{ "metadata": { "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python", "version": "3.12.12", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" }, "kaggle": { "accelerator": "none", "dataSources": [], "dockerImageVersionId": 31259, "isInternetEnabled": true, "language": "python", "sourceType": "notebook", "isGpuEnabled": false }, "colab": { "provenance": [], "gpuType": "T4" }, "accelerator": "GPU" }, "nbformat_minor": 0, "nbformat": 4, "cells": [ { "cell_type": "code", "source": [], "metadata": { "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "trusted": true, "execution": { "iopub.status.busy": "2026-01-22T11:23:22.240664Z", "iopub.execute_input": "2026-01-22T11:23:22.240957Z", "iopub.status.idle": "2026-01-22T11:23:22.246018Z", "shell.execute_reply.started": "2026-01-22T11:23:22.240936Z", "shell.execute_reply": "2026-01-22T11:23:22.245074Z" }, "id": "yhVNR6GETKyA" }, "outputs": [], "execution_count": null }, { "cell_type": "code", "source": [ "# =====================================================================\n", "# biplet_dino_mast3r_ps2_gs_colab_01.ipynb\n", "# ASMK を DINO に置き換えたバージョン\n", "# =====================================================================\n", "\n", "# =====================================================================\n", "# CELL 1: Install Dependencies\n", "# =====================================================================\n", "!pip install roma einops timm huggingface_hub\n", "!pip install opencv-python pillow tqdm pyaml cython plyfile\n", "!pip install pycolmap trimesh\n", "!pip install transformers==4.40.0 # DINOに必要\n", "!pip uninstall -y numpy scipy\n", "!pip install numpy==1.26.4 scipy==1.11.4\n", "break" ], "metadata": { "trusted": true, "id": "6C3QGJD8TKyC", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "b362f97d-fbc1-474f-f2cb-b84b565acdb9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting roma\n", " Downloading roma-1.5.4-py3-none-any.whl.metadata (5.5 kB)\n", "Requirement already satisfied: einops in /usr/local/lib/python3.12/dist-packages (0.8.1)\n", "Requirement already satisfied: timm in /usr/local/lib/python3.12/dist-packages (1.0.24)\n", "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.12/dist-packages (0.36.0)\n", "Requirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (from timm) (2.9.0+cu126)\n", "Requirement already satisfied: torchvision in /usr/local/lib/python3.12/dist-packages (from timm) (0.24.0+cu126)\n", "Requirement already satisfied: pyyaml in /usr/local/lib/python3.12/dist-packages (from timm) (6.0.3)\n", "Requirement already satisfied: safetensors in /usr/local/lib/python3.12/dist-packages (from timm) (0.7.0)\n", 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requests->huggingface_hub) (3.4.4)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub) (3.11)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub) (2.5.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub) (2026.1.4)\n", "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch->timm) (75.2.0)\n", "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (1.14.0)\n", "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (3.6.1)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (3.1.6)\n", "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in 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pyaml-25.7.0\n", "Collecting pycolmap\n", " Downloading pycolmap-3.13.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (10 kB)\n", "Collecting trimesh\n", " Downloading trimesh-4.11.1-py3-none-any.whl.metadata (13 kB)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from pycolmap) (2.0.2)\n", "Downloading pycolmap-3.13.0-cp312-cp312-manylinux_2_28_x86_64.whl (20.3 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.3/20.3 MB\u001b[0m \u001b[31m58.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading trimesh-4.11.1-py3-none-any.whl (740 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m740.4/740.4 kB\u001b[0m \u001b[31m62.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: trimesh, pycolmap\n", "Successfully installed pycolmap-3.13.0 trimesh-4.11.1\n", "Collecting transformers==4.40.0\n", " Downloading 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tokenizers-0.19.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m87.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: tokenizers, transformers\n", " Attempting uninstall: tokenizers\n", " Found existing installation: tokenizers 0.22.2\n", " Uninstalling tokenizers-0.22.2:\n", " Successfully uninstalled tokenizers-0.22.2\n", " Attempting uninstall: transformers\n", " Found existing installation: transformers 4.57.6\n", " Uninstalling transformers-4.57.6:\n", " Successfully uninstalled transformers-4.57.6\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "sentence-transformers 5.2.0 requires transformers<6.0.0,>=4.41.0, but you have transformers 4.40.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed tokenizers-0.19.1 transformers-4.40.0\n", "Found existing installation: numpy 2.0.2\n", "Uninstalling numpy-2.0.2:\n", " Successfully uninstalled numpy-2.0.2\n", "Found existing installation: scipy 1.16.3\n", "Uninstalling scipy-1.16.3:\n", " Successfully uninstalled scipy-1.16.3\n", "Collecting numpy==1.26.4\n", " Downloading numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.0/61.0 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hCollecting scipy==1.11.4\n", " Downloading scipy-1.11.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.4/60.4 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.0 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.0/18.0 MB\u001b[0m \u001b[31m71.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading scipy-1.11.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (35.8 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.8/35.8 MB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: numpy, scipy\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "mapclassify 2.10.0 requires scipy>=1.12, but you have scipy 1.11.4 which is incompatible.\n", "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", "tsfresh 0.21.1 requires scipy>=1.14.0; python_version >= \"3.10\", but you have scipy 1.11.4 which is incompatible.\n", "pytensor 2.36.3 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "spopt 0.7.0 requires scipy>=1.12.0, but you have scipy 1.11.4 which is incompatible.\n", "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", "opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", "shap 0.50.0 requires numpy>=2, but you have numpy 1.26.4 which is incompatible.\n", "sentence-transformers 5.2.0 requires transformers<6.0.0,>=4.41.0, but you have transformers 4.40.0 which is incompatible.\n", "jax 0.7.2 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "jax 0.7.2 requires scipy>=1.13, but you have scipy 1.11.4 which is incompatible.\n", "libpysal 4.14.1 requires scipy>=1.12.0, but you have scipy 1.11.4 which is incompatible.\n", "rasterio 1.5.0 requires numpy>=2, but you have numpy 1.26.4 which is incompatible.\n", "access 1.1.10.post3 requires scipy>=1.14.1, but you have scipy 1.11.4 which is incompatible.\n", "tobler 0.13.0 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "tobler 0.13.0 requires scipy>=1.13, but you have scipy 1.11.4 which is incompatible.\n", "esda 2.8.1 requires scipy>=1.12, but you have scipy 1.11.4 which is incompatible.\n", "inequality 1.1.2 requires scipy>=1.12, but you have scipy 1.11.4 which is incompatible.\n", "giddy 2.3.8 requires scipy>=1.12, but you have scipy 1.11.4 which is incompatible.\n", "jaxlib 0.7.2 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "jaxlib 0.7.2 requires scipy>=1.13, but you have scipy 1.11.4 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed numpy-1.26.4 scipy-1.11.4\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "numpy" ] }, "id": "c6df9411f82f41ceb400a90d4bec5f90" } }, "metadata": {} }, { "output_type": "error", "ename": "SyntaxError", "evalue": "'break' outside loop (ipython-input-2150635115.py, line 15)", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"/tmp/ipython-input-2150635115.py\"\u001b[0;36m, line \u001b[0;32m15\u001b[0m\n\u001b[0;31m break\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m 'break' outside loop\n" ] } ], "execution_count": 1 }, { "cell_type": "code", "source": [], "metadata": { "id": "49QM1qVmdm4k" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "bSUbLgHpeeJ4" }, "execution_count": 27, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "TPcj5qcmedBw" }, "execution_count": 27, "outputs": [] }, { "cell_type": "code", "source": [ "# restart & run after\n", "# =====================================================================\n", "# CELL 2: Mount Drive and Verify\n", "# =====================================================================\n", "from google.colab import drive\n", "drive.mount('/content/drive')\n", "\n", "import numpy as np\n", "print(f\"✓ np: {np.__version__} - {np.__file__}\")\n", "!pip show numpy | grep Version\n", "\n", "try:\n", " import roma\n", " print(\"✓ roma is installed\")\n", "except ModuleNotFoundError:\n", " print(\"⚠️ roma not found, installing...\")\n", " !pip install roma\n", " import roma\n", " print(\"✓ roma installed\")\n", "\n", "# =====================================================================\n", "# CELL 3: Clone Repositories\n", "# =====================================================================\n", "import os\n", "import sys\n", "\n", "# MASt3Rをクローン\n", "if not os.path.exists('/content/mast3r'):\n", " print(\"Cloning MASt3R repository...\")\n", " !git clone --recursive https://github.com/naver/mast3r.git /content/mast3r\n", " print(\"✓ MASt3R cloned\")\n", "else:\n", " print(\"✓ MASt3R already exists\")\n", "\n", "# DUSt3Rをクローン（MASt3R内に必要）\n", "if not os.path.exists('/content/mast3r/dust3r'):\n", " print(\"Cloning DUSt3R repository...\")\n", " !git clone --recursive https://github.com/naver/dust3r.git /content/mast3r/dust3r\n", " print(\"✓ DUSt3R cloned\")\n", "else:\n", " print(\"✓ DUSt3R already exists\")\n", "\n", "# パスを追加\n", "sys.path.insert(0, '/content/mast3r')\n", "sys.path.insert(0, '/content/mast3r/dust3r')\n", "\n", "# 確認\n", "try:\n", " from dust3r.model import AsymmetricCroCo3DStereo\n", " print(\"✓ dust3r.model imported successfully\")\n", "except ImportError as e:\n", " print(f\"✗ Import error: {e}\")\n", "\n", "# croco（MASt3Rの依存関係）もクローン\n", "if not os.path.exists('/content/mast3r/croco'):\n", " print(\"Cloning CroCo repository...\")\n", " !git clone --recursive https://github.com/naver/croco.git /content/mast3r/croco\n", " print(\"✓ CroCo cloned\")\n", "\n", "# =====================================================================\n", "# CELL 4: Clone and Build Gaussian Splatting\n", "# =====================================================================\n", "print(\"\\n\" + \"=\"*70)\n", "print(\"STEP: Clone Gaussian Splatting\")\n", "print(\"=\"*70)\n", "WORK_DIR = \"/content/gaussian-splatting\"\n", "\n", "import subprocess\n", "if not os.path.exists(WORK_DIR):\n", " subprocess.run([\n", " \"git\", \"clone\", \"--recursive\",\n", " \"https://github.com/graphdeco-inria/gaussian-splatting.git\",\n", " WORK_DIR\n", " ], capture_output=True)\n", " print(\"✓ Cloned\")\n", "else:\n", " print(\"✓ Already exists\")\n", "\n", "# インストールが必要なディレクトリ\n", "submodules = [\n", " \"/content/gaussian-splatting/submodules/diff-gaussian-rasterization\",\n", " \"/content/gaussian-splatting/submodules/simple-knn\"\n", "]\n", "\n", "for path in submodules:\n", " print(f\"Installing {path}...\")\n", " subprocess.run([\"pip\", \"install\", path], check=True)\n", "\n", "print(\"✓ Custom CUDA modules installed.\")\n", "\n", "print(f\"✓ np: {np.__version__} - {np.__file__}\")\n", "!pip show numpy | grep Version\n", "\n", "# =====================================================================\n", "# CELL 5: Import Core Libraries and Configure Memory\n", "# =====================================================================\n", "import os\n", "import sys\n", "import gc\n", "import torch\n", "import numpy as np\n", "from pathlib import Path\n", "from tqdm import tqdm\n", "import torch.nn.functional as F\n", "import shutil\n", "from PIL import Image\n", "from transformers import AutoImageProcessor, AutoModel\n", "\n", "# MEMORY MANAGEMENT\n", "os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'\n", "\n", "def clear_memory():\n", " \"\"\"メモリクリア関数\"\"\"\n", " gc.collect()\n", " if torch.cuda.is_available():\n", " torch.cuda.empty_cache()\n", " torch.cuda.synchronize()\n", "\n", "def get_memory_info():\n", " \"\"\"Get current memory usage\"\"\"\n", " if torch.cuda.is_available():\n", " allocated = torch.cuda.memory_allocated() / 1024**3\n", " reserved = torch.cuda.memory_reserved() / 1024**3\n", " print(f\"GPU Memory - Allocated: {allocated:.2f}GB, Reserved: {reserved:.2f}GB\")\n", "\n", " import psutil\n", " cpu_mem = psutil.virtual_memory().percent\n", " print(f\"CPU Memory Usage: {cpu_mem:.1f}%\")\n", "\n", "# CONFIGURATION\n", "class Config:\n", " DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", " MAST3R_WEIGHTS = \"naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric\"\n", " DUST3R_WEIGHTS = \"naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt\"\n", "\n", " # DINO設定\n", " DINO_MODEL = \"facebook/dinov2-base\"\n", " GLOBAL_TOPK = 20 # 各画像がペアを組む上位K個\n", "\n", " IMAGE_SIZE = 224\n", "\n", "# =====================================================================\n", "# CELL 6: Image Preprocessing Functions (Biplet)\n", "# =====================================================================\n", "def normalize_image_sizes_biplet(input_dir, output_dir=None, size=1024):\n", " \"\"\"\n", " Generates two square crops (Left & Right or Top & Bottom)\n", " from each image in a directory.\n", " \"\"\"\n", " if output_dir is None:\n", " output_dir = input_dir + \"_biplet\"\n", "\n", " os.makedirs(output_dir, exist_ok=True)\n", "\n", " print(f\"\\n=== Generating Biplet Crops ({size}x{size}) ===\")\n", "\n", " converted_count = 0\n", " size_stats = {}\n", "\n", " for img_file in tqdm(sorted(os.listdir(input_dir)), desc=\"Creating biplets\"):\n", " if not img_file.lower().endswith(('.jpg', '.jpeg', '.png')):\n", " continue\n", "\n", " input_path = os.path.join(input_dir, img_file)\n", "\n", " try:\n", " img = Image.open(input_path)\n", " original_size = img.size\n", "\n", " size_key = f\"{original_size[0]}x{original_size[1]}\"\n", " size_stats[size_key] = size_stats.get(size_key, 0) + 1\n", "\n", " # Generate 2 crops\n", " crops = generate_two_crops(img, size)\n", "\n", " base_name, ext = os.path.splitext(img_file)\n", " for mode, cropped_img in crops.items():\n", " output_path = os.path.join(output_dir, f\"{base_name}_{mode}{ext}\")\n", " cropped_img.save(output_path, quality=95)\n", "\n", " converted_count += 1\n", "\n", " except Exception as e:\n", " print(f\" ✗ Error processing {img_file}: {e}\")\n", "\n", " print(f\"\\n✓ Biplet generation complete:\")\n", " print(f\" Source images: {converted_count}\")\n", " print(f\" Biplet crops generated: {converted_count * 2}\")\n", " print(f\" Original size distribution: {size_stats}\")\n", "\n", " return output_dir\n", "\n", "\n", "def generate_two_crops(img, size):\n", " \"\"\"\n", " Crops the image into a square and returns 2 variations\n", " \"\"\"\n", " width, height = img.size\n", " crop_size = min(width, height)\n", " crops = {}\n", "\n", " if width > height:\n", " # Landscape → Left & Right\n", " positions = {\n", " 'left': 0,\n", " 'right': width - crop_size\n", " }\n", " for mode, x_offset in positions.items():\n", " box = (x_offset, 0, x_offset + crop_size, crop_size)\n", " crops[mode] = img.crop(box).resize(\n", " (size, size),\n", " Image.Resampling.LANCZOS\n", " )\n", " else:\n", " # Portrait or Square → Top & Bottom\n", " positions = {\n", " 'top': 0,\n", " 'bottom': height - crop_size\n", " }\n", " for mode, y_offset in positions.items():\n", " box = (0, y_offset, crop_size, y_offset + crop_size)\n", " crops[mode] = img.crop(box).resize(\n", " (size, size),\n", " Image.Resampling.LANCZOS\n", " )\n", "\n", " return crops\n", "\n", "# =====================================================================\n", "# CELL 7: Image Loading Function\n", "# =====================================================================\n", "def load_images_from_directory(image_dir, max_images=200):\n", " \"\"\"ディレクトリから画像をロード\"\"\"\n", " print(f\"\\nLoading images from: {image_dir}\")\n", "\n", " valid_extensions = {'.jpg', '.jpeg', '.png', '.bmp'}\n", " image_paths = []\n", "\n", " for ext in valid_extensions:\n", " image_paths.extend(sorted(Path(image_dir).glob(f'*{ext}')))\n", " image_paths.extend(sorted(Path(image_dir).glob(f'*{ext.upper()}')))\n", "\n", " image_paths = sorted(set(str(p) for p in image_paths))\n", "\n", " if len(image_paths) > max_images:\n", " print(f\"⚠️ Limiting from {len(image_paths)} to {max_images} images\")\n", " image_paths = image_paths[:max_images]\n", "\n", " print(f\"✓ Found {len(image_paths)} images\")\n", " return image_paths\n", "\n", "# =====================================================================\n", "# CELL 8: MASt3R Model Loading\n", "# =====================================================================\n", "def load_mast3r_model(device):\n", " \"\"\"MASt3Rモデルをロード\"\"\"\n", " print(\"\\n=== Loading MASt3R Model ===\")\n", "\n", " if '/content/mast3r' not in sys.path:\n", " sys.path.insert(0, '/content/mast3r')\n", " if '/content/mast3r/dust3r' not in sys.path:\n", " sys.path.insert(0, '/content/mast3r/dust3r')\n", "\n", " from dust3r.model import AsymmetricCroCo3DStereo\n", "\n", " try:\n", " print(f\"Attempting to load: {Config.MAST3R_WEIGHTS}\")\n", " model = AsymmetricCroCo3DStereo.from_pretrained(Config.MAST3R_WEIGHTS).to(device)\n", " print(\"✓ Loaded MASt3R model\")\n", " except Exception as e:\n", " print(f\"⚠️ Failed to load MASt3R: {e}\")\n", " print(f\"Trying DUSt3R instead: {Config.DUST3R_WEIGHTS}\")\n", " model = AsymmetricCroCo3DStereo.from_pretrained(Config.DUST3R_WEIGHTS).to(device)\n", " print(\"✓ Loaded DUSt3R model as fallback\")\n", "\n", " model.eval()\n", " print(f\"✓ Model loaded on {device}\")\n", " return model\n", "\n", "# =====================================================================\n", "# CELL 9: DINO Pair Selection (REPLACES ASMK)\n", "# =====================================================================\n", "def load_torch_image(fname, device):\n", " \"\"\"Load image as torch tensor\"\"\"\n", " import torchvision.transforms as T\n", "\n", " img = Image.open(fname).convert('RGB')\n", " transform = T.Compose([\n", " T.ToTensor(),\n", " ])\n", " return transform(img).unsqueeze(0).to(device)\n", "\n", "def extract_dino_global(image_paths, model_path, device):\n", " \"\"\"Extract DINO global descriptors with memory management\"\"\"\n", " print(\"\\n=== Extracting DINO Global Features ===\")\n", " print(\"Initial memory state:\")\n", " get_memory_info()\n", "\n", " processor = AutoImageProcessor.from_pretrained(model_path)\n", " model = AutoModel.from_pretrained(model_path).eval().to(device)\n", "\n", " global_descs = []\n", " batch_size = 4 # Small batch to save memory\n", "\n", " for i in tqdm(range(0, len(image_paths), batch_size), desc=\"DINO extraction\"):\n", " batch_paths = image_paths[i:i+batch_size]\n", " batch_imgs = []\n", "\n", " for img_path in batch_paths:\n", " img = load_torch_image(img_path, device)\n", " batch_imgs.append(img)\n", "\n", " batch_tensor = torch.cat(batch_imgs, dim=0)\n", "\n", " with torch.no_grad():\n", " inputs = processor(images=batch_tensor, return_tensors=\"pt\", do_rescale=False).to(device)\n", " outputs = model(**inputs)\n", " desc = F.normalize(outputs.last_hidden_state[:, 1:].max(dim=1)[0], dim=1, p=2)\n", " global_descs.append(desc.cpu())\n", "\n", " # Clear batch memory\n", " del batch_tensor, inputs, outputs, desc\n", " clear_memory()\n", "\n", " global_descs = torch.cat(global_descs, dim=0)\n", "\n", " del model, processor\n", " clear_memory()\n", "\n", " print(\"After DINO extraction:\")\n", " get_memory_info()\n", "\n", " return global_descs\n", "\n", "def build_topk_pairs(global_feats, k, device):\n", " \"\"\"Build top-k similar pairs from global features\"\"\"\n", " g = global_feats.to(device)\n", " sim = g @ g.T\n", " sim.fill_diagonal_(-1)\n", "\n", " N = sim.size(0)\n", " k = min(k, N - 1)\n", "\n", " topk_indices = torch.topk(sim, k, dim=1).indices.cpu()\n", "\n", " pairs = []\n", " for i in range(N):\n", " for j in topk_indices[i]:\n", " j = j.item()\n", " if i < j:\n", " pairs.append((i, j))\n", "\n", " # Remove duplicates\n", " pairs = list(set(pairs))\n", "\n", " return pairs\n", "\n", "def select_diverse_pairs(pairs, max_pairs, num_images):\n", " \"\"\"\n", " Select diverse pairs to ensure good image coverage\n", " \"\"\"\n", " import random\n", " random.seed(42)\n", "\n", " if len(pairs) <= max_pairs:\n", " return pairs\n", "\n", " print(f\"Selecting {max_pairs} diverse pairs from {len(pairs)} candidates...\")\n", "\n", " # Count how many times each image appears in pairs\n", " image_counts = {i: 0 for i in range(num_images)}\n", " for i, j in pairs:\n", " image_counts[i] += 1\n", " image_counts[j] += 1\n", "\n", " # Sort pairs by: prefer pairs with less-connected images\n", " def pair_score(pair):\n", " i, j = pair\n", " return image_counts[i] + image_counts[j]\n", "\n", " pairs_scored = [(pair, pair_score(pair)) for pair in pairs]\n", " pairs_scored.sort(key=lambda x: x[1])\n", "\n", " # Select pairs greedily to maximize coverage\n", " selected = []\n", " selected_images = set()\n", "\n", " # Phase 1: Select pairs that add new images\n", " for pair, score in pairs_scored:\n", " if len(selected) >= max_pairs:\n", " break\n", " i, j = pair\n", " if i not in selected_images or j not in selected_images:\n", " selected.append(pair)\n", " selected_images.add(i)\n", " selected_images.add(j)\n", "\n", " # Phase 2: Fill remaining slots\n", " if len(selected) < max_pairs:\n", " remaining = [p for p, s in pairs_scored if p not in selected]\n", " random.shuffle(remaining)\n", " selected.extend(remaining[:max_pairs - len(selected)])\n", "\n", " print(f\"Selected pairs cover {len(selected_images)} / {num_images} images ({100*len(selected_images)/num_images:.1f}%)\")\n", "\n", " return selected\n", "\n", "def get_image_pairs_dino(image_paths, max_pairs=None):\n", " \"\"\"DINO-based pair selection\"\"\"\n", " device = Config.DEVICE\n", "\n", " # DINO global features\n", " global_feats = extract_dino_global(image_paths, Config.DINO_MODEL, device)\n", " pairs = build_topk_pairs(global_feats, Config.GLOBAL_TOPK, device)\n", "\n", " print(f\"Initial pairs from DINO: {len(pairs)}\")\n", "\n", " # Apply intelligent pair selection if limit specified\n", " if max_pairs and len(pairs) > max_pairs:\n", " pairs = select_diverse_pairs(pairs, max_pairs, len(image_paths))\n", "\n", " return pairs\n", "\n", "# =====================================================================\n", "# CELL 10: MASt3R Reconstruction\n", "# =====================================================================\n", "def run_mast3r_pairs(model, image_paths, pairs, device, batch_size=1, max_pairs=None):\n", " \"\"\"Run MASt3R on selected pairs with memory management\"\"\"\n", " print(\"\\n=== Running MASt3R Reconstruction ===\")\n", " print(\"Initial memory state:\")\n", " get_memory_info()\n", "\n", " from dust3r.inference import inference\n", " from dust3r.cloud_opt import global_aligner, GlobalAlignerMode\n", " from dust3r.utils.image import load_images\n", "\n", " # Limit number of pairs if specified\n", " if max_pairs and len(pairs) > max_pairs:\n", " print(f\"Limiting pairs from {len(pairs)} to {max_pairs}\")\n", " step = max(1, len(pairs) // max_pairs)\n", " pairs = pairs[::step][:max_pairs]\n", "\n", " print(f\"Processing {len(pairs)} pairs...\")\n", "\n", " # Load images in smaller size\n", " print(f\"Loading {len(image_paths)} images at {Config.IMAGE_SIZE}x{Config.IMAGE_SIZE}...\")\n", " images = load_images(image_paths, size=Config.IMAGE_SIZE)\n", "\n", " print(f\"Loaded {len(images)} images\")\n", " print(\"After loading images:\")\n", " get_memory_info()\n", "\n", " # Create all image pairs\n", " print(f\"Creating {len(pairs)} image pairs...\")\n", " mast3r_pairs = []\n", " for idx1, idx2 in tqdm(pairs, desc=\"Preparing pairs\"):\n", " mast3r_pairs.append((images[idx1], images[idx2]))\n", "\n", " print(f\"Running MASt3R inference on {len(mast3r_pairs)} pairs...\")\n", "\n", " # Run inference\n", " output = inference(mast3r_pairs, model, device, batch_size=batch_size, verbose=True)\n", "\n", " del mast3r_pairs\n", " clear_memory()\n", "\n", " print(\"✓ MASt3R inference complete\")\n", " print(\"After inference:\")\n", " get_memory_info()\n", "\n", " # Global alignment\n", " print(\"Running global alignment...\")\n", " scene = global_aligner(\n", " output,\n", " device=device,\n", " mode=GlobalAlignerMode.PointCloudOptimizer\n", " )\n", "\n", " del output\n", " clear_memory()\n", "\n", " print(\"Computing global alignment...\")\n", " loss = scene.compute_global_alignment(\n", " init=\"mst\",\n", " niter=50, # Reduced iterations\n", " schedule='cosine',\n", " lr=0.01\n", " )\n", "\n", " print(f\"✓ Global alignment complete (final loss: {loss:.6f})\")\n", " print(\"Final memory state:\")\n", " get_memory_info()\n", "\n", " return scene, images\n", "\n", "\n", "\n" ], "metadata": { "trusted": true, "id": "OWJEB1oQTKyD", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "fa123527-2b15-4fa5-8d3c-c830ccc43365" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n", "✓ np: 1.26.4 - /usr/local/lib/python3.12/dist-packages/numpy/__init__.py\n", "Version: 1.26.4\n", "Version 3.1, 31 March 2009\n", " Version 3, 29 June 2007\n", " 5. Conveying Modified Source Versions.\n", " 14. Revised Versions of this License.\n", "✓ roma is installed\n", "✓ MASt3R already exists\n", "✓ DUSt3R already exists\n", "✓ dust3r.model imported successfully\n", "\n", "======================================================================\n", "STEP: Clone Gaussian Splatting\n", "======================================================================\n", "✓ Already exists\n", "Installing /content/gaussian-splatting/submodules/diff-gaussian-rasterization...\n", "Installing /content/gaussian-splatting/submodules/simple-knn...\n", "✓ Custom CUDA modules installed.\n", "✓ np: 1.26.4 - /usr/local/lib/python3.12/dist-packages/numpy/__init__.py\n", "Version: 1.26.4\n", "Version 3.1, 31 March 2009\n", " Version 3, 29 June 2007\n", " 5. Conveying Modified Source Versions.\n", " 14. Revised Versions of this License.\n" ] } ], "execution_count": 28 }, { "cell_type": "code", "source": [ "# =====================================================================\n", "# CELL 11: Camera Parameter Extraction (修正版)\n", "# =====================================================================\n", "def extract_camera_params_process2(scene, image_paths, conf_threshold=1.5):\n", " \"\"\"sceneからカメラパラメータと3D点を抽出（スケーリング修正版）\"\"\"\n", " print(\"\\n=== Extracting Camera Parameters ===\")\n", "\n", " cameras_dict = {}\n", " all_pts3d = []\n", " all_confidence = []\n", "\n", " try:\n", " if hasattr(scene, 'get_im_poses'):\n", " poses = scene.get_im_poses()\n", " elif hasattr(scene, 'im_poses'):\n", " poses = scene.im_poses\n", " else:\n", " poses = None\n", "\n", " if hasattr(scene, 'get_focals'):\n", " focals = scene.get_focals()\n", " elif hasattr(scene, 'im_focals'):\n", " focals = scene.im_focals\n", " else:\n", " focals = None\n", "\n", " if hasattr(scene, 'get_principal_points'):\n", " pps = scene.get_principal_points()\n", " elif hasattr(scene, 'im_pp'):\n", " pps = scene.im_pp\n", " else:\n", " pps = None\n", " except Exception as e:\n", " print(f\"⚠️ Error getting camera parameters: {e}\")\n", " poses = None\n", " focals = None\n", " pps = None\n", "\n", " # 【重要】MASt3Rの処理サイズ\n", " mast3r_size = 224.0\n", "\n", " n_images = min(len(poses) if poses is not None else len(image_paths), len(image_paths))\n", "\n", " for idx in range(n_images):\n", " img_name = os.path.basename(image_paths[idx])\n", "\n", " try:\n", " # 元画像のサイズを取得\n", " img = Image.open(image_paths[idx])\n", " W, H = img.size\n", " img.close()\n", "\n", " # スケール比を計算\n", " scale = W / mast3r_size\n", "\n", " # Poseを取得（camera-to-worldをworld-to-cameraに変換）\n", " if poses is not None and idx < len(poses):\n", " pose_c2w = poses[idx]\n", " if isinstance(pose_c2w, torch.Tensor):\n", " pose_c2w = pose_c2w.detach().cpu().numpy()\n", " if not isinstance(pose_c2w, np.ndarray) or pose_c2w.shape != (4, 4):\n", " pose_c2w = np.eye(4)\n", "\n", " # world-to-camera に変換\n", " pose = np.linalg.inv(pose_c2w)\n", " else:\n", " pose = np.eye(4)\n", "\n", " # Focalを取得してスケーリング\n", " if focals is not None and idx < len(focals):\n", " focal_mast3r = focals[idx]\n", " if isinstance(focal_mast3r, torch.Tensor):\n", " focal_mast3r = focal_mast3r.detach().cpu().item()\n", " else:\n", " focal_mast3r = float(focal_mast3r)\n", "\n", " # 🔧 スケーリング適用\n", " if focals.shape[1] == 1:\n", " # 等方性カメラ（fx = fy）\n", " focal = focal_mast3r * scale\n", " else:\n", " # 異方性カメラ\n", " focal = float(focals[idx, 0]) * scale\n", " else:\n", " focal = 1000.0\n", "\n", " # Principal pointを取得してスケーリング\n", " if pps is not None and idx < len(pps):\n", " pp_mast3r = pps[idx]\n", " if isinstance(pp_mast3r, torch.Tensor):\n", " pp_mast3r = pp_mast3r.detach().cpu().numpy()\n", "\n", " # 🔧 スケーリング適用\n", " pp = pp_mast3r * scale\n", " else:\n", " pp = np.array([W / 2.0, H / 2.0])\n", "\n", " # カメラパラメータを保存\n", " cameras_dict[img_name] = {\n", " 'focal': focal,\n", " 'pp': pp,\n", " 'pose': pose,\n", " 'rotation': pose[:3, :3],\n", " 'translation': pose[:3, 3],\n", " 'width': W,\n", " 'height': H\n", " }\n", "\n", " # デバッグ情報（最初の画像のみ）\n", " if idx == 0:\n", " print(f\"\\nExample camera 0:\")\n", " print(f\" Image size: {W}x{H}\")\n", " print(f\" MASt3R size: {mast3r_size}\")\n", " print(f\" Scale factor: {scale:.3f}\")\n", " print(f\" MASt3R focal: {focal_mast3r:.2f}\")\n", " print(f\" Scaled focal: {focal:.2f}\")\n", " print(f\" MASt3R pp: [{pp_mast3r[0]:.2f}, {pp_mast3r[1]:.2f}]\")\n", " print(f\" Scaled pp: [{pp[0]:.2f}, {pp[1]:.2f}]\")\n", "\n", " # 3D点を取得\n", " if hasattr(scene, 'im_pts3d') and idx < len(scene.im_pts3d):\n", " pts3d_img = scene.im_pts3d[idx]\n", " elif hasattr(scene, 'get_pts3d'):\n", " pts3d_all = scene.get_pts3d()\n", " if idx < len(pts3d_all):\n", " pts3d_img = pts3d_all[idx]\n", " else:\n", " pts3d_img = None\n", " else:\n", " pts3d_img = None\n", "\n", " # Confidenceを取得\n", " if hasattr(scene, 'im_conf') and idx < len(scene.im_conf):\n", " conf_img = scene.im_conf[idx]\n", " elif hasattr(scene, 'get_conf'):\n", " conf_all = scene.get_conf()\n", " if idx < len(conf_all):\n", " conf_img = conf_all[idx]\n", " else:\n", " conf_img = None\n", " else:\n", " conf_img = None\n", "\n", " # 3D点とconfidenceを処理\n", " if pts3d_img is not None:\n", " if isinstance(pts3d_img, torch.Tensor):\n", " pts3d_img = pts3d_img.detach().cpu().numpy()\n", "\n", " if pts3d_img.ndim == 3:\n", " pts3d_flat = pts3d_img.reshape(-1, 3)\n", " else:\n", " pts3d_flat = pts3d_img\n", "\n", " all_pts3d.append(pts3d_flat)\n", "\n", " # confidenceを処理\n", " if conf_img is not None:\n", " if isinstance(conf_img, list):\n", " conf_img = np.array(conf_img)\n", " elif isinstance(conf_img, torch.Tensor):\n", " conf_img = conf_img.detach().cpu().numpy()\n", "\n", " if conf_img.ndim > 1:\n", " conf_flat = conf_img.reshape(-1)\n", " else:\n", " conf_flat = conf_img\n", "\n", " if len(conf_flat) != len(pts3d_flat):\n", " conf_flat = np.ones(len(pts3d_flat))\n", "\n", " all_confidence.append(conf_flat)\n", " else:\n", " all_confidence.append(np.ones(len(pts3d_flat)))\n", "\n", " except Exception as e:\n", " print(f\"⚠️ Error processing image {idx} ({img_name}): {e}\")\n", " # デフォルト値でもスケーリングを適用\n", " img = Image.open(image_paths[idx])\n", " W, H = img.size\n", " img.close()\n", "\n", " cameras_dict[img_name] = {\n", " 'focal': 1000.0 * (W / mast3r_size),\n", " 'pp': np.array([W / 2.0, H / 2.0]),\n", " 'pose': np.eye(4),\n", " 'rotation': np.eye(3),\n", " 'translation': np.zeros(3),\n", " 'width': W,\n", " 'height': H\n", " }\n", " continue\n", "\n", " # 全3D点を結合\n", " if all_pts3d:\n", " pts3d = np.vstack(all_pts3d)\n", " confidence = np.concatenate(all_confidence)\n", " else:\n", " pts3d = np.zeros((0, 3))\n", " confidence = np.zeros(0)\n", "\n", " print(f\"✓ Extracted camera parameters for {len(cameras_dict)} cameras\")\n", " print(f\"✓ Total 3D points: {len(pts3d)}\")\n", "\n", " # Confidenceでフィルタリング\n", " if len(confidence) > 0:\n", " valid_mask = confidence > conf_threshold\n", " pts3d = pts3d[valid_mask]\n", " confidence = confidence[valid_mask]\n", " print(f\"✓ After confidence filtering (>{conf_threshold}): {len(pts3d)} points\")\n", "\n", " return cameras_dict, pts3d, confidence\n" ], "metadata": { "id": "YSt2RDqmviUa" }, "execution_count": 42, "outputs": [] }, { "cell_type": "code", "source": [ "# =====================================================================\n", "# CELL 12: COLMAP Export Functions (PINHOLE版)\n", "# =====================================================================\n", "\n", "import struct\n", "import numpy as np\n", "from pathlib import Path\n", "\n", "def rotmat_to_qvec(R):\n", " \"\"\"回転行列をクォータニオンに変換\"\"\"\n", " R = np.asarray(R, dtype=np.float64)\n", " trace = np.trace(R)\n", "\n", " if trace > 0:\n", " s = 0.5 / np.sqrt(trace + 1.0)\n", " w = 0.25 / s\n", " x = (R[2, 1] - R[1, 2]) * s\n", " y = (R[0, 2] - R[2, 0]) * s\n", " z = (R[1, 0] - R[0, 1]) * s\n", " elif R[0, 0] > R[1, 1] and R[0, 0] > R[2, 2]:\n", " s = 2.0 * np.sqrt(1.0 + R[0, 0] - R[1, 1] - R[2, 2])\n", " w = (R[2, 1] - R[1, 2]) / s\n", " x = 0.25 * s\n", " y = (R[0, 1] + R[1, 0]) / s\n", " z = (R[0, 2] + R[2, 0]) / s\n", " elif R[1, 1] > R[2, 2]:\n", " s = 2.0 * np.sqrt(1.0 + R[1, 1] - R[0, 0] - R[2, 2])\n", " w = (R[0, 2] - R[2, 0]) / s\n", " x = (R[0, 1] + R[1, 0]) / s\n", " y = 0.25 * s\n", " z = (R[1, 2] + R[2, 1]) / s\n", " else:\n", " s = 2.0 * np.sqrt(1.0 + R[2, 2] - R[0, 0] - R[1, 1])\n", " w = (R[1, 0] - R[0, 1]) / s\n", " x = (R[0, 2] + R[2, 0]) / s\n", " y = (R[1, 2] + R[2, 1]) / s\n", " z = 0.25 * s\n", "\n", " qvec = np.array([w, x, y, z], dtype=np.float64)\n", " qvec = qvec / np.linalg.norm(qvec)\n", "\n", " return qvec\n", "\n", "\n", "def write_cameras_binary(cameras_dict, image_size, output_file):\n", " \"\"\"\n", " cameras.binを出力（PINHOLEモデル使用）\n", " \"\"\"\n", " width, height = image_size\n", " num_cameras = len(cameras_dict)\n", "\n", " # COLMAP camera models\n", " PINHOLE = 1 # 🔧 SIMPLE_PINHOLE (0) から PINHOLE (1) に変更\n", "\n", " with open(output_file, 'wb') as f:\n", " f.write(struct.pack('Q', num_cameras))\n", "\n", " for camera_id, (img_id, cam_params) in enumerate(cameras_dict.items(), start=1):\n", " focal = cam_params['focal']\n", "\n", " # PINHOLEの場合: fx, fy, cx, cy\n", " fx = fy = focal # 等方性カメラを仮定\n", "\n", " # Principal pointを取得（存在しない場合は中心）\n", " if 'pp' in cam_params:\n", " pp = cam_params['pp']\n", " cx = float(pp[0])\n", " cy = float(pp[1])\n", " else:\n", " cx = width / 2.0\n", " cy = height / 2.0\n", "\n", " # camera_id\n", " f.write(struct.pack('I', camera_id))\n", " # model_id (PINHOLE = 1)\n", " f.write(struct.pack('i', PINHOLE))\n", " # width\n", " f.write(struct.pack('Q', width))\n", " # height\n", " f.write(struct.pack('Q', height))\n", " # params: fx, fy, cx, cy (4パラメータ)\n", " f.write(struct.pack('d', fx))\n", " f.write(struct.pack('d', fy))\n", " f.write(struct.pack('d', cx))\n", " f.write(struct.pack('d', cy))\n", "\n", " print(f\"COLMAP cameras.bin saved to {output_file}\")\n", "\n", "\n", "def write_images_binary(cameras_dict, output_file):\n", " \"\"\"images.binを出力\"\"\"\n", " num_images = len(cameras_dict)\n", "\n", " with open(output_file, 'wb') as f:\n", " f.write(struct.pack('Q', num_images))\n", "\n", " for image_id, (img_id, cam_params) in enumerate(cameras_dict.items(), start=1):\n", " R = cam_params['rotation']\n", " quat = rotmat_to_qvec(R)\n", " t = cam_params['translation']\n", " camera_id = image_id\n", "\n", " f.write(struct.pack('I', image_id))\n", " for q in quat:\n", " f.write(struct.pack('d', q))\n", " for ti in t:\n", " f.write(struct.pack('d', ti))\n", " f.write(struct.pack('I', camera_id))\n", "\n", " name_bytes = img_id.encode('utf-8') + b'\\x00'\n", " f.write(name_bytes)\n", " f.write(struct.pack('Q', 0))\n", "\n", " print(f\"COLMAP images.bin saved to {output_file}\")\n", "\n", "\n", "def write_points3D_binary(pts3d, confidence, output_file):\n", " \"\"\"points3D.binを出力\"\"\"\n", " num_points = len(pts3d)\n", "\n", " with open(output_file, 'wb') as f:\n", " f.write(struct.pack('Q', num_points))\n", "\n", " for point_id, pt in enumerate(pts3d, start=1):\n", " x, y, z = pt\n", "\n", " f.write(struct.pack('Q', point_id))\n", " f.write(struct.pack('d', x))\n", " f.write(struct.pack('d', y))\n", " f.write(struct.pack('d', z))\n", "\n", " # RGB (グレー)\n", " f.write(struct.pack('B', 128))\n", " f.write(struct.pack('B', 128))\n", " f.write(struct.pack('B', 128))\n", "\n", " # error\n", " if confidence is not None and point_id <= len(confidence):\n", " error = 1.0 / max(confidence[point_id-1], 0.001)\n", " else:\n", " error = 1.0\n", " f.write(struct.pack('d', error))\n", "\n", " # track_length\n", " f.write(struct.pack('Q', 0))\n", "\n", " print(f\"COLMAP points3D.bin saved to {output_file}\")\n", "\n", "\n", "def export_colmap_binary(cameras_dict, pts3d, confidence, image_size, output_dir):\n", " \"\"\"COLMAPバイナリファイルを出力\"\"\"\n", " output_path = Path(output_dir)\n", " output_path.mkdir(parents=True, exist_ok=True)\n", "\n", " write_cameras_binary(\n", " cameras_dict,\n", " image_size,\n", " output_path / 'cameras.bin'\n", " )\n", "\n", " write_images_binary(\n", " cameras_dict,\n", " output_path / 'images.bin'\n", " )\n", "\n", " write_points3D_binary(\n", " pts3d,\n", " confidence,\n", " output_path / 'points3D.bin'\n", " )\n", "\n", " print(f\"\\nCOLMAP binary files exported to {output_dir}/\")\n", " print(f\" - cameras.bin: {len(cameras_dict)} cameras (PINHOLE model)\")\n", " print(f\" - images.bin: {len(cameras_dict)} images\")\n", " print(f\" - points3D.bin: {len(pts3d)} points\")" ], "metadata": { "id": "jNk5C0k1zkLD" }, "execution_count": 46, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "gDbmwRKsEkYi" }, "execution_count": 30, "outputs": [] }, { "cell_type": "markdown", "source": [ "**comparison**" ], "metadata": { "id": "yGny_hJrElYL" } }, { "cell_type": "code", "source": [ "# =====================================================================\n", "# CELL 20: Traditional Method Functions (for comparison)\n", "# =====================================================================\n", "import struct\n", "import numpy as np\n", "from pathlib import Path\n", "\n", "# ===== 従来法: extract_colmap_data =====\n", "def extract_colmap_data_traditional(scene, image_paths, max_points=1000000):\n", " \"\"\"\n", " 従来法: MASt3Rシーンから COLMAP互換データを抽出\n", " （dino-mast3r-gs-kg-34oo.ipynb からの抽出）\n", " \"\"\"\n", " print(\"\\n=== [TRADITIONAL] Extracting COLMAP-compatible data ===\")\n", "\n", " # Extract point cloud\n", " pts_all = scene.get_pts3d()\n", " print(f\"pts_all type: {type(pts_all)}\")\n", "\n", " if isinstance(pts_all, list):\n", " print(f\"pts_all is a list with {len(pts_all)} elements\")\n", " if len(pts_all) > 0:\n", " print(f\"First element type: {type(pts_all[0])}\")\n", " if hasattr(pts_all[0], 'shape'):\n", " print(f\"First element shape: {pts_all[0].shape}\")\n", "\n", " pts_all = torch.stack([p if isinstance(p, torch.Tensor) else torch.tensor(p)\n", " for p in pts_all])\n", " print(f\"pts_all shape after conversion: {pts_all.shape}\")\n", "\n", " if len(pts_all.shape) == 4:\n", " print(f\"Found batched point cloud: {pts_all.shape}\")\n", " B, H, W, _ = pts_all.shape\n", " pts3d = pts_all.reshape(-1, 3).detach().cpu().numpy()\n", "\n", " # Extract colors\n", " colors = []\n", " for img_path in image_paths:\n", " img = Image.open(img_path).resize((W, H))\n", " colors.append(np.array(img))\n", " colors = np.stack(colors).reshape(-1, 3) / 255.0\n", " else:\n", " pts3d = pts_all.detach().cpu().numpy() if isinstance(pts_all, torch.Tensor) else pts_all\n", " colors = np.ones((len(pts3d), 3)) * 0.5\n", "\n", " print(f\"✓ Extracted {len(pts3d)} 3D points from {len(image_paths)} images\")\n", "\n", " # Downsample points\n", " if len(pts3d) > max_points:\n", " print(f\"\\n⚠ Downsampling from {len(pts3d)} to {max_points} points...\")\n", " valid_mask = ~(np.isnan(pts3d).any(axis=1) | np.isinf(pts3d).any(axis=1))\n", " pts3d_valid = pts3d[valid_mask]\n", " colors_valid = colors[valid_mask]\n", " indices = np.random.choice(len(pts3d_valid), size=max_points, replace=False)\n", " pts3d = pts3d_valid[indices]\n", " colors = colors_valid[indices]\n", " print(f\"✓ Downsampled to {len(pts3d)} points\")\n", "\n", " # Extract camera parameters\n", " print(\"Extracting camera parameters...\")\n", "\n", " # 【重要】camera-to-world を world-to-camera に変換\n", " poses_c2w = scene.get_im_poses().detach().cpu().numpy()\n", " print(f\"Retrieved camera-to-world poses: shape {poses_c2w.shape}\")\n", "\n", " poses = []\n", " for i, pose_c2w in enumerate(poses_c2w):\n", " pose_w2c = np.linalg.inv(pose_c2w)\n", " poses.append(pose_w2c)\n", " poses = np.array(poses)\n", " print(f\"Converted to world-to-camera poses for COLMAP\")\n", "\n", " focals = scene.get_focals().detach().cpu().numpy()\n", " pp = scene.get_principal_points().detach().cpu().numpy()\n", " print(f\"Focals shape: {focals.shape}\")\n", " print(f\"Principal points shape: {pp.shape}\")\n", "\n", " mast3r_size = 224.0\n", "\n", " cameras = []\n", " for i, img_path in enumerate(image_paths):\n", " img = Image.open(img_path)\n", " W, H = img.size\n", " scale = W / mast3r_size\n", "\n", " if focals.shape[1] == 1:\n", " focal_mast3r = float(focals[i, 0])\n", " fx = fy = focal_mast3r * scale\n", " else:\n", " fx = float(focals[i, 0]) * scale\n", " fy = float(focals[i, 1]) * scale\n", "\n", " cx = float(pp[i, 0]) * scale\n", " cy = float(pp[i, 1]) * scale\n", "\n", " camera = {\n", " 'camera_id': i + 1,\n", " 'model': 'PINHOLE',\n", " 'width': W,\n", " 'height': H,\n", " 'params': [fx, fy, cx, cy]\n", " }\n", " cameras.append(camera)\n", "\n", " if i == 0:\n", " print(f\"\\nExample camera 0:\")\n", " print(f\" Image size: {W}x{H}\")\n", " print(f\" MASt3R focal: {focal_mast3r:.2f}, pp: ({pp[i,0]:.2f}, {pp[i,1]:.2f})\")\n", " print(f\" Scaled fx={fx:.2f}, fy={fy:.2f}, cx={cx:.2f}, cy={cy:.2f}\")\n", " print(f\" Pose (first row): {poses[i][0]}\")\n", "\n", " print(f\"\\n✓ Extracted {len(cameras)} cameras and {len(poses)} poses\")\n", "\n", " return pts3d, colors, cameras, poses\n", "\n", "\n", "# ===== 従来法: rotmat2qvec =====\n", "def rotmat2qvec_traditional(R):\n", " \"\"\"従来法: 回転行列をクォータニオンに変換\"\"\"\n", " R = np.asarray(R, dtype=np.float64)\n", " trace = np.trace(R)\n", "\n", " if trace > 0:\n", " s = 0.5 / np.sqrt(trace + 1.0)\n", " w = 0.25 / s\n", " x = (R[2, 1] - R[1, 2]) * s\n", " y = (R[0, 2] - R[2, 0]) * s\n", " z = (R[1, 0] - R[0, 1]) * s\n", " elif R[0, 0] > R[1, 1] and R[0, 0] > R[2, 2]:\n", " s = 2.0 * np.sqrt(1.0 + R[0, 0] - R[1, 1] - R[2, 2])\n", " w = (R[2, 1] - R[1, 2]) / s\n", " x = 0.25 * s\n", " y = (R[0, 1] + R[1, 0]) / s\n", " z = (R[0, 2] + R[2, 0]) / s\n", " elif R[1, 1] > R[2, 2]:\n", " s = 2.0 * np.sqrt(1.0 + R[1, 1] - R[0, 0] - R[2, 2])\n", " w = (R[0, 2] - R[2, 0]) / s\n", " x = (R[0, 1] + R[1, 0]) / s\n", " y = 0.25 * s\n", " z = (R[1, 2] + R[2, 1]) / s\n", " else:\n", " s = 2.0 * np.sqrt(1.0 + R[2, 2] - R[0, 0] - R[1, 1])\n", " w = (R[1, 0] - R[0, 1]) / s\n", " x = (R[0, 2] + R[2, 0]) / s\n", " y = (R[1, 2] + R[2, 1]) / s\n", " z = 0.25 * s\n", "\n", " qvec = np.array([w, x, y, z], dtype=np.float64)\n", " qvec = qvec / np.linalg.norm(qvec)\n", "\n", " return qvec\n", "\n", "\n", "# ===== 従来法: save関数群 =====\n", "def write_cameras_binary_traditional(cameras, output_file):\n", " \"\"\"従来法: cameras.binを書き込み\"\"\"\n", " with open(output_file, 'wb') as f:\n", " f.write(struct.pack('Q', len(cameras)))\n", "\n", " for i, cam in enumerate(cameras):\n", " camera_id = cam.get('camera_id', i + 1)\n", " model_id = 1 # PINHOLE\n", " width = cam['width']\n", " height = cam['height']\n", " params = cam['params']\n", "\n", " f.write(struct.pack('i', camera_id))\n", " f.write(struct.pack('i', model_id))\n", " f.write(struct.pack('Q', width))\n", " f.write(struct.pack('Q', height))\n", "\n", " for param in params[:4]:\n", " f.write(struct.pack('d', param))\n", "\n", "\n", "def write_images_binary_traditional(image_paths, cameras, poses, output_file):\n", " \"\"\"従来法: images.binを書き込み\"\"\"\n", " with open(output_file, 'wb') as f:\n", " f.write(struct.pack('Q', len(image_paths)))\n", "\n", " for i, (img_path, pose) in enumerate(zip(image_paths, poses)):\n", " image_id = i + 1\n", " camera_id = cameras[i].get('camera_id', i + 1)\n", " image_name = os.path.basename(img_path)\n", "\n", " R = pose[:3, :3]\n", " t = pose[:3, 3]\n", " qvec = rotmat2qvec_traditional(R)\n", " tvec = t\n", "\n", " f.write(struct.pack('i', image_id))\n", " for q in qvec:\n", " f.write(struct.pack('d', float(q)))\n", " for tv in tvec:\n", " f.write(struct.pack('d', float(tv)))\n", " f.write(struct.pack('i', camera_id))\n", " f.write(image_name.encode('utf-8') + b'\\x00')\n", " f.write(struct.pack('Q', 0))\n", "\n", "\n", "def write_points3d_binary_traditional(pts3d, colors, output_file):\n", " \"\"\"従来法: points3D.binを書き込み\"\"\"\n", " valid_indices = []\n", " for i, pt in enumerate(pts3d):\n", " if not (np.isnan(pt).any() or np.isinf(pt).any()):\n", " valid_indices.append(i)\n", "\n", " with open(output_file, 'wb') as f:\n", " f.write(struct.pack('Q', len(valid_indices)))\n", "\n", " for idx, point_id in enumerate(valid_indices):\n", " pt = pts3d[point_id]\n", " color = colors[point_id]\n", "\n", " f.write(struct.pack('Q', point_id))\n", " for coord in pt:\n", " f.write(struct.pack('d', float(coord)))\n", "\n", " col_int = (color * 255).astype(np.uint8)\n", " for c in col_int:\n", " f.write(struct.pack('B', int(c)))\n", "\n", " f.write(struct.pack('d', 0.0))\n", " f.write(struct.pack('Q', 0))\n", "\n", " return len(valid_indices)\n", "\n", "\n", "def save_colmap_reconstruction_traditional(pts3d, colors, cameras, poses, image_paths, output_dir):\n", " \"\"\"従来法: COLMAP再構成を保存\"\"\"\n", " print(\"\\n=== [TRADITIONAL] Saving COLMAP reconstruction ===\")\n", "\n", " sparse_dir = Path(output_dir) / 'sparse_traditional' / '0'\n", " sparse_dir.mkdir(parents=True, exist_ok=True)\n", "\n", " write_cameras_binary_traditional(cameras, sparse_dir / 'cameras.bin')\n", " print(f\" ✓ Wrote {len(cameras)} cameras\")\n", "\n", " write_images_binary_traditional(image_paths, cameras, poses, sparse_dir / 'images.bin')\n", " print(f\" ✓ Wrote {len(image_paths)} images\")\n", "\n", " num_points = write_points3d_binary_traditional(pts3d, colors, sparse_dir / 'points3D.bin')\n", " print(f\" ✓ Wrote {num_points} 3D points\")\n", "\n", " print(f\"\\n✓ Traditional COLMAP reconstruction saved to {sparse_dir}\")\n", "\n", " return sparse_dir" ], "metadata": { "id": "kIrrlZXQEkSA" }, "execution_count": 31, "outputs": [] }, { "cell_type": "code", "source": [ "# =====================================================================\n", "# CELL 21: Convert BIN to CSV for Easy Comparison\n", "# =====================================================================\n", "import pandas as pd\n", "import struct\n", "\n", "def bin_to_csv_cameras(bin_file, csv_file):\n", " \"\"\"cameras.bin → CSV\"\"\"\n", " data = []\n", " with open(bin_file, 'rb') as f:\n", " num_cameras = struct.unpack('Q', f.read(8))[0]\n", " for _ in range(num_cameras):\n", " camera_id = struct.unpack('i', f.read(4))[0]\n", " model_id = struct.unpack('i', f.read(4))[0]\n", " width = struct.unpack('Q', f.read(8))[0]\n", " height = struct.unpack('Q', f.read(8))[0]\n", "\n", " # PINHOLE: 4 params\n", " if model_id == 1:\n", " params = struct.unpack('dddd', f.read(32))\n", " # SIMPLE_PINHOLE: 3 params\n", " else:\n", " params = struct.unpack('ddd', f.read(24))\n", "\n", " data.append({\n", " 'camera_id': camera_id,\n", " 'model_id': model_id,\n", " 'width': width,\n", " 'height': height,\n", " 'fx': params[0] if len(params) >= 1 else None,\n", " 'fy': params[1] if len(params) >= 2 else params[0] if len(params) == 1 else None,\n", " 'cx': params[2] if len(params) >= 3 else None,\n", " 'cy': params[3] if len(params) >= 4 else None\n", " })\n", "\n", " df = pd.DataFrame(data)\n", " df.to_csv(csv_file, index=False)\n", " print(f\"✓ Cameras CSV saved: {csv_file}\")\n", " return df\n", "\n", "\n", "def bin_to_csv_images(bin_file, csv_file):\n", " \"\"\"images.bin → CSV\"\"\"\n", " data = []\n", " with open(bin_file, 'rb') as f:\n", " num_images = struct.unpack('Q', f.read(8))[0]\n", " for _ in range(num_images):\n", " image_id = struct.unpack('i', f.read(4))[0]\n", " qvec = struct.unpack('dddd', f.read(32))\n", " tvec = struct.unpack('ddd', f.read(24))\n", " camera_id = struct.unpack('i', f.read(4))[0]\n", "\n", " name = b''\n", " while True:\n", " char = f.read(1)\n", " if char == b'\\x00':\n", " break\n", " name += char\n", " name = name.decode('utf-8')\n", "\n", " num_points2D = struct.unpack('Q', f.read(8))[0]\n", " f.read(num_points2D * 24)\n", "\n", " data.append({\n", " 'image_id': image_id,\n", " 'qw': qvec[0],\n", " 'qx': qvec[1],\n", " 'qy': qvec[2],\n", " 'qz': qvec[3],\n", " 'tx': tvec[0],\n", " 'ty': tvec[1],\n", " 'tz': tvec[2],\n", " 'camera_id': camera_id,\n", " 'name': name\n", " })\n", "\n", " df = pd.DataFrame(data)\n", " df.to_csv(csv_file, index=False)\n", " print(f\"✓ Images CSV saved: {csv_file}\")\n", " return df\n", "\n", "\n", "def bin_to_csv_points3d(bin_file, csv_file, max_rows=10000):\n", " \"\"\"points3D.bin → CSV (サンプリング)\"\"\"\n", " data = []\n", " with open(bin_file, 'rb') as f:\n", " num_points = struct.unpack('Q', f.read(8))[0]\n", "\n", " # サンプリング間隔を計算\n", " step = max(1, num_points // max_rows)\n", "\n", " for i in range(num_points):\n", " point_id = struct.unpack('Q', f.read(8))[0]\n", " xyz = struct.unpack('ddd', f.read(24))\n", " rgb = struct.unpack('BBB', f.read(3))\n", " error = struct.unpack('d', f.read(8))[0]\n", " track_length = struct.unpack('Q', f.read(8))[0]\n", " f.read(track_length * 8)\n", "\n", " # サンプリング\n", " if i % step == 0:\n", " data.append({\n", " 'point_id': point_id,\n", " 'x': xyz[0],\n", " 'y': xyz[1],\n", " 'z': xyz[2],\n", " 'r': rgb[0],\n", " 'g': rgb[1],\n", " 'b': rgb[2],\n", " 'error': error\n", " })\n", "\n", " df = pd.DataFrame(data)\n", " df.to_csv(csv_file, index=False)\n", " print(f\"✓ Points3D CSV saved: {csv_file} (sampled {len(df)} / {num_points} points)\")\n", " return df\n", "\n", "\n", "def convert_colmap_bins_to_csv(sparse_dir, output_prefix):\n", " \"\"\"全BINファイルをCSVに変換\"\"\"\n", " print(f\"\\n=== Converting {sparse_dir} to CSV ===\")\n", "\n", " cameras_df = bin_to_csv_cameras(\n", " os.path.join(sparse_dir, 'cameras.bin'),\n", " f\"{output_prefix}_cameras.csv\"\n", " )\n", "\n", " images_df = bin_to_csv_images(\n", " os.path.join(sparse_dir, 'images.bin'),\n", " f\"{output_prefix}_images.csv\"\n", " )\n", "\n", " points_df = bin_to_csv_points3d(\n", " os.path.join(sparse_dir, 'points3D.bin'),\n", " f\"{output_prefix}_points3d.csv\",\n", " max_rows=10000\n", " )\n", "\n", " return cameras_df, images_df, points_df" ], "metadata": { "id": "c7A05pXLFt2E" }, "execution_count": 32, "outputs": [] }, { "cell_type": "code", "source": [ "# =====================================================================\n", "# CELL 22: Comparison Function\n", "# =====================================================================\n", "\n", "def compare_extraction_methods(scene, image_paths, output_dir, conf_threshold=0.5, max_points=500000):\n", " \"\"\"\n", " 新方式と従来法の両方でCOLMAP形式を出力し、比較する\n", "\n", " Args:\n", " scene: MASt3Rのシーンオブジェクト\n", " image_paths: 画像パスのリスト\n", " output_dir: 出力ディレクトリ\n", " conf_threshold: 信頼度閾値（新方式用）\n", " max_points: 最大点数（従来法用）\n", "\n", " Returns:\n", " dict: 比較結果の辞書\n", " \"\"\"\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"COMPARISON: New vs Traditional Extraction Methods\")\n", " print(\"=\"*70)\n", "\n", " # ===== METHOD 1: 新方式 (extract_camera_params_process2) =====\n", " print(\"\\n--- METHOD 1: Current Implementation (extract_camera_params_process2) ---\")\n", "\n", " cameras_dict_new, pts3d_new, confidence_new = extract_camera_params_process2(\n", " scene=scene,\n", " image_paths=image_paths,\n", " conf_threshold=conf_threshold\n", " )\n", "\n", " # 画像サイズを取得\n", " first_img = Image.open(image_paths[0])\n", " image_size = (first_img.width, first_img.height)\n", " first_img.close()\n", "\n", " # 新方式のBIN保存\n", " sparse_dir_new = os.path.join(output_dir, \"sparse_new/0\")\n", " os.makedirs(sparse_dir_new, exist_ok=True)\n", "\n", " export_colmap_binary(\n", " cameras_dict=cameras_dict_new,\n", " pts3d=pts3d_new,\n", " confidence=confidence_new,\n", " image_size=image_size,\n", " output_dir=sparse_dir_new\n", " )\n", "\n", " # ===== METHOD 2: 従来法 (extract_colmap_data_traditional) =====\n", " print(\"\\n--- METHOD 2: Traditional Implementation (extract_colmap_data) ---\")\n", "\n", " pts3d_trad, colors_trad, cameras_trad, poses_trad = extract_colmap_data_traditional(\n", " scene=scene,\n", " image_paths=image_paths,\n", " max_points=max_points\n", " )\n", "\n", " # 従来法のBIN保存\n", " sparse_dir_trad = save_colmap_reconstruction_traditional(\n", " pts3d=pts3d_trad,\n", " colors=colors_trad,\n", " cameras=cameras_trad,\n", " poses=poses_trad,\n", " image_paths=image_paths,\n", " output_dir=output_dir\n", " )\n", "\n", " # ===== CSVに変換 =====\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"Converting to CSV for comparison\")\n", " print(\"=\"*70)\n", "\n", " csv_prefix_new = os.path.join(output_dir, \"comparison_new\")\n", " csv_prefix_trad = os.path.join(output_dir, \"comparison_traditional\")\n", "\n", " cam_new, img_new, pts_new = convert_colmap_bins_to_csv(\n", " sparse_dir_new,\n", " csv_prefix_new\n", " )\n", "\n", " cam_trad, img_trad, pts_trad = convert_colmap_bins_to_csv(\n", " str(sparse_dir_trad),\n", " csv_prefix_trad\n", " )\n", "\n", " # ===== 比較サマリー =====\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"COMPARISON SUMMARY\")\n", " print(\"=\"*70)\n", "\n", " comparison_results = {\n", " 'cameras': {\n", " 'new_count': len(cam_new),\n", " 'trad_count': len(cam_trad),\n", " 'new_focal': float(cam_new.iloc[0]['fx']) if len(cam_new) > 0 else None,\n", " 'trad_focal': float(cam_trad.iloc[0]['fx']) if len(cam_trad) > 0 else None,\n", " },\n", " 'images': {\n", " 'new_count': len(img_new),\n", " 'trad_count': len(img_trad),\n", " 'new_tvec': [float(img_new.iloc[0]['tx']), float(img_new.iloc[0]['ty']), float(img_new.iloc[0]['tz'])] if len(img_new) > 0 else None,\n", " 'trad_tvec': [float(img_trad.iloc[0]['tx']), float(img_trad.iloc[0]['ty']), float(img_trad.iloc[0]['tz'])] if len(img_trad) > 0 else None,\n", " },\n", " 'points': {\n", " 'new_count': len(pts_new),\n", " 'trad_count': len(pts_trad),\n", " 'new_center': [float(pts_new['x'].mean()), float(pts_new['y'].mean()), float(pts_new['z'].mean())] if len(pts_new) > 0 else None,\n", " 'trad_center': [float(pts_trad['x'].mean()), float(pts_trad['y'].mean()), float(pts_trad['z'].mean())] if len(pts_trad) > 0 else None,\n", " }\n", " }\n", "\n", " # 結果を表示\n", " print(\"\\nCAMERAS:\")\n", " print(f\" New method: {comparison_results['cameras']['new_count']} cameras\")\n", " print(f\" Traditional method: {comparison_results['cameras']['trad_count']} cameras\")\n", " if comparison_results['cameras']['new_focal'] and comparison_results['cameras']['trad_focal']:\n", " print(f\"\\n Sample focal lengths:\")\n", " print(f\" New: fx={comparison_results['cameras']['new_focal']:.2f}\")\n", " print(f\" Traditional: fx={comparison_results['cameras']['trad_focal']:.2f}\")\n", " focal_diff = abs(comparison_results['cameras']['new_focal'] - comparison_results['cameras']['trad_focal'])\n", " print(f\" Difference: {focal_diff:.2f}\")\n", "\n", " print(\"\\nIMAGES:\")\n", " print(f\" New method: {comparison_results['images']['new_count']} images\")\n", " print(f\" Traditional method: {comparison_results['images']['trad_count']} images\")\n", " if comparison_results['images']['new_tvec'] and comparison_results['images']['trad_tvec']:\n", " print(f\"\\n Sample translation (first image):\")\n", " print(f\" New: {comparison_results['images']['new_tvec']}\")\n", " print(f\" Traditional: {comparison_results['images']['trad_tvec']}\")\n", " tvec_diff = np.linalg.norm(\n", " np.array(comparison_results['images']['new_tvec']) -\n", " np.array(comparison_results['images']['trad_tvec'])\n", " )\n", " print(f\" Distance: {tvec_diff:.3f}\")\n", "\n", " print(\"\\nPOINTS3D:\")\n", " print(f\" New method: {comparison_results['points']['new_count']} points (sampled)\")\n", " print(f\" Traditional method: {comparison_results['points']['trad_count']} points (sampled)\")\n", " if comparison_results['points']['new_center'] and comparison_results['points']['trad_center']:\n", " print(f\"\\n Center of points:\")\n", " print(f\" New: {comparison_results['points']['new_center']}\")\n", " print(f\" Traditional: {comparison_results['points']['trad_center']}\")\n", " center_diff = np.linalg.norm(\n", " np.array(comparison_results['points']['new_center']) -\n", " np.array(comparison_results['points']['trad_center'])\n", " )\n", " print(f\" Distance: {center_diff:.3f}\")\n", "\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"CSV FILES SAVED:\")\n", " print(\"=\"*70)\n", " print(f\" New method:\")\n", " print(f\" - {csv_prefix_new}_cameras.csv\")\n", " print(f\" - {csv_prefix_new}_images.csv\")\n", " print(f\" - {csv_prefix_new}_points3d.csv\")\n", " print(f\" Traditional method:\")\n", " print(f\" - {csv_prefix_trad}_cameras.csv\")\n", " print(f\" - {csv_prefix_trad}_images.csv\")\n", " print(f\" - {csv_prefix_trad}_points3d.csv\")\n", "\n", " print(\"\\n✓ Comparison complete! Review CSV files for detailed analysis.\")\n", "\n", " return comparison_results" ], "metadata": { "id": "SN1a_CbWEkIg" }, "execution_count": 33, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "KJPwr7rbE1kO" }, "execution_count": 33, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "E_ZQUzrhE1b_" }, "execution_count": 33, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "lHdqGcsaDLfb" }, "execution_count": 33, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "3iOFnjrhDLV0" }, "execution_count": 33, "outputs": [] }, { "cell_type": "code", "source": [ "# =====================================================================\n", "# CELL 13: Gaussian Splatting Runner\n", "# =====================================================================\n", "def run_gaussian_splatting(source_dir, output_dir, iterations=30000):\n", " \"\"\"Gaussian Splattingを実行\"\"\"\n", " print(\"\\n=== Running Gaussian Splatting ===\")\n", "\n", " os.makedirs(output_dir, exist_ok=True)\n", "\n", " cmd = [\n", " \"python\", \"/content/gaussian-splatting/train.py\",\n", " \"-s\", source_dir,\n", " \"-m\", output_dir,\n", " \"--iterations\", str(iterations),\n", " \"--eval\"\n", " ]\n", "\n", " print(f\"Command: {' '.join(cmd)}\")\n", " print(f\" Source: {source_dir}\")\n", " print(f\" Output: {output_dir}\")\n", "\n", " result = subprocess.run(cmd, capture_output=False, text=True)\n", "\n", " if result.returncode == 0:\n", " print(f\"\\n✓ Gaussian Splatting complete\")\n", "\n", " point_cloud_dir = os.path.join(output_dir, \"point_cloud\")\n", " if os.path.exists(point_cloud_dir):\n", " print(f\"\\n✓ Point cloud directory found: {point_cloud_dir}\")\n", "\n", " for item in sorted(os.listdir(point_cloud_dir)):\n", " item_path = os.path.join(point_cloud_dir, item)\n", " if os.path.isdir(item_path) and item.startswith(\"iteration_\"):\n", " ply_file = os.path.join(item_path, \"point_cloud.ply\")\n", " if os.path.exists(ply_file):\n", " file_size = os.path.getsize(ply_file) / (1024 * 1024)\n", " print(f\" ✓ {item}/point_cloud.ply ({file_size:.2f} MB)\")\n", " else:\n", " print(f\"\\n✗ Gaussian Splatting failed with return code {result.returncode}\")\n", "\n", " return output_dir" ], "metadata": { "id": "o0n2RL3Ep5_Y" }, "execution_count": 34, "outputs": [] }, { "cell_type": "code", "source": [ "# =====================================================================\n", "# CELL 14: Main Pipeline\n", "# =====================================================================\n", "def main_pipeline(image_dir, output_dir, square_size=1024, iterations=30000,\n", " max_images=200, max_pairs=100, max_points=500000,\n", " conf_threshold=1.001, preprocess_mode='none'):\n", " \"\"\"メインパイプライン（DINO + CELL 11/12対応版）\"\"\"\n", "\n", " # STEP 0: Image Preprocessing\n", " if preprocess_mode == 'biplet':\n", " print(\"=\"*70)\n", " print(\"STEP 0: Image Preprocessing (Biplet Crops)\")\n", " print(\"=\"*70)\n", "\n", " temp_biplet_dir = os.path.join(output_dir, \"temp_biplet\")\n", " biplet_dir = normalize_image_sizes_biplet(image_dir, temp_biplet_dir, size=square_size)\n", "\n", " images_dir = os.path.join(output_dir, \"images\")\n", " os.makedirs(images_dir, exist_ok=True)\n", "\n", " biplet_suffixes = ['_left', '_right', '_top', '_bottom']\n", " copied_count = 0\n", "\n", " for img_file in os.listdir(temp_biplet_dir):\n", " if any(suffix in img_file for suffix in biplet_suffixes):\n", " src = os.path.join(temp_biplet_dir, img_file)\n", " dst = os.path.join(images_dir, img_file)\n", " shutil.copy2(src, dst)\n", " copied_count += 1\n", "\n", " print(f\"✓ Copied {copied_count} biplet images to {images_dir}\")\n", "\n", " original_images_dir = os.path.join(output_dir, \"original_images\")\n", " os.makedirs(original_images_dir, exist_ok=True)\n", "\n", " original_count = 0\n", " valid_extensions = ('.jpg', '.jpeg', '.png', '.bmp')\n", " for img_file in os.listdir(image_dir):\n", " if img_file.lower().endswith(valid_extensions):\n", " src = os.path.join(image_dir, img_file)\n", " dst = os.path.join(original_images_dir, img_file)\n", " shutil.copy2(src, dst)\n", " original_count += 1\n", "\n", " print(f\"✓ Saved {original_count} original images to {original_images_dir}\")\n", " shutil.rmtree(temp_biplet_dir)\n", " image_dir = images_dir\n", " clear_memory()\n", " else:\n", " images_dir = os.path.join(output_dir, \"images\")\n", " if not os.path.exists(images_dir):\n", " print(\"=\"*70)\n", " print(\"STEP 0: Copying images to output directory\")\n", " print(\"=\"*70)\n", " shutil.copytree(image_dir, images_dir)\n", " print(f\"✓ Copied images to {images_dir}\")\n", " image_dir = images_dir\n", "\n", " # STEP 1: Loading Images\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 1: Loading and Preparing Images\")\n", " print(\"=\"*70)\n", "\n", " image_paths = load_images_from_directory(image_dir, max_images=max_images)\n", " print(f\"Loaded {len(image_paths)} images\")\n", " clear_memory()\n", "\n", " # STEP 2: Image Pair Selection (DINO)\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 2: Image Pair Selection (DINO)\")\n", " print(\"=\"*70)\n", "\n", " max_pairs = min(max_pairs, 50)\n", " pairs = get_image_pairs_dino(image_paths, max_pairs=max_pairs)\n", " print(f\"Selected {len(pairs)} image pairs\")\n", " clear_memory()\n", "\n", " # STEP 3: MASt3R 3D Reconstruction\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 3: MASt3R 3D Reconstruction\")\n", " print(\"=\"*70)\n", "\n", " device = Config.DEVICE\n", " model = load_mast3r_model(device)\n", " scene, mast3r_images = run_mast3r_pairs(model, image_paths, pairs, device)\n", "\n", " del model\n", " clear_memory()\n", "\n", "\n", "\n", " # STEP 4: Converting to COLMAP (CELL 11/12使用)\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 4: Converting to COLMAP (PINHOLE)\")\n", " print(\"=\"*70)\n", "\n", " # 画像ファイル名のリストを作成\n", " image_names = [os.path.basename(p) for p in image_paths]\n", "\n", " # CELL 11: カメラパラメータの抽出（修正版関数を使用）\n", " cameras_dict, pts3d, confidence = extract_camera_params_process2(\n", " scene=scene,\n", " image_paths=image_paths,\n", " conf_threshold=conf_threshold\n", " )\n", "\n", " print(f\"Extracted {len(cameras_dict)} cameras with conf >= {conf_threshold}\")\n", "\n", " # 画像サイズを取得（最初の画像から）\n", " from PIL import Image\n", " first_img = Image.open(image_paths[0])\n", " image_size = (first_img.width, first_img.height)\n", " first_img.close()\n", "\n", " # COLMAP出力ディレクトリ\n", " colmap_dir = os.path.join(output_dir, \"sparse/0\")\n", " os.makedirs(colmap_dir, exist_ok=True)\n", "\n", " # CELL 12: COLMAPバイナリ形式でエクスポート（修正版関数を使用）\n", " export_colmap_binary(\n", " cameras_dict=cameras_dict,\n", " pts3d=pts3d,\n", " confidence=confidence,\n", " image_size=image_size,\n", " output_dir=colmap_dir\n", " )\n", "\n", " #del scene\n", " clear_memory()\n", "\n", "\n", "\n", " # STEP 5: Running Gaussian Splatting\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 5: Running Gaussian Splatting\")\n", " print(\"=\"*70)\n", "\n", " source_dir = output_dir\n", " model_output_dir = os.path.join(output_dir, \"gaussian_splatting\")\n", "\n", " gs_output = run_gaussian_splatting(\n", " source_dir=source_dir,\n", " output_dir=model_output_dir,\n", " iterations=iterations\n", " )\n", "\n", " # STEP 6: Verify Output\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"PIPELINE COMPLETE\")\n", " print(\"=\"*70)\n", "\n", " ply_path = os.path.join(\n", " model_output_dir,\n", " \"point_cloud\",\n", " f\"iteration_{iterations}\",\n", " \"point_cloud.ply\"\n", " )\n", "\n", " if os.path.exists(ply_path):\n", " file_size = os.path.getsize(ply_path) / (1024 * 1024)\n", " print(f\"✓ Point cloud generated: {ply_path}\")\n", " print(f\" Size: {file_size:.2f} MB\")\n", " else:\n", " print(f\"⚠️ Point cloud not found at: {ply_path}\")\n", "\n", " print(f\"\\nOutput directory structure:\")\n", " print(f\" {output_dir}/\")\n", " print(f\" ├── images/ (processed images)\")\n", " if preprocess_mode == 'biplet':\n", " print(f\" ├── original_images/ (original source images)\")\n", " print(f\" ├── sparse/0/ (COLMAP data)\")\n", " print(f\" │ ├── cameras.bin\")\n", " print(f\" │ ├── images.bin\")\n", " print(f\" │ └── points3D.bin\")\n", " print(f\" └── gaussian_splatting/ (GS output)\")\n", "\n", "\n", " comparison_results = compare_extraction_methods(\n", " scene=scene,\n", " image_paths=image_paths,\n", " output_dir=\"/content/output\",\n", " conf_threshold=0.5,\n", " max_points=100000\n", " )\n", "\n", "\n", " return gs_output" ], "metadata": { "trusted": true, "id": "U7Lk41hLTKyF" }, "outputs": [], "execution_count": 43 }, { "cell_type": "code", "source": [ "# =====================================================================\n", "# CELL 15: Run Pipeline\n", "# =====================================================================\n", "if __name__ == \"__main__\":\n", " IMAGE_DIR = \"/content/drive/MyDrive/your_folder/fountain\"\n", " OUTPUT_DIR = \"/content/output\"\n", "\n", "\n", " gs_output = main_pipeline(\n", " image_dir=IMAGE_DIR,\n", " output_dir=OUTPUT_DIR,\n", " square_size=512,\n", " iterations=1000,\n", " max_images=20,\n", " max_pairs=400,\n", " max_points=1000000,\n", " conf_threshold=0.5,\n", " preprocess_mode='biplet' # or 'none'\n", " )\n", "\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"PIPELINE COMPLETE\")\n", " print(\"=\"*70)\n", " print(f\"Output directory: {gs_output}\")" ], "metadata": { "trusted": true, "id": "_-8kDLieTKyG", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "beafd1de-a25c-4273-dfcb-10ca5301abb7" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "======================================================================\n", "STEP 0: Image Preprocessing (Biplet Crops)\n", "======================================================================\n", "\n", "=== Generating Biplet Crops (512x512) ===\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Creating biplets: 100%|██████████| 30/30 [00:02<00:00, 11.36it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "✓ Biplet generation complete:\n", " Source images: 30\n", " Biplet crops generated: 60\n", " Original size distribution: {'1440x1920': 30}\n", "✓ Copied 60 biplet images to /content/output/images\n", "✓ Saved 30 original images to /content/output/original_images\n", "\n", "======================================================================\n", "STEP 1: Loading and Preparing Images\n", "======================================================================\n", "\n", "Loading images from: /content/output/images\n", "⚠️ Limiting from 60 to 20 images\n", "✓ Found 20 images\n", "Loaded 20 images\n", "\n", "======================================================================\n", "STEP 2: Image Pair Selection (DINO)\n", "======================================================================\n", "\n", "=== Extracting DINO Global Features ===\n", "Initial memory state:\n", "GPU Memory - Allocated: 0.02GB, Reserved: 0.06GB\n", "CPU Memory Usage: 43.3%\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:942: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n", "DINO extraction: 100%|██████████| 5/5 [00:02<00:00, 2.00it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "After DINO extraction:\n", "GPU Memory - Allocated: 0.03GB, Reserved: 0.08GB\n", "CPU Memory Usage: 43.4%\n", "Initial pairs from DINO: 190\n", "Selecting 50 diverse pairs from 190 candidates...\n", "Selected pairs cover 20 / 20 images (100.0%)\n", "Selected 50 image pairs\n", "\n", "======================================================================\n", "STEP 3: MASt3R 3D Reconstruction\n", "======================================================================\n", "\n", "=== Loading MASt3R Model ===\n", "Attempting to load: naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric\n", "⚠️ Failed to load MASt3R: tried to load naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric from huggingface, but failed\n", "Trying DUSt3R instead: naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt\n", "✓ Loaded DUSt3R model as fallback\n", "✓ Model loaded on cuda\n", "\n", "=== Running MASt3R Reconstruction ===\n", "Initial memory state:\n", "GPU Memory - Allocated: 2.14GB, Reserved: 2.16GB\n", "CPU Memory Usage: 43.4%\n", "Processing 50 pairs...\n", "Loading 20 images at 224x224...\n", ">> Loading a list of 20 images\n", " - adding /content/output/images/image_001_bottom.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_001_top.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_002_bottom.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_002_top.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_003_bottom.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_003_top.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_004_bottom.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_004_top.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_005_bottom.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_005_top.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_006_bottom.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_006_top.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_007_bottom.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_007_top.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_008_bottom.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_008_top.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_009_bottom.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_009_top.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_010_bottom.jpeg with resolution 512x512 --> 224x224\n", " - adding /content/output/images/image_010_top.jpeg with resolution 512x512 --> 224x224\n", " (Found 20 images)\n", "Loaded 20 images\n", "After loading images:\n", "GPU Memory - Allocated: 2.14GB, Reserved: 2.16GB\n", "CPU Memory Usage: 43.4%\n", "Creating 50 image pairs...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Preparing pairs: 100%|██████████| 50/50 [00:00<00:00, 483214.75it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Running MASt3R inference on 50 pairs...\n", ">> Inference with model on 50 image pairs\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\r 0%| | 0/50 [00:000.5): 1003520 points\n", "Extracted 20 cameras with conf >= 0.5\n", "COLMAP cameras.bin saved to /content/output/sparse/0/cameras.bin\n", "COLMAP images.bin saved to /content/output/sparse/0/images.bin\n", "COLMAP points3D.bin saved to /content/output/sparse/0/points3D.bin\n", "\n", "COLMAP binary files exported to /content/output/sparse/0/\n", " - cameras.bin: 20 cameras (PINHOLE model)\n", " - images.bin: 20 images\n", " - points3D.bin: 1003520 points\n", "\n", "======================================================================\n", "STEP 5: Running Gaussian Splatting\n", "======================================================================\n", "\n", "=== Running Gaussian Splatting ===\n", "Command: python /content/gaussian-splatting/train.py -s /content/output -m /content/output/gaussian_splatting --iterations 1000 --eval\n", " Source: /content/output\n", " Output: /content/output/gaussian_splatting\n", "\n", "✓ Gaussian Splatting complete\n", "\n", "✓ Point cloud directory found: /content/output/gaussian_splatting/point_cloud\n", " ✓ iteration_1000/point_cloud.ply (129.66 MB)\n", "\n", "======================================================================\n", "PIPELINE COMPLETE\n", "======================================================================\n", "✓ Point cloud generated: /content/output/gaussian_splatting/point_cloud/iteration_1000/point_cloud.ply\n", " Size: 129.66 MB\n", "\n", "Output directory structure:\n", " /content/output/\n", " ├── images/ (processed images)\n", " ├── original_images/ (original source images)\n", " ├── sparse/0/ (COLMAP data)\n", " │ ├── cameras.bin\n", " │ ├── images.bin\n", " │ └── points3D.bin\n", " └── gaussian_splatting/ (GS output)\n", "\n", "======================================================================\n", "COMPARISON: New vs Traditional Extraction Methods\n", "======================================================================\n", "\n", "--- METHOD 1: Current Implementation (extract_camera_params_process2) ---\n", "\n", "=== Extracting Camera Parameters ===\n", "\n", "Example camera 0:\n", " Image size: 512x512\n", " MASt3R size: 224.0\n", " Scale factor: 2.286\n", " MASt3R focal: 388.52\n", " Scaled focal: 888.04\n", " MASt3R pp: [112.00, 112.00]\n", " Scaled pp: [256.00, 256.00]\n", "✓ Extracted camera parameters for 20 cameras\n", "✓ Total 3D points: 1003520\n", "✓ After confidence filtering (>0.5): 1003520 points\n", "COLMAP cameras.bin saved to /content/output/sparse_new/0/cameras.bin\n", "COLMAP images.bin saved to /content/output/sparse_new/0/images.bin\n", "COLMAP points3D.bin saved to /content/output/sparse_new/0/points3D.bin\n", "\n", "COLMAP binary files exported to /content/output/sparse_new/0/\n", " - cameras.bin: 20 cameras (PINHOLE model)\n", " - images.bin: 20 images\n", " - points3D.bin: 1003520 points\n", "\n", "--- METHOD 2: Traditional Implementation (extract_colmap_data) ---\n", "\n", "=== [TRADITIONAL] Extracting COLMAP-compatible data ===\n", "pts_all type: \n", "pts_all is a list with 20 elements\n", "First element type: \n", "First element shape: torch.Size([224, 224, 3])\n", "pts_all shape after conversion: torch.Size([20, 224, 224, 3])\n", "Found batched point cloud: torch.Size([20, 224, 224, 3])\n", "✓ Extracted 1003520 3D points from 20 images\n", "\n", "⚠ Downsampling from 1003520 to 100000 points...\n", "✓ Downsampled to 100000 points\n", "Extracting camera parameters...\n", "Retrieved camera-to-world poses: shape (20, 4, 4)\n", "Converted to world-to-camera poses for COLMAP\n", "Focals shape: (20, 1)\n", "Principal points shape: (20, 2)\n", "\n", "Example camera 0:\n", " Image size: 512x512\n", " MASt3R focal: 388.52, pp: (112.00, 112.00)\n", " Scaled fx=888.04, fy=888.04, cx=256.00, cy=256.00\n", " Pose (first row): [ 0.99998546 0.00527871 -0.00112329 0. ]\n", "\n", "✓ Extracted 20 cameras and 20 poses\n", "\n", "=== [TRADITIONAL] Saving COLMAP reconstruction ===\n", " ✓ Wrote 20 cameras\n", " ✓ Wrote 20 images\n", " ✓ Wrote 100000 3D points\n", "\n", "✓ Traditional COLMAP reconstruction saved to /content/output/sparse_traditional/0\n", "\n", "======================================================================\n", "Converting to CSV for comparison\n", "======================================================================\n", "\n", "=== Converting /content/output/sparse_new/0 to CSV ===\n", "✓ Cameras CSV saved: /content/output/comparison_new_cameras.csv\n", "✓ Images CSV saved: /content/output/comparison_new_images.csv\n", "✓ Points3D CSV saved: /content/output/comparison_new_points3d.csv (sampled 10036 / 1003520 points)\n", "\n", "=== Converting /content/output/sparse_traditional/0 to CSV ===\n", "✓ Cameras CSV saved: /content/output/comparison_traditional_cameras.csv\n", "✓ Images CSV saved: /content/output/comparison_traditional_images.csv\n", "✓ Points3D CSV saved: /content/output/comparison_traditional_points3d.csv (sampled 10000 / 100000 points)\n", "\n", "======================================================================\n", "COMPARISON SUMMARY\n", "======================================================================\n", "\n", "CAMERAS:\n", " New method: 20 cameras\n", " Traditional method: 20 cameras\n", "\n", " Sample focal lengths:\n", " New: fx=888.04\n", " Traditional: fx=888.04\n", " Difference: 0.00\n", "\n", "IMAGES:\n", " New method: 20 images\n", " Traditional method: 20 images\n", "\n", " Sample translation (first image):\n", " New: [0.0, 0.0, 0.0]\n", " Traditional: [0.0, 0.0, 0.0]\n", " Distance: 0.000\n", "\n", "POINTS3D:\n", " New method: 10036 points (sampled)\n", " Traditional method: 10000 points (sampled)\n", "\n", " Center of points:\n", " New: [0.0202701605206711, -0.026698642946412735, 0.30150085233594376]\n", " Traditional: [0.02198630629032996, -0.026773571838538193, 0.30177652681320905]\n", " Distance: 0.002\n", "\n", "======================================================================\n", "CSV FILES SAVED:\n", "======================================================================\n", " New method:\n", " - /content/output/comparison_new_cameras.csv\n", " - /content/output/comparison_new_images.csv\n", " - /content/output/comparison_new_points3d.csv\n", " Traditional method:\n", " - /content/output/comparison_traditional_cameras.csv\n", " - /content/output/comparison_traditional_images.csv\n", " - /content/output/comparison_traditional_points3d.csv\n", "\n", "✓ Comparison complete! Review CSV files for detailed analysis.\n", "\n", "======================================================================\n", "PIPELINE COMPLETE\n", "======================================================================\n", "Output directory: /content/output/gaussian_splatting\n" ] } ], "execution_count": 47 }, { "cell_type": "code", "source": [ "# =====================================================================\n", "# CELL 23: Detailed Quaternion and Pose Comparison\n", "# =====================================================================\n", "import pandas as pd\n", "import numpy as np\n", "\n", "def detailed_pose_comparison(output_dir=\"/content/output\"):\n", " \"\"\"クォータニオンとポーズの詳細比較\"\"\"\n", "\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"DETAILED POSE COMPARISON\")\n", " print(\"=\"*70)\n", "\n", " # CSVを読み込み\n", " img_new = pd.read_csv(f\"{output_dir}/comparison_new_images.csv\")\n", " img_trad = pd.read_csv(f\"{output_dir}/comparison_traditional_images.csv\")\n", "\n", " print(f\"\\nComparing {len(img_new)} images...\")\n", "\n", " # 最初の5画像について詳細比較\n", " num_samples = min(5, len(img_new))\n", "\n", " for i in range(num_samples):\n", " print(f\"\\n--- Image {i}: {img_new.iloc[i]['name']} ---\")\n", "\n", " # クォータニオン\n", " qvec_new = np.array([\n", " img_new.iloc[i]['qw'],\n", " img_new.iloc[i]['qx'],\n", " img_new.iloc[i]['qy'],\n", " img_new.iloc[i]['qz']\n", " ])\n", " qvec_trad = np.array([\n", " img_trad.iloc[i]['qw'],\n", " img_trad.iloc[i]['qx'],\n", " img_trad.iloc[i]['qy'],\n", " img_trad.iloc[i]['qz']\n", " ])\n", "\n", " # 並進ベクトル\n", " tvec_new = np.array([\n", " img_new.iloc[i]['tx'],\n", " img_new.iloc[i]['ty'],\n", " img_new.iloc[i]['tz']\n", " ])\n", " tvec_trad = np.array([\n", " img_trad.iloc[i]['tx'],\n", " img_trad.iloc[i]['ty'],\n", " img_trad.iloc[i]['tz']\n", " ])\n", "\n", " # クォータニオンの差\n", " # 正規化\n", " qvec_new_norm = qvec_new / np.linalg.norm(qvec_new)\n", " qvec_trad_norm = qvec_trad / np.linalg.norm(qvec_trad)\n", "\n", " # 内積（類似度）: 1.0に近いほど似ている\n", " q_similarity = abs(np.dot(qvec_new_norm, qvec_trad_norm))\n", "\n", " # 角度差（度）\n", " angle_diff = 2 * np.arccos(np.clip(q_similarity, 0, 1)) * 180 / np.pi\n", "\n", " print(f\" Quaternion (New): [{qvec_new[0]:.6f}, {qvec_new[1]:.6f}, {qvec_new[2]:.6f}, {qvec_new[3]:.6f}]\")\n", " print(f\" Quaternion (Trad): [{qvec_trad[0]:.6f}, {qvec_trad[1]:.6f}, {qvec_trad[2]:.6f}, {qvec_trad[3]:.6f}]\")\n", " print(f\" Similarity: {q_similarity:.6f} (1.0 = identical)\")\n", " print(f\" Angle diff: {angle_diff:.3f}°\")\n", "\n", " print(f\"\\n Translation (New): [{tvec_new[0]:.6f}, {tvec_new[1]:.6f}, {tvec_new[2]:.6f}]\")\n", " print(f\" Translation (Trad): [{tvec_trad[0]:.6f}, {tvec_trad[1]:.6f}, {tvec_trad[2]:.6f}]\")\n", "\n", " tvec_diff = np.linalg.norm(tvec_new - tvec_trad)\n", " print(f\" Distance: {tvec_diff:.6f}\")\n", "\n", " # 🔴 問題チェック\n", " if angle_diff > 1.0:\n", " print(f\" ⚠️ WARNING: Large rotation difference!\")\n", " if tvec_diff > 0.01:\n", " print(f\" ⚠️ WARNING: Large translation difference!\")\n", "\n", " # 全体統計\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"OVERALL STATISTICS\")\n", " print(\"=\"*70)\n", "\n", " all_angle_diffs = []\n", " all_tvec_diffs = []\n", "\n", " for i in range(len(img_new)):\n", " qvec_new = np.array([img_new.iloc[i]['qw'], img_new.iloc[i]['qx'],\n", " img_new.iloc[i]['qy'], img_new.iloc[i]['qz']])\n", " qvec_trad = np.array([img_trad.iloc[i]['qw'], img_trad.iloc[i]['qx'],\n", " img_trad.iloc[i]['qy'], img_trad.iloc[i]['qz']])\n", "\n", " qvec_new_norm = qvec_new / np.linalg.norm(qvec_new)\n", " qvec_trad_norm = qvec_trad / np.linalg.norm(qvec_trad)\n", " q_similarity = abs(np.dot(qvec_new_norm, qvec_trad_norm))\n", " angle_diff = 2 * np.arccos(np.clip(q_similarity, 0, 1)) * 180 / np.pi\n", " all_angle_diffs.append(angle_diff)\n", "\n", " tvec_new = np.array([img_new.iloc[i]['tx'], img_new.iloc[i]['ty'], img_new.iloc[i]['tz']])\n", " tvec_trad = np.array([img_trad.iloc[i]['tx'], img_trad.iloc[i]['ty'], img_trad.iloc[i]['tz']])\n", " tvec_diff = np.linalg.norm(tvec_new - tvec_trad)\n", " all_tvec_diffs.append(tvec_diff)\n", "\n", " print(f\"\\nRotation differences:\")\n", " print(f\" Mean: {np.mean(all_angle_diffs):.3f}°\")\n", " print(f\" Max: {np.max(all_angle_diffs):.3f}°\")\n", " print(f\" Min: {np.min(all_angle_diffs):.3f}°\")\n", "\n", " print(f\"\\nTranslation differences:\")\n", " print(f\" Mean: {np.mean(all_tvec_diffs):.6f}\")\n", " print(f\" Max: {np.max(all_tvec_diffs):.6f}\")\n", " print(f\" Min: {np.min(all_tvec_diffs):.6f}\")\n", "\n", " # 🔴 判定\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"DIAGNOSIS\")\n", " print(\"=\"*70)\n", "\n", " if np.max(all_angle_diffs) > 5.0:\n", " print(\"🔴 CRITICAL: Rotation differences > 5° detected!\")\n", " print(\" → Problem: Quaternion conversion or pose transformation is wrong\")\n", " elif np.max(all_angle_diffs) > 1.0:\n", " print(\"⚠️ WARNING: Rotation differences > 1° detected\")\n", " print(\" → May cause issues in 3DGS\")\n", " else:\n", " print(\"✓ Rotations look OK (< 1° difference)\")\n", "\n", " if np.max(all_tvec_diffs) > 0.1:\n", " print(\"🔴 CRITICAL: Translation differences > 0.1 detected!\")\n", " print(\" → Problem: Camera position is significantly different\")\n", " elif np.max(all_tvec_diffs) > 0.01:\n", " print(\"⚠️ WARNING: Translation differences > 0.01 detected\")\n", " else:\n", " print(\"✓ Translations look OK (< 0.01 difference)\")\n", "\n", "# 実行\n", "detailed_pose_comparison()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R8EaiIDrKxRG", "outputId": "b251f780-7736-4b45-eb1e-be939ed3ae55" }, "execution_count": 48, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "======================================================================\n", "DETAILED POSE COMPARISON\n", "======================================================================\n", "\n", "Comparing 20 images...\n", "\n", "--- Image 0: image_001_bottom.jpeg ---\n", " Quaternion (New): [0.999995, 0.001439, -0.000558, -0.002640]\n", " Quaternion (Trad): [0.999995, 0.001439, -0.000558, -0.002640]\n", " Similarity: 1.000000 (1.0 = identical)\n", " Angle diff: 0.000°\n", "\n", " Translation (New): [0.000000, 0.000000, 0.000000]\n", " Translation (Trad): [0.000000, 0.000000, 0.000000]\n", " Distance: 0.000000\n", "\n", "--- Image 1: image_001_top.jpeg ---\n", " Quaternion (New): [0.991558, -0.128225, -0.012587, -0.014562]\n", " Quaternion (Trad): [0.991558, -0.128225, -0.012587, -0.014562]\n", " Similarity: 1.000000 (1.0 = identical)\n", " Angle diff: 0.000°\n", "\n", " Translation (New): [0.007832, -0.026141, -0.068950]\n", " Translation (Trad): [0.007832, -0.026141, -0.068950]\n", " Distance: 0.000000\n", "\n", "--- Image 2: image_002_bottom.jpeg ---\n", " Quaternion (New): [0.999702, 0.015601, -0.009351, -0.016260]\n", " Quaternion (Trad): [0.999702, 0.015601, -0.009351, -0.016260]\n", " Similarity: 1.000000 (1.0 = identical)\n", " Angle diff: 0.000°\n", "\n", " Translation (New): [0.002856, 0.007463, -0.054111]\n", " Translation (Trad): [0.002856, 0.007463, -0.054111]\n", " Distance: 0.000000\n", "\n", "--- Image 3: image_002_top.jpeg ---\n", " Quaternion (New): [0.989615, -0.129472, -0.053992, -0.031363]\n", " Quaternion (Trad): [0.989615, -0.129472, -0.053992, -0.031363]\n", " Similarity: 1.000000 (1.0 = identical)\n", " Angle diff: 0.000°\n", "\n", " Translation (New): [0.024814, -0.030227, -0.057101]\n", " Translation (Trad): [0.024814, -0.030227, -0.057101]\n", " Distance: 0.000000\n", "\n", "--- Image 4: image_003_bottom.jpeg ---\n", " Quaternion (New): [0.995844, -0.002876, -0.071390, -0.056486]\n", " Quaternion (Trad): [0.995844, -0.002876, -0.071390, -0.056486]\n", " Similarity: 1.000000 (1.0 = identical)\n", " Angle diff: 0.000°\n", "\n", " Translation (New): [0.028993, -0.008757, 0.041457]\n", " Translation (Trad): [0.028993, -0.008757, 0.041457]\n", " Distance: 0.000000\n", "\n", "======================================================================\n", "OVERALL STATISTICS\n", "======================================================================\n", "\n", "Rotation differences:\n", " Mean: 0.000°\n", " Max: 0.000°\n", " Min: 0.000°\n", "\n", "Translation differences:\n", " Mean: 0.000000\n", " Max: 0.000000\n", " Min: 0.000000\n", "\n", "======================================================================\n", "DIAGNOSIS\n", "======================================================================\n", "✓ Rotations look OK (< 1° difference)\n", "✓ Translations look OK (< 0.01 difference)\n" ] } ] } ] }