{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8e5ecff9",
   "metadata": {
    "papermill": {
     "duration": 0.018623,
     "end_time": "2026-01-11T03:29:14.859357",
     "exception": false,
     "start_time": "2026-01-11T03:29:14.840734",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Inspired by the [first-place solution](https://www.kaggle.com/competitions/image-matching-challenge-2025/discussion/583058) by [@ns6464](https://www.kaggle.com/ns6464), I've put together a demo showcasing how to run MASt3R within Kaggle.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d990286",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:29:14.895164Z",
     "iopub.status.busy": "2026-01-11T03:29:14.894929Z",
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     "shell.execute_reply": "2026-01-11T03:29:14.900035Z"
    },
    "papermill": {
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     "end_time": "2026-01-11T03:29:14.901828",
     "exception": false,
     "start_time": "2026-01-11T03:29:14.877049",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3327fcd4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:29:14.937173Z",
     "iopub.status.busy": "2026-01-11T03:29:14.936909Z",
     "iopub.status.idle": "2026-01-11T03:29:14.940692Z",
     "shell.execute_reply": "2026-01-11T03:29:14.939885Z"
    },
    "papermill": {
     "duration": 0.022659,
     "end_time": "2026-01-11T03:29:14.941930",
     "exception": false,
     "start_time": "2026-01-11T03:29:14.919271",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class CONFIG:\n",
    "    # DEBUG Settings\n",
    "    DRY_RUN = False\n",
    "    DRY_RUN_MAX_IMAGES = 10\n",
    "\n",
    "    # Pipeline settings\n",
    "    NUM_CORES = 2\n",
    "    MAST3R_MIN_PAIR = 15\n",
    "    MATCH_CONF_TH = 1.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ad4a7ad",
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2026-01-11T03:29:14.978848Z",
     "iopub.status.busy": "2026-01-11T03:29:14.978637Z",
     "iopub.status.idle": "2026-01-11T03:31:36.842679Z",
     "shell.execute_reply": "2026-01-11T03:31:36.841924Z"
    },
    "papermill": {
     "duration": 141.883397,
     "end_time": "2026-01-11T03:31:36.844015",
     "exception": false,
     "start_time": "2026-01-11T03:29:14.960618",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in links: /kaggle/input/mast3r-fix/mast3r-wheels\r\n",
      "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.6.0+cu124)\r\n",
      "Requirement already satisfied: torchvision in /usr/local/lib/python3.11/dist-packages (0.21.0+cu124)\r\n",
      "Requirement already satisfied: torchaudio in /usr/local/lib/python3.11/dist-packages (2.6.0+cu124)\r\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch) (3.18.0)\r\n",
      "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch) (4.13.2)\r\n",
      "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch) (3.4.2)\r\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.6)\r\n",
      "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch) (2025.3.2)\r\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\r\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\r\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (from torch)\r\n",
      "INFO: pip is looking at multiple versions of torch to determine which version is compatible with other requirements. This could take a while.\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/torch-2.7.1-cp311-cp311-manylinux_2_28_x86_64.whl\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/sympy-1.14.0-py3-none-any.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_nccl_cu12-2.26.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/triton-3.3.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (from torch)\r\n",
      "Requirement already satisfied: setuptools>=40.8.0 in /usr/local/lib/python3.11/dist-packages (from triton==3.3.1->torch) (75.2.0)\r\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from torchvision) (1.26.4)\r\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.11/dist-packages (from torchvision) (11.1.0)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/torchvision-0.22.1-cp311-cp311-manylinux_2_28_x86_64.whl\r\n",
      "INFO: pip is looking at multiple versions of torchaudio to determine which version is compatible with other requirements. This could take a while.\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/torchaudio-2.5.1+cu121-cp311-cp311-linux_x86_64.whl\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/torchvision-0.20.1+cu121-cp311-cp311-linux_x86_64.whl\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/torch-2.5.1+cu121-cp311-cp311-linux_x86_64.whl\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (from nvidia-cudnn-cu12==9.5.1.17->torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (from nvidia-cusolver-cu12==11.7.1.2->torch)\r\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch) (2.21.5)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (from torch)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/triton-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from torch)\r\n",
      "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch) (1.13.1)\r\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.11/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.9.41)\r\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch) (1.3.0)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch) (3.0.2)\r\n",
      "Requirement already satisfied: mkl_fft in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (1.3.8)\r\n",
      "Requirement already satisfied: mkl_random in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (1.2.4)\r\n",
      "Requirement already satisfied: mkl_umath in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (0.1.1)\r\n",
      "Requirement already satisfied: mkl in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (2025.1.0)\r\n",
      "Requirement already satisfied: tbb4py in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (2022.1.0)\r\n",
      "Requirement already satisfied: mkl-service in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (2.4.1)\r\n",
      "Requirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy->torchvision) (2024.2.0)\r\n",
      "Requirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy->torchvision) (2022.1.0)\r\n",
      "Requirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->mkl->numpy->torchvision) (1.3.0)\r\n",
      "Requirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.11/dist-packages (from mkl_umath->numpy->torchvision) (2024.2.0)\r\n",
      "Requirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.11/dist-packages (from intel-openmp<2026,>=2024->mkl->numpy->torchvision) (2024.2.0)\r\n",
      "Installing collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, torchaudio, torchvision\r\n",
      "  Attempting uninstall: triton\r\n",
      "    Found existing installation: triton 3.2.0\r\n",
      "    Uninstalling triton-3.2.0:\r\n",
      "      Successfully uninstalled triton-3.2.0\r\n",
      "  Attempting uninstall: nvidia-nvtx-cu12\r\n",
      "    Found existing installation: nvidia-nvtx-cu12 12.4.127\r\n",
      "    Uninstalling nvidia-nvtx-cu12-12.4.127:\r\n",
      "      Successfully uninstalled nvidia-nvtx-cu12-12.4.127\r\n",
      "  Attempting uninstall: nvidia-nvjitlink-cu12\r\n",
      "    Found existing installation: nvidia-nvjitlink-cu12 12.9.41\r\n",
      "    Uninstalling nvidia-nvjitlink-cu12-12.9.41:\r\n",
      "      Successfully uninstalled nvidia-nvjitlink-cu12-12.9.41\r\n",
      "  Attempting uninstall: nvidia-curand-cu12\r\n",
      "    Found existing installation: nvidia-curand-cu12 10.3.10.19\r\n",
      "    Uninstalling nvidia-curand-cu12-10.3.10.19:\r\n",
      "      Successfully uninstalled nvidia-curand-cu12-10.3.10.19\r\n",
      "  Attempting uninstall: nvidia-cufft-cu12\r\n",
      "    Found existing installation: nvidia-cufft-cu12 11.4.0.6\r\n",
      "    Uninstalling nvidia-cufft-cu12-11.4.0.6:\r\n",
      "      Successfully uninstalled nvidia-cufft-cu12-11.4.0.6\r\n",
      "  Attempting uninstall: nvidia-cuda-runtime-cu12\r\n",
      "    Found existing installation: nvidia-cuda-runtime-cu12 12.4.127\r\n",
      "    Uninstalling nvidia-cuda-runtime-cu12-12.4.127:\r\n",
      "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.4.127\r\n",
      "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\r\n",
      "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.4.127\r\n",
      "    Uninstalling nvidia-cuda-nvrtc-cu12-12.4.127:\r\n",
      "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.4.127\r\n",
      "  Attempting uninstall: nvidia-cuda-cupti-cu12\r\n",
      "    Found existing installation: nvidia-cuda-cupti-cu12 12.4.127\r\n",
      "    Uninstalling nvidia-cuda-cupti-cu12-12.4.127:\r\n",
      "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.4.127\r\n",
      "  Attempting uninstall: nvidia-cublas-cu12\r\n",
      "    Found existing installation: nvidia-cublas-cu12 12.9.0.13\r\n",
      "    Uninstalling nvidia-cublas-cu12-12.9.0.13:\r\n",
      "      Successfully uninstalled nvidia-cublas-cu12-12.9.0.13\r\n",
      "  Attempting uninstall: nvidia-cusparse-cu12\r\n",
      "    Found existing installation: nvidia-cusparse-cu12 12.5.9.5\r\n",
      "    Uninstalling nvidia-cusparse-cu12-12.5.9.5:\r\n",
      "      Successfully uninstalled nvidia-cusparse-cu12-12.5.9.5\r\n",
      "  Attempting uninstall: nvidia-cudnn-cu12\r\n",
      "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\r\n",
      "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\r\n",
      "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\r\n",
      "  Attempting uninstall: nvidia-cusolver-cu12\r\n",
      "    Found existing installation: nvidia-cusolver-cu12 11.7.4.40\r\n",
      "    Uninstalling nvidia-cusolver-cu12-11.7.4.40:\r\n",
      "      Successfully uninstalled nvidia-cusolver-cu12-11.7.4.40\r\n",
      "  Attempting uninstall: torch\r\n",
      "    Found existing installation: torch 2.6.0+cu124\r\n",
      "    Uninstalling torch-2.6.0+cu124:\r\n",
      "      Successfully uninstalled torch-2.6.0+cu124\r\n",
      "  Attempting uninstall: torchaudio\r\n",
      "    Found existing installation: torchaudio 2.6.0+cu124\r\n",
      "    Uninstalling torchaudio-2.6.0+cu124:\r\n",
      "      Successfully uninstalled torchaudio-2.6.0+cu124\r\n",
      "  Attempting uninstall: torchvision\r\n",
      "    Found existing installation: torchvision 0.21.0+cu124\r\n",
      "    Uninstalling torchvision-0.21.0+cu124:\r\n",
      "      Successfully uninstalled torchvision-0.21.0+cu124\r\n",
      "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nvjitlink-cu12-12.6.85 nvidia-nvtx-cu12-12.1.105 torch-2.5.1+cu121 torchaudio-2.5.1+cu121 torchvision-0.20.1+cu121 triton-3.1.0\r\n"
     ]
    }
   ],
   "source": [
    "!pip install torch torchvision torchaudio --no-index --find-links=/kaggle/input/mast3r-fix/mast3r-wheels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "062c291c",
   "metadata": {
    "_kg_hide-output": false,
    "execution": {
     "iopub.execute_input": "2026-01-11T03:31:36.889033Z",
     "iopub.status.busy": "2026-01-11T03:31:36.888780Z",
     "iopub.status.idle": "2026-01-11T03:31:41.228798Z",
     "shell.execute_reply": "2026-01-11T03:31:41.228028Z"
    },
    "papermill": {
     "duration": 4.363873,
     "end_time": "2026-01-11T03:31:41.230502",
     "exception": false,
     "start_time": "2026-01-11T03:31:36.866629",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in links: /kaggle/input/mast3r-fix/mast3r-wheels\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/faiss_gpu_cu12-1.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\n",
      "Requirement already satisfied: numpy<2 in /usr/local/lib/python3.11/dist-packages (from faiss-gpu-cu12) (1.26.4)\r\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from faiss-gpu-cu12) (25.0)\r\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12>=12.1.105 in /usr/local/lib/python3.11/dist-packages (from faiss-gpu-cu12) (12.1.105)\r\n",
      "Requirement already satisfied: nvidia-cublas-cu12>=12.1.3.1 in /usr/local/lib/python3.11/dist-packages (from faiss-gpu-cu12) (12.1.3.1)\r\n",
      "Requirement already satisfied: mkl_fft in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (1.3.8)\r\n",
      "Requirement already satisfied: mkl_random in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (1.2.4)\r\n",
      "Requirement already satisfied: mkl_umath in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (0.1.1)\r\n",
      "Requirement already satisfied: mkl in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (2025.1.0)\r\n",
      "Requirement already satisfied: tbb4py in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (2022.1.0)\r\n",
      "Requirement already satisfied: mkl-service in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (2.4.1)\r\n",
      "Requirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy<2->faiss-gpu-cu12) (2024.2.0)\r\n",
      "Requirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy<2->faiss-gpu-cu12) (2022.1.0)\r\n",
      "Requirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->mkl->numpy<2->faiss-gpu-cu12) (1.3.0)\r\n",
      "Requirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.11/dist-packages (from mkl_umath->numpy<2->faiss-gpu-cu12) (2024.2.0)\r\n",
      "Requirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.11/dist-packages (from intel-openmp<2026,>=2024->mkl->numpy<2->faiss-gpu-cu12) (2024.2.0)\r\n",
      "Installing collected packages: faiss-gpu-cu12\r\n",
      "Successfully installed faiss-gpu-cu12-1.11.0\r\n"
     ]
    }
   ],
   "source": [
    "!pip install faiss-gpu-cu12 --no-index --find-links=/kaggle/input/mast3r-fix/mast3r-wheels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cda955bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:31:41.275022Z",
     "iopub.status.busy": "2026-01-11T03:31:41.274542Z",
     "iopub.status.idle": "2026-01-11T03:31:58.595780Z",
     "shell.execute_reply": "2026-01-11T03:31:58.594959Z"
    },
    "papermill": {
     "duration": 17.344561,
     "end_time": "2026-01-11T03:31:58.597248",
     "exception": false,
     "start_time": "2026-01-11T03:31:41.252687",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in links: /kaggle/input/mast3r-fix/mast3r-wheels\r\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (1.2.2)\r\n",
      "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (2.5.1+cu121)\r\n",
      "Requirement already satisfied: torchvision in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 2)) (0.20.1+cu121)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/roma-1.5.3-py3-none-any.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 3))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/gradio-5.33.0-py3-none-any.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 5)) (3.7.2)\r\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 6)) (4.67.1)\r\n",
      "Requirement already satisfied: opencv-python in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 7)) (4.11.0.86)\r\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 8)) (1.15.2)\r\n",
      "Requirement already satisfied: einops in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 9)) (0.8.1)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/trimesh-4.6.11-py3-none-any.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 10))\r\n",
      "Requirement already satisfied: tensorboard in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 11)) (2.18.0)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/pyglet-1.5.31-py3-none-any.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 12))\r\n",
      "Requirement already satisfied: huggingface-hub>=0.22 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub[torch]>=0.22->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 13)) (0.31.1)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/pillow_heif-0.22.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 1))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/pyrender-0.1.45-py3-none-any.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 2))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/kapture-1.1.10-py3-none-any.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/kapture_localization-1.1.10-py3-none-any.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 4))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/numpy_quaternion-2024.0.8-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 5))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/pycolmap-3.11.1-cp311-cp311-manylinux_2_28_x86_64.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 6))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/poselib-2.0.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 7))\r\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (1.26.4)\r\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (1.5.0)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (3.6.0)\r\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (3.18.0)\r\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (4.13.2)\r\n",
      "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (3.4.2)\r\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (3.1.6)\r\n",
      "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (2025.3.2)\r\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (12.1.105)\r\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (12.1.105)\r\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (12.1.105)\r\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (9.1.0.70)\r\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (12.1.3.1)\r\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (11.0.2.54)\r\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (10.3.2.106)\r\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (11.4.5.107)\r\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (12.1.0.106)\r\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (2.21.5)\r\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (12.1.105)\r\n",
      "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (3.1.0)\r\n",
      "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (1.13.1)\r\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.11/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (12.6.85)\r\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (1.3.0)\r\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.11/dist-packages (from torchvision->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 2)) (11.1.0)\r\n",
      "Requirement already satisfied: aiofiles<25.0,>=22.0 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (22.1.0)\r\n",
      "Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (4.9.0)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/fastapi-0.115.12-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/ffmpy-0.6.0-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/gradio_client-1.10.2-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/groovy-0.1.2-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Requirement already satisfied: httpx>=0.24.1 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (0.28.1)\r\n",
      "Requirement already satisfied: markupsafe<4.0,>=2.0 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (3.0.2)\r\n",
      "Requirement already satisfied: orjson~=3.0 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (3.10.16)\r\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (25.0)\r\n",
      "Requirement already satisfied: pandas<3.0,>=1.0 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (2.2.3)\r\n",
      "Requirement already satisfied: pydantic<2.12,>=2.0 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (2.11.4)\r\n",
      "Requirement already satisfied: pydub in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (0.25.1)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/python_multipart-0.0.20-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Requirement already satisfied: pyyaml<7.0,>=5.0 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (6.0.2)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/ruff-0.11.13-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/safehttpx-0.1.6-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/semantic_version-2.10.0-py2.py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/starlette-0.46.2-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/tomlkit-0.13.3-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Requirement already satisfied: typer<1.0,>=0.12 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (0.15.2)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/uvicorn-0.34.3-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\r\n",
      "Requirement already satisfied: websockets<16.0,>=10.0 in /usr/local/lib/python3.11/dist-packages (from gradio-client==1.10.2->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (15.0.1)\r\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 5)) (1.3.1)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 5)) (0.12.1)\r\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 5)) (4.57.0)\r\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 5)) (1.4.8)\r\n",
      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 5)) (3.0.9)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 5)) (2.9.0.post0)\r\n",
      "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.11/dist-packages (from tensorboard->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 11)) (1.4.0)\r\n",
      "Requirement already satisfied: grpcio>=1.48.2 in /usr/local/lib/python3.11/dist-packages (from tensorboard->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 11)) (1.72.0rc1)\r\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 11)) (3.7)\r\n",
      "Requirement already satisfied: protobuf!=4.24.0,>=3.19.6 in /usr/local/lib/python3.11/dist-packages (from tensorboard->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 11)) (3.20.3)\r\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 11)) (75.2.0)\r\n",
      "Requirement already satisfied: six>1.9 in /usr/local/lib/python3.11/dist-packages (from tensorboard->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 11)) (1.17.0)\r\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 11)) (0.7.2)\r\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 11)) (3.1.3)\r\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.22->huggingface-hub[torch]>=0.22->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 13)) (2.32.3)\r\n",
      "Requirement already satisfied: hf-xet<2.0.0,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.22->huggingface-hub[torch]>=0.22->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 13)) (1.1.0)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/freetype_py-2.5.1-py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (from pyrender->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 2))\r\n",
      "Requirement already satisfied: imageio in /usr/local/lib/python3.11/dist-packages (from pyrender->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 2)) (2.37.0)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/PyOpenGL-3.1.0.zip (from pyrender->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 2))\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\r\n",
      "Requirement already satisfied: numba>=0.42 in /usr/local/lib/python3.11/dist-packages (from kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3)) (0.60.0)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/piexif-1.1.3-py2.py3-none-any.whl (from kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3))\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/wcmatch-10.0-py3-none-any.whl (from kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3))\r\n",
      "Requirement already satisfied: tabulate>=0.8.7 in /usr/local/lib/python3.11/dist-packages (from kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3)) (0.9.0)\r\n",
      "Requirement already satisfied: pytz>=2021.1 in /usr/local/lib/python3.11/dist-packages (from kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3)) (2025.2)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/build-1.2.2.post1-py3-none-any.whl (from kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3))\r\n",
      "Requirement already satisfied: cvxpy>=1.1.6 in /usr/local/lib/python3.11/dist-packages (from kapture-localization->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 4)) (1.6.4)\r\n",
      "Requirement already satisfied: safetensors[torch] in /usr/local/lib/python3.11/dist-packages (from huggingface-hub[torch]>=0.22->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 13)) (0.5.3)\r\n",
      "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.11/dist-packages (from anyio<5.0,>=3.0->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (3.10)\r\n",
      "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.11/dist-packages (from anyio<5.0,>=3.0->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (1.3.1)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/pyproject_hooks-1.2.0-py3-none-any.whl (from build>=1.0.3->kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3))\r\n",
      "Requirement already satisfied: osqp>=0.6.2 in /usr/local/lib/python3.11/dist-packages (from cvxpy>=1.1.6->kapture-localization->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 4)) (1.0.3)\r\n",
      "Requirement already satisfied: clarabel>=0.5.0 in /usr/local/lib/python3.11/dist-packages (from cvxpy>=1.1.6->kapture-localization->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 4)) (0.10.0)\r\n",
      "Requirement already satisfied: scs>=3.2.4.post1 in /usr/local/lib/python3.11/dist-packages (from cvxpy>=1.1.6->kapture-localization->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 4)) (3.2.7.post2)\r\n",
      "Requirement already satisfied: certifi in /usr/local/lib/python3.11/dist-packages (from httpx>=0.24.1->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (2025.4.26)\r\n",
      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.11/dist-packages (from httpx>=0.24.1->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (1.0.7)\r\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.11/dist-packages (from httpcore==1.*->httpx>=0.24.1->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (0.14.0)\r\n",
      "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.11/dist-packages (from numba>=0.42->kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3)) (0.43.0)\r\n",
      "Requirement already satisfied: mkl_fft in /usr/local/lib/python3.11/dist-packages (from numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (1.3.8)\r\n",
      "Requirement already satisfied: mkl_random in /usr/local/lib/python3.11/dist-packages (from numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (1.2.4)\r\n",
      "Requirement already satisfied: mkl_umath in /usr/local/lib/python3.11/dist-packages (from numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (0.1.1)\r\n",
      "Requirement already satisfied: mkl in /usr/local/lib/python3.11/dist-packages (from numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (2025.1.0)\r\n",
      "Requirement already satisfied: tbb4py in /usr/local/lib/python3.11/dist-packages (from numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (2022.1.0)\r\n",
      "Requirement already satisfied: mkl-service in /usr/local/lib/python3.11/dist-packages (from numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (2.4.1)\r\n",
      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas<3.0,>=1.0->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (2025.2)\r\n",
      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic<2.12,>=2.0->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (0.7.0)\r\n",
      "Requirement already satisfied: pydantic-core==2.33.2 in /usr/local/lib/python3.11/dist-packages (from pydantic<2.12,>=2.0->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (2.33.2)\r\n",
      "Requirement already satisfied: typing-inspection>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from pydantic<2.12,>=2.0->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (0.4.0)\r\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.22->huggingface-hub[torch]>=0.22->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 13)) (3.4.2)\r\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.22->huggingface-hub[torch]>=0.22->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 13)) (2.4.0)\r\n",
      "Requirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.11/dist-packages (from typer<1.0,>=0.12->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (8.1.8)\r\n",
      "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.11/dist-packages (from typer<1.0,>=0.12->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (1.5.4)\r\n",
      "Requirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.11/dist-packages (from typer<1.0,>=0.12->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (14.0.0)\r\n",
      "Processing /kaggle/input/mast3r-fix/mast3r-wheels/bracex-2.5.post1-py3-none-any.whl (from wcmatch>=5.0->kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3))\r\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (3.0.0)\r\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (2.19.1)\r\n",
      "Requirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (2024.2.0)\r\n",
      "Requirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (2022.1.0)\r\n",
      "Requirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->mkl->numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (1.3.0)\r\n",
      "Requirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.11/dist-packages (from mkl_umath->numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (2024.2.0)\r\n",
      "Requirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.11/dist-packages (from intel-openmp<2026,>=2024->mkl->numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (2024.2.0)\r\n",
      "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (0.1.2)\r\n",
      "Building wheels for collected packages: PyOpenGL\r\n",
      "  Building wheel for PyOpenGL (setup.py) ... \u001b[?25l\u001b[?25hdone\r\n",
      "  Created wheel for PyOpenGL: filename=PyOpenGL-3.1.0-py3-none-any.whl size=1745193 sha256=44a62ea4690a706134202dc7a25977e655029ef4c4b9998f589936b53dff68d5\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/1e/f6/cf/2c2644cdb0ce0cc3bbbbe4338fb0e2eff890c26d0a1a225363\r\n",
      "Successfully built PyOpenGL\r\n",
      "Installing collected packages: PyOpenGL, pyglet, uvicorn, tomlkit, semantic-version, ruff, roma, python-multipart, pyproject_hooks, pillow-heif, piexif, groovy, freetype-py, ffmpy, bracex, wcmatch, starlette, build, safehttpx, gradio-client, fastapi, numpy-quaternion, trimesh, kapture, pyrender, pycolmap, poselib, kapture-localization, gradio\r\n",
      "  Attempting uninstall: PyOpenGL\r\n",
      "    Found existing installation: PyOpenGL 3.1.9\r\n",
      "    Uninstalling PyOpenGL-3.1.9:\r\n",
      "      Successfully uninstalled PyOpenGL-3.1.9\r\n",
      "Successfully installed PyOpenGL-3.1.0 bracex-2.5.post1 build-1.2.2.post1 fastapi-0.115.12 ffmpy-0.6.0 freetype-py-2.5.1 gradio-5.33.0 gradio-client-1.10.2 groovy-0.1.2 kapture-1.1.10 kapture-localization-1.1.10 numpy-quaternion-2024.0.8 piexif-1.1.3 pillow-heif-0.22.0 poselib-2.0.4 pycolmap-3.11.1 pyglet-1.5.31 pyproject_hooks-1.2.0 pyrender-0.1.45 python-multipart-0.0.20 roma-1.5.3 ruff-0.11.13 safehttpx-0.1.6 semantic-version-2.10.0 starlette-0.46.2 tomlkit-0.13.3 trimesh-4.6.11 uvicorn-0.34.3 wcmatch-10.0\r\n"
     ]
    }
   ],
   "source": [
    "# 离线安装所有依赖（不联网）\n",
    "!pip install --no-index --find-links=/kaggle/input/mast3r-fix/mast3r-wheels \\\n",
    "    -r /kaggle/input/mast3r-fix/mast3r/requirements.txt \\\n",
    "    -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt \\\n",
    "    -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ad2389e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:31:58.645782Z",
     "iopub.status.busy": "2026-01-11T03:31:58.645233Z",
     "iopub.status.idle": "2026-01-11T03:32:00.103264Z",
     "shell.execute_reply": "2026-01-11T03:32:00.102541Z"
    },
    "papermill": {
     "duration": 1.482866,
     "end_time": "2026-01-11T03:32:00.104602",
     "exception": false,
     "start_time": "2026-01-11T03:31:58.621736",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing /kaggle/input/imc2024-packages-lightglue-rerun-kornia/kornia-0.7.2-py2.py3-none-any.whl\r\n",
      "Processing /kaggle/input/imc2024-packages-lightglue-rerun-kornia/kornia_moons-0.2.9-py3-none-any.whl\r\n",
      "\u001b[31mERROR: kornia_rs-0.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl is not a supported wheel on this platform.\u001b[0m\u001b[31m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install --no-index /kaggle/input/imc2024-packages-lightglue-rerun-kornia/* --no-deps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d22a2f7a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:00.154544Z",
     "iopub.status.busy": "2026-01-11T03:32:00.153753Z",
     "iopub.status.idle": "2026-01-11T03:32:01.720630Z",
     "shell.execute_reply": "2026-01-11T03:32:01.719835Z"
    },
    "papermill": {
     "duration": 1.591858,
     "end_time": "2026-01-11T03:32:01.722084",
     "exception": false,
     "start_time": "2026-01-11T03:32:00.130226",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing /kaggle/input/pycolmap3-11/pycolmap-3.11.1-cp311-cp311-manylinux_2_28_x86_64.whl\r\n",
      "pycolmap is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\r\n"
     ]
    }
   ],
   "source": [
    "!pip install --no-index /kaggle/input/pycolmap3-11/pycolmap-3.11.1-cp311-cp311-manylinux_2_28_x86_64.whl --no-deps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b519cc0c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:01.772093Z",
     "iopub.status.busy": "2026-01-11T03:32:01.771406Z",
     "iopub.status.idle": "2026-01-11T03:32:01.774908Z",
     "shell.execute_reply": "2026-01-11T03:32:01.774373Z"
    },
    "papermill": {
     "duration": 0.028583,
     "end_time": "2026-01-11T03:32:01.775955",
     "exception": false,
     "start_time": "2026-01-11T03:32:01.747372",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install kornia_rs\n",
    "# !pip install pycolmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "04e572ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:01.825016Z",
     "iopub.status.busy": "2026-01-11T03:32:01.824375Z",
     "iopub.status.idle": "2026-01-11T03:32:01.828128Z",
     "shell.execute_reply": "2026-01-11T03:32:01.827589Z"
    },
    "papermill": {
     "duration": 0.028944,
     "end_time": "2026-01-11T03:32:01.829149",
     "exception": false,
     "start_time": "2026-01-11T03:32:01.800205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 加入源码主目录（包含 mast3r, dust3r 等子目录）\n",
    "sys.path.insert(0, \"/kaggle/input/mast3r-fix/mast3r\")\n",
    "sys.path.insert(0, '/kaggle/input/mast3r-fix/mast3r/asmk')\n",
    "sys.path.insert(0, '/kaggle/input/mast3r-fix/mast3r/dust3r/croco/models/curope')"
   ]
  },
  {
   "attachments": {
    "4aba1fc2-1244-4b2b-95e4-ce07df4566a2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "2dd332ef",
   "metadata": {
    "papermill": {
     "duration": 0.02268,
     "end_time": "2026-01-11T03:32:01.875600",
     "exception": false,
     "start_time": "2026-01-11T03:32:01.852920",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Run gradio demo with internet on mode:\n",
    "\n",
    "> !PYTHONPATH=\"/kaggle/input/mast3r-fix/mast3r/asmk:$PYTHONPATH\" python3 /kaggle/input/mast3r-fix/mast3r/demo.py --weights /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth --share\n",
    "\n",
    "![WechatIMG10.png](attachment:4aba1fc2-1244-4b2b-95e4-ce07df4566a2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "07df81dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:01.922242Z",
     "iopub.status.busy": "2026-01-11T03:32:01.921698Z",
     "iopub.status.idle": "2026-01-11T03:32:01.924819Z",
     "shell.execute_reply": "2026-01-11T03:32:01.924185Z"
    },
    "papermill": {
     "duration": 0.027509,
     "end_time": "2026-01-11T03:32:01.925844",
     "exception": false,
     "start_time": "2026-01-11T03:32:01.898335",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # %cd /kaggle/working/mast3r\n",
    "# !PYTHONPATH=\"/kaggle/input/mast3r-fix/mast3r/asmk:$PYTHONPATH\" python3 /kaggle/input/mast3r-fix/mast3r/demo.py --weights /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth --share"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d5248d0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:01.975324Z",
     "iopub.status.busy": "2026-01-11T03:32:01.975119Z",
     "iopub.status.idle": "2026-01-11T03:32:02.313074Z",
     "shell.execute_reply": "2026-01-11T03:32:02.312285Z"
    },
    "papermill": {
     "duration": 0.363546,
     "end_time": "2026-01-11T03:32:02.314290",
     "exception": false,
     "start_time": "2026-01-11T03:32:01.950744",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -rf /kaggle/working/visualization_output\n",
    "!rm -rf /kaggle/working/temp\n",
    "!rm -rf /kaggle/working/result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df524eac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:02.362847Z",
     "iopub.status.busy": "2026-01-11T03:32:02.362608Z",
     "iopub.status.idle": "2026-01-11T03:32:04.110382Z",
     "shell.execute_reply": "2026-01-11T03:32:04.109745Z"
    },
    "papermill": {
     "duration": 1.773251,
     "end_time": "2026-01-11T03:32:04.111831",
     "exception": false,
     "start_time": "2026-01-11T03:32:02.338580",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import dataclasses\n",
    "\n",
    "def seed_everything(seed: int = 42):\n",
    "    \"\"\"Set seed for reproducibility across random, numpy, torch (CPU + CUDA).\"\"\"\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)  # for multi-GPU\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "\n",
    "seed_everything()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d16173e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:04.162408Z",
     "iopub.status.busy": "2026-01-11T03:32:04.162115Z",
     "iopub.status.idle": "2026-01-11T03:32:04.165159Z",
     "shell.execute_reply": "2026-01-11T03:32:04.164659Z"
    },
    "papermill": {
     "duration": 0.028108,
     "end_time": "2026-01-11T03:32:04.166164",
     "exception": false,
     "start_time": "2026-01-11T03:32:04.138056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip uninstall -y pycolmap\n",
    "# !pip install --no-cache-dir pycolmap==3.11.1 --index-url https://pypi.org/simple\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "850ef041",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:04.261802Z",
     "iopub.status.busy": "2026-01-11T03:32:04.261231Z",
     "iopub.status.idle": "2026-01-11T03:32:06.163110Z",
     "shell.execute_reply": "2026-01-11T03:32:06.162243Z"
    },
    "papermill": {
     "duration": 1.927527,
     "end_time": "2026-01-11T03:32:06.164537",
     "exception": false,
     "start_time": "2026-01-11T03:32:04.237010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: pycolmap\r\n",
      "Version: 3.11.1\r\n",
      "Summary: COLMAP bindings\r\n",
      "Home-page: \r\n",
      "Author: \r\n",
      "Author-email: =?utf-8?q?Johannes_Sch=C3=B6nberger?= <jsch@demuc.de>, Mihai Dusmanu <mihai.dusmanu@gmail.com>, Paul-Edouard Sarlin <psarlin@ethz.ch>, Shaohui Liu <b1ueber2y@gmail.com>, Philipp Lindenberger <plindenbe@ethz.ch>\r\n",
      "License: BSD-3-Clause\r\n",
      "Location: /usr/local/lib/python3.11/dist-packages\r\n",
      "Requires: numpy\r\n",
      "Required-by: \r\n"
     ]
    }
   ],
   "source": [
    "import pycolmap\n",
    "!pip show pycolmap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31fdbec4",
   "metadata": {
    "papermill": {
     "duration": 0.023397,
     "end_time": "2026-01-11T03:32:06.213884",
     "exception": false,
     "start_time": "2026-01-11T03:32:06.190487",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc8ab2e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:06.261522Z",
     "iopub.status.busy": "2026-01-11T03:32:06.260903Z",
     "iopub.status.idle": "2026-01-11T03:32:06.265577Z",
     "shell.execute_reply": "2026-01-11T03:32:06.264997Z"
    },
    "papermill": {
     "duration": 0.029417,
     "end_time": "2026-01-11T03:32:06.266661",
     "exception": false,
     "start_time": "2026-01-11T03:32:06.237244",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__init__.py', 'cost_functions', '__pycache__', 'py.typed', 'utils.py', '_core', '_core.cpython-311-x86_64-linux-gnu.so', 'manifold']\n"
     ]
    }
   ],
   "source": [
    "import pycolmap\n",
    "import os\n",
    "print(os.listdir(os.path.dirname(pycolmap.__file__)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a7fafcf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:06.312885Z",
     "iopub.status.busy": "2026-01-11T03:32:06.312705Z",
     "iopub.status.idle": "2026-01-11T03:32:27.451484Z",
     "shell.execute_reply": "2026-01-11T03:32:27.450604Z"
    },
    "papermill": {
     "duration": 21.163527,
     "end_time": "2026-01-11T03:32:27.453104",
     "exception": false,
     "start_time": "2026-01-11T03:32:06.289577",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-01-11 03:32:14.453743: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1768102334.634863      20 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1768102334.689467      20 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "from time import time, sleep\n",
    "import gc\n",
    "import numpy as np\n",
    "import h5py\n",
    "import dataclasses\n",
    "import pandas as pd\n",
    "from IPython.display import clear_output\n",
    "from collections import defaultdict\n",
    "from copy import deepcopy\n",
    "from PIL import Image\n",
    "\n",
    "import cv2\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import torch\n",
    "from transformers import AutoImageProcessor, AutoModel\n",
    "\n",
    "\n",
    "# IMPORTANT Utilities: importing data into colmap and competition metric\n",
    "import pycolmap\n",
    "\n",
    "\n",
    "sys.path.append('/kaggle/input/pycolmap3-11-imc-utils')\n",
    "# from database import *\n",
    "from h5_to_db import *\n",
    "import metric\n",
    "from pycolmap import verify_matches, TwoViewGeometryOptions\n",
    "\n",
    "from fastprogress import progress_bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ce355579",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:27.502697Z",
     "iopub.status.busy": "2026-01-11T03:32:27.502008Z",
     "iopub.status.idle": "2026-01-11T03:32:27.505641Z",
     "shell.execute_reply": "2026-01-11T03:32:27.505029Z"
    },
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     "exception": false,
     "start_time": "2026-01-11T03:32:27.477643",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 主线程先 preload，确保子线程里不会挂\n",
    "_ = verify_matches\n",
    "_ = TwoViewGeometryOptions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e47656d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:27.554918Z",
     "iopub.status.busy": "2026-01-11T03:32:27.554676Z",
     "iopub.status.idle": "2026-01-11T03:32:27.918962Z",
     "shell.execute_reply": "2026-01-11T03:32:27.918198Z"
    },
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     "end_time": "2026-01-11T03:32:27.920273",
     "exception": false,
     "start_time": "2026-01-11T03:32:27.529734",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from mast3r.model import AsymmetricMASt3R\n",
    "from mast3r.fast_nn import fast_reciprocal_NNs, extract_correspondences_nonsym\n",
    "\n",
    "import mast3r.utils.path_to_dust3r\n",
    "from dust3r.inference import inference\n",
    "from dust3r.utils.image import load_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "68c8d9d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:27.969215Z",
     "iopub.status.busy": "2026-01-11T03:32:27.968982Z",
     "iopub.status.idle": "2026-01-11T03:32:28.114610Z",
     "shell.execute_reply": "2026-01-11T03:32:28.113386Z"
    },
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     "duration": 0.171322,
     "end_time": "2026-01-11T03:32:28.116416",
     "exception": false,
     "start_time": "2026-01-11T03:32:27.945094",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -rf /kaggle/working/result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1e65bab1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:28.185064Z",
     "iopub.status.busy": "2026-01-11T03:32:28.184766Z",
     "iopub.status.idle": "2026-01-11T03:32:58.674044Z",
     "shell.execute_reply": "2026-01-11T03:32:58.673293Z"
    },
    "papermill": {
     "duration": 30.547881,
     "end_time": "2026-01-11T03:32:58.699267",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n",
      "Loading model from local path: /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\n",
      "... loading model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/kaggle/input/mast3r-fix/mast3r/mast3r/model.py:24: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(model_path, map_location='cpu')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instantiating : AsymmetricMASt3R(enc_depth=24, dec_depth=12, enc_embed_dim=1024, dec_embed_dim=768, enc_num_heads=16, dec_num_heads=12, pos_embed='RoPE100',img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), patch_embed_cls='PatchEmbedDust3R', two_confs=True, desc_conf_mode=('exp', 0, inf), landscape_only=False)\n",
      "<All keys matched successfully>\n",
      "Model loaded successfully.\n"
     ]
    }
   ],
   "source": [
    "# Configuration\n",
    "import torch\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu' # Automatically use GPU if available\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "schedule = 'cosine' # These seem to be unused in the provided snippet, but keep for context\n",
    "lr = 0.01\n",
    "niter = 300\n",
    "local_model_path = \"/kaggle/input/mast3r-fix/mast3r/checkpoints/\"\n",
    "local_model_directory = \"/kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\"\n",
    "retrival_model_dir = '/kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth'\n",
    "\n",
    "# Now, we manually call `load_model` as suggested by `mast3r/model.py`'s `from_pretrained` logic\n",
    "from mast3r.model import load_model # Assuming load_model is defined in mast3r/model.py or accessible\n",
    "\n",
    "print(f\"Loading model from local path: {local_model_directory}\")\n",
    "mast3r_model = load_model(local_model_directory, device=device) # Pass device to load_model\n",
    "print(\"Model loaded successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "502338e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:32:58.748092Z",
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     "shell.execute_reply": "2026-01-11T03:32:58.755166Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def transform_keypoints_to_original(\n",
    "    kpts_crop: np.ndarray,\n",
    "    original_size: tuple[int, int],#H,W\n",
    "    size_param: int = 512, # The 'size' parameter (e.g., 224, 512) used in load_images\n",
    "    square_ok: bool = False\n",
    ") -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Transforms keypoint coordinates from a DUST3R-processed (resized and cropped)\n",
    "    image back to the original image's coordinate system.\n",
    "\n",
    "    Args:\n",
    "        kpts_crop: A NumPy array of shape (N, 2) where N is the number of keypoints,\n",
    "                   and each row is (x, y) coordinate on the processed image.\n",
    "        original_size: A tuple (original_width, original_height) of the original image.\n",
    "        resized_crop_size: A tuple (processed_width, processed_height) of the\n",
    "                           image after resizing and cropping (i.e., the dimensions\n",
    "                           of the input image to DUST3R). This is W2, H2 from the\n",
    "                           load_images function.\n",
    "        size_param: The 'size' parameter (e.g., 224, 512) used in the\n",
    "                    original load_images function.\n",
    "        square_ok: The 'square_ok' parameter used in the original load_images function.\n",
    "\n",
    "    Returns:\n",
    "        A NumPy array of shape (N, 2) with the transformed keypoint coordinates\n",
    "        on the original image.\n",
    "    \"\"\"\n",
    "    # print(f\"original_size: {original_size}\")\n",
    "    original_height, original_width = original_size\n",
    "    original_height = float(original_height)\n",
    "    original_width = float(original_width)\n",
    "\n",
    "    # --- 1. Determine the dimensions after resizing but *before* cropping (W_res, H_res) ---\n",
    "    # This logic mirrors the _resize_pil_image call in load_images\n",
    "    if size_param == 224:\n",
    "        # Target long side is used for resizing.\n",
    "        target_long_side = round(size_param * max(original_width / original_height, original_height / original_width))\n",
    "        if original_width >= original_height:\n",
    "            W_res = target_long_side\n",
    "            H_res = round(original_height * (target_long_side / original_width))\n",
    "        else:\n",
    "            H_res = target_long_side\n",
    "            W_res = round(original_width * (target_long_side / original_height))\n",
    "    else:\n",
    "        # Long side is resized to size_param.\n",
    "        if original_width >= original_height:\n",
    "            W_res = size_param\n",
    "            H_res = round(original_height * (size_param / original_width))\n",
    "        else:\n",
    "            H_res = size_param\n",
    "            W_res = round(original_width * (size_param / original_height))\n",
    "\n",
    "    # print(f\"H_res, W_res: {H_res}_{W_res}\")\n",
    "\n",
    "    # --- 2. Calculate the cropping offsets used during processing ---\n",
    "    cx, cy = W_res // 2, H_res // 2\n",
    "\n",
    "    if size_param == 224:\n",
    "        half = min(cx, cy)\n",
    "        crop_left = cx - half\n",
    "        crop_top = cy - half\n",
    "    else:\n",
    "        halfw = ((2 * cx) // 16) * 8\n",
    "        halfh = ((2 * cy) // 16) * 8\n",
    "        if not square_ok and W_res == H_res:\n",
    "            halfh = round(3 * halfw / 4)\n",
    "        \n",
    "        crop_left = cx - halfw\n",
    "        crop_top = cy - halfh\n",
    "\n",
    "    \n",
    "\n",
    "    # --- 4. Reverse the Resizing ---\n",
    "    # Determine the actual scaling factor applied during the initial resize\n",
    "    if original_width >= original_height:\n",
    "        scale_factor = size_param / original_width\n",
    "    else:\n",
    "        scale_factor = size_param / original_height\n",
    "    # --- 3. Reverse the Cropping ---\n",
    "    # Add the crop offsets to the keypoints from the cropped image\n",
    "    # print(crop_left, crop_top)\n",
    "    kpts_resized = kpts_crop.astype(float) # Ensure float for accurate division\n",
    "    kpts_resized[:, 0] = kpts_resized[:, 0] + crop_left\n",
    "    kpts_resized[:, 1] = kpts_resized[:, 1] + crop_top\n",
    "\n",
    "\n",
    "    \n",
    "    # Divide by the scale factor to get original coordinates\n",
    "    kpts_original = kpts_resized/ scale_factor \n",
    "\n",
    "    return kpts_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7f827797",
   "metadata": {
    "execution": {
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   "source": [
    "import os\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def draw_matches_on_original_images(img_path1, img_path2, matches_im0, matches_im1, save_path, n_viz=100):\n",
    "    \"\"\"\n",
    "    画出两张原图上的匹配点连线，并保存图像\n",
    "\n",
    "    Args:\n",
    "        img_path1: str, 原图1路径\n",
    "        img_path2: str, 原图2路径\n",
    "        matches_im0: (N, 2) numpy array，图1上的匹配点坐标（在原图上）\n",
    "        matches_im1: (N, 2) numpy array，图2上的匹配点坐标（在原图上）\n",
    "        save_path: str, 输出图像路径\n",
    "        n_viz: int, 可视化前n个匹配（默认100）\n",
    "    \"\"\"\n",
    "    os.makedirs(os.path.dirname(save_path), exist_ok=True)\n",
    "\n",
    "    # 读取图像\n",
    "    img0 = cv2.imread(img_path1)\n",
    "    img1 = cv2.imread(img_path2)\n",
    "    key1 = os.path.basename(img_path1)\n",
    "    key2 = os.path.basename(img_path2)\n",
    "\n",
    "\n",
    "    if img0 is None or img1 is None:\n",
    "        print(f\"Error: Cannot load {img_path1} or {img_path2}\")\n",
    "        return\n",
    "\n",
    "    img0 = cv2.cvtColor(img0, cv2.COLOR_BGR2RGB)\n",
    "    img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "    H0, W0 = img0.shape[:2]\n",
    "    H1, W1 = img1.shape[:2]\n",
    "    canvas_h = max(H0, H1)\n",
    "    canvas = np.zeros((canvas_h, W0 + W1, 3), dtype=np.uint8)\n",
    "    canvas[:H0, :W0] = img0\n",
    "    canvas[:H1, W0:] = img1\n",
    "\n",
    "    # 可视化子集\n",
    "    num_matches = min(len(matches_im0), n_viz)\n",
    "    idxs = np.round(np.linspace(0, len(matches_im0) - 1, num_matches)).astype(int)\n",
    "    cmap = plt.get_cmap('rainbow')\n",
    "\n",
    "    for i, idx in enumerate(idxs):\n",
    "        (x0, y0) = matches_im0[idx]\n",
    "        (x1, y1) = matches_im1[idx]\n",
    "        color = tuple((np.array(cmap(i / n_viz))[:3] * 255).astype(int).tolist())\n",
    "\n",
    "        pt1 = (int(round(x0)), int(round(y0)))\n",
    "        pt2 = (int(round(x1 + W0)), int(round(y1)))\n",
    "\n",
    "        cv2.line(canvas, pt1, pt2, color, thickness=1, lineType=cv2.LINE_AA)\n",
    "        cv2.circle(canvas, pt1, 2, color, -1, lineType=cv2.LINE_AA)\n",
    "        cv2.circle(canvas, pt2, 2, color, -1, lineType=cv2.LINE_AA)\n",
    "\n",
    "    # 保存\n",
    "    cv2.imwrite(f'{save_path}/{key1}_{key2}.jpg', cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR))\n",
    "    # print(f\"Saved match debug image to {save_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fa88b442",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> Loading a list of 2 images\n",
      " - adding /kaggle/input/image-matching-challenge-2025/train/ETs/et_et003.png with resolution 480x640 --> 384x512\n",
      " - adding /kaggle/input/image-matching-challenge-2025/train/ETs/et_et006.png with resolution 480x640 --> 384x512\n",
      " (Found 2 images)\n",
      ">> Inference with model on 1 image pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/inference.py:44: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(enabled=bool(use_amp)):\n",
      "/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/model.py:205: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(enabled=False):\n",
      "/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/inference.py:48: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(enabled=False):\n",
      "100%|██████████| 1/1 [00:00<00:00,  1.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "desc1 shape: torch.Size([512, 384, 24])\n",
      "conf1 shape: torch.Size([512, 384])\n",
      "corres[0].shape: torch.Size([1866, 2])\n",
      "corres[2].shape: torch.Size([1866])\n",
      "conf min: 0.02820398658514023\n",
      "conf max: 3.819314956665039\n",
      "conf mean: 1.5521736145019531\n",
      "conf std: 0.7250813841819763\n",
      "conf median: 1.4392729997634888\n",
      "matches_im0.shape: (1388, 2)\n",
      "512 384 512 384\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_path1 = '/kaggle/input/image-matching-challenge-2025/train/ETs/et_et003.png'\n",
    "img_path2 = '/kaggle/input/image-matching-challenge-2025/train/ETs/et_et006.png'\n",
    "images = load_images([img_path1, img_path2], size=512)\n",
    "output = inference([tuple(images)], mast3r_model, device, batch_size=1, verbose=True)\n",
    "\n",
    "# at this stage, you have the raw dust3r predictions\n",
    "view1, pred1 = output['view1'], output['pred1']\n",
    "view2, pred2 = output['view2'], output['pred2']\n",
    "\n",
    "desc1, desc2 = pred1['desc'].squeeze(0).detach(), pred2['desc'].squeeze(0).detach()\n",
    "\n",
    "# find 2D-2D matches between the two images\n",
    "# matches_im0, matches_im1 = fast_reciprocal_NNs(desc1, desc2, subsample_or_initxy1=8,\n",
    "#                                                device=device, dist='dot', block_size=2**13)\n",
    "conf1, conf2 = pred1['desc_conf'].squeeze(0).detach(), pred2['desc_conf'].squeeze(0).detach()\n",
    "print(f\"desc1 shape: {desc1.shape}\")\n",
    "print(f\"conf1 shape: {conf1.shape}\")\n",
    "corres = extract_correspondences_nonsym(desc1, desc2, conf1, conf2,\n",
    "                                        device=device, subsample=8, pixel_tol=5)\n",
    "print(f\"corres[0].shape: {corres[0].shape}\")\n",
    "print(f\"corres[2].shape: {corres[2].shape}\")\n",
    "score = corres[2]\n",
    "\n",
    "print(\"conf min:\",score.min().item())\n",
    "print(\"conf max:\", score.max().item())\n",
    "print(\"conf mean:\", score.float().mean().item())\n",
    "print(\"conf std:\", score.float().std().item())\n",
    "print(\"conf median:\", score.float().median().item())\n",
    "\n",
    "mask = score >= CONFIG.MATCH_CONF_TH\n",
    "matches_im0 = corres[0][mask].cpu().numpy()\n",
    "matches_im1 = corres[1][mask].cpu().numpy()\n",
    "print(f\"matches_im0.shape: {matches_im0.shape}\")\n",
    "\n",
    "# matches_im0 = corres[0].cpu().numpy()\n",
    "# matches_im1 = corres[1].cpu().numpy()\n",
    "\n",
    "# ignore small border around the edge\n",
    "H0, W0 = view1['true_shape'][0]\n",
    "valid_matches_im0 = (matches_im0[:, 0] >= 3) & (matches_im0[:, 0] < int(W0) - 3) & (\n",
    "    matches_im0[:, 1] >= 3) & (matches_im0[:, 1] < int(H0) - 3)\n",
    "\n",
    "H1, W1 = view2['true_shape'][0]\n",
    "valid_matches_im1 = (matches_im1[:, 0] >= 3) & (matches_im1[:, 0] < int(W1) - 3) & (\n",
    "    matches_im1[:, 1] >= 3) & (matches_im1[:, 1] < int(H1) - 3)\n",
    "\n",
    "valid_matches = valid_matches_im0 & valid_matches_im1\n",
    "matches_im0, matches_im1 = matches_im0[valid_matches], matches_im1[valid_matches]\n",
    "\n",
    "# visualize a few matches\n",
    "import numpy as np\n",
    "import torch\n",
    "import torchvision.transforms.functional\n",
    "from matplotlib import pyplot as pl\n",
    "\n",
    "n_viz = 100\n",
    "num_matches = matches_im0.shape[0]\n",
    "match_idx_to_viz = np.round(np.linspace(0, num_matches - 1, n_viz)).astype(int)\n",
    "viz_matches_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], matches_im1[match_idx_to_viz]\n",
    "\n",
    "image_mean = torch.as_tensor([0.5, 0.5, 0.5], device='cpu').reshape(1, 3, 1, 1)\n",
    "image_std = torch.as_tensor([0.5, 0.5, 0.5], device='cpu').reshape(1, 3, 1, 1)\n",
    "\n",
    "viz_imgs = []\n",
    "for i, view in enumerate([view1, view2]):\n",
    "    rgb_tensor = view['img'] * image_std + image_mean\n",
    "    viz_imgs.append(rgb_tensor.squeeze(0).permute(1, 2, 0).cpu().numpy())\n",
    "\n",
    "H0, W0, H1, W1 = *viz_imgs[0].shape[:2], *viz_imgs[1].shape[:2]\n",
    "print(H0,W0,H1,W1)\n",
    "img0 = np.pad(viz_imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)\n",
    "img1 = np.pad(viz_imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)\n",
    "img = np.concatenate((img0, img1), axis=1)\n",
    "pl.figure()\n",
    "pl.imshow(img)\n",
    "cmap = pl.get_cmap('jet')\n",
    "for i in range(n_viz):\n",
    "    (x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T\n",
    "    pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False)\n",
    "pl.show(block=True)\n",
    "\n",
    "img0 = cv2.imread(img_path1)\n",
    "img1 = cv2.imread(img_path2)\n",
    "H0, W0 = img0.shape[:2]\n",
    "H1, W1 = img1.shape[:2]\n",
    "viz_matches_im0_org = transform_keypoints_to_original(viz_matches_im0, (H0, W0))\n",
    "viz_matches_im1_org = transform_keypoints_to_original(viz_matches_im1, (H1, W1))\n",
    "\n",
    "out_org_dir= os.path.join(\"/kaggle/working\", \"temp\")\n",
    "os.makedirs(out_org_dir, exist_ok=True)\n",
    "\n",
    "draw_matches_on_original_images(img_path1, img_path2, viz_matches_im0_org, viz_matches_im1_org, out_org_dir, n_viz=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a6508918",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:33:00.803022Z",
     "iopub.status.busy": "2026-01-11T03:33:00.802410Z",
     "iopub.status.idle": "2026-01-11T03:33:00.806154Z",
     "shell.execute_reply": "2026-01-11T03:33:00.805614Z"
    },
    "papermill": {
     "duration": 0.035032,
     "end_time": "2026-01-11T03:33:00.807152",
     "exception": false,
     "start_time": "2026-01-11T03:33:00.772120",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_img_pairs_exhaustive(img_fnames):\n",
    "    index_pairs = []\n",
    "    for i in range(len(img_fnames)):\n",
    "        for j in range(i+1, len(img_fnames)):\n",
    "            index_pairs.append((i,j))\n",
    "    return index_pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5c0b79d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:33:00.868944Z",
     "iopub.status.busy": "2026-01-11T03:33:00.868741Z",
     "iopub.status.idle": "2026-01-11T03:33:01.033005Z",
     "shell.execute_reply": "2026-01-11T03:33:01.032324Z"
    },
    "papermill": {
     "duration": 0.196128,
     "end_time": "2026-01-11T03:33:01.034205",
     "exception": false,
     "start_time": "2026-01-11T03:33:00.838077",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import PIL\n",
    "import numpy as np # Ensure numpy is imported for checking np.ndarray\n",
    "from PIL import Image\n",
    "from mast3r.retrieval.processor import Retriever\n",
    "from mast3r.image_pairs import make_pairs\n",
    "from mast3r.model import AsymmetricMASt3R\n",
    "\n",
    "\n",
    "def get_image_list(images_path):\n",
    "    \"\"\"\n",
    "    Scans the specified path for all image files and returns their relative paths.\n",
    "    Skips unidentifiable or corrupt image files.\n",
    "    \"\"\"\n",
    "    file_list = [os.path.relpath(os.path.join(dirpath, filename), images_path)\n",
    "                 for dirpath, _, filenames in os.walk(images_path)\n",
    "                 for filename in filenames]\n",
    "    file_list = sorted(file_list)\n",
    "    image_list = []\n",
    "    for filename in file_list:\n",
    "        try:\n",
    "            with Image.open(os.path.join(images_path, filename)) as im:\n",
    "                im.verify()  # Verify image file integrity\n",
    "                image_list.append(filename)\n",
    "        except (OSError, PIL.UnidentifiedImageError):\n",
    "            print(f'Skipping invalid image file: {filename}')\n",
    "    return image_list\n",
    "\n",
    "\n",
    "def make_pair_with_mast3r_return_pairs(\n",
    "    image_dir: str,\n",
    "    weights_path: str,  # Path to the AsymmetricMASt3R model weights\n",
    "    retrieval_model_path: str,  # Path to the retrieval model (e.g., \"trainingfree.pth\")\n",
    "    scene_graph: str = 'retrieval-20-25',\n",
    "    device: str = 'cuda'\n",
    "):\n",
    "    \"\"\"\n",
    "    Generates image pairs using MASt3R + ASMK retrieval, returning a list of pairs.\n",
    "\n",
    "    Args:\n",
    "        image_dir (str): Path to the directory containing images.\n",
    "        weights_path (str): Path to the AsymmetricMASt3R model weights.\n",
    "        retrieval_model_path (str): Path to the retrieval model (e.g., \"trainingfree.pth\").\n",
    "        scene_graph (str, optional): String defining the scene graph construction strategy. \n",
    "                                     Defaults to 'retrieval-20-1-10-1'.\n",
    "        device (str, optional): PyTorch device to use ('cuda' or 'cpu'). Defaults to 'cuda'.\n",
    "\n",
    "    Returns:\n",
    "        sorted_pairs: List[Tuple[str, str]], where each tuple contains \n",
    "                      the relative paths of the paired images (img1, img2).\n",
    "    \"\"\"\n",
    "    print(\"🖼️ Scanning images...\")\n",
    "    imgs = get_image_list(image_dir)\n",
    "    imgs_fp = [os.path.join(image_dir, f) for f in imgs]\n",
    "\n",
    "    if not imgs:\n",
    "        print(\"⚠️ No valid images found in the directory. Returning empty pairs.\")\n",
    "        return []\n",
    "\n",
    "    print(f\"⚙️ Loading backbone model from {weights_path}...\")\n",
    "    backbone = AsymmetricMASt3R.from_pretrained(weights_path).to(device).eval()\n",
    "\n",
    "    # print(\"🔍 Running ASMK retrieval...\")\n",
    "    retriever = Retriever(retrieval_model_path, backbone=backbone)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        sim_matrix_np = retriever(imgs_fp) \n",
    "        \n",
    "    # Cleanup GPU cache\n",
    "    del retriever\n",
    "    del backbone \n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "    # print(\"🧮 Generating image pairs...\")\n",
    "    # make_pairs 应该期望一个 PyTorch 张量作为 sim_mat\n",
    "    raw_pairs = make_pairs(imgs, scene_graph, prefilter=None, symmetrize=True, sim_mat=sim_matrix_np)\n",
    "    # print(raw_pairs)\n",
    "    \n",
    "    sorted_pairs = sorted(set(tuple(sorted([a, b])) for a, b in raw_pairs))\n",
    "\n",
    "    print(f\"✅ Generated {len(sorted_pairs)} unique image pairs.\")\n",
    "    return sorted_pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7b48f7d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:33:01.097491Z",
     "iopub.status.busy": "2026-01-11T03:33:01.096965Z",
     "iopub.status.idle": "2026-01-11T03:33:01.959573Z",
     "shell.execute_reply": "2026-01-11T03:33:01.958804Z"
    },
    "papermill": {
     "duration": 0.895392,
     "end_time": "2026-01-11T03:33:01.961096",
     "exception": false,
     "start_time": "2026-01-11T03:33:01.065704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import kornia as K\n",
    "import kornia.feature as KF\n",
    "# --- Helper function for image loading (if not already defined) ---\n",
    "def load_torch_image(fname, device=torch.device('cpu')):\n",
    "    img = K.io.load_image(fname, K.io.ImageLoadType.RGB32, device=device)[None, ...]\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "146c063a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:33:02.034827Z",
     "iopub.status.busy": "2026-01-11T03:33:02.034084Z",
     "iopub.status.idle": "2026-01-11T03:33:02.041108Z",
     "shell.execute_reply": "2026-01-11T03:33:02.040379Z"
    },
    "papermill": {
     "duration": 0.044124,
     "end_time": "2026-01-11T03:33:02.042213",
     "exception": false,
     "start_time": "2026-01-11T03:33:01.998089",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Must Use efficientnet global descriptor to get matching shortlists.\n",
    "def get_global_desc(fnames, device = torch.device('cpu')):\n",
    "    processor = AutoImageProcessor.from_pretrained('/kaggle/input/dinov2/pytorch/base/1')\n",
    "    model = AutoModel.from_pretrained('/kaggle/input/dinov2/pytorch/base/1')\n",
    "    model = model.eval()\n",
    "    model = model.to(device)\n",
    "    global_descs_dinov2 = []\n",
    "    for i, img_fname_full in tqdm(enumerate(fnames),total= len(fnames)):\n",
    "        key = os.path.splitext(os.path.basename(img_fname_full))[0]\n",
    "        timg = load_torch_image(img_fname_full)\n",
    "        with torch.inference_mode():\n",
    "            inputs = processor(images=timg, return_tensors=\"pt\", do_rescale=False).to(device)\n",
    "            outputs = model(**inputs)\n",
    "            dino_mac = F.normalize(outputs.last_hidden_state[:,1:].max(dim=1)[0], dim=1, p=2)\n",
    "        global_descs_dinov2.append(dino_mac.detach().cpu())\n",
    "    global_descs_dinov2 = torch.cat(global_descs_dinov2, dim=0)\n",
    "    return global_descs_dinov2\n",
    "\n",
    "\n",
    "def get_img_pairs_exhaustive(img_fnames):\n",
    "    index_pairs = []\n",
    "    for i in range(len(img_fnames)):\n",
    "        for j in range(i+1, len(img_fnames)):\n",
    "            index_pairs.append((i,j))\n",
    "    return index_pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3f759e88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:33:02.105967Z",
     "iopub.status.busy": "2026-01-11T03:33:02.105414Z",
     "iopub.status.idle": "2026-01-11T03:33:02.111376Z",
     "shell.execute_reply": "2026-01-11T03:33:02.110781Z"
    },
    "papermill": {
     "duration": 0.038436,
     "end_time": "2026-01-11T03:33:02.112376",
     "exception": false,
     "start_time": "2026-01-11T03:33:02.073940",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_image_pairs_shortlist_org(fnames,\n",
    "                              sim_th = 0.6, # should be strict\n",
    "                              min_pairs = 60,\n",
    "                              exhaustive_if_less = 20,\n",
    "                              device=torch.device('cpu')):\n",
    "    num_imgs = len(fnames)\n",
    "    if num_imgs <= exhaustive_if_less:\n",
    "        return get_img_pairs_exhaustive(fnames)\n",
    "    descs = get_global_desc(fnames, device=device)\n",
    "    dm = torch.cdist(descs, descs, p=2).detach().cpu().numpy()\n",
    "\n",
    "    \n",
    "    mask = dm <= sim_th\n",
    "    total = 0\n",
    "    matching_list = []\n",
    "    ar = np.arange(num_imgs)\n",
    "    already_there_set = []\n",
    "    for st_idx in range(num_imgs-1):\n",
    "        mask_idx = mask[st_idx]\n",
    "        to_match = ar[mask_idx]\n",
    "        if len(to_match) < min_pairs:\n",
    "            to_match = np.argsort(dm[st_idx])[:min_pairs]  \n",
    "        for idx in to_match:\n",
    "            if st_idx == idx:\n",
    "                continue\n",
    "            if dm[st_idx, idx] < 10000:\n",
    "                matching_list.append(tuple(sorted((st_idx, idx.item()))))\n",
    "                total+=1\n",
    "    matching_list = sorted(list(set(matching_list)))\n",
    "    return matching_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3451a92f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:33:02.178518Z",
     "iopub.status.busy": "2026-01-11T03:33:02.178238Z",
     "iopub.status.idle": "2026-01-11T03:33:02.182595Z",
     "shell.execute_reply": "2026-01-11T03:33:02.181874Z"
    },
    "papermill": {
     "duration": 0.039337,
     "end_time": "2026-01-11T03:33:02.183719",
     "exception": false,
     "start_time": "2026-01-11T03:33:02.144382",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pycolmap version: 3.11.1\n"
     ]
    }
   ],
   "source": [
    "import pycolmap\n",
    "print(f\"pycolmap version: {pycolmap.__version__}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ce68d2a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:33:02.250717Z",
     "iopub.status.busy": "2026-01-11T03:33:02.250105Z",
     "iopub.status.idle": "2026-01-11T03:33:02.394708Z",
     "shell.execute_reply": "2026-01-11T03:33:02.393893Z"
    },
    "papermill": {
     "duration": 0.178359,
     "end_time": "2026-01-11T03:33:02.395849",
     "exception": false,
     "start_time": "2026-01-11T03:33:02.217490",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"ETs\" -> num_images=22\n",
      "Dataset \"amy_gardens\" -> num_images=200\n",
      "Dataset \"fbk_vineyard\" -> num_images=163\n",
      "Dataset \"imc2023_haiper\" -> num_images=54\n",
      "Dataset \"imc2023_heritage\" -> num_images=209\n",
      "Dataset \"imc2023_theather_imc2024_church\" -> num_images=76\n",
      "Dataset \"imc2024_dioscuri_baalshamin\" -> num_images=138\n",
      "Dataset \"imc2024_lizard_pond\" -> num_images=214\n",
      "Dataset \"pt_brandenburg_british_buckingham\" -> num_images=225\n",
      "Dataset \"pt_piazzasanmarco_grandplace\" -> num_images=168\n",
      "Dataset \"pt_sacrecoeur_trevi_tajmahal\" -> num_images=225\n",
      "Dataset \"pt_stpeters_stpauls\" -> num_images=200\n",
      "Dataset \"stairs\" -> num_images=51\n"
     ]
    }
   ],
   "source": [
    "# Collect vital info from the dataset\n",
    "\n",
    "@dataclasses.dataclass\n",
    "class Prediction:\n",
    "    image_id: str | None  # A unique identifier for the row -- unused otherwise. Used only on the hidden test set.\n",
    "    dataset: str\n",
    "    filename: str\n",
    "    cluster_index: int | None = None\n",
    "    rotation: np.ndarray | None = None\n",
    "    translation: np.ndarray | None = None\n",
    "\n",
    "# Set is_train=True to run the notebook on the training data.\n",
    "# Set is_train=False if submitting an entry to the competition (test data is hidden, and different from what you see on the \"test\" folder).\n",
    "is_train = False\n",
    "data_dir = '/kaggle/input/image-matching-challenge-2025'\n",
    "workdir = '/kaggle/working/result/'\n",
    "os.makedirs(workdir, exist_ok=True)\n",
    "\n",
    "if is_train:\n",
    "    sample_submission_csv = os.path.join(data_dir, 'train_labels.csv')\n",
    "else:\n",
    "    sample_submission_csv = os.path.join(data_dir, 'sample_submission.csv')\n",
    "\n",
    "samples = {}\n",
    "competition_data = pd.read_csv(sample_submission_csv)\n",
    "for _, row in competition_data.iterrows():\n",
    "    # Note: For the test data, the \"scene\" column has no meaning, and the rotation_matrix and translation_vector columns are random.\n",
    "    if row.dataset not in samples:\n",
    "        samples[row.dataset] = []\n",
    "    samples[row.dataset].append(\n",
    "        Prediction(\n",
    "            image_id=None if is_train else row.image_id,\n",
    "            dataset=row.dataset,\n",
    "            filename=row.image\n",
    "        )\n",
    "    )\n",
    "\n",
    "for dataset in samples:\n",
    "    print(f'Dataset \"{dataset}\" -> num_images={len(samples[dataset])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a527ca1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:33:02.457393Z",
     "iopub.status.busy": "2026-01-11T03:33:02.457153Z",
     "iopub.status.idle": "2026-01-11T03:33:02.460248Z",
     "shell.execute_reply": "2026-01-11T03:33:02.459754Z"
    },
    "papermill": {
     "duration": 0.034561,
     "end_time": "2026-01-11T03:33:02.461261",
     "exception": false,
     "start_time": "2026-01-11T03:33:02.426700",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import multiprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8b006275",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:33:02.524346Z",
     "iopub.status.busy": "2026-01-11T03:33:02.524091Z",
     "iopub.status.idle": "2026-01-11T03:33:02.533740Z",
     "shell.execute_reply": "2026-01-11T03:33:02.533171Z"
    },
    "papermill": {
     "duration": 0.041871,
     "end_time": "2026-01-11T03:33:02.534713",
     "exception": false,
     "start_time": "2026-01-11T03:33:02.492842",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "\n",
    "\n",
    "def save_match_viz_image(\n",
    "    key1,\n",
    "    key2,\n",
    "    view1,\n",
    "    view2,\n",
    "    matches_im0,\n",
    "    matches_im1,\n",
    "    feature_dir,\n",
    "    n_viz: int = 100\n",
    "):\n",
    "    \"\"\"\n",
    "    Save a visual match image for a pair of images using descriptor matches.\n",
    "\n",
    "    Parameters:\n",
    "        key1, key2: str\n",
    "            Base filenames of the matched image pair (used for naming the output).\n",
    "        view1, view2: dict\n",
    "            MASt3R inference outputs containing 'img' and 'true_shape'.\n",
    "        matches_im0, matches_im1: np.ndarray of shape (N, 2)\n",
    "            Coordinates of matched keypoints in image 0 and image 1.\n",
    "        feature_dir: str\n",
    "            Path to save the visualized match image.\n",
    "        n_viz: int\n",
    "            Number of matches to visualize (default: 100).\n",
    "    \"\"\"\n",
    "    if matches_im0.shape[0] == 0:\n",
    "        return  # nothing to draw\n",
    "\n",
    "    n_viz = min(n_viz, matches_im0.shape[0])\n",
    "    idx = np.round(np.linspace(0, matches_im0.shape[0] - 1, n_viz)).astype(int)\n",
    "    viz_matches_im0 = matches_im0[idx]\n",
    "    viz_matches_im1 = matches_im1[idx]\n",
    "\n",
    "    image_mean = torch.tensor([0.5, 0.5, 0.5]).reshape(1, 3, 1, 1)\n",
    "    image_std = torch.tensor([0.5, 0.5, 0.5]).reshape(1, 3, 1, 1)\n",
    "\n",
    "    viz_imgs = []\n",
    "    for view in [view1, view2]:\n",
    "        rgb_tensor = view['img'].cpu() * image_std + image_mean\n",
    "        rgb_np = rgb_tensor.squeeze(0).permute(1, 2, 0).clamp(0, 1).numpy()\n",
    "        viz_imgs.append((rgb_np * 255).astype(np.uint8))\n",
    "\n",
    "    H0, W0 = viz_imgs[0].shape[:2]\n",
    "    H1, W1 = viz_imgs[1].shape[:2]\n",
    "    img0 = np.pad(viz_imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant')\n",
    "    img1 = np.pad(viz_imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant')\n",
    "    img = np.concatenate((img0, img1), axis=1)\n",
    "\n",
    "    cmap = plt.get_cmap('jet')\n",
    "    for i in range(n_viz):\n",
    "        (x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T\n",
    "        color = tuple(int(c * 255) for c in cmap(i / (n_viz - 1))[:3])\n",
    "        cv2.line(img, (int(x0), int(y0)), (int(x1 + W0), int(y1)), color, thickness=1)\n",
    "        cv2.circle(img, (int(x0), int(y0)), radius=2, color=color, thickness=-1)\n",
    "        cv2.circle(img, (int(x1 + W0), int(y1)), radius=2, color=color, thickness=-1)\n",
    "\n",
    "    out_dir = os.path.join(feature_dir, \"debug_vis\")\n",
    "    os.makedirs(out_dir, exist_ok=True)\n",
    "    out_path = os.path.join(out_dir, f\"{key1}-{key2}.jpg\")\n",
    "    cv2.imwrite(out_path, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))\n",
    "    print(f\"[Match Debug] Saved to {out_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0cf01de4",
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   "source": [
    "from typing import Dict, Tuple\n",
    "from collections import defaultdict\n",
    "import numpy as np\n",
    "\n",
    "def unify_keypoints_and_matches(\n",
    "    out_match: Dict[str, Dict[str, np.ndarray]],\n",
    "    round_digits: int = 1\n",
    ") -> Tuple[\n",
    "    Dict[str, np.ndarray],                    # global_keypoints[img] = (N, 2)\n",
    "    Dict[Tuple[str, str], np.ndarray]         # global_matches[(img1, img2)] = (M, 2) (global IDs)\n",
    "]:\n",
    "    # Step 1: 收集每张图所有坐标\n",
    "    keypoints_per_image = defaultdict(list)\n",
    "\n",
    "    for img1, subdict in out_match.items():\n",
    "        for img2, match in subdict.items():\n",
    "            pts1 = np.round(match[:, :2], decimals=round_digits)\n",
    "            pts2 = np.round(match[:, 2:], decimals=round_digits)\n",
    "            keypoints_per_image[img1].append(pts1)\n",
    "            keypoints_per_image[img2].append(pts2)\n",
    "\n",
    "    # Step 2: 为每张图构建 unique keypoints 和坐标 -> id 映射\n",
    "    global_keypoints = {}\n",
    "    coord_to_id = {}\n",
    "\n",
    "    for img, kpt_list in keypoints_per_image.items():\n",
    "        all_pts = np.concatenate(kpt_list, axis=0)\n",
    "        all_pts = np.round(all_pts, decimals=round_digits)\n",
    "        unique_pts, inverse = np.unique(all_pts, axis=0, return_inverse=True)\n",
    "        global_keypoints[img] = unique_pts\n",
    "\n",
    "        # 建立映射 coord → id\n",
    "        coord_to_id[img] = {tuple(pt): idx for idx, pt in enumerate(unique_pts)}\n",
    "\n",
    "    # Step 3: 将匹配对转换为 global_id 匹配\n",
    "    global_matches = {}\n",
    "\n",
    "    for img1, subdict in out_match.items():\n",
    "        for img2, match in subdict.items():\n",
    "            pts1 = np.round(match[:, :2], decimals=round_digits)\n",
    "            pts2 = np.round(match[:, 2:], decimals=round_digits)\n",
    "\n",
    "            ids1 = np.array([coord_to_id[img1][tuple(pt)] for pt in pts1])\n",
    "            ids2 = np.array([coord_to_id[img2][tuple(pt)] for pt in pts2])\n",
    "\n",
    "            global_matches[(img1, img2)] = np.stack([ids1, ids2], axis=1)\n",
    "\n",
    "    return global_keypoints, global_matches\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f668b674",
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   "source": [
    "import os\n",
    "import h5py\n",
    "import numpy as np\n",
    "\n",
    "def save_unified_keypoints_and_matches(global_keypoints, global_matches, feature_dir, lock=None):\n",
    "    os.makedirs(feature_dir, exist_ok=True)\n",
    "    save_kpts_file = os.path.join(feature_dir, 'keypoints.h5')\n",
    "    save_matches_file = os.path.join(feature_dir, 'matches.h5')\n",
    "    save_matches_txt_file = os.path.join(feature_dir, 'pairs.txt')\n",
    "\n",
    "    # 删除旧文件\n",
    "    for f in [save_kpts_file, save_matches_file, save_matches_txt_file]:\n",
    "        if os.path.exists(f):\n",
    "            os.remove(f)\n",
    "\n",
    "    # 保存关键点坐标\n",
    "    with h5py.File(save_kpts_file, 'w') as f_kp:\n",
    "        for img_name, kpts in global_keypoints.items():\n",
    "            f_kp[img_name] = kpts\n",
    "        #     f_kp.create_dataset(img_name, data=np.asarray(kpts, dtype=np.float32))\n",
    "        # f_kp.flush()\n",
    "    print(f\"✅ Saved keypoints to {save_kpts_file}\")\n",
    "\n",
    "    # 保存匹配\n",
    "    with h5py.File(save_matches_file, 'w') as f_match:\n",
    "        for (img1, img2), match in global_matches.items():\n",
    "            group = f_match.require_group(img1)\n",
    "            if len(match) >= CONFIG.MAST3R_MIN_PAIR:\n",
    "            # match = np.asarray(match, dtype=np.int32)\n",
    "                group.create_dataset(img2, data=match)\n",
    "        # f_match.flush()\n",
    "    print(f\"✅ Saved matches to {save_matches_file}\")\n",
    "    \n",
    "    with h5py.File(save_matches_file, 'r') as f, open(save_matches_txt_file, 'w') as fout:\n",
    "        for k1 in f.keys():\n",
    "            group = f[k1]\n",
    "            for k2 in group.keys():\n",
    "                fout.write(f\"{k1} {k2}\\n\")\n",
    "    print(f\"✅ Saved matches to {save_matches_txt_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4a79df03",
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   "source": [
    "def match_with_mast3r_and_save(index_pairs, image_list, feature_dir, model, device, lock):\n",
    "    os.makedirs(feature_dir, exist_ok=True)\n",
    "    cache = {}\n",
    "    unique_keypoints = defaultdict(list)\n",
    "    out_match = defaultdict(dict)\n",
    "    local_cache = {}\n",
    "    \n",
    "    out_dir = os.path.join(feature_dir, \"pair_res\")\n",
    "    out_org_dir= os.path.join(feature_dir, \"pair_res_onorg\")\n",
    "    os.makedirs(out_dir, exist_ok=True)\n",
    "    os.makedirs(out_org_dir, exist_ok=True)\n",
    "    \n",
    "    for idx1, idx2 in tqdm(index_pairs):\n",
    "        name1, name2 = image_list[idx1], image_list[idx2]\n",
    "        key1, key2 = os.path.basename(name1), os.path.basename(name2)\n",
    "\n",
    "        # Only re-run inference for key1 if not in cache\n",
    "        images = load_images([name1, name2], size=512, verbose=False)\n",
    "        output = inference([tuple(images)], mast3r_model, device, batch_size=1, verbose=False)\n",
    "        \n",
    "        # at this stage, you have the raw dust3r predictions\n",
    "        view1, pred1 = output['view1'], output['pred1']\n",
    "        view2, pred2 = output['view2'], output['pred2']\n",
    "        \n",
    "        desc1, desc2 = pred1['desc'].squeeze(0).detach(), pred2['desc'].squeeze(0).detach()\n",
    "\n",
    "        # matches_im0, matches_im1 = fast_reciprocal_NNs(desc1, desc2, subsample_or_initxy1=8, device=device)\n",
    "        # print(f\"get pair for {key1}_{key2}, {len(matches_im0)}\")\n",
    "\n",
    "        conf1, conf2 = pred1['desc_conf'].squeeze(0).detach(), pred2['desc_conf'].squeeze(0).detach()\n",
    "        corres = extract_correspondences_nonsym(desc1, desc2, conf1, conf2,\n",
    "                                        device=device, subsample=8, pixel_tol=5)\n",
    "        score = corres[2]\n",
    "        mask = score >= CONFIG.MATCH_CONF_TH\n",
    "        matches_im0 = corres[0][mask].cpu().numpy()\n",
    "        matches_im1 = corres[1][mask].cpu().numpy()\n",
    "\n",
    "        # matches_im0 = corres[0].cpu().numpy()\n",
    "        # matches_im1 = corres[1].cpu().numpy()\n",
    "\n",
    "        if len(matches_im0) < CONFIG.MAST3R_MIN_PAIR:\n",
    "            continue\n",
    "\n",
    "        H0, W0 = view1['true_shape'][0].tolist()\n",
    "        H1, W1 = view2['true_shape'][0].tolist()\n",
    "        \n",
    "        valid0 = (matches_im0[:, 0] >= 3) & (matches_im0[:, 0] < W0 - 3) & (matches_im0[:, 1] >= 3) & (matches_im0[:, 1] < H0 - 3)\n",
    "        valid1 = (matches_im1[:, 0] >= 3) & (matches_im1[:, 0] < W1 - 3) & (matches_im1[:, 1] >= 3) & (matches_im1[:, 1] < H1 - 3)\n",
    "        valid = valid0 & valid1\n",
    "\n",
    "        matches_im0 = matches_im0[valid]\n",
    "        matches_im1 = matches_im1[valid]\n",
    "        if len(matches_im0) < CONFIG.MAST3R_MIN_PAIR:\n",
    "            continue\n",
    "        # print(\"transform_keypoints_to_original begin\")\n",
    "        # print(f\"{key1}_{key2}: {len(matches_im0)} matches\")\n",
    "        img0 = cv2.imread(name1)\n",
    "        img1 = cv2.imread(name2)\n",
    "        H0, W0 = img0.shape[:2]\n",
    "        H1, W1 = img1.shape[:2]\n",
    "        matches_im0_org = transform_keypoints_to_original(matches_im0, (H0, W0))\n",
    "        matches_im1_org = transform_keypoints_to_original(matches_im1, (H1, W1))\n",
    "\n",
    "        # matches_im0_org = transform_keypoints_to_original(matches_im0, view1['true_shape'][0].tolist())\n",
    "        # matches_im1_org = transform_keypoints_to_original(matches_im1, view2['true_shape'][0].tolist())\n",
    "        # print(\"transform_keypoints_to_original end\")\n",
    "\n",
    "        unique_keypoints[key1].append(matches_im0_org)\n",
    "        unique_keypoints[key2].append(matches_im1_org)\n",
    "        out_match[key1][key2] = np.concatenate([matches_im0_org, matches_im1_org], axis=1)\n",
    "        if False:\n",
    "            save_match_viz_image(key1, key2, view1, view2, matches_im0, matches_im1, out_dir)\n",
    "        if False:\n",
    "            draw_matches_on_original_images(name1, name2, matches_im0_org, matches_im1_org, out_org_dir)\n",
    "    \n",
    "    # print(\"out of loop\")\n",
    "\n",
    "    keypoints_unified = {}\n",
    "    keypoints_id_map = {}\n",
    "    out_match_unified = defaultdict(dict)\n",
    "\n",
    "    global_keypoints, global_matches = unify_keypoints_and_matches(out_match)\n",
    "    # print(\"points and matches unified\")\n",
    "\n",
    "    save_unified_keypoints_and_matches(global_keypoints, global_matches, feature_dir, lock)\n",
    "    # print(f\"Saved keypoints and matches to {feature_dir}\")\n"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "import cv2\n",
    "import h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "\n",
    "def draw_keypoints_and_matches(images_input, unified_kp_path, remapped_matches_path, feature_dir='visualization_output'):\n",
    "    output_dir = os.path.join(feature_dir, 'visualization_output')\n",
    "    os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "    # Load images and determine image_keys for HDF5 lookup\n",
    "    if isinstance(images_input[0], str):\n",
    "        loaded_images = [cv2.imread(img_path) for img_path in images_input]\n",
    "        image_keys = [os.path.basename(img_path) for img_path in images_input]\n",
    "    else:\n",
    "        loaded_images = images_input\n",
    "        # If images_input are already arrays, you need to provide the corresponding keys\n",
    "        # This part is crucial: image_keys MUST align with the HDF5 keys\n",
    "        image_keys = image_keys_in_h5 # Use the predefined list for the dummy case\n",
    "\n",
    "    # Load unified keypoints\n",
    "    keypoints_data = {}\n",
    "    with h5py.File(unified_kp_path, 'r') as f_kp:\n",
    "        for img_name_raw in f_kp.keys():\n",
    "            img_name = img_name_raw.decode('utf-8') if isinstance(img_name_raw, bytes) else img_name_raw\n",
    "            keypoints_data[img_name] = f_kp[img_name_raw][()] # Access with raw key if bytes\n",
    "\n",
    "    # Load remapped matches - CORRECTED LOGIC\n",
    "    # Store (img1_key, img2_key) directly with matches for robust iteration\n",
    "    matches_data_pairs = [] # Will store (img1_key, img2_key, matches_array)\n",
    "    with h5py.File(remapped_matches_path, 'r') as f_matches:\n",
    "        print(\"\\n--- Loading remapped matches from HDF5 ---\")\n",
    "        for img1_group_key_candidate in tqdm(f_matches.keys(), desc=\"Loading matches\"):\n",
    "            img1_key = img1_group_key_candidate.decode('utf-8') if isinstance(img1_group_key_candidate, bytes) else img1_group_key_candidate\n",
    "\n",
    "            img1_group = f_matches[img1_group_key_candidate] # Access with raw key\n",
    "\n",
    "            if isinstance(img1_group, h5py.Group):\n",
    "                for img2_dataset_key_candidate in img1_group.keys():\n",
    "                    img2_key = img2_dataset_key_candidate.decode('utf-8') if isinstance(img2_dataset_key_candidate, bytes) else img2_dataset_key_candidate\n",
    "\n",
    "                    try:\n",
    "                        matches_array = img1_group[img2_dataset_key_candidate][()]\n",
    "                        matches_data_pairs.append((img1_key, img2_key, matches_array))\n",
    "                    except Exception as e:\n",
    "                        print(f\"Error loading matches for pair ({img1_key}, {img2_key}): {e}\")\n",
    "            else:\n",
    "                print(f\"Warning: Expected '{img1_key}' to be a group, but found {type(img1_group)}. Skipping its contents.\")\n",
    "\n",
    "\n",
    "    # --- Drawing Keypoints ---\n",
    "    print(\"\\n--- Drawing Keypoints ---\")\n",
    "    for i, img_key in enumerate(image_keys):\n",
    "        if img_key in keypoints_data:\n",
    "            img = loaded_images[i].copy()\n",
    "            kpts = keypoints_data[img_key]\n",
    "\n",
    "            for kp in kpts:\n",
    "                x, y = int(kp[0]), int(kp[1])\n",
    "                cv2.circle(img, (x, y), 3, (0, 255, 0), -1) # Green circle for keypoint\n",
    "\n",
    "            output_kp_path = os.path.join(output_dir, f\"keypoints_{img_key}\")\n",
    "            if len(img.shape) == 2:\n",
    "                img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\n",
    "            cv2.imwrite(output_kp_path, img)\n",
    "            print(f\"Keypoints drawn on {img_key}, saved to {output_kp_path}\")\n",
    "        else:\n",
    "            print(f\"No keypoints found for {img_key} in unified keypoints file.\")\n",
    "\n",
    "    # --- Drawing Matches ---\n",
    "    print(\"\\n--- Drawing Matches ---\")\n",
    "    # Iterate through the (img1_key, img2_key, matches) tuples directly\n",
    "    for img_name1, img_name2, matches in matches_data_pairs:\n",
    "        # We no longer need to split img_pair_key, as we have img_name1 and img_name2 directly\n",
    "\n",
    "        # Find the actual image objects and their keypoints using image_keys list\n",
    "        try:\n",
    "            img1_idx = image_keys.index(img_name1)\n",
    "            img2_idx = image_keys.index(img_name2)\n",
    "        except ValueError:\n",
    "            print(f\"Skipping matches for {img_name1}-{img_name2}: One or both image names not found in the provided 'images' list/keys.\")\n",
    "            continue\n",
    "\n",
    "        img1 = loaded_images[img1_idx].copy()\n",
    "        img2 = loaded_images[img2_idx].copy()\n",
    "\n",
    "        kpts1 = keypoints_data.get(img_name1)\n",
    "        kpts2 = keypoints_data.get(img_name2)\n",
    "\n",
    "        if kpts1 is None or kpts2 is None:\n",
    "            print(f\"Skipping matches for {img_name1}-{img_name2}: keypoints not found for one or both images in unified keypoints.\")\n",
    "            continue\n",
    "        if len(matches) == 0:\n",
    "            print(f\"No matches to draw for {img_name1}-{img_name2}.\")\n",
    "            continue\n",
    "\n",
    "        # Ensure images are 3 channels for drawing lines\n",
    "        if len(img1.shape) == 2:\n",
    "            img1 = cv2.cvtColor(img1, cv2.COLOR_GRAY2BGR)\n",
    "        if len(img2.shape) == 2:\n",
    "            img2 = cv2.cvtColor(img2, cv2.COLOR_GRAY2BGR)\n",
    "\n",
    "        # Create a concatenated image for drawing matches\n",
    "        h1, w1 = img1.shape[:2]\n",
    "        h2, w2 = img2.shape[:2]\n",
    "        max_h = max(h1, h2)\n",
    "        matched_img = np.zeros((max_h, w1 + w2, 3), dtype=np.uint8)\n",
    "        matched_img[0:h1, 0:w1] = img1\n",
    "        matched_img[0:h2, w1:w1+w2] = img2\n",
    "\n",
    "        num_matches_to_draw = min(len(matches), 200) # Draw up to 200 matches to avoid clutter, adjust as needed\n",
    "\n",
    "        for i in np.linspace(0, len(matches) - 1, num_matches_to_draw, dtype=int):\n",
    "            match = matches[i]\n",
    "            kp1_idx, kp2_idx = int(match[0]), int(match[1])\n",
    "\n",
    "            # Bounds check for keypoint indices\n",
    "            if kp1_idx >= len(kpts1) or kp2_idx >= len(kpts2):\n",
    "                # print(f\"Warning: Match index out of bounds for {img_name1}-{img_name2}. Skipping match {kp1_idx}-{kp2_idx}.\")\n",
    "                continue\n",
    "\n",
    "            pt1 = tuple(map(int, kpts1[kp1_idx][:2]))\n",
    "            pt2 = tuple(map(int, kpts2[kp2_idx][:2]))\n",
    "\n",
    "            # Draw circles on the concatenated image\n",
    "            cv2.circle(matched_img, pt1, 5, (0, 0, 255), 2) # Red circle on img1 side\n",
    "            cv2.circle(matched_img, (pt2[0] + w1, pt2[1]), 5, (255, 0, 0), 2) # Blue circle on img2 side\n",
    "\n",
    "            # Draw a line connecting the matched keypoints\n",
    "            color = tuple(np.random.randint(0, 255, 3).tolist())\n",
    "            cv2.line(matched_img, pt1, (pt2[0] + w1, pt2[1]), color, 1)\n",
    "\n",
    "        output_match_path = os.path.join(output_dir, f\"matches_{img_name1}_{img_name2}.png\")\n",
    "        cv2.imwrite(output_match_path, matched_img)\n",
    "        print(f\"Matches drawn between {img_name1} and {img_name2}, saved to {output_match_path}\")\n",
    "\n",
    "\n",
    "# Example call (replace with your actual 'images' list)\n",
    "# If your 'images' are file paths:\n",
    "# images_file_paths = ['path/to/your/image1.jpg', 'path/to/your/image2.jpg', ...]\n",
    "# draw_keypoints_and_matches(images_file_paths, unified_kp_path, remapped_matches_path)\n",
    "\n",
    "# If your 'images' are loaded numpy arrays (as in the dummy example above):\n",
    "# draw_keypoints_and_matches(images, unified_kp_path, remapped_matches_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1e0fe2db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:33:02.890095Z",
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     "iopub.status.idle": "2026-01-11T03:33:02.906279Z",
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    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import gc\n",
    "import time\n",
    "import numpy as np\n",
    "import concurrent.futures\n",
    "import multiprocessing\n",
    "from pathlib import Path\n",
    "from time import sleep, time\n",
    "# from pycolmap import verify_matches, TwoViewGeometryOptions\n",
    "\n",
    "def run_verify_matches_safe(database_path, pairs_path, max_retries=5):\n",
    "    def _safe_verify():\n",
    "        verify_matches(\n",
    "            database_path=database_path,\n",
    "            pairs_path=pairs_path,\n",
    "            options=TwoViewGeometryOptions()\n",
    "        )\n",
    "\n",
    "    for attempt in range(max_retries):\n",
    "        print(f\"🔁 Attempt {attempt + 1} to run verify_matches\")\n",
    "        proc = multiprocessing.Process(target=_safe_verify)\n",
    "        proc.start()\n",
    "        proc.join()\n",
    "\n",
    "        if proc.exitcode in [0, 1]:\n",
    "            print(\"✅ verify_matches succeeded\")\n",
    "            return\n",
    "        else:\n",
    "            print(f\"⚠️ verify_matches crashed with code {proc.exitcode}\")\n",
    "    raise RuntimeError(\"❌ verify_matches failed after multiple retries.\")\n",
    "\n",
    "def reconstruct_from_db(feature_dir, img_dir):\n",
    "    result = {}\n",
    "    local_timings = {'RANSAC': [], 'Reconstruction': []}\n",
    "\n",
    "    database_path = f'{feature_dir}/colmap.db'\n",
    "    pairs_txt = f'{feature_dir}/pairs.txt'\n",
    "    if os.path.isfile(database_path):\n",
    "        os.remove(database_path)\n",
    "    gc.collect()\n",
    "    sleep(1)\n",
    "\n",
    "    import_into_colmap(img_dir, feature_dir=feature_dir, database_path=database_path)\n",
    "    sleep(1)\n",
    "    print(f\"import {database_path} done!\")\n",
    "    output_path = f'{feature_dir}/colmap_rec'\n",
    "    os.makedirs(output_path, exist_ok=True)\n",
    "    print(\"colmap database\")\n",
    "\n",
    "    t = time()\n",
    "    run_verify_matches_safe(database_path, pairs_txt)\n",
    "    print(\"verify matching done!!!!\")\n",
    "    local_timings['RANSAC'].append(time() - t)\n",
    "    print(f'RANSAC in {local_timings[\"RANSAC\"][-1]:.4f} sec')\n",
    "\n",
    "    t = time()\n",
    "    mapper_options = pycolmap.IncrementalPipelineOptions()\n",
    "    mapper_options.min_model_size = 3\n",
    "    mapper_options.max_num_models = 5\n",
    "    maps = pycolmap.incremental_mapping(database_path=database_path, image_path=img_dir, \n",
    "                                        output_path=output_path, options=mapper_options)\n",
    "    print(maps)\n",
    "    for map_index, rec in maps.items():\n",
    "        result[map_index]={}\n",
    "        for img_id, image in rec.images.items():\n",
    "            result[map_index][image.name] = {\n",
    "                'R': image.cam_from_world.rotation.matrix().tolist(),\n",
    "                't': image.cam_from_world.translation.tolist()\n",
    "            }\n",
    "    local_timings['Reconstruction'].append(time() - t)\n",
    "    print(f'Reconstruction done in {local_timings[\"Reconstruction\"][-1]:.4f} sec')\n",
    "\n",
    "    return result, local_timings\n",
    "\n",
    "def run_one_dataset(dataset, predictions, data_dir, workdir, is_train, model, device, lock=None):\n",
    "    timings = {\n",
    "        \"shortlisting\": [],\n",
    "        \"feature_matching\": [],\n",
    "        \"RANSAC\": [],\n",
    "        \"Reconstruction\": [],\n",
    "    }\n",
    "\n",
    "    try:\n",
    "        images_dir = os.path.join(data_dir, 'train' if is_train else 'test', dataset)\n",
    "        images = [os.path.join(images_dir, p.filename) for p in predictions]\n",
    "\n",
    "        print(f'Processing dataset \"{dataset}\": {len(images)} images')\n",
    "        filename_to_index = {p.filename: idx for idx, p in enumerate(predictions)}\n",
    "        feature_dir = os.path.join(workdir, 'featureout', dataset)\n",
    "        os.makedirs(feature_dir, exist_ok=True)\n",
    "\n",
    "        t = time()\n",
    "        # index_pairs = get_image_pairs_shortlist_org(\n",
    "        #     images, sim_th=0.2, min_pairs=10,\n",
    "        #     exhaustive_if_less=20, device=device)\n",
    "\n",
    "        index_img_pairs = make_pair_with_mast3r_return_pairs(image_dir=images_dir,\n",
    "                      weights_path = local_model_directory,  # Path to the AsymmetricMASt3R model weights (e.g., \"naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric\" or local path)\n",
    "                      retrieval_model_path = retrival_model_dir,  # Path to the retrieval model (e.g., \"trainingfree.pth\")\n",
    "                      device = device)\n",
    "\n",
    "        indexed_pairs = []\n",
    "        for filename1, filename2 in index_img_pairs:\n",
    "            try:\n",
    "                idx1 = filename_to_index[filename1]\n",
    "                idx2 = filename_to_index[filename2]\n",
    "                indexed_pairs.append((idx1, idx2))\n",
    "            except KeyError as e:\n",
    "                print(f\"Warning: Filename not found in mapping: {e}. Skipping pair ({filename1}, {filename2}).\")\n",
    "\n",
    "        timings['shortlisting'].append(time() - t)\n",
    "        print(f'Shortlisting done: {len(indexed_pairs)} pairs')\n",
    "        gc.collect()\n",
    "\n",
    "        t = time()\n",
    "        match_with_mast3r_and_save(indexed_pairs, images, feature_dir, model, device, lock)\n",
    "        timings['feature_matching'].append(time() - t)\n",
    "        print(f'MASt3R matching done in {time() - t:.2f} sec')\n",
    "        gc.collect()\n",
    "\n",
    "        maps, local_timings = reconstruct_from_db(feature_dir, images_dir)\n",
    "        # print(maps)\n",
    "        \n",
    "        # Sort map clusters by number of registered images (ascending)\n",
    "        sorted_map_items = sorted(maps.items(), key=lambda x: len(x[1]))\n",
    "        # print(sorted_map_items)\n",
    "        \n",
    "        registered = 0\n",
    "        for new_cluster_idx, (original_map_index, cur_map) in enumerate(sorted_map_items):\n",
    "            for image_name, pose in cur_map.items():\n",
    "                idx = filename_to_index[image_name]\n",
    "                pred = predictions[idx]\n",
    "                pred.cluster_index = new_cluster_idx  # use the sorted order\n",
    "                pred.rotation = np.array(pose['R'])\n",
    "                pred.translation = np.array(pose['t'])\n",
    "                registered += 1\n",
    "\n",
    "        mapping_result_str = f\"Dataset {dataset} -> Registered {registered} / {len(images)} images with {len(maps)} clusters\"\n",
    "        return mapping_result_str, timings\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"Error in dataset {dataset}: {e}\")\n",
    "        return f\"Dataset \\\"{dataset}\\\" -> Failed!\", timings\n",
    "\n",
    "def run_mast3r_pipeline(samples, data_dir, workdir, is_train, model, device):\n",
    "    max_images = None\n",
    "    datasets_to_process = ['ETs'] if is_train else list(samples.keys())\n",
    "\n",
    "    overall_timings = {\n",
    "        \"shortlisting\": [],\n",
    "        \"feature_matching\": [],\n",
    "        \"RANSAC\": [],\n",
    "        \"Reconstruction\": [],\n",
    "    }\n",
    "    mapping_result_strs = []\n",
    "    lock = multiprocessing.Lock()\n",
    "\n",
    "    with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:\n",
    "        futures = []\n",
    "        for dataset, predictions in samples.items():\n",
    "            if datasets_to_process and dataset not in datasets_to_process:\n",
    "                print(f\"Skipping {dataset}\")\n",
    "                continue\n",
    "            futures.append(executor.submit(run_one_dataset, dataset, predictions, data_dir, workdir, is_train, model, device, lock))\n",
    "\n",
    "        for future in concurrent.futures.as_completed(futures):\n",
    "            result_str, timings = future.result()\n",
    "            mapping_result_strs.append(result_str)\n",
    "            for k in timings:\n",
    "                overall_timings[k].extend(timings[k])\n",
    "\n",
    "    print('\\nResults')\n",
    "    for s in mapping_result_strs:\n",
    "        print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "617e3afa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:33:02.969400Z",
     "iopub.status.busy": "2026-01-11T03:33:02.968974Z",
     "iopub.status.idle": "2026-01-11T03:58:04.971822Z",
     "shell.execute_reply": "2026-01-11T03:58:04.970723Z"
    },
    "papermill": {
     "duration": 1502.03538,
     "end_time": "2026-01-11T03:58:04.973467",
     "exception": false,
     "start_time": "2026-01-11T03:33:02.938087",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataset \"ETs\": 22 images\n",
      "Processing dataset \"amy_gardens\": 200 images\n",
      "🖼️ Scanning images...\n",
      "🖼️ Scanning images...\n",
      "⚠️ No valid images found in the directory. Returning empty pairs.\n",
      "Shortlisting done: 0 pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/amy_gardens/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/amy_gardens/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/amy_gardens/pairs.txt\n",
      "MASt3R matching done in 0.01 sec\n",
      "Skipping invalid image file: LICENSE.txt\n",
      "⚙️ Loading backbone model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth...\n",
      "... loading model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/kaggle/input/mast3r-fix/mast3r/mast3r/model.py:24: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(model_path, map_location='cpu')\n",
      "Adding keypoints and images: 0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import /kaggle/working/result/featureout/amy_gardens/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:06.157667 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:06.158731 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:06.158757 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "I20260111 03:33:06.159031 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:06.159485 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:06.159504 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "I20260111 03:33:06.159228 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:06.159537 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:06.159547 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:06.159610 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:06.159150 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:06.159764 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:06.159794 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "*** Aborted at 1768102386 (unix time) try \"date -d @1768102386\" if you are using GNU date ***\n",
      "terminate called recursively\n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x62) received by PID 98 (TID 0x791fa6ffd640) from PID 98; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instantiating : AsymmetricMASt3R(enc_depth=24, dec_depth=12, enc_embed_dim=1024, dec_embed_dim=768, enc_num_heads=16, dec_num_heads=12, pos_embed='RoPE100',img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), patch_embed_cls='PatchEmbedDust3R', two_confs=True, desc_conf_mode=('exp', 0, inf), landscape_only=False)\n",
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 2 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:06.617487 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:06.618443 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:06.618487 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:06.619049 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:06.618564 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "*** Aborted at 1768102386 (unix time) try \"date -d @1768102386\" if you are using GNU date ***\n",
      "E20260111 03:33:06.619257 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:06.619299 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:06.618443 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:06.618663 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:06.619366 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:06.619394 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:06.619435 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:06.619470 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x6c) received by PID 108 (TID 0x791fa6ffd640) from PID 108; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 3 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:06.955990 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:06.957063 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:06.957087 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:06.957686 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102386 (unix time) try \"date -d @1768102386\" if you are using GNU date ***\n",
      "I20260111 03:33:06.957173 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:06.958932 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:06.958980 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:06.959200 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:06.959218 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:06.959257 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:06.960078 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:06.960118 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:06.960172 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x76) received by PID 118 (TID 0x791fa5ffb640) from PID 118; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 4 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:07.255142 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:07.255929 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:07.255957 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:07.256455 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:07.256643 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "*** Aborted at 1768102387 (unix time) try \"date -d @1768102387\" if you are using GNU date ***\n",
      "E20260111 03:33:07.256704 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:07.256752 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:07.256733 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:07.257590 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:07.257632 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:07.256663 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:07.257684 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:07.257711 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x80) received by PID 128 (TID 0x791fa5ffb640) from PID 128; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 5 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:07.620398 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:07.621406 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:07.621426 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:07.621974 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102387 (unix time) try \"date -d @1768102387\" if you are using GNU date ***\n",
      "I20260111 03:33:07.622681 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:07.622698 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:07.622750 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:07.621588 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:07.624602 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:07.624663 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:07.621748 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:07.624730 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:07.624755 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x8a) received by PID 138 (TID 0x791fa5ffb640) from PID 138; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "Error in dataset amy_gardens: ❌ verify_matches failed after multiple retries.\n",
      "Processing dataset \"fbk_vineyard\": 163 images\n",
      "🖼️ Scanning images...\n",
      "⚠️ No valid images found in the directory. Returning empty pairs.\n",
      "Shortlisting done: 0 pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/fbk_vineyard/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/fbk_vineyard/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/fbk_vineyard/pairs.txt\n",
      "MASt3R matching done in 0.01 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adding keypoints and images: 0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import /kaggle/working/result/featureout/fbk_vineyard/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:11.060955 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:11.061728 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:11.061751 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:11.062199 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:11.062427 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "*** Aborted at 1768102391 (unix time) try \"date -d @1768102391\" if you are using GNU date ***\n",
      "E20260111 03:33:11.062445 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:11.062473 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:11.062767 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:11.062780 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:11.062815 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:11.062908 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:11.062914 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:11.062940 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x94) received by PID 148 (TID 0x791fa5ffb640) from PID 148; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 2 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:11.462098 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:11.463005 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:11.463031 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:11.463453 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:11.463087 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:11.463602 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "*** Aborted at 1768102391 (unix time) try \"date -d @1768102391\" if you are using GNU date ***\n",
      "E20260111 03:33:11.463654 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:11.464647 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:11.464668 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:11.464709 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:11.465380 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:11.465402 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:11.465433 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x9e) received by PID 158 (TID 0x791fa5ffb640) from PID 158; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 3 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:11.836431 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:11.837273 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:11.837295 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:11.837751 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102391 (unix time) try \"date -d @1768102391\" if you are using GNU date ***\n",
      "I20260111 03:33:11.838149 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:11.838171 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:11.838210 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:11.838355 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:11.838365 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:11.838387 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:11.840648 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:11.840677 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:11.840726 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0xa8) received by PID 168 (TID 0x791fa5ffb640) from PID 168; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 4 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:12.252895 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:12.253995 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:12.254042 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:12.254023 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:12.254710 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102392 (unix time) try \"date -d @1768102392\" if you are using GNU date ***\n",
      "E20260111 03:33:12.254058 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:12.255333 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:12.254248 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:12.255542 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:12.255605 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:12.254139 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:12.255853 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:12.255894 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0xb2) received by PID 178 (TID 0x791fa6ffd640) from PID 178; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 5 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:12.609483 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:12.610470 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:12.610488 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:12.610986 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102392 (unix time) try \"date -d @1768102392\" if you are using GNU date ***\n",
      "I20260111 03:33:12.610652 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:12.612277 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:12.612338 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:12.610737 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:12.612521 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:12.612544 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:12.610825 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:12.612724 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:12.612756 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0xbc) received by PID 188 (TID 0x791fa5ffb640) from PID 188; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "Error in dataset fbk_vineyard: ❌ verify_matches failed after multiple retries.\n",
      "Processing dataset \"imc2023_haiper\": 54 images\n",
      "🖼️ Scanning images...\n",
      "⚠️ No valid images found in the directory. Returning empty pairs.\n",
      "Shortlisting done: 0 pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/imc2023_haiper/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/imc2023_haiper/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/imc2023_haiper/pairs.txt\n",
      "MASt3R matching done in 0.01 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adding keypoints and images: 0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import /kaggle/working/result/featureout/imc2023_haiper/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:16.146444 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:16.147486 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:16.147501 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "I20260111 03:33:16.147689 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:16.148098 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:16.148132 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102396 (unix time) try \"date -d @1768102396\" if you are using GNU date ***\n",
      "I20260111 03:33:16.148637 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:16.148652 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:16.148698 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:16.148024 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:16.149610 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:16.149655 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:16.150507 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0xc6) received by PID 198 (TID 0x791fa67fc640) from PID 198; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 2 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:16.533862 133176606766656 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:16.534911 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:16.534955 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:16.535493 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102396 (unix time) try \"date -d @1768102396\" if you are using GNU date ***\n",
      "I20260111 03:33:16.536988 133176861124160 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:16.537007 133176861124160 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:16.537045 133176861124160 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:16.535315 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:16.537690 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:16.537746 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:16.535406 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:16.537835 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:16.537870 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0xd0) received by PID 208 (TID 0x791fa6ffd640) from PID 208; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 3 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:17.041729 133176580023872 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:17.044702 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:17.044733 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.045138 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102397 (unix time) try \"date -d @1768102397\" if you are using GNU date ***\n",
      "I20260111 03:33:17.045441 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:17.045456 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.045507 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:17.045778 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:17.045794 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.045833 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:17.046350 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:17.046411 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.046739 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0xdb) received by PID 219 (TID 0x791f9856b640) from PID 219; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 4 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:17.415436 133176580023872 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:17.416591 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:17.416611 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.417099 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102397 (unix time) try \"date -d @1768102397\" if you are using GNU date ***\n",
      "I20260111 03:33:17.416887 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:17.418864 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.418921 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:17.417047 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:17.418994 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.419030 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:17.419376 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:17.419392 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.419432 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0xe5) received by PID 229 (TID 0x791fa5ffb640) from PID 229; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 5 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:17.779097 133176580023872 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:17.780317 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:17.780383 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:17.780404 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.780981 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102397 (unix time) try \"date -d @1768102397\" if you are using GNU date ***\n",
      "E20260111 03:33:17.780340 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.781635 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:17.780533 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:17.780567 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:17.781715 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.781760 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:17.781731 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:17.782436 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0xef) received by PID 239 (TID 0x791f9856b640) from PID 239; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "Error in dataset imc2023_haiper: ❌ verify_matches failed after multiple retries.\n",
      "Processing dataset \"imc2023_heritage\": 209 images\n",
      "🖼️ Scanning images...\n",
      "⚠️ No valid images found in the directory. Returning empty pairs.\n",
      "Shortlisting done: 0 pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/imc2023_heritage/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/imc2023_heritage/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/imc2023_heritage/pairs.txt\n",
      "MASt3R matching done in 0.01 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adding keypoints and images: 0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<All keys matched successfully>\n",
      "Loading retrieval model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth\n",
      "import /kaggle/working/result/featureout/imc2023_heritage/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/kaggle/input/mast3r-fix/mast3r/mast3r/retrieval/processor.py:70: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(modelname, 'cpu')  # TODO from pretrained to download it automatically\n",
      "I20260111 03:33:21.318810 133176580023872 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:21.319973 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:21.320014 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:21.320021 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:21.320668 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102401 (unix time) try \"date -d @1768102401\" if you are using GNU date ***\n",
      "E20260111 03:33:21.320037 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:21.320991 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:21.320582 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:21.320653 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:21.321331 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:21.321390 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:21.321138 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:21.321609 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0xf9) received by PID 249 (TID 0x791f9856b640) from PID 249; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 2 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:21.691292 133176580023872 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:21.692156 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:21.692290 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:21.692882 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:21.692370 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:21.692923 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:21.692965 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:21.692456 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "*** Aborted at 1768102401 (unix time) try \"date -d @1768102401\" if you are using GNU date ***\n",
      "E20260111 03:33:21.693075 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:21.693103 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:21.692523 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:21.693154 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:21.693180 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x103) received by PID 259 (TID 0x791f9856b640) from PID 259; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a019c42a __gnu_cxx::__verbose_terminate_handler()\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 3 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:22.050204 133176580023872 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:22.050969 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:22.051000 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:22.051728 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:22.051224 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:22.051827 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:22.051873 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "I20260111 03:33:22.051308 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102402 (unix time) try \"date -d @1768102402\" if you are using GNU date ***\n",
      "E20260111 03:33:22.051947 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:22.051987 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "terminate called after throwing an instance of 'I20260111 03:33:22.051393 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:22.052130 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:22.052187 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x10d) received by PID 269 (TID 0x791fa5ffb640) from PID 269; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 4 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:22.404716 133176580023872 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:22.405742 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:22.405777 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "I20260111 03:33:22.405889 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:22.406401 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:22.406429 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:22.405965 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:22.406188 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "E20260111 03:33:22.406538 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:22.406627 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:22.406637 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102402 (unix time) try \"date -d @1768102402\" if you are using GNU date ***\n",
      "terminate called recursively\n",
      "E20260111 03:33:22.406534 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:22.406735 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x117) received by PID 279 (TID 0x791f9856b640) from PID 279; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a019c42a __gnu_cxx::__verbose_terminate_handler()\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 5 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:22.764315 133176580023872 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:22.765190 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:22.765240 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:22.765777 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:22.765857 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:22.765876 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "E20260111 03:33:22.765930 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102402 (unix time) try \"date -d @1768102402\" if you are using GNU date ***\n",
      "terminate called recursively\n",
      "I20260111 03:33:22.765512 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:22.766160 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:22.766209 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:22.765699 133176852731456 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:22.766267 133176852731456 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:22.766303 133176852731456 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x121) received by PID 289 (TID 0x791f9856b640) from PID 289; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "Error in dataset imc2023_heritage: ❌ verify_matches failed after multiple retries.\n",
      "Processing dataset \"imc2023_theather_imc2024_church\": 76 images\n",
      "🖼️ Scanning images...\n",
      "⚠️ No valid images found in the directory. Returning empty pairs.\n",
      "Shortlisting done: 0 pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/imc2023_theather_imc2024_church/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/imc2023_theather_imc2024_church/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/imc2023_theather_imc2024_church/pairs.txt\n",
      "MASt3R matching done in 0.01 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 22 images\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/22 [00:00<?, ?it/s]\n",
      "Adding keypoints and images: 0it [00:00, ?it/s]\n",
      " 32%|███▏      | 7/22 [00:01<00:02,  6.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import /kaggle/working/result/featureout/imc2023_theather_imc2024_church/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|███▋      | 8/22 [00:01<00:02,  6.69it/s]I20260111 03:33:26.420477 133174973683264 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:26.421219 133174990468672 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:26.421219 133174982075968 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:26.421234 133174990468672 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:26.421580 133174990468672 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:26.421250 133174982075968 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "*** Aborted at 1768102406 (unix time) try \"date -d @1768102406\" if you are using GNU date ***\n",
      "E20260111 03:33:26.421762 133174982075968 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:26.422176 133176315860544 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:26.422192 133176315860544 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:26.422227 133176315860544 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:26.422873 133176324253248 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:26.423203 133176324253248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:26.423483 133176324253248 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x168) received by PID 360 (TID 0x791f37fff640) from PID 360; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      " 41%|████      | 9/22 [00:01<00:01,  7.01it/s]    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      " 45%|████▌     | 10/22 [00:02<00:01,  7.27it/s]    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 2 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 11/22 [00:02<00:01,  6.08it/s]I20260111 03:33:26.901491 133174973683264 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:26.903697 133174982075968 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:26.903726 133174982075968 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:26.904176 133174982075968 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102406 (unix time) try \"date -d @1768102406\" if you are using GNU date ***\n",
      "I20260111 03:33:26.906688 133174990468672 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:26.906711 133174990468672 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:26.906747 133174990468672 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:26.906907 133176324253248 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:26.906919 133176324253248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:26.906944 133176324253248 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:26.907140 133176315860544 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:26.907162 133176315860544 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:26.907188 133176315860544 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x172) received by PID 370 (TID 0x791f377fe640) from PID 370; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      " 55%|█████▍    | 12/22 [00:02<00:01,  6.00it/s]    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      " 59%|█████▉    | 13/22 [00:02<00:01,  5.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 3 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:27.358428 133174973683264 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:27.360681 133174990468672 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:27.360706 133174990468672 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:27.361085 133174990468672 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102407 (unix time) try \"date -d @1768102407\" if you are using GNU date ***\n",
      "I20260111 03:33:27.361849 133174982075968 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:27.361867 133174982075968 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:27.361917 133174982075968 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:27.362100 133176315860544 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:27.362109 133176324253248 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:27.362129 133176324253248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:27.362158 133176324253248 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:27.362108 133176315860544 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:27.362221 133176315860544 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x17c) received by PID 380 (TID 0x791f37fff640) from PID 380; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      " 64%|██████▎   | 14/22 [00:02<00:01,  5.93it/s]    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      " 68%|██████▊   | 15/22 [00:02<00:01,  5.90it/s]    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 4 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 73%|███████▎  | 16/22 [00:03<00:01,  5.67it/s]I20260111 03:33:27.776594 133174973683264 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:27.777294 133174982075968 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:27.777312 133174990468672 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:27.777327 133174990468672 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:27.777694 133174990468672 sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:27.777334 133174982075968 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:27.777771 133174982075968 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102407 (unix time) try \"date -d @1768102407\" if you are using GNU date ***\n",
      "I20260111 03:33:27.778174 133176324253248 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:27.778189 133176324253248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:27.778224 133176324253248 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:27.778508 133176315860544 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:27.778530 133176315860544 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:27.779608 133176315860544 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x186) received by PID 390 (TID 0x791f377fe640) from PID 390; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      " 77%|███████▋  | 17/22 [00:03<00:00,  5.72it/s]    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      " 82%|████████▏ | 18/22 [00:03<00:00,  5.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 5 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:28.206289 133174973683264 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:28.207351 133174982075968 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:28.207370 133174982075968 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:28.207892 133174982075968 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102408 (unix time) try \"date -d @1768102408\" if you are using GNU date ***\n",
      "I20260111 03:33:28.207692 133176315860544 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:28.209225 133176315860544 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:28.209272 133176315860544 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:28.207782 133176324253248 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:28.209356 133176324253248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:28.209384 133176324253248 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:28.209645 133174990468672 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:28.209657 133174990468672 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:28.209685 133174990468672 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x190) received by PID 400 (TID 0x791f377fe640) from PID 400; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      " 86%|████████▋ | 19/22 [00:03<00:00,  5.82it/s]    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      " 91%|█████████ | 20/22 [00:03<00:00,  6.29it/s]    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "Error in dataset imc2023_theather_imc2024_church: ❌ verify_matches failed after multiple retries.\n",
      "Processing dataset \"imc2024_dioscuri_baalshamin\": 138 images\n",
      "🖼️ Scanning images...\n",
      "⚠️ No valid images found in the directory. Returning empty pairs.\n",
      "Shortlisting done: 0 pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|█████████▌| 21/22 [00:04<00:00,  2.92it/s]\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/imc2024_dioscuri_baalshamin/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/imc2024_dioscuri_baalshamin/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/imc2024_dioscuri_baalshamin/pairs.txt\n",
      "MASt3R matching done in 0.01 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22/22 [00:05<00:00,  3.79it/s]\n",
      "Adding keypoints and images: 0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Generated 231 unique image pairs.\n",
      "Shortlisting done: 231 pairs\n",
      "import /kaggle/working/result/featureout/imc2024_dioscuri_baalshamin/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/231 [00:00<?, ?it/s]I20260111 03:33:32.030927 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:32.032120 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:32.032151 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.032528 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:32.032580 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:32.032597 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.032644 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102412 (unix time) try \"date -d @1768102412\" if you are using GNU date ***\n",
      "I20260111 03:33:32.032199 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:32.033199 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.033247 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  I20260111 03:33:32.033679 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:32.034116 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.034165 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "[sift.cc:979] Check failed: options_.Check() \n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x19b) received by PID 411 (TID 0x791fa5ffb640) from PID 411; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/inference.py:44: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(enabled=bool(use_amp)):\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 2 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/model.py:205: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(enabled=False):\n",
      "/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/inference.py:48: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(enabled=False):\n",
      "I20260111 03:33:32.481982 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:32.483210 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:32.483279 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.484059 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:32.483627 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:32.483826 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:32.483743 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:32.484131 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.484139 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.484212 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:32.484188 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() E20260111 03:33:32.484198 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.484505 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "*** Aborted at 1768102412 (unix time) try \"date -d @1768102412\" if you are using GNU date ***\n",
      "\n",
      "terminate called recursively\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x1a5) received by PID 421 (TID 0x791fa67fc640) from PID 421; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a019c42a __gnu_cxx::__verbose_terminate_handler()\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "  0%|          | 1/231 [00:00<03:10,  1.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 3 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:32.928733 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:32.930138 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:32.930161 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.931191 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:32.930631 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:32.931243 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.931284 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argumentterminate called after throwing an instance of 'std::invalid_argument'\n",
      "'\n",
      "I20260111 03:33:32.930647 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102412 (unix time) try \"date -d @1768102412\" if you are using GNU date ***\n",
      "E20260111 03:33:32.931432 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.931470 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:32.930517 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:32.931567 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:32.931603 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x1af) received by PID 431 (TID 0x791fa5ffb640) from PID 431; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 4 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:33.329518 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:33.330828 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:33.330864 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:33.331337 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102413 (unix time) try \"date -d @1768102413\" if you are using GNU date ***\n",
      "I20260111 03:33:33.332139 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:33.332160 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:33.332212 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:33.330939 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:33.331027 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:33.332900 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:33.332933 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:33.332626 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:33.333028 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x1b9) received by PID 441 (TID 0x791fa5ffb640) from PID 441; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 5 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:33.771754 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:33.773115 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:33.773137 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:33.773686 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102413 (unix time) try \"date -d @1768102413\" if you are using GNU date ***\n",
      "I20260111 03:33:33.773888 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:33.773987 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:33.774171 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:33.774605 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:33.774633 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:33.774647 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:33.774691 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:33.774627 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:33.774740 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x1c3) received by PID 451 (TID 0x791f96bea640) from PID 451; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "  1%|          | 2/231 [00:01<03:25,  1.11it/s]    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "Error in dataset imc2024_dioscuri_baalshamin: ❌ verify_matches failed after multiple retries.\n",
      "Processing dataset \"imc2024_lizard_pond\": 214 images\n",
      "🖼️ Scanning images...\n",
      "⚠️ No valid images found in the directory. Returning empty pairs.\n",
      "Shortlisting done: 0 pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/imc2024_lizard_pond/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/imc2024_lizard_pond/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/imc2024_lizard_pond/pairs.txt\n",
      "MASt3R matching done in 0.01 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 4/231 [00:04<03:52,  1.02s/it]\n",
      "Adding keypoints and images: 0it [00:00, ?it/s]\n",
      "  2%|▏         | 5/231 [00:04<03:21,  1.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import /kaggle/working/result/featureout/imc2024_lizard_pond/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:37.366067 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:37.367816 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:37.367845 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:37.368377 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102417 (unix time) try \"date -d @1768102417\" if you are using GNU date ***\n",
      "I20260111 03:33:37.367905 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:37.370603 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:37.370655 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:37.367976 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:37.370737 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:37.370758 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:37.367907 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:37.370800 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:37.370829 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x1cd) received by PID 461 (TID 0x791fa5ffb640) from PID 461; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "  3%|▎         | 6/231 [00:05<03:12,  1.17it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 2 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:37.818714 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:37.819548 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:37.819596 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:37.820325 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102417 (unix time) try \"date -d @1768102417\" if you are using GNU date ***\n",
      "I20260111 03:33:37.821191 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:37.821206 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:37.821239 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:37.821396 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:37.821403 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:37.821434 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:37.821535 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:37.821541 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:37.821590 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x1d7) received by PID 471 (TID 0x791f96bea640) from PID 471; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 3 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:38.325325 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:38.326728 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:38.326721 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:38.326766 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:38.327303 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102418 (unix time) try \"date -d @1768102418\" if you are using GNU date ***\n",
      "E20260111 03:33:38.326756 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:38.328997 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:38.326873 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:38.329600 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:38.329641 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:38.326933 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:38.329708 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:38.329743 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x1e1) received by PID 481 (TID 0x791fa67fc640) from PID 481; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "  3%|▎         | 7/231 [00:06<03:09,  1.18it/s]    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 4 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:38.720630 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:38.721639 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:38.721665 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:38.722313 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:38.722485 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:38.722500 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "*** Aborted at 1768102418 (unix time) try \"date -d @1768102418\" if you are using GNU date ***\n",
      "E20260111 03:33:38.722540 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:38.722093 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:38.722118 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:38.723007 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:38.723033 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:38.722976 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:38.723152 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x1eb) received by PID 491 (TID 0x791f96bea640) from PID 491; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 5 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:39.088850 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:39.090424 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:39.090446 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:39.090936 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:39.090609 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:39.090765 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:39.090999 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:39.091058 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'I20260111 03:33:39.090880 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:39.091163 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "*** Aborted at 1768102419 (unix time) try \"date -d @1768102419\" if you are using GNU date ***\n",
      "E20260111 03:33:39.091206 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:39.091264 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:39.091302 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x1f5) received by PID 501 (TID 0x791f96bea640) from PID 501; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "  3%|▎         | 8/231 [00:07<03:05,  1.20it/s]    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "Error in dataset imc2024_lizard_pond: ❌ verify_matches failed after multiple retries.\n",
      "Processing dataset \"pt_brandenburg_british_buckingham\": 225 images\n",
      "🖼️ Scanning images...\n",
      "⚠️ No valid images found in the directory. Returning empty pairs.\n",
      "Shortlisting done: 0 pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/pt_brandenburg_british_buckingham/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/pt_brandenburg_british_buckingham/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/pt_brandenburg_british_buckingham/pairs.txt\n",
      "MASt3R matching done in 0.01 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|▍         | 9/231 [00:08<04:03,  1.10s/it]\n",
      "Adding keypoints and images: 0it [00:00, ?it/s]\n",
      "  5%|▍         | 11/231 [00:10<03:22,  1.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import /kaggle/working/result/featureout/pt_brandenburg_british_buckingham/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:42.725628 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:42.726752 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:42.726779 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:42.727481 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102422 (unix time) try \"date -d @1768102422\" if you are using GNU date ***\n",
      "I20260111 03:33:42.727872 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:42.727889 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:42.727923 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:42.728100 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:42.728108 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:42.728140 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:42.728239 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:42.728246 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:42.728288 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x1ff) received by PID 511 (TID 0x791f96bea640) from PID 511; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 2 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:43.175833 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:43.177633 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:43.177646 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:43.177660 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:43.177672 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:43.178375 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:43.177834 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:43.178610 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "I20260111 03:33:43.178149 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:43.178676 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:43.178694 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "*** Aborted at 1768102423 (unix time) try \"date -d @1768102423\" if you are using GNU date ***\n",
      "E20260111 03:33:43.178886 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:43.178648 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x209) received by PID 521 (TID 0x791f9856b640) from PID 521; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a019c42a __gnu_cxx::__verbose_terminate_handler()\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "  5%|▌         | 12/231 [00:11<03:19,  1.10it/s]    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 3 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:43.615289 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:43.616138 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:43.616166 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:43.616815 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102423 (unix time) try \"date -d @1768102423\" if you are using GNU date ***\n",
      "I20260111 03:33:43.617347 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:43.617368 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:43.617399 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:43.617581 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:43.617596 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:43.617643 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:43.618837 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:43.618874 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:43.618949 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x213) received by PID 531 (TID 0x791f96bea640) from PID 531; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 4 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:44.041907 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:44.042871 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:44.042900 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:44.043534 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102424 (unix time) try \"date -d @1768102424\" if you are using GNU date ***\n",
      "I20260111 03:33:44.043172 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:44.043941 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:44.044031 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:44.043262 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:44.044148 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:44.044222 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:44.043347 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:44.044353 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:44.044401 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x21d) received by PID 541 (TID 0x791f96bea640) from PID 541; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "  6%|▌         | 13/231 [00:12<03:18,  1.10it/s]    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 5 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:44.498989 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:44.499903 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:44.499934 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:44.500753 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:44.500996 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "*** Aborted at 1768102424 (unix time) try \"date -d @1768102424\" if you are using GNU date ***\n",
      "E20260111 03:33:44.501013 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:44.501044 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:44.501201 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:44.501210 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:44.501244 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:44.501492 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:44.501500 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:44.501531 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x227) received by PID 551 (TID 0x791f96bea640) from PID 551; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "Error in dataset pt_brandenburg_british_buckingham: ❌ verify_matches failed after multiple retries.\n",
      "Processing dataset \"pt_piazzasanmarco_grandplace\": 168 images\n",
      "🖼️ Scanning images...\n",
      "⚠️ No valid images found in the directory. Returning empty pairs.\n",
      "Shortlisting done: 0 pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/pt_piazzasanmarco_grandplace/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/pt_piazzasanmarco_grandplace/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/pt_piazzasanmarco_grandplace/pairs.txt\n",
      "MASt3R matching done in 0.01 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|▋         | 15/231 [00:14<03:53,  1.08s/it]\n",
      "Adding keypoints and images: 0it [00:00, ?it/s]\n",
      "  7%|▋         | 16/231 [00:15<03:30,  1.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import /kaggle/working/result/featureout/pt_piazzasanmarco_grandplace/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:48.030881 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:48.032059 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:48.032074 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:48.032639 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:48.032123 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:48.032266 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:48.032841 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "*** Aborted at 1768102428 (unix time) try \"date -d @1768102428\" if you are using GNU date ***\n",
      "E20260111 03:33:48.032886 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:48.032309 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:48.032943 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:48.032978 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:48.032826 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:48.033101 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x231) received by PID 561 (TID 0x791f9856b640) from PID 561; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 2 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:48.480828 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:48.481729 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:48.481776 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "I20260111 03:33:48.482442 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:48.482450 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:48.482457 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:48.482521 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102428 (unix time) try \"date -d @1768102428\" if you are using GNU date ***\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:48.483378 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:48.483396 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:48.483457 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:48.483434 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:48.483981 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:48.484034 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x23b) received by PID 571 (TID 0x791f9856b640) from PID 571; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "  7%|▋         | 17/231 [00:16<03:25,  1.04it/s]    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 3 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:48.923098 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:48.924223 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:48.924269 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "I20260111 03:33:48.924937 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:48.924942 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:48.924879 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:48.925001 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:48.925033 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'terminate called after throwing an instance of 'std::invalid_argument'\n",
      "E20260111 03:33:48.924947 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:48.925259 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102428 (unix time) try \"date -d @1768102428\" if you are using GNU date ***\n",
      "terminate called recursively\n",
      "std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:48.924800 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:48.926660 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:48.926708 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x245) received by PID 581 (TID 0x791f96bea640) from PID 581; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 4 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:49.375025 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:49.375972 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:49.376014 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:49.376738 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102429 (unix time) try \"date -d @1768102429\" if you are using GNU date ***\n",
      "I20260111 03:33:49.377019 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:49.377039 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:49.377082 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:49.377253 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:49.377258 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:49.377262 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:49.377311 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:49.377273 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:49.377429 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x24f) received by PID 591 (TID 0x791f96bea640) from PID 591; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "  8%|▊         | 18/231 [00:17<03:22,  1.05it/s]    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 5 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:49.805326 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:49.806391 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:49.806416 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:49.807033 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:49.806828 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:49.807090 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:49.807128 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() *** Aborted at 1768102429 (unix time) try \"date -d @1768102429\" if you are using GNU date ***\n",
      "\n",
      "I20260111 03:33:49.807410 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:49.807424 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:49.807464 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:49.806741 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:49.807633 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:49.807671 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x259) received by PID 601 (TID 0x791fa5ffb640) from PID 601; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "Error in dataset pt_piazzasanmarco_grandplace: ❌ verify_matches failed after multiple retries.\n",
      "Processing dataset \"pt_sacrecoeur_trevi_tajmahal\": 225 images\n",
      "🖼️ Scanning images...\n",
      "⚠️ No valid images found in the directory. Returning empty pairs.\n",
      "Shortlisting done: 0 pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/pt_sacrecoeur_trevi_tajmahal/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/pt_sacrecoeur_trevi_tajmahal/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/pt_sacrecoeur_trevi_tajmahal/pairs.txt\n",
      "MASt3R matching done in 0.01 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9%|▊         | 20/231 [00:20<03:47,  1.08s/it]\n",
      "Adding keypoints and images: 0it [00:00, ?it/s]\n",
      "  9%|▉         | 21/231 [00:20<03:24,  1.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import /kaggle/working/result/featureout/pt_sacrecoeur_trevi_tajmahal/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:53.366956 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:53.368114 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:53.368129 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:53.368511 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102433 (unix time) try \"date -d @1768102433\" if you are using GNU date ***\n",
      "I20260111 03:33:53.369696 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:53.369713 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:53.369759 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:53.370650 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:53.370660 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:53.370686 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:53.370778 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:53.370784 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:53.370807 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x263) received by PID 611 (TID 0x791f96bea640) from PID 611; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      " 10%|▉         | 22/231 [00:21<03:13,  1.08it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 2 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:53.793772 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:53.794707 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:53.794756 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:53.795366 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102433 (unix time) try \"date -d @1768102433\" if you are using GNU date ***\n",
      "I20260111 03:33:53.795810 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:53.795828 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:53.795864 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:53.796248 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:53.796263 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:53.796304 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:53.796431 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:53.796441 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:53.796476 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x26d) received by PID 621 (TID 0x791f96bea640) from PID 621; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 3 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:54.220283 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:54.221493 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:54.221520 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:54.222093 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102434 (unix time) try \"date -d @1768102434\" if you are using GNU date ***\n",
      "I20260111 03:33:54.221917 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:54.222674 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:54.222743 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:54.222798 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:54.222809 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:54.222847 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:54.222725 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:54.222913 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:54.222946 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x277) received by PID 631 (TID 0x791f9856b640) from PID 631; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      " 10%|▉         | 23/231 [00:22<03:09,  1.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 4 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:54.685822 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:54.686770 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:54.686813 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:54.687658 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102434 (unix time) try \"date -d @1768102434\" if you are using GNU date ***\n",
      "I20260111 03:33:54.687920 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:54.687936 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:54.687965 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:54.688138 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:54.688146 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:54.688175 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:54.688271 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:54.688277 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:54.688312 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x281) received by PID 641 (TID 0x791f96bea640) from PID 641; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 5 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:55.126400 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:55.127474 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:55.127512 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:55.128122 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102435 (unix time) try \"date -d @1768102435\" if you are using GNU date ***\n",
      "I20260111 03:33:55.127566 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:55.128448 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:55.128502 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:55.127649 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:55.129500 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "I20260111 03:33:55.127938 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:55.129873 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:55.129923 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:55.130004 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x28b) received by PID 651 (TID 0x791fa5ffb640) from PID 651; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      " 10%|█         | 24/231 [00:23<02:59,  1.15it/s]    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "Error in dataset pt_sacrecoeur_trevi_tajmahal: ❌ verify_matches failed after multiple retries.\n",
      "Processing dataset \"pt_stpeters_stpauls\": 200 images\n",
      "🖼️ Scanning images...\n",
      "⚠️ No valid images found in the directory. Returning empty pairs.\n",
      "Shortlisting done: 0 pairs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/pt_stpeters_stpauls/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/pt_stpeters_stpauls/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/pt_stpeters_stpauls/pairs.txt\n",
      "MASt3R matching done in 0.01 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 11%|█▏        | 26/231 [00:25<03:17,  1.04it/s]\n",
      "Adding keypoints and images: 0it [00:00, ?it/s]\n",
      " 12%|█▏        | 27/231 [00:26<02:57,  1.15it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import /kaggle/working/result/featureout/pt_stpeters_stpauls/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:58.700724 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:58.701858 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:58.701898 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:58.702437 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:58.702001 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:58.702479 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:58.702527 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102438 (unix time) try \"date -d @1768102438\" if you are using GNU date ***\n",
      "terminate called recursively\n",
      "I20260111 03:33:58.702092 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:58.702271 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:58.704608 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:58.704615 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:58.704679 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:33:58.704658 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x295) received by PID 661 (TID 0x791f96bea640) from PID 661; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      " 12%|█▏        | 28/231 [00:26<02:50,  1.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 2 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:59.128514 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:59.130238 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:59.130255 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:59.130767 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:59.130618 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:33:59.130653 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:59.130818 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:59.130833 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:59.130906 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "E20260111 03:33:59.130882 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102439 (unix time) try \"date -d @1768102439\" if you are using GNU date ***\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:59.130719 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:59.131026 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:59.131073 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x29f) received by PID 671 (TID 0x791fa5ffb640) from PID 671; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a019c42a __gnu_cxx::__verbose_terminate_handler()\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 3 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:33:59.540039 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:59.541125 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:59.541148 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:59.541623 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:59.541726 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102439 (unix time) try \"date -d @1768102439\" if you are using GNU date ***\n",
      "E20260111 03:33:59.541751 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:59.542019 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:59.542659 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:59.542676 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "I20260111 03:33:59.542716 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:59.542717 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "E20260111 03:33:59.542735 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:59.542807 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x2a9) received by PID 681 (TID 0x791f96bea640) from PID 681; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      " 13%|█▎        | 29/231 [00:27<02:48,  1.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 4 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:33:59.965798 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:33:59.966545 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:59.966588 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:59.967179 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "I20260111 03:33:59.967364 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "*** Aborted at 1768102439 (unix time) try \"date -d @1768102439\" if you are using GNU date ***\n",
      "E20260111 03:33:59.967384 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:59.967416 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:59.967648 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:59.967662 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:59.967713 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "I20260111 03:33:59.967860 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:33:59.967873 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:33:59.967911 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x2b3) received by PID 691 (TID 0x791f96bea640) from PID 691; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "🔁 Attempt 5 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    @     0x7921a1744a04 clone\n",
      "I20260111 03:34:00.385465 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:34:00.386658 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:34:00.386675 133176580023872 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:34:00.387104 133176580023872 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called after throwing an instance of 'std::invalid_argument'\n",
      "  what():  [sift.cc:979] Check failed: options_.Check() \n",
      "*** Aborted at 1768102440 (unix time) try \"date -d @1768102440\" if you are using GNU date ***\n",
      "I20260111 03:34:00.387798 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:34:00.387798 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:34:00.387819 133176606766656 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:34:00.387856 133176835946048 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:34:00.387889 133176606766656 sift.cc:979] Check failed: options_.Check() \n",
      "E20260111 03:34:00.387915 133176835946048 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "terminate called recursively\n",
      "I20260111 03:34:00.388000 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "E20260111 03:34:00.388023 133176844338752 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n",
      "E20260111 03:34:00.388081 133176844338752 sift.cc:979] Check failed: options_.Check() \n",
      "terminate called recursively\n",
      "PC: @     0x7921a16b59fc pthread_kill\n",
      "*** SIGABRT (@0x2bd) received by PID 701 (TID 0x791f96bea640) from PID 701; stack trace: ***\n",
      "    @     0x7921a16b8ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n",
      "    @     0x7920b437c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n",
      "    @     0x7921a1661520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n",
      "    @     0x7921a16b59fc pthread_kill\n",
      "    @     0x7921a1661476 raise\n",
      "    @     0x7921a16477f3 abort\n",
      "    @     0x7921a018eb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n",
      "    @     0x7921a019a20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n",
      "    @     0x7921a019a277 std::terminate()\n",
      "    @     0x7921a019a4d8 __cxa_throw\n",
      "    @     0x7920b3eea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n",
      "    @     0x7920b4598881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n",
      "    @     0x7920b450da4b colmap::FeatureMatcherWorker::Run()\n",
      "    @     0x7920b4ece872 colmap::Thread::RunFunc()\n",
      "    @     0x7921a01c8253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n",
      "    @     0x7921a16b3ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n",
      "    @     0x7921a1744a04 clone\n",
      " 13%|█▎        | 30/231 [00:28<02:51,  1.17it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ verify_matches crashed with code -6\n",
      "Error in dataset pt_stpeters_stpauls: ❌ verify_matches failed after multiple retries.\n",
      "Processing dataset \"stairs\": 51 images\n",
      "🖼️ Scanning images...\n",
      "Skipping invalid image file: LICENSE.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 13%|█▎        | 31/231 [00:29<02:46,  1.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚙️ Loading backbone model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth...\n",
      "... loading model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instantiating : AsymmetricMASt3R(enc_depth=24, dec_depth=12, enc_embed_dim=1024, dec_embed_dim=768, enc_num_heads=16, dec_num_heads=12, pos_embed='RoPE100',img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), patch_embed_cls='PatchEmbedDust3R', two_confs=True, desc_conf_mode=('exp', 0, inf), landscape_only=False)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 19%|█▉        | 45/231 [00:39<02:09,  1.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<All keys matched successfully>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|█▉        | 46/231 [00:41<02:44,  1.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading retrieval model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth\n",
      "Found 51 images\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "text": [
      "✅ Generated 880 unique image pairs.\n",
      "Shortlisting done: 880 pairs\n"
     ]
    },
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/ETs/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/ETs/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/ETs/pairs.txt\n",
      "MASt3R matching done in 340.93 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Adding keypoints and images: 100%|██████████| 19/19 [00:00<00:00, 61.73it/s]\n",
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import /kaggle/working/result/featureout/ETs/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:39:16.067333 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:39:16.068337 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:39:16.068684 133176598373952 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:39:16.068787 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:39:16.068850 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:39:16.068870 133176571631168 pairing.cc:742] Importing image pairs...\n",
      "I20260111 03:39:16.069183 133176571631168 pairing.cc:775] Matching block [1/1]\n",
      "\n",
      " 15%|█▍        | 131/880 [04:46<23:33,  1.89s/it]\u001b[AI20260111 03:39:17.897362 133176571631168 feature_matching.cc:46] in 1.828s\n",
      "I20260111 03:39:17.900922 133176571631168 timer.cc:91] Elapsed time: 0.031 [minutes]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ verify_matches succeeded\n",
      "verify matching done!!!!\n",
      "RANSAC in 2.1768 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:39:18.122069 133176606766656 incremental_pipeline.cc:237] Loading database\n",
      "I20260111 03:39:18.123888 133176606766656 database_cache.cc:66] Loading cameras...\n",
      "I20260111 03:39:18.123970 133176606766656 database_cache.cc:76]  19 in 0.000s\n",
      "I20260111 03:39:18.123992 133176606766656 database_cache.cc:84] Loading matches...\n",
      "I20260111 03:39:18.126515 133176606766656 database_cache.cc:89]  80 in 0.003s\n",
      "I20260111 03:39:18.126541 133176606766656 database_cache.cc:105] Loading images...\n",
      "I20260111 03:39:18.137031 133176606766656 database_cache.cc:153]  19 in 0.010s (connected 19)\n",
      "I20260111 03:39:18.137064 133176606766656 database_cache.cc:164] Loading pose priors...\n",
      "I20260111 03:39:18.137215 133176606766656 database_cache.cc:175]  0 in 0.000s\n",
      "I20260111 03:39:18.137227 133176606766656 database_cache.cc:184] Building correspondence graph...\n",
      "W20260111 03:39:18.139413 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5259 in image_id=1 and point2D_idx=5353 in image_id=2\n",
      "W20260111 03:39:18.139726 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8284 in image_id=1 and point2D_idx=9131 in image_id=2\n",
      "W20260111 03:39:18.139783 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5266 in image_id=1 and point2D_idx=5362 in image_id=2\n",
      "W20260111 03:39:18.139876 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3272 in image_id=1 and point2D_idx=3211 in image_id=2\n",
      "W20260111 03:39:18.139968 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5745 in image_id=1 and point2D_idx=5790 in image_id=2\n",
      "W20260111 03:39:18.140133 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1531 in image_id=1 and point2D_idx=1214 in image_id=2\n",
      "W20260111 03:39:18.140225 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10825 in image_id=1 and point2D_idx=11839 in image_id=2\n",
      "W20260111 03:39:18.140249 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9393 in image_id=1 and point2D_idx=10189 in image_id=2\n",
      "W20260111 03:39:18.140260 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10826 in image_id=1 and point2D_idx=11840 in image_id=2\n",
      "W20260111 03:39:18.140362 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7912 in image_id=1 and point2D_idx=8329 in image_id=2\n",
      "W20260111 03:39:18.140472 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9409 in image_id=1 and point2D_idx=10160 in image_id=2\n",
      "W20260111 03:39:18.140488 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11142 in image_id=1 and point2D_idx=12071 in image_id=2\n",
      "W20260111 03:39:18.140640 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5709 in image_id=1 and point2D_idx=3338 in image_id=3\n",
      "W20260111 03:39:18.140681 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7832 in image_id=1 and point2D_idx=5753 in image_id=3\n",
      "W20260111 03:39:18.140719 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2764 in image_id=1 and point2D_idx=522 in image_id=3\n",
      "W20260111 03:39:18.140787 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6981 in image_id=1 and point2D_idx=4727 in image_id=3\n",
      "W20260111 03:39:18.140844 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6565 in image_id=1 and point2D_idx=4287 in image_id=3\n",
      "W20260111 03:39:18.140855 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8312 in image_id=1 and point2D_idx=5820 in image_id=3\n",
      "W20260111 03:39:18.140866 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9019 in image_id=1 and point2D_idx=6434 in image_id=3\n",
      "W20260111 03:39:18.140902 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9725 in image_id=1 and point2D_idx=7014 in image_id=3\n",
      "W20260111 03:39:18.140941 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1909 in image_id=1 and point2D_idx=473 in image_id=3\n",
      "W20260111 03:39:18.140955 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9729 in image_id=1 and point2D_idx=7016 in image_id=3\n",
      "W20260111 03:39:18.140987 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2796 in image_id=1 and point2D_idx=1316 in image_id=3\n",
      "W20260111 03:39:18.141042 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2332 in image_id=1 and point2D_idx=555 in image_id=3\n",
      "W20260111 03:39:18.141114 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2808 in image_id=1 and point2D_idx=818 in image_id=3\n",
      "W20260111 03:39:18.141142 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3842 in image_id=1 and point2D_idx=1989 in image_id=3\n",
      "W20260111 03:39:18.141221 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6204 in image_id=1 and point2D_idx=3841 in image_id=3\n",
      "W20260111 03:39:18.141424 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9434 in image_id=1 and point2D_idx=6935 in image_id=4\n",
      "W20260111 03:39:18.141456 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7869 in image_id=1 and point2D_idx=6225 in image_id=4\n",
      "W20260111 03:39:18.141490 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9747 in image_id=1 and point2D_idx=8356 in image_id=4\n",
      "W20260111 03:39:18.141570 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10132 in image_id=1 and point2D_idx=8820 in image_id=4\n",
      "W20260111 03:39:18.141626 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3327 in image_id=1 and point2D_idx=2099 in image_id=4\n",
      "W20260111 03:39:18.141867 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6164 in image_id=1 and point2D_idx=5418 in image_id=5\n",
      "W20260111 03:39:18.141942 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6996 in image_id=1 and point2D_idx=6760 in image_id=5\n",
      "W20260111 03:39:18.142009 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7011 in image_id=1 and point2D_idx=6773 in image_id=5\n",
      "W20260111 03:39:18.142041 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1149 in image_id=1 and point2D_idx=357 in image_id=5\n",
      "W20260111 03:39:18.142139 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10814 in image_id=1 and point2D_idx=10615 in image_id=5\n",
      "W20260111 03:39:18.142198 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10822 in image_id=1 and point2D_idx=10670 in image_id=5\n",
      "W20260111 03:39:18.142319 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7442 in image_id=1 and point2D_idx=7565 in image_id=5\n",
      "W20260111 03:39:18.142404 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9406 in image_id=1 and point2D_idx=9213 in image_id=5\n",
      "W20260111 03:39:18.142428 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3876 in image_id=1 and point2D_idx=3296 in image_id=5\n",
      "W20260111 03:39:18.142474 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5353 in image_id=1 and point2D_idx=4575 in image_id=5\n",
      "W20260111 03:39:18.142615 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7483 in image_id=1 and point2D_idx=9277 in image_id=6\n",
      "W20260111 03:39:18.142673 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11072 in image_id=1 and point2D_idx=3305 in image_id=6\n",
      "W20260111 03:39:18.142704 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9363 in image_id=1 and point2D_idx=9287 in image_id=6\n",
      "W20260111 03:39:18.142723 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9365 in image_id=1 and point2D_idx=9374 in image_id=6\n",
      "W20260111 03:39:18.142759 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8760 in image_id=1 and point2D_idx=9298 in image_id=6\n",
      "W20260111 03:39:18.142813 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9380 in image_id=1 and point2D_idx=10880 in image_id=6\n",
      "W20260111 03:39:18.142833 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=864 in image_id=1 and point2D_idx=1570 in image_id=6\n",
      "W20260111 03:39:18.142868 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10129 in image_id=1 and point2D_idx=11931 in image_id=6\n",
      "W20260111 03:39:18.142883 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2345 in image_id=1 and point2D_idx=382 in image_id=6\n",
      "W20260111 03:39:18.142986 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7438 in image_id=1 and point2D_idx=8304 in image_id=6\n",
      "W20260111 03:39:18.143035 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1561 in image_id=1 and point2D_idx=2338 in image_id=6\n",
      "W20260111 03:39:18.143180 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4431 in image_id=1 and point2D_idx=9857 in image_id=6\n",
      "W20260111 03:39:18.143270 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4762 in image_id=1 and point2D_idx=5714 in image_id=7\n",
      "W20260111 03:39:18.143307 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6171 in image_id=1 and point2D_idx=4707 in image_id=7\n",
      "W20260111 03:39:18.143352 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4332 in image_id=1 and point2D_idx=3075 in image_id=7\n",
      "W20260111 03:39:18.143371 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1512 in image_id=1 and point2D_idx=1061 in image_id=7\n",
      "W20260111 03:39:18.143381 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2329 in image_id=1 and point2D_idx=1542 in image_id=7\n",
      "W20260111 03:39:18.143401 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1912 in image_id=1 and point2D_idx=1743 in image_id=7\n",
      "W20260111 03:39:18.143424 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1519 in image_id=1 and point2D_idx=2246 in image_id=7\n",
      "W20260111 03:39:18.143437 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7416 in image_id=1 and point2D_idx=6949 in image_id=7\n",
      "W20260111 03:39:18.143479 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2340 in image_id=1 and point2D_idx=4737 in image_id=7\n",
      "W20260111 03:39:18.143508 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2813 in image_id=1 and point2D_idx=6077 in image_id=7\n",
      "W20260111 03:39:18.143538 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2820 in image_id=1 and point2D_idx=526 in image_id=7\n",
      "W20260111 03:39:18.143570 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11923 in image_id=1 and point2D_idx=8636 in image_id=7\n",
      "W20260111 03:39:18.143590 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2824 in image_id=1 and point2D_idx=2501 in image_id=7\n",
      "W20260111 03:39:18.143599 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3857 in image_id=1 and point2D_idx=3919 in image_id=7\n",
      "W20260111 03:39:18.143619 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11924 in image_id=1 and point2D_idx=9135 in image_id=7\n",
      "W20260111 03:39:18.143673 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3338 in image_id=1 and point2D_idx=4323 in image_id=7\n",
      "W20260111 03:39:18.143693 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1956 in image_id=1 and point2D_idx=2852 in image_id=7\n",
      "W20260111 03:39:18.143747 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6613 in image_id=1 and point2D_idx=9400 in image_id=7\n",
      "W20260111 03:39:18.143764 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3876 in image_id=1 and point2D_idx=8011 in image_id=7\n",
      "W20260111 03:39:18.143821 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4308 in image_id=1 and point2D_idx=7931 in image_id=8\n",
      "W20260111 03:39:18.143830 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4748 in image_id=1 and point2D_idx=8321 in image_id=8\n",
      "W20260111 03:39:18.143851 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3269 in image_id=1 and point2D_idx=6193 in image_id=8\n",
      "W20260111 03:39:18.143869 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6167 in image_id=1 and point2D_idx=3768 in image_id=8\n",
      "W20260111 03:39:18.143900 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2326 in image_id=1 and point2D_idx=1457 in image_id=8\n",
      "W20260111 03:39:18.143924 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2796 in image_id=1 and point2D_idx=2043 in image_id=8\n",
      "W20260111 03:39:18.143936 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9880 in image_id=1 and point2D_idx=6507 in image_id=8\n",
      "W20260111 03:39:18.143962 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1151 in image_id=1 and point2D_idx=3208 in image_id=8\n",
      "W20260111 03:39:18.143980 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7951 in image_id=1 and point2D_idx=6132 in image_id=8\n",
      "W20260111 03:39:18.143990 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9745 in image_id=1 and point2D_idx=8356 in image_id=8\n",
      "W20260111 03:39:18.144002 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7875 in image_id=1 and point2D_idx=6136 in image_id=8\n",
      "W20260111 03:39:18.144060 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=435 in image_id=1 and point2D_idx=1065 in image_id=8\n",
      "W20260111 03:39:18.144081 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1956 in image_id=1 and point2D_idx=3394 in image_id=8\n",
      "W20260111 03:39:18.144102 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7970 in image_id=1 and point2D_idx=7227 in image_id=8\n",
      "W20260111 03:39:18.144128 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3358 in image_id=1 and point2D_idx=7281 in image_id=8\n",
      "W20260111 03:39:18.144174 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5742 in image_id=1 and point2D_idx=3015 in image_id=9\n",
      "W20260111 03:39:18.144197 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6174 in image_id=1 and point2D_idx=4222 in image_id=9\n",
      "W20260111 03:39:18.144209 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4780 in image_id=1 and point2D_idx=3553 in image_id=9\n",
      "W20260111 03:39:18.144248 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9386 in image_id=1 and point2D_idx=6198 in image_id=9\n",
      "W20260111 03:39:18.144279 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1547 in image_id=1 and point2D_idx=1140 in image_id=9\n",
      "W20260111 03:39:18.144332 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3258 in image_id=1 and point2D_idx=5313 in image_id=10\n",
      "W20260111 03:39:18.144344 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2491 in image_id=1 and point2D_idx=4866 in image_id=10\n",
      "W20260111 03:39:18.144360 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3285 in image_id=1 and point2D_idx=1783 in image_id=10\n",
      "W20260111 03:39:18.144388 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2817 in image_id=1 and point2D_idx=421 in image_id=10\n",
      "W20260111 03:39:18.144401 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2820 in image_id=1 and point2D_idx=754 in image_id=10\n",
      "W20260111 03:39:18.144413 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1941 in image_id=1 and point2D_idx=1141 in image_id=10\n",
      "W20260111 03:39:18.144422 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3316 in image_id=1 and point2D_idx=2978 in image_id=10\n",
      "W20260111 03:39:18.144507 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5755 in image_id=2 and point2D_idx=3328 in image_id=3\n",
      "W20260111 03:39:18.144548 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5351 in image_id=2 and point2D_idx=2894 in image_id=3\n",
      "W20260111 03:39:18.144558 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6576 in image_id=2 and point2D_idx=4001 in image_id=3\n",
      "W20260111 03:39:18.144695 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8681 in image_id=2 and point2D_idx=5812 in image_id=3\n",
      "W20260111 03:39:18.144749 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4479 in image_id=2 and point2D_idx=2675 in image_id=3\n",
      "W20260111 03:39:18.144771 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4943 in image_id=2 and point2D_idx=2922 in image_id=3\n",
      "W20260111 03:39:18.144826 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7450 in image_id=2 and point2D_idx=4733 in image_id=3\n",
      "W20260111 03:39:18.144944 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7860 in image_id=2 and point2D_idx=4928 in image_id=3\n",
      "W20260111 03:39:18.144967 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9097 in image_id=2 and point2D_idx=5840 in image_id=3\n",
      "W20260111 03:39:18.144989 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10844 in image_id=2 and point2D_idx=7237 in image_id=3\n",
      "W20260111 03:39:18.145008 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10122 in image_id=2 and point2D_idx=6638 in image_id=3\n",
      "W20260111 03:39:18.145040 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5424 in image_id=2 and point2D_idx=3189 in image_id=3\n",
      "W20260111 03:39:18.145066 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5429 in image_id=2 and point2D_idx=3108 in image_id=3\n",
      "W20260111 03:39:18.145121 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6166 in image_id=2 and point2D_idx=4450 in image_id=4\n",
      "W20260111 03:39:18.145145 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7412 in image_id=2 and point2D_idx=5478 in image_id=4\n",
      "W20260111 03:39:18.145194 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7418 in image_id=2 and point2D_idx=5481 in image_id=4\n",
      "W20260111 03:39:18.145282 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6178 in image_id=2 and point2D_idx=4465 in image_id=4\n",
      "W20260111 03:39:18.145341 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4927 in image_id=2 and point2D_idx=3499 in image_id=4\n",
      "W20260111 03:39:18.145383 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8252 in image_id=2 and point2D_idx=6146 in image_id=4\n",
      "W20260111 03:39:18.145422 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8260 in image_id=2 and point2D_idx=6154 in image_id=4\n",
      "W20260111 03:39:18.145486 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=873 in image_id=2 and point2D_idx=66 in image_id=4\n",
      "W20260111 03:39:18.145499 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7034 in image_id=2 and point2D_idx=5265 in image_id=4\n",
      "W20260111 03:39:18.145534 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4066 in image_id=2 and point2D_idx=2911 in image_id=4\n",
      "W20260111 03:39:18.145725 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1209 in image_id=2 and point2D_idx=74 in image_id=4\n",
      "W20260111 03:39:18.145780 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4071 in image_id=2 and point2D_idx=2916 in image_id=4\n",
      "W20260111 03:39:18.145829 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9473 in image_id=2 and point2D_idx=7233 in image_id=4\n",
      "W20260111 03:39:18.145884 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5826 in image_id=2 and point2D_idx=4303 in image_id=4\n",
      "W20260111 03:39:18.145985 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3266 in image_id=2 and point2D_idx=2101 in image_id=4\n",
      "W20260111 03:39:18.146090 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10442 in image_id=2 and point2D_idx=10652 in image_id=5\n",
      "W20260111 03:39:18.146220 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6597 in image_id=2 and point2D_idx=5705 in image_id=5\n",
      "W20260111 03:39:18.146379 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11821 in image_id=2 and point2D_idx=10668 in image_id=5\n",
      "W20260111 03:39:18.146444 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12049 in image_id=2 and point2D_idx=10853 in image_id=5\n",
      "W20260111 03:39:18.146482 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2815 in image_id=2 and point2D_idx=2884 in image_id=5\n",
      "W20260111 03:39:18.146531 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6637 in image_id=2 and point2D_idx=6797 in image_id=5\n",
      "W20260111 03:39:18.146757 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7087 in image_id=2 and point2D_idx=7216 in image_id=6\n",
      "W20260111 03:39:18.146868 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5464 in image_id=2 and point2D_idx=7234 in image_id=6\n",
      "W20260111 03:39:18.146915 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9064 in image_id=2 and point2D_idx=10239 in image_id=6\n",
      "W20260111 03:39:18.146950 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9776 in image_id=2 and point2D_idx=1163 in image_id=6\n",
      "W20260111 03:39:18.146999 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10173 in image_id=2 and point2D_idx=1906 in image_id=6\n",
      "W20260111 03:39:18.147050 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3224 in image_id=2 and point2D_idx=1913 in image_id=6\n",
      "W20260111 03:39:18.147131 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4950 in image_id=2 and point2D_idx=4896 in image_id=6\n",
      "W20260111 03:39:18.147181 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10902 in image_id=2 and point2D_idx=10592 in image_id=6\n",
      "W20260111 03:39:18.147280 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9101 in image_id=2 and point2D_idx=9315 in image_id=6\n",
      "W20260111 03:39:18.147336 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1225 in image_id=2 and point2D_idx=409 in image_id=6\n",
      "W20260111 03:39:18.147401 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10493 in image_id=2 and point2D_idx=11381 in image_id=6\n",
      "W20260111 03:39:18.147463 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5503 in image_id=2 and point2D_idx=6293 in image_id=6\n",
      "W20260111 03:39:18.147537 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6712 in image_id=2 and point2D_idx=8827 in image_id=6\n",
      "W20260111 03:39:18.147585 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=337 in image_id=2 and point2D_idx=2345 in image_id=6\n",
      "W20260111 03:39:18.147643 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7081 in image_id=2 and point2D_idx=9914 in image_id=6\n",
      "W20260111 03:39:18.147677 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9533 in image_id=2 and point2D_idx=11401 in image_id=6\n",
      "W20260111 03:39:18.147704 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3760 in image_id=2 and point2D_idx=7309 in image_id=6\n",
      "W20260111 03:39:18.147751 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4022 in image_id=2 and point2D_idx=5112 in image_id=7\n",
      "W20260111 03:39:18.147783 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6999 in image_id=2 and point2D_idx=9290 in image_id=7\n",
      "W20260111 03:39:18.147800 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7412 in image_id=2 and point2D_idx=9666 in image_id=7\n",
      "W20260111 03:39:18.147842 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7097 in image_id=2 and point2D_idx=9675 in image_id=7\n",
      "W20260111 03:39:18.147951 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9083 in image_id=2 and point2D_idx=7947 in image_id=7\n",
      "W20260111 03:39:18.148014 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11530 in image_id=2 and point2D_idx=9327 in image_id=7\n",
      "W20260111 03:39:18.148034 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=880 in image_id=2 and point2D_idx=1503 in image_id=7\n",
      "W20260111 03:39:18.148045 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2426 in image_id=2 and point2D_idx=3900 in image_id=7\n",
      "W20260111 03:39:18.148066 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2365 in image_id=2 and point2D_idx=4302 in image_id=7\n",
      "W20260111 03:39:18.148079 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12089 in image_id=2 and point2D_idx=810 in image_id=7\n",
      "W20260111 03:39:18.148106 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10479 in image_id=2 and point2D_idx=10201 in image_id=7\n",
      "W20260111 03:39:18.148123 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7051 in image_id=2 and point2D_idx=6505 in image_id=7\n",
      "W20260111 03:39:18.148133 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9525 in image_id=2 and point2D_idx=7962 in image_id=7\n",
      "W20260111 03:39:18.148183 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1225 in image_id=2 and point2D_idx=217 in image_id=7\n",
      "W20260111 03:39:18.148251 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=118 in image_id=2 and point2D_idx=407 in image_id=7\n",
      "W20260111 03:39:18.148340 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6233 in image_id=2 and point2D_idx=7481 in image_id=7\n",
      "W20260111 03:39:18.148441 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4909 in image_id=2 and point2D_idx=8313 in image_id=8\n",
      "W20260111 03:39:18.148481 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4124 in image_id=2 and point2D_idx=7193 in image_id=8\n",
      "W20260111 03:39:18.148494 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4045 in image_id=2 and point2D_idx=6925 in image_id=8\n",
      "W20260111 03:39:18.148511 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6184 in image_id=2 and point2D_idx=7941 in image_id=8\n",
      "W20260111 03:39:18.148548 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4945 in image_id=2 and point2D_idx=3775 in image_id=8\n",
      "W20260111 03:39:18.148660 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2562 in image_id=2 and point2D_idx=3720 in image_id=8\n",
      "W20260111 03:39:18.148708 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=882 in image_id=2 and point2D_idx=3105 in image_id=8\n",
      "W20260111 03:39:18.148769 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1559 in image_id=2 and point2D_idx=5408 in image_id=8\n",
      "W20260111 03:39:18.148798 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10542 in image_id=2 and point2D_idx=8761 in image_id=8\n",
      "W20260111 03:39:18.148826 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3245 in image_id=2 and point2D_idx=7472 in image_id=8\n",
      "W20260111 03:39:18.148975 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3294 in image_id=2 and point2D_idx=7240 in image_id=8\n",
      "W20260111 03:39:18.149261 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2786 in image_id=2 and point2D_idx=2092 in image_id=9\n",
      "W20260111 03:39:18.149288 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4947 in image_id=2 and point2D_idx=3550 in image_id=9\n",
      "W20260111 03:39:18.149301 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4949 in image_id=2 and point2D_idx=3654 in image_id=9\n",
      "W20260111 03:39:18.149336 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=883 in image_id=2 and point2D_idx=4748 in image_id=9\n",
      "W20260111 03:39:18.149405 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=329 in image_id=2 and point2D_idx=2075 in image_id=9\n",
      "W20260111 03:39:18.149427 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1981 in image_id=2 and point2D_idx=3929 in image_id=9\n",
      "W20260111 03:39:18.149498 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1962 in image_id=2 and point2D_idx=1018 in image_id=10\n",
      "W20260111 03:39:18.149574 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3697 in image_id=2 and point2D_idx=4900 in image_id=10\n",
      "W20260111 03:39:18.149702 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3127 in image_id=3 and point2D_idx=3965 in image_id=4\n",
      "W20260111 03:39:18.149772 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3135 in image_id=3 and point2D_idx=3975 in image_id=4\n",
      "W20260111 03:39:18.149785 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4659 in image_id=3 and point2D_idx=5407 in image_id=4\n",
      "W20260111 03:39:18.149809 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3136 in image_id=3 and point2D_idx=3976 in image_id=4\n",
      "W20260111 03:39:18.149838 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6235 in image_id=3 and point2D_idx=6913 in image_id=4\n",
      "W20260111 03:39:18.149866 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4955 in image_id=3 and point2D_idx=5658 in image_id=4\n",
      "W20260111 03:39:18.149883 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3145 in image_id=3 and point2D_idx=3990 in image_id=4\n",
      "W20260111 03:39:18.149897 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3148 in image_id=3 and point2D_idx=3993 in image_id=4\n",
      "W20260111 03:39:18.149952 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1042 in image_id=3 and point2D_idx=1447 in image_id=4\n",
      "W20260111 03:39:18.149986 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6057 in image_id=3 and point2D_idx=6945 in image_id=4\n",
      "W20260111 03:39:18.150012 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6629 in image_id=3 and point2D_idx=7648 in image_id=4\n",
      "W20260111 03:39:18.150199 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6799 in image_id=3 and point2D_idx=8118 in image_id=4\n",
      "W20260111 03:39:18.150209 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7691 in image_id=3 and point2D_idx=9008 in image_id=4\n",
      "W20260111 03:39:18.150337 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1014 in image_id=3 and point2D_idx=1786 in image_id=5\n",
      "W20260111 03:39:18.150355 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2155 in image_id=3 and point2D_idx=3207 in image_id=5\n",
      "W20260111 03:39:18.150367 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=55 in image_id=3 and point2D_idx=604 in image_id=5\n",
      "W20260111 03:39:18.150387 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=57 in image_id=3 and point2D_idx=605 in image_id=5\n",
      "W20260111 03:39:18.150454 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3627 in image_id=3 and point2D_idx=4924 in image_id=5\n",
      "W20260111 03:39:18.150481 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5108 in image_id=3 and point2D_idx=6743 in image_id=5\n",
      "W20260111 03:39:18.150497 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4023 in image_id=3 and point2D_idx=5707 in image_id=5\n",
      "W20260111 03:39:18.150534 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4913 in image_id=3 and point2D_idx=7118 in image_id=5\n",
      "W20260111 03:39:18.150550 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7208 in image_id=3 and point2D_idx=9768 in image_id=5\n",
      "W20260111 03:39:18.150579 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1500 in image_id=3 and point2D_idx=2793 in image_id=5\n",
      "W20260111 03:39:18.150594 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5826 in image_id=3 and point2D_idx=8138 in image_id=5\n",
      "W20260111 03:39:18.150656 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5607 in image_id=3 and point2D_idx=8155 in image_id=5\n",
      "W20260111 03:39:18.150666 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7679 in image_id=3 and point2D_idx=10084 in image_id=5\n",
      "W20260111 03:39:18.150676 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3608 in image_id=3 and point2D_idx=5743 in image_id=5\n",
      "W20260111 03:39:18.150711 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7227 in image_id=3 and point2D_idx=9461 in image_id=5\n",
      "W20260111 03:39:18.150741 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3838 in image_id=3 and point2D_idx=6095 in image_id=5\n",
      "W20260111 03:39:18.150837 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4389 in image_id=3 and point2D_idx=7711 in image_id=6\n",
      "W20260111 03:39:18.150899 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1533 in image_id=3 and point2D_idx=5265 in image_id=6\n",
      "W20260111 03:39:18.150927 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6034 in image_id=3 and point2D_idx=10228 in image_id=6\n",
      "W20260111 03:39:18.150942 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6605 in image_id=3 and point2D_idx=10572 in image_id=6\n",
      "W20260111 03:39:18.150981 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4280 in image_id=3 and point2D_idx=8756 in image_id=6\n",
      "W20260111 03:39:18.150995 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2958 in image_id=3 and point2D_idx=7232 in image_id=6\n",
      "W20260111 03:39:18.151016 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4658 in image_id=3 and point2D_idx=9266 in image_id=6\n",
      "W20260111 03:39:18.151069 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5291 in image_id=3 and point2D_idx=9779 in image_id=6\n",
      "W20260111 03:39:18.151081 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1719 in image_id=3 and point2D_idx=4799 in image_id=6\n",
      "W20260111 03:39:18.151091 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3351 in image_id=3 and point2D_idx=7740 in image_id=6\n",
      "W20260111 03:39:18.151121 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4239 in image_id=3 and point2D_idx=8247 in image_id=6\n",
      "W20260111 03:39:18.151142 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4427 in image_id=3 and point2D_idx=6244 in image_id=6\n",
      "W20260111 03:39:18.151163 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5582 in image_id=3 and point2D_idx=8259 in image_id=6\n",
      "W20260111 03:39:18.151174 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6963 in image_id=3 and point2D_idx=9287 in image_id=6\n",
      "W20260111 03:39:18.151251 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3822 in image_id=3 and point2D_idx=6259 in image_id=6\n",
      "W20260111 03:39:18.151303 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6632 in image_id=3 and point2D_idx=9805 in image_id=6\n",
      "W20260111 03:39:18.151330 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4205 in image_id=3 and point2D_idx=7272 in image_id=6\n",
      "W20260111 03:39:18.151384 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6192 in image_id=3 and point2D_idx=9815 in image_id=6\n",
      "W20260111 03:39:18.151405 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=819 in image_id=3 and point2D_idx=4838 in image_id=6\n",
      "W20260111 03:39:18.151451 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5646 in image_id=3 and point2D_idx=9321 in image_id=6\n",
      "W20260111 03:39:18.151463 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6920 in image_id=3 and point2D_idx=11375 in image_id=6\n",
      "W20260111 03:39:18.151499 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4868 in image_id=3 and point2D_idx=8435 in image_id=7\n",
      "W20260111 03:39:18.151524 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5153 in image_id=3 and point2D_idx=9283 in image_id=7\n",
      "W20260111 03:39:18.151547 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6214 in image_id=3 and point2D_idx=10047 in image_id=7\n",
      "W20260111 03:39:18.151590 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2371 in image_id=3 and point2D_idx=7404 in image_id=7\n",
      "W20260111 03:39:18.151657 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5066 in image_id=3 and point2D_idx=10062 in image_id=7\n",
      "W20260111 03:39:18.151701 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1719 in image_id=3 and point2D_idx=5565 in image_id=7\n",
      "W20260111 03:39:18.151719 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7852 in image_id=3 and point2D_idx=373 in image_id=7\n",
      "W20260111 03:39:18.151738 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3037 in image_id=3 and point2D_idx=6922 in image_id=7\n",
      "W20260111 03:39:18.151760 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7481 in image_id=3 and point2D_idx=379 in image_id=7\n",
      "W20260111 03:39:18.151806 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5591 in image_id=3 and point2D_idx=6937 in image_id=7\n",
      "W20260111 03:39:18.151844 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4257 in image_id=3 and point2D_idx=6051 in image_id=7\n",
      "W20260111 03:39:18.151863 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=275 in image_id=3 and point2D_idx=2427 in image_id=7\n",
      "W20260111 03:39:18.151882 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7484 in image_id=3 and point2D_idx=9328 in image_id=7\n",
      "W20260111 03:39:18.151918 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4266 in image_id=3 and point2D_idx=6433 in image_id=7\n",
      "W20260111 03:39:18.151929 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5371 in image_id=3 and point2D_idx=7458 in image_id=7\n",
      "W20260111 03:39:18.151943 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6451 in image_id=3 and point2D_idx=8118 in image_id=7\n",
      "W20260111 03:39:18.151962 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4705 in image_id=3 and point2D_idx=6963 in image_id=7\n",
      "W20260111 03:39:18.151973 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7463 in image_id=3 and point2D_idx=10764 in image_id=7\n",
      "W20260111 03:39:18.152020 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=825 in image_id=3 and point2D_idx=2849 in image_id=7\n",
      "W20260111 03:39:18.152036 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4567 in image_id=3 and point2D_idx=8295 in image_id=8\n",
      "W20260111 03:39:18.152058 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2891 in image_id=3 and point2D_idx=7914 in image_id=8\n",
      "W20260111 03:39:18.152077 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3198 in image_id=3 and point2D_idx=8306 in image_id=8\n",
      "W20260111 03:39:18.152094 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4879 in image_id=3 and point2D_idx=9199 in image_id=8\n",
      "W20260111 03:39:18.152107 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3544 in image_id=3 and point2D_idx=8719 in image_id=8\n",
      "W20260111 03:39:18.152120 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6221 in image_id=3 and point2D_idx=9631 in image_id=8\n",
      "W20260111 03:39:18.152146 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1022 in image_id=3 and point2D_idx=7182 in image_id=8\n",
      "W20260111 03:39:18.152161 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4009 in image_id=3 and point2D_idx=9209 in image_id=8\n",
      "W20260111 03:39:18.152193 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3350 in image_id=3 and point2D_idx=8732 in image_id=8\n",
      "W20260111 03:39:18.152223 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4667 in image_id=3 and point2D_idx=9225 in image_id=8\n",
      "W20260111 03:39:18.152272 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1269 in image_id=3 and point2D_idx=1212 in image_id=8\n",
      "W20260111 03:39:18.152284 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3629 in image_id=3 and point2D_idx=4094 in image_id=8\n",
      "W20260111 03:39:18.152297 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2922 in image_id=3 and point2D_idx=3347 in image_id=8\n",
      "W20260111 03:39:18.152360 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7867 in image_id=3 and point2D_idx=7046 in image_id=8\n",
      "W20260111 03:39:18.152408 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=824 in image_id=3 and point2D_idx=2073 in image_id=8\n",
      "W20260111 03:39:18.152426 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1916 in image_id=3 and point2D_idx=7342 in image_id=9\n",
      "W20260111 03:39:18.152447 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1711 in image_id=3 and point2D_idx=7357 in image_id=9\n",
      "W20260111 03:39:18.152456 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=364 in image_id=3 and point2D_idx=5867 in image_id=9\n",
      "W20260111 03:39:18.152471 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1029 in image_id=3 and point2D_idx=6363 in image_id=9\n",
      "W20260111 03:39:18.152481 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1715 in image_id=3 and point2D_idx=7074 in image_id=9\n",
      "W20260111 03:39:18.152495 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3203 in image_id=3 and point2D_idx=3201 in image_id=9\n",
      "W20260111 03:39:18.152510 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3207 in image_id=3 and point2D_idx=3546 in image_id=9\n",
      "W20260111 03:39:18.152545 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1751 in image_id=3 and point2D_idx=3233 in image_id=9\n",
      "W20260111 03:39:18.152567 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7486 in image_id=3 and point2D_idx=6194 in image_id=9\n",
      "W20260111 03:39:18.152603 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6121 in image_id=4 and point2D_idx=6724 in image_id=5\n",
      "W20260111 03:39:18.152647 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7597 in image_id=4 and point2D_idx=8379 in image_id=5\n",
      "W20260111 03:39:18.152682 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2616 in image_id=4 and point2D_idx=2758 in image_id=5\n",
      "W20260111 03:39:18.152739 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3686 in image_id=4 and point2D_idx=4068 in image_id=5\n",
      "W20260111 03:39:18.152780 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5916 in image_id=4 and point2D_idx=6740 in image_id=5\n",
      "W20260111 03:39:18.152817 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3500 in image_id=4 and point2D_idx=4076 in image_id=5\n",
      "W20260111 03:39:18.152997 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8592 in image_id=4 and point2D_idx=10079 in image_id=5\n",
      "W20260111 03:39:18.153008 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5735 in image_id=4 and point2D_idx=7123 in image_id=5\n",
      "W20260111 03:39:18.153194 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8371 in image_id=4 and point2D_idx=9478 in image_id=5\n",
      "W20260111 03:39:18.153239 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7930 in image_id=4 and point2D_idx=9198 in image_id=5\n",
      "W20260111 03:39:18.153254 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1832 in image_id=4 and point2D_idx=2827 in image_id=5\n",
      "W20260111 03:39:18.153267 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6448 in image_id=4 and point2D_idx=8180 in image_id=5\n",
      "W20260111 03:39:18.153318 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5241 in image_id=4 and point2D_idx=8750 in image_id=6\n",
      "W20260111 03:39:18.153348 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1415 in image_id=4 and point2D_idx=3803 in image_id=6\n",
      "W20260111 03:39:18.153374 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2358 in image_id=4 and point2D_idx=5275 in image_id=6\n",
      "W20260111 03:39:18.153420 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4533 in image_id=4 and point2D_idx=7745 in image_id=6\n",
      "W20260111 03:39:18.153449 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5495 in image_id=4 and point2D_idx=8249 in image_id=6\n",
      "W20260111 03:39:18.153482 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3704 in image_id=4 and point2D_idx=4332 in image_id=6\n",
      "W20260111 03:39:18.153500 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2381 in image_id=4 and point2D_idx=1981 in image_id=6\n",
      "W20260111 03:39:18.153548 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7863 in image_id=4 and point2D_idx=9797 in image_id=6\n",
      "W20260111 03:39:18.153751 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1499 in image_id=4 and point2D_idx=3856 in image_id=6\n",
      "W20260111 03:39:18.153808 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6422 in image_id=4 and point2D_idx=8796 in image_id=6\n",
      "W20260111 03:39:18.153839 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7655 in image_id=4 and point2D_idx=9621 in image_id=6\n",
      "W20260111 03:39:18.153866 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=903 in image_id=4 and point2D_idx=3863 in image_id=6\n",
      "W20260111 03:39:18.153906 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6361 in image_id=4 and point2D_idx=8800 in image_id=6\n",
      "W20260111 03:39:18.154003 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1166 in image_id=4 and point2D_idx=1583 in image_id=6\n",
      "W20260111 03:39:18.154044 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5737 in image_id=4 and point2D_idx=7796 in image_id=6\n",
      "W20260111 03:39:18.154096 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6632 in image_id=4 and point2D_idx=10047 in image_id=7\n",
      "W20260111 03:39:18.154124 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6451 in image_id=4 and point2D_idx=10050 in image_id=7\n",
      "W20260111 03:39:18.154189 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1093 in image_id=4 and point2D_idx=4255 in image_id=7\n",
      "W20260111 03:39:18.154288 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5188 in image_id=4 and point2D_idx=9315 in image_id=7\n",
      "W20260111 03:39:18.154324 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6136 in image_id=4 and point2D_idx=9685 in image_id=7\n",
      "W20260111 03:39:18.154352 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5744 in image_id=4 and point2D_idx=9316 in image_id=7\n",
      "W20260111 03:39:18.154386 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3700 in image_id=4 and point2D_idx=3130 in image_id=7\n",
      "W20260111 03:39:18.154418 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5257 in image_id=4 and point2D_idx=5145 in image_id=7\n",
      "W20260111 03:39:18.154472 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=617 in image_id=4 and point2D_idx=1280 in image_id=7\n",
      "W20260111 03:39:18.154490 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1454 in image_id=4 and point2D_idx=1742 in image_id=7\n",
      "W20260111 03:39:18.154514 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2909 in image_id=4 and point2D_idx=2872 in image_id=7\n",
      "W20260111 03:39:18.154527 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6732 in image_id=4 and point2D_idx=7955 in image_id=7\n",
      "W20260111 03:39:18.154590 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8357 in image_id=4 and point2D_idx=10095 in image_id=7\n",
      "W20260111 03:39:18.154612 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=117 in image_id=4 and point2D_idx=4309 in image_id=7\n",
      "W20260111 03:39:18.154634 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9010 in image_id=4 and point2D_idx=11615 in image_id=7\n",
      "W20260111 03:39:18.154681 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8128 in image_id=4 and point2D_idx=10777 in image_id=7\n",
      "W20260111 03:39:18.154694 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=329 in image_id=4 and point2D_idx=1312 in image_id=7\n",
      "W20260111 03:39:18.154728 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7678 in image_id=4 and point2D_idx=9345 in image_id=7\n",
      "W20260111 03:39:18.154739 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=126 in image_id=4 and point2D_idx=1773 in image_id=7\n",
      "W20260111 03:39:18.154750 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1484 in image_id=4 and point2D_idx=3927 in image_id=7\n",
      "W20260111 03:39:18.154780 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3752 in image_id=4 and point2D_idx=7914 in image_id=8\n",
      "W20260111 03:39:18.154793 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2602 in image_id=4 and point2D_idx=7536 in image_id=8\n",
      "W20260111 03:39:18.154817 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5005 in image_id=4 and point2D_idx=9205 in image_id=8\n",
      "W20260111 03:39:18.154829 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3969 in image_id=4 and point2D_idx=8723 in image_id=8\n",
      "W20260111 03:39:18.154849 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1421 in image_id=4 and point2D_idx=6491 in image_id=8\n",
      "W20260111 03:39:18.154887 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5197 in image_id=4 and point2D_idx=4484 in image_id=8\n",
      "W20260111 03:39:18.154913 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6678 in image_id=4 and point2D_idx=6116 in image_id=8\n",
      "W20260111 03:39:18.154933 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1808 in image_id=4 and point2D_idx=1726 in image_id=8\n",
      "W20260111 03:39:18.154944 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3502 in image_id=4 and point2D_idx=3349 in image_id=8\n",
      "W20260111 03:39:18.154962 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4766 in image_id=4 and point2D_idx=4895 in image_id=8\n",
      "W20260111 03:39:18.154981 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8342 in image_id=4 and point2D_idx=6507 in image_id=8\n",
      "W20260111 03:39:18.155108 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2907 in image_id=4 and point2D_idx=2682 in image_id=9\n",
      "W20260111 03:39:18.155138 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1819 in image_id=4 and point2D_idx=481 in image_id=9\n",
      "W20260111 03:39:18.155150 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1504 in image_id=4 and point2D_idx=575 in image_id=9\n",
      "W20260111 03:39:18.155173 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1167 in image_id=4 and point2D_idx=3293 in image_id=9\n",
      "W20260111 03:39:18.155240 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1306 in image_id=5 and point2D_idx=3918 in image_id=6\n",
      "W20260111 03:39:18.155273 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8112 in image_id=5 and point2D_idx=10582 in image_id=6\n",
      "W20260111 03:39:18.155471 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8400 in image_id=5 and point2D_idx=9294 in image_id=6\n",
      "W20260111 03:39:18.155509 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3367 in image_id=5 and point2D_idx=2794 in image_id=6\n",
      "W20260111 03:39:18.155533 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10603 in image_id=5 and point2D_idx=11086 in image_id=6\n",
      "W20260111 03:39:18.155635 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7488 in image_id=5 and point2D_idx=8289 in image_id=6\n",
      "W20260111 03:39:18.155696 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1385 in image_id=5 and point2D_idx=123 in image_id=6\n",
      "W20260111 03:39:18.155767 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3278 in image_id=5 and point2D_idx=4368 in image_id=6\n",
      "W20260111 03:39:18.155789 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6802 in image_id=5 and point2D_idx=6855 in image_id=6\n",
      "W20260111 03:39:18.155800 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11101 in image_id=5 and point2D_idx=12409 in image_id=6\n",
      "W20260111 03:39:18.155849 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10150 in image_id=5 and point2D_idx=11392 in image_id=6\n",
      "W20260111 03:39:18.155908 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4626 in image_id=5 and point2D_idx=7812 in image_id=6\n",
      "W20260111 03:39:18.155920 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10118 in image_id=5 and point2D_idx=11956 in image_id=6\n",
      "W20260111 03:39:18.155937 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9855 in image_id=5 and point2D_idx=11958 in image_id=6\n",
      "W20260111 03:39:18.155955 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11340 in image_id=5 and point2D_idx=12782 in image_id=6\n",
      "W20260111 03:39:18.155978 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10879 in image_id=5 and point2D_idx=12784 in image_id=6\n",
      "W20260111 03:39:18.156042 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7440 in image_id=5 and point2D_idx=10446 in image_id=7\n",
      "W20260111 03:39:18.156078 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6043 in image_id=5 and point2D_idx=9680 in image_id=7\n",
      "W20260111 03:39:18.156107 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6509 in image_id=5 and point2D_idx=9316 in image_id=7\n",
      "W20260111 03:39:18.156200 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=611 in image_id=5 and point2D_idx=1498 in image_id=7\n",
      "W20260111 03:39:18.156219 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3788 in image_id=5 and point2D_idx=2423 in image_id=7\n",
      "W20260111 03:39:18.156242 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3790 in image_id=5 and point2D_idx=2424 in image_id=7\n",
      "W20260111 03:39:18.156281 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6184 in image_id=5 and point2D_idx=6061 in image_id=7\n",
      "W20260111 03:39:18.156298 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9465 in image_id=5 and point2D_idx=10765 in image_id=7\n",
      "W20260111 03:39:18.156328 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1851 in image_id=5 and point2D_idx=6515 in image_id=7\n",
      "W20260111 03:39:18.156392 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=633 in image_id=5 and point2D_idx=888 in image_id=7\n",
      "W20260111 03:39:18.156429 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3282 in image_id=5 and point2D_idx=4323 in image_id=7\n",
      "W20260111 03:39:18.156446 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7204 in image_id=5 and point2D_idx=6983 in image_id=7\n",
      "W20260111 03:39:18.156457 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6492 in image_id=5 and point2D_idx=6527 in image_id=7\n",
      "W20260111 03:39:18.156506 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6814 in image_id=5 and point2D_idx=9399 in image_id=7\n",
      "W20260111 03:39:18.156521 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1942 in image_id=5 and point2D_idx=6544 in image_id=7\n",
      "W20260111 03:39:18.156532 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10118 in image_id=5 and point2D_idx=11361 in image_id=7\n",
      "W20260111 03:39:18.156542 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11362 in image_id=5 and point2D_idx=11843 in image_id=7\n",
      "W20260111 03:39:18.156678 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7568 in image_id=5 and point2D_idx=11372 in image_id=7\n",
      "W20260111 03:39:18.156749 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5710 in image_id=5 and point2D_idx=3698 in image_id=8\n",
      "W20260111 03:39:18.156784 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1835 in image_id=5 and point2D_idx=1213 in image_id=8\n",
      "W20260111 03:39:18.156806 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=948 in image_id=5 and point2D_idx=1297 in image_id=8\n",
      "W20260111 03:39:18.156822 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=949 in image_id=5 and point2D_idx=1893 in image_id=8\n",
      "W20260111 03:39:18.156852 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10716 in image_id=5 and point2D_idx=8750 in image_id=8\n",
      "W20260111 03:39:18.156863 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7813 in image_id=5 and point2D_idx=6129 in image_id=8\n",
      "W20260111 03:39:18.156905 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2329 in image_id=5 and point2D_idx=7588 in image_id=8\n",
      "W20260111 03:39:18.156917 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9292 in image_id=5 and point2D_idx=8763 in image_id=8\n",
      "W20260111 03:39:18.156984 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1413 in image_id=5 and point2D_idx=4549 in image_id=8\n",
      "W20260111 03:39:18.157081 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2264 in image_id=5 and point2D_idx=7120 in image_id=9\n",
      "W20260111 03:39:18.157116 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6047 in image_id=5 and point2D_idx=7082 in image_id=9\n",
      "W20260111 03:39:18.157142 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2786 in image_id=5 and point2D_idx=2305 in image_id=9\n",
      "W20260111 03:39:18.157155 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6072 in image_id=5 and point2D_idx=4222 in image_id=9\n",
      "W20260111 03:39:18.157170 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3786 in image_id=5 and point2D_idx=2616 in image_id=9\n",
      "W20260111 03:39:18.157196 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10144 in image_id=5 and point2D_idx=6482 in image_id=9\n",
      "W20260111 03:39:18.157206 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3256 in image_id=5 and point2D_idx=2052 in image_id=9\n",
      "W20260111 03:39:18.157227 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2814 in image_id=5 and point2D_idx=320 in image_id=9\n",
      "W20260111 03:39:18.157241 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=24 in image_id=5 and point2D_idx=698 in image_id=9\n",
      "W20260111 03:39:18.157264 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=29 in image_id=5 and point2D_idx=1818 in image_id=9\n",
      "W20260111 03:39:18.157276 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=640 in image_id=5 and point2D_idx=2648 in image_id=9\n",
      "W20260111 03:39:18.157327 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5069 in image_id=5 and point2D_idx=1978 in image_id=10\n",
      "W20260111 03:39:18.157357 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3259 in image_id=5 and point2D_idx=2599 in image_id=10\n",
      "W20260111 03:39:18.157368 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2813 in image_id=5 and point2D_idx=501 in image_id=10\n",
      "W20260111 03:39:18.157386 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1393 in image_id=5 and point2D_idx=1648 in image_id=10\n",
      "W20260111 03:39:18.157401 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=632 in image_id=5 and point2D_idx=2252 in image_id=10\n",
      "W20260111 03:39:18.157417 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1398 in image_id=5 and point2D_idx=2485 in image_id=10\n",
      "W20260111 03:39:18.157542 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3295 in image_id=6 and point2D_idx=4262 in image_id=7\n",
      "W20260111 03:39:18.157611 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12386 in image_id=6 and point2D_idx=7511 in image_id=7\n",
      "W20260111 03:39:18.157625 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12586 in image_id=6 and point2D_idx=7940 in image_id=7\n",
      "W20260111 03:39:18.157648 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12254 in image_id=6 and point2D_idx=7434 in image_id=7\n",
      "W20260111 03:39:18.157673 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12636 in image_id=6 and point2D_idx=8474 in image_id=7\n",
      "W20260111 03:39:18.157706 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12594 in image_id=6 and point2D_idx=9780 in image_id=7\n",
      "W20260111 03:39:18.157733 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5310 in image_id=6 and point2D_idx=4723 in image_id=7\n",
      "W20260111 03:39:18.157776 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3332 in image_id=6 and point2D_idx=3900 in image_id=7\n",
      "W20260111 03:39:18.157814 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1567 in image_id=6 and point2D_idx=2432 in image_id=7\n",
      "W20260111 03:39:18.157827 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12184 in image_id=6 and point2D_idx=11617 in image_id=7\n",
      "W20260111 03:39:18.157847 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11934 in image_id=6 and point2D_idx=11342 in image_id=7\n",
      "W20260111 03:39:18.157931 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9908 in image_id=6 and point2D_idx=8512 in image_id=7\n",
      "W20260111 03:39:18.157941 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11388 in image_id=6 and point2D_idx=10164 in image_id=7\n",
      "W20260111 03:39:18.157964 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7291 in image_id=6 and point2D_idx=6086 in image_id=7\n",
      "W20260111 03:39:18.158048 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2336 in image_id=6 and point2D_idx=2811 in image_id=7\n",
      "W20260111 03:39:18.158072 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11961 in image_id=6 and point2D_idx=11643 in image_id=7\n",
      "W20260111 03:39:18.158282 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6737 in image_id=6 and point2D_idx=4489 in image_id=8\n",
      "W20260111 03:39:18.158297 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3833 in image_id=6 and point2D_idx=2705 in image_id=8\n",
      "W20260111 03:39:18.158406 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11931 in image_id=6 and point2D_idx=9965 in image_id=8\n",
      "W20260111 03:39:18.158442 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1627 in image_id=6 and point2D_idx=4529 in image_id=8\n",
      "W20260111 03:39:18.158459 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1199 in image_id=6 and point2D_idx=176 in image_id=8\n",
      "W20260111 03:39:18.158474 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2813 in image_id=6 and point2D_idx=2400 in image_id=8\n",
      "W20260111 03:39:18.158484 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12409 in image_id=6 and point2D_idx=9723 in image_id=8\n",
      "W20260111 03:39:18.158527 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2903 in image_id=6 and point2D_idx=3386 in image_id=8\n",
      "W20260111 03:39:18.158599 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4375 in image_id=6 and point2D_idx=4544 in image_id=8\n",
      "W20260111 03:39:18.158616 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9415 in image_id=6 and point2D_idx=7602 in image_id=8\n",
      "W20260111 03:39:18.158737 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2414 in image_id=6 and point2D_idx=2273 in image_id=9\n",
      "W20260111 03:39:18.158787 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4404 in image_id=6 and point2D_idx=3194 in image_id=9\n",
      "W20260111 03:39:18.158808 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7250 in image_id=6 and point2D_idx=3871 in image_id=9\n",
      "W20260111 03:39:18.158834 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2786 in image_id=6 and point2D_idx=2362 in image_id=9\n",
      "W20260111 03:39:18.158851 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4339 in image_id=6 and point2D_idx=3277 in image_id=9\n",
      "W20260111 03:39:18.158863 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1917 in image_id=6 and point2D_idx=2307 in image_id=9\n",
      "W20260111 03:39:18.158891 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10924 in image_id=6 and point2D_idx=5147 in image_id=9\n",
      "W20260111 03:39:18.158908 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11650 in image_id=6 and point2D_idx=6191 in image_id=9\n",
      "W20260111 03:39:18.158938 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7778 in image_id=6 and point2D_idx=4494 in image_id=9\n",
      "W20260111 03:39:18.158966 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1199 in image_id=6 and point2D_idx=263 in image_id=9\n",
      "W20260111 03:39:18.158976 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2314 in image_id=6 and point2D_idx=5910 in image_id=9\n",
      "W20260111 03:39:18.158987 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2896 in image_id=6 and point2D_idx=2326 in image_id=9\n",
      "W20260111 03:39:18.159000 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2813 in image_id=6 and point2D_idx=2399 in image_id=9\n",
      "W20260111 03:39:18.159041 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3886 in image_id=6 and point2D_idx=4520 in image_id=9\n",
      "W20260111 03:39:18.159067 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=606 in image_id=6 and point2D_idx=2381 in image_id=9\n",
      "W20260111 03:39:18.159081 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3891 in image_id=6 and point2D_idx=4527 in image_id=9\n",
      "W20260111 03:39:18.159142 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2750 in image_id=6 and point2D_idx=4599 in image_id=10\n",
      "W20260111 03:39:18.159154 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4879 in image_id=6 and point2D_idx=5313 in image_id=10\n",
      "W20260111 03:39:18.159185 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3465 in image_id=6 and point2D_idx=1621 in image_id=10\n",
      "W20260111 03:39:18.159211 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10872 in image_id=6 and point2D_idx=2942 in image_id=10\n",
      "W20260111 03:39:18.159235 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2303 in image_id=6 and point2D_idx=4271 in image_id=10\n",
      "W20260111 03:39:18.159266 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=858 in image_id=6 and point2D_idx=656 in image_id=10\n",
      "W20260111 03:39:18.159315 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=132 in image_id=6 and point2D_idx=2255 in image_id=10\n",
      "W20260111 03:39:18.159477 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8908 in image_id=7 and point2D_idx=8732 in image_id=8\n",
      "W20260111 03:39:18.159498 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6468 in image_id=7 and point2D_idx=7193 in image_id=8\n",
      "W20260111 03:39:18.159757 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3955 in image_id=7 and point2D_idx=3708 in image_id=8\n",
      "W20260111 03:39:18.159878 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=816 in image_id=7 and point2D_idx=1223 in image_id=8\n",
      "W20260111 03:39:18.159921 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2776 in image_id=7 and point2D_idx=3424 in image_id=8\n",
      "W20260111 03:39:18.159957 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11062 in image_id=7 and point2D_idx=9330 in image_id=8\n",
      "W20260111 03:39:18.159998 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=586 in image_id=7 and point2D_idx=36 in image_id=8\n",
      "W20260111 03:39:18.160041 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=587 in image_id=7 and point2D_idx=67 in image_id=8\n",
      "W20260111 03:39:18.160116 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4309 in image_id=7 and point2D_idx=5754 in image_id=8\n",
      "W20260111 03:39:18.160227 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=427 in image_id=7 and point2D_idx=488 in image_id=8\n",
      "W20260111 03:39:18.160456 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11581 in image_id=7 and point2D_idx=7 in image_id=9\n",
      "W20260111 03:39:18.160474 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11867 in image_id=7 and point2D_idx=105 in image_id=9\n",
      "W20260111 03:39:18.160496 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1590 in image_id=7 and point2D_idx=1012 in image_id=9\n",
      "W20260111 03:39:18.160550 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1792 in image_id=7 and point2D_idx=1324 in image_id=9\n",
      "W20260111 03:39:18.160598 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2398 in image_id=7 and point2D_idx=2591 in image_id=9\n",
      "W20260111 03:39:18.160610 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7422 in image_id=7 and point2D_idx=7075 in image_id=9\n",
      "W20260111 03:39:18.160627 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6919 in image_id=7 and point2D_idx=6786 in image_id=9\n",
      "W20260111 03:39:18.160643 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2747 in image_id=7 and point2D_idx=2959 in image_id=9\n",
      "W20260111 03:39:18.160653 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3122 in image_id=7 and point2D_idx=3189 in image_id=9\n",
      "W20260111 03:39:18.160663 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6564 in image_id=7 and point2D_idx=6477 in image_id=9\n",
      "W20260111 03:39:18.160700 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3877 in image_id=7 and point2D_idx=3201 in image_id=9\n",
      "W20260111 03:39:18.160717 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10081 in image_id=7 and point2D_idx=657 in image_id=9\n",
      "W20260111 03:39:18.160730 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3881 in image_id=7 and point2D_idx=3274 in image_id=9\n",
      "W20260111 03:39:18.160789 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=579 in image_id=7 and point2D_idx=1208 in image_id=9\n",
      "W20260111 03:39:18.160805 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=581 in image_id=7 and point2D_idx=1389 in image_id=9\n",
      "W20260111 03:39:18.160818 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11390 in image_id=7 and point2D_idx=6794 in image_id=9\n",
      "W20260111 03:39:18.160833 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5274 in image_id=7 and point2D_idx=4494 in image_id=9\n",
      "W20260111 03:39:18.160858 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3156 in image_id=7 and point2D_idx=4742 in image_id=9\n",
      "W20260111 03:39:18.160902 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6965 in image_id=7 and point2D_idx=6720 in image_id=9\n",
      "W20260111 03:39:18.160917 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2787 in image_id=7 and point2D_idx=4956 in image_id=9\n",
      "W20260111 03:39:18.161000 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=847 in image_id=7 and point2D_idx=1371 in image_id=9\n",
      "W20260111 03:39:18.161029 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11997 in image_id=7 and point2D_idx=7588 in image_id=9\n",
      "W20260111 03:39:18.161046 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11856 in image_id=7 and point2D_idx=7589 in image_id=9\n",
      "W20260111 03:39:18.161094 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=860 in image_id=7 and point2D_idx=2654 in image_id=9\n",
      "W20260111 03:39:18.161183 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2041 in image_id=7 and point2D_idx=2282 in image_id=10\n",
      "W20260111 03:39:18.161194 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3117 in image_id=7 and point2D_idx=4105 in image_id=10\n",
      "W20260111 03:39:18.161219 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2748 in image_id=7 and point2D_idx=2204 in image_id=10\n",
      "W20260111 03:39:18.161233 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11442 in image_id=7 and point2D_idx=113 in image_id=10\n",
      "W20260111 03:39:18.161266 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3510 in image_id=7 and point2D_idx=1776 in image_id=10\n",
      "W20260111 03:39:18.161280 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10460 in image_id=7 and point2D_idx=60 in image_id=10\n",
      "W20260111 03:39:18.161301 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1731 in image_id=7 and point2D_idx=1834 in image_id=10\n",
      "W20260111 03:39:18.161319 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11661 in image_id=7 and point2D_idx=672 in image_id=10\n",
      "W20260111 03:39:18.161344 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11974 in image_id=7 and point2D_idx=933 in image_id=10\n",
      "W20260111 03:39:18.161387 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=582 in image_id=7 and point2D_idx=2468 in image_id=10\n",
      "W20260111 03:39:18.161400 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10473 in image_id=7 and point2D_idx=682 in image_id=10\n",
      "W20260111 03:39:18.161425 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10772 in image_id=7 and point2D_idx=3445 in image_id=10\n",
      "W20260111 03:39:18.161446 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1750 in image_id=7 and point2D_idx=4276 in image_id=10\n",
      "W20260111 03:39:18.161491 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=61 in image_id=7 and point2D_idx=493 in image_id=10\n",
      "W20260111 03:39:18.161554 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2455 in image_id=7 and point2D_idx=2879 in image_id=10\n",
      "W20260111 03:39:18.161577 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=222 in image_id=7 and point2D_idx=2254 in image_id=10\n",
      "W20260111 03:39:18.161590 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=606 in image_id=7 and point2D_idx=2517 in image_id=10\n",
      "W20260111 03:39:18.161627 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5245 in image_id=7 and point2D_idx=4742 in image_id=10\n",
      "W20260111 03:39:18.161659 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11654 in image_id=7 and point2D_idx=5501 in image_id=10\n",
      "W20260111 03:39:18.161720 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1437 in image_id=8 and point2D_idx=1314 in image_id=9\n",
      "W20260111 03:39:18.161732 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4471 in image_id=8 and point2D_idx=4979 in image_id=9\n",
      "W20260111 03:39:18.161752 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5697 in image_id=8 and point2D_idx=5862 in image_id=9\n",
      "W20260111 03:39:18.161761 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6581 in image_id=8 and point2D_idx=6467 in image_id=9\n",
      "W20260111 03:39:18.161842 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2028 in image_id=8 and point2D_idx=1402 in image_id=9\n",
      "W20260111 03:39:18.161941 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2043 in image_id=8 and point2D_idx=2312 in image_id=9\n",
      "W20260111 03:39:18.161973 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=466 in image_id=8 and point2D_idx=271 in image_id=9\n",
      "W20260111 03:39:18.162046 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=172 in image_id=8 and point2D_idx=145 in image_id=9\n",
      "W20260111 03:39:18.162114 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1240 in image_id=8 and point2D_idx=1187 in image_id=9\n",
      "W20260111 03:39:18.162206 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2419 in image_id=8 and point2D_idx=3257 in image_id=9\n",
      "W20260111 03:39:18.162238 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7170 in image_id=8 and point2D_idx=4245 in image_id=10\n",
      "W20260111 03:39:18.162292 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9684 in image_id=8 and point2D_idx=55 in image_id=10\n",
      "W20260111 03:39:18.162387 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6500 in image_id=8 and point2D_idx=14 in image_id=10\n",
      "W20260111 03:39:18.162400 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3701 in image_id=8 and point2D_idx=2210 in image_id=10\n",
      "W20260111 03:39:18.162488 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1736 in image_id=8 and point2D_idx=3266 in image_id=10\n",
      "W20260111 03:39:18.162719 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2401 in image_id=8 and point2D_idx=2475 in image_id=10\n",
      "W20260111 03:39:18.162752 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=48 in image_id=8 and point2D_idx=721 in image_id=10\n",
      "W20260111 03:39:18.162764 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3380 in image_id=8 and point2D_idx=3235 in image_id=10\n",
      "W20260111 03:39:18.162801 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1485 in image_id=8 and point2D_idx=2484 in image_id=10\n",
      "W20260111 03:39:18.162841 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9752 in image_id=8 and point2D_idx=5338 in image_id=10\n",
      "W20260111 03:39:18.162878 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6183 in image_id=8 and point2D_idx=5445 in image_id=10\n",
      "W20260111 03:39:18.162892 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3508 in image_id=9 and point2D_idx=2258 in image_id=10\n",
      "W20260111 03:39:18.162930 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1517 in image_id=9 and point2D_idx=1203 in image_id=10\n",
      "W20260111 03:39:18.163109 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3212 in image_id=9 and point2D_idx=2695 in image_id=10\n",
      "W20260111 03:39:18.163120 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4229 in image_id=9 and point2D_idx=2950 in image_id=10\n",
      "W20260111 03:39:18.163135 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4231 in image_id=9 and point2D_idx=2951 in image_id=10\n",
      "W20260111 03:39:18.163200 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2972 in image_id=9 and point2D_idx=3712 in image_id=10\n",
      "W20260111 03:39:18.163230 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=897 in image_id=9 and point2D_idx=911 in image_id=10\n",
      "W20260111 03:39:18.163261 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4508 in image_id=9 and point2D_idx=4582 in image_id=10\n",
      "W20260111 03:39:18.163285 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3233 in image_id=9 and point2D_idx=3018 in image_id=10\n",
      "W20260111 03:39:18.163344 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4520 in image_id=9 and point2D_idx=3946 in image_id=10\n",
      "W20260111 03:39:18.163392 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=807 in image_id=9 and point2D_idx=1492 in image_id=10\n",
      "W20260111 03:39:18.163432 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1611 in image_id=9 and point2D_idx=2998 in image_id=10\n",
      "W20260111 03:39:18.163607 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10655 in image_id=11 and point2D_idx=14697 in image_id=12\n",
      "W20260111 03:39:18.163622 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7063 in image_id=11 and point2D_idx=12658 in image_id=12\n",
      "W20260111 03:39:18.163875 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7093 in image_id=11 and point2D_idx=7680 in image_id=12\n",
      "W20260111 03:39:18.164009 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6790 in image_id=11 and point2D_idx=6986 in image_id=12\n",
      "W20260111 03:39:18.164113 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3518 in image_id=11 and point2D_idx=4797 in image_id=12\n",
      "W20260111 03:39:18.164181 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9865 in image_id=11 and point2D_idx=12067 in image_id=12\n",
      "W20260111 03:39:18.164204 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=298 in image_id=11 and point2D_idx=1215 in image_id=12\n",
      "W20260111 03:39:18.164220 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9440 in image_id=11 and point2D_idx=11743 in image_id=12\n",
      "W20260111 03:39:18.164340 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1015 in image_id=11 and point2D_idx=1449 in image_id=12\n",
      "W20260111 03:39:18.164509 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1518 in image_id=11 and point2D_idx=7796 in image_id=13\n",
      "W20260111 03:39:18.164532 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1609 in image_id=11 and point2D_idx=7376 in image_id=13\n",
      "W20260111 03:39:18.164626 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=693 in image_id=11 and point2D_idx=5064 in image_id=13\n",
      "W20260111 03:39:18.164706 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11088 in image_id=11 and point2D_idx=10742 in image_id=13\n",
      "W20260111 03:39:18.164729 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5858 in image_id=11 and point2D_idx=4702 in image_id=13\n",
      "W20260111 03:39:18.164816 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8184 in image_id=11 and point2D_idx=8682 in image_id=13\n",
      "W20260111 03:39:18.164871 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=63 in image_id=11 and point2D_idx=1788 in image_id=13\n",
      "W20260111 03:39:18.164925 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8270 in image_id=11 and point2D_idx=8268 in image_id=13\n",
      "W20260111 03:39:18.164984 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=112 in image_id=11 and point2D_idx=1229 in image_id=13\n",
      "W20260111 03:39:18.165228 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7916 in image_id=11 and point2D_idx=6634 in image_id=13\n",
      "W20260111 03:39:18.165265 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7175 in image_id=11 and point2D_idx=5503 in image_id=13\n",
      "W20260111 03:39:18.165877 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2637 in image_id=11 and point2D_idx=1645 in image_id=14\n",
      "W20260111 03:39:18.166386 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5854 in image_id=11 and point2D_idx=5694 in image_id=15\n",
      "W20260111 03:39:18.166433 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11462 in image_id=11 and point2D_idx=12136 in image_id=15\n",
      "W20260111 03:39:18.166456 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3693 in image_id=11 and point2D_idx=7931 in image_id=15\n",
      "W20260111 03:39:18.166498 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3282 in image_id=11 and point2D_idx=6582 in image_id=15\n",
      "W20260111 03:39:18.166670 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1565 in image_id=11 and point2D_idx=3119 in image_id=15\n",
      "W20260111 03:39:18.166734 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10695 in image_id=11 and point2D_idx=10665 in image_id=15\n",
      "W20260111 03:39:18.166810 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5603 in image_id=11 and point2D_idx=4346 in image_id=15\n",
      "W20260111 03:39:18.166871 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=294 in image_id=11 and point2D_idx=579 in image_id=15\n",
      "W20260111 03:39:18.166993 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3322 in image_id=11 and point2D_idx=2013 in image_id=15\n",
      "W20260111 03:39:18.167023 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7483 in image_id=11 and point2D_idx=5744 in image_id=15\n",
      "W20260111 03:39:18.167116 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3120 in image_id=11 and point2D_idx=1020 in image_id=15\n",
      "W20260111 03:39:18.167272 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9416 in image_id=11 and point2D_idx=6903 in image_id=16\n",
      "W20260111 03:39:18.167283 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10695 in image_id=11 and point2D_idx=9672 in image_id=16\n",
      "W20260111 03:39:18.167344 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11763 in image_id=11 and point2D_idx=8399 in image_id=16\n",
      "W20260111 03:39:18.167526 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7141 in image_id=11 and point2D_idx=1937 in image_id=16\n",
      "W20260111 03:39:18.167551 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3743 in image_id=11 and point2D_idx=1123 in image_id=16\n",
      "W20260111 03:39:18.167692 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11072 in image_id=11 and point2D_idx=7836 in image_id=17\n",
      "W20260111 03:39:18.167755 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11093 in image_id=11 and point2D_idx=6936 in image_id=17\n",
      "W20260111 03:39:18.167795 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8547 in image_id=11 and point2D_idx=3847 in image_id=17\n",
      "W20260111 03:39:18.167819 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10680 in image_id=11 and point2D_idx=6284 in image_id=17\n",
      "W20260111 03:39:18.167879 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2442 in image_id=11 and point2D_idx=11344 in image_id=17\n",
      "W20260111 03:39:18.167911 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7160 in image_id=11 and point2D_idx=5400 in image_id=17\n",
      "W20260111 03:39:18.167976 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6483 in image_id=11 and point2D_idx=4777 in image_id=17\n",
      "W20260111 03:39:18.168071 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9869 in image_id=11 and point2D_idx=7422 in image_id=17\n",
      "W20260111 03:39:18.168166 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8667 in image_id=11 and point2D_idx=1714 in image_id=17\n",
      "W20260111 03:39:18.168179 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9787 in image_id=11 and point2D_idx=6369 in image_id=18\n",
      "W20260111 03:39:18.168205 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11271 in image_id=11 and point2D_idx=6503 in image_id=18\n",
      "W20260111 03:39:18.168218 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11322 in image_id=11 and point2D_idx=6504 in image_id=18\n",
      "W20260111 03:39:18.168252 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1031 in image_id=11 and point2D_idx=10768 in image_id=18\n",
      "W20260111 03:39:18.168279 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2848 in image_id=11 and point2D_idx=11204 in image_id=18\n",
      "W20260111 03:39:18.168300 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6177 in image_id=11 and point2D_idx=4547 in image_id=18\n",
      "W20260111 03:39:18.168323 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8559 in image_id=11 and point2D_idx=5219 in image_id=18\n",
      "W20260111 03:39:18.168361 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8811 in image_id=11 and point2D_idx=5169 in image_id=18\n",
      "W20260111 03:39:18.168404 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10716 in image_id=11 and point2D_idx=7279 in image_id=18\n",
      "W20260111 03:39:18.168449 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7482 in image_id=11 and point2D_idx=3336 in image_id=18\n",
      "W20260111 03:39:18.168461 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1899 in image_id=11 and point2D_idx=1902 in image_id=18\n",
      "W20260111 03:39:18.168474 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1601 in image_id=11 and point2D_idx=1658 in image_id=18\n",
      "W20260111 03:39:18.168489 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9496 in image_id=11 and point2D_idx=2509 in image_id=18\n",
      "W20260111 03:39:18.168556 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=278 in image_id=11 and point2D_idx=7450 in image_id=19\n",
      "W20260111 03:39:18.168627 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11776 in image_id=11 and point2D_idx=4910 in image_id=19\n",
      "W20260111 03:39:18.168646 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12190 in image_id=11 and point2D_idx=4880 in image_id=19\n",
      "W20260111 03:39:18.168730 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1236 in image_id=12 and point2D_idx=3477 in image_id=13\n",
      "W20260111 03:39:18.168785 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7286 in image_id=12 and point2D_idx=11482 in image_id=13\n",
      "W20260111 03:39:18.169116 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=766 in image_id=12 and point2D_idx=1480 in image_id=13\n",
      "W20260111 03:39:18.169132 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2563 in image_id=12 and point2D_idx=3509 in image_id=13\n",
      "W20260111 03:39:18.169235 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12685 in image_id=12 and point2D_idx=13962 in image_id=13\n",
      "W20260111 03:39:18.169284 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5534 in image_id=12 and point2D_idx=4315 in image_id=13\n",
      "W20260111 03:39:18.169389 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10237 in image_id=12 and point2D_idx=8331 in image_id=13\n",
      "W20260111 03:39:18.169451 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10764 in image_id=12 and point2D_idx=8280 in image_id=13\n",
      "W20260111 03:39:18.169479 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8145 in image_id=12 and point2D_idx=6621 in image_id=13\n",
      "W20260111 03:39:18.169499 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=94 in image_id=12 and point2D_idx=161 in image_id=13\n",
      "W20260111 03:39:18.169531 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8507 in image_id=12 and point2D_idx=7016 in image_id=13\n",
      "W20260111 03:39:18.169544 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13788 in image_id=12 and point2D_idx=12866 in image_id=13\n",
      "W20260111 03:39:18.169657 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12722 in image_id=12 and point2D_idx=10723 in image_id=13\n",
      "W20260111 03:39:18.169754 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5926 in image_id=12 and point2D_idx=3189 in image_id=13\n",
      "W20260111 03:39:18.169832 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14165 in image_id=12 and point2D_idx=8326 in image_id=14\n",
      "W20260111 03:39:18.169922 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5151 in image_id=12 and point2D_idx=1902 in image_id=14\n",
      "W20260111 03:39:18.169934 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8840 in image_id=12 and point2D_idx=4024 in image_id=14\n",
      "W20260111 03:39:18.170085 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6602 in image_id=12 and point2D_idx=5880 in image_id=14\n",
      "W20260111 03:39:18.170518 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12046 in image_id=12 and point2D_idx=8804 in image_id=14\n",
      "W20260111 03:39:18.170575 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4203 in image_id=12 and point2D_idx=1686 in image_id=14\n",
      "W20260111 03:39:18.170620 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3556 in image_id=12 and point2D_idx=1181 in image_id=14\n",
      "W20260111 03:39:18.170808 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7713 in image_id=12 and point2D_idx=5656 in image_id=14\n",
      "W20260111 03:39:18.170878 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1707 in image_id=12 and point2D_idx=414 in image_id=14\n",
      "W20260111 03:39:18.170902 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2292 in image_id=12 and point2D_idx=1440 in image_id=14\n",
      "W20260111 03:39:18.170936 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4562 in image_id=12 and point2D_idx=3404 in image_id=14\n",
      "W20260111 03:39:18.171014 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13341 in image_id=12 and point2D_idx=10924 in image_id=14\n",
      "W20260111 03:39:18.171073 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12354 in image_id=12 and point2D_idx=12602 in image_id=15\n",
      "W20260111 03:39:18.171204 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11636 in image_id=12 and point2D_idx=11936 in image_id=15\n",
      "W20260111 03:39:18.171258 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6601 in image_id=12 and point2D_idx=6302 in image_id=15\n",
      "W20260111 03:39:18.171284 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7324 in image_id=12 and point2D_idx=6932 in image_id=15\n",
      "W20260111 03:39:18.171296 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11193 in image_id=12 and point2D_idx=11255 in image_id=15\n",
      "W20260111 03:39:18.171338 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14038 in image_id=12 and point2D_idx=12801 in image_id=15\n",
      "W20260111 03:39:18.171411 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2881 in image_id=12 and point2D_idx=2811 in image_id=15\n",
      "W20260111 03:39:18.171423 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7332 in image_id=12 and point2D_idx=6613 in image_id=15\n",
      "W20260111 03:39:18.171491 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4526 in image_id=12 and point2D_idx=4276 in image_id=15\n",
      "W20260111 03:39:18.171531 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5191 in image_id=12 and point2D_idx=4343 in image_id=15\n",
      "W20260111 03:39:18.171658 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8086 in image_id=12 and point2D_idx=6326 in image_id=15\n",
      "W20260111 03:39:18.171719 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10756 in image_id=12 and point2D_idx=8342 in image_id=15\n",
      "W20260111 03:39:18.171900 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14739 in image_id=12 and point2D_idx=11927 in image_id=15\n",
      "W20260111 03:39:18.171953 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9341 in image_id=12 and point2D_idx=6048 in image_id=15\n",
      "W20260111 03:39:18.172010 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2930 in image_id=12 and point2D_idx=1021 in image_id=15\n",
      "W20260111 03:39:18.172038 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6662 in image_id=12 and point2D_idx=3777 in image_id=15\n",
      "W20260111 03:39:18.172053 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4572 in image_id=12 and point2D_idx=2031 in image_id=15\n",
      "W20260111 03:39:18.172070 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9731 in image_id=12 and point2D_idx=7140 in image_id=16\n",
      "W20260111 03:39:18.172144 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9749 in image_id=12 and point2D_idx=5022 in image_id=16\n",
      "W20260111 03:39:18.172221 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10738 in image_id=12 and point2D_idx=8383 in image_id=16\n",
      "W20260111 03:39:18.172241 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10742 in image_id=12 and point2D_idx=7873 in image_id=16\n",
      "W20260111 03:39:18.172269 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10226 in image_id=12 and point2D_idx=6257 in image_id=16\n",
      "W20260111 03:39:18.172330 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12628 in image_id=12 and point2D_idx=10192 in image_id=16\n",
      "W20260111 03:39:18.172386 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13295 in image_id=12 and point2D_idx=7393 in image_id=16\n",
      "W20260111 03:39:18.172465 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11247 in image_id=12 and point2D_idx=3647 in image_id=16\n",
      "W20260111 03:39:18.172523 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4569 in image_id=12 and point2D_idx=1127 in image_id=16\n",
      "W20260111 03:39:18.172738 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9746 in image_id=12 and point2D_idx=4825 in image_id=17\n",
      "W20260111 03:39:18.172806 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8864 in image_id=12 and point2D_idx=6857 in image_id=17\n",
      "W20260111 03:39:18.172825 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2007 in image_id=12 and point2D_idx=10117 in image_id=17\n",
      "W20260111 03:39:18.172873 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3926 in image_id=12 and point2D_idx=11336 in image_id=17\n",
      "W20260111 03:39:18.172891 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7678 in image_id=12 and point2D_idx=5674 in image_id=17\n",
      "W20260111 03:39:18.172919 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7683 in image_id=12 and point2D_idx=5400 in image_id=17\n",
      "W20260111 03:39:18.172983 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6612 in image_id=12 and point2D_idx=5096 in image_id=17\n",
      "W20260111 03:39:18.173011 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9299 in image_id=12 and point2D_idx=6298 in image_id=17\n",
      "W20260111 03:39:18.173078 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10730 in image_id=12 and point2D_idx=8589 in image_id=17\n",
      "W20260111 03:39:18.173167 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5887 in image_id=12 and point2D_idx=3855 in image_id=17\n",
      "W20260111 03:39:18.173231 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6319 in image_id=12 and point2D_idx=2792 in image_id=17\n",
      "W20260111 03:39:18.173330 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12711 in image_id=12 and point2D_idx=8131 in image_id=17\n",
      "W20260111 03:39:18.173369 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10770 in image_id=12 and point2D_idx=6892 in image_id=17\n",
      "W20260111 03:39:18.173388 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12072 in image_id=12 and point2D_idx=6924 in image_id=17\n",
      "W20260111 03:39:18.173459 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5563 in image_id=12 and point2D_idx=2368 in image_id=17\n",
      "W20260111 03:39:18.173474 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10779 in image_id=12 and point2D_idx=3892 in image_id=17\n",
      "W20260111 03:39:18.173505 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8524 in image_id=12 and point2D_idx=2607 in image_id=17\n",
      "W20260111 03:39:18.173578 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4926 in image_id=12 and point2D_idx=774 in image_id=17\n",
      "W20260111 03:39:18.173606 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9355 in image_id=12 and point2D_idx=1340 in image_id=17\n",
      "W20260111 03:39:18.173629 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11066 in image_id=12 and point2D_idx=6977 in image_id=18\n",
      "W20260111 03:39:18.173660 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9063 in image_id=12 and point2D_idx=4819 in image_id=18\n",
      "W20260111 03:39:18.173682 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10181 in image_id=12 and point2D_idx=5438 in image_id=18\n",
      "W20260111 03:39:18.173750 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8863 in image_id=12 and point2D_idx=3611 in image_id=18\n",
      "W20260111 03:39:18.173831 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2262 in image_id=12 and point2D_idx=8190 in image_id=18\n",
      "W20260111 03:39:18.173849 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6976 in image_id=12 and point2D_idx=4233 in image_id=18\n",
      "W20260111 03:39:18.173858 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7688 in image_id=12 and point2D_idx=4236 in image_id=18\n",
      "W20260111 03:39:18.173923 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12432 in image_id=12 and point2D_idx=6538 in image_id=18\n",
      "W20260111 03:39:18.173973 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12403 in image_id=12 and point2D_idx=6336 in image_id=18\n",
      "W20260111 03:39:18.173999 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10254 in image_id=12 and point2D_idx=4271 in image_id=18\n",
      "W20260111 03:39:18.174015 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8916 in image_id=12 and point2D_idx=3040 in image_id=18\n",
      "W20260111 03:39:18.174035 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5224 in image_id=12 and point2D_idx=1670 in image_id=18\n",
      "W20260111 03:39:18.174046 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8919 in image_id=12 and point2D_idx=2303 in image_id=18\n",
      "W20260111 03:39:18.174060 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5568 in image_id=12 and point2D_idx=1459 in image_id=18\n",
      "W20260111 03:39:18.174087 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5271 in image_id=12 and point2D_idx=732 in image_id=18\n",
      "W20260111 03:39:18.174097 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7736 in image_id=12 and point2D_idx=1067 in image_id=18\n",
      "W20260111 03:39:18.174136 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8866 in image_id=12 and point2D_idx=4604 in image_id=19\n",
      "W20260111 03:39:18.174148 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9755 in image_id=12 and point2D_idx=6772 in image_id=19\n",
      "W20260111 03:39:18.174175 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10730 in image_id=12 and point2D_idx=5520 in image_id=19\n",
      "W20260111 03:39:18.174198 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10218 in image_id=12 and point2D_idx=4616 in image_id=19\n",
      "W20260111 03:39:18.174229 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10745 in image_id=12 and point2D_idx=4858 in image_id=19\n",
      "W20260111 03:39:18.174257 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=592 in image_id=12 and point2D_idx=6422 in image_id=19\n",
      "W20260111 03:39:18.174279 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11219 in image_id=12 and point2D_idx=6791 in image_id=19\n",
      "W20260111 03:39:18.174347 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14565 in image_id=12 and point2D_idx=3821 in image_id=19\n",
      "W20260111 03:39:18.174357 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9241 in image_id=12 and point2D_idx=3493 in image_id=19\n",
      "W20260111 03:39:18.174418 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9407 in image_id=12 and point2D_idx=465 in image_id=19\n",
      "W20260111 03:39:18.174447 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13899 in image_id=13 and point2D_idx=5296 in image_id=14\n",
      "W20260111 03:39:18.174515 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13930 in image_id=13 and point2D_idx=5023 in image_id=14\n",
      "W20260111 03:39:18.174573 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13936 in image_id=13 and point2D_idx=5028 in image_id=14\n",
      "W20260111 03:39:18.174692 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6582 in image_id=13 and point2D_idx=6842 in image_id=14\n",
      "W20260111 03:39:18.174723 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4910 in image_id=13 and point2D_idx=888 in image_id=14\n",
      "W20260111 03:39:18.174767 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3216 in image_id=13 and point2D_idx=75 in image_id=14\n",
      "W20260111 03:39:18.174814 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3157 in image_id=13 and point2D_idx=88 in image_id=14\n",
      "W20260111 03:39:18.174841 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3531 in image_id=13 and point2D_idx=4292 in image_id=14\n",
      "W20260111 03:39:18.174861 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3926 in image_id=13 and point2D_idx=4856 in image_id=14\n",
      "W20260111 03:39:18.174944 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5838 in image_id=13 and point2D_idx=5369 in image_id=14\n",
      "W20260111 03:39:18.174992 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4749 in image_id=13 and point2D_idx=4863 in image_id=14\n",
      "W20260111 03:39:18.175043 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13343 in image_id=13 and point2D_idx=11087 in image_id=14\n",
      "W20260111 03:39:18.175120 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2155 in image_id=13 and point2D_idx=3627 in image_id=14\n",
      "W20260111 03:39:18.175164 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2500 in image_id=13 and point2D_idx=4324 in image_id=14\n",
      "W20260111 03:39:18.175225 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9604 in image_id=13 and point2D_idx=11016 in image_id=15\n",
      "W20260111 03:39:18.175276 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6192 in image_id=13 and point2D_idx=7250 in image_id=15\n",
      "W20260111 03:39:18.175287 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11065 in image_id=13 and point2D_idx=11877 in image_id=15\n",
      "W20260111 03:39:18.175389 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5804 in image_id=13 and point2D_idx=6606 in image_id=15\n",
      "W20260111 03:39:18.175465 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5448 in image_id=13 and point2D_idx=4265 in image_id=15\n",
      "W20260111 03:39:18.175490 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9274 in image_id=13 and point2D_idx=6907 in image_id=15\n",
      "W20260111 03:39:18.175518 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14141 in image_id=13 and point2D_idx=12123 in image_id=15\n",
      "W20260111 03:39:18.175693 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2116 in image_id=13 and point2D_idx=1510 in image_id=15\n",
      "W20260111 03:39:18.175765 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2122 in image_id=13 and point2D_idx=1450 in image_id=15\n",
      "W20260111 03:39:18.175830 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3155 in image_id=13 and point2D_idx=2251 in image_id=15\n",
      "W20260111 03:39:18.175883 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=965 in image_id=13 and point2D_idx=425 in image_id=15\n",
      "W20260111 03:39:18.175940 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3162 in image_id=13 and point2D_idx=2547 in image_id=15\n",
      "W20260111 03:39:18.176070 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10198 in image_id=13 and point2D_idx=8347 in image_id=15\n",
      "W20260111 03:39:18.176214 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12578 in image_id=13 and point2D_idx=10630 in image_id=15\n",
      "W20260111 03:39:18.176257 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2160 in image_id=13 and point2D_idx=2587 in image_id=15\n",
      "W20260111 03:39:18.176285 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=244 in image_id=13 and point2D_idx=92 in image_id=15\n",
      "W20260111 03:39:18.176333 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11502 in image_id=13 and point2D_idx=8516 in image_id=16\n",
      "W20260111 03:39:18.176404 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1777 in image_id=13 and point2D_idx=10401 in image_id=16\n",
      "W20260111 03:39:18.176417 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10666 in image_id=13 and point2D_idx=7351 in image_id=16\n",
      "W20260111 03:39:18.176434 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9142 in image_id=13 and point2D_idx=5423 in image_id=16\n",
      "W20260111 03:39:18.176470 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7052 in image_id=13 and point2D_idx=5636 in image_id=16\n",
      "W20260111 03:39:18.176481 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8686 in image_id=13 and point2D_idx=6869 in image_id=16\n",
      "W20260111 03:39:18.176619 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7434 in image_id=13 and point2D_idx=3912 in image_id=16\n",
      "W20260111 03:39:18.176717 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8725 in image_id=13 and point2D_idx=6308 in image_id=16\n",
      "W20260111 03:39:18.176766 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10208 in image_id=13 and point2D_idx=5680 in image_id=16\n",
      "W20260111 03:39:18.176802 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5143 in image_id=13 and point2D_idx=2698 in image_id=16\n",
      "W20260111 03:39:18.176830 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11138 in image_id=13 and point2D_idx=4804 in image_id=16\n",
      "W20260111 03:39:18.176900 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1182 in image_id=13 and point2D_idx=9517 in image_id=17\n",
      "W20260111 03:39:18.176922 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5792 in image_id=13 and point2D_idx=4243 in image_id=17\n",
      "W20260111 03:39:18.176947 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10647 in image_id=13 and point2D_idx=7434 in image_id=17\n",
      "W20260111 03:39:18.177100 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5521 in image_id=13 and point2D_idx=4767 in image_id=17\n",
      "W20260111 03:39:18.177149 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=114 in image_id=13 and point2D_idx=4260 in image_id=17\n",
      "W20260111 03:39:18.177163 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6700 in image_id=13 and point2D_idx=5102 in image_id=17\n",
      "W20260111 03:39:18.177198 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6224 in image_id=13 and point2D_idx=4837 in image_id=17\n",
      "W20260111 03:39:18.177231 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7424 in image_id=13 and point2D_idx=5478 in image_id=17\n",
      "W20260111 03:39:18.177382 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2150 in image_id=13 and point2D_idx=2362 in image_id=17\n",
      "W20260111 03:39:18.177407 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5847 in image_id=13 and point2D_idx=3882 in image_id=17\n",
      "W20260111 03:39:18.177416 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7504 in image_id=13 and point2D_idx=5143 in image_id=17\n",
      "W20260111 03:39:18.177512 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10647 in image_id=13 and point2D_idx=6258 in image_id=18\n",
      "W20260111 03:39:18.177541 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2782 in image_id=13 and point2D_idx=10520 in image_id=18\n",
      "W20260111 03:39:18.177614 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=738 in image_id=13 and point2D_idx=8632 in image_id=18\n",
      "W20260111 03:39:18.177666 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6981 in image_id=13 and point2D_idx=4835 in image_id=18\n",
      "W20260111 03:39:18.177676 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8249 in image_id=13 and point2D_idx=5792 in image_id=18\n",
      "W20260111 03:39:18.177691 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9632 in image_id=13 and point2D_idx=7684 in image_id=18\n",
      "W20260111 03:39:18.177743 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4792 in image_id=13 and point2D_idx=3309 in image_id=18\n",
      "W20260111 03:39:18.177753 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10169 in image_id=13 and point2D_idx=7253 in image_id=18\n",
      "W20260111 03:39:18.177795 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10088 in image_id=13 and point2D_idx=7012 in image_id=18\n",
      "W20260111 03:39:18.177812 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7005 in image_id=13 and point2D_idx=3023 in image_id=18\n",
      "W20260111 03:39:18.177854 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9186 in image_id=13 and point2D_idx=3695 in image_id=18\n",
      "W20260111 03:39:18.177898 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11617 in image_id=13 and point2D_idx=6550 in image_id=18\n",
      "W20260111 03:39:18.177951 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5859 in image_id=13 and point2D_idx=1682 in image_id=18\n",
      "W20260111 03:39:18.177967 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8299 in image_id=13 and point2D_idx=2120 in image_id=18\n",
      "W20260111 03:39:18.177981 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6265 in image_id=13 and point2D_idx=1310 in image_id=18\n",
      "W20260111 03:39:18.177998 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=156 in image_id=13 and point2D_idx=8388 in image_id=19\n",
      "W20260111 03:39:18.178020 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7835 in image_id=13 and point2D_idx=4070 in image_id=19\n",
      "W20260111 03:39:18.178077 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9649 in image_id=13 and point2D_idx=4848 in image_id=19\n",
      "W20260111 03:39:18.178102 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10172 in image_id=13 and point2D_idx=5135 in image_id=19\n",
      "W20260111 03:39:18.178117 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10176 in image_id=13 and point2D_idx=5090 in image_id=19\n",
      "W20260111 03:39:18.178162 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11115 in image_id=13 and point2D_idx=6966 in image_id=19\n",
      "W20260111 03:39:18.178187 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11118 in image_id=13 and point2D_idx=5828 in image_id=19\n",
      "W20260111 03:39:18.178211 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13984 in image_id=13 and point2D_idx=5762 in image_id=19\n",
      "W20260111 03:39:18.178249 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13318 in image_id=13 and point2D_idx=3516 in image_id=19\n",
      "W20260111 03:39:18.178298 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13322 in image_id=13 and point2D_idx=1678 in image_id=19\n",
      "W20260111 03:39:18.178341 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13024 in image_id=13 and point2D_idx=845 in image_id=19\n",
      "W20260111 03:39:18.178377 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6164 in image_id=14 and point2D_idx=6286 in image_id=15\n",
      "W20260111 03:39:18.178453 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10637 in image_id=14 and point2D_idx=11883 in image_id=15\n",
      "W20260111 03:39:18.178731 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6520 in image_id=14 and point2D_idx=6626 in image_id=15\n",
      "W20260111 03:39:18.178806 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2322 in image_id=14 and point2D_idx=4012 in image_id=15\n",
      "W20260111 03:39:18.178892 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11538 in image_id=14 and point2D_idx=12445 in image_id=15\n",
      "W20260111 03:39:18.178939 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3612 in image_id=14 and point2D_idx=3453 in image_id=15\n",
      "W20260111 03:39:18.178982 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10906 in image_id=14 and point2D_idx=11000 in image_id=15\n",
      "W20260111 03:39:18.179015 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=103 in image_id=14 and point2D_idx=587 in image_id=15\n",
      "W20260111 03:39:18.179175 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5389 in image_id=14 and point2D_idx=3773 in image_id=15\n",
      "W20260111 03:39:18.179238 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8352 in image_id=14 and point2D_idx=4131 in image_id=16\n",
      "W20260111 03:39:18.179253 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10232 in image_id=14 and point2D_idx=5986 in image_id=16\n",
      "W20260111 03:39:18.179264 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8353 in image_id=14 and point2D_idx=4132 in image_id=16\n",
      "W20260111 03:39:18.179303 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8364 in image_id=14 and point2D_idx=7362 in image_id=16\n",
      "W20260111 03:39:18.179354 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9099 in image_id=14 and point2D_idx=9671 in image_id=16\n",
      "W20260111 03:39:18.179384 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10251 in image_id=14 and point2D_idx=8649 in image_id=16\n",
      "W20260111 03:39:18.179434 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8760 in image_id=14 and point2D_idx=7642 in image_id=16\n",
      "W20260111 03:39:18.179455 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7627 in image_id=14 and point2D_idx=6275 in image_id=16\n",
      "W20260111 03:39:18.179468 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8396 in image_id=14 and point2D_idx=6003 in image_id=16\n",
      "W20260111 03:39:18.179541 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2582 in image_id=14 and point2D_idx=1697 in image_id=16\n",
      "W20260111 03:39:18.179573 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1898 in image_id=14 and point2D_idx=1478 in image_id=16\n",
      "W20260111 03:39:18.179617 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9792 in image_id=14 and point2D_idx=7113 in image_id=17\n",
      "W20260111 03:39:18.179626 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10204 in image_id=14 and point2D_idx=7605 in image_id=17\n",
      "W20260111 03:39:18.179640 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6827 in image_id=14 and point2D_idx=3544 in image_id=17\n",
      "W20260111 03:39:18.179655 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10633 in image_id=14 and point2D_idx=7611 in image_id=17\n",
      "W20260111 03:39:18.179750 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6867 in image_id=14 and point2D_idx=3328 in image_id=17\n",
      "W20260111 03:39:18.179766 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7973 in image_id=14 and point2D_idx=4493 in image_id=17\n",
      "W20260111 03:39:18.179781 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8576 in image_id=14 and point2D_idx=4790 in image_id=17\n",
      "W20260111 03:39:18.179792 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9111 in image_id=14 and point2D_idx=5130 in image_id=17\n",
      "W20260111 03:39:18.179808 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10271 in image_id=14 and point2D_idx=10166 in image_id=17\n",
      "W20260111 03:39:18.179894 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9792 in image_id=14 and point2D_idx=5718 in image_id=18\n",
      "W20260111 03:39:18.179923 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=608 in image_id=14 and point2D_idx=11189 in image_id=18\n",
      "W20260111 03:39:18.180006 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8751 in image_id=14 and point2D_idx=3636 in image_id=18\n",
      "W20260111 03:39:18.180029 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10677 in image_id=14 and point2D_idx=7945 in image_id=18\n",
      "W20260111 03:39:18.180038 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7501 in image_id=14 and point2D_idx=6039 in image_id=18\n",
      "W20260111 03:39:18.180055 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6880 in image_id=14 and point2D_idx=5486 in image_id=18\n",
      "W20260111 03:39:18.180102 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10642 in image_id=14 and point2D_idx=4337 in image_id=19\n",
      "W20260111 03:39:18.180179 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9127 in image_id=14 and point2D_idx=2575 in image_id=19\n",
      "W20260111 03:39:18.180257 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3484 in image_id=15 and point2D_idx=10724 in image_id=16\n",
      "W20260111 03:39:18.180289 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2596 in image_id=15 and point2D_idx=9636 in image_id=16\n",
      "W20260111 03:39:18.180301 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10945 in image_id=15 and point2D_idx=7330 in image_id=16\n",
      "W20260111 03:39:18.180391 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3426 in image_id=15 and point2D_idx=10479 in image_id=16\n",
      "W20260111 03:39:18.180419 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10955 in image_id=15 and point2D_idx=6235 in image_id=16\n",
      "W20260111 03:39:18.180482 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8709 in image_id=15 and point2D_idx=6872 in image_id=16\n",
      "W20260111 03:39:18.180547 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8725 in image_id=15 and point2D_idx=6550 in image_id=16\n",
      "W20260111 03:39:18.180665 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8352 in image_id=15 and point2D_idx=6913 in image_id=16\n",
      "W20260111 03:39:18.180690 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1523 in image_id=15 and point2D_idx=2427 in image_id=16\n",
      "W20260111 03:39:18.180727 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7995 in image_id=15 and point2D_idx=6003 in image_id=16\n",
      "W20260111 03:39:18.180911 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4052 in image_id=15 and point2D_idx=11336 in image_id=17\n",
      "W20260111 03:39:18.180931 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3782 in image_id=15 and point2D_idx=11051 in image_id=17\n",
      "W20260111 03:39:18.180941 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7945 in image_id=15 and point2D_idx=5065 in image_id=17\n",
      "W20260111 03:39:18.180979 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3995 in image_id=15 and point2D_idx=11056 in image_id=17\n",
      "W20260111 03:39:18.180998 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10178 in image_id=15 and point2D_idx=6060 in image_id=17\n",
      "W20260111 03:39:18.181103 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7274 in image_id=15 and point2D_idx=5687 in image_id=17\n",
      "W20260111 03:39:18.181114 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8029 in image_id=15 and point2D_idx=5981 in image_id=17\n",
      "W20260111 03:39:18.181161 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10971 in image_id=15 and point2D_idx=10451 in image_id=17\n",
      "W20260111 03:39:18.181177 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8725 in image_id=15 and point2D_idx=6648 in image_id=17\n",
      "W20260111 03:39:18.181196 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8729 in image_id=15 and point2D_idx=6945 in image_id=17\n",
      "W20260111 03:39:18.181237 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4859 in image_id=15 and point2D_idx=2645 in image_id=17\n",
      "W20260111 03:39:18.181297 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2057 in image_id=15 and point2D_idx=2592 in image_id=17\n",
      "W20260111 03:39:18.181330 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11318 in image_id=15 and point2D_idx=9140 in image_id=17\n",
      "W20260111 03:39:18.181380 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5145 in image_id=15 and point2D_idx=3298 in image_id=17\n",
      "W20260111 03:39:18.181485 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1333 in image_id=15 and point2D_idx=9373 in image_id=18\n",
      "W20260111 03:39:18.181724 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10949 in image_id=15 and point2D_idx=5433 in image_id=18\n",
      "W20260111 03:39:18.181769 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10950 in image_id=15 and point2D_idx=5500 in image_id=18\n",
      "W20260111 03:39:18.181809 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3120 in image_id=15 and point2D_idx=9038 in image_id=18\n",
      "W20260111 03:39:18.181929 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10269 in image_id=15 and point2D_idx=8411 in image_id=18\n",
      "W20260111 03:39:18.181986 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6067 in image_id=15 and point2D_idx=3927 in image_id=18\n",
      "W20260111 03:39:18.182040 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11032 in image_id=15 and point2D_idx=7926 in image_id=18\n",
      "W20260111 03:39:18.182082 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11303 in image_id=15 and point2D_idx=10057 in image_id=18\n",
      "W20260111 03:39:18.182162 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9858 in image_id=15 and point2D_idx=7034 in image_id=18\n",
      "W20260111 03:39:18.182225 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3766 in image_id=15 and point2D_idx=2101 in image_id=18\n",
      "W20260111 03:39:18.182270 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4070 in image_id=15 and point2D_idx=1675 in image_id=18\n",
      "W20260111 03:39:18.182299 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4612 in image_id=15 and point2D_idx=1921 in image_id=18\n",
      "W20260111 03:39:18.182350 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6053 in image_id=15 and point2D_idx=1927 in image_id=18\n",
      "W20260111 03:39:18.182411 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11557 in image_id=15 and point2D_idx=4585 in image_id=19\n",
      "W20260111 03:39:18.182453 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11875 in image_id=15 and point2D_idx=4825 in image_id=19\n",
      "W20260111 03:39:18.182499 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=207 in image_id=15 and point2D_idx=6588 in image_id=19\n",
      "W20260111 03:39:18.182511 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3123 in image_id=15 and point2D_idx=8194 in image_id=19\n",
      "W20260111 03:39:18.182533 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3428 in image_id=15 and point2D_idx=8427 in image_id=19\n",
      "W20260111 03:39:18.182573 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7963 in image_id=15 and point2D_idx=4128 in image_id=19\n",
      "W20260111 03:39:18.182597 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4013 in image_id=15 and point2D_idx=7076 in image_id=19\n",
      "W20260111 03:39:18.182662 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11596 in image_id=15 and point2D_idx=6464 in image_id=19\n",
      "W20260111 03:39:18.182711 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10615 in image_id=15 and point2D_idx=4648 in image_id=19\n",
      "W20260111 03:39:18.182796 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11326 in image_id=15 and point2D_idx=1871 in image_id=19\n",
      "W20260111 03:39:18.182819 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11616 in image_id=15 and point2D_idx=1651 in image_id=19\n",
      "W20260111 03:39:18.182863 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11229 in image_id=16 and point2D_idx=9764 in image_id=17\n",
      "W20260111 03:39:18.182928 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8066 in image_id=16 and point2D_idx=7358 in image_id=17\n",
      "W20260111 03:39:18.183010 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7335 in image_id=16 and point2D_idx=6838 in image_id=17\n",
      "W20260111 03:39:18.183022 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10111 in image_id=16 and point2D_idx=9056 in image_id=17\n",
      "W20260111 03:39:18.183066 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4710 in image_id=16 and point2D_idx=4458 in image_id=17\n",
      "W20260111 03:39:18.183088 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4123 in image_id=16 and point2D_idx=3839 in image_id=17\n",
      "W20260111 03:39:18.183173 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9102 in image_id=16 and point2D_idx=8567 in image_id=17\n",
      "W20260111 03:39:18.183422 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4149 in image_id=16 and point2D_idx=4183 in image_id=17\n",
      "W20260111 03:39:18.183522 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10761 in image_id=16 and point2D_idx=11075 in image_id=17\n",
      "W20260111 03:39:18.183532 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11032 in image_id=16 and point2D_idx=11367 in image_id=17\n",
      "W20260111 03:39:18.183585 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=826 in image_id=16 and point2D_idx=772 in image_id=17\n",
      "W20260111 03:39:18.183767 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9142 in image_id=16 and point2D_idx=9831 in image_id=17\n",
      "W20260111 03:39:18.183912 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4404 in image_id=16 and point2D_idx=3276 in image_id=18\n",
      "W20260111 03:39:18.184061 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4129 in image_id=16 and point2D_idx=2990 in image_id=18\n",
      "W20260111 03:39:18.184221 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=704 in image_id=16 and point2D_idx=573 in image_id=18\n",
      "W20260111 03:39:18.184576 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2457 in image_id=16 and point2D_idx=1929 in image_id=18\n",
      "W20260111 03:39:18.184648 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5387 in image_id=16 and point2D_idx=7428 in image_id=19\n",
      "W20260111 03:39:18.184682 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8087 in image_id=16 and point2D_idx=8252 in image_id=19\n",
      "W20260111 03:39:18.184730 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7325 in image_id=16 and point2D_idx=7563 in image_id=19\n",
      "W20260111 03:39:18.184754 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8843 in image_id=16 and point2D_idx=8301 in image_id=19\n",
      "W20260111 03:39:18.184827 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=131 in image_id=16 and point2D_idx=1598 in image_id=19\n",
      "W20260111 03:39:18.184854 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=101 in image_id=16 and point2D_idx=1599 in image_id=19\n",
      "W20260111 03:39:18.184924 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8861 in image_id=16 and point2D_idx=6254 in image_id=19\n",
      "W20260111 03:39:18.184954 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6295 in image_id=16 and point2D_idx=3773 in image_id=19\n",
      "W20260111 03:39:18.184985 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6296 in image_id=16 and point2D_idx=3775 in image_id=19\n",
      "W20260111 03:39:18.184997 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10480 in image_id=16 and point2D_idx=8427 in image_id=19\n",
      "W20260111 03:39:18.185044 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4442 in image_id=16 and point2D_idx=2513 in image_id=19\n",
      "W20260111 03:39:18.185123 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9705 in image_id=16 and point2D_idx=6270 in image_id=19\n",
      "W20260111 03:39:18.185145 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2871 in image_id=16 and point2D_idx=810 in image_id=19\n",
      "W20260111 03:39:18.185167 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6305 in image_id=16 and point2D_idx=3151 in image_id=19\n",
      "W20260111 03:39:18.185337 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6576 in image_id=16 and point2D_idx=3171 in image_id=19\n",
      "W20260111 03:39:18.185356 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3645 in image_id=16 and point2D_idx=1014 in image_id=19\n",
      "W20260111 03:39:18.185399 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8925 in image_id=16 and point2D_idx=4876 in image_id=19\n",
      "W20260111 03:39:18.185496 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4454 in image_id=17 and point2D_idx=3589 in image_id=18\n",
      "W20260111 03:39:18.185526 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4456 in image_id=17 and point2D_idx=3592 in image_id=18\n",
      "W20260111 03:39:18.185659 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5084 in image_id=17 and point2D_idx=3910 in image_id=18\n",
      "W20260111 03:39:18.185867 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10748 in image_id=17 and point2D_idx=10327 in image_id=18\n",
      "W20260111 03:39:18.186070 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9449 in image_id=17 and point2D_idx=7927 in image_id=18\n",
      "W20260111 03:39:18.186121 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6322 in image_id=17 and point2D_idx=5481 in image_id=18\n",
      "W20260111 03:39:18.186228 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6895 in image_id=17 and point2D_idx=5775 in image_id=18\n",
      "W20260111 03:39:18.186289 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7670 in image_id=17 and point2D_idx=6315 in image_id=18\n",
      "W20260111 03:39:18.186369 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8570 in image_id=17 and point2D_idx=8311 in image_id=19\n",
      "W20260111 03:39:18.186392 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9414 in image_id=17 and point2D_idx=8518 in image_id=19\n",
      "W20260111 03:39:18.186515 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8105 in image_id=17 and point2D_idx=5089 in image_id=19\n",
      "W20260111 03:39:18.186532 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5119 in image_id=17 and point2D_idx=2520 in image_id=19\n",
      "W20260111 03:39:18.186570 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11070 in image_id=17 and point2D_idx=6940 in image_id=19\n",
      "W20260111 03:39:18.186590 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=638 in image_id=17 and point2D_idx=1610 in image_id=19\n",
      "W20260111 03:39:18.186662 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10453 in image_id=17 and point2D_idx=7082 in image_id=19\n",
      "W20260111 03:39:18.186723 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10157 in image_id=17 and point2D_idx=6307 in image_id=19\n",
      "W20260111 03:39:18.186750 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11084 in image_id=17 and point2D_idx=6458 in image_id=19\n",
      "W20260111 03:39:18.186821 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7426 in image_id=17 and point2D_idx=2839 in image_id=19\n",
      "W20260111 03:39:18.186874 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12 in image_id=18 and point2D_idx=1786 in image_id=19\n",
      "W20260111 03:39:18.186903 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4827 in image_id=18 and point2D_idx=2778 in image_id=19\n",
      "W20260111 03:39:18.187039 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6284 in image_id=18 and point2D_idx=4349 in image_id=19\n",
      "W20260111 03:39:18.187083 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7914 in image_id=18 and point2D_idx=6098 in image_id=19\n",
      "W20260111 03:39:18.187116 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10793 in image_id=18 and point2D_idx=8293 in image_id=19\n",
      "W20260111 03:39:18.187159 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6527 in image_id=18 and point2D_idx=4406 in image_id=19\n",
      "W20260111 03:39:18.187193 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7476 in image_id=18 and point2D_idx=5323 in image_id=19\n",
      "W20260111 03:39:18.187339 133176606766656 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8177 in image_id=18 and point2D_idx=5338 in image_id=19\n",
      "I20260111 03:39:18.205006 133176606766656 database_cache.cc:210]  in 0.068s (ignored 0)\n",
      "I20260111 03:39:18.205054 133176606766656 timer.cc:91] Elapsed time: 0.001 [minutes]\n",
      "I20260111 03:39:18.208254 133176606766656 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "\n",
      " 15%|█▌        | 132/880 [04:47<22:02,  1.77s/it]\u001b[AI20260111 03:39:18.759405 133176606766656 incremental_pipeline.cc:306] Initializing with image pair #12 and #16\n",
      "I20260111 03:39:18.766332 133176606766656 incremental_pipeline.cc:311] Global bundle adjustment\n",
      "I20260111 03:39:19.582602 133176606766656 incremental_pipeline.cc:390] Registering image #17 (3)\n",
      "I20260111 03:39:19.582636 133176606766656 incremental_pipeline.cc:393] => Image sees 1378 / 11365 points\n",
      "\n",
      " 15%|█▌        | 133/880 [04:48<19:52,  1.60s/it]\u001b[AI20260111 03:39:20.829866 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "\n",
      " 15%|█▌        | 134/880 [04:50<18:19,  1.47s/it]\u001b[AI20260111 03:39:21.096229 133176606766656 incremental_pipeline.cc:390] Registering image #18 (4)\n",
      "I20260111 03:39:21.096267 133176606766656 incremental_pipeline.cc:393] => Image sees 4563 / 11224 points\n",
      "\n",
      " 15%|█▌        | 135/880 [04:51<17:12,  1.39s/it]\u001b[AI20260111 03:39:22.224721 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:39:22.794375 133176606766656 incremental_pipeline.cc:390] Registering image #19 (5)\n",
      "I20260111 03:39:22.794412 133176606766656 incremental_pipeline.cc:393] => Image sees 4385 / 8745 points\n",
      "\n",
      " 15%|█▌        | 136/880 [04:52<16:31,  1.33s/it]\u001b[A\n",
      " 16%|█▌        | 137/880 [04:53<16:08,  1.30s/it]\u001b[AI20260111 03:39:24.749858 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:39:25.409918 133176606766656 incremental_pipeline.cc:390] Registering image #15 (6)\n",
      "I20260111 03:39:25.409956 133176606766656 incremental_pipeline.cc:393] => Image sees 4918 / 12808 points\n",
      "\n",
      " 16%|█▌        | 138/880 [04:54<15:50,  1.28s/it]\u001b[A\n",
      " 16%|█▌        | 139/880 [04:56<15:46,  1.28s/it]\u001b[A\n",
      " 16%|█▌        | 140/880 [04:57<15:41,  1.27s/it]\u001b[A\n",
      " 16%|█▌        | 141/880 [04:58<16:03,  1.30s/it]\u001b[AI20260111 03:39:30.007513 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:39:31.011406 133176606766656 incremental_pipeline.cc:390] Registering image #13 (7)\n",
      "I20260111 03:39:31.011456 133176606766656 incremental_pipeline.cc:393] => Image sees 7707 / 14171 points\n",
      "\n",
      " 16%|█▌        | 142/880 [05:00<16:19,  1.33s/it]\u001b[A\n",
      " 16%|█▋        | 143/880 [05:01<16:16,  1.32s/it]\u001b[A\n",
      " 16%|█▋        | 144/880 [05:02<16:01,  1.31s/it]\u001b[AI20260111 03:39:34.759974 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "\n",
      " 16%|█▋        | 145/880 [05:04<15:45,  1.29s/it]\u001b[A\n",
      " 17%|█▋        | 146/880 [05:05<15:26,  1.26s/it]\u001b[AI20260111 03:39:36.302867 133176606766656 incremental_pipeline.cc:390] Registering image #11 (8)\n",
      "I20260111 03:39:36.302940 133176606766656 incremental_pipeline.cc:393] => Image sees 7758 / 12944 points\n",
      "\n",
      " 17%|█▋        | 147/880 [05:06<15:21,  1.26s/it]\u001b[A\n",
      " 17%|█▋        | 148/880 [05:07<15:19,  1.26s/it]\u001b[A\n",
      " 17%|█▋        | 149/880 [05:09<15:17,  1.25s/it]\u001b[AI20260111 03:39:40.606695 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "\n",
      " 17%|█▋        | 150/880 [05:10<15:00,  1.23s/it]\u001b[A\n",
      " 17%|█▋        | 151/880 [05:11<15:01,  1.24s/it]\u001b[A\n",
      " 17%|█▋        | 152/880 [05:12<15:09,  1.25s/it]\u001b[AI20260111 03:39:43.811171 133176606766656 incremental_pipeline.cc:390] Registering image #14 (9)\n",
      "I20260111 03:39:43.811214 133176606766656 incremental_pipeline.cc:393] => Image sees 9204 / 11803 points\n",
      "\n",
      " 17%|█▋        | 153/880 [05:13<15:12,  1.26s/it]\u001b[A\n",
      " 18%|█▊        | 154/880 [05:15<15:13,  1.26s/it]\u001b[A\n",
      " 18%|█▊        | 155/880 [05:16<15:17,  1.26s/it]\u001b[A\n",
      " 18%|█▊        | 156/880 [05:17<15:16,  1.27s/it]\u001b[A\n",
      " 18%|█▊        | 157/880 [05:19<15:12,  1.26s/it]\u001b[AI20260111 03:39:49.990200 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "\n",
      " 18%|█▊        | 158/880 [05:20<15:07,  1.26s/it]\u001b[A\n",
      " 18%|█▊        | 159/880 [05:21<15:05,  1.26s/it]\u001b[A\n",
      " 18%|█▊        | 160/880 [05:22<14:55,  1.24s/it]\u001b[AI20260111 03:39:53.950694 133176606766656 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:39:54.432027 133176606766656 incremental_pipeline.cc:306] Initializing with image pair #2 and #9\n",
      "I20260111 03:39:54.436816 133176606766656 incremental_pipeline.cc:311] Global bundle adjustment\n",
      "\n",
      " 18%|█▊        | 161/880 [05:24<14:52,  1.24s/it]\u001b[AI20260111 03:39:54.829903 133176606766656 incremental_pipeline.cc:390] Registering image #8 (3)\n",
      "I20260111 03:39:54.829934 133176606766656 incremental_pipeline.cc:393] => Image sees 893 / 10473 points\n",
      "I20260111 03:39:55.257784 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:39:55.361214 133176606766656 incremental_pipeline.cc:390] Registering image #7 (4)\n",
      "I20260111 03:39:55.361255 133176606766656 incremental_pipeline.cc:393] => Image sees 3047 / 11979 points\n",
      "\n",
      " 18%|█▊        | 162/880 [05:25<14:39,  1.22s/it]\u001b[AI20260111 03:39:56.305744 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:39:56.642847 133176606766656 incremental_pipeline.cc:390] Registering image #6 (5)\n",
      "I20260111 03:39:56.642881 133176606766656 incremental_pipeline.cc:393] => Image sees 4464 / 12771 points\n",
      "\n",
      " 19%|█▊        | 163/880 [05:26<14:36,  1.22s/it]\u001b[AI20260111 03:39:58.112436 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "\n",
      " 19%|█▊        | 164/880 [05:27<14:42,  1.23s/it]\u001b[AI20260111 03:39:58.679359 133176606766656 incremental_pipeline.cc:390] Registering image #5 (6)\n",
      "I20260111 03:39:58.679397 133176606766656 incremental_pipeline.cc:393] => Image sees 4704 / 11542 points\n",
      "\n",
      " 19%|█▉        | 165/880 [05:28<14:50,  1.25s/it]\u001b[A\n",
      " 19%|█▉        | 166/880 [05:30<14:54,  1.25s/it]\u001b[AI20260111 03:40:01.155376 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "\n",
      " 19%|█▉        | 167/880 [05:31<14:54,  1.26s/it]\u001b[AI20260111 03:40:02.672521 133176606766656 incremental_pipeline.cc:390] Registering image #4 (7)\n",
      "I20260111 03:40:02.672574 133176606766656 incremental_pipeline.cc:393] => Image sees 4763 / 8977 points\n",
      "\n",
      " 19%|█▉        | 168/880 [05:32<14:58,  1.26s/it]\u001b[A\n",
      " 19%|█▉        | 169/880 [05:34<14:55,  1.26s/it]\u001b[A\n",
      " 19%|█▉        | 170/880 [05:35<15:02,  1.27s/it]\u001b[AI20260111 03:40:06.210884 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "\n",
      " 19%|█▉        | 171/880 [05:36<14:51,  1.26s/it]\u001b[AI20260111 03:40:07.506254 133176606766656 incremental_pipeline.cc:390] Registering image #3 (8)\n",
      "I20260111 03:40:07.506297 133176606766656 incremental_pipeline.cc:393] => Image sees 5400 / 7840 points\n",
      "\n",
      " 20%|█▉        | 172/880 [05:37<14:52,  1.26s/it]\u001b[A\n",
      " 20%|█▉        | 173/880 [05:39<15:18,  1.30s/it]\u001b[AI20260111 03:40:11.002658 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "\n",
      " 20%|█▉        | 174/880 [05:40<15:03,  1.28s/it]\u001b[A\n",
      " 20%|█▉        | 175/880 [05:41<15:22,  1.31s/it]\u001b[AI20260111 03:40:13.593442 133176606766656 incremental_pipeline.cc:390] Registering image #1 (9)\n",
      "I20260111 03:40:13.593480 133176606766656 incremental_pipeline.cc:393] => Image sees 9926 / 12149 points\n",
      "\n",
      " 20%|██        | 176/880 [05:43<15:12,  1.30s/it]\u001b[A\n",
      " 20%|██        | 177/880 [05:44<15:20,  1.31s/it]\u001b[A\n",
      " 20%|██        | 178/880 [05:45<15:09,  1.30s/it]\u001b[A\n",
      " 20%|██        | 179/880 [05:46<14:59,  1.28s/it]\u001b[AI20260111 03:40:18.296714 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "\n",
      " 20%|██        | 180/880 [05:48<14:51,  1.27s/it]\u001b[A\n",
      " 21%|██        | 181/880 [05:49<14:42,  1.26s/it]\u001b[A\n",
      " 21%|██        | 182/880 [05:50<14:30,  1.25s/it]\u001b[AI20260111 03:40:21.629604 133176606766656 incremental_pipeline.cc:390] Registering image #10 (10)\n",
      "I20260111 03:40:21.629674 133176606766656 incremental_pipeline.cc:393] => Image sees 4012 / 5534 points\n",
      "\n",
      " 21%|██        | 183/880 [05:51<14:28,  1.25s/it]\u001b[A\n",
      " 21%|██        | 184/880 [05:53<14:32,  1.25s/it]\u001b[AI20260111 03:40:24.939690 133176606766656 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "\n",
      " 21%|██        | 185/880 [05:54<14:27,  1.25s/it]\u001b[A\n",
      " 21%|██        | 186/880 [05:55<14:45,  1.28s/it]\u001b[A\n",
      " 21%|██▏       | 187/880 [05:56<14:36,  1.26s/it]\u001b[AI20260111 03:40:28.425405 133176606766656 timer.cc:91] Elapsed time: 1.172 [minutes]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: Reconstruction(num_cameras=9, num_images=9, num_reg_images=9, num_points3D=17537), 1: Reconstruction(num_cameras=10, num_images=10, num_reg_images=10, num_points3D=14668)}\n",
      "Reconstruction done in 70.3405 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 21%|██▏       | 188/880 [05:58<14:46,  1.28s/it]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved keypoints to /kaggle/working/result/featureout/stairs/keypoints.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/stairs/matches.h5\n",
      "✅ Saved matches to /kaggle/working/result/featureout/stairs/pairs.txt\n",
      "MASt3R matching done in 1210.12 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adding keypoints and images: 100%|██████████| 51/51 [00:02<00:00, 21.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import /kaggle/working/result/featureout/stairs/colmap.db done!\n",
      "colmap database\n",
      "🔁 Attempt 1 to run verify_matches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:54:46.206044 133176571631168 misc.cc:44] \n",
      "==============================================================================\n",
      "Feature matching\n",
      "==============================================================================\n",
      "I20260111 03:54:46.207135 133176580023872 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:54:46.208663 133176606766656 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:54:46.208716 133176835946048 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:54:46.208774 133176844338752 sift.cc:1432] Creating SIFT CPU feature matcher\n",
      "I20260111 03:54:46.208896 133176571631168 pairing.cc:742] Importing image pairs...\n",
      "I20260111 03:54:46.209408 133176571631168 pairing.cc:775] Matching block [1/1]\n",
      "I20260111 03:54:52.065088 133176571631168 feature_matching.cc:46] in 5.856s\n",
      "I20260111 03:54:52.071030 133176571631168 timer.cc:91] Elapsed time: 0.098 [minutes]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ verify_matches succeeded\n",
      "verify matching done!!!!\n",
      "RANSAC in 6.1955 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:54:52.284176 133176598373952 incremental_pipeline.cc:237] Loading database\n",
      "I20260111 03:54:52.285412 133176598373952 database_cache.cc:66] Loading cameras...\n",
      "I20260111 03:54:52.285502 133176598373952 database_cache.cc:76]  51 in 0.000s\n",
      "I20260111 03:54:52.285523 133176598373952 database_cache.cc:84] Loading matches...\n",
      "I20260111 03:54:52.288722 133176598373952 database_cache.cc:89]  287 in 0.003s\n",
      "I20260111 03:54:52.288746 133176598373952 database_cache.cc:105] Loading images...\n",
      "I20260111 03:54:52.300302 133176598373952 database_cache.cc:153]  51 in 0.012s (connected 51)\n",
      "I20260111 03:54:52.300334 133176598373952 database_cache.cc:164] Loading pose priors...\n",
      "I20260111 03:54:52.300615 133176598373952 database_cache.cc:175]  0 in 0.000s\n",
      "I20260111 03:54:52.300630 133176598373952 database_cache.cc:184] Building correspondence graph...\n",
      "W20260111 03:54:52.302975 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3942 in image_id=1 and point2D_idx=8187 in image_id=2\n",
      "W20260111 03:54:52.303075 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=187 in image_id=1 and point2D_idx=2345 in image_id=2\n",
      "W20260111 03:54:52.303130 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=193 in image_id=1 and point2D_idx=2021 in image_id=2\n",
      "W20260111 03:54:52.303162 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4064 in image_id=1 and point2D_idx=8249 in image_id=2\n",
      "W20260111 03:54:52.303186 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4066 in image_id=1 and point2D_idx=8251 in image_id=2\n",
      "W20260111 03:54:52.303217 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=201 in image_id=1 and point2D_idx=1759 in image_id=2\n",
      "W20260111 03:54:52.303271 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1563 in image_id=1 and point2D_idx=4472 in image_id=2\n",
      "W20260111 03:54:52.303329 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4217 in image_id=1 and point2D_idx=8363 in image_id=2\n",
      "W20260111 03:54:52.303403 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=220 in image_id=1 and point2D_idx=794 in image_id=2\n",
      "W20260111 03:54:52.303428 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3969 in image_id=1 and point2D_idx=8220 in image_id=2\n",
      "W20260111 03:54:52.303466 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=224 in image_id=1 and point2D_idx=699 in image_id=2\n",
      "W20260111 03:54:52.303480 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=802 in image_id=1 and point2D_idx=1716 in image_id=2\n",
      "W20260111 03:54:52.303496 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2546 in image_id=1 and point2D_idx=6451 in image_id=2\n",
      "W20260111 03:54:52.303541 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=228 in image_id=1 and point2D_idx=398 in image_id=2\n",
      "W20260111 03:54:52.303650 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2829 in image_id=1 and point2D_idx=6657 in image_id=2\n",
      "W20260111 03:54:52.303675 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3547 in image_id=1 and point2D_idx=7678 in image_id=2\n",
      "W20260111 03:54:52.303717 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2334 in image_id=1 and point2D_idx=5317 in image_id=2\n",
      "W20260111 03:54:52.303760 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4374 in image_id=1 and point2D_idx=8491 in image_id=2\n",
      "W20260111 03:54:52.303824 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2790 in image_id=1 and point2D_idx=6553 in image_id=3\n",
      "W20260111 03:54:52.303843 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2797 in image_id=1 and point2D_idx=6557 in image_id=3\n",
      "W20260111 03:54:52.303871 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=587 in image_id=1 and point2D_idx=400 in image_id=3\n",
      "W20260111 03:54:52.303889 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2313 in image_id=1 and point2D_idx=6014 in image_id=3\n",
      "W20260111 03:54:52.303902 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2531 in image_id=1 and point2D_idx=6353 in image_id=3\n",
      "W20260111 03:54:52.303955 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3979 in image_id=1 and point2D_idx=8004 in image_id=9\n",
      "W20260111 03:54:52.303976 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3253 in image_id=1 and point2D_idx=7229 in image_id=9\n",
      "W20260111 03:54:52.304056 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2313 in image_id=1 and point2D_idx=6521 in image_id=24\n",
      "W20260111 03:54:52.304107 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2816 in image_id=1 and point2D_idx=7345 in image_id=24\n",
      "W20260111 03:54:52.304142 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=599 in image_id=1 and point2D_idx=740 in image_id=24\n",
      "W20260111 03:54:52.304186 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3209 in image_id=1 and point2D_idx=601 in image_id=25\n",
      "W20260111 03:54:52.304208 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3213 in image_id=1 and point2D_idx=584 in image_id=25\n",
      "W20260111 03:54:52.304222 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4421 in image_id=1 and point2D_idx=1941 in image_id=25\n",
      "W20260111 03:54:52.304246 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4217 in image_id=1 and point2D_idx=1604 in image_id=25\n",
      "W20260111 03:54:52.304331 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2521 in image_id=1 and point2D_idx=7938 in image_id=28\n",
      "W20260111 03:54:52.304355 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=777 in image_id=1 and point2D_idx=4279 in image_id=28\n",
      "W20260111 03:54:52.304370 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2525 in image_id=1 and point2D_idx=7997 in image_id=28\n",
      "W20260111 03:54:52.304408 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2774 in image_id=1 and point2D_idx=8422 in image_id=28\n",
      "W20260111 03:54:52.304427 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3197 in image_id=1 and point2D_idx=8999 in image_id=28\n",
      "W20260111 03:54:52.304451 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2781 in image_id=1 and point2D_idx=8426 in image_id=28\n",
      "W20260111 03:54:52.304461 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3201 in image_id=1 and point2D_idx=9001 in image_id=28\n",
      "W20260111 03:54:52.304475 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3521 in image_id=1 and point2D_idx=9328 in image_id=28\n",
      "W20260111 03:54:52.304505 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2797 in image_id=1 and point2D_idx=8470 in image_id=28\n",
      "W20260111 03:54:52.304535 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=585 in image_id=1 and point2D_idx=2405 in image_id=28\n",
      "W20260111 03:54:52.304585 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1815 in image_id=1 and point2D_idx=6206 in image_id=28\n",
      "W20260111 03:54:52.304633 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1447 in image_id=1 and point2D_idx=4511 in image_id=28\n",
      "W20260111 03:54:52.304701 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1678 in image_id=1 and point2D_idx=5515 in image_id=28\n",
      "W20260111 03:54:52.304722 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1822 in image_id=1 and point2D_idx=6073 in image_id=28\n",
      "W20260111 03:54:52.304736 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3227 in image_id=1 and point2D_idx=9317 in image_id=28\n",
      "W20260111 03:54:52.304777 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1161 in image_id=1 and point2D_idx=2675 in image_id=28\n",
      "W20260111 03:54:52.304787 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1315 in image_id=1 and point2D_idx=3366 in image_id=28\n",
      "W20260111 03:54:52.304799 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1958 in image_id=1 and point2D_idx=6645 in image_id=28\n",
      "W20260111 03:54:52.304805 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2108 in image_id=1 and point2D_idx=7159 in image_id=28\n",
      "W20260111 03:54:52.304813 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2325 in image_id=1 and point2D_idx=7829 in image_id=28\n",
      "W20260111 03:54:52.304836 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=601 in image_id=1 and point2D_idx=537 in image_id=28\n",
      "W20260111 03:54:52.304868 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3235 in image_id=1 and point2D_idx=5364 in image_id=36\n",
      "W20260111 03:54:52.304881 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2807 in image_id=1 and point2D_idx=3534 in image_id=40\n",
      "W20260111 03:54:52.304893 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1953 in image_id=1 and point2D_idx=1920 in image_id=40\n",
      "W20260111 03:54:52.304905 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2826 in image_id=1 and point2D_idx=4042 in image_id=40\n",
      "W20260111 03:54:52.304917 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=805 in image_id=1 and point2D_idx=1273 in image_id=45\n",
      "W20260111 03:54:52.304937 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1670 in image_id=1 and point2D_idx=4843 in image_id=51\n",
      "W20260111 03:54:52.304947 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2797 in image_id=1 and point2D_idx=6912 in image_id=51\n",
      "W20260111 03:54:52.304954 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3527 in image_id=1 and point2D_idx=7737 in image_id=51\n",
      "W20260111 03:54:52.304964 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3218 in image_id=1 and point2D_idx=7429 in image_id=51\n",
      "W20260111 03:54:52.304976 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1676 in image_id=1 and point2D_idx=4503 in image_id=51\n",
      "W20260111 03:54:52.304996 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3539 in image_id=1 and point2D_idx=7800 in image_id=51\n",
      "W20260111 03:54:52.305023 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3773 in image_id=1 and point2D_idx=8172 in image_id=51\n",
      "W20260111 03:54:52.305080 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8 in image_id=1 and point2D_idx=547 in image_id=51\n",
      "W20260111 03:54:52.305122 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6578 in image_id=2 and point2D_idx=6016 in image_id=3\n",
      "W20260111 03:54:52.305132 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6183 in image_id=2 and point2D_idx=5706 in image_id=3\n",
      "W20260111 03:54:52.305148 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5800 in image_id=2 and point2D_idx=5445 in image_id=3\n",
      "W20260111 03:54:52.305161 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5056 in image_id=2 and point2D_idx=4721 in image_id=3\n",
      "W20260111 03:54:52.305173 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4651 in image_id=2 and point2D_idx=4224 in image_id=3\n",
      "W20260111 03:54:52.305197 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4460 in image_id=2 and point2D_idx=4065 in image_id=3\n",
      "W20260111 03:54:52.305214 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5255 in image_id=2 and point2D_idx=5051 in image_id=3\n",
      "W20260111 03:54:52.305222 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6979 in image_id=2 and point2D_idx=6302 in image_id=3\n",
      "W20260111 03:54:52.305246 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3298 in image_id=2 and point2D_idx=2779 in image_id=3\n",
      "W20260111 03:54:52.305266 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4280 in image_id=2 and point2D_idx=3943 in image_id=3\n",
      "W20260111 03:54:52.305274 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6591 in image_id=2 and point2D_idx=6071 in image_id=3\n",
      "W20260111 03:54:52.305312 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2275 in image_id=2 and point2D_idx=451 in image_id=3\n",
      "W20260111 03:54:52.305338 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2031 in image_id=2 and point2D_idx=84 in image_id=3\n",
      "W20260111 03:54:52.305362 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2715 in image_id=2 and point2D_idx=879 in image_id=3\n",
      "W20260111 03:54:52.305373 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7655 in image_id=2 and point2D_idx=6885 in image_id=3\n",
      "W20260111 03:54:52.305401 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6400 in image_id=2 and point2D_idx=6094 in image_id=3\n",
      "W20260111 03:54:52.305427 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6390 in image_id=2 and point2D_idx=6435 in image_id=9\n",
      "W20260111 03:54:52.305440 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6392 in image_id=2 and point2D_idx=6437 in image_id=9\n",
      "W20260111 03:54:52.305452 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4467 in image_id=2 and point2D_idx=5005 in image_id=9\n",
      "W20260111 03:54:52.305463 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1124 in image_id=2 and point2D_idx=2969 in image_id=9\n",
      "W20260111 03:54:52.305471 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1125 in image_id=2 and point2D_idx=2970 in image_id=9\n",
      "W20260111 03:54:52.305519 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5466 in image_id=2 and point2D_idx=6139 in image_id=9\n",
      "W20260111 03:54:52.305540 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2684 in image_id=2 and point2D_idx=4321 in image_id=9\n",
      "W20260111 03:54:52.305629 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=523 in image_id=2 and point2D_idx=1208 in image_id=13\n",
      "W20260111 03:54:52.305656 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1522 in image_id=2 and point2D_idx=1997 in image_id=13\n",
      "W20260111 03:54:52.305675 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1692 in image_id=2 and point2D_idx=1411 in image_id=14\n",
      "W20260111 03:54:52.305690 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1141 in image_id=2 and point2D_idx=702 in image_id=14\n",
      "W20260111 03:54:52.305710 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3496 in image_id=2 and point2D_idx=2798 in image_id=23\n",
      "W20260111 03:54:52.305728 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3983 in image_id=2 and point2D_idx=3085 in image_id=23\n",
      "W20260111 03:54:52.305734 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4469 in image_id=2 and point2D_idx=3360 in image_id=23\n",
      "W20260111 03:54:52.305782 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6650 in image_id=2 and point2D_idx=4499 in image_id=23\n",
      "W20260111 03:54:52.305789 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7372 in image_id=2 and point2D_idx=4763 in image_id=23\n",
      "W20260111 03:54:52.305830 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7043 in image_id=2 and point2D_idx=4658 in image_id=23\n",
      "W20260111 03:54:52.305844 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=551 in image_id=2 and point2D_idx=1111 in image_id=23\n",
      "W20260111 03:54:52.305868 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5476 in image_id=2 and point2D_idx=4176 in image_id=23\n",
      "W20260111 03:54:52.305875 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7177 in image_id=2 and point2D_idx=4771 in image_id=23\n",
      "W20260111 03:54:52.305884 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2057 in image_id=2 and point2D_idx=2608 in image_id=23\n",
      "W20260111 03:54:52.305905 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=642 in image_id=2 and point2D_idx=1313 in image_id=23\n",
      "W20260111 03:54:52.305926 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3935 in image_id=2 and point2D_idx=3626 in image_id=23\n",
      "W20260111 03:54:52.305943 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7044 in image_id=2 and point2D_idx=4775 in image_id=23\n",
      "W20260111 03:54:52.305951 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6370 in image_id=2 and point2D_idx=4585 in image_id=23\n",
      "W20260111 03:54:52.305958 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3776 in image_id=2 and point2D_idx=3628 in image_id=23\n",
      "W20260111 03:54:52.305966 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4940 in image_id=2 and point2D_idx=4067 in image_id=23\n",
      "W20260111 03:54:52.305981 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6205 in image_id=2 and point2D_idx=6358 in image_id=24\n",
      "W20260111 03:54:52.305995 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6402 in image_id=2 and point2D_idx=6640 in image_id=24\n",
      "W20260111 03:54:52.306084 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7726 in image_id=2 and point2D_idx=9158 in image_id=28\n",
      "W20260111 03:54:52.306125 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=424 in image_id=2 and point2D_idx=39 in image_id=28\n",
      "W20260111 03:54:52.306187 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2498 in image_id=2 and point2D_idx=2166 in image_id=28\n",
      "W20260111 03:54:52.306199 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7354 in image_id=2 and point2D_idx=8728 in image_id=28\n",
      "W20260111 03:54:52.306235 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=935 in image_id=2 and point2D_idx=236 in image_id=28\n",
      "W20260111 03:54:52.306317 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7369 in image_id=2 and point2D_idx=8960 in image_id=28\n",
      "W20260111 03:54:52.306343 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4290 in image_id=2 and point2D_idx=5091 in image_id=28\n",
      "W20260111 03:54:52.306351 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5270 in image_id=2 and point2D_idx=6476 in image_id=28\n",
      "W20260111 03:54:52.306385 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4481 in image_id=2 and point2D_idx=5517 in image_id=28\n",
      "W20260111 03:54:52.306403 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2731 in image_id=2 and point2D_idx=2428 in image_id=28\n",
      "W20260111 03:54:52.306425 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3920 in image_id=2 and point2D_idx=4576 in image_id=28\n",
      "W20260111 03:54:52.306433 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4676 in image_id=2 and point2D_idx=5897 in image_id=28\n",
      "W20260111 03:54:52.306442 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7531 in image_id=2 and point2D_idx=9423 in image_id=28\n",
      "W20260111 03:54:52.306465 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4487 in image_id=2 and point2D_idx=5707 in image_id=28\n",
      "W20260111 03:54:52.306487 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5639 in image_id=2 and point2D_idx=7507 in image_id=28\n",
      "W20260111 03:54:52.306503 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2739 in image_id=2 and point2D_idx=2439 in image_id=28\n",
      "W20260111 03:54:52.306528 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=940 in image_id=2 and point2D_idx=497 in image_id=29\n",
      "W20260111 03:54:52.306536 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=743 in image_id=2 and point2D_idx=499 in image_id=29\n",
      "W20260111 03:54:52.306544 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3306 in image_id=2 and point2D_idx=2809 in image_id=29\n",
      "W20260111 03:54:52.306585 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1336 in image_id=2 and point2D_idx=980 in image_id=29\n",
      "W20260111 03:54:52.306622 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1890 in image_id=2 and point2D_idx=2021 in image_id=29\n",
      "W20260111 03:54:52.306647 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6550 in image_id=2 and point2D_idx=6070 in image_id=29\n",
      "W20260111 03:54:52.306668 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1136 in image_id=2 and point2D_idx=1644 in image_id=29\n",
      "W20260111 03:54:52.306680 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7535 in image_id=2 and point2D_idx=6735 in image_id=29\n",
      "W20260111 03:54:52.306697 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=214 in image_id=2 and point2D_idx=512 in image_id=29\n",
      "W20260111 03:54:52.306706 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2522 in image_id=2 and point2D_idx=3405 in image_id=29\n",
      "W20260111 03:54:52.306725 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5497 in image_id=2 and point2D_idx=5531 in image_id=29\n",
      "W20260111 03:54:52.306757 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3965 in image_id=2 and point2D_idx=4639 in image_id=29\n",
      "W20260111 03:54:52.306773 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6046 in image_id=2 and point2D_idx=6252 in image_id=29\n",
      "W20260111 03:54:52.306788 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3335 in image_id=2 and point2D_idx=4641 in image_id=29\n",
      "W20260111 03:54:52.306821 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1844 in image_id=2 and point2D_idx=2968 in image_id=34\n",
      "W20260111 03:54:52.306875 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=987 in image_id=2 and point2D_idx=2977 in image_id=34\n",
      "W20260111 03:54:52.306903 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3756 in image_id=2 and point2D_idx=6853 in image_id=34\n",
      "W20260111 03:54:52.306912 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=642 in image_id=2 and point2D_idx=2982 in image_id=34\n",
      "W20260111 03:54:52.306934 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9 in image_id=2 and point2D_idx=1099 in image_id=34\n",
      "W20260111 03:54:52.306969 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1555 in image_id=2 and point2D_idx=5353 in image_id=34\n",
      "W20260111 03:54:52.306995 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1346 in image_id=2 and point2D_idx=1917 in image_id=39\n",
      "W20260111 03:54:52.307019 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=452 in image_id=2 and point2D_idx=2160 in image_id=39\n",
      "W20260111 03:54:52.307053 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=281 in image_id=2 and point2D_idx=805 in image_id=43\n",
      "W20260111 03:54:52.307087 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5476 in image_id=2 and point2D_idx=5625 in image_id=43\n",
      "W20260111 03:54:52.307097 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=197 in image_id=2 and point2D_idx=1127 in image_id=43\n",
      "W20260111 03:54:52.307105 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3355 in image_id=2 and point2D_idx=4017 in image_id=43\n",
      "W20260111 03:54:52.307131 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3531 in image_id=2 and point2D_idx=4419 in image_id=43\n",
      "W20260111 03:54:52.307145 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3936 in image_id=2 and point2D_idx=4846 in image_id=43\n",
      "W20260111 03:54:52.307159 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=780 in image_id=2 and point2D_idx=2597 in image_id=43\n",
      "W20260111 03:54:52.307170 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13 in image_id=2 and point2D_idx=1075 in image_id=43\n",
      "W20260111 03:54:52.307202 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1323 in image_id=2 and point2D_idx=1355 in image_id=51\n",
      "W20260111 03:54:52.307224 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1123 in image_id=2 and point2D_idx=1362 in image_id=51\n",
      "W20260111 03:54:52.307231 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1834 in image_id=2 and point2D_idx=1715 in image_id=51\n",
      "W20260111 03:54:52.307242 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3102 in image_id=2 and point2D_idx=2951 in image_id=51\n",
      "W20260111 03:54:52.307254 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5675 in image_id=2 and point2D_idx=5508 in image_id=51\n",
      "W20260111 03:54:52.307299 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=912 in image_id=2 and point2D_idx=2112 in image_id=51\n",
      "W20260111 03:54:52.307338 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=160 in image_id=2 and point2D_idx=1389 in image_id=51\n",
      "W20260111 03:54:52.307428 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=217 in image_id=3 and point2D_idx=1927 in image_id=4\n",
      "W20260111 03:54:52.307437 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1458 in image_id=3 and point2D_idx=2048 in image_id=4\n",
      "W20260111 03:54:52.307444 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1946 in image_id=3 and point2D_idx=2131 in image_id=4\n",
      "W20260111 03:54:52.307474 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1559 in image_id=3 and point2D_idx=490 in image_id=10\n",
      "W20260111 03:54:52.307495 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1025 in image_id=3 and point2D_idx=283 in image_id=10\n",
      "W20260111 03:54:52.307520 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2600 in image_id=3 and point2D_idx=1366 in image_id=10\n",
      "W20260111 03:54:52.307545 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=569 in image_id=3 and point2D_idx=185 in image_id=10\n",
      "W20260111 03:54:52.307617 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=750 in image_id=3 and point2D_idx=304 in image_id=10\n",
      "W20260111 03:54:52.307656 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3163 in image_id=3 and point2D_idx=1878 in image_id=10\n",
      "W20260111 03:54:52.307675 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=316 in image_id=3 and point2D_idx=35 in image_id=10\n",
      "W20260111 03:54:52.307702 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2616 in image_id=3 and point2D_idx=1635 in image_id=10\n",
      "W20260111 03:54:52.307716 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3330 in image_id=3 and point2D_idx=2016 in image_id=10\n",
      "W20260111 03:54:52.307735 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=730 in image_id=3 and point2D_idx=380 in image_id=10\n",
      "W20260111 03:54:52.307784 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1350 in image_id=3 and point2D_idx=808 in image_id=10\n",
      "W20260111 03:54:52.307823 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2279 in image_id=3 and point2D_idx=1645 in image_id=10\n",
      "W20260111 03:54:52.307844 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1353 in image_id=3 and point2D_idx=894 in image_id=10\n",
      "W20260111 03:54:52.307877 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1831 in image_id=3 and point2D_idx=1409 in image_id=10\n",
      "W20260111 03:54:52.307897 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2282 in image_id=3 and point2D_idx=1742 in image_id=10\n",
      "W20260111 03:54:52.307923 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2630 in image_id=3 and point2D_idx=1965 in image_id=10\n",
      "W20260111 03:54:52.307995 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2437 in image_id=3 and point2D_idx=2801 in image_id=14\n",
      "W20260111 03:54:52.308032 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1968 in image_id=3 and point2D_idx=1926 in image_id=24\n",
      "W20260111 03:54:52.308066 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2816 in image_id=3 and point2D_idx=2780 in image_id=24\n",
      "W20260111 03:54:52.308127 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=522 in image_id=3 and point2D_idx=1488 in image_id=24\n",
      "W20260111 03:54:52.308149 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3342 in image_id=3 and point2D_idx=3251 in image_id=24\n",
      "W20260111 03:54:52.308226 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2949 in image_id=3 and point2D_idx=2842 in image_id=24\n",
      "W20260111 03:54:52.308250 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4747 in image_id=3 and point2D_idx=4699 in image_id=24\n",
      "W20260111 03:54:52.308365 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4447 in image_id=3 and point2D_idx=4352 in image_id=24\n",
      "W20260111 03:54:52.308384 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5289 in image_id=3 and point2D_idx=5221 in image_id=24\n",
      "W20260111 03:54:52.308457 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4078 in image_id=3 and point2D_idx=3954 in image_id=24\n",
      "W20260111 03:54:52.308488 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5913 in image_id=3 and point2D_idx=6192 in image_id=24\n",
      "W20260111 03:54:52.308512 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6618 in image_id=3 and point2D_idx=7311 in image_id=24\n",
      "W20260111 03:54:52.308563 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4251 in image_id=3 and point2D_idx=4168 in image_id=24\n",
      "W20260111 03:54:52.308591 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3322 in image_id=3 and point2D_idx=3031 in image_id=24\n",
      "W20260111 03:54:52.308638 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3486 in image_id=3 and point2D_idx=3529 in image_id=24\n",
      "W20260111 03:54:52.308687 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5831 in image_id=3 and point2D_idx=6201 in image_id=24\n",
      "W20260111 03:54:52.308717 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2445 in image_id=3 and point2D_idx=2818 in image_id=24\n",
      "W20260111 03:54:52.308733 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5833 in image_id=3 and point2D_idx=6248 in image_id=24\n",
      "W20260111 03:54:52.308749 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6091 in image_id=3 and point2D_idx=6569 in image_id=24\n",
      "W20260111 03:54:52.308775 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5 in image_id=3 and point2D_idx=541 in image_id=27\n",
      "W20260111 03:54:52.308831 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4568 in image_id=3 and point2D_idx=4921 in image_id=27\n",
      "W20260111 03:54:52.308892 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3604 in image_id=3 and point2D_idx=4105 in image_id=27\n",
      "W20260111 03:54:52.308911 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4905 in image_id=3 and point2D_idx=5053 in image_id=27\n",
      "W20260111 03:54:52.308929 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=21 in image_id=3 and point2D_idx=344 in image_id=27\n",
      "W20260111 03:54:52.309002 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4763 in image_id=3 and point2D_idx=4787 in image_id=27\n",
      "W20260111 03:54:52.309015 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4589 in image_id=3 and point2D_idx=4626 in image_id=27\n",
      "W20260111 03:54:52.309033 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3762 in image_id=3 and point2D_idx=4045 in image_id=27\n",
      "W20260111 03:54:52.309040 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4075 in image_id=3 and point2D_idx=4259 in image_id=27\n",
      "W20260111 03:54:52.309047 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5062 in image_id=3 and point2D_idx=4987 in image_id=27\n",
      "W20260111 03:54:52.309090 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5189 in image_id=3 and point2D_idx=5044 in image_id=27\n",
      "W20260111 03:54:52.309103 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1330 in image_id=3 and point2D_idx=1859 in image_id=27\n",
      "W20260111 03:54:52.309118 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4773 in image_id=3 and point2D_idx=4635 in image_id=27\n",
      "W20260111 03:54:52.309134 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4597 in image_id=3 and point2D_idx=4493 in image_id=27\n",
      "W20260111 03:54:52.309153 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2792 in image_id=3 and point2D_idx=2753 in image_id=27\n",
      "W20260111 03:54:52.309201 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4556 in image_id=3 and point2D_idx=5649 in image_id=28\n",
      "W20260111 03:54:52.309229 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6498 in image_id=3 and point2D_idx=8283 in image_id=28\n",
      "W20260111 03:54:52.309242 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4386 in image_id=3 and point2D_idx=5464 in image_id=28\n",
      "W20260111 03:54:52.309254 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6978 in image_id=3 and point2D_idx=9156 in image_id=28\n",
      "W20260111 03:54:52.309272 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=146 in image_id=3 and point2D_idx=2134 in image_id=28\n",
      "W20260111 03:54:52.309350 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3132 in image_id=3 and point2D_idx=3805 in image_id=28\n",
      "W20260111 03:54:52.309366 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=290 in image_id=3 and point2D_idx=2143 in image_id=28\n",
      "W20260111 03:54:52.309402 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=421 in image_id=3 and point2D_idx=2147 in image_id=28\n",
      "W20260111 03:54:52.309414 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6684 in image_id=3 and point2D_idx=8656 in image_id=28\n",
      "W20260111 03:54:52.309426 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3610 in image_id=3 and point2D_idx=4293 in image_id=28\n",
      "W20260111 03:54:52.309445 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6368 in image_id=3 and point2D_idx=7955 in image_id=28\n",
      "W20260111 03:54:52.309510 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6955 in image_id=3 and point2D_idx=9172 in image_id=28\n",
      "W20260111 03:54:52.309532 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=893 in image_id=3 and point2D_idx=2166 in image_id=28\n",
      "W20260111 03:54:52.309544 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6000 in image_id=3 and point2D_idx=7310 in image_id=28\n",
      "W20260111 03:54:52.309598 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5195 in image_id=3 and point2D_idx=6061 in image_id=28\n",
      "W20260111 03:54:52.309618 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3326 in image_id=3 and point2D_idx=4065 in image_id=28\n",
      "W20260111 03:54:52.309652 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=534 in image_id=3 and point2D_idx=1942 in image_id=28\n",
      "W20260111 03:54:52.309666 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6964 in image_id=3 and point2D_idx=9182 in image_id=28\n",
      "W20260111 03:54:52.309708 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4261 in image_id=3 and point2D_idx=6064 in image_id=28\n",
      "W20260111 03:54:52.309749 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3642 in image_id=3 and point2D_idx=5507 in image_id=28\n",
      "W20260111 03:54:52.309796 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=892 in image_id=4 and point2D_idx=1942 in image_id=5\n",
      "W20260111 03:54:52.309808 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1374 in image_id=4 and point2D_idx=2350 in image_id=5\n",
      "W20260111 03:54:52.309870 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=648 in image_id=4 and point2D_idx=1593 in image_id=5\n",
      "W20260111 03:54:52.309986 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=325 in image_id=4 and point2D_idx=47 in image_id=5\n",
      "W20260111 03:54:52.310012 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=273 in image_id=4 and point2D_idx=30 in image_id=5\n",
      "W20260111 03:54:52.310039 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1040 in image_id=4 and point2D_idx=1393 in image_id=5\n",
      "W20260111 03:54:52.310069 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1280 in image_id=4 and point2D_idx=1429 in image_id=5\n",
      "W20260111 03:54:52.310100 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=208 in image_id=4 and point2D_idx=600 in image_id=5\n",
      "W20260111 03:54:52.310143 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=802 in image_id=4 and point2D_idx=775 in image_id=5\n",
      "W20260111 03:54:52.310192 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=903 in image_id=4 and point2D_idx=3077 in image_id=12\n",
      "W20260111 03:54:52.310218 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=517 in image_id=4 and point2D_idx=2750 in image_id=12\n",
      "W20260111 03:54:52.310228 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=782 in image_id=4 and point2D_idx=2915 in image_id=12\n",
      "W20260111 03:54:52.310262 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2352 in image_id=4 and point2D_idx=1411 in image_id=27\n",
      "W20260111 03:54:52.310287 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1247 in image_id=4 and point2D_idx=111 in image_id=27\n",
      "W20260111 03:54:52.310294 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2359 in image_id=4 and point2D_idx=1416 in image_id=27\n",
      "W20260111 03:54:52.310306 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1248 in image_id=4 and point2D_idx=105 in image_id=27\n",
      "W20260111 03:54:52.310337 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3647 in image_id=4 and point2D_idx=2982 in image_id=27\n",
      "W20260111 03:54:52.310379 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3821 in image_id=4 and point2D_idx=3247 in image_id=27\n",
      "W20260111 03:54:52.310405 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3547 in image_id=4 and point2D_idx=2855 in image_id=27\n",
      "W20260111 03:54:52.310417 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3877 in image_id=4 and point2D_idx=3387 in image_id=27\n",
      "W20260111 03:54:52.310433 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3255 in image_id=4 and point2D_idx=2509 in image_id=27\n",
      "W20260111 03:54:52.310471 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1273 in image_id=4 and point2D_idx=27 in image_id=27\n",
      "W20260111 03:54:52.310500 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4132 in image_id=4 and point2D_idx=3810 in image_id=27\n",
      "W20260111 03:54:52.310550 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2171 in image_id=4 and point2D_idx=1738 in image_id=27\n",
      "W20260111 03:54:52.310603 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3364 in image_id=4 and point2D_idx=2745 in image_id=27\n",
      "W20260111 03:54:52.310635 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4067 in image_id=4 and point2D_idx=3820 in image_id=27\n",
      "W20260111 03:54:52.310645 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2064 in image_id=4 and point2D_idx=2078 in image_id=27\n",
      "W20260111 03:54:52.310696 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3559 in image_id=4 and point2D_idx=2864 in image_id=28\n",
      "W20260111 03:54:52.310704 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3378 in image_id=4 and point2D_idx=2642 in image_id=28\n",
      "W20260111 03:54:52.310729 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2413 in image_id=5 and point2D_idx=620 in image_id=11\n",
      "W20260111 03:54:52.310750 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2499 in image_id=5 and point2D_idx=818 in image_id=11\n",
      "W20260111 03:54:52.310762 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2502 in image_id=5 and point2D_idx=843 in image_id=11\n",
      "W20260111 03:54:52.310797 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=89 in image_id=5 and point2D_idx=522 in image_id=12\n",
      "W20260111 03:54:52.310805 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=118 in image_id=5 and point2D_idx=627 in image_id=12\n",
      "W20260111 03:54:52.310822 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=267 in image_id=5 and point2D_idx=1216 in image_id=12\n",
      "W20260111 03:54:52.310831 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=804 in image_id=5 and point2D_idx=2109 in image_id=12\n",
      "W20260111 03:54:52.310879 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1743 in image_id=5 and point2D_idx=3026 in image_id=12\n",
      "W20260111 03:54:52.310908 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=537 in image_id=5 and point2D_idx=1636 in image_id=12\n",
      "W20260111 03:54:52.310928 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1375 in image_id=5 and point2D_idx=2592 in image_id=12\n",
      "W20260111 03:54:52.310940 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=274 in image_id=5 and point2D_idx=3076 in image_id=13\n",
      "W20260111 03:54:52.310948 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=478 in image_id=5 and point2D_idx=3637 in image_id=13\n",
      "W20260111 03:54:52.310967 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=540 in image_id=5 and point2D_idx=3061 in image_id=13\n",
      "W20260111 03:54:52.310980 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=485 in image_id=5 and point2D_idx=2632 in image_id=13\n",
      "W20260111 03:54:52.310989 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1455 in image_id=6 and point2D_idx=1880 in image_id=7\n",
      "W20260111 03:54:52.311004 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1459 in image_id=6 and point2D_idx=1885 in image_id=7\n",
      "W20260111 03:54:52.311011 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1569 in image_id=6 and point2D_idx=2129 in image_id=7\n",
      "W20260111 03:54:52.311033 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1927 in image_id=6 and point2D_idx=2467 in image_id=7\n",
      "W20260111 03:54:52.311072 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2126 in image_id=6 and point2D_idx=2569 in image_id=7\n",
      "W20260111 03:54:52.311097 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1023 in image_id=6 and point2D_idx=34 in image_id=7\n",
      "W20260111 03:54:52.311106 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1938 in image_id=6 and point2D_idx=2475 in image_id=7\n",
      "W20260111 03:54:52.311138 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=340 in image_id=6 and point2D_idx=561 in image_id=9\n",
      "W20260111 03:54:52.311163 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=506 in image_id=6 and point2D_idx=753 in image_id=9\n",
      "W20260111 03:54:52.311176 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1037 in image_id=6 and point2D_idx=2542 in image_id=9\n",
      "W20260111 03:54:52.311202 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=107 in image_id=6 and point2D_idx=86 in image_id=9\n",
      "W20260111 03:54:52.311216 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=844 in image_id=6 and point2D_idx=1601 in image_id=9\n",
      "W20260111 03:54:52.311234 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=520 in image_id=6 and point2D_idx=583 in image_id=9\n",
      "W20260111 03:54:52.311242 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1052 in image_id=6 and point2D_idx=2305 in image_id=9\n",
      "W20260111 03:54:52.311250 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1093 in image_id=6 and point2D_idx=2556 in image_id=9\n",
      "W20260111 03:54:52.311263 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1884 in image_id=6 and point2D_idx=623 in image_id=12\n",
      "W20260111 03:54:52.311276 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2098 in image_id=6 and point2D_idx=951 in image_id=12\n",
      "W20260111 03:54:52.311287 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2289 in image_id=6 and point2D_idx=1388 in image_id=12\n",
      "W20260111 03:54:52.311294 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1204 in image_id=6 and point2D_idx=60 in image_id=12\n",
      "W20260111 03:54:52.311312 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1478 in image_id=6 and point2D_idx=317 in image_id=12\n",
      "W20260111 03:54:52.311320 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1995 in image_id=6 and point2D_idx=954 in image_id=12\n",
      "W20260111 03:54:52.311332 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1669 in image_id=6 and point2D_idx=630 in image_id=12\n",
      "W20260111 03:54:52.311346 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1350 in image_id=6 and point2D_idx=319 in image_id=12\n",
      "W20260111 03:54:52.311364 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2189 in image_id=6 and point2D_idx=1396 in image_id=12\n",
      "W20260111 03:54:52.311371 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2400 in image_id=6 and point2D_idx=1707 in image_id=12\n",
      "W20260111 03:54:52.311392 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2374 in image_id=6 and point2D_idx=5200 in image_id=13\n",
      "W20260111 03:54:52.311456 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=715 in image_id=6 and point2D_idx=865 in image_id=13\n",
      "W20260111 03:54:52.311481 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1930 in image_id=6 and point2D_idx=4394 in image_id=23\n",
      "W20260111 03:54:52.311494 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1576 in image_id=6 and point2D_idx=3611 in image_id=26\n",
      "W20260111 03:54:52.311513 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=336 in image_id=6 and point2D_idx=3601 in image_id=32\n",
      "W20260111 03:54:52.311527 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=139 in image_id=6 and point2D_idx=3110 in image_id=32\n",
      "W20260111 03:54:52.311546 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=219 in image_id=6 and point2D_idx=2943 in image_id=32\n",
      "W20260111 03:54:52.311594 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=435 in image_id=6 and point2D_idx=2295 in image_id=33\n",
      "W20260111 03:54:52.311610 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=201 in image_id=6 and point2D_idx=1737 in image_id=33\n",
      "W20260111 03:54:52.311621 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=65 in image_id=6 and point2D_idx=1225 in image_id=33\n",
      "W20260111 03:54:52.311646 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=354 in image_id=6 and point2D_idx=5178 in image_id=42\n",
      "W20260111 03:54:52.311655 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=206 in image_id=6 and point2D_idx=4652 in image_id=42\n",
      "W20260111 03:54:52.311669 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=769 in image_id=6 and point2D_idx=4173 in image_id=48\n",
      "W20260111 03:54:52.311695 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=970 in image_id=6 and point2D_idx=4564 in image_id=48\n",
      "W20260111 03:54:52.311703 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=437 in image_id=6 and point2D_idx=2612 in image_id=48\n",
      "W20260111 03:54:52.311718 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1041 in image_id=6 and point2D_idx=4568 in image_id=48\n",
      "W20260111 03:54:52.311732 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=844 in image_id=6 and point2D_idx=3809 in image_id=48\n",
      "W20260111 03:54:52.311751 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=627 in image_id=7 and point2D_idx=3633 in image_id=8\n",
      "W20260111 03:54:52.311758 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1084 in image_id=7 and point2D_idx=3861 in image_id=8\n",
      "W20260111 03:54:52.311769 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=852 in image_id=7 and point2D_idx=3571 in image_id=8\n",
      "W20260111 03:54:52.311776 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1045 in image_id=7 and point2D_idx=3619 in image_id=8\n",
      "W20260111 03:54:52.311798 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=824 in image_id=7 and point2D_idx=2967 in image_id=8\n",
      "W20260111 03:54:52.311819 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1163 in image_id=7 and point2D_idx=3048 in image_id=26\n",
      "W20260111 03:54:52.311830 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=600 in image_id=7 and point2D_idx=2662 in image_id=26\n",
      "W20260111 03:54:52.311847 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=211 in image_id=7 and point2D_idx=2206 in image_id=26\n",
      "W20260111 03:54:52.311860 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1470 in image_id=7 and point2D_idx=3284 in image_id=26\n",
      "W20260111 03:54:52.311875 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1677 in image_id=7 and point2D_idx=3396 in image_id=26\n",
      "W20260111 03:54:52.311893 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=135 in image_id=7 and point2D_idx=276 in image_id=26\n",
      "W20260111 03:54:52.311909 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=611 in image_id=7 and point2D_idx=2527 in image_id=26\n",
      "W20260111 03:54:52.311939 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1481 in image_id=7 and point2D_idx=3069 in image_id=26\n",
      "W20260111 03:54:52.311954 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1791 in image_id=7 and point2D_idx=3301 in image_id=26\n",
      "W20260111 03:54:52.311964 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=619 in image_id=7 and point2D_idx=2333 in image_id=26\n",
      "W20260111 03:54:52.311986 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2137 in image_id=7 and point2D_idx=3571 in image_id=26\n",
      "W20260111 03:54:52.312005 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2299 in image_id=7 and point2D_idx=3751 in image_id=26\n",
      "W20260111 03:54:52.312024 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1907 in image_id=7 and point2D_idx=3204 in image_id=26\n",
      "W20260111 03:54:52.312032 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2261 in image_id=7 and point2D_idx=3702 in image_id=26\n",
      "W20260111 03:54:52.312062 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=969 in image_id=7 and point2D_idx=2102 in image_id=26\n",
      "W20260111 03:54:52.312104 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=476 in image_id=7 and point2D_idx=1630 in image_id=26\n",
      "W20260111 03:54:52.312121 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=973 in image_id=7 and point2D_idx=1823 in image_id=26\n",
      "W20260111 03:54:52.312137 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2649 in image_id=8 and point2D_idx=526 in image_id=16\n",
      "W20260111 03:54:52.312146 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2209 in image_id=8 and point2D_idx=197 in image_id=16\n",
      "W20260111 03:54:52.312169 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2037 in image_id=8 and point2D_idx=467 in image_id=16\n",
      "W20260111 03:54:52.312179 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2386 in image_id=8 and point2D_idx=792 in image_id=16\n",
      "W20260111 03:54:52.312234 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=421 in image_id=8 and point2D_idx=349 in image_id=16\n",
      "W20260111 03:54:52.312252 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=78 in image_id=8 and point2D_idx=1807 in image_id=22\n",
      "W20260111 03:54:52.312259 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=472 in image_id=8 and point2D_idx=2459 in image_id=22\n",
      "W20260111 03:54:52.312266 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=79 in image_id=8 and point2D_idx=1637 in image_id=22\n",
      "W20260111 03:54:52.312273 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=133 in image_id=8 and point2D_idx=1731 in image_id=22\n",
      "W20260111 03:54:52.312299 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1607 in image_id=8 and point2D_idx=3486 in image_id=25\n",
      "W20260111 03:54:52.312313 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1862 in image_id=8 and point2D_idx=3566 in image_id=25\n",
      "W20260111 03:54:52.312329 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=833 in image_id=8 and point2D_idx=2883 in image_id=25\n",
      "W20260111 03:54:52.312370 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=490 in image_id=8 and point2D_idx=2246 in image_id=25\n",
      "W20260111 03:54:52.312389 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1249 in image_id=8 and point2D_idx=2942 in image_id=25\n",
      "W20260111 03:54:52.312424 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1256 in image_id=8 and point2D_idx=2719 in image_id=25\n",
      "W20260111 03:54:52.312466 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=264 in image_id=8 and point2D_idx=594 in image_id=25\n",
      "W20260111 03:54:52.312488 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3419 in image_id=8 and point2D_idx=678 in image_id=26\n",
      "W20260111 03:54:52.312513 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3050 in image_id=8 and point2D_idx=412 in image_id=26\n",
      "W20260111 03:54:52.312522 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2375 in image_id=8 and point2D_idx=6 in image_id=26\n",
      "W20260111 03:54:52.312541 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3530 in image_id=8 and point2D_idx=949 in image_id=26\n",
      "W20260111 03:54:52.312553 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2301 in image_id=8 and point2D_idx=116 in image_id=26\n",
      "W20260111 03:54:52.312908 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2245 in image_id=8 and point2D_idx=211 in image_id=26\n",
      "W20260111 03:54:52.312964 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1045 in image_id=8 and point2D_idx=47 in image_id=26\n",
      "W20260111 03:54:52.313014 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3266 in image_id=8 and point2D_idx=1251 in image_id=26\n",
      "W20260111 03:54:52.313048 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3812 in image_id=8 and point2D_idx=1833 in image_id=26\n",
      "W20260111 03:54:52.313064 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=609 in image_id=8 and point2D_idx=98 in image_id=26\n",
      "W20260111 03:54:52.313087 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2533 in image_id=8 and point2D_idx=611 in image_id=26\n",
      "W20260111 03:54:52.313116 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1764 in image_id=8 and point2D_idx=387 in image_id=26\n",
      "W20260111 03:54:52.313131 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2879 in image_id=8 and point2D_idx=849 in image_id=26\n",
      "W20260111 03:54:52.313153 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=783 in image_id=8 and point2D_idx=267 in image_id=26\n",
      "W20260111 03:54:52.313168 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1153 in image_id=8 and point2D_idx=290 in image_id=26\n",
      "W20260111 03:54:52.313184 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3065 in image_id=8 and point2D_idx=1255 in image_id=26\n",
      "W20260111 03:54:52.313221 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1516 in image_id=8 and point2D_idx=472 in image_id=26\n",
      "W20260111 03:54:52.313257 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3081 in image_id=8 and point2D_idx=1510 in image_id=26\n",
      "W20260111 03:54:52.313319 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3809 in image_id=8 and point2D_idx=2227 in image_id=26\n",
      "W20260111 03:54:52.313341 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3260 in image_id=8 and point2D_idx=1934 in image_id=26\n",
      "W20260111 03:54:52.313377 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1796 in image_id=8 and point2D_idx=860 in image_id=26\n",
      "W20260111 03:54:52.313407 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2910 in image_id=8 and point2D_idx=1740 in image_id=26\n",
      "W20260111 03:54:52.313433 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2954 in image_id=8 and point2D_idx=1847 in image_id=26\n",
      "W20260111 03:54:52.313479 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4051 in image_id=8 and point2D_idx=2543 in image_id=26\n",
      "W20260111 03:54:52.313493 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3744 in image_id=8 and point2D_idx=2451 in image_id=26\n",
      "W20260111 03:54:52.313510 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1073 in image_id=8 and point2D_idx=864 in image_id=26\n",
      "W20260111 03:54:52.313521 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1282 in image_id=8 and point2D_idx=977 in image_id=26\n",
      "W20260111 03:54:52.313548 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=964 in image_id=8 and point2D_idx=979 in image_id=26\n",
      "W20260111 03:54:52.313579 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1657 in image_id=8 and point2D_idx=1405 in image_id=26\n",
      "W20260111 03:54:52.313629 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=266 in image_id=9 and point2D_idx=110 in image_id=13\n",
      "W20260111 03:54:52.313657 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2975 in image_id=9 and point2D_idx=3028 in image_id=18\n",
      "W20260111 03:54:52.313695 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4146 in image_id=9 and point2D_idx=4775 in image_id=18\n",
      "W20260111 03:54:52.313744 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6196 in image_id=9 and point2D_idx=7625 in image_id=18\n",
      "W20260111 03:54:52.313767 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2743 in image_id=9 and point2D_idx=2766 in image_id=18\n",
      "W20260111 03:54:52.313816 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4556 in image_id=9 and point2D_idx=5247 in image_id=18\n",
      "W20260111 03:54:52.313944 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1574 in image_id=9 and point2D_idx=1389 in image_id=18\n",
      "W20260111 03:54:52.314003 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2289 in image_id=9 and point2D_idx=2144 in image_id=18\n",
      "W20260111 03:54:52.314020 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2041 in image_id=9 and point2D_idx=1662 in image_id=18\n",
      "W20260111 03:54:52.314091 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3772 in image_id=9 and point2D_idx=1678 in image_id=24\n",
      "W20260111 03:54:52.314163 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3586 in image_id=9 and point2D_idx=521 in image_id=24\n",
      "W20260111 03:54:52.314171 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4853 in image_id=9 and point2D_idx=2389 in image_id=24\n",
      "W20260111 03:54:52.314194 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6309 in image_id=9 and point2D_idx=220 in image_id=25\n",
      "W20260111 03:54:52.314224 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7938 in image_id=9 and point2D_idx=1257 in image_id=25\n",
      "W20260111 03:54:52.314243 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7484 in image_id=9 and point2D_idx=999 in image_id=25\n",
      "W20260111 03:54:52.314257 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5857 in image_id=9 and point2D_idx=479 in image_id=25\n",
      "W20260111 03:54:52.314298 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7485 in image_id=9 and point2D_idx=1134 in image_id=25\n",
      "W20260111 03:54:52.314314 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5817 in image_id=9 and point2D_idx=626 in image_id=25\n",
      "W20260111 03:54:52.314331 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4363 in image_id=9 and point2D_idx=238 in image_id=25\n",
      "W20260111 03:54:52.314343 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5557 in image_id=9 and point2D_idx=628 in image_id=25\n",
      "W20260111 03:54:52.314436 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5063 in image_id=9 and point2D_idx=4287 in image_id=29\n",
      "W20260111 03:54:52.314450 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5019 in image_id=9 and point2D_idx=4464 in image_id=29\n",
      "W20260111 03:54:52.314472 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2510 in image_id=9 and point2D_idx=1660 in image_id=29\n",
      "W20260111 03:54:52.314490 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2997 in image_id=9 and point2D_idx=2444 in image_id=29\n",
      "W20260111 03:54:52.314532 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1576 in image_id=9 and point2D_idx=1403 in image_id=29\n",
      "W20260111 03:54:52.314596 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2776 in image_id=9 and point2D_idx=3441 in image_id=29\n",
      "W20260111 03:54:52.314726 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7027 in image_id=9 and point2D_idx=9523 in image_id=34\n",
      "W20260111 03:54:52.314780 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6044 in image_id=9 and point2D_idx=7787 in image_id=34\n",
      "W20260111 03:54:52.314826 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1560 in image_id=9 and point2D_idx=879 in image_id=34\n",
      "W20260111 03:54:52.314874 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6045 in image_id=9 and point2D_idx=8099 in image_id=34\n",
      "W20260111 03:54:52.314923 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4515 in image_id=9 and point2D_idx=5708 in image_id=34\n",
      "W20260111 03:54:52.314962 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5729 in image_id=9 and point2D_idx=7804 in image_id=34\n",
      "W20260111 03:54:52.314984 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8074 in image_id=9 and point2D_idx=12213 in image_id=34\n",
      "W20260111 03:54:52.315010 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=886 in image_id=9 and point2D_idx=610 in image_id=34\n",
      "W20260111 03:54:52.315021 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6467 in image_id=9 and point2D_idx=9283 in image_id=34\n",
      "W20260111 03:54:52.315089 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8240 in image_id=9 and point2D_idx=12639 in image_id=34\n",
      "W20260111 03:54:52.315119 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=710 in image_id=9 and point2D_idx=842 in image_id=34\n",
      "W20260111 03:54:52.315129 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5546 in image_id=9 and point2D_idx=8122 in image_id=34\n",
      "W20260111 03:54:52.315136 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6629 in image_id=9 and point2D_idx=9848 in image_id=34\n",
      "W20260111 03:54:52.315185 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6202 in image_id=9 and point2D_idx=9294 in image_id=34\n",
      "W20260111 03:54:52.315202 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7691 in image_id=9 and point2D_idx=11563 in image_id=34\n",
      "W20260111 03:54:52.315223 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8086 in image_id=9 and point2D_idx=12440 in image_id=34\n",
      "W20260111 03:54:52.315251 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2815 in image_id=9 and point2D_idx=4462 in image_id=34\n",
      "W20260111 03:54:52.315297 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6798 in image_id=9 and point2D_idx=10393 in image_id=34\n",
      "W20260111 03:54:52.315347 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8031 in image_id=9 and point2D_idx=12453 in image_id=34\n",
      "W20260111 03:54:52.315382 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1602 in image_id=9 and point2D_idx=3817 in image_id=34\n",
      "W20260111 03:54:52.315392 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5398 in image_id=9 and point2D_idx=8733 in image_id=34\n",
      "W20260111 03:54:52.315413 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5056 in image_id=9 and point2D_idx=8447 in image_id=34\n",
      "W20260111 03:54:52.315469 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5387 in image_id=9 and point2D_idx=4761 in image_id=38\n",
      "W20260111 03:54:52.315490 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1410 in image_id=9 and point2D_idx=2400 in image_id=38\n",
      "W20260111 03:54:52.315507 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1662 in image_id=9 and point2D_idx=2640 in image_id=38\n",
      "W20260111 03:54:52.315519 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2496 in image_id=9 and point2D_idx=1176 in image_id=39\n",
      "W20260111 03:54:52.315639 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2311 in image_id=9 and point2D_idx=7226 in image_id=42\n",
      "W20260111 03:54:52.315702 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1353 in image_id=9 and point2D_idx=5674 in image_id=42\n",
      "W20260111 03:54:52.315741 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3765 in image_id=9 and point2D_idx=8843 in image_id=42\n",
      "W20260111 03:54:52.315780 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5090 in image_id=9 and point2D_idx=10243 in image_id=42\n",
      "W20260111 03:54:52.315806 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=855 in image_id=9 and point2D_idx=4650 in image_id=42\n",
      "W20260111 03:54:52.315830 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=134 in image_id=9 and point2D_idx=3137 in image_id=42\n",
      "W20260111 03:54:52.315872 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3457 in image_id=9 and point2D_idx=7252 in image_id=42\n",
      "W20260111 03:54:52.315885 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=334 in image_id=9 and point2D_idx=3641 in image_id=42\n",
      "W20260111 03:54:52.315933 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=212 in image_id=9 and point2D_idx=3161 in image_id=42\n",
      "W20260111 03:54:52.315963 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8290 in image_id=9 and point2D_idx=6005 in image_id=49\n",
      "W20260111 03:54:52.315983 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6312 in image_id=9 and point2D_idx=4117 in image_id=49\n",
      "W20260111 03:54:52.315997 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6754 in image_id=9 and point2D_idx=4552 in image_id=49\n",
      "W20260111 03:54:52.316021 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7899 in image_id=9 and point2D_idx=5651 in image_id=49\n",
      "W20260111 03:54:52.316080 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7680 in image_id=9 and point2D_idx=5566 in image_id=49\n",
      "W20260111 03:54:52.316109 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5032 in image_id=9 and point2D_idx=3410 in image_id=49\n",
      "W20260111 03:54:52.316126 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=422 in image_id=9 and point2D_idx=46 in image_id=49\n",
      "W20260111 03:54:52.316137 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5854 in image_id=9 and point2D_idx=4090 in image_id=49\n",
      "W20260111 03:54:52.316155 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3802 in image_id=9 and point2D_idx=2638 in image_id=49\n",
      "W20260111 03:54:52.316175 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1019 in image_id=9 and point2D_idx=493 in image_id=49\n",
      "W20260111 03:54:52.316195 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3410 in image_id=9 and point2D_idx=2479 in image_id=49\n",
      "W20260111 03:54:52.316316 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=721 in image_id=10 and point2D_idx=960 in image_id=11\n",
      "W20260111 03:54:52.316342 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2611 in image_id=10 and point2D_idx=5036 in image_id=24\n",
      "W20260111 03:54:52.316367 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3097 in image_id=10 and point2D_idx=6337 in image_id=24\n",
      "W20260111 03:54:52.316406 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2707 in image_id=10 and point2D_idx=5382 in image_id=24\n",
      "W20260111 03:54:52.316441 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3119 in image_id=10 and point2D_idx=6487 in image_id=24\n",
      "W20260111 03:54:52.316447 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3243 in image_id=10 and point2D_idx=6749 in image_id=24\n",
      "W20260111 03:54:52.316455 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3393 in image_id=10 and point2D_idx=7046 in image_id=24\n",
      "W20260111 03:54:52.316468 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2070 in image_id=10 and point2D_idx=3740 in image_id=24\n",
      "W20260111 03:54:52.316495 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3081 in image_id=10 and point2D_idx=6490 in image_id=24\n",
      "W20260111 03:54:52.316524 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2408 in image_id=10 and point2D_idx=4549 in image_id=24\n",
      "W20260111 03:54:52.316532 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2754 in image_id=10 and point2D_idx=5565 in image_id=24\n",
      "W20260111 03:54:52.316585 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3252 in image_id=10 and point2D_idx=6756 in image_id=24\n",
      "W20260111 03:54:52.316610 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3405 in image_id=10 and point2D_idx=7053 in image_id=24\n",
      "W20260111 03:54:52.316621 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2969 in image_id=10 and point2D_idx=6035 in image_id=24\n",
      "W20260111 03:54:52.316696 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2964 in image_id=10 and point2D_idx=5881 in image_id=24\n",
      "W20260111 03:54:52.316731 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1476 in image_id=10 and point2D_idx=2595 in image_id=24\n",
      "W20260111 03:54:52.316775 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2316 in image_id=10 and point2D_idx=4173 in image_id=24\n",
      "W20260111 03:54:52.316795 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1564 in image_id=10 and point2D_idx=2819 in image_id=24\n",
      "W20260111 03:54:52.316812 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1689 in image_id=10 and point2D_idx=3041 in image_id=24\n",
      "W20260111 03:54:52.316850 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1704 in image_id=10 and point2D_idx=3586 in image_id=28\n",
      "W20260111 03:54:52.316928 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3070 in image_id=10 and point2D_idx=1901 in image_id=50\n",
      "W20260111 03:54:52.316988 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3085 in image_id=10 and point2D_idx=2627 in image_id=50\n",
      "W20260111 03:54:52.316999 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3300 in image_id=10 and point2D_idx=2888 in image_id=50\n",
      "W20260111 03:54:52.317031 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=370 in image_id=11 and point2D_idx=3094 in image_id=12\n",
      "W20260111 03:54:52.317040 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=180 in image_id=11 and point2D_idx=2925 in image_id=12\n",
      "W20260111 03:54:52.317083 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1642 in image_id=11 and point2D_idx=3500 in image_id=14\n",
      "W20260111 03:54:52.317103 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1852 in image_id=11 and point2D_idx=3009 in image_id=24\n",
      "W20260111 03:54:52.317118 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1512 in image_id=11 and point2D_idx=2349 in image_id=24\n",
      "W20260111 03:54:52.317127 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1343 in image_id=11 and point2D_idx=1940 in image_id=24\n",
      "W20260111 03:54:52.317145 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1225 in image_id=11 and point2D_idx=1797 in image_id=24\n",
      "W20260111 03:54:52.317159 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1583 in image_id=11 and point2D_idx=2803 in image_id=24\n",
      "W20260111 03:54:52.317170 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1866 in image_id=11 and point2D_idx=3505 in image_id=24\n",
      "W20260111 03:54:52.317207 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1724 in image_id=11 and point2D_idx=4101 in image_id=24\n",
      "W20260111 03:54:52.317217 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1004 in image_id=11 and point2D_idx=2814 in image_id=24\n",
      "W20260111 03:54:52.317350 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=346 in image_id=12 and point2D_idx=286 in image_id=13\n",
      "W20260111 03:54:52.317372 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=736 in image_id=12 and point2D_idx=4458 in image_id=17\n",
      "W20260111 03:54:52.317393 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1 in image_id=12 and point2D_idx=1114 in image_id=17\n",
      "W20260111 03:54:52.317450 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=872 in image_id=12 and point2D_idx=1579 in image_id=17\n",
      "W20260111 03:54:52.317470 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=456 in image_id=12 and point2D_idx=152 in image_id=17\n",
      "W20260111 03:54:52.317486 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2995 in image_id=13 and point2D_idx=4124 in image_id=14\n",
      "W20260111 03:54:52.317498 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3427 in image_id=13 and point2D_idx=4781 in image_id=14\n",
      "W20260111 03:54:52.317505 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3676 in image_id=13 and point2D_idx=5274 in image_id=14\n",
      "W20260111 03:54:52.317515 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2579 in image_id=13 and point2D_idx=3437 in image_id=14\n",
      "W20260111 03:54:52.317522 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2997 in image_id=13 and point2D_idx=4125 in image_id=14\n",
      "W20260111 03:54:52.317530 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3430 in image_id=13 and point2D_idx=4782 in image_id=14\n",
      "W20260111 03:54:52.317590 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4313 in image_id=13 and point2D_idx=6104 in image_id=14\n",
      "W20260111 03:54:52.317604 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3528 in image_id=13 and point2D_idx=4978 in image_id=14\n",
      "W20260111 03:54:52.317616 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3006 in image_id=13 and point2D_idx=4262 in image_id=17\n",
      "W20260111 03:54:52.317623 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3162 in image_id=13 and point2D_idx=4449 in image_id=17\n",
      "W20260111 03:54:52.317747 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=871 in image_id=13 and point2D_idx=1561 in image_id=17\n",
      "W20260111 03:54:52.317774 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=143 in image_id=13 and point2D_idx=578 in image_id=17\n",
      "W20260111 03:54:52.317837 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=32 in image_id=13 and point2D_idx=308 in image_id=17\n",
      "W20260111 03:54:52.317887 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1048 in image_id=13 and point2D_idx=1791 in image_id=17\n",
      "W20260111 03:54:52.317902 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1907 in image_id=13 and point2D_idx=2641 in image_id=17\n",
      "W20260111 03:54:52.317944 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5052 in image_id=13 and point2D_idx=5017 in image_id=23\n",
      "W20260111 03:54:52.318039 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=523 in image_id=13 and point2D_idx=6 in image_id=23\n",
      "W20260111 03:54:52.318071 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2585 in image_id=13 and point2D_idx=5626 in image_id=34\n",
      "W20260111 03:54:52.318117 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4693 in image_id=13 and point2D_idx=10873 in image_id=34\n",
      "W20260111 03:54:52.318135 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4360 in image_id=13 and point2D_idx=10084 in image_id=34\n",
      "W20260111 03:54:52.318177 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3481 in image_id=13 and point2D_idx=8122 in image_id=34\n",
      "W20260111 03:54:52.318238 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=150 in image_id=13 and point2D_idx=3707 in image_id=42\n",
      "W20260111 03:54:52.318275 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=220 in image_id=13 and point2D_idx=3632 in image_id=42\n",
      "W20260111 03:54:52.318332 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=339 in image_id=13 and point2D_idx=3161 in image_id=42\n",
      "W20260111 03:54:52.318387 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1461 in image_id=13 and point2D_idx=4162 in image_id=42\n",
      "W20260111 03:54:52.318449 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1415 in image_id=13 and point2D_idx=1975 in image_id=43\n",
      "W20260111 03:54:52.318550 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4209 in image_id=13 and point2D_idx=6213 in image_id=43\n",
      "W20260111 03:54:52.318586 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5070 in image_id=13 and point2D_idx=7429 in image_id=43\n",
      "W20260111 03:54:52.318598 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5341 in image_id=13 and point2D_idx=7853 in image_id=43\n",
      "W20260111 03:54:52.318622 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5103 in image_id=13 and point2D_idx=7430 in image_id=43\n",
      "W20260111 03:54:52.318736 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3713 in image_id=13 and point2D_idx=5682 in image_id=43\n",
      "W20260111 03:54:52.318771 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5086 in image_id=13 and point2D_idx=7590 in image_id=43\n",
      "W20260111 03:54:52.318886 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1731 in image_id=13 and point2D_idx=1571 in image_id=49\n",
      "W20260111 03:54:52.318906 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4937 in image_id=13 and point2D_idx=5059 in image_id=49\n",
      "W20260111 03:54:52.318985 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3448 in image_id=13 and point2D_idx=3530 in image_id=49\n",
      "W20260111 03:54:52.319003 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4856 in image_id=13 and point2D_idx=5084 in image_id=49\n",
      "W20260111 03:54:52.319024 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4700 in image_id=13 and point2D_idx=4951 in image_id=49\n",
      "W20260111 03:54:52.319047 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2873 in image_id=13 and point2D_idx=3004 in image_id=49\n",
      "W20260111 03:54:52.319114 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4086 in image_id=13 and point2D_idx=4361 in image_id=49\n",
      "W20260111 03:54:52.319145 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4955 in image_id=13 and point2D_idx=5353 in image_id=49\n",
      "W20260111 03:54:52.319156 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5079 in image_id=13 and point2D_idx=5465 in image_id=49\n",
      "W20260111 03:54:52.319320 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4437 in image_id=13 and point2D_idx=7816 in image_id=51\n",
      "W20260111 03:54:52.319343 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4547 in image_id=13 and point2D_idx=8188 in image_id=51\n",
      "W20260111 03:54:52.319372 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1612 in image_id=14 and point2D_idx=381 in image_id=15\n",
      "W20260111 03:54:52.319417 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4186 in image_id=14 and point2D_idx=2550 in image_id=15\n",
      "W20260111 03:54:52.319504 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5458 in image_id=14 and point2D_idx=3352 in image_id=15\n",
      "W20260111 03:54:52.319548 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3702 in image_id=14 and point2D_idx=2162 in image_id=15\n",
      "W20260111 03:54:52.319586 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5065 in image_id=14 and point2D_idx=3022 in image_id=15\n",
      "W20260111 03:54:52.319648 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3503 in image_id=14 and point2D_idx=2020 in image_id=15\n",
      "W20260111 03:54:52.319706 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1632 in image_id=14 and point2D_idx=262 in image_id=15\n",
      "W20260111 03:54:52.319725 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2141 in image_id=14 and point2D_idx=932 in image_id=15\n",
      "W20260111 03:54:52.319739 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2804 in image_id=14 and point2D_idx=1531 in image_id=15\n",
      "W20260111 03:54:52.319754 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3612 in image_id=14 and point2D_idx=2016 in image_id=15\n",
      "W20260111 03:54:52.319772 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4381 in image_id=14 and point2D_idx=2467 in image_id=15\n",
      "W20260111 03:54:52.319787 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4955 in image_id=14 and point2D_idx=2899 in image_id=15\n",
      "W20260111 03:54:52.319800 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5156 in image_id=14 and point2D_idx=3051 in image_id=15\n",
      "W20260111 03:54:52.319865 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3188 in image_id=14 and point2D_idx=1663 in image_id=15\n",
      "W20260111 03:54:52.319887 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4956 in image_id=14 and point2D_idx=2853 in image_id=15\n",
      "W20260111 03:54:52.319904 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5653 in image_id=14 and point2D_idx=3466 in image_id=15\n",
      "W20260111 03:54:52.319944 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3711 in image_id=14 and point2D_idx=1998 in image_id=15\n",
      "W20260111 03:54:52.319963 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4854 in image_id=14 and point2D_idx=2774 in image_id=15\n",
      "W20260111 03:54:52.320052 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3715 in image_id=14 and point2D_idx=2004 in image_id=15\n",
      "W20260111 03:54:52.320070 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4757 in image_id=14 and point2D_idx=2673 in image_id=15\n",
      "W20260111 03:54:52.320124 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2151 in image_id=14 and point2D_idx=557 in image_id=15\n",
      "W20260111 03:54:52.320146 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2702 in image_id=14 and point2D_idx=1084 in image_id=15\n",
      "W20260111 03:54:52.320206 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1991 in image_id=14 and point2D_idx=4292 in image_id=18\n",
      "W20260111 03:54:52.320281 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1699 in image_id=14 and point2D_idx=4299 in image_id=18\n",
      "W20260111 03:54:52.320302 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2417 in image_id=14 and point2D_idx=5011 in image_id=18\n",
      "W20260111 03:54:52.320333 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2460 in image_id=14 and point2D_idx=5015 in image_id=18\n",
      "W20260111 03:54:52.320381 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1688 in image_id=14 and point2D_idx=4546 in image_id=18\n",
      "W20260111 03:54:52.320490 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1082 in image_id=14 and point2D_idx=992 in image_id=20\n",
      "W20260111 03:54:52.320619 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1607 in image_id=14 and point2D_idx=2334 in image_id=23\n",
      "W20260111 03:54:52.320676 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6036 in image_id=14 and point2D_idx=4747 in image_id=23\n",
      "W20260111 03:54:52.320779 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3334 in image_id=14 and point2D_idx=3226 in image_id=23\n",
      "W20260111 03:54:52.320804 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3065 in image_id=14 and point2D_idx=3088 in image_id=23\n",
      "W20260111 03:54:52.320854 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2600 in image_id=14 and point2D_idx=2770 in image_id=23\n",
      "W20260111 03:54:52.320899 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3074 in image_id=14 and point2D_idx=3094 in image_id=23\n",
      "W20260111 03:54:52.320916 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3335 in image_id=14 and point2D_idx=3233 in image_id=23\n",
      "W20260111 03:54:52.320950 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=22 in image_id=14 and point2D_idx=1105 in image_id=23\n",
      "W20260111 03:54:52.321042 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3394 in image_id=14 and point2D_idx=3280 in image_id=24\n",
      "W20260111 03:54:52.321070 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3987 in image_id=14 and point2D_idx=4362 in image_id=24\n",
      "W20260111 03:54:52.321093 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4188 in image_id=14 and point2D_idx=4733 in image_id=24\n",
      "W20260111 03:54:52.321110 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1596 in image_id=14 and point2D_idx=1183 in image_id=24\n",
      "W20260111 03:54:52.321135 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4535 in image_id=14 and point2D_idx=5394 in image_id=24\n",
      "W20260111 03:54:52.321218 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1055 in image_id=14 and point2D_idx=963 in image_id=29\n",
      "W20260111 03:54:52.321238 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1678 in image_id=14 and point2D_idx=1624 in image_id=29\n",
      "W20260111 03:54:52.321252 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1251 in image_id=14 and point2D_idx=1342 in image_id=29\n",
      "W20260111 03:54:52.321268 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1638 in image_id=14 and point2D_idx=1627 in image_id=29\n",
      "W20260111 03:54:52.321286 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2039 in image_id=14 and point2D_idx=2213 in image_id=29\n",
      "W20260111 03:54:52.321318 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=941 in image_id=14 and point2D_idx=1838 in image_id=29\n",
      "W20260111 03:54:52.321337 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2020 in image_id=14 and point2D_idx=2614 in image_id=29\n",
      "W20260111 03:54:52.321347 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=391 in image_id=14 and point2D_idx=1371 in image_id=29\n",
      "W20260111 03:54:52.321369 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2675 in image_id=14 and point2D_idx=3584 in image_id=29\n",
      "W20260111 03:54:52.321391 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2637 in image_id=14 and point2D_idx=1144 in image_id=30\n",
      "W20260111 03:54:52.321405 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2301 in image_id=14 and point2D_idx=1148 in image_id=30\n",
      "W20260111 03:54:52.321433 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1068 in image_id=14 and point2D_idx=620 in image_id=30\n",
      "W20260111 03:54:52.321446 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1826 in image_id=14 and point2D_idx=1129 in image_id=30\n",
      "W20260111 03:54:52.321469 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=314 in image_id=14 and point2D_idx=80 in image_id=30\n",
      "W20260111 03:54:52.321491 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1509 in image_id=14 and point2D_idx=1505 in image_id=30\n",
      "W20260111 03:54:52.321510 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=262 in image_id=14 and point2D_idx=3556 in image_id=33\n",
      "W20260111 03:54:52.321546 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2710 in image_id=14 and point2D_idx=4414 in image_id=34\n",
      "W20260111 03:54:52.321573 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=207 in image_id=14 and point2D_idx=1657 in image_id=34\n",
      "W20260111 03:54:52.321595 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2951 in image_id=14 and point2D_idx=5103 in image_id=34\n",
      "W20260111 03:54:52.321607 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3521 in image_id=14 and point2D_idx=5926 in image_id=34\n",
      "W20260111 03:54:52.321634 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=802 in image_id=14 and point2D_idx=2704 in image_id=34\n",
      "W20260111 03:54:52.321678 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2536 in image_id=14 and point2D_idx=5034 in image_id=34\n",
      "W20260111 03:54:52.321702 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3746 in image_id=14 and point2D_idx=6545 in image_id=34\n",
      "W20260111 03:54:52.321736 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=92 in image_id=14 and point2D_idx=1098 in image_id=39\n",
      "W20260111 03:54:52.321779 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=215 in image_id=14 and point2D_idx=201 in image_id=45\n",
      "W20260111 03:54:52.321789 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1021 in image_id=14 and point2D_idx=4533 in image_id=48\n",
      "W20260111 03:54:52.321802 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=725 in image_id=15 and point2D_idx=2132 in image_id=16\n",
      "W20260111 03:54:52.321811 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=617 in image_id=15 and point2D_idx=2084 in image_id=16\n",
      "W20260111 03:54:52.321841 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1337 in image_id=15 and point2D_idx=2163 in image_id=16\n",
      "W20260111 03:54:52.321873 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=171 in image_id=15 and point2D_idx=1051 in image_id=16\n",
      "W20260111 03:54:52.321881 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1249 in image_id=15 and point2D_idx=1588 in image_id=16\n",
      "W20260111 03:54:52.321899 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=534 in image_id=15 and point2D_idx=989 in image_id=16\n",
      "W20260111 03:54:52.321918 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=749 in image_id=15 and point2D_idx=828 in image_id=16\n",
      "W20260111 03:54:52.321936 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1062 in image_id=15 and point2D_idx=813 in image_id=16\n",
      "W20260111 03:54:52.321953 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=48 in image_id=16 and point2D_idx=4607 in image_id=22\n",
      "W20260111 03:54:52.321960 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=49 in image_id=16 and point2D_idx=4585 in image_id=22\n",
      "W20260111 03:54:52.321989 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=179 in image_id=16 and point2D_idx=4443 in image_id=22\n",
      "W20260111 03:54:52.322016 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=129 in image_id=16 and point2D_idx=1619 in image_id=22\n",
      "W20260111 03:54:52.322045 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1772 in image_id=16 and point2D_idx=628 in image_id=27\n",
      "W20260111 03:54:52.322093 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2315 in image_id=16 and point2D_idx=1009 in image_id=27\n",
      "W20260111 03:54:52.322105 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1383 in image_id=16 and point2D_idx=306 in image_id=27\n",
      "W20260111 03:54:52.322116 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2266 in image_id=16 and point2D_idx=963 in image_id=27\n",
      "W20260111 03:54:52.322159 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1582 in image_id=16 and point2D_idx=417 in image_id=27\n",
      "W20260111 03:54:52.322166 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1794 in image_id=16 and point2D_idx=571 in image_id=27\n",
      "W20260111 03:54:52.322172 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1986 in image_id=16 and point2D_idx=711 in image_id=27\n",
      "W20260111 03:54:52.322180 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2169 in image_id=16 and point2D_idx=851 in image_id=27\n",
      "W20260111 03:54:52.322212 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1325 in image_id=17 and point2D_idx=265 in image_id=18\n",
      "W20260111 03:54:52.322219 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6841 in image_id=17 and point2D_idx=10519 in image_id=18\n",
      "W20260111 03:54:52.322230 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6591 in image_id=17 and point2D_idx=10182 in image_id=18\n",
      "W20260111 03:54:52.322237 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6846 in image_id=17 and point2D_idx=10495 in image_id=18\n",
      "W20260111 03:54:52.322258 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4465 in image_id=17 and point2D_idx=6211 in image_id=18\n",
      "W20260111 03:54:52.322291 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2841 in image_id=17 and point2D_idx=1879 in image_id=18\n",
      "W20260111 03:54:52.322301 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1969 in image_id=17 and point2D_idx=271 in image_id=19\n",
      "W20260111 03:54:52.322328 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6833 in image_id=17 and point2D_idx=3906 in image_id=19\n",
      "W20260111 03:54:52.322338 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6052 in image_id=17 and point2D_idx=3509 in image_id=19\n",
      "W20260111 03:54:52.322369 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8750 in image_id=17 and point2D_idx=2621 in image_id=22\n",
      "W20260111 03:54:52.322408 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8500 in image_id=17 and point2D_idx=2806 in image_id=22\n",
      "W20260111 03:54:52.322444 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8609 in image_id=17 and point2D_idx=3193 in image_id=22\n",
      "W20260111 03:54:52.322472 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4654 in image_id=17 and point2D_idx=1056 in image_id=22\n",
      "W20260111 03:54:52.322494 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4293 in image_id=17 and point2D_idx=919 in image_id=22\n",
      "W20260111 03:54:52.322506 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5896 in image_id=17 and point2D_idx=1872 in image_id=22\n",
      "W20260111 03:54:52.322552 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6618 in image_id=17 and point2D_idx=2459 in image_id=22\n",
      "W20260111 03:54:52.322644 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8561 in image_id=17 and point2D_idx=3764 in image_id=22\n",
      "W20260111 03:54:52.322670 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5505 in image_id=17 and point2D_idx=2161 in image_id=22\n",
      "W20260111 03:54:52.322686 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8008 in image_id=17 and point2D_idx=3387 in image_id=22\n",
      "W20260111 03:54:52.322723 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7135 in image_id=17 and point2D_idx=3023 in image_id=22\n",
      "W20260111 03:54:52.322752 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5731 in image_id=17 and point2D_idx=2465 in image_id=22\n",
      "W20260111 03:54:52.322820 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5945 in image_id=17 and point2D_idx=2836 in image_id=22\n",
      "W20260111 03:54:52.322884 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9087 in image_id=17 and point2D_idx=4273 in image_id=22\n",
      "W20260111 03:54:52.322914 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7861 in image_id=17 and point2D_idx=3937 in image_id=22\n",
      "W20260111 03:54:52.322959 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8291 in image_id=17 and point2D_idx=4127 in image_id=22\n",
      "W20260111 03:54:52.322990 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8547 in image_id=17 and point2D_idx=4206 in image_id=22\n",
      "W20260111 03:54:52.323033 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9184 in image_id=17 and point2D_idx=4376 in image_id=22\n",
      "W20260111 03:54:52.323063 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3769 in image_id=17 and point2D_idx=2325 in image_id=22\n",
      "W20260111 03:54:52.323084 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4690 in image_id=17 and point2D_idx=2847 in image_id=22\n",
      "W20260111 03:54:52.323099 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7154 in image_id=17 and point2D_idx=3943 in image_id=22\n",
      "W20260111 03:54:52.323121 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7501 in image_id=17 and point2D_idx=1794 in image_id=25\n",
      "W20260111 03:54:52.323131 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9142 in image_id=17 and point2D_idx=2667 in image_id=25\n",
      "W20260111 03:54:52.323154 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7431 in image_id=17 and point2D_idx=1868 in image_id=25\n",
      "W20260111 03:54:52.323166 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9233 in image_id=17 and point2D_idx=2775 in image_id=25\n",
      "W20260111 03:54:52.323181 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8644 in image_id=17 and point2D_idx=2461 in image_id=25\n",
      "W20260111 03:54:52.323195 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9066 in image_id=17 and point2D_idx=2701 in image_id=25\n",
      "W20260111 03:54:52.323205 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7120 in image_id=17 and point2D_idx=1951 in image_id=25\n",
      "W20260111 03:54:52.323226 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8652 in image_id=17 and point2D_idx=2543 in image_id=25\n",
      "W20260111 03:54:52.323259 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4516 in image_id=17 and point2D_idx=1519 in image_id=25\n",
      "W20260111 03:54:52.323276 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8382 in image_id=17 and point2D_idx=2550 in image_id=25\n",
      "W20260111 03:54:52.323284 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8888 in image_id=17 and point2D_idx=2868 in image_id=25\n",
      "W20260111 03:54:52.323355 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5866 in image_id=17 and point2D_idx=8666 in image_id=33\n",
      "W20260111 03:54:52.323458 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8639 in image_id=17 and point2D_idx=10340 in image_id=33\n",
      "W20260111 03:54:52.323479 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8141 in image_id=17 and point2D_idx=10079 in image_id=33\n",
      "W20260111 03:54:52.323537 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7510 in image_id=17 and point2D_idx=9586 in image_id=33\n",
      "W20260111 03:54:52.323662 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6357 in image_id=17 and point2D_idx=8327 in image_id=33\n",
      "W20260111 03:54:52.323725 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7927 in image_id=17 and point2D_idx=9864 in image_id=33\n",
      "W20260111 03:54:52.323758 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7967 in image_id=17 and point2D_idx=9867 in image_id=33\n",
      "W20260111 03:54:52.323830 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8375 in image_id=17 and point2D_idx=10093 in image_id=33\n",
      "W20260111 03:54:52.323939 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8382 in image_id=17 and point2D_idx=10015 in image_id=33\n",
      "W20260111 03:54:52.323946 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8916 in image_id=17 and point2D_idx=10282 in image_id=33\n",
      "W20260111 03:54:52.323963 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3295 in image_id=17 and point2D_idx=4612 in image_id=33\n",
      "W20260111 03:54:52.324005 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8118 in image_id=17 and point2D_idx=9633 in image_id=33\n",
      "W20260111 03:54:52.324108 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=97 in image_id=17 and point2D_idx=236 in image_id=33\n",
      "W20260111 03:54:52.324145 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1185 in image_id=17 and point2D_idx=1257 in image_id=33\n",
      "W20260111 03:54:52.324168 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5472 in image_id=17 and point2D_idx=6407 in image_id=33\n",
      "W20260111 03:54:52.324252 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6840 in image_id=17 and point2D_idx=6511 in image_id=38\n",
      "W20260111 03:54:52.324282 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3960 in image_id=17 and point2D_idx=4489 in image_id=38\n",
      "W20260111 03:54:52.324297 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5286 in image_id=17 and point2D_idx=5457 in image_id=38\n",
      "W20260111 03:54:52.324304 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5434 in image_id=17 and point2D_idx=5566 in image_id=38\n",
      "W20260111 03:54:52.324317 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3961 in image_id=17 and point2D_idx=4491 in image_id=38\n",
      "W20260111 03:54:52.324359 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=573 in image_id=17 and point2D_idx=957 in image_id=38\n",
      "W20260111 03:54:52.324465 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2466 in image_id=17 and point2D_idx=2652 in image_id=38\n",
      "W20260111 03:54:52.324486 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2433 in image_id=17 and point2D_idx=2421 in image_id=38\n",
      "W20260111 03:54:52.324494 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1150 in image_id=17 and point2D_idx=1183 in image_id=38\n",
      "W20260111 03:54:52.324513 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1381 in image_id=17 and point2D_idx=1191 in image_id=38\n",
      "W20260111 03:54:52.324524 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2441 in image_id=17 and point2D_idx=2198 in image_id=38\n",
      "W20260111 03:54:52.324535 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=462 in image_id=17 and point2D_idx=308 in image_id=38\n",
      "W20260111 03:54:52.324547 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5861 in image_id=17 and point2D_idx=14022 in image_id=42\n",
      "W20260111 03:54:52.324586 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=564 in image_id=17 and point2D_idx=4333 in image_id=42\n",
      "W20260111 03:54:52.324631 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=926 in image_id=17 and point2D_idx=5157 in image_id=42\n",
      "W20260111 03:54:52.324677 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=571 in image_id=17 and point2D_idx=3769 in image_id=42\n",
      "W20260111 03:54:52.324758 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3278 in image_id=17 and point2D_idx=7255 in image_id=42\n",
      "W20260111 03:54:52.324883 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7132 in image_id=17 and point2D_idx=11764 in image_id=42\n",
      "W20260111 03:54:52.324902 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=957 in image_id=17 and point2D_idx=2680 in image_id=42\n",
      "W20260111 03:54:52.324910 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5943 in image_id=17 and point2D_idx=10273 in image_id=42\n",
      "W20260111 03:54:52.324918 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=86 in image_id=17 and point2D_idx=1154 in image_id=42\n",
      "W20260111 03:54:52.325003 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5946 in image_id=17 and point2D_idx=8891 in image_id=42\n",
      "W20260111 03:54:52.325083 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3313 in image_id=17 and point2D_idx=3188 in image_id=42\n",
      "W20260111 03:54:52.325103 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5091 in image_id=17 and point2D_idx=5738 in image_id=42\n",
      "W20260111 03:54:52.325135 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2470 in image_id=17 and point2D_idx=2293 in image_id=42\n",
      "W20260111 03:54:52.325152 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2909 in image_id=17 and point2D_idx=2702 in image_id=42\n",
      "W20260111 03:54:52.325214 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7946 in image_id=17 and point2D_idx=10179 in image_id=51\n",
      "W20260111 03:54:52.325248 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7312 in image_id=17 and point2D_idx=9415 in image_id=51\n",
      "W20260111 03:54:52.325279 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8868 in image_id=17 and point2D_idx=10781 in image_id=51\n",
      "W20260111 03:54:52.325337 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8688 in image_id=17 and point2D_idx=10708 in image_id=51\n",
      "W20260111 03:54:52.325416 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8239 in image_id=17 and point2D_idx=10604 in image_id=51\n",
      "W20260111 03:54:52.325467 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8242 in image_id=17 and point2D_idx=10662 in image_id=51\n",
      "W20260111 03:54:52.325539 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8389 in image_id=18 and point2D_idx=2791 in image_id=19\n",
      "W20260111 03:54:52.325646 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7414 in image_id=18 and point2D_idx=2099 in image_id=19\n",
      "W20260111 03:54:52.325772 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10157 in image_id=18 and point2D_idx=3846 in image_id=19\n",
      "W20260111 03:54:52.325821 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9803 in image_id=18 and point2D_idx=3749 in image_id=19\n",
      "W20260111 03:54:52.325875 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7427 in image_id=18 and point2D_idx=1981 in image_id=19\n",
      "W20260111 03:54:52.325893 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10326 in image_id=18 and point2D_idx=3902 in image_id=19\n",
      "W20260111 03:54:52.325992 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11091 in image_id=18 and point2D_idx=1037 in image_id=22\n",
      "W20260111 03:54:52.326050 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10736 in image_id=18 and point2D_idx=1199 in image_id=22\n",
      "W20260111 03:54:52.326071 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11079 in image_id=18 and point2D_idx=1398 in image_id=22\n",
      "W20260111 03:54:52.326135 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8937 in image_id=18 and point2D_idx=776 in image_id=22\n",
      "W20260111 03:54:52.326215 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7815 in image_id=18 and point2D_idx=5039 in image_id=29\n",
      "W20260111 03:54:52.326267 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8613 in image_id=18 and point2D_idx=5887 in image_id=29\n",
      "W20260111 03:54:52.326307 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2122 in image_id=18 and point2D_idx=512 in image_id=29\n",
      "W20260111 03:54:52.326504 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7468 in image_id=18 and point2D_idx=6267 in image_id=29\n",
      "W20260111 03:54:52.326526 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7432 in image_id=18 and point2D_idx=6302 in image_id=29\n",
      "W20260111 03:54:52.326544 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=158 in image_id=18 and point2D_idx=14 in image_id=29\n",
      "W20260111 03:54:52.326566 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1389 in image_id=18 and point2D_idx=1034 in image_id=29\n",
      "W20260111 03:54:52.326687 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10213 in image_id=18 and point2D_idx=7698 in image_id=29\n",
      "W20260111 03:54:52.326766 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5086 in image_id=18 and point2D_idx=5026 in image_id=29\n",
      "W20260111 03:54:52.326809 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2335 in image_id=18 and point2D_idx=2651 in image_id=29\n",
      "W20260111 03:54:52.326947 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6608 in image_id=18 and point2D_idx=6594 in image_id=29\n",
      "W20260111 03:54:52.326987 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8254 in image_id=18 and point2D_idx=7320 in image_id=29\n",
      "W20260111 03:54:52.327023 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11220 in image_id=18 and point2D_idx=8030 in image_id=29\n",
      "W20260111 03:54:52.327068 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8436 in image_id=18 and point2D_idx=7507 in image_id=29\n",
      "W20260111 03:54:52.327095 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9132 in image_id=18 and point2D_idx=7664 in image_id=29\n",
      "W20260111 03:54:52.327120 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6190 in image_id=18 and point2D_idx=6732 in image_id=29\n",
      "W20260111 03:54:52.327149 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10640 in image_id=18 and point2D_idx=7961 in image_id=29\n",
      "W20260111 03:54:52.327179 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8999 in image_id=18 and point2D_idx=7716 in image_id=29\n",
      "W20260111 03:54:52.327229 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=560 in image_id=18 and point2D_idx=1723 in image_id=33\n",
      "W20260111 03:54:52.327272 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=249 in image_id=18 and point2D_idx=1457 in image_id=33\n",
      "W20260111 03:54:52.327353 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3067 in image_id=18 and point2D_idx=4263 in image_id=33\n",
      "W20260111 03:54:52.327369 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10228 in image_id=18 and point2D_idx=9418 in image_id=33\n",
      "W20260111 03:54:52.327391 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6426 in image_id=18 and point2D_idx=6577 in image_id=33\n",
      "W20260111 03:54:52.327409 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7023 in image_id=18 and point2D_idx=6971 in image_id=33\n",
      "W20260111 03:54:52.327440 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8943 in image_id=18 and point2D_idx=8328 in image_id=33\n",
      "W20260111 03:54:52.327464 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2370 in image_id=18 and point2D_idx=3599 in image_id=33\n",
      "W20260111 03:54:52.327489 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1658 in image_id=18 and point2D_idx=2909 in image_id=33\n",
      "W20260111 03:54:52.327506 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9122 in image_id=18 and point2D_idx=8531 in image_id=33\n",
      "W20260111 03:54:52.327524 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4858 in image_id=18 and point2D_idx=5525 in image_id=33\n",
      "W20260111 03:54:52.327573 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3075 in image_id=18 and point2D_idx=4270 in image_id=33\n",
      "W20260111 03:54:52.327611 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6184 in image_id=18 and point2D_idx=6384 in image_id=33\n",
      "W20260111 03:54:52.327635 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6827 in image_id=18 and point2D_idx=6787 in image_id=33\n",
      "W20260111 03:54:52.327682 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=888 in image_id=18 and point2D_idx=2311 in image_id=33\n",
      "W20260111 03:54:52.327829 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10470 in image_id=18 and point2D_idx=11753 in image_id=34\n",
      "W20260111 03:54:52.327879 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5955 in image_id=18 and point2D_idx=6548 in image_id=34\n",
      "W20260111 03:54:52.327922 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5998 in image_id=18 and point2D_idx=6549 in image_id=34\n",
      "W20260111 03:54:52.327943 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5031 in image_id=18 and point2D_idx=5650 in image_id=34\n",
      "W20260111 03:54:52.328011 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10775 in image_id=18 and point2D_idx=12431 in image_id=34\n",
      "W20260111 03:54:52.328055 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8054 in image_id=18 and point2D_idx=9848 in image_id=34\n",
      "W20260111 03:54:52.328088 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8902 in image_id=18 and point2D_idx=10899 in image_id=34\n",
      "W20260111 03:54:52.328173 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10937 in image_id=18 and point2D_idx=13043 in image_id=34\n",
      "W20260111 03:54:52.328194 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11224 in image_id=18 and point2D_idx=13341 in image_id=34\n",
      "W20260111 03:54:52.328225 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9661 in image_id=18 and point2D_idx=12016 in image_id=34\n",
      "W20260111 03:54:52.328329 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9961 in image_id=18 and point2D_idx=9300 in image_id=35\n",
      "W20260111 03:54:52.328481 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4551 in image_id=18 and point2D_idx=3737 in image_id=35\n",
      "W20260111 03:54:52.328577 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10476 in image_id=18 and point2D_idx=10207 in image_id=35\n",
      "W20260111 03:54:52.328649 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9984 in image_id=18 and point2D_idx=9862 in image_id=35\n",
      "W20260111 03:54:52.328778 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10621 in image_id=18 and point2D_idx=10534 in image_id=35\n",
      "W20260111 03:54:52.328880 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1853 in image_id=18 and point2D_idx=2093 in image_id=35\n",
      "W20260111 03:54:52.328904 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8420 in image_id=18 and point2D_idx=8944 in image_id=35\n",
      "W20260111 03:54:52.328939 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10781 in image_id=18 and point2D_idx=10878 in image_id=35\n",
      "W20260111 03:54:52.328989 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1247 in image_id=18 and point2D_idx=1215 in image_id=35\n",
      "W20260111 03:54:52.329006 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9287 in image_id=18 and point2D_idx=9705 in image_id=35\n",
      "W20260111 03:54:52.329032 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3338 in image_id=18 and point2D_idx=3707 in image_id=35\n",
      "W20260111 03:54:52.329051 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9322 in image_id=18 and point2D_idx=9706 in image_id=35\n",
      "W20260111 03:54:52.329094 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5555 in image_id=18 and point2D_idx=6134 in image_id=35\n",
      "W20260111 03:54:52.329136 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8949 in image_id=18 and point2D_idx=9523 in image_id=35\n",
      "W20260111 03:54:52.329235 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4825 in image_id=18 and point2D_idx=6139 in image_id=35\n",
      "W20260111 03:54:52.329262 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10136 in image_id=18 and point2D_idx=10707 in image_id=35\n",
      "W20260111 03:54:52.329300 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=570 in image_id=18 and point2D_idx=1227 in image_id=35\n",
      "W20260111 03:54:52.329328 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9827 in image_id=18 and point2D_idx=10575 in image_id=35\n",
      "W20260111 03:54:52.329439 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5293 in image_id=18 and point2D_idx=7136 in image_id=35\n",
      "W20260111 03:54:52.329455 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4593 in image_id=18 and point2D_idx=6403 in image_id=35\n",
      "W20260111 03:54:52.329530 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9971 in image_id=18 and point2D_idx=7200 in image_id=39\n",
      "W20260111 03:54:52.329602 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5731 in image_id=18 and point2D_idx=4242 in image_id=39\n",
      "W20260111 03:54:52.329629 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8973 in image_id=18 and point2D_idx=6711 in image_id=39\n",
      "W20260111 03:54:52.329658 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8974 in image_id=18 and point2D_idx=6712 in image_id=39\n",
      "W20260111 03:54:52.329701 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=512 in image_id=18 and point2D_idx=200 in image_id=39\n",
      "W20260111 03:54:52.329720 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=664 in image_id=18 and point2D_idx=254 in image_id=39\n",
      "W20260111 03:54:52.329740 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8275 in image_id=18 and point2D_idx=6221 in image_id=39\n",
      "W20260111 03:54:52.329762 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=832 in image_id=18 and point2D_idx=374 in image_id=39\n",
      "W20260111 03:54:52.329777 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3052 in image_id=18 and point2D_idx=2545 in image_id=39\n",
      "W20260111 03:54:52.329814 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2784 in image_id=18 and point2D_idx=2407 in image_id=39\n",
      "W20260111 03:54:52.329831 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5265 in image_id=18 and point2D_idx=4248 in image_id=39\n",
      "W20260111 03:54:52.329874 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9459 in image_id=18 and point2D_idx=7210 in image_id=39\n",
      "W20260111 03:54:52.329894 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9629 in image_id=18 and point2D_idx=7309 in image_id=39\n",
      "W20260111 03:54:52.329928 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9460 in image_id=18 and point2D_idx=7211 in image_id=39\n",
      "W20260111 03:54:52.329989 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5269 in image_id=18 and point2D_idx=4252 in image_id=39\n",
      "W20260111 03:54:52.330021 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=49 in image_id=18 and point2D_idx=6 in image_id=39\n",
      "W20260111 03:54:52.330083 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1390 in image_id=18 and point2D_idx=1088 in image_id=39\n",
      "W20260111 03:54:52.330115 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10172 in image_id=18 and point2D_idx=7548 in image_id=39\n",
      "W20260111 03:54:52.330148 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=386 in image_id=18 and point2D_idx=266 in image_id=39\n",
      "W20260111 03:54:52.330182 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9813 in image_id=18 and point2D_idx=7472 in image_id=39\n",
      "W20260111 03:54:52.330214 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11022 in image_id=18 and point2D_idx=7774 in image_id=39\n",
      "W20260111 03:54:52.330298 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=882 in image_id=18 and point2D_idx=634 in image_id=39\n",
      "W20260111 03:54:52.330384 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=272 in image_id=18 and point2D_idx=278 in image_id=39\n",
      "W20260111 03:54:52.330461 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11221 in image_id=18 and point2D_idx=7951 in image_id=39\n",
      "W20260111 03:54:52.330495 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=147 in image_id=18 and point2D_idx=286 in image_id=39\n",
      "W20260111 03:54:52.330511 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=545 in image_id=18 and point2D_idx=726 in image_id=39\n",
      "W20260111 03:54:52.330585 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5549 in image_id=18 and point2D_idx=9356 in image_id=42\n",
      "W20260111 03:54:52.330640 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4088 in image_id=18 and point2D_idx=7252 in image_id=42\n",
      "W20260111 03:54:52.330665 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2564 in image_id=18 and point2D_idx=5696 in image_id=42\n",
      "W20260111 03:54:52.330738 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7034 in image_id=18 and point2D_idx=9824 in image_id=42\n",
      "W20260111 03:54:52.330755 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9291 in image_id=18 and point2D_idx=12081 in image_id=42\n",
      "W20260111 03:54:52.330783 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9326 in image_id=18 and point2D_idx=11753 in image_id=42\n",
      "W20260111 03:54:52.330806 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8504 in image_id=18 and point2D_idx=10655 in image_id=42\n",
      "W20260111 03:54:52.330898 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=971 in image_id=18 and point2D_idx=2608 in image_id=48\n",
      "W20260111 03:54:52.330931 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=677 in image_id=18 and point2D_idx=2276 in image_id=48\n",
      "W20260111 03:54:52.330951 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=52 in image_id=18 and point2D_idx=1673 in image_id=48\n",
      "W20260111 03:54:52.330977 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3340 in image_id=18 and point2D_idx=4572 in image_id=48\n",
      "W20260111 03:54:52.331018 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3086 in image_id=18 and point2D_idx=4148 in image_id=48\n",
      "W20260111 03:54:52.331041 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=256 in image_id=18 and point2D_idx=58 in image_id=49\n",
      "W20260111 03:54:52.331106 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2669 in image_id=19 and point2D_idx=4702 in image_id=20\n",
      "W20260111 03:54:52.331132 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3198 in image_id=19 and point2D_idx=6194 in image_id=20\n",
      "W20260111 03:54:52.331196 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1604 in image_id=19 and point2D_idx=3459 in image_id=20\n",
      "W20260111 03:54:52.331231 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2778 in image_id=19 and point2D_idx=5612 in image_id=20\n",
      "W20260111 03:54:52.331283 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3206 in image_id=19 and point2D_idx=6448 in image_id=20\n",
      "W20260111 03:54:52.331311 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3292 in image_id=19 and point2D_idx=6575 in image_id=20\n",
      "W20260111 03:54:52.331339 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1715 in image_id=19 and point2D_idx=3866 in image_id=20\n",
      "W20260111 03:54:52.331383 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2985 in image_id=19 and point2D_idx=6208 in image_id=20\n",
      "W20260111 03:54:52.331401 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3210 in image_id=19 and point2D_idx=6454 in image_id=20\n",
      "W20260111 03:54:52.331449 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1138 in image_id=19 and point2D_idx=2894 in image_id=20\n",
      "W20260111 03:54:52.331472 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3594 in image_id=19 and point2D_idx=7027 in image_id=20\n",
      "W20260111 03:54:52.331536 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1723 in image_id=19 and point2D_idx=4117 in image_id=20\n",
      "W20260111 03:54:52.331604 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3407 in image_id=19 and point2D_idx=6973 in image_id=20\n",
      "W20260111 03:54:52.331652 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2991 in image_id=19 and point2D_idx=6832 in image_id=20\n",
      "W20260111 03:54:52.331688 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1385 in image_id=19 and point2D_idx=960 in image_id=21\n",
      "W20260111 03:54:52.331740 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2269 in image_id=19 and point2D_idx=2857 in image_id=21\n",
      "W20260111 03:54:52.331803 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=179 in image_id=19 and point2D_idx=3201 in image_id=33\n",
      "W20260111 03:54:52.331819 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=41 in image_id=19 and point2D_idx=2880 in image_id=33\n",
      "W20260111 03:54:52.331842 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=358 in image_id=19 and point2D_idx=3928 in image_id=33\n",
      "W20260111 03:54:52.331863 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=741 in image_id=19 and point2D_idx=5271 in image_id=33\n",
      "W20260111 03:54:52.331885 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=128 in image_id=19 and point2D_idx=2602 in image_id=38\n",
      "W20260111 03:54:52.331908 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3547 in image_id=19 and point2D_idx=7425 in image_id=51\n",
      "W20260111 03:54:52.331934 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2666 in image_id=19 and point2D_idx=5857 in image_id=51\n",
      "W20260111 03:54:52.331951 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2856 in image_id=19 and point2D_idx=6179 in image_id=51\n",
      "W20260111 03:54:52.331997 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3215 in image_id=19 and point2D_idx=6931 in image_id=51\n",
      "W20260111 03:54:52.332057 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3568 in image_id=19 and point2D_idx=7812 in image_id=51\n",
      "W20260111 03:54:52.332136 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1463 in image_id=19 and point2D_idx=4865 in image_id=51\n",
      "W20260111 03:54:52.332159 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2296 in image_id=19 and point2D_idx=5879 in image_id=51\n",
      "W20260111 03:54:52.332194 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3321 in image_id=19 and point2D_idx=7455 in image_id=51\n",
      "W20260111 03:54:52.332236 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1964 in image_id=19 and point2D_idx=5536 in image_id=51\n",
      "W20260111 03:54:52.332272 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2085 in image_id=19 and point2D_idx=5753 in image_id=51\n",
      "W20260111 03:54:52.332299 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3824 in image_id=19 and point2D_idx=8604 in image_id=51\n",
      "W20260111 03:54:52.332355 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5756 in image_id=20 and point2D_idx=2131 in image_id=21\n",
      "W20260111 03:54:52.332415 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6933 in image_id=20 and point2D_idx=3686 in image_id=21\n",
      "W20260111 03:54:52.332484 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6152 in image_id=20 and point2D_idx=2528 in image_id=21\n",
      "W20260111 03:54:52.332645 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6860 in image_id=20 and point2D_idx=3825 in image_id=21\n",
      "W20260111 03:54:52.332693 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5607 in image_id=20 and point2D_idx=2843 in image_id=21\n",
      "W20260111 03:54:52.332723 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3241 in image_id=20 and point2D_idx=1044 in image_id=21\n",
      "W20260111 03:54:52.332819 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3132 in image_id=20 and point2D_idx=962 in image_id=21\n",
      "W20260111 03:54:52.332861 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3463 in image_id=20 and point2D_idx=1438 in image_id=21\n",
      "W20260111 03:54:52.332880 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4523 in image_id=20 and point2D_idx=2363 in image_id=21\n",
      "W20260111 03:54:52.332957 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6542 in image_id=20 and point2D_idx=7477 in image_id=24\n",
      "W20260111 03:54:52.332986 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4191 in image_id=20 and point2D_idx=6428 in image_id=28\n",
      "W20260111 03:54:52.333039 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3569 in image_id=20 and point2D_idx=5502 in image_id=28\n",
      "W20260111 03:54:52.333055 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3694 in image_id=20 and point2D_idx=5685 in image_id=28\n",
      "W20260111 03:54:52.333108 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=438 in image_id=20 and point2D_idx=971 in image_id=29\n",
      "W20260111 03:54:52.333133 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=415 in image_id=20 and point2D_idx=974 in image_id=29\n",
      "W20260111 03:54:52.333162 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=172 in image_id=20 and point2D_idx=1002 in image_id=29\n",
      "W20260111 03:54:52.333205 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=441 in image_id=20 and point2D_idx=152 in image_id=30\n",
      "W20260111 03:54:52.333265 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=119 in image_id=20 and point2D_idx=100 in image_id=30\n",
      "W20260111 03:54:52.333295 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=997 in image_id=20 and point2D_idx=1179 in image_id=30\n",
      "W20260111 03:54:52.333385 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2731 in image_id=20 and point2D_idx=1132 in image_id=36\n",
      "W20260111 03:54:52.333427 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4927 in image_id=20 and point2D_idx=3464 in image_id=36\n",
      "W20260111 03:54:52.333500 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5075 in image_id=20 and point2D_idx=3598 in image_id=36\n",
      "W20260111 03:54:52.333519 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5350 in image_id=20 and point2D_idx=3861 in image_id=36\n",
      "W20260111 03:54:52.333650 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1133 in image_id=20 and point2D_idx=156 in image_id=36\n",
      "W20260111 03:54:52.333673 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1849 in image_id=20 and point2D_idx=622 in image_id=36\n",
      "W20260111 03:54:52.333698 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2632 in image_id=20 and point2D_idx=1235 in image_id=36\n",
      "W20260111 03:54:52.333714 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2722 in image_id=20 and point2D_idx=1322 in image_id=36\n",
      "W20260111 03:54:52.333726 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2845 in image_id=20 and point2D_idx=1423 in image_id=36\n",
      "W20260111 03:54:52.333746 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2372 in image_id=20 and point2D_idx=1072 in image_id=36\n",
      "W20260111 03:54:52.333775 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2508 in image_id=20 and point2D_idx=1150 in image_id=36\n",
      "W20260111 03:54:52.333827 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5959 in image_id=20 and point2D_idx=2975 in image_id=40\n",
      "W20260111 03:54:52.333886 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5965 in image_id=20 and point2D_idx=2982 in image_id=40\n",
      "W20260111 03:54:52.333912 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4521 in image_id=20 and point2D_idx=1704 in image_id=40\n",
      "W20260111 03:54:52.333985 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4992 in image_id=20 and point2D_idx=2446 in image_id=40\n",
      "W20260111 03:54:52.334027 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3114 in image_id=20 and point2D_idx=2572 in image_id=45\n",
      "W20260111 03:54:52.334112 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=953 in image_id=20 and point2D_idx=335 in image_id=45\n",
      "W20260111 03:54:52.334166 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1882 in image_id=20 and point2D_idx=1226 in image_id=45\n",
      "W20260111 03:54:52.334213 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1126 in image_id=20 and point2D_idx=580 in image_id=45\n",
      "W20260111 03:54:52.334242 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=673 in image_id=20 and point2D_idx=207 in image_id=45\n",
      "W20260111 03:54:52.334366 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=439 in image_id=20 and point2D_idx=96 in image_id=45\n",
      "W20260111 03:54:52.334444 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1581 in image_id=20 and point2D_idx=1387 in image_id=45\n",
      "W20260111 03:54:52.334582 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5399 in image_id=20 and point2D_idx=6104 in image_id=45\n",
      "W20260111 03:54:52.334693 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4559 in image_id=20 and point2D_idx=5467 in image_id=45\n",
      "W20260111 03:54:52.334817 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2901 in image_id=20 and point2D_idx=3421 in image_id=45\n",
      "W20260111 03:54:52.334874 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1005 in image_id=20 and point2D_idx=1412 in image_id=45\n",
      "W20260111 03:54:52.334902 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=837 in image_id=20 and point2D_idx=4533 in image_id=48\n",
      "W20260111 03:54:52.334926 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=989 in image_id=20 and point2D_idx=4542 in image_id=48\n",
      "W20260111 03:54:52.334962 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1538 in image_id=20 and point2D_idx=2101 in image_id=51\n",
      "W20260111 03:54:52.335041 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3025 in image_id=21 and point2D_idx=4797 in image_id=36\n",
      "W20260111 03:54:52.335101 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=956 in image_id=21 and point2D_idx=2371 in image_id=36\n",
      "W20260111 03:54:52.335134 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3400 in image_id=21 and point2D_idx=4905 in image_id=36\n",
      "W20260111 03:54:52.335184 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1607 in image_id=21 and point2D_idx=4919 in image_id=45\n",
      "W20260111 03:54:52.335224 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2756 in image_id=21 and point2D_idx=6532 in image_id=45\n",
      "W20260111 03:54:52.335299 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=332 in image_id=21 and point2D_idx=182 in image_id=46\n",
      "W20260111 03:54:52.335351 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2959 in image_id=21 and point2D_idx=1624 in image_id=46\n",
      "W20260111 03:54:52.335422 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2119 in image_id=21 and point2D_idx=1143 in image_id=50\n",
      "W20260111 03:54:52.335445 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1135 in image_id=21 and point2D_idx=536 in image_id=50\n",
      "W20260111 03:54:52.335493 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3718 in image_id=21 and point2D_idx=3771 in image_id=50\n",
      "W20260111 03:54:52.335526 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=717 in image_id=21 and point2D_idx=240 in image_id=50\n",
      "W20260111 03:54:52.335584 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3015 in image_id=21 and point2D_idx=2022 in image_id=50\n",
      "W20260111 03:54:52.335636 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3375 in image_id=21 and point2D_idx=3652 in image_id=50\n",
      "W20260111 03:54:52.335658 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=824 in image_id=21 and point2D_idx=354 in image_id=50\n",
      "W20260111 03:54:52.335695 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=828 in image_id=21 and point2D_idx=367 in image_id=50\n",
      "W20260111 03:54:52.335728 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3842 in image_id=21 and point2D_idx=3848 in image_id=50\n",
      "W20260111 03:54:52.335813 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3862 in image_id=21 and point2D_idx=3858 in image_id=50\n",
      "W20260111 03:54:52.335875 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3600 in image_id=21 and point2D_idx=3867 in image_id=50\n",
      "W20260111 03:54:52.335905 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=518 in image_id=21 and point2D_idx=202 in image_id=50\n",
      "W20260111 03:54:52.335934 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3290 in image_id=21 and point2D_idx=3545 in image_id=50\n",
      "W20260111 03:54:52.335979 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3420 in image_id=21 and point2D_idx=6888 in image_id=51\n",
      "W20260111 03:54:52.335996 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4318 in image_id=21 and point2D_idx=8475 in image_id=51\n",
      "W20260111 03:54:52.336021 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3394 in image_id=21 and point2D_idx=6903 in image_id=51\n",
      "W20260111 03:54:52.336052 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4195 in image_id=22 and point2D_idx=3060 in image_id=25\n",
      "W20260111 03:54:52.336080 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4040 in image_id=22 and point2D_idx=2872 in image_id=25\n",
      "W20260111 03:54:52.336106 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4476 in image_id=22 and point2D_idx=3536 in image_id=25\n",
      "W20260111 03:54:52.336136 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4379 in image_id=22 and point2D_idx=3396 in image_id=41\n",
      "W20260111 03:54:52.336146 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4407 in image_id=22 and point2D_idx=3273 in image_id=41\n",
      "W20260111 03:54:52.336191 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=354 in image_id=22 and point2D_idx=6737 in image_id=42\n",
      "W20260111 03:54:52.336228 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=563 in image_id=22 and point2D_idx=6250 in image_id=42\n",
      "W20260111 03:54:52.336279 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=441 in image_id=22 and point2D_idx=4162 in image_id=42\n",
      "W20260111 03:54:52.336324 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=307 in image_id=22 and point2D_idx=2280 in image_id=42\n",
      "W20260111 03:54:52.336377 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2039 in image_id=22 and point2D_idx=3192 in image_id=42\n",
      "W20260111 03:54:52.336481 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3738 in image_id=22 and point2D_idx=11129 in image_id=51\n",
      "W20260111 03:54:52.336589 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3566 in image_id=22 and point2D_idx=10972 in image_id=51\n",
      "W20260111 03:54:52.336650 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3011 in image_id=22 and point2D_idx=10595 in image_id=51\n",
      "W20260111 03:54:52.336669 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3197 in image_id=22 and point2D_idx=10718 in image_id=51\n",
      "W20260111 03:54:52.336721 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2302 in image_id=22 and point2D_idx=9656 in image_id=51\n",
      "W20260111 03:54:52.336919 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2783 in image_id=23 and point2D_idx=5342 in image_id=34\n",
      "W20260111 03:54:52.337067 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1364 in image_id=23 and point2D_idx=2529 in image_id=35\n",
      "W20260111 03:54:52.337129 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=972 in image_id=23 and point2D_idx=2302 in image_id=35\n",
      "W20260111 03:54:52.337153 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=254 in image_id=23 and point2D_idx=632 in image_id=35\n",
      "W20260111 03:54:52.337193 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=336 in image_id=23 and point2D_idx=1004 in image_id=35\n",
      "W20260111 03:54:52.337229 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=260 in image_id=23 and point2D_idx=1008 in image_id=35\n",
      "W20260111 03:54:52.337285 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1088 in image_id=23 and point2D_idx=8836 in image_id=42\n",
      "W20260111 03:54:52.337314 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1565 in image_id=23 and point2D_idx=9344 in image_id=42\n",
      "W20260111 03:54:52.337380 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1549 in image_id=23 and point2D_idx=2413 in image_id=43\n",
      "W20260111 03:54:52.337445 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1815 in image_id=23 and point2D_idx=3026 in image_id=43\n",
      "W20260111 03:54:52.337507 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2111 in image_id=23 and point2D_idx=3485 in image_id=43\n",
      "W20260111 03:54:52.337605 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1120 in image_id=23 and point2D_idx=1225 in image_id=44\n",
      "W20260111 03:54:52.337670 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=409 in image_id=23 and point2D_idx=527 in image_id=44\n",
      "W20260111 03:54:52.337695 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1130 in image_id=23 and point2D_idx=1607 in image_id=44\n",
      "W20260111 03:54:52.337708 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4318 in image_id=23 and point2D_idx=4394 in image_id=44\n",
      "W20260111 03:54:52.337733 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=254 in image_id=23 and point2D_idx=414 in image_id=44\n",
      "W20260111 03:54:52.337753 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3390 in image_id=23 and point2D_idx=3409 in image_id=44\n",
      "W20260111 03:54:52.337777 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1561 in image_id=23 and point2D_idx=2064 in image_id=44\n",
      "W20260111 03:54:52.337798 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=336 in image_id=23 and point2D_idx=653 in image_id=44\n",
      "W20260111 03:54:52.337812 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2647 in image_id=23 and point2D_idx=2743 in image_id=44\n",
      "W20260111 03:54:52.337848 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1566 in image_id=23 and point2D_idx=2342 in image_id=44\n",
      "W20260111 03:54:52.337893 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1791 in image_id=23 and point2D_idx=685 in image_id=45\n",
      "W20260111 03:54:52.337915 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1304 in image_id=23 and point2D_idx=157 in image_id=45\n",
      "W20260111 03:54:52.337928 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1306 in image_id=23 and point2D_idx=328 in image_id=45\n",
      "W20260111 03:54:52.337949 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1315 in image_id=23 and point2D_idx=5347 in image_id=48\n",
      "W20260111 03:54:52.337979 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2974 in image_id=23 and point2D_idx=6330 in image_id=48\n",
      "W20260111 03:54:52.338007 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1282 in image_id=23 and point2D_idx=5355 in image_id=48\n",
      "W20260111 03:54:52.338024 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2813 in image_id=23 and point2D_idx=6206 in image_id=48\n",
      "W20260111 03:54:52.338067 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=432 in image_id=23 and point2D_idx=4176 in image_id=48\n",
      "W20260111 03:54:52.338094 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=805 in image_id=23 and point2D_idx=4859 in image_id=48\n",
      "W20260111 03:54:52.338148 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=750 in image_id=23 and point2D_idx=1079 in image_id=51\n",
      "W20260111 03:54:52.338272 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2997 in image_id=24 and point2D_idx=4024 in image_id=28\n",
      "W20260111 03:54:52.338344 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5536 in image_id=24 and point2D_idx=6596 in image_id=28\n",
      "W20260111 03:54:52.338457 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6735 in image_id=24 and point2D_idx=7794 in image_id=28\n",
      "W20260111 03:54:52.338535 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5048 in image_id=24 and point2D_idx=6047 in image_id=28\n",
      "W20260111 03:54:52.338629 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2144 in image_id=24 and point2D_idx=2877 in image_id=28\n",
      "W20260111 03:54:52.338706 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2812 in image_id=24 and point2D_idx=3635 in image_id=28\n",
      "W20260111 03:54:52.338724 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3285 in image_id=24 and point2D_idx=4061 in image_id=28\n",
      "W20260111 03:54:52.339001 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1200 in image_id=24 and point2D_idx=1455 in image_id=30\n",
      "W20260111 03:54:52.339021 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=936 in image_id=24 and point2D_idx=1192 in image_id=30\n",
      "W20260111 03:54:52.339030 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=669 in image_id=24 and point2D_idx=871 in image_id=30\n",
      "W20260111 03:54:52.339111 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=38 in image_id=24 and point2D_idx=1098 in image_id=39\n",
      "W20260111 03:54:52.339139 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=356 in image_id=24 and point2D_idx=1539 in image_id=43\n",
      "W20260111 03:54:52.339182 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=348 in image_id=24 and point2D_idx=68 in image_id=45\n",
      "W20260111 03:54:52.339206 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=277 in image_id=24 and point2D_idx=72 in image_id=45\n",
      "W20260111 03:54:52.339239 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=356 in image_id=24 and point2D_idx=412 in image_id=45\n",
      "W20260111 03:54:52.339259 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=444 in image_id=24 and point2D_idx=746 in image_id=45\n",
      "W20260111 03:54:52.339313 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=28 in image_id=24 and point2D_idx=3327 in image_id=48\n",
      "W20260111 03:54:52.339350 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3424 in image_id=27 and point2D_idx=3313 in image_id=28\n",
      "W20260111 03:54:52.339377 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4004 in image_id=27 and point2D_idx=4023 in image_id=28\n",
      "W20260111 03:54:52.339417 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3790 in image_id=27 and point2D_idx=3796 in image_id=28\n",
      "W20260111 03:54:52.339505 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2857 in image_id=27 and point2D_idx=2868 in image_id=28\n",
      "W20260111 03:54:52.339528 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3672 in image_id=27 and point2D_idx=3811 in image_id=28\n",
      "W20260111 03:54:52.339542 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4613 in image_id=27 and point2D_idx=5270 in image_id=28\n",
      "W20260111 03:54:52.339651 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3274 in image_id=27 and point2D_idx=3592 in image_id=28\n",
      "W20260111 03:54:52.339682 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3713 in image_id=27 and point2D_idx=4061 in image_id=28\n",
      "W20260111 03:54:52.339707 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1358 in image_id=28 and point2D_idx=1830 in image_id=29\n",
      "W20260111 03:54:52.339764 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1136 in image_id=28 and point2D_idx=616 in image_id=30\n",
      "W20260111 03:54:52.339795 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1142 in image_id=28 and point2D_idx=866 in image_id=30\n",
      "W20260111 03:54:52.339856 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1337 in image_id=28 and point2D_idx=1607 in image_id=30\n",
      "W20260111 03:54:52.339904 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1696 in image_id=28 and point2D_idx=2045 in image_id=30\n",
      "W20260111 03:54:52.339943 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=118 in image_id=28 and point2D_idx=1662 in image_id=34\n",
      "W20260111 03:54:52.340010 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5878 in image_id=28 and point2D_idx=2663 in image_id=36\n",
      "W20260111 03:54:52.340083 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4839 in image_id=28 and point2D_idx=2102 in image_id=36\n",
      "W20260111 03:54:52.340125 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1953 in image_id=28 and point2D_idx=677 in image_id=36\n",
      "W20260111 03:54:52.340183 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=706 in image_id=28 and point2D_idx=1338 in image_id=39\n",
      "W20260111 03:54:52.340229 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1957 in image_id=28 and point2D_idx=2700 in image_id=39\n",
      "W20260111 03:54:52.340253 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=332 in image_id=28 and point2D_idx=1638 in image_id=39\n",
      "W20260111 03:54:52.340265 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=724 in image_id=28 and point2D_idx=2149 in image_id=39\n",
      "W20260111 03:54:52.340299 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=76 in image_id=28 and point2D_idx=1361 in image_id=39\n",
      "W20260111 03:54:52.340363 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1149 in image_id=28 and point2D_idx=964 in image_id=45\n",
      "W20260111 03:54:52.340380 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1749 in image_id=28 and point2D_idx=1526 in image_id=45\n",
      "W20260111 03:54:52.340412 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7030 in image_id=29 and point2D_idx=6210 in image_id=30\n",
      "W20260111 03:54:52.340451 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6223 in image_id=29 and point2D_idx=4246 in image_id=30\n",
      "W20260111 03:54:52.340507 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1820 in image_id=29 and point2D_idx=944 in image_id=30\n",
      "W20260111 03:54:52.340528 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3196 in image_id=29 and point2D_idx=1931 in image_id=30\n",
      "W20260111 03:54:52.340544 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4615 in image_id=29 and point2D_idx=2830 in image_id=30\n",
      "W20260111 03:54:52.340610 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5512 in image_id=29 and point2D_idx=4563 in image_id=30\n",
      "W20260111 03:54:52.340659 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5163 in image_id=29 and point2D_idx=4154 in image_id=30\n",
      "W20260111 03:54:52.340698 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1347 in image_id=29 and point2D_idx=469 in image_id=30\n",
      "W20260111 03:54:52.340720 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4268 in image_id=29 and point2D_idx=3023 in image_id=30\n",
      "W20260111 03:54:52.340764 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3752 in image_id=29 and point2D_idx=2635 in image_id=30\n",
      "W20260111 03:54:52.340788 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4983 in image_id=29 and point2D_idx=4176 in image_id=30\n",
      "W20260111 03:54:52.340859 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5341 in image_id=29 and point2D_idx=4536 in image_id=30\n",
      "W20260111 03:54:52.340903 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3213 in image_id=29 and point2D_idx=2293 in image_id=30\n",
      "W20260111 03:54:52.340966 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2914 in image_id=29 and point2D_idx=2127 in image_id=30\n",
      "W20260111 03:54:52.340992 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1634 in image_id=29 and point2D_idx=843 in image_id=30\n",
      "W20260111 03:54:52.341005 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2421 in image_id=29 and point2D_idx=1696 in image_id=30\n",
      "W20260111 03:54:52.341015 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2514 in image_id=29 and point2D_idx=1792 in image_id=30\n",
      "W20260111 03:54:52.341040 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1636 in image_id=29 and point2D_idx=878 in image_id=30\n",
      "W20260111 03:54:52.341174 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2075 in image_id=29 and point2D_idx=1399 in image_id=30\n",
      "W20260111 03:54:52.341193 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2226 in image_id=29 and point2D_idx=1619 in image_id=30\n",
      "W20260111 03:54:52.341205 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2588 in image_id=29 and point2D_idx=1968 in image_id=30\n",
      "W20260111 03:54:52.341223 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4636 in image_id=29 and point2D_idx=4435 in image_id=30\n",
      "W20260111 03:54:52.341255 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1653 in image_id=29 and point2D_idx=5010 in image_id=32\n",
      "W20260111 03:54:52.341317 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3229 in image_id=29 and point2D_idx=5265 in image_id=33\n",
      "W20260111 03:54:52.341349 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1851 in image_id=29 and point2D_idx=3928 in image_id=33\n",
      "W20260111 03:54:52.341372 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7542 in image_id=29 and point2D_idx=9555 in image_id=33\n",
      "W20260111 03:54:52.341428 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7834 in image_id=29 and point2D_idx=9243 in image_id=33\n",
      "W20260111 03:54:52.341465 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=19 in image_id=29 and point2D_idx=11 in image_id=34\n",
      "W20260111 03:54:52.341500 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1860 in image_id=29 and point2D_idx=3604 in image_id=37\n",
      "W20260111 03:54:52.341538 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3430 in image_id=29 and point2D_idx=3574 in image_id=37\n",
      "W20260111 03:54:52.341579 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3058 in image_id=29 and point2D_idx=3392 in image_id=37\n",
      "W20260111 03:54:52.341612 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1873 in image_id=29 and point2D_idx=3074 in image_id=37\n",
      "W20260111 03:54:52.341677 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4433 in image_id=29 and point2D_idx=983 in image_id=40\n",
      "W20260111 03:54:52.341695 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5689 in image_id=29 and point2D_idx=1889 in image_id=40\n",
      "W20260111 03:54:52.341707 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6223 in image_id=29 and point2D_idx=2407 in image_id=40\n",
      "W20260111 03:54:52.341741 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3560 in image_id=29 and point2D_idx=533 in image_id=40\n",
      "W20260111 03:54:52.341811 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5516 in image_id=29 and point2D_idx=2601 in image_id=40\n",
      "W20260111 03:54:52.341845 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4983 in image_id=29 and point2D_idx=2334 in image_id=40\n",
      "W20260111 03:54:52.341901 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3602 in image_id=29 and point2D_idx=7896 in image_id=42\n",
      "W20260111 03:54:52.341923 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4031 in image_id=29 and point2D_idx=8347 in image_id=42\n",
      "W20260111 03:54:52.341950 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1034 in image_id=29 and point2D_idx=4657 in image_id=42\n",
      "W20260111 03:54:52.341971 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2898 in image_id=29 and point2D_idx=5696 in image_id=42\n",
      "W20260111 03:54:52.341992 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3059 in image_id=29 and point2D_idx=5702 in image_id=42\n",
      "W20260111 03:54:52.342016 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7592 in image_id=29 and point2D_idx=7740 in image_id=43\n",
      "W20260111 03:54:52.342031 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7654 in image_id=29 and point2D_idx=7741 in image_id=43\n",
      "W20260111 03:54:52.342097 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3591 in image_id=29 and point2D_idx=5914 in image_id=48\n",
      "W20260111 03:54:52.342116 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1654 in image_id=29 and point2D_idx=4542 in image_id=48\n",
      "W20260111 03:54:52.342143 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4132 in image_id=29 and point2D_idx=6067 in image_id=48\n",
      "W20260111 03:54:52.342190 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1590 in image_id=29 and point2D_idx=3789 in image_id=48\n",
      "W20260111 03:54:52.342235 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=143 in image_id=29 and point2D_idx=2264 in image_id=48\n",
      "W20260111 03:54:52.342277 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5563 in image_id=29 and point2D_idx=5929 in image_id=48\n",
      "W20260111 03:54:52.342315 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5349 in image_id=30 and point2D_idx=1816 in image_id=31\n",
      "W20260111 03:54:52.342371 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6047 in image_id=30 and point2D_idx=2635 in image_id=31\n",
      "W20260111 03:54:52.342438 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3665 in image_id=30 and point2D_idx=979 in image_id=31\n",
      "W20260111 03:54:52.342479 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3268 in image_id=30 and point2D_idx=840 in image_id=31\n",
      "W20260111 03:54:52.342495 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5362 in image_id=30 and point2D_idx=2651 in image_id=31\n",
      "W20260111 03:54:52.342529 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3399 in image_id=30 and point2D_idx=1041 in image_id=31\n",
      "W20260111 03:54:52.342549 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4653 in image_id=30 and point2D_idx=2162 in image_id=31\n",
      "W20260111 03:54:52.342627 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5072 in image_id=30 and point2D_idx=2876 in image_id=31\n",
      "W20260111 03:54:52.342675 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4927 in image_id=30 and point2D_idx=2974 in image_id=31\n",
      "W20260111 03:54:52.342711 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3280 in image_id=30 and point2D_idx=1404 in image_id=31\n",
      "W20260111 03:54:52.342771 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5373 in image_id=30 and point2D_idx=3324 in image_id=31\n",
      "W20260111 03:54:52.342806 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4938 in image_id=30 and point2D_idx=3153 in image_id=31\n",
      "W20260111 03:54:52.342847 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4528 in image_id=30 and point2D_idx=6830 in image_id=35\n",
      "W20260111 03:54:52.342913 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5656 in image_id=30 and point2D_idx=7323 in image_id=35\n",
      "W20260111 03:54:52.343033 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5117 in image_id=30 and point2D_idx=5535 in image_id=39\n",
      "W20260111 03:54:52.343051 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5864 in image_id=30 and point2D_idx=6209 in image_id=39\n",
      "W20260111 03:54:52.343063 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6096 in image_id=30 and point2D_idx=6703 in image_id=39\n",
      "W20260111 03:54:52.343182 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3270 in image_id=30 and point2D_idx=1463 in image_id=40\n",
      "W20260111 03:54:52.343223 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5367 in image_id=30 and point2D_idx=3510 in image_id=40\n",
      "W20260111 03:54:52.343249 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4522 in image_id=30 and point2D_idx=2624 in image_id=40\n",
      "W20260111 03:54:52.343265 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4658 in image_id=30 and point2D_idx=2728 in image_id=40\n",
      "W20260111 03:54:52.343287 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4659 in image_id=30 and point2D_idx=2697 in image_id=40\n",
      "W20260111 03:54:52.343328 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3818 in image_id=30 and point2D_idx=1920 in image_id=40\n",
      "W20260111 03:54:52.343349 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3819 in image_id=30 and point2D_idx=1921 in image_id=40\n",
      "W20260111 03:54:52.343438 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5965 in image_id=30 and point2D_idx=7520 in image_id=45\n",
      "W20260111 03:54:52.343484 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5983 in image_id=30 and point2D_idx=7521 in image_id=45\n",
      "W20260111 03:54:52.343538 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5839 in image_id=30 and point2D_idx=7313 in image_id=45\n",
      "W20260111 03:54:52.343638 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3421 in image_id=30 and point2D_idx=4584 in image_id=45\n",
      "W20260111 03:54:52.343712 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4949 in image_id=30 and point2D_idx=6268 in image_id=45\n",
      "W20260111 03:54:52.343733 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5095 in image_id=30 and point2D_idx=6414 in image_id=45\n",
      "W20260111 03:54:52.343748 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5263 in image_id=30 and point2D_idx=6555 in image_id=45\n",
      "W20260111 03:54:52.343814 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2542 in image_id=30 and point2D_idx=3061 in image_id=45\n",
      "W20260111 03:54:52.343832 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2637 in image_id=30 and point2D_idx=3237 in image_id=45\n",
      "W20260111 03:54:52.343891 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1405 in image_id=30 and point2D_idx=1397 in image_id=45\n",
      "W20260111 03:54:52.343923 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2039 in image_id=30 and point2D_idx=2198 in image_id=45\n",
      "W20260111 03:54:52.343963 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2041 in image_id=30 and point2D_idx=2200 in image_id=45\n",
      "W20260111 03:54:52.343996 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2129 in image_id=30 and point2D_idx=2338 in image_id=45\n",
      "W20260111 03:54:52.344054 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=980 in image_id=30 and point2D_idx=906 in image_id=45\n",
      "W20260111 03:54:52.344082 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2307 in image_id=30 and point2D_idx=2608 in image_id=45\n",
      "W20260111 03:54:52.344112 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2404 in image_id=30 and point2D_idx=2751 in image_id=45\n",
      "W20260111 03:54:52.344149 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3173 in image_id=30 and point2D_idx=4113 in image_id=45\n",
      "W20260111 03:54:52.344221 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2630 in image_id=30 and point2D_idx=3073 in image_id=45\n",
      "W20260111 03:54:52.344260 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1088 in image_id=30 and point2D_idx=1016 in image_id=45\n",
      "W20260111 03:54:52.344280 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3454 in image_id=30 and point2D_idx=4280 in image_id=45\n",
      "W20260111 03:54:52.344311 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6259 in image_id=30 and point2D_idx=4642 in image_id=49\n",
      "W20260111 03:54:52.344369 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2237 in image_id=30 and point2D_idx=1547 in image_id=50\n",
      "W20260111 03:54:52.344410 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2767 in image_id=30 and point2D_idx=1415 in image_id=50\n",
      "W20260111 03:54:52.344433 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2538 in image_id=30 and point2D_idx=1353 in image_id=50\n",
      "W20260111 03:54:52.344473 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3275 in image_id=31 and point2D_idx=1476 in image_id=32\n",
      "W20260111 03:54:52.344510 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2643 in image_id=31 and point2D_idx=2081 in image_id=32\n",
      "W20260111 03:54:52.344585 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1843 in image_id=31 and point2D_idx=3228 in image_id=32\n",
      "W20260111 03:54:52.344623 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2048 in image_id=31 and point2D_idx=3942 in image_id=32\n",
      "W20260111 03:54:52.344652 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2655 in image_id=31 and point2D_idx=4428 in image_id=32\n",
      "W20260111 03:54:52.344779 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2033 in image_id=31 and point2D_idx=3365 in image_id=40\n",
      "W20260111 03:54:52.344813 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2271 in image_id=31 and point2D_idx=3497 in image_id=40\n",
      "W20260111 03:54:52.344834 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1938 in image_id=31 and point2D_idx=3104 in image_id=40\n",
      "W20260111 03:54:52.344868 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=742 in image_id=31 and point2D_idx=1458 in image_id=40\n",
      "W20260111 03:54:52.344953 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=254 in image_id=31 and point2D_idx=125 in image_id=41\n",
      "W20260111 03:54:52.344972 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=725 in image_id=31 and point2D_idx=641 in image_id=41\n",
      "W20260111 03:54:52.344985 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2406 in image_id=31 and point2D_idx=3019 in image_id=41\n",
      "W20260111 03:54:52.345095 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=186 in image_id=31 and point2D_idx=126 in image_id=41\n",
      "W20260111 03:54:52.345114 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1746 in image_id=31 and point2D_idx=2805 in image_id=41\n",
      "W20260111 03:54:52.345171 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=373 in image_id=31 and point2D_idx=356 in image_id=41\n",
      "W20260111 03:54:52.345210 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3070 in image_id=31 and point2D_idx=3976 in image_id=41\n",
      "W20260111 03:54:52.345237 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=905 in image_id=31 and point2D_idx=1726 in image_id=41\n",
      "W20260111 03:54:52.345258 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2902 in image_id=31 and point2D_idx=3899 in image_id=41\n",
      "W20260111 03:54:52.345281 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1414 in image_id=31 and point2D_idx=2703 in image_id=41\n",
      "W20260111 03:54:52.345300 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1775 in image_id=31 and point2D_idx=3146 in image_id=41\n",
      "W20260111 03:54:52.345404 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4667 in image_id=32 and point2D_idx=9259 in image_id=33\n",
      "W20260111 03:54:52.345430 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5042 in image_id=32 and point2D_idx=9879 in image_id=33\n",
      "W20260111 03:54:52.345476 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4759 in image_id=32 and point2D_idx=2370 in image_id=38\n",
      "W20260111 03:54:52.345581 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=885 in image_id=32 and point2D_idx=2661 in image_id=41\n",
      "W20260111 03:54:52.345617 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1044 in image_id=32 and point2D_idx=2774 in image_id=41\n",
      "W20260111 03:54:52.345648 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=943 in image_id=32 and point2D_idx=2395 in image_id=41\n",
      "W20260111 03:54:52.345689 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=514 in image_id=32 and point2D_idx=1527 in image_id=41\n",
      "W20260111 03:54:52.345716 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=923 in image_id=32 and point2D_idx=2111 in image_id=41\n",
      "W20260111 03:54:52.345762 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=182 in image_id=32 and point2D_idx=987 in image_id=41\n",
      "W20260111 03:54:52.345796 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=587 in image_id=32 and point2D_idx=1251 in image_id=41\n",
      "W20260111 03:54:52.345843 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1338 in image_id=32 and point2D_idx=2118 in image_id=41\n",
      "W20260111 03:54:52.345873 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1308 in image_id=32 and point2D_idx=1974 in image_id=41\n",
      "W20260111 03:54:52.345931 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=590 in image_id=32 and point2D_idx=891 in image_id=41\n",
      "W20260111 03:54:52.345960 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1431 in image_id=32 and point2D_idx=1831 in image_id=41\n",
      "W20260111 03:54:52.345997 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2092 in image_id=32 and point2D_idx=2124 in image_id=41\n",
      "W20260111 03:54:52.346014 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=428 in image_id=32 and point2D_idx=707 in image_id=41\n",
      "W20260111 03:54:52.346026 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3099 in image_id=32 and point2D_idx=2911 in image_id=41\n",
      "W20260111 03:54:52.346039 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=94 in image_id=32 and point2D_idx=539 in image_id=41\n",
      "W20260111 03:54:52.346064 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1703 in image_id=32 and point2D_idx=1688 in image_id=41\n",
      "W20260111 03:54:52.346109 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2349 in image_id=32 and point2D_idx=1983 in image_id=41\n",
      "W20260111 03:54:52.346129 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=945 in image_id=32 and point2D_idx=896 in image_id=41\n",
      "W20260111 03:54:52.346171 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2931 in image_id=32 and point2D_idx=1323 in image_id=42\n",
      "W20260111 03:54:52.346189 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3596 in image_id=32 and point2D_idx=2495 in image_id=42\n",
      "W20260111 03:54:52.346239 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3040 in image_id=32 and point2D_idx=1726 in image_id=42\n",
      "W20260111 03:54:52.346284 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3504 in image_id=32 and point2D_idx=2654 in image_id=42\n",
      "W20260111 03:54:52.346325 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1760 in image_id=32 and point2D_idx=183 in image_id=42\n",
      "W20260111 03:54:52.346379 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3090 in image_id=32 and point2D_idx=2263 in image_id=42\n",
      "W20260111 03:54:52.346426 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3612 in image_id=32 and point2D_idx=4243 in image_id=42\n",
      "W20260111 03:54:52.346490 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2071 in image_id=32 and point2D_idx=586 in image_id=42\n",
      "W20260111 03:54:52.346598 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3186 in image_id=32 and point2D_idx=7294 in image_id=42\n",
      "W20260111 03:54:52.346638 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1665 in image_id=32 and point2D_idx=985 in image_id=42\n",
      "W20260111 03:54:52.346653 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4276 in image_id=32 and point2D_idx=10666 in image_id=42\n",
      "W20260111 03:54:52.346738 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2577 in image_id=32 and point2D_idx=6782 in image_id=42\n",
      "W20260111 03:54:52.346903 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5054 in image_id=32 and point2D_idx=13870 in image_id=42\n",
      "W20260111 03:54:52.346932 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4557 in image_id=32 and point2D_idx=13874 in image_id=42\n",
      "W20260111 03:54:52.346954 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3367 in image_id=32 and point2D_idx=13732 in image_id=42\n",
      "W20260111 03:54:52.346994 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2582 in image_id=33 and point2D_idx=216 in image_id=34\n",
      "W20260111 03:54:52.347015 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2282 in image_id=33 and point2D_idx=76 in image_id=34\n",
      "W20260111 03:54:52.347030 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2586 in image_id=33 and point2D_idx=324 in image_id=34\n",
      "W20260111 03:54:52.347062 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3073 in image_id=33 and point2D_idx=1142 in image_id=34\n",
      "W20260111 03:54:52.347114 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9235 in image_id=33 and point2D_idx=13905 in image_id=34\n",
      "W20260111 03:54:52.347177 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5569 in image_id=33 and point2D_idx=3248 in image_id=37\n",
      "W20260111 03:54:52.347192 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3670 in image_id=33 and point2D_idx=2639 in image_id=37\n",
      "W20260111 03:54:52.347211 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2012 in image_id=33 and point2D_idx=2338 in image_id=37\n",
      "W20260111 03:54:52.347258 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3673 in image_id=33 and point2D_idx=2305 in image_id=37\n",
      "W20260111 03:54:52.347283 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2669 in image_id=33 and point2D_idx=2068 in image_id=37\n",
      "W20260111 03:54:52.347300 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4815 in image_id=33 and point2D_idx=2419 in image_id=37\n",
      "W20260111 03:54:52.347318 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4816 in image_id=33 and point2D_idx=2421 in image_id=37\n",
      "W20260111 03:54:52.347347 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2926 in image_id=33 and point2D_idx=1969 in image_id=37\n",
      "W20260111 03:54:52.347382 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1488 in image_id=33 and point2D_idx=1547 in image_id=37\n",
      "W20260111 03:54:52.347406 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4283 in image_id=33 and point2D_idx=1972 in image_id=37\n",
      "W20260111 03:54:52.347427 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2626 in image_id=33 and point2D_idx=1651 in image_id=37\n",
      "W20260111 03:54:52.347453 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2627 in image_id=33 and point2D_idx=1653 in image_id=37\n",
      "W20260111 03:54:52.347506 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5796 in image_id=33 and point2D_idx=1979 in image_id=37\n",
      "W20260111 03:54:52.347578 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2568 in image_id=33 and point2D_idx=1794 in image_id=38\n",
      "W20260111 03:54:52.347614 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8862 in image_id=33 and point2D_idx=6118 in image_id=38\n",
      "W20260111 03:54:52.347627 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9198 in image_id=33 and point2D_idx=6350 in image_id=38\n",
      "W20260111 03:54:52.347653 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8120 in image_id=33 and point2D_idx=5672 in image_id=38\n",
      "W20260111 03:54:52.347675 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5028 in image_id=33 and point2D_idx=3559 in image_id=38\n",
      "W20260111 03:54:52.347875 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3584 in image_id=33 and point2D_idx=2620 in image_id=38\n",
      "W20260111 03:54:52.348008 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=645 in image_id=33 and point2D_idx=309 in image_id=38\n",
      "W20260111 03:54:52.348025 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=826 in image_id=33 and point2D_idx=386 in image_id=38\n",
      "W20260111 03:54:52.348110 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6586 in image_id=33 and point2D_idx=4895 in image_id=38\n",
      "W20260111 03:54:52.348195 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=841 in image_id=33 and point2D_idx=538 in image_id=38\n",
      "W20260111 03:54:52.348221 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=295 in image_id=33 and point2D_idx=55 in image_id=38\n",
      "W20260111 03:54:52.348274 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=668 in image_id=33 and point2D_idx=408 in image_id=38\n",
      "W20260111 03:54:52.348329 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10336 in image_id=33 and point2D_idx=4319 in image_id=41\n",
      "W20260111 03:54:52.348375 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1511 in image_id=33 and point2D_idx=4100 in image_id=42\n",
      "W20260111 03:54:52.348409 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9048 in image_id=33 and point2D_idx=14026 in image_id=42\n",
      "W20260111 03:54:52.348422 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9846 in image_id=33 and point2D_idx=14194 in image_id=42\n",
      "W20260111 03:54:52.348454 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9781 in image_id=33 and point2D_idx=14180 in image_id=42\n",
      "W20260111 03:54:52.348476 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=414 in image_id=33 and point2D_idx=2234 in image_id=42\n",
      "W20260111 03:54:52.348538 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9053 in image_id=33 and point2D_idx=13958 in image_id=42\n",
      "W20260111 03:54:52.348617 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=234 in image_id=33 and point2D_idx=1577 in image_id=42\n",
      "W20260111 03:54:52.348664 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2280 in image_id=33 and point2D_idx=4642 in image_id=42\n",
      "W20260111 03:54:52.348698 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7212 in image_id=33 and point2D_idx=11381 in image_id=42\n",
      "W20260111 03:54:52.348740 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1067 in image_id=33 and point2D_idx=2651 in image_id=42\n",
      "W20260111 03:54:52.348775 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9 in image_id=33 and point2D_idx=763 in image_id=42\n",
      "W20260111 03:54:52.348817 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7944 in image_id=33 and point2D_idx=12072 in image_id=42\n",
      "W20260111 03:54:52.348872 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8748 in image_id=33 and point2D_idx=12695 in image_id=42\n",
      "W20260111 03:54:52.348904 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9420 in image_id=33 and point2D_idx=13519 in image_id=42\n",
      "W20260111 03:54:52.348984 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7768 in image_id=33 and point2D_idx=11036 in image_id=42\n",
      "W20260111 03:54:52.349011 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5533 in image_id=33 and point2D_idx=7265 in image_id=42\n",
      "W20260111 03:54:52.349052 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6792 in image_id=33 and point2D_idx=9383 in image_id=42\n",
      "W20260111 03:54:52.349116 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4559 in image_id=33 and point2D_idx=5720 in image_id=42\n",
      "W20260111 03:54:52.349152 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=293 in image_id=33 and point2D_idx=1114 in image_id=42\n",
      "W20260111 03:54:52.349269 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1761 in image_id=33 and point2D_idx=2285 in image_id=42\n",
      "W20260111 03:54:52.349342 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6628 in image_id=33 and point2D_idx=7837 in image_id=42\n",
      "W20260111 03:54:52.349399 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8173 in image_id=33 and point2D_idx=10288 in image_id=42\n",
      "W20260111 03:54:52.349516 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5087 in image_id=33 and point2D_idx=4705 in image_id=42\n",
      "W20260111 03:54:52.349669 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8512 in image_id=33 and point2D_idx=7209 in image_id=48\n",
      "W20260111 03:54:52.349693 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8693 in image_id=33 and point2D_idx=7273 in image_id=48\n",
      "W20260111 03:54:52.349736 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8873 in image_id=33 and point2D_idx=7339 in image_id=48\n",
      "W20260111 03:54:52.349809 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7751 in image_id=33 and point2D_idx=6782 in image_id=48\n",
      "W20260111 03:54:52.349875 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5737 in image_id=33 and point2D_idx=5381 in image_id=48\n",
      "W20260111 03:54:52.349941 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=694 in image_id=33 and point2D_idx=622 in image_id=48\n",
      "W20260111 03:54:52.350027 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4817 in image_id=33 and point2D_idx=3830 in image_id=48\n",
      "W20260111 03:54:52.350144 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3235 in image_id=33 and point2D_idx=745 in image_id=51\n",
      "W20260111 03:54:52.350221 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8332 in image_id=33 and point2D_idx=9447 in image_id=51\n",
      "W20260111 03:54:52.350326 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12837 in image_id=34 and point2D_idx=11810 in image_id=35\n",
      "W20260111 03:54:52.350425 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10086 in image_id=34 and point2D_idx=8933 in image_id=35\n",
      "W20260111 03:54:52.350476 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7797 in image_id=34 and point2D_idx=6855 in image_id=35\n",
      "W20260111 03:54:52.350555 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10366 in image_id=34 and point2D_idx=9326 in image_id=35\n",
      "W20260111 03:54:52.350616 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9573 in image_id=34 and point2D_idx=8726 in image_id=35\n",
      "W20260111 03:54:52.350657 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1677 in image_id=34 and point2D_idx=634 in image_id=35\n",
      "W20260111 03:54:52.350735 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5943 in image_id=34 and point2D_idx=5154 in image_id=35\n",
      "W20260111 03:54:52.350820 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1688 in image_id=34 and point2D_idx=595 in image_id=35\n",
      "W20260111 03:54:52.350845 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6250 in image_id=34 and point2D_idx=5398 in image_id=35\n",
      "W20260111 03:54:52.350859 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7183 in image_id=34 and point2D_idx=6386 in image_id=35\n",
      "W20260111 03:54:52.350870 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8118 in image_id=34 and point2D_idx=7361 in image_id=35\n",
      "W20260111 03:54:52.350886 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10647 in image_id=34 and point2D_idx=9883 in image_id=35\n",
      "W20260111 03:54:52.350932 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5949 in image_id=34 and point2D_idx=5158 in image_id=35\n",
      "W20260111 03:54:52.350949 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8425 in image_id=34 and point2D_idx=7842 in image_id=35\n",
      "W20260111 03:54:52.351121 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12002 in image_id=34 and point2D_idx=11161 in image_id=35\n",
      "W20260111 03:54:52.351166 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3554 in image_id=34 and point2D_idx=2499 in image_id=35\n",
      "W20260111 03:54:52.351304 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3001 in image_id=34 and point2D_idx=1897 in image_id=35\n",
      "W20260111 03:54:52.351325 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3558 in image_id=34 and point2D_idx=2574 in image_id=35\n",
      "W20260111 03:54:52.351338 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4149 in image_id=34 and point2D_idx=3261 in image_id=35\n",
      "W20260111 03:54:52.351379 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12457 in image_id=34 and point2D_idx=11849 in image_id=35\n",
      "W20260111 03:54:52.351447 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7157 in image_id=34 and point2D_idx=3772 in image_id=36\n",
      "W20260111 03:54:52.351463 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7787 in image_id=34 and point2D_idx=4279 in image_id=36\n",
      "W20260111 03:54:52.351498 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2722 in image_id=34 and point2D_idx=3788 in image_id=37\n",
      "W20260111 03:54:52.351580 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2512 in image_id=34 and point2D_idx=3074 in image_id=37\n",
      "W20260111 03:54:52.351614 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=209 in image_id=34 and point2D_idx=2515 in image_id=37\n",
      "W20260111 03:54:52.351642 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6584 in image_id=34 and point2D_idx=3395 in image_id=37\n",
      "W20260111 03:54:52.351697 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5693 in image_id=34 and point2D_idx=2908 in image_id=37\n",
      "W20260111 03:54:52.351724 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6320 in image_id=34 and point2D_idx=2937 in image_id=37\n",
      "W20260111 03:54:52.351759 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2460 in image_id=34 and point2D_idx=2616 in image_id=38\n",
      "W20260111 03:54:52.351797 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2475 in image_id=34 and point2D_idx=2387 in image_id=38\n",
      "W20260111 03:54:52.351820 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3858 in image_id=34 and point2D_idx=3051 in image_id=38\n",
      "W20260111 03:54:52.351842 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4462 in image_id=34 and point2D_idx=3249 in image_id=38\n",
      "W20260111 03:54:52.351866 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5069 in image_id=34 and point2D_idx=3420 in image_id=38\n",
      "W20260111 03:54:52.351894 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8139 in image_id=34 and point2D_idx=4507 in image_id=38\n",
      "W20260111 03:54:52.351915 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=59 in image_id=34 and point2D_idx=689 in image_id=38\n",
      "W20260111 03:54:52.351972 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=314 in image_id=34 and point2D_idx=968 in image_id=38\n",
      "W20260111 03:54:52.352021 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9 in image_id=34 and point2D_idx=392 in image_id=38\n",
      "W20260111 03:54:52.352059 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=330 in image_id=34 and point2D_idx=807 in image_id=38\n",
      "W20260111 03:54:52.352118 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10346 in image_id=34 and point2D_idx=4130 in image_id=40\n",
      "W20260111 03:54:52.352171 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1097 in image_id=34 and point2D_idx=5673 in image_id=42\n",
      "W20260111 03:54:52.352242 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7190 in image_id=34 and point2D_idx=9808 in image_id=42\n",
      "W20260111 03:54:52.352342 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8491 in image_id=34 and point2D_idx=8370 in image_id=42\n",
      "W20260111 03:54:52.352380 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7908 in image_id=34 and point2D_idx=7267 in image_id=42\n",
      "W20260111 03:54:52.352440 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10417 in image_id=34 and point2D_idx=8380 in image_id=42\n",
      "W20260111 03:54:52.352486 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7155 in image_id=34 and point2D_idx=4608 in image_id=43\n",
      "W20260111 03:54:52.352527 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7158 in image_id=34 and point2D_idx=4611 in image_id=43\n",
      "W20260111 03:54:52.352578 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7875 in image_id=34 and point2D_idx=5048 in image_id=43\n",
      "W20260111 03:54:52.352659 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10364 in image_id=34 and point2D_idx=6538 in image_id=43\n",
      "W20260111 03:54:52.352687 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10092 in image_id=34 and point2D_idx=6355 in image_id=43\n",
      "W20260111 03:54:52.352706 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10636 in image_id=34 and point2D_idx=6738 in image_id=43\n",
      "W20260111 03:54:52.352760 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12219 in image_id=34 and point2D_idx=7778 in image_id=43\n",
      "W20260111 03:54:52.352853 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=295 in image_id=34 and point2D_idx=347 in image_id=43\n",
      "W20260111 03:54:52.352885 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=296 in image_id=34 and point2D_idx=256 in image_id=43\n",
      "W20260111 03:54:52.353155 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5016 in image_id=34 and point2D_idx=3452 in image_id=43\n",
      "W20260111 03:54:52.353185 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7853 in image_id=34 and point2D_idx=5093 in image_id=43\n",
      "W20260111 03:54:52.353197 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=678 in image_id=34 and point2D_idx=623 in image_id=43\n",
      "W20260111 03:54:52.353263 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7907 in image_id=34 and point2D_idx=5099 in image_id=43\n",
      "W20260111 03:54:52.353347 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=74 in image_id=34 and point2D_idx=165 in image_id=43\n",
      "W20260111 03:54:52.353477 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13378 in image_id=34 and point2D_idx=8270 in image_id=43\n",
      "W20260111 03:54:52.353500 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11189 in image_id=34 and point2D_idx=6973 in image_id=43\n",
      "W20260111 03:54:52.353590 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8403 in image_id=34 and point2D_idx=3849 in image_id=44\n",
      "W20260111 03:54:52.353652 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10359 in image_id=34 and point2D_idx=4874 in image_id=44\n",
      "W20260111 03:54:52.353731 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2981 in image_id=34 and point2D_idx=1228 in image_id=44\n",
      "W20260111 03:54:52.353754 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5939 in image_id=34 and point2D_idx=2920 in image_id=44\n",
      "W20260111 03:54:52.353848 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5359 in image_id=34 and point2D_idx=2712 in image_id=44\n",
      "W20260111 03:54:52.353870 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9063 in image_id=34 and point2D_idx=4528 in image_id=44\n",
      "W20260111 03:54:52.353892 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6260 in image_id=34 and point2D_idx=3167 in image_id=44\n",
      "W20260111 03:54:52.353920 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4452 in image_id=34 and point2D_idx=2205 in image_id=44\n",
      "W20260111 03:54:52.353938 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4743 in image_id=34 and point2D_idx=2347 in image_id=44\n",
      "W20260111 03:54:52.354019 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5965 in image_id=34 and point2D_idx=3200 in image_id=44\n",
      "W20260111 03:54:52.354052 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=632 in image_id=34 and point2D_idx=172 in image_id=44\n",
      "W20260111 03:54:52.354089 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9308 in image_id=34 and point2D_idx=4775 in image_id=44\n",
      "W20260111 03:54:52.354145 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=638 in image_id=34 and point2D_idx=178 in image_id=44\n",
      "W20260111 03:54:52.354182 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=852 in image_id=34 and point2D_idx=240 in image_id=44\n",
      "W20260111 03:54:52.354247 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1722 in image_id=34 and point2D_idx=677 in image_id=44\n",
      "W20260111 03:54:52.354287 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6894 in image_id=34 and point2D_idx=3760 in image_id=44\n",
      "W20260111 03:54:52.354343 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2951 in image_id=34 and point2D_idx=4554 in image_id=48\n",
      "W20260111 03:54:52.354383 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3292 in image_id=34 and point2D_idx=4183 in image_id=48\n",
      "W20260111 03:54:52.354444 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5972 in image_id=34 and point2D_idx=5147 in image_id=48\n",
      "W20260111 03:54:52.354493 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=471 in image_id=34 and point2D_idx=1681 in image_id=48\n",
      "W20260111 03:54:52.354523 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1169 in image_id=34 and point2D_idx=1993 in image_id=48\n",
      "W20260111 03:54:52.354541 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7747 in image_id=34 and point2D_idx=5384 in image_id=48\n",
      "W20260111 03:54:52.354589 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=326 in image_id=34 and point2D_idx=1421 in image_id=48\n",
      "W20260111 03:54:52.354692 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4728 in image_id=34 and point2D_idx=2322 in image_id=49\n",
      "W20260111 03:54:52.354773 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4732 in image_id=34 and point2D_idx=2366 in image_id=49\n",
      "W20260111 03:54:52.354806 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12854 in image_id=34 and point2D_idx=6155 in image_id=49\n",
      "W20260111 03:54:52.354864 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13034 in image_id=34 and point2D_idx=6214 in image_id=49\n",
      "W20260111 03:54:52.354996 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8432 in image_id=34 and point2D_idx=4235 in image_id=49\n",
      "W20260111 03:54:52.355015 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8718 in image_id=34 and point2D_idx=4409 in image_id=49\n",
      "W20260111 03:54:52.355037 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3289 in image_id=34 and point2D_idx=1607 in image_id=49\n",
      "W20260111 03:54:52.355098 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1710 in image_id=34 and point2D_idx=715 in image_id=49\n",
      "W20260111 03:54:52.355229 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13685 in image_id=34 and point2D_idx=9817 in image_id=51\n",
      "W20260111 03:54:52.355260 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13599 in image_id=34 and point2D_idx=9820 in image_id=51\n",
      "W20260111 03:54:52.355290 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13027 in image_id=34 and point2D_idx=9412 in image_id=51\n",
      "W20260111 03:54:52.355320 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13330 in image_id=34 and point2D_idx=9618 in image_id=51\n",
      "W20260111 03:54:52.355383 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13572 in image_id=34 and point2D_idx=9834 in image_id=51\n",
      "W20260111 03:54:52.355403 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=616 in image_id=34 and point2D_idx=403 in image_id=51\n",
      "W20260111 03:54:52.355477 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5369 in image_id=34 and point2D_idx=3288 in image_id=51\n",
      "W20260111 03:54:52.355532 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=890 in image_id=34 and point2D_idx=407 in image_id=51\n",
      "W20260111 03:54:52.355607 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11150 in image_id=34 and point2D_idx=7847 in image_id=51\n",
      "W20260111 03:54:52.355641 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5077 in image_id=34 and point2D_idx=2999 in image_id=51\n",
      "W20260111 03:54:52.355702 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4205 in image_id=34 and point2D_idx=2430 in image_id=51\n",
      "W20260111 03:54:52.355727 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10711 in image_id=34 and point2D_idx=6975 in image_id=51\n",
      "W20260111 03:54:52.355741 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11803 in image_id=34 and point2D_idx=8209 in image_id=51\n",
      "W20260111 03:54:52.355799 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2504 in image_id=35 and point2D_idx=620 in image_id=36\n",
      "W20260111 03:54:52.355913 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3203 in image_id=35 and point2D_idx=1048 in image_id=36\n",
      "W20260111 03:54:52.355982 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5116 in image_id=35 and point2D_idx=2855 in image_id=36\n",
      "W20260111 03:54:52.356023 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6604 in image_id=35 and point2D_idx=4137 in image_id=36\n",
      "W20260111 03:54:52.356063 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2284 in image_id=35 and point2D_idx=437 in image_id=36\n",
      "W20260111 03:54:52.356148 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5857 in image_id=35 and point2D_idx=3640 in image_id=36\n",
      "W20260111 03:54:52.356405 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=623 in image_id=35 and point2D_idx=616 in image_id=39\n",
      "W20260111 03:54:52.356442 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1398 in image_id=35 and point2D_idx=1431 in image_id=39\n",
      "W20260111 03:54:52.356454 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1674 in image_id=35 and point2D_idx=1658 in image_id=39\n",
      "W20260111 03:54:52.356677 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3013 in image_id=35 and point2D_idx=2729 in image_id=39\n",
      "W20260111 03:54:52.356720 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3053 in image_id=35 and point2D_idx=2730 in image_id=39\n",
      "W20260111 03:54:52.356734 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3276 in image_id=35 and point2D_idx=2899 in image_id=39\n",
      "W20260111 03:54:52.356767 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3054 in image_id=35 and point2D_idx=2731 in image_id=39\n",
      "W20260111 03:54:52.356782 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3277 in image_id=35 and point2D_idx=2900 in image_id=39\n",
      "W20260111 03:54:52.356801 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3949 in image_id=35 and point2D_idx=3421 in image_id=39\n",
      "W20260111 03:54:52.356837 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3745 in image_id=35 and point2D_idx=3254 in image_id=39\n",
      "W20260111 03:54:52.356850 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4422 in image_id=35 and point2D_idx=3760 in image_id=39\n",
      "W20260111 03:54:52.356886 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10549 in image_id=35 and point2D_idx=7652 in image_id=39\n",
      "W20260111 03:54:52.356956 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10551 in image_id=35 and point2D_idx=7654 in image_id=39\n",
      "W20260111 03:54:52.357001 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10711 in image_id=35 and point2D_idx=7682 in image_id=39\n",
      "W20260111 03:54:52.357066 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10735 in image_id=35 and point2D_idx=7684 in image_id=39\n",
      "W20260111 03:54:52.357139 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10264 in image_id=35 and point2D_idx=7565 in image_id=39\n",
      "W20260111 03:54:52.357160 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10400 in image_id=35 and point2D_idx=7623 in image_id=39\n",
      "W20260111 03:54:52.357341 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7789 in image_id=35 and point2D_idx=3381 in image_id=40\n",
      "W20260111 03:54:52.357361 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1386 in image_id=35 and point2D_idx=8320 in image_id=42\n",
      "W20260111 03:54:52.357383 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2524 in image_id=35 and point2D_idx=8832 in image_id=42\n",
      "W20260111 03:54:52.357428 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3227 in image_id=35 and point2D_idx=8332 in image_id=42\n",
      "W20260111 03:54:52.357464 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4697 in image_id=35 and point2D_idx=9350 in image_id=42\n",
      "W20260111 03:54:52.357497 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4413 in image_id=35 and point2D_idx=8342 in image_id=42\n",
      "W20260111 03:54:52.357533 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=480 in image_id=35 and point2D_idx=4650 in image_id=42\n",
      "W20260111 03:54:52.357599 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=213 in image_id=35 and point2D_idx=3641 in image_id=42\n",
      "W20260111 03:54:52.357623 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6151 in image_id=35 and point2D_idx=7264 in image_id=42\n",
      "W20260111 03:54:52.357782 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4148 in image_id=35 and point2D_idx=2449 in image_id=44\n",
      "W20260111 03:54:52.357824 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4390 in image_id=35 and point2D_idx=2595 in image_id=44\n",
      "W20260111 03:54:52.357864 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4629 in image_id=35 and point2D_idx=2728 in image_id=44\n",
      "W20260111 03:54:52.358007 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5137 in image_id=35 and point2D_idx=3013 in image_id=44\n",
      "W20260111 03:54:52.358023 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5377 in image_id=35 and point2D_idx=3153 in image_id=44\n",
      "W20260111 03:54:52.358051 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5622 in image_id=35 and point2D_idx=3295 in image_id=44\n",
      "W20260111 03:54:52.358184 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5873 in image_id=35 and point2D_idx=3481 in image_id=44\n",
      "W20260111 03:54:52.358200 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6373 in image_id=35 and point2D_idx=3734 in image_id=44\n",
      "W20260111 03:54:52.358228 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=319 in image_id=35 and point2D_idx=253 in image_id=44\n",
      "W20260111 03:54:52.358334 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=635 in image_id=35 and point2D_idx=415 in image_id=44\n",
      "W20260111 03:54:52.358390 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8071 in image_id=35 and point2D_idx=4647 in image_id=44\n",
      "W20260111 03:54:52.358441 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1871 in image_id=35 and point2D_idx=1242 in image_id=44\n",
      "W20260111 03:54:52.358478 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1250 in image_id=35 and point2D_idx=802 in image_id=44\n",
      "W20260111 03:54:52.358519 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8783 in image_id=35 and point2D_idx=5010 in image_id=44\n",
      "W20260111 03:54:52.358569 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8959 in image_id=35 and point2D_idx=5150 in image_id=44\n",
      "W20260111 03:54:52.358612 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9346 in image_id=35 and point2D_idx=5355 in image_id=44\n",
      "W20260111 03:54:52.358632 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9569 in image_id=35 and point2D_idx=5457 in image_id=44\n",
      "W20260111 03:54:52.358753 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4914 in image_id=35 and point2D_idx=3041 in image_id=44\n",
      "W20260111 03:54:52.358789 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4670 in image_id=35 and point2D_idx=2902 in image_id=44\n",
      "W20260111 03:54:52.358968 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1409 in image_id=35 and point2D_idx=4173 in image_id=48\n",
      "W20260111 03:54:52.358996 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=86 in image_id=35 and point2D_idx=2601 in image_id=48\n",
      "W20260111 03:54:52.359012 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10673 in image_id=35 and point2D_idx=8761 in image_id=51\n",
      "W20260111 03:54:52.359020 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10354 in image_id=35 and point2D_idx=8593 in image_id=51\n",
      "W20260111 03:54:52.359032 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10023 in image_id=35 and point2D_idx=8492 in image_id=51\n",
      "W20260111 03:54:52.359053 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10025 in image_id=35 and point2D_idx=8494 in image_id=51\n",
      "W20260111 03:54:52.359180 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9127 in image_id=35 and point2D_idx=7460 in image_id=51\n",
      "W20260111 03:54:52.359204 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5926 in image_id=35 and point2D_idx=4529 in image_id=51\n",
      "W20260111 03:54:52.359224 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2765 in image_id=35 and point2D_idx=2404 in image_id=51\n",
      "W20260111 03:54:52.359241 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1919 in image_id=35 and point2D_idx=1738 in image_id=51\n",
      "W20260111 03:54:52.359266 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1001 in image_id=35 and point2D_idx=1031 in image_id=51\n",
      "W20260111 03:54:52.359295 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9874 in image_id=35 and point2D_idx=7827 in image_id=51\n",
      "W20260111 03:54:52.359310 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4173 in image_id=35 and point2D_idx=3273 in image_id=51\n",
      "W20260111 03:54:52.359319 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4651 in image_id=35 and point2D_idx=3586 in image_id=51\n",
      "W20260111 03:54:52.359363 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1003 in image_id=35 and point2D_idx=1035 in image_id=51\n",
      "W20260111 03:54:52.359382 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6125 in image_id=35 and point2D_idx=4531 in image_id=51\n",
      "W20260111 03:54:52.359394 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7159 in image_id=35 and point2D_idx=5197 in image_id=51\n",
      "W20260111 03:54:52.359437 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10384 in image_id=35 and point2D_idx=8515 in image_id=51\n",
      "W20260111 03:54:52.359485 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1366 in image_id=35 and point2D_idx=1400 in image_id=51\n",
      "W20260111 03:54:52.359540 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7408 in image_id=35 and point2D_idx=5203 in image_id=51\n",
      "W20260111 03:54:52.359568 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6881 in image_id=35 and point2D_idx=4877 in image_id=51\n",
      "W20260111 03:54:52.359585 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8786 in image_id=35 and point2D_idx=6215 in image_id=51\n",
      "W20260111 03:54:52.359597 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9189 in image_id=35 and point2D_idx=6547 in image_id=51\n",
      "W20260111 03:54:52.359648 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8964 in image_id=35 and point2D_idx=6216 in image_id=51\n",
      "W20260111 03:54:52.359682 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9892 in image_id=35 and point2D_idx=7473 in image_id=51\n",
      "W20260111 03:54:52.359720 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7854 in image_id=35 and point2D_idx=5210 in image_id=51\n",
      "W20260111 03:54:52.359736 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8317 in image_id=35 and point2D_idx=5552 in image_id=51\n",
      "W20260111 03:54:52.359808 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5449 in image_id=36 and point2D_idx=7847 in image_id=45\n",
      "W20260111 03:54:52.359858 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3602 in image_id=36 and point2D_idx=5269 in image_id=45\n",
      "W20260111 03:54:52.359881 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5792 in image_id=36 and point2D_idx=8196 in image_id=45\n",
      "W20260111 03:54:52.360067 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4917 in image_id=36 and point2D_idx=7197 in image_id=45\n",
      "W20260111 03:54:52.360093 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5013 in image_id=36 and point2D_idx=7308 in image_id=45\n",
      "W20260111 03:54:52.360110 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5570 in image_id=36 and point2D_idx=8011 in image_id=45\n",
      "W20260111 03:54:52.360135 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6035 in image_id=36 and point2D_idx=8470 in image_id=45\n",
      "W20260111 03:54:52.360175 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6036 in image_id=36 and point2D_idx=8446 in image_id=45\n",
      "W20260111 03:54:52.360642 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1636 in image_id=36 and point2D_idx=3884 in image_id=51\n",
      "W20260111 03:54:52.360675 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1989 in image_id=36 and point2D_idx=4190 in image_id=51\n",
      "W20260111 03:54:52.360685 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3853 in image_id=36 and point2D_idx=6507 in image_id=51\n",
      "W20260111 03:54:52.360701 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=323 in image_id=36 and point2D_idx=2101 in image_id=51\n",
      "W20260111 03:54:52.360719 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5600 in image_id=36 and point2D_idx=8160 in image_id=51\n",
      "W20260111 03:54:52.360750 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4845 in image_id=36 and point2D_idx=6914 in image_id=51\n",
      "W20260111 03:54:52.360770 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5104 in image_id=36 and point2D_idx=7429 in image_id=51\n",
      "W20260111 03:54:52.360810 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4158 in image_id=36 and point2D_idx=5858 in image_id=51\n",
      "W20260111 03:54:52.360842 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1046 in image_id=36 and point2D_idx=2953 in image_id=51\n",
      "W20260111 03:54:52.360903 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1084 in image_id=36 and point2D_idx=2956 in image_id=51\n",
      "W20260111 03:54:52.361002 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4065 in image_id=37 and point2D_idx=5356 in image_id=38\n",
      "W20260111 03:54:52.361055 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4395 in image_id=37 and point2D_idx=6844 in image_id=38\n",
      "W20260111 03:54:52.361084 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4359 in image_id=37 and point2D_idx=6786 in image_id=38\n",
      "W20260111 03:54:52.361111 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3523 in image_id=37 and point2D_idx=5712 in image_id=38\n",
      "W20260111 03:54:52.361149 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3860 in image_id=37 and point2D_idx=6617 in image_id=38\n",
      "W20260111 03:54:52.361181 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1635 in image_id=37 and point2D_idx=882 in image_id=46\n",
      "W20260111 03:54:52.361203 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1278 in image_id=37 and point2D_idx=654 in image_id=46\n",
      "W20260111 03:54:52.361215 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2412 in image_id=37 and point2D_idx=1186 in image_id=46\n",
      "W20260111 03:54:52.361237 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2401 in image_id=37 and point2D_idx=1189 in image_id=46\n",
      "W20260111 03:54:52.361287 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=920 in image_id=37 and point2D_idx=500 in image_id=47\n",
      "W20260111 03:54:52.361307 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=404 in image_id=37 and point2D_idx=185 in image_id=47\n",
      "W20260111 03:54:52.361323 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=883 in image_id=37 and point2D_idx=490 in image_id=47\n",
      "W20260111 03:54:52.361362 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=780 in image_id=37 and point2D_idx=388 in image_id=47\n",
      "W20260111 03:54:52.361388 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=763 in image_id=37 and point2D_idx=360 in image_id=47\n",
      "W20260111 03:54:52.361409 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3252 in image_id=37 and point2D_idx=1430 in image_id=47\n",
      "W20260111 03:54:52.361452 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4071 in image_id=37 and point2D_idx=1511 in image_id=47\n",
      "W20260111 03:54:52.361476 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3118 in image_id=37 and point2D_idx=1230 in image_id=47\n",
      "W20260111 03:54:52.361535 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2950 in image_id=37 and point2D_idx=1071 in image_id=47\n",
      "W20260111 03:54:52.361571 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2083 in image_id=37 and point2D_idx=792 in image_id=47\n",
      "W20260111 03:54:52.361593 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3528 in image_id=37 and point2D_idx=1305 in image_id=47\n",
      "W20260111 03:54:52.361627 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3821 in image_id=37 and point2D_idx=1245 in image_id=47\n",
      "W20260111 03:54:52.361654 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3947 in image_id=37 and point2D_idx=1317 in image_id=47\n",
      "W20260111 03:54:52.361669 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3874 in image_id=37 and point2D_idx=1253 in image_id=47\n",
      "W20260111 03:54:52.361697 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1076 in image_id=37 and point2D_idx=92 in image_id=48\n",
      "W20260111 03:54:52.361720 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=121 in image_id=37 and point2D_idx=3192 in image_id=50\n",
      "W20260111 03:54:52.361746 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=178 in image_id=37 and point2D_idx=3196 in image_id=50\n",
      "W20260111 03:54:52.361790 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=298 in image_id=37 and point2D_idx=3201 in image_id=50\n",
      "W20260111 03:54:52.361818 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1254 in image_id=37 and point2D_idx=3732 in image_id=50\n",
      "W20260111 03:54:52.361839 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=676 in image_id=37 and point2D_idx=3304 in image_id=50\n",
      "W20260111 03:54:52.361865 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=251 in image_id=37 and point2D_idx=2763 in image_id=50\n",
      "W20260111 03:54:52.361890 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=167 in image_id=37 and point2D_idx=2373 in image_id=50\n",
      "W20260111 03:54:52.362008 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=252 in image_id=37 and point2D_idx=1914 in image_id=50\n",
      "W20260111 03:54:52.362036 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1039 in image_id=37 and point2D_idx=2272 in image_id=50\n",
      "W20260111 03:54:52.362083 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1690 in image_id=37 and point2D_idx=2275 in image_id=50\n",
      "W20260111 03:54:52.362121 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1677 in image_id=37 and point2D_idx=2302 in image_id=50\n",
      "W20260111 03:54:52.362190 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2149 in image_id=38 and point2D_idx=4 in image_id=39\n",
      "W20260111 03:54:52.362248 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5569 in image_id=38 and point2D_idx=7492 in image_id=39\n",
      "W20260111 03:54:52.362271 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6020 in image_id=38 and point2D_idx=7748 in image_id=39\n",
      "W20260111 03:54:52.362372 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5776 in image_id=38 and point2D_idx=13690 in image_id=42\n",
      "W20260111 03:54:52.362390 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5940 in image_id=38 and point2D_idx=13849 in image_id=42\n",
      "W20260111 03:54:52.362420 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6859 in image_id=38 and point2D_idx=14180 in image_id=42\n",
      "W20260111 03:54:52.362448 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3031 in image_id=38 and point2D_idx=7765 in image_id=42\n",
      "W20260111 03:54:52.362473 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1597 in image_id=38 and point2D_idx=5151 in image_id=42\n",
      "W20260111 03:54:52.362492 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4623 in image_id=38 and point2D_idx=10997 in image_id=42\n",
      "W20260111 03:54:52.362574 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=829 in image_id=38 and point2D_idx=3615 in image_id=42\n",
      "W20260111 03:54:52.362634 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=595 in image_id=38 and point2D_idx=3128 in image_id=42\n",
      "W20260111 03:54:52.362828 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1565 in image_id=38 and point2D_idx=3645 in image_id=42\n",
      "W20260111 03:54:52.362906 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5158 in image_id=38 and point2D_idx=9830 in image_id=42\n",
      "W20260111 03:54:52.362937 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4391 in image_id=38 and point2D_idx=7821 in image_id=42\n",
      "W20260111 03:54:52.362978 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3071 in image_id=38 and point2D_idx=5210 in image_id=42\n",
      "W20260111 03:54:52.363047 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6042 in image_id=38 and point2D_idx=11412 in image_id=42\n",
      "W20260111 03:54:52.363100 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10 in image_id=38 and point2D_idx=937 in image_id=42\n",
      "W20260111 03:54:52.363128 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1208 in image_id=38 and point2D_idx=2281 in image_id=42\n",
      "W20260111 03:54:52.363150 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=136 in image_id=38 and point2D_idx=1119 in image_id=42\n",
      "W20260111 03:54:52.363196 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3303 in image_id=38 and point2D_idx=4693 in image_id=42\n",
      "W20260111 03:54:52.363226 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=205 in image_id=38 and point2D_idx=1122 in image_id=42\n",
      "W20260111 03:54:52.363284 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4064 in image_id=38 and point2D_idx=5734 in image_id=42\n",
      "W20260111 03:54:52.363338 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3558 in image_id=38 and point2D_idx=5352 in image_id=48\n",
      "W20260111 03:54:52.363361 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3708 in image_id=38 and point2D_idx=5562 in image_id=48\n",
      "W20260111 03:54:52.363456 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2158 in image_id=38 and point2D_idx=2612 in image_id=48\n",
      "W20260111 03:54:52.363512 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5002 in image_id=38 and point2D_idx=6450 in image_id=48\n",
      "W20260111 03:54:52.363706 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3638 in image_id=38 and point2D_idx=3831 in image_id=48\n",
      "W20260111 03:54:52.363736 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3278 in image_id=38 and point2D_idx=2992 in image_id=48\n",
      "W20260111 03:54:52.363744 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4234 in image_id=38 and point2D_idx=4893 in image_id=48\n",
      "W20260111 03:54:52.363765 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2186 in image_id=38 and point2D_idx=1704 in image_id=48\n",
      "W20260111 03:54:52.363849 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2883 in image_id=38 and point2D_idx=2020 in image_id=48\n",
      "W20260111 03:54:52.363868 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1990 in image_id=38 and point2D_idx=1252 in image_id=48\n",
      "W20260111 03:54:52.363901 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1895 in image_id=39 and point2D_idx=55 in image_id=40\n",
      "W20260111 03:54:52.363928 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5369 in image_id=39 and point2D_idx=1358 in image_id=40\n",
      "W20260111 03:54:52.363967 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1897 in image_id=39 and point2D_idx=57 in image_id=40\n",
      "W20260111 03:54:52.364015 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3030 in image_id=39 and point2D_idx=301 in image_id=40\n",
      "W20260111 03:54:52.364061 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5373 in image_id=39 and point2D_idx=2637 in image_id=40\n",
      "W20260111 03:54:52.364072 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6030 in image_id=39 and point2D_idx=3516 in image_id=40\n",
      "W20260111 03:54:52.364106 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2332 in image_id=39 and point2D_idx=150 in image_id=40\n",
      "W20260111 03:54:52.364119 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3035 in image_id=39 and point2D_idx=328 in image_id=40\n",
      "W20260111 03:54:52.364133 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5035 in image_id=39 and point2D_idx=2175 in image_id=40\n",
      "W20260111 03:54:52.364175 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6033 in image_id=39 and point2D_idx=3387 in image_id=40\n",
      "W20260111 03:54:52.364216 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2336 in image_id=39 and point2D_idx=154 in image_id=40\n",
      "W20260111 03:54:52.364233 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3215 in image_id=39 and point2D_idx=380 in image_id=40\n",
      "W20260111 03:54:52.364249 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4225 in image_id=39 and point2D_idx=1381 in image_id=40\n",
      "W20260111 03:54:52.364260 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4716 in image_id=39 and point2D_idx=2058 in image_id=40\n",
      "W20260111 03:54:52.364270 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5205 in image_id=39 and point2D_idx=2745 in image_id=40\n",
      "W20260111 03:54:52.364280 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6208 in image_id=39 and point2D_idx=3989 in image_id=40\n",
      "W20260111 03:54:52.364322 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3725 in image_id=39 and point2D_idx=794 in image_id=40\n",
      "W20260111 03:54:52.364370 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=933 in image_id=39 and point2D_idx=4135 in image_id=42\n",
      "W20260111 03:54:52.364396 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=503 in image_id=39 and point2D_idx=3641 in image_id=42\n",
      "W20260111 03:54:52.364412 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2561 in image_id=39 and point2D_idx=5194 in image_id=42\n",
      "W20260111 03:54:52.364429 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=635 in image_id=39 and point2D_idx=3645 in image_id=42\n",
      "W20260111 03:54:52.364511 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7890 in image_id=39 and point2D_idx=6433 in image_id=44\n",
      "W20260111 03:54:52.364582 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3059 in image_id=39 and point2D_idx=5135 in image_id=48\n",
      "W20260111 03:54:52.364605 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2765 in image_id=39 and point2D_idx=4859 in image_id=48\n",
      "W20260111 03:54:52.364624 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2131 in image_id=39 and point2D_idx=4178 in image_id=48\n",
      "W20260111 03:54:52.364643 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2364 in image_id=39 and point2D_idx=4183 in image_id=48\n",
      "W20260111 03:54:52.364665 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=889 in image_id=39 and point2D_idx=2961 in image_id=48\n",
      "W20260111 03:54:52.364716 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1891 in image_id=39 and point2D_idx=2972 in image_id=48\n",
      "W20260111 03:54:52.364750 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1964 in image_id=39 and point2D_idx=2628 in image_id=48\n",
      "W20260111 03:54:52.364783 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=726 in image_id=39 and point2D_idx=1996 in image_id=48\n",
      "W20260111 03:54:52.364804 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1409 in image_id=39 and point2D_idx=2294 in image_id=48\n",
      "W20260111 03:54:52.364847 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7141 in image_id=39 and point2D_idx=7804 in image_id=51\n",
      "W20260111 03:54:52.364869 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1174 in image_id=39 and point2D_idx=1379 in image_id=51\n",
      "W20260111 03:54:52.364907 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6870 in image_id=39 and point2D_idx=7448 in image_id=51\n",
      "W20260111 03:54:52.364926 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5216 in image_id=39 and point2D_idx=5184 in image_id=51\n",
      "W20260111 03:54:52.364949 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=743 in image_id=39 and point2D_idx=1017 in image_id=51\n",
      "W20260111 03:54:52.365005 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3233 in image_id=39 and point2D_idx=2972 in image_id=51\n",
      "W20260111 03:54:52.365040 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3578 in image_id=39 and point2D_idx=3268 in image_id=51\n",
      "W20260111 03:54:52.365057 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4735 in image_id=39 and point2D_idx=4524 in image_id=51\n",
      "W20260111 03:54:52.365072 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6914 in image_id=39 and point2D_idx=6945 in image_id=51\n",
      "W20260111 03:54:52.365091 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7069 in image_id=39 and point2D_idx=7455 in image_id=51\n",
      "W20260111 03:54:52.365151 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1673 in image_id=40 and point2D_idx=212 in image_id=41\n",
      "W20260111 03:54:52.365216 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1793 in image_id=40 and point2D_idx=470 in image_id=41\n",
      "W20260111 03:54:52.365253 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1178 in image_id=40 and point2D_idx=276 in image_id=41\n",
      "W20260111 03:54:52.365294 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=995 in image_id=40 and point2D_idx=277 in image_id=41\n",
      "W20260111 03:54:52.365323 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1286 in image_id=40 and point2D_idx=444 in image_id=41\n",
      "W20260111 03:54:52.365337 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1796 in image_id=40 and point2D_idx=931 in image_id=41\n",
      "W20260111 03:54:52.365372 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1083 in image_id=40 and point2D_idx=445 in image_id=41\n",
      "W20260111 03:54:52.365390 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2152 in image_id=40 and point2D_idx=1694 in image_id=41\n",
      "W20260111 03:54:52.365429 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=720 in image_id=40 and point2D_idx=272 in image_id=41\n",
      "W20260111 03:54:52.365446 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=842 in image_id=40 and point2D_idx=394 in image_id=41\n",
      "W20260111 03:54:52.365463 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2970 in image_id=40 and point2D_idx=2911 in image_id=41\n",
      "W20260111 03:54:52.365508 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1463 in image_id=40 and point2D_idx=1583 in image_id=41\n",
      "W20260111 03:54:52.365626 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3573 in image_id=41 and point2D_idx=500 in image_id=42\n",
      "W20260111 03:54:52.365659 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3115 in image_id=41 and point2D_idx=1408 in image_id=42\n",
      "W20260111 03:54:52.365683 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3232 in image_id=41 and point2D_idx=2306 in image_id=42\n",
      "W20260111 03:54:52.365802 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3693 in image_id=41 and point2D_idx=14212 in image_id=42\n",
      "W20260111 03:54:52.365836 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14074 in image_id=42 and point2D_idx=6926 in image_id=43\n",
      "W20260111 03:54:52.365856 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14125 in image_id=42 and point2D_idx=7578 in image_id=43\n",
      "W20260111 03:54:52.365883 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13858 in image_id=42 and point2D_idx=7586 in image_id=43\n",
      "W20260111 03:54:52.365902 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12970 in image_id=42 and point2D_idx=6830 in image_id=43\n",
      "W20260111 03:54:52.365949 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11740 in image_id=42 and point2D_idx=7481 in image_id=43\n",
      "W20260111 03:54:52.365978 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12394 in image_id=42 and point2D_idx=8021 in image_id=43\n",
      "W20260111 03:54:52.365999 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3652 in image_id=42 and point2D_idx=1820 in image_id=43\n",
      "W20260111 03:54:52.366013 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13262 in image_id=42 and point2D_idx=8490 in image_id=43\n",
      "W20260111 03:54:52.366041 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7265 in image_id=42 and point2D_idx=5296 in image_id=43\n",
      "W20260111 03:54:52.366083 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6753 in image_id=42 and point2D_idx=5110 in image_id=43\n",
      "W20260111 03:54:52.366099 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8372 in image_id=42 and point2D_idx=6404 in image_id=43\n",
      "W20260111 03:54:52.366114 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12984 in image_id=42 and point2D_idx=8370 in image_id=43\n",
      "W20260111 03:54:52.366166 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13267 in image_id=42 and point2D_idx=8734 in image_id=43\n",
      "W20260111 03:54:52.366242 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4101 in image_id=42 and point2D_idx=1395 in image_id=48\n",
      "W20260111 03:54:52.366264 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1572 in image_id=42 and point2D_idx=319 in image_id=48\n",
      "W20260111 03:54:52.366289 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1094 in image_id=42 and point2D_idx=216 in image_id=48\n",
      "W20260111 03:54:52.366310 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11371 in image_id=42 and point2D_idx=6436 in image_id=48\n",
      "W20260111 03:54:52.366401 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7234 in image_id=42 and point2D_idx=4250 in image_id=48\n",
      "W20260111 03:54:52.366441 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4116 in image_id=42 and point2D_idx=1665 in image_id=48\n",
      "W20260111 03:54:52.366481 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11724 in image_id=42 and point2D_idx=6627 in image_id=48\n",
      "W20260111 03:54:52.366523 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6226 in image_id=42 and point2D_idx=3357 in image_id=48\n",
      "W20260111 03:54:52.366584 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13311 in image_id=42 and point2D_idx=7213 in image_id=48\n",
      "W20260111 03:54:52.366639 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=770 in image_id=42 and point2D_idx=182 in image_id=48\n",
      "W20260111 03:54:52.366687 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12391 in image_id=42 and point2D_idx=6927 in image_id=48\n",
      "W20260111 03:54:52.366781 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9377 in image_id=42 and point2D_idx=5940 in image_id=48\n",
      "W20260111 03:54:52.366854 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7758 in image_id=42 and point2D_idx=732 in image_id=49\n",
      "W20260111 03:54:52.366904 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11724 in image_id=42 and point2D_idx=3810 in image_id=49\n",
      "W20260111 03:54:52.366939 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12391 in image_id=42 and point2D_idx=4995 in image_id=49\n",
      "W20260111 03:54:52.366977 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13523 in image_id=42 and point2D_idx=5989 in image_id=49\n",
      "W20260111 03:54:52.367027 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2842 in image_id=43 and point2D_idx=4190 in image_id=48\n",
      "W20260111 03:54:52.367047 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1573 in image_id=43 and point2D_idx=2678 in image_id=48\n",
      "W20260111 03:54:52.367070 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1139 in image_id=43 and point2D_idx=1991 in image_id=48\n",
      "W20260111 03:54:52.367145 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3450 in image_id=43 and point2D_idx=3820 in image_id=48\n",
      "W20260111 03:54:52.367172 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=623 in image_id=43 and point2D_idx=1269 in image_id=48\n",
      "W20260111 03:54:52.367195 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=97 in image_id=43 and point2D_idx=758 in image_id=48\n",
      "W20260111 03:54:52.367229 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5844 in image_id=43 and point2D_idx=5395 in image_id=48\n",
      "W20260111 03:54:52.367282 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=23 in image_id=43 and point2D_idx=421 in image_id=48\n",
      "W20260111 03:54:52.367490 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1785 in image_id=43 and point2D_idx=1133 in image_id=49\n",
      "W20260111 03:54:52.367570 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=421 in image_id=43 and point2D_idx=242 in image_id=49\n",
      "W20260111 03:54:52.367653 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6419 in image_id=43 and point2D_idx=4822 in image_id=49\n",
      "W20260111 03:54:52.367704 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2437 in image_id=43 and point2D_idx=1759 in image_id=49\n",
      "W20260111 03:54:52.367747 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1142 in image_id=43 and point2D_idx=713 in image_id=49\n",
      "W20260111 03:54:52.367810 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4665 in image_id=43 and point2D_idx=3562 in image_id=49\n",
      "W20260111 03:54:52.367850 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=38 in image_id=43 and point2D_idx=5 in image_id=49\n",
      "W20260111 03:54:52.367946 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5429 in image_id=43 and point2D_idx=6194 in image_id=51\n",
      "W20260111 03:54:52.368011 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2874 in image_id=43 and point2D_idx=2697 in image_id=51\n",
      "W20260111 03:54:52.368032 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5245 in image_id=43 and point2D_idx=5885 in image_id=51\n",
      "W20260111 03:54:52.368043 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6156 in image_id=43 and point2D_idx=7460 in image_id=51\n",
      "W20260111 03:54:52.368143 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5636 in image_id=43 and point2D_idx=6216 in image_id=51\n",
      "W20260111 03:54:52.368166 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6742 in image_id=43 and point2D_idx=8196 in image_id=51\n",
      "W20260111 03:54:52.368179 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=644 in image_id=43 and point2D_idx=286 in image_id=51\n",
      "W20260111 03:54:52.368254 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5226 in image_id=43 and point2D_idx=5555 in image_id=51\n",
      "W20260111 03:54:52.368337 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6045 in image_id=43 and point2D_idx=6569 in image_id=51\n",
      "W20260111 03:54:52.368463 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6788 in image_id=43 and point2D_idx=7857 in image_id=51\n",
      "W20260111 03:54:52.368495 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5284 in image_id=43 and point2D_idx=5231 in image_id=51\n",
      "W20260111 03:54:52.368541 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=164 in image_id=43 and point2D_idx=33 in image_id=51\n",
      "W20260111 03:54:52.368608 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3260 in image_id=43 and point2D_idx=2449 in image_id=51\n",
      "W20260111 03:54:52.368642 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=859 in image_id=43 and point2D_idx=161 in image_id=51\n",
      "W20260111 03:54:52.368659 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5314 in image_id=43 and point2D_idx=5237 in image_id=51\n",
      "W20260111 03:54:52.368755 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2595 in image_id=44 and point2D_idx=4122 in image_id=45\n",
      "W20260111 03:54:52.368821 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1771 in image_id=44 and point2D_idx=2237 in image_id=45\n",
      "W20260111 03:54:52.368859 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2460 in image_id=44 and point2D_idx=3795 in image_id=45\n",
      "W20260111 03:54:52.368893 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5429 in image_id=44 and point2D_idx=8168 in image_id=51\n",
      "W20260111 03:54:52.368931 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=781 in image_id=44 and point2D_idx=1384 in image_id=51\n",
      "W20260111 03:54:52.368958 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4128 in image_id=44 and point2D_idx=5939 in image_id=51\n",
      "W20260111 03:54:52.368975 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5329 in image_id=44 and point2D_idx=8176 in image_id=51\n",
      "W20260111 03:54:52.368998 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7508 in image_id=45 and point2D_idx=2276 in image_id=46\n",
      "W20260111 03:54:52.369038 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7705 in image_id=45 and point2D_idx=2244 in image_id=46\n",
      "W20260111 03:54:52.369118 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1233 in image_id=45 and point2D_idx=33 in image_id=46\n",
      "W20260111 03:54:52.369165 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5613 in image_id=45 and point2D_idx=1356 in image_id=46\n",
      "W20260111 03:54:52.369214 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8009 in image_id=45 and point2D_idx=2550 in image_id=46\n",
      "W20260111 03:54:52.369247 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5282 in image_id=45 and point2D_idx=1459 in image_id=46\n",
      "W20260111 03:54:52.369308 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5288 in image_id=45 and point2D_idx=1470 in image_id=46\n",
      "W20260111 03:54:52.369336 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7525 in image_id=45 and point2D_idx=2401 in image_id=46\n",
      "W20260111 03:54:52.369382 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3406 in image_id=45 and point2D_idx=141 in image_id=47\n",
      "W20260111 03:54:52.369410 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7345 in image_id=45 and point2D_idx=4342 in image_id=49\n",
      "W20260111 03:54:52.369433 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3026 in image_id=45 and point2D_idx=136 in image_id=50\n",
      "W20260111 03:54:52.369473 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2869 in image_id=45 and point2D_idx=94 in image_id=50\n",
      "W20260111 03:54:52.369501 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=969 in image_id=45 and point2D_idx=2209 in image_id=51\n",
      "W20260111 03:54:52.369526 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7181 in image_id=45 and point2D_idx=6908 in image_id=51\n",
      "W20260111 03:54:52.369547 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=822 in image_id=45 and point2D_idx=2101 in image_id=51\n",
      "W20260111 03:54:52.369629 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1856 in image_id=46 and point2D_idx=2620 in image_id=50\n",
      "W20260111 03:54:52.369647 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2373 in image_id=46 and point2D_idx=3854 in image_id=50\n",
      "W20260111 03:54:52.369708 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=419 in image_id=46 and point2D_idx=423 in image_id=50\n",
      "W20260111 03:54:52.369757 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1395 in image_id=46 and point2D_idx=2057 in image_id=50\n",
      "W20260111 03:54:52.369804 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1294 in image_id=46 and point2D_idx=1924 in image_id=50\n",
      "W20260111 03:54:52.369853 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1615 in image_id=46 and point2D_idx=2783 in image_id=50\n",
      "W20260111 03:54:52.369874 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1451 in image_id=46 and point2D_idx=2407 in image_id=50\n",
      "W20260111 03:54:52.369918 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=516 in image_id=47 and point2D_idx=535 in image_id=48\n",
      "W20260111 03:54:52.369954 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=906 in image_id=47 and point2D_idx=6111 in image_id=48\n",
      "W20260111 03:54:52.369991 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1070 in image_id=47 and point2D_idx=7090 in image_id=48\n",
      "W20260111 03:54:52.370021 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=94 in image_id=47 and point2D_idx=2498 in image_id=50\n",
      "W20260111 03:54:52.370040 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=129 in image_id=47 and point2D_idx=2501 in image_id=50\n",
      "W20260111 03:54:52.370113 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6452 in image_id=48 and point2D_idx=5265 in image_id=49\n",
      "W20260111 03:54:52.370133 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6550 in image_id=48 and point2D_idx=5399 in image_id=49\n",
      "W20260111 03:54:52.370167 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6575 in image_id=48 and point2D_idx=5853 in image_id=49\n",
      "W20260111 03:54:52.370187 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6931 in image_id=48 and point2D_idx=6244 in image_id=49\n",
      "W20260111 03:54:52.370203 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7502 in image_id=48 and point2D_idx=6475 in image_id=49\n",
      "W20260111 03:54:52.370233 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7287 in image_id=48 and point2D_idx=6523 in image_id=49\n",
      "W20260111 03:54:52.370282 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=981 in image_id=49 and point2D_idx=773 in image_id=51\n",
      "W20260111 03:54:52.370344 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2368 in image_id=49 and point2D_idx=2724 in image_id=51\n",
      "W20260111 03:54:52.370363 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2870 in image_id=49 and point2D_idx=3604 in image_id=51\n",
      "W20260111 03:54:52.370391 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=580 in image_id=49 and point2D_idx=297 in image_id=51\n",
      "W20260111 03:54:52.370455 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3164 in image_id=49 and point2D_idx=3935 in image_id=51\n",
      "W20260111 03:54:52.370486 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6108 in image_id=49 and point2D_idx=9851 in image_id=51\n",
      "W20260111 03:54:52.370526 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5258 in image_id=49 and point2D_idx=7857 in image_id=51\n",
      "W20260111 03:54:52.370567 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2881 in image_id=49 and point2D_idx=3307 in image_id=51\n",
      "W20260111 03:54:52.370604 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5259 in image_id=49 and point2D_idx=7859 in image_id=51\n",
      "W20260111 03:54:52.370617 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5376 in image_id=49 and point2D_idx=8219 in image_id=51\n",
      "W20260111 03:54:52.370637 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2370 in image_id=49 and point2D_idx=2442 in image_id=51\n",
      "W20260111 03:54:52.370664 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3835 in image_id=49 and point2D_idx=4904 in image_id=51\n",
      "W20260111 03:54:52.370712 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4389 in image_id=49 and point2D_idx=5919 in image_id=51\n",
      "W20260111 03:54:52.370764 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6286 in image_id=49 and point2D_idx=10170 in image_id=51\n",
      "W20260111 03:54:52.370791 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5124 in image_id=49 and point2D_idx=7498 in image_id=51\n",
      "W20260111 03:54:52.370806 133176598373952 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4393 in image_id=49 and point2D_idx=5923 in image_id=51\n",
      "I20260111 03:54:52.388768 133176598373952 database_cache.cc:210]  in 0.088s (ignored 0)\n",
      "I20260111 03:54:52.388842 133176598373952 timer.cc:91] Elapsed time: 0.002 [minutes]\n",
      "I20260111 03:54:52.392413 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:54:52.646381 133176598373952 incremental_pipeline.cc:306] Initializing with image pair #34 and #44\n",
      "I20260111 03:54:52.653054 133176598373952 incremental_pipeline.cc:311] Global bundle adjustment\n",
      "I20260111 03:54:53.617927 133176598373952 incremental_pipeline.cc:390] Registering image #35 (3)\n",
      "I20260111 03:54:53.617962 133176598373952 incremental_pipeline.cc:393] => Image sees 1350 / 12026 points\n",
      "I20260111 03:54:55.183135 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:55:00.765048 133176598373952 incremental_pipeline.cc:390] Registering image #39 (4)\n",
      "I20260111 03:55:00.765089 133176598373952 incremental_pipeline.cc:393] => Image sees 1983 / 8129 points\n",
      "I20260111 03:55:02.568431 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:55:03.783258 133176598373952 incremental_pipeline.cc:390] Registering image #18 (5)\n",
      "I20260111 03:55:03.783291 133176598373952 incremental_pipeline.cc:393] => Image sees 2332 / 11292 points\n",
      "I20260111 03:55:07.088180 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:55:08.159031 133176598373952 incremental_pipeline.cc:390] Registering image #29 (6)\n",
      "I20260111 03:55:08.159071 133176598373952 incremental_pipeline.cc:393] => Image sees 1746 / 7948 points\n",
      "I20260111 03:55:10.827418 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:55:11.510264 133176598373952 incremental_pipeline.cc:390] Registering image #9 (7)\n",
      "I20260111 03:55:11.510305 133176598373952 incremental_pipeline.cc:393] => Image sees 2511 / 8243 points\n",
      "I20260111 03:55:13.545624 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:55:15.504306 133176598373952 incremental_pipeline.cc:390] Registering image #49 (8)\n",
      "I20260111 03:55:15.504354 133176598373952 incremental_pipeline.cc:393] => Image sees 1294 / 6412 points\n",
      "I20260111 03:55:17.238167 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:55:18.131313 133176598373952 incremental_pipeline.cc:390] Registering image #43 (9)\n",
      "I20260111 03:55:18.131359 133176598373952 incremental_pipeline.cc:393] => Image sees 1996 / 8867 points\n",
      "I20260111 03:55:20.213049 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:55:24.117595 133176598373952 incremental_pipeline.cc:390] Registering image #51 (10)\n",
      "I20260111 03:55:24.117663 133176598373952 incremental_pipeline.cc:393] => Image sees 4447 / 11095 points\n",
      "I20260111 03:55:26.370433 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:55:29.890960 133176598373952 incremental_pipeline.cc:390] Registering image #13 (11)\n",
      "I20260111 03:55:29.891000 133176598373952 incremental_pipeline.cc:393] => Image sees 1098 / 5582 points\n",
      "I20260111 03:55:31.611928 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:55:33.001906 133176598373952 incremental_pipeline.cc:390] Registering image #2 (12)\n",
      "I20260111 03:55:33.001944 133176598373952 incremental_pipeline.cc:393] => Image sees 1444 / 8456 points\n",
      "I20260111 03:55:34.758415 133176598373952 incremental_pipeline.cc:390] Registering image #19 (13)\n",
      "I20260111 03:55:34.758454 133176598373952 incremental_pipeline.cc:393] => Image sees 885 / 3975 points\n",
      "I20260111 03:55:36.216741 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:55:44.796986 133176598373952 incremental_pipeline.cc:390] Registering image #33 (14)\n",
      "I20260111 03:55:44.797032 133176598373952 incremental_pipeline.cc:393] => Image sees 1584 / 10190 points\n",
      "I20260111 03:55:46.653143 133176598373952 incremental_pipeline.cc:390] Registering image #17 (15)\n",
      "I20260111 03:55:46.653185 133176598373952 incremental_pipeline.cc:393] => Image sees 1308 / 9335 points\n",
      "I20260111 03:55:48.860758 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:55:53.562447 133176598373952 incremental_pipeline.cc:390] Registering image #38 (16)\n",
      "I20260111 03:55:53.562510 133176598373952 incremental_pipeline.cc:393] => Image sees 2583 / 6851 points\n",
      "I20260111 03:55:55.575229 133176598373952 incremental_pipeline.cc:390] Registering image #42 (17)\n",
      "I20260111 03:55:55.575305 133176598373952 incremental_pipeline.cc:393] => Image sees 7444 / 14117 points\n",
      "I20260111 03:55:58.075974 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:56:02.454670 133176598373952 incremental_pipeline.cc:390] Registering image #48 (18)\n",
      "I20260111 03:56:02.454722 133176598373952 incremental_pipeline.cc:393] => Image sees 4794 / 7452 points\n",
      "I20260111 03:56:04.661257 133176598373952 incremental_pipeline.cc:390] Registering image #22 (19)\n",
      "I20260111 03:56:04.661321 133176598373952 incremental_pipeline.cc:393] => Image sees 1795 / 4612 points\n",
      "I20260111 03:56:06.898198 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:56:12.048709 133176598373952 incremental_pipeline.cc:390] Registering image #23 (20)\n",
      "I20260111 03:56:12.048756 133176598373952 incremental_pipeline.cc:393] => Image sees 2159 / 5065 points\n",
      "I20260111 03:56:14.047941 133176598373952 incremental_pipeline.cc:390] Registering image #32 (21)\n",
      "I20260111 03:56:14.047990 133176598373952 incremental_pipeline.cc:393] => Image sees 1085 / 5032 points\n",
      "I20260111 03:56:15.836336 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:56:24.866515 133176598373952 incremental_pipeline.cc:390] Registering image #25 (22)\n",
      "I20260111 03:56:24.866590 133176598373952 incremental_pipeline.cc:393] => Image sees 1244 / 3533 points\n",
      "I20260111 03:56:25.734248 133176598373952 incremental_pipeline.cc:390] Registering image #37 (23)\n",
      "I20260111 03:56:25.734316 133176598373952 incremental_pipeline.cc:393] => Image sees 1471 / 4431 points\n",
      "I20260111 03:56:26.850900 133176598373952 incremental_pipeline.cc:390] Registering image #12 (24)\n",
      "I20260111 03:56:26.850949 133176598373952 incremental_pipeline.cc:393] => Image sees 636 / 3256 points\n",
      "I20260111 03:56:27.799184 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:56:31.024764 133176598373952 incremental_pipeline.cc:390] Registering image #6 (25)\n",
      "I20260111 03:56:31.024811 133176598373952 incremental_pipeline.cc:393] => Image sees 761 / 2480 points\n",
      "I20260111 03:56:33.009045 133176598373952 incremental_pipeline.cc:390] Registering image #36 (26)\n",
      "I20260111 03:56:33.009093 133176598373952 incremental_pipeline.cc:393] => Image sees 475 / 6195 points\n",
      "I20260111 03:56:34.903630 133176598373952 incremental_pipeline.cc:390] Registering image #45 (27)\n",
      "I20260111 03:56:34.903669 133176598373952 incremental_pipeline.cc:393] => Image sees 968 / 8479 points\n",
      "I20260111 03:56:36.927315 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:56:40.199183 133176598373952 incremental_pipeline.cc:390] Registering image #20 (28)\n",
      "I20260111 03:56:40.199224 133176598373952 incremental_pipeline.cc:393] => Image sees 1507 / 6989 points\n",
      "I20260111 03:56:41.943767 133176598373952 incremental_pipeline.cc:390] Registering image #30 (29)\n",
      "I20260111 03:56:41.943809 133176598373952 incremental_pipeline.cc:393] => Image sees 1531 / 6265 points\n",
      "I20260111 03:56:43.587473 133176598373952 incremental_pipeline.cc:390] Registering image #14 (30)\n",
      "I20260111 03:56:43.587518 133176598373952 incremental_pipeline.cc:393] => Image sees 1285 / 6018 points\n",
      "I20260111 03:56:45.428836 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:56:53.606993 133176598373952 incremental_pipeline.cc:390] Registering image #28 (31)\n",
      "I20260111 03:56:53.607035 133176598373952 incremental_pipeline.cc:393] => Image sees 1249 / 9389 points\n",
      "I20260111 03:56:55.804190 133176598373952 incremental_pipeline.cc:390] Registering image #24 (32)\n",
      "I20260111 03:56:55.804278 133176598373952 incremental_pipeline.cc:393] => Image sees 1701 / 7636 points\n",
      "I20260111 03:56:57.993036 133176598373952 incremental_pipeline.cc:390] Registering image #3 (33)\n",
      "I20260111 03:56:57.993094 133176598373952 incremental_pipeline.cc:393] => Image sees 1541 / 6962 points\n",
      "I20260111 03:56:59.913177 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:57:12.636397 133176598373952 incremental_pipeline.cc:390] Registering image #10 (34)\n",
      "I20260111 03:57:12.636447 133176598373952 incremental_pipeline.cc:393] => Image sees 765 / 3477 points\n",
      "I20260111 03:57:14.623093 133176598373952 incremental_pipeline.cc:390] Registering image #27 (35)\n",
      "I20260111 03:57:14.623147 133176598373952 incremental_pipeline.cc:393] => Image sees 658 / 5028 points\n",
      "I20260111 03:57:15.985024 133176598373952 incremental_pipeline.cc:390] Registering image #21 (36)\n",
      "I20260111 03:57:15.985083 133176598373952 incremental_pipeline.cc:393] => Image sees 749 / 4330 points\n",
      "I20260111 03:57:17.980326 133176598373952 incremental_pipeline.cc:390] Registering image #4 (37)\n",
      "I20260111 03:57:17.980376 133176598373952 incremental_pipeline.cc:393] => Image sees 648 / 4174 points\n",
      "I20260111 03:57:19.063102 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:57:28.643926 133176598373952 incremental_pipeline.cc:390] Registering image #46 (38)\n",
      "I20260111 03:57:28.644010 133176598373952 incremental_pipeline.cc:393] => Image sees 729 / 2536 points\n",
      "I20260111 03:57:30.336460 133176598373952 incremental_pipeline.cc:390] Registering image #40 (39)\n",
      "I20260111 03:57:30.336498 133176598373952 incremental_pipeline.cc:393] => Image sees 667 / 4094 points\n",
      "I20260111 03:57:31.738301 133176598373952 incremental_pipeline.cc:390] Registering image #1 (40)\n",
      "I20260111 03:57:31.738345 133176598373952 incremental_pipeline.cc:393] => Image sees 1935 / 4408 points\n",
      "I20260111 03:57:33.445414 133176598373952 incremental_pipeline.cc:390] Registering image #31 (41)\n",
      "I20260111 03:57:33.445453 133176598373952 incremental_pipeline.cc:393] => Image sees 657 / 3435 points\n",
      "I20260111 03:57:33.901851 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:57:42.994997 133176598373952 incremental_pipeline.cc:390] Registering image #41 (42)\n",
      "I20260111 03:57:42.995040 133176598373952 incremental_pipeline.cc:393] => Image sees 860 / 4332 points\n",
      "I20260111 03:57:44.436594 133176598373952 incremental_pipeline.cc:390] Registering image #50 (43)\n",
      "I20260111 03:57:44.436634 133176598373952 incremental_pipeline.cc:393] => Image sees 621 / 4372 points\n",
      "I20260111 03:57:45.994513 133176598373952 incremental_pipeline.cc:390] Registering image #11 (44)\n",
      "I20260111 03:57:45.994566 133176598373952 incremental_pipeline.cc:393] => Image sees 358 / 1893 points\n",
      "I20260111 03:57:46.944604 133176598373952 incremental_pipeline.cc:390] Registering image #15 (45)\n",
      "I20260111 03:57:46.944640 133176598373952 incremental_pipeline.cc:393] => Image sees 262 / 3627 points\n",
      "I20260111 03:57:48.000475 133176598373952 incremental_pipeline.cc:390] Registering image #16 (46)\n",
      "I20260111 03:57:48.000514 133176598373952 incremental_pipeline.cc:393] => Image sees 209 / 2608 points\n",
      "I20260111 03:57:48.527909 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:57:57.848018 133176598373952 incremental_pipeline.cc:390] Registering image #47 (47)\n",
      "I20260111 03:57:57.848057 133176598373952 incremental_pipeline.cc:393] => Image sees 189 / 1510 points\n",
      "I20260111 03:57:58.492296 133176598373952 incremental_pipeline.cc:390] Registering image #5 (48)\n",
      "I20260111 03:57:58.492343 133176598373952 incremental_pipeline.cc:393] => Image sees 130 / 2539 points\n",
      "I20260111 03:57:59.091314 133176598373952 incremental_pipeline.cc:390] Registering image #8 (49)\n",
      "I20260111 03:57:59.091383 133176598373952 incremental_pipeline.cc:393] => Image sees 60 / 4073 points\n",
      "I20260111 03:58:00.035067 133176598373952 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n",
      "I20260111 03:58:04.468599 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.514859 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.517918 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.518205 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.520969 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.521284 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.523715 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.523992 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.526077 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.526392 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.528498 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.528819 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.530825 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.531125 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.533071 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.533353 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.535235 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.535527 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.537387 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.537674 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.539433 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.539751 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.541501 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.541797 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.543490 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.543788 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.545478 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.545797 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.547615 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.547899 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.549675 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.549955 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.551756 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.552055 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.553821 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.554105 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.555868 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.556209 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.557963 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.558244 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.559980 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.560254 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.562020 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.562310 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.563999 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.564282 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.566012 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.566315 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.568107 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.568395 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.570235 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.570524 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.572405 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.572689 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.574523 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.574826 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.576746 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.577044 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.578915 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.579198 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.581013 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.581299 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.583091 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.583415 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.585226 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.585522 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.587381 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.587687 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.589485 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.589772 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.591580 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.591864 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.593678 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.593955 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.595782 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.596080 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.597866 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.598149 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.599845 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.600127 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.601784 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.602070 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.603703 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.603981 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.605648 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.605955 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.607626 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.607900 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.609501 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.609799 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.611479 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.611795 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.613436 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.613733 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.615372 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.615694 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.617305 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.617623 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.619193 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.619481 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.621062 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.621353 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.623002 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.623340 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.625011 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.625313 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.626955 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.627282 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.628926 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.629208 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.630797 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.631113 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.632743 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.633023 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.634611 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.634910 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.636651 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.636946 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.638629 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.638904 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.640499 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.640786 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.642471 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.642835 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.644540 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.644839 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.646769 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.647102 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.648835 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.649125 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.650804 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.651136 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.652792 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.653082 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.654788 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.655095 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.656812 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.657122 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.658736 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.659023 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.660641 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.660924 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.662527 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.662842 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.664426 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.664726 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.666372 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.666714 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.668343 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.668642 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.670429 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.670769 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.672610 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.672924 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.674594 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.674875 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.676516 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.676845 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.678442 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.678743 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.680308 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.680620 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.682179 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.682472 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.684040 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.684336 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.685955 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.686260 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.687906 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.688193 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.689818 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.690107 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.691726 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.692021 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.693615 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.693906 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.695448 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.695771 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.697409 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.697728 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.699300 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.699604 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.701169 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.701461 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.703014 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.703302 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.704861 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.705152 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.706776 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.707080 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.708689 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.708982 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.710521 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.710859 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.712422 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.712736 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.714283 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.714594 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.716207 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.716522 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.718127 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.718418 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.719996 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.720283 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.721887 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.722176 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.723787 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.724076 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.725669 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.725994 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.727611 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.727963 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.729543 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.729865 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.731447 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.731746 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.733299 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.733613 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.735186 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.735496 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.737186 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.737487 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.739185 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.739481 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.741134 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.741427 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.743024 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.743313 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.744934 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.745223 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.746893 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.747207 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.748826 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.749113 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.750712 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.751007 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.752617 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.752905 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.754509 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.754829 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.756445 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.756768 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.758352 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.758651 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.760206 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.760518 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.762112 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.762404 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.763964 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.764254 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.765868 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.766174 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.767803 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.768107 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.769826 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.770167 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.771861 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.772161 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.773770 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.774064 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.775663 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.775990 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.777631 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.777925 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.779502 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.779806 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.781371 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.781673 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.783231 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.783525 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.785153 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.785446 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.787081 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.787374 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.788972 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.789261 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.790877 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.791180 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.792791 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.793094 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.794709 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.795006 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.796635 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.796950 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.798538 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.798859 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.800441 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.800743 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.802309 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.802621 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.804185 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.804481 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.806135 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.806447 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.808094 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.808400 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.809996 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.810295 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.811913 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.812226 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.813833 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.814122 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.815714 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.816032 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.817652 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.817932 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.819478 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.819779 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.821357 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.821680 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.823249 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.823544 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.825126 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.825419 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.827037 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.827332 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.828898 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.829186 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.830783 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.831085 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.832681 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.832977 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.834539 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.834843 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.836434 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.836751 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.838308 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.838621 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.840169 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.840465 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.842042 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.842339 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.843906 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.844196 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.845813 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.846117 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.847772 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.848059 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.849672 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.849965 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.851667 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.851979 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.853601 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.853894 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.855471 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.855808 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.857409 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.857715 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.859392 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.859709 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.861299 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.861613 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.863196 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.863490 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.865054 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.865348 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.866969 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.867257 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.868826 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.869114 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.870705 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: Reconstruction(num_cameras=49, num_images=49, num_reg_images=49, num_points3D=48121)}\n",
      "Reconstruction done in 192.6754 sec\n",
      "\n",
      "Results\n",
      "Dataset \"amy_gardens\" -> Failed!\n",
      "Dataset \"fbk_vineyard\" -> Failed!\n",
      "Dataset \"imc2023_haiper\" -> Failed!\n",
      "Dataset \"imc2023_heritage\" -> Failed!\n",
      "Dataset \"imc2023_theather_imc2024_church\" -> Failed!\n",
      "Dataset \"imc2024_dioscuri_baalshamin\" -> Failed!\n",
      "Dataset \"imc2024_lizard_pond\" -> Failed!\n",
      "Dataset \"pt_brandenburg_british_buckingham\" -> Failed!\n",
      "Dataset \"pt_piazzasanmarco_grandplace\" -> Failed!\n",
      "Dataset \"pt_sacrecoeur_trevi_tajmahal\" -> Failed!\n",
      "Dataset \"pt_stpeters_stpauls\" -> Failed!\n",
      "Dataset ETs -> Registered 19 / 22 images with 2 clusters\n",
      "Dataset stairs -> Registered 49 / 51 images with 1 clusters\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I20260111 03:58:04.871004 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.872825 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.873123 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.875001 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.875296 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.876988 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.877284 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.878891 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.879181 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.880780 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.881069 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.882663 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.882957 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.884513 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.884816 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.886405 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.886726 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.888295 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.888611 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.890193 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.890487 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.892117 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.892436 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.893997 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.894286 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.895911 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.896239 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.897856 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.898145 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.899772 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.900094 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.901839 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.902123 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.903785 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.904059 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.905717 133176598373952 incremental_pipeline.cc:282] Finding good initial image pair\n",
      "I20260111 03:58:04.906021 133176598373952 incremental_pipeline.cc:286] => No good initial image pair found.\n",
      "I20260111 03:58:04.906170 133176598373952 timer.cc:91] Elapsed time: 3.210 [minutes]\n"
     ]
    }
   ],
   "source": [
    "run_mast3r_pipeline(samples, data_dir, workdir, is_train, mast3r_model, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aee780e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:58:05.338168Z",
     "iopub.status.busy": "2026-01-11T03:58:05.337265Z",
     "iopub.status.idle": "2026-01-11T03:58:05.767804Z",
     "shell.execute_reply": "2026-01-11T03:58:05.766836Z"
    },
    "papermill": {
     "duration": 0.621565,
     "end_time": "2026-01-11T03:58:05.769430",
     "exception": false,
     "start_time": "2026-01-11T03:58:05.147865",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_id,dataset,scene,image,rotation_matrix,translation_vector\r\n",
      "ETs_another_et_another_et001.png_public,ETs,cluster1,another_et_another_et001.png,0.999901122;-0.006022093;0.012707487;0.005732015;0.999724943;0.022741628;-0.012840944;-0.022666539;0.999660611,-2.810862083;-1.938197515;2.734789485\r\n",
      "ETs_another_et_another_et002.png_public,ETs,cluster1,another_et_another_et002.png,0.999990905;-0.003492048;-0.002448712;0.003485311;0.999990144;-0.002750249;0.002458292;0.002741690;0.999993220,-2.656899281;-1.163056178;1.202037024\r\n",
      "ETs_another_et_another_et003.png_public,ETs,cluster1,another_et_another_et003.png,0.997897729;-0.043131049;0.048371835;0.046276132;0.996752351;-0.065903486;-0.045372254;0.068003401;0.996652846,-2.864245076;0.351968712;-0.599432525\r\n",
      "ETs_another_et_another_et004.png_public,ETs,cluster1,another_et_another_et004.png,0.999324094;-0.012796008;0.034461810;0.008239038;0.991575723;0.129266022;-0.035825583;-0.128894718;0.991010938,-2.772501780;-1.431491010;-0.140841990\r\n",
      "ETs_another_et_another_et005.png_public,ETs,cluster1,another_et_another_et005.png,0.995498998;0.001146848;0.094765134;-0.007473143;0.997763119;0.066429746;-0.094476971;-0.066838939;0.993280755,-3.298440810;-2.123986635;1.325921346\r\n",
      "ETs_another_et_another_et006.png_public,ETs,cluster1,another_et_another_et006.png,0.919834772;0.189919983;-0.343270146;-0.215986817;0.975617624;-0.038986502;0.327496088;0.110002966;0.938427227,-0.573114075;-0.731431989;1.084820369\r\n",
      "ETs_another_et_another_et007.png_public,ETs,cluster1,another_et_another_et007.png,0.779595606;0.259614760;-0.569939354;-0.307037104;0.951602015;0.013484158;0.545856124;0.164480338;0.821576114,1.132292675;-0.432583411;0.585784571\r\n",
      "ETs_another_et_another_et008.png_public,ETs,cluster1,another_et_another_et008.png,0.565464703;0.305603269;-0.766065475;-0.389897638;0.917529529;0.078226566;0.726793988;0.254452758;0.637984555,2.830325819;-0.746015949;1.381209091\r\n",
      "ETs_another_et_another_et009.png_public,ETs,cluster1,another_et_another_et009.png,0.312116576;0.340570588;-0.886901865;-0.486884755;0.858965166;0.158499462;0.815798062;0.382348687;0.433915894,4.484310177;-1.071450912;2.003105554\r\n"
     ]
    }
   ],
   "source": [
    "array_to_str = lambda array: ';'.join([f\"{x:.09f}\" for x in array])\n",
    "none_to_str = lambda n: ';'.join(['nan'] * n)\n",
    "submission_file = '/kaggle/working/submission.csv'\n",
    "with open(submission_file, 'w') as f:\n",
    "    if is_train:\n",
    "        f.write('dataset,scene,image,rotation_matrix,translation_vector\\n')\n",
    "        for dataset, predictions in samples.items():\n",
    "            for prediction in predictions:\n",
    "                cluster_name = 'outliers' if prediction.cluster_index is None else f'cluster{prediction.cluster_index}'\n",
    "\n",
    "                # ✅ `rotation` is a list of lists, flatten it\n",
    "                if prediction.rotation is None:\n",
    "                    rotation_str = none_to_str(9)\n",
    "                else:\n",
    "                    rotation_flat =  prediction.rotation.flatten()  # flatten 3x3 list -> 9 elems\n",
    "                    rotation_str = array_to_str(rotation_flat)\n",
    "\n",
    "                # ✅ `translation` is a flat list\n",
    "                if prediction.translation is None:\n",
    "                    translation_str = none_to_str(3)\n",
    "                else:\n",
    "                    translation_str = array_to_str(prediction.translation)\n",
    "\n",
    "                f.write(f'{prediction.dataset},{cluster_name},{prediction.filename},{rotation_str},{translation_str}\\n')\n",
    "    else:\n",
    "        f.write('image_id,dataset,scene,image,rotation_matrix,translation_vector\\n')\n",
    "        for dataset, predictions in samples.items():\n",
    "            for prediction in predictions:\n",
    "                cluster_name = 'outliers' if prediction.cluster_index is None else f'cluster{prediction.cluster_index}'\n",
    "\n",
    "                if prediction.rotation is None:\n",
    "                    rotation_str = none_to_str(9)\n",
    "                else:\n",
    "                    rotation_flat =  prediction.rotation.flatten()\n",
    "                    rotation_str = array_to_str(rotation_flat)\n",
    "\n",
    "                if prediction.translation is None:\n",
    "                    translation_str = none_to_str(3)\n",
    "                else:\n",
    "                    translation_str = array_to_str(prediction.translation)\n",
    "\n",
    "                f.write(f'{prediction.image_id},{prediction.dataset},{cluster_name},{prediction.filename},{rotation_str},{translation_str}\\n')\n",
    "\n",
    "# Preview the output\n",
    "!head {submission_file}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "91a6ba56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-11T03:58:06.139613Z",
     "iopub.status.busy": "2026-01-11T03:58:06.139247Z",
     "iopub.status.idle": "2026-01-11T03:58:06.144318Z",
     "shell.execute_reply": "2026-01-11T03:58:06.143740Z"
    },
    "papermill": {
     "duration": 0.189,
     "end_time": "2026-01-11T03:58:06.145511",
     "exception": false,
     "start_time": "2026-01-11T03:58:05.956511",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Definitely Compute results if running on the training set.\n",
    "# Do not do this when submitting a notebook for scoring. All you have to do is save your submission to /kaggle/working/submission.csv.\n",
    "\n",
    "if is_train:\n",
    "    t = time()\n",
    "    final_score, dataset_scores = metric.score(\n",
    "        gt_csv='/kaggle/input/image-matching-challenge-2025/train_labels.csv',\n",
    "        user_csv=submission_file,\n",
    "        thresholds_csv='/kaggle/input/image-matching-challenge-2025/train_thresholds.csv',\n",
    "        mask_csv=None if is_train else os.path.join(data_dir, 'mask.csv'),\n",
    "        inl_cf=0,\n",
    "        strict_cf=-1,\n",
    "        verbose=True,\n",
    "    )\n",
    "    print(f'Computed metric in: {time() - t:.02f} sec.')"
   ]
  }
 ],
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