File size: 50,615 Bytes
fd2ff16 | 1 | {"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"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"},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU","kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":14569969,"sourceType":"datasetVersion","datasetId":1429416}],"dockerImageVersionId":31236,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **asmk-mast3r-ps2-gs-kg** \n\n","metadata":{"id":"qDQLX3PArmh8"}},{"cell_type":"markdown","source":"https://www.kaggle.com/code/stpeteishii/dino-mast3r-gs-kg-34","metadata":{}},{"cell_type":"code","source":"!pip install roma einops timm huggingface_hub\n!pip install opencv-python pillow tqdm pyaml cython\n!pip install pycolmap trimesh\n!pip uninstall -y numpy scipy\n!pip install numpy==1.26.4 scipy==1.11.4\n\nbreak","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T12:17:14.624596Z","iopub.execute_input":"2026-01-21T12:17:14.624834Z","iopub.status.idle":"2026-01-21T12:17:42.086930Z","shell.execute_reply.started":"2026-01-21T12:17:14.624811Z","shell.execute_reply":"2026-01-21T12:17:42.085419Z"}},"outputs":[{"name":"stdout","text":"Collecting roma\n Downloading roma-1.5.4-py3-none-any.whl.metadata (5.5 kB)\nRequirement already satisfied: einops in /usr/local/lib/python3.12/dist-packages (0.8.1)\nRequirement already satisfied: timm in /usr/local/lib/python3.12/dist-packages (1.0.20)\nRequirement already satisfied: huggingface_hub in /usr/local/lib/python3.12/dist-packages (0.36.0)\nRequirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (from timm) (2.8.0+cu126)\nRequirement already satisfied: torchvision in /usr/local/lib/python3.12/dist-packages (from timm) (0.23.0+cu126)\nRequirement already satisfied: pyyaml in /usr/local/lib/python3.12/dist-packages (from timm) (6.0.3)\nRequirement already satisfied: safetensors in /usr/local/lib/python3.12/dist-packages (from timm) (0.6.2)\nRequirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (3.20.1)\nRequirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (2025.10.0)\nRequirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (25.0)\nRequirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (2.32.5)\nRequirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (4.67.1)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (4.15.0)\nRequirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (1.2.1rc0)\nRequirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub) (3.4.4)\nRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub) (3.11)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub) (2.6.2)\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub) (2025.11.12)\nRequirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch->timm) (75.2.0)\nRequirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (1.13.3)\nRequirement already satisfied: networkx in /usr/local/lib/python3.12/dist-packages (from torch->timm) (3.5)\nRequirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (3.1.6)\nRequirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.77)\nRequirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.77)\nRequirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.80)\nRequirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (9.10.2.21)\nRequirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.4.1)\nRequirement already satisfied: nvidia-cufft-cu12==11.3.0.4 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (11.3.0.4)\nRequirement already satisfied: nvidia-curand-cu12==10.3.7.77 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (10.3.7.77)\nRequirement already satisfied: nvidia-cusolver-cu12==11.7.1.2 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (11.7.1.2)\nRequirement already satisfied: nvidia-cusparse-cu12==12.5.4.2 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.5.4.2)\nRequirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (0.7.1)\nRequirement already satisfied: nvidia-nccl-cu12==2.27.3 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (2.27.3)\nRequirement already satisfied: nvidia-nvtx-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.77)\nRequirement already satisfied: nvidia-nvjitlink-cu12==12.6.85 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.85)\nRequirement already satisfied: nvidia-cufile-cu12==1.11.1.6 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (1.11.1.6)\nRequirement already satisfied: triton==3.4.0 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (3.4.0)\nRequirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from torchvision->timm) (2.0.2)\nRequirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.12/dist-packages (from torchvision->timm) (11.3.0)\nRequirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch->timm) (1.3.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch->timm) (3.0.3)\nDownloading roma-1.5.4-py3-none-any.whl (25 kB)\nInstalling collected packages: roma\nSuccessfully installed roma-1.5.4\nRequirement already satisfied: opencv-python in /usr/local/lib/python3.12/dist-packages (4.12.0.88)\nRequirement already satisfied: pillow in /usr/local/lib/python3.12/dist-packages (11.3.0)\nRequirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (4.67.1)\nRequirement already satisfied: pyaml in /usr/local/lib/python3.12/dist-packages (25.7.0)\nRequirement already satisfied: cython in /usr/local/lib/python3.12/dist-packages (3.0.12)\nRequirement already satisfied: numpy<2.3.0,>=2 in /usr/local/lib/python3.12/dist-packages (from opencv-python) (2.0.2)\nRequirement already satisfied: PyYAML in /usr/local/lib/python3.12/dist-packages (from pyaml) (6.0.3)\nCollecting pycolmap\n Downloading pycolmap-3.13.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (10 kB)\nCollecting trimesh\n Downloading trimesh-4.11.1-py3-none-any.whl.metadata (13 kB)\nRequirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from pycolmap) (2.0.2)\nDownloading 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[31m80.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\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[31m40.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hInstalling collected packages: trimesh, pycolmap\nSuccessfully installed pycolmap-3.13.0 trimesh-4.11.1\nFound existing installation: numpy 2.0.2\nUninstalling numpy-2.0.2:\n Successfully uninstalled numpy-2.0.2\nFound existing installation: scipy 1.15.3\nUninstalling scipy-1.15.3:\n Successfully uninstalled scipy-1.15.3\nCollecting 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[31m2.4 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[31m4.9 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[31m85.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\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[31m59.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\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.\nbigframes 2.26.0 requires google-cloud-bigquery-storage<3.0.0,>=2.30.0, which is not installed.\ncesium 0.12.4 requires numpy<3.0,>=2.0, but you have numpy 1.26.4 which is incompatible.\ngoogle-colab 1.0.0 requires jupyter-server==2.14.0, but you have jupyter-server 2.12.5 which is incompatible.\ngoogle-colab 1.0.0 requires requests==2.32.4, but you have requests 2.32.5 which is incompatible.\ndopamine-rl 4.1.2 requires gymnasium>=1.0.0, but you have gymnasium 0.29.0 which is incompatible.\njaxlib 0.7.2 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\njaxlib 0.7.2 requires scipy>=1.13, but you have scipy 1.11.4 which is incompatible.\nthinc 8.3.6 requires numpy<3.0.0,>=2.0.0, but you have numpy 1.26.4 which is incompatible.\ntsfresh 0.21.1 requires scipy>=1.14.0; python_version >= \"3.10\", but you have scipy 1.11.4 which is incompatible.\nopencv-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.\nopencv-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.\njax 0.7.2 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\njax 0.7.2 requires scipy>=1.13, but you have scipy 1.11.4 which is incompatible.\ncudf-cu12 25.6.0 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 22.0.0 which is incompatible.\ngradio 5.49.1 requires pydantic<2.12,>=2.0, but you have pydantic 2.12.5 which is incompatible.\nbigframes 2.26.0 requires rich<14,>=12.4.4, but you have rich 14.2.0 which is incompatible.\nopencv-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.\npytensor 2.35.1 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\npylibcudf-cu12 25.6.0 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 22.0.0 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed numpy-1.26.4 scipy-1.11.4\n","output_type":"stream"},{"traceback":["\u001b[0;36m File \u001b[0;32m\"/tmp/ipykernel_55/690471039.py\"\u001b[0;36m, line \u001b[0;32m7\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"],"ename":"SyntaxError","evalue":"'break' outside loop (690471039.py, line 7)","output_type":"error"}],"execution_count":1},{"cell_type":"code","source":"# restart, then run after","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T12:17:42.088099Z","iopub.status.idle":"2026-01-21T12:17:42.088365Z","shell.execute_reply.started":"2026-01-21T12:17:42.088248Z","shell.execute_reply":"2026-01-21T12:17:42.088264Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nprint(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T12:17:42.089661Z","iopub.status.idle":"2026-01-21T12:17:42.089949Z","shell.execute_reply.started":"2026-01-21T12:17:42.089822Z","shell.execute_reply":"2026-01-21T12:17:42.089844Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport sys\n\n# MASt3Rをクローン\nif not os.path.exists('/kaggle/working/mast3r'):\n print(\"Cloning MASt3R repository...\")\n !git clone --recursive https://github.com/naver/mast3r.git /kaggle/working/mast3r\n print(\"✓ MASt3R cloned\")\nelse:\n print(\"✓ MASt3R already exists\")\n\n# DUSt3Rをクローン（MASt3R内に必要）\nif not os.path.exists('/kaggle/working/mast3r/dust3r'):\n print(\"Cloning DUSt3R repository...\")\n !git clone --recursive https://github.com/naver/dust3r.git /kaggle/working/mast3r/dust3r\n print(\"✓ DUSt3R cloned\")\nelse:\n print(\"✓ DUSt3R already exists\")\n\n# ASMKをクローン\nif not os.path.exists('/kaggle/working/asmk'):\n print(\"Cloning ASMK repository...\")\n !git clone https://github.com/jenicek/asmk.git /kaggle/working/asmk\n print(\"✓ ASMK cloned\")\nelse:\n print(\"✓ ASMK already exists\")\n\n# パスを追加\nsys.path.insert(0, '/kaggle/working/mast3r')\nsys.path.insert(0, '/kaggle/working/mast3r/dust3r')\nsys.path.insert(0, '/kaggle/working/asmk')\n\n# 確認\ntry:\n from dust3r.model import AsymmetricCroCo3DStereo\n print(\"✓ dust3r.model imported successfully\")\nexcept ImportError as e:\n print(f\"✗ Import error: {e}\")\n\n# croco（MASt3Rの依存関係）もクローン\nif not os.path.exists('/kaggle/working/mast3r/croco'):\n print(\"Cloning CroCo repository...\")\n !git clone --recursive https://github.com/naver/croco.git /kaggle/working/mast3r/croco\n print(\"✓ CroCo cloned\")\n\n# CroCo v2の依存関係\nif not os.path.exists('/kaggle/working/mast3r/croco/models/curope'):\n print(\"Cloning CuRoPe...\")\n !git clone --recursive https://github.com/naver/curope.git /kaggle/working/mast3r/croco/models/curope\n print(\"✓ CuRoPe cloned\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T12:17:42.091165Z","iopub.status.idle":"2026-01-21T12:17:42.091907Z","shell.execute_reply.started":"2026-01-21T12:17:42.091760Z","shell.execute_reply":"2026-01-21T12:17:42.091778Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# =====================================================================\n# STEP 2: Clone Gaussian Splatting\n# =====================================================================\nprint(\"\\n\" + \"=\"*70)\nprint(\"STEP 2: Clone Gaussian Splatting\")\nprint(\"=\"*70)\n\nif 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\")\nelse:\n print(\"✓ Already exists\")\n\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nprint(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T12:17:42.093479Z","iopub.status.idle":"2026-01-21T12:17:42.093758Z","shell.execute_reply.started":"2026-01-21T12:17:42.093637Z","shell.execute_reply":"2026-01-21T12:17:42.093651Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\"\"\"\nComplete MASt3R to Gaussian Splatting Pipeline\nProcess2 Only - Memory Optimized Version\n\"\"\"\n\nimport os\nimport sys\nimport gc\nimport torch\nimport numpy as np\nfrom pathlib import Path\nfrom tqdm import tqdm\nimport torch.nn.functional as F\n\n# ======================================================================\n# MEMORY MANAGEMENT\n# ======================================================================\n\nos.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'\n\ndef 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\n# ======================================================================\n# CONFIGURATION\n# ======================================================================\n\nclass Config:\n DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n # 正しいMASt3Rモデル名\n MAST3R_WEIGHTS = \"naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric\"\n DUST3R_WEIGHTS = \"naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt\" # フォールバック用\n RETRIEVAL_TOPK = 10\n IMAGE_SIZE = 224 # メモリ節約のため224に設定\n\n\n# ======================================================================\n# IMAGE LOADING\n# ======================================================================\n\ndef 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# ======================================================================\n# MAST3R MODEL\n# ======================================================================\n\ndef load_mast3r_model(device):\n \"\"\"MASt3Rモデルをロード\"\"\"\n print(\"\\n=== Loading MASt3R Model ===\")\n \n # mast3rのパスを追加\n if '/kaggle/working/mast3r' not in sys.path:\n sys.path.insert(0, '/kaggle/working/mast3r')\n if '/kaggle/working/mast3r/dust3r' not in sys.path:\n sys.path.insert(0, '/kaggle/working/mast3r/dust3r')\n \n from dust3r.model import AsymmetricCroCo3DStereo\n \n try:\n # MASt3Rモデルを試す\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 try:\n model = AsymmetricCroCo3DStereo.from_pretrained(Config.DUST3R_WEIGHTS).to(device)\n print(\"✓ Loaded DUSt3R model as fallback\")\n except Exception as e2:\n print(f\"⚠️ Failed to load DUSt3R: {e2}\")\n raise Exception(\"Could not load any model. Please check model names and internet connection.\")\n \n model.eval()\n \n print(f\"✓ Model loaded on {device}\")\n return model\n\n\n# ======================================================================\n# FEATURE EXTRACTION & PAIR SELECTION\n# ======================================================================\n\ndef load_asmk_retrieval_model(device):\n \"\"\"ASMKリトリーバルモデルをロード\"\"\"\n print(\"\\n=== Loading ASMK Retrieval Model ===\")\n \n # mast3rとasmkのパスを追加\n if '/kaggle/working/mast3r' not in sys.path:\n sys.path.insert(0, '/kaggle/working/mast3r')\n if '/kaggle/working/mast3r/dust3r' not in sys.path:\n sys.path.insert(0, '/kaggle/working/mast3r/dust3r')\n if '/kaggle/working/asmk' not in sys.path:\n sys.path.insert(0, '/kaggle/working/asmk')\n \n from dust3r.model import AsymmetricCroCo3DStereo\n \n try:\n # MASt3Rモデルを試す\n model = AsymmetricCroCo3DStereo.from_pretrained(Config.MAST3R_WEIGHTS).to(device)\n print(\"✓ Loaded MASt3R model for retrieval\")\n except Exception as e:\n print(f\"⚠️ Failed to load MASt3R: {e}\")\n print(f\"Trying DUSt3R instead\")\n model = AsymmetricCroCo3DStereo.from_pretrained(Config.DUST3R_WEIGHTS).to(device)\n print(\"✓ Loaded DUSt3R model for retrieval\")\n \n model.eval()\n \n # Codebookの初期化（簡易版）\n codebook = np.random.randn(1024, 24).astype(np.float32)\n \n print(\"✓ ASMK model loaded\")\n return model, codebook\n\n\ndef extract_mast3r_features(model, image_paths, device, batch_size=1):\n \"\"\"MASt3Rモデルを使用して特徴量を抽出（ペア画像として処理）\"\"\"\n print(\"\\n=== Extracting MASt3R Features ===\")\n from dust3r.utils.image import load_images\n from dust3r.inference import inference\n \n all_features = []\n \n # 各画像を自分自身とペアにして処理\n for i in tqdm(range(len(image_paths)), desc=\"Features\"):\n img_path = image_paths[i]\n \n # 同じ画像を2回ロード（ペアとして）\n images = load_images([img_path, img_path], size=Config.IMAGE_SIZE)\n \n # ペア形式で推論\n pairs = [(images[0], images[1])]\n \n with torch.no_grad():\n output = inference(pairs, model, device, batch_size=1)\n \n # outputの構造を確認してデータを抽出\n try:\n # outputが辞書の場合 (DUSt3R形式)\n if isinstance(output, dict):\n # pred1から3D点または特徴量を取得\n if 'pred1' in output:\n pred1 = output['pred1']\n if isinstance(pred1, dict):\n # pts3dまたはdescを探す\n if 'pts3d' in pred1:\n desc = pred1['pts3d']\n elif 'desc' in pred1:\n desc = pred1['desc']\n else:\n # 利用可能な最初のテンソルを使用\n for key, val in pred1.items():\n if isinstance(val, torch.Tensor):\n desc = val\n break\n else:\n desc = pred1\n elif 'view1' in output:\n desc = output['view1']\n else:\n # 最初の値を使用\n desc = list(output.values())[0]\n # outputがタプル (view1, view2) の形式\n elif isinstance(output, tuple) and len(output) == 2:\n view1, view2 = output\n # view1から特徴量を取得\n if isinstance(view1, dict):\n if 'pts3d' in view1:\n desc = view1['pts3d']\n elif 'desc' in view1:\n desc = view1['desc']\n else:\n desc = list(view1.values())[0]\n else:\n desc = view1\n # outputがリストの場合\n elif isinstance(output, list):\n if len(output) > 0:\n item = output[0]\n if isinstance(item, dict):\n if 'pts3d' in item:\n desc = item['pts3d']\n elif 'desc' in item:\n desc = item['desc']\n else:\n desc = list(item.values())[0]\n else:\n desc = item\n else:\n raise ValueError(\"Empty output\")\n else:\n # その他の形式\n desc = output\n \n # テンソルの次元を調整\n if isinstance(desc, torch.Tensor):\n if desc.dim() == 4:\n desc = desc.squeeze(0) # [1, H, W, C] -> [H, W, C]\n elif desc.dim() == 2:\n # [H*W, C] の場合、適切な形状に変換\n h = w = int(np.sqrt(desc.shape[0]))\n if h * w == desc.shape[0]:\n desc = desc.reshape(h, w, desc.shape[1])\n \n all_features.append(desc)\n \n except Exception as e:\n print(f\"⚠️ Error extracting features for image {i}: {e}\")\n print(f\" Output type: {type(output)}\")\n if isinstance(output, (list, tuple)):\n print(f\" Output length: {len(output)}\")\n if len(output) > 0:\n print(f\" First item type: {type(output[0])}\")\n if isinstance(output[0], dict):\n print(f\" Keys: {output[0].keys()}\")\n # デフォルトの特徴量を追加\n all_features.append(torch.zeros((Config.IMAGE_SIZE, Config.IMAGE_SIZE, 24)))\n \n # メモリクリア\n del output, images, pairs\n if i % 10 == 0:\n torch.cuda.empty_cache()\n \n print(f\"✓ Extracted features for {len(all_features)} images\")\n if all_features:\n first_feat = all_features[0]\n if isinstance(first_feat, torch.Tensor):\n print(f\" Feature shape: {first_feat.shape}\")\n elif isinstance(first_feat, dict):\n print(f\" Feature type: dict with keys: {first_feat.keys()}\")\n elif isinstance(first_feat, np.ndarray):\n print(f\" Feature shape: {first_feat.shape}\")\n else:\n print(f\" Feature type: {type(first_feat)}\")\n \n return all_features\n\n\ndef compute_asmk_similarity(features, codebook):\n \"\"\"ASMKを使用して類似度行列を計算\"\"\"\n print(\"\\n=== Computing ASMK Similarity ===\")\n \n n_images = len(features)\n similarity_matrix = np.zeros((n_images, n_images), dtype=np.float32)\n \n # 各特徴量をグローバル記述子に変換\n global_features = []\n \n for feat in features:\n # featが辞書の場合、テンソルを抽出\n if isinstance(feat, dict):\n if 'pts3d' in feat:\n feat = feat['pts3d']\n elif 'desc' in feat:\n feat = feat['desc']\n elif 'pred1' in feat:\n pred1 = feat['pred1']\n if isinstance(pred1, dict) and 'pts3d' in pred1:\n feat = pred1['pts3d']\n else:\n feat = pred1\n else:\n # 最初のテンソル値を使用\n for val in feat.values():\n if isinstance(val, (torch.Tensor, np.ndarray)):\n feat = val\n break\n \n if isinstance(feat, torch.Tensor):\n feat = feat.cpu().numpy()\n \n # featの形状を確認\n if isinstance(feat, np.ndarray):\n if feat.ndim == 3: # [H, W, C]\n h, w, c = feat.shape\n feat_flat = feat.reshape(-1, c)\n elif feat.ndim == 2: # [N, C]\n feat_flat = feat\n else:\n print(f\"⚠️ Unexpected feature shape: {feat.shape}\")\n feat_flat = feat.reshape(-1, feat.shape[-1])\n \n global_desc = np.mean(feat_flat, axis=0)\n global_features.append(global_desc)\n else:\n print(f\"⚠️ Unexpected feature type: {type(feat)}\")\n # ダミー特徴量\n global_features.append(np.zeros(24))\n \n global_features = np.stack(global_features)\n \n if codebook is not None and len(codebook) > 0:\n try:\n print(f\"Using ASMK with codebook size: {len(codebook)}\")\n \n for i in range(n_images):\n feat_i = features[i]\n \n # 辞書からテンソルを抽出\n if isinstance(feat_i, dict):\n if 'pts3d' in feat_i:\n feat_i = feat_i['pts3d']\n elif 'pred1' in feat_i and isinstance(feat_i['pred1'], dict):\n feat_i = feat_i['pred1'].get('pts3d', feat_i['pred1'])\n \n if isinstance(feat_i, torch.Tensor):\n feat_i = feat_i.cpu().numpy()\n \n if feat_i.ndim == 3:\n feat_i = feat_i.reshape(-1, feat_i.shape[-1])\n \n for j in range(i+1, n_images):\n feat_j = features[j]\n \n # 辞書からテンソルを抽出\n if isinstance(feat_j, dict):\n if 'pts3d' in feat_j:\n feat_j = feat_j['pts3d']\n elif 'pred1' in feat_j and isinstance(feat_j['pred1'], dict):\n feat_j = feat_j['pred1'].get('pts3d', feat_j['pred1'])\n \n if isinstance(feat_j, torch.Tensor):\n feat_j = feat_j.cpu().numpy()\n \n if feat_j.ndim == 3:\n feat_j = feat_j.reshape(-1, feat_j.shape[-1])\n \n dist_i = np.linalg.norm(feat_i[:, None, :] - codebook[None, :, :], axis=2)\n dist_j = np.linalg.norm(feat_j[:, None, :] - codebook[None, :, :], axis=2)\n \n assign_i = np.argmin(dist_i, axis=1)\n assign_j = np.argmin(dist_j, axis=1)\n \n common = len(set(assign_i) & set(assign_j))\n sim = common / max(len(set(assign_i)), len(set(assign_j)))\n \n similarity_matrix[i, j] = sim\n similarity_matrix[j, i] = sim\n \n if (i + 1) % 10 == 0:\n print(f\"Processed {i+1}/{n_images} images\")\n \n print(\"✓ ASMK similarity computation completed\")\n \n except Exception as e:\n print(f\"⚠️ ASMK failed: {e}, using cosine similarity\")\n global_features_norm = global_features / (np.linalg.norm(global_features, axis=1, keepdims=True) + 1e-8)\n similarity_matrix = global_features_norm @ global_features_norm.T\n \n else:\n print(\"No codebook provided, using cosine similarity\")\n global_features_norm = global_features / (np.linalg.norm(global_features, axis=1, keepdims=True) + 1e-8)\n similarity_matrix = global_features_norm @ global_features_norm.T\n \n np.fill_diagonal(similarity_matrix, -1)\n \n print(f\"Similarity matrix shape: {similarity_matrix.shape}\")\n print(f\"Similarity range: [{similarity_matrix.min():.3f}, {similarity_matrix.max():.3f}]\")\n \n return similarity_matrix\n\n\ndef build_pairs_from_similarity(similarity_matrix, top_k=10):\n \"\"\"類似度行列からペアを構築\"\"\"\n n_images = similarity_matrix.shape[0]\n pairs = []\n \n for i in range(n_images):\n similarities = similarity_matrix[i]\n top_indices = np.argsort(similarities)[::-1][:top_k]\n \n for j in top_indices:\n if j > i:\n pairs.append((i, j))\n \n pairs = list(set(pairs))\n print(f\"✓ Built {len(pairs)} unique pairs\")\n \n return pairs\n\n\ndef get_image_pairs_asmk(image_paths, max_pairs=100):\n \"\"\"ASMKを使用して画像ペアを取得\"\"\"\n print(\"\\n=== Getting Image Pairs with ASMK ===\")\n \n device = Config.DEVICE\n model, codebook = load_asmk_retrieval_model(device)\n features = extract_mast3r_features(model, image_paths, device)\n similarity_matrix = compute_asmk_similarity(features, codebook)\n pairs = build_pairs_from_similarity(similarity_matrix, Config.RETRIEVAL_TOPK)\n \n # モデルを解放\n del model\n clear_memory()\n \n if len(pairs) > max_pairs:\n pairs = pairs[:max_pairs]\n print(f\"Limited to {max_pairs} pairs\")\n \n return pairs\n\n\n# ======================================================================\n# MAST3R RECONSTRUCTION\n# ======================================================================\n\ndef run_mast3r_pairs(model, image_paths, pairs, device, batch_size=1):\n \"\"\"MASt3Rでペア画像を処理（メモリ最適化版）\"\"\"\n print(\"\\n=== Running MASt3R Reconstruction ===\")\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 # ペアを制限（メモリ節約）\n max_pairs_for_memory = 50\n if len(pairs) > max_pairs_for_memory:\n print(f\"⚠️ Limiting pairs from {len(pairs)} to {max_pairs_for_memory} for memory\")\n pairs = pairs[:max_pairs_for_memory]\n \n # ペアから画像インデックスを取得\n pair_indices = []\n for i, j in pairs:\n pair_indices.extend([i, j])\n unique_indices = sorted(set(pair_indices))\n \n selected_paths = [image_paths[i] for i in unique_indices]\n print(f\"Selected {len(selected_paths)} unique images from {len(pairs)} pairs\")\n \n # 画像をロード\n images = load_images(selected_paths, size=Config.IMAGE_SIZE)\n \n clear_memory()\n \n # インデックスマッピング（元のインデックス → 新しいインデックス）\n index_map = {old_idx: new_idx for new_idx, old_idx in enumerate(unique_indices)}\n \n # ペアを新しいインデックスに変換してペア画像リストを作成\n image_pairs = []\n for i, j in pairs:\n new_i = index_map[i]\n new_j = index_map[j]\n image_pairs.append((images[new_i], images[new_j]))\n \n print(f\"Created {len(image_pairs)} image pairs\")\n \n clear_memory()\n \n # バッチサイズを動的に調整\n available_memory = torch.cuda.get_device_properties(device).total_memory\n used_memory = torch.cuda.memory_allocated(device)\n free_memory = available_memory - used_memory\n \n if free_memory < 2e9:\n batch_size = 1\n print(f\"⚠️ Low memory, using batch_size=1\")\n \n # 推論を実行\n print(f\"Running inference on {len(image_pairs)} pairs...\")\n with torch.no_grad():\n output = inference(image_pairs, model, device, batch_size=batch_size)\n \n print(f\"✓ Processed {len(output)} predictions\")\n \n clear_memory()\n \n # Global alignmentの準備\n scene = global_aligner(\n dust3r_output=output,\n device=device,\n mode=GlobalAlignerMode.PointCloudOptimizer,\n verbose=True\n )\n \n clear_memory()\n \n # Global alignment\n print(\"Running global alignment...\")\n try:\n loss = scene.compute_global_alignment(\n init=\"mst\", \n niter=50,\n schedule='cosine', \n lr=0.01\n )\n print(f\"✓ Alignment complete (loss: {loss:.6f})\")\n except RuntimeError as e:\n if \"out of memory\" in str(e).lower():\n print(\"⚠️ OOM during alignment, trying with fewer iterations...\")\n clear_memory()\n \n loss = scene.compute_global_alignment(\n init=\"mst\", \n niter=20,\n schedule='cosine', \n lr=0.01\n )\n print(f\"✓ Alignment complete with reduced iterations (loss: {loss:.6f})\")\n else:\n raise\n \n clear_memory()\n \n return scene, images\n\n\n# ======================================================================\n# CAMERA PARAMETER EXTRACTION (PROCESS2)\n# ======================================================================\n\ndef extract_camera_params_process2(scene, image_paths, conf_threshold=1.5):\n \"\"\"Process2: sceneから直接カメラパラメータと3D点を抽出\"\"\"\n print(\"\\n=== Extracting Camera Parameters (Process2) ===\")\n \n cameras_dict = {}\n all_pts3d = []\n all_confidence = []\n \n n_images = len(image_paths)\n \n for idx in range(n_images):\n img_name = os.path.basename(image_paths[idx])\n \n try:\n # カメラパラメータを取得\n if hasattr(scene, 'im_poses'):\n pose = scene.im_poses[idx]\n elif hasattr(scene, 'get_im_poses'):\n pose = scene.get_im_poses()[idx]\n else:\n pose = torch.eye(4)\n \n if hasattr(scene, 'im_focals'):\n focal = scene.im_focals[idx]\n elif hasattr(scene, 'get_focals'):\n focal = scene.get_focals()[idx]\n else:\n focal = 1000.0\n \n if hasattr(scene, 'im_pp'):\n pp = scene.im_pp[idx]\n elif hasattr(scene, 'get_principal_points'):\n pp = scene.get_principal_points()[idx]\n else:\n pp = torch.tensor([112.0, 112.0])\n \n # テンソルをnumpyに変換\n if isinstance(pose, torch.Tensor):\n pose = pose.detach().cpu().numpy()\n if isinstance(focal, torch.Tensor):\n focal = focal.detach().cpu().item()\n if isinstance(pp, torch.Tensor):\n pp = pp.detach().cpu().numpy()\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 }\n \n # 3D点を取得\n if hasattr(scene, 'im_pts3d'):\n pts3d_img = scene.im_pts3d[idx]\n elif hasattr(scene, 'get_pts3d'):\n pts3d_img = scene.get_pts3d()[idx]\n else:\n pts3d_img = None\n \n # Confidenceを取得\n if hasattr(scene, 'im_conf'):\n conf_img = scene.im_conf[idx]\n elif hasattr(scene, 'get_conf'):\n conf_img = scene.get_conf()[idx]\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 cameras_dict[img_name] = {\n 'focal': 1000.0,\n 'pp': np.array([112.0, 112.0]),\n 'pose': np.eye(4)\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)} images\")\n print(f\"✓ Total 3D points: {len(pts3d)}\")\n print(f\"✓ Confidence shape: {confidence.shape}\")\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\n\n# ======================================================================\n# COLMAP EXPORT\n# ======================================================================\n\ndef write_colmap_sparse(cameras_dict, pts3d, confidence, image_paths, output_dir):\n \"\"\"COLMAPフォーマットでスパース再構成を書き出し\"\"\"\n print(\"\\n=== Writing COLMAP Sparse Reconstruction ===\")\n \n import pycolmap\n \n os.makedirs(output_dir, exist_ok=True)\n \n # Reconstruction オブジェクトを作成\n reconstruction = pycolmap.Reconstruction()\n \n # カメラを追加\n camera_id = reconstruction.add_camera(\n pycolmap.Camera(\n model=\"SIMPLE_PINHOLE\",\n width=Config.IMAGE_SIZE,\n height=Config.IMAGE_SIZE,\n params=[1000.0, Config.IMAGE_SIZE/2, Config.IMAGE_SIZE/2]\n )\n )\n \n # 画像を追加\n for img_idx, img_path in enumerate(image_paths):\n img_name = os.path.basename(img_path)\n \n if img_name in cameras_dict:\n cam_params = cameras_dict[img_name]\n pose = cam_params['pose']\n \n # Rotation and translation\n R = pose[:3, :3]\n t = pose[:3, 3]\n \n qvec = pycolmap.rotmat_to_qvec(R)\n tvec = t\n \n reconstruction.add_image(\n pycolmap.Image(\n id=img_idx + 1,\n name=img_name,\n camera_id=camera_id,\n qvec=qvec,\n tvec=tvec\n )\n )\n \n # 3D点を追加\n for i, pt in enumerate(pts3d):\n if i >= 100000: # 点数制限\n break\n \n reconstruction.add_point3D(\n pycolmap.Point3D(\n xyz=pt,\n color=np.array([128, 128, 128], dtype=np.uint8),\n error=1.0 - confidence[i] if i < len(confidence) else 1.0\n )\n )\n \n # 保存\n reconstruction.write(output_dir)\n \n print(f\"✓ Wrote COLMAP reconstruction to: {output_dir}\")\n print(f\" - Cameras: {len(reconstruction.cameras)}\")\n print(f\" - Images: {len(reconstruction.images)}\")\n print(f\" - Points: {len(reconstruction.points3D)}\")\n\n\n# ======================================================================\n# GAUSSIAN SPLATTING\n# ======================================================================\n\ndef run_gaussian_splatting(output_dir, iterations=30000):\n \"\"\"Gaussian Splattingを実行\"\"\"\n print(\"\\n=== Running Gaussian Splatting ===\")\n \n gs_source = output_dir\n gs_model = os.path.join(output_dir, \"output\")\n \n cmd = f\"\"\"\n python /kaggle/working/gaussian-splatting/train.py \\\n -s {gs_source} \\\n -m {gs_model} \\\n --iterations {iterations} \\\n --eval\n \"\"\"\n \n print(f\"Command: {cmd}\")\n os.system(cmd)\n \n print(f\"✓ Gaussian Splatting complete\")\n print(f\" Output: {gs_model}\")\n \n return gs_model\n\n\n# ======================================================================\n# MAIN PIPELINE\n# ======================================================================\n\ndef main_pipeline(image_dir, output_dir, square_size=256, iterations=30000, \n max_images=200, max_pairs=100, max_points=500000, \n conf_threshold=1.5):\n \"\"\"メインパイプライン（Process2のみ、メモリ最適化版）\"\"\"\n \n print(\"=\"*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 \n clear_memory()\n \n print(\"\\n\" + \"=\"*70)\n print(\"STEP 2: Image Pair Selection\")\n print(\"=\"*70)\n \n max_pairs = min(max_pairs, 50)\n pairs = get_image_pairs_asmk(image_paths, max_pairs=max_pairs)\n print(f\"Selected {len(pairs)} image pairs\")\n \n clear_memory()\n \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 \n scene, mast3r_images = run_mast3r_pairs(model, image_paths, pairs, device)\n \n # モデルを解放\n del model\n clear_memory()\n \n print(\"\\n\" + \"=\"*70)\n print(\"STEP 4: Converting to COLMAP (Process2 Method)\")\n print(\"=\"*70)\n \n cameras_dict, pts3d, confidence = extract_camera_params_process2(\n scene, image_paths, conf_threshold=conf_threshold\n )\n \n # sceneを解放\n del scene\n clear_memory()\n \n # 点数を制限\n if len(pts3d) > max_points:\n print(f\"⚠️ Limiting points from {len(pts3d)} to {max_points}\")\n indices = np.random.choice(len(pts3d), max_points, replace=False)\n pts3d = pts3d[indices]\n confidence = confidence[indices]\n \n print(f\"Final point count: {len(pts3d)}\")\n \n # COLMAP変換\n colmap_dir = os.path.join(output_dir, \"sparse/0\")\n os.makedirs(colmap_dir, exist_ok=True)\n \n write_colmap_sparse(cameras_dict, pts3d, confidence, image_paths, colmap_dir)\n \n clear_memory()\n \n print(\"\\n\" + \"=\"*70)\n print(\"STEP 5: Running Gaussian Splatting\")\n print(\"=\"*70)\n \n gs_output = run_gaussian_splatting(\n output_dir=output_dir,\n iterations=iterations\n )\n \n return gs_output\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T12:17:42.095184Z","iopub.status.idle":"2026-01-21T12:17:42.095586Z","shell.execute_reply.started":"2026-01-21T12:17:42.095383Z","shell.execute_reply":"2026-01-21T12:17:42.095410Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T12:17:42.096753Z","iopub.status.idle":"2026-01-21T12:17:42.097018Z","shell.execute_reply.started":"2026-01-21T12:17:42.096899Z","shell.execute_reply":"2026-01-21T12:17:42.096912Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ======================================================================\n# USAGE EXAMPLE\n# ======================================================================\n\nif __name__ == \"__main__\":\n IMAGE_DIR = \"/kaggle/input/two-dogs/fountain80/fountain80\"\n OUTPUT_DIR = \"/kaggle/working/output\"\n \n # メモリ制約を考慮したパラメータ\n gs_output = main_pipeline(\n image_dir=IMAGE_DIR,\n output_dir=OUTPUT_DIR,\n square_size=256,\n iterations=1000,\n max_images=10, # 画像数を制限\n max_pairs=10, # ペア数を制限\n max_points=3000, # 点数を制限\n conf_threshold=1.5\n )\n \n print(\"\\n\" + \"=\"*70)\n print(\"PIPELINE COMPLETE\")\n print(\"=\"*70)\n print(f\"Output directory: {gs_output}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T12:17:42.098014Z","iopub.status.idle":"2026-01-21T12:17:42.098318Z","shell.execute_reply.started":"2026-01-21T12:17:42.098147Z","shell.execute_reply":"2026-01-21T12:17:42.098166Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T12:17:42.099569Z","iopub.status.idle":"2026-01-21T12:17:42.099849Z","shell.execute_reply.started":"2026-01-21T12:17:42.099731Z","shell.execute_reply":"2026-01-21T12:17:42.099745Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]} |