Delete biplet-asmk-mast3r-ps2-gs-kg-16.ipynb

biplet-asmk-mast3r-ps2-gs-kg-16.ipynb

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-{"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":[{"sourceType":"datasetVersion","sourceId":14571475,"datasetId":1429416,"databundleVersionId":15404368}],"dockerImageVersionId":31259,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **biplet-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 plyfile\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":"2026-01-21T16:44:52.975424Z","iopub.execute_input":"2026-01-21T16:44:52.975701Z","iopub.status.idle":"2026-01-21T16:45:23.107536Z","shell.execute_reply.started":"2026-01-21T16:44:52.975666Z","shell.execute_reply":"2026-01-21T16:45:23.106517Z"}},"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) (3.20.3)\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) (26.0rc2)\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) (2.6.3)\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub) (2026.1.4)\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)\nCollecting plyfile\n  Downloading plyfile-1.1.3-py3-none-any.whl.metadata (43 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.3/43.3 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement 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)\nDownloading plyfile-1.1.3-py3-none-any.whl (36 kB)\nInstalling collected packages: plyfile\nSuccessfully installed plyfile-1.1.3\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[31m98.2 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[31m34.9 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[31m2.1 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[31m1.9 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[31m101.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\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[31m59.9 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 google-auth==2.38.0, but you have google-auth 2.47.0 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.\nfastai 2.8.4 requires fastcore<1.9,>=1.8.0, but you have fastcore 1.11.3 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_55/2184368426.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 (2184368426.py, line 7)","output_type":"error"}],"execution_count":1},{"cell_type":"code","source":"# restart, then run after","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T17:02:51.949411Z","iopub.execute_input":"2026-01-21T17:02:51.950027Z","iopub.status.idle":"2026-01-21T17:02:51.953541Z","shell.execute_reply.started":"2026-01-21T17:02:51.949996Z","shell.execute_reply":"2026-01-21T17:02:51.952667Z"}},"outputs":[],"execution_count":17},{"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":"2026-01-21T17:02:51.954854Z","iopub.execute_input":"2026-01-21T17:02:51.955067Z","iopub.status.idle":"2026-01-21T17:02:54.087211Z","shell.execute_reply.started":"2026-01-21T17:02:51.955047Z","shell.execute_reply":"2026-01-21T17:02:54.086431Z"}},"outputs":[{"name":"stdout","text":"✓ np: 2.0.2 - /usr/local/lib/python3.12/dist-packages/numpy/__init__.py\nVersion: 1.26.4\nVersion 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","output_type":"stream"}],"execution_count":18},{"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":"2026-01-21T17:02:54.088775Z","iopub.execute_input":"2026-01-21T17:02:54.089009Z","iopub.status.idle":"2026-01-21T17:02:54.105691Z","shell.execute_reply.started":"2026-01-21T17:02:54.088984Z","shell.execute_reply":"2026-01-21T17:02:54.104974Z"}},"outputs":[{"name":"stdout","text":"✓ MASt3R already exists\n✓ DUSt3R already exists\n✓ ASMK already exists\n✓ dust3r.model imported successfully\n","output_type":"stream"}],"execution_count":19},{"cell_type":"code","source":"# =====================================================================\n# STEP 2: Clone Gaussian Splatting\n# =====================================================================\nprint(\"\\n\" + \"=\"*70)\nprint(\"STEP 2: Clone Gaussian Splatting\")\nprint(\"=\"*70)\nWORK_DIR = \"/kaggle/working/gaussian-splatting\"\n\nimport subprocess\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# インストールが必要なディレクトリ\nsubmodules = [\n    \"/kaggle/working/gaussian-splatting/submodules/diff-gaussian-rasterization\",\n    \"/kaggle/working/gaussian-splatting/submodules/simple-knn\"\n]\n\nfor path in submodules:\n    print(f\"Installing {path}...\")\n    # -e は編集可能モード、不要なら外してもOKです\n    subprocess.run([\"pip\", \"install\", path], check=True)\n\nprint(\"✓ Custom CUDA modules installed.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T17:02:54.106692Z","iopub.execute_input":"2026-01-21T17:02:54.106965Z","iopub.status.idle":"2026-01-21T17:03:16.561765Z","shell.execute_reply.started":"2026-01-21T17:02:54.106937Z","shell.execute_reply":"2026-01-21T17:03:16.560946Z"}},"outputs":[{"name":"stdout","text":"\n======================================================================\nSTEP 2: Clone Gaussian Splatting\n======================================================================\n✓ Already exists\nInstalling /kaggle/working/gaussian-splatting/submodules/diff-gaussian-rasterization...\nProcessing ./gaussian-splatting/submodules/diff-gaussian-rasterization\n  Preparing metadata (setup.py): started\n  Preparing metadata (setup.py): finished with status 'done'\nBuilding wheels for collected packages: diff_gaussian_rasterization\n  Building wheel for diff_gaussian_rasterization (setup.py): started\n  Building wheel for diff_gaussian_rasterization (setup.py): finished with status 'done'\n  Created wheel for diff_gaussian_rasterization: filename=diff_gaussian_rasterization-0.0.0-cp312-cp312-linux_x86_64.whl size=3455609 sha256=76353663da1e49e76787e2bd9ebeb652e975a7e0277688c44bd2ab9f5c736de7\n  Stored in directory: /root/.cache/pip/wheels/ba/99/d3/014520068aca8c2e8bdc358ca774581380cadb65788559b3ea\nSuccessfully built diff_gaussian_rasterization\nInstalling collected packages: diff_gaussian_rasterization\n  Attempting uninstall: diff_gaussian_rasterization\n    Found existing installation: diff_gaussian_rasterization 0.0.0\n    Uninstalling diff_gaussian_rasterization-0.0.0:\n      Successfully uninstalled diff_gaussian_rasterization-0.0.0\nSuccessfully installed diff_gaussian_rasterization-0.0.0\nInstalling /kaggle/working/gaussian-splatting/submodules/simple-knn...\nProcessing ./gaussian-splatting/submodules/simple-knn\n  Preparing metadata (setup.py): started\n  Preparing metadata (setup.py): finished with status 'done'\nBuilding wheels for collected packages: simple_knn\n  Building wheel for simple_knn (setup.py): started\n  Building wheel for simple_knn (setup.py): finished with status 'done'\n  Created wheel for simple_knn: filename=simple_knn-0.0.0-cp312-cp312-linux_x86_64.whl size=3212332 sha256=24991f6133023b1cf1771bb1e2f3b89e03dd3571670f857f3fca1dfc2c64f5ef\n  Stored in directory: /root/.cache/pip/wheels/ca/30/df/7f4f362d12edead48c699acde5962cbb06ca05033b9d970934\nSuccessfully built simple_knn\nInstalling collected packages: simple_knn\n  Attempting uninstall: simple_knn\n    Found existing installation: simple_knn 0.0.0\n    Uninstalling simple_knn-0.0.0:\n      Successfully uninstalled simple_knn-0.0.0\nSuccessfully installed simple_knn-0.0.0\n✓ Custom CUDA modules installed.\n","output_type":"stream"}],"execution_count":20},{"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":"2026-01-21T17:03:16.563482Z","iopub.execute_input":"2026-01-21T17:03:16.563769Z","iopub.status.idle":"2026-01-21T17:03:18.691963Z","shell.execute_reply.started":"2026-01-21T17:03:16.563746Z","shell.execute_reply":"2026-01-21T17:03:18.691030Z"}},"outputs":[{"name":"stdout","text":"✓ np: 2.0.2 - /usr/local/lib/python3.12/dist-packages/numpy/__init__.py\nVersion: 1.26.4\nVersion 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","output_type":"stream"}],"execution_count":21},{"cell_type":"code","source":"import 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 PREPROCESSING\n# ======================================================================\n\ndef 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    from PIL import Image\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\"  Output images: {converted_count * 2}\")\n    print(f\"  Original size distribution: {size_stats}\")\n    \n    return output_dir\n\n\ndef generate_two_crops(img, size):\n    \"\"\"\n    Crops the image into a square and returns 2 variations\n    (Left/Right for landscape, Top/Bottom for portrait).\n    \"\"\"\n    from PIL import Image\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    \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# ======================================================================\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���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    # sceneから実際の画像数を取得\n    try:\n        if hasattr(scene, 'get_im_poses'):\n            poses = scene.get_im_poses()\n            n_images = len(poses)\n        elif hasattr(scene, 'im_poses'):\n            poses = scene.im_poses\n            n_images = len(poses)\n        else:\n            n_images = len(image_paths)\n            poses = None\n        \n        print(f\"Scene has {n_images} images, image_paths has {len(image_paths)} images\")\n        \n        # 実際のscene画像数とimage_pathsの小さい方を使用\n        n_images = min(n_images, len(image_paths))\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            \n    except Exception as e:\n        print(f\"⚠️  Error getting camera parameters: {e}\")\n        n_images = len(image_paths)\n        poses = None\n        focals = None\n        pps = None\n    \n    for idx in range(n_images):\n        #-------------------------------------------------------------------\n        print(f\"\\n=== Image {idx}: {os.path.basename(image_paths[idx])} ===\")\n        \n        # Poseを取得の後に追加\n        if poses is not None and idx < len(poses):\n            pose = poses[idx]\n            if isinstance(pose, torch.Tensor):\n                pose = pose.detach().cpu().numpy()\n            \n            print(f\"  Pose type: {type(pose)}\")\n            print(f\"  Pose shape: {pose.shape if hasattr(pose, 'shape') else 'N/A'}\")\n            if hasattr(pose, 'shape') and pose.shape == (4, 4):\n                print(f\"  Rotation det: {np.linalg.det(pose[:3, :3]):.3f}\")\n        #-------------------------------------------------------------------\n        \n        img_name = os.path.basename(image_paths[idx])\n        \n        try:\n            # Poseを取得\n            if poses is not None and idx < len(poses):\n                pose = poses[idx]\n                if isinstance(pose, torch.Tensor):\n                    pose = pose.detach().cpu().numpy()\n                \n                # poseが正しい形状でない場合、単位行列を使用\n                if not isinstance(pose, np.ndarray) or pose.shape != (4, 4):\n                    print(f\"  → Image {idx}: pose shape {pose.shape if hasattr(pose, 'shape') else 'N/A'}, using identity\")\n                    pose = np.eye(4)\n            else:\n                pose = np.eye(4)\n            \n            # Focalを取得\n            if focals is not None and idx < len(focals):\n                focal = focals[idx]\n                if isinstance(focal, torch.Tensor):\n                    focal = focal.detach().cpu().item()\n                else:\n                    focal = float(focal)\n            else:\n                focal = 1000.0\n            \n            # Principal pointを取得\n            if pps is not None and idx < len(pps):\n                pp = pps[idx]\n                if isinstance(pp, torch.Tensor):\n                    pp = pp.detach().cpu().numpy()\n            else:\n                pp = np.array([112.0, 112.0])\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            }\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            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    \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","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T17:03:18.693472Z","iopub.execute_input":"2026-01-21T17:03:18.693729Z","iopub.status.idle":"2026-01-21T17:03:18.757175Z","shell.execute_reply.started":"2026-01-21T17:03:18.693701Z","shell.execute_reply":"2026-01-21T17:03:18.756463Z"}},"outputs":[],"execution_count":22},{"cell_type":"code","source":"# ======================================================================\n# COLMAP EXPORT\n# ======================================================================\nimport os\nimport numpy as np\nimport pycolmap\nfrom pycolmap import Rigid3d\n#from scipy.spatial.transform import Rotation as R\n\ndef write_colmap_sparse(cameras_dict, pts3d, confidence, image_paths, output_dir):\n    \"\"\"\n    pycolmap 3.13.0 の API に準拠した実装\n    \"\"\"\n    reconstruction = pycolmap.Reconstruction()\n\n    # --- 1. カメラの設定 ---\n    if not cameras_dict:\n        raise ValueError(\"cameras_dict is empty\")\n    \n    first_key = list(cameras_dict.keys())[0]\n    first_cam = cameras_dict[first_key]\n    \n    w = int(first_cam.get('width', 1920))\n    h = int(first_cam.get('height', 1080))\n    f = float(first_cam.get('focal', max(w, h) * 1.2))\n    \n    # PINHOLEモデル: [fx, fy, cx, cy]\n    camera = pycolmap.Camera(\n        model=pycolmap.CameraModelId.PINHOLE,\n        width=w,\n        height=h,\n        params=[f, f, w / 2.0, h / 2.0]\n    )\n    camera.camera_id = 1\n    reconstruction.add_camera(camera)\n\n    # --- 2. 画像 (Pose) の登録 ---\n    for i, img_path in enumerate(image_paths):\n        img_name = os.path.basename(img_path)\n        \n        cam_info = cameras_dict.get(i) or cameras_dict.get(str(i)) or cameras_dict.get(img_name)\n        if cam_info is None:\n            continue\n            \n        # Pose (C2W -> W2C)\n        c2w = cam_info['pose']\n        try:\n            w2c = np.linalg.inv(c2w)\n        except np.linalg.LinAlgError:\n            continue\n\n        rot_mat = w2c[:3, :3]\n        tvec = w2c[:3, 3]\n        quat = R.from_matrix(rot_mat).as_quat() # [x, y, z, w]\n        qvec = np.array([quat[3], quat[0], quat[1], quat[2]]) # [w, x, y, z]\n\n        # 【v3.13 重要ポイント】\n        # 1. まず空のイメージを登録してIDを取得\n        image_id = i + 1\n        image = pycolmap.Image(name=img_name, camera_id=camera.camera_id)\n        image.image_id = image_id\n        \n        # 2. Rigid3d オブジェクトを作成してセット\n        # pycolmap v3.x では cam_from_world を使用します\n        image.cam_from_world = Rigid3d(qvec, tvec)\n        \n        reconstruction.add_image(image)\n\n    # --- 3. 3Dポイントの登録 ---\n    for point in pts3d:\n        xyz = point.astype(np.float64)\n        # 第2引数は Track (点と画像の対応情報)\n        reconstruction.add_point3D(xyz, pycolmap.Track(), [255, 255, 255])\n\n    # --- 4. 書き出し ---\n    os.makedirs(output_dir, exist_ok=True)\n    reconstruction.write(output_dir)\n    \n    print(f\"✓ COLMAP sparse reconstruction saved (v3.13.0 API)\")\n    print(f\"  - Images: {reconstruction.num_images()}\")\n    print(f\"  - Points: {reconstruction.num_points3D()}\")\n\n    return reconstruction","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T17:03:18.758280Z","iopub.execute_input":"2026-01-21T17:03:18.758594Z","iopub.status.idle":"2026-01-21T17:03:18.781442Z","shell.execute_reply.started":"2026-01-21T17:03:18.758571Z","shell.execute_reply":"2026-01-21T17:03:18.780870Z"}},"outputs":[],"execution_count":23},{"cell_type":"code","source":"# ======================================================================\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=1024, iterations=30000, \n                  max_images=200, max_pairs=100, max_points=500000, \n                  conf_threshold=1.5, preprocess_mode='none'):\n    \"\"\"\n    メインパイプライン（Process2のみ、メモリ最適化版）\n    \n    Args:\n        image_dir: 入力画像ディレクトリ\n        output_dir: 出力ディレクトリ\n        square_size: Biplet前処理時の正方形サイズ\n        iterations: Gaussian Splattingの反復回数\n        max_images: 最大画像数\n        max_pairs: 最大ペア数\n        max_points: 最大点群数\n        conf_threshold: Confidence閾値\n        preprocess_mode: 前処理モード\n            - 'none': 前処理なし（元画像をそのまま使用）\n            - 'biplet': 2つの正方形クロップを生成（画像数2倍）\n    \"\"\"\n    \n    # 前処理\n    if preprocess_mode == 'biplet':\n        print(\"=\"*70)\n        print(\"STEP 0: Image Preprocessing (Biplet Crops)\")\n        print(\"=\"*70)\n        preprocessed_dir = os.path.join(output_dir, \"preprocessed_biplet\")\n        image_dir = normalize_image_sizes_biplet(\n            image_dir,\n            preprocessed_dir,\n            size=square_size\n        )\n        clear_memory()\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","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T17:03:18.782408Z","iopub.execute_input":"2026-01-21T17:03:18.782607Z","iopub.status.idle":"2026-01-21T17:03:18.805461Z","shell.execute_reply.started":"2026-01-21T17:03:18.782588Z","shell.execute_reply":"2026-01-21T17:03:18.804753Z"}},"outputs":[],"execution_count":24},{"cell_type":"markdown","source":"    # ======================================================================\n    # USAGE EXAMPLE\n    # ======================================================================\n    \n    if __name__ == \"__main__\":\n        IMAGE_DIR = \"/kaggle/input/your-dataset/images\"\n        OUTPUT_DIR = \"/kaggle/working/output\"\n        \n        # 使用例1: 前処理なし（元画像をそのまま使用）\n        gs_output = main_pipeline(\n            image_dir=IMAGE_DIR,\n            output_dir=OUTPUT_DIR,\n            iterations=30000,\n            max_images=100,\n            max_pairs=50,\n            max_points=300000,\n            conf_threshold=1.5,\n            preprocess_mode='none'  # 前処理なし\n        )\n        \n        # 使用例2: Bipletクロップ（画像数が2倍になる）\n        gs_output = main_pipeline(\n            image_dir=IMAGE_DIR,\n            output_dir=OUTPUT_DIR,\n            square_size=1024,      # クロップサイズ\n            iterations=30000,\n            max_images=50,         # Bipletで2倍になるので少なめに\n            max_pairs=50,\n            max_points=300000,\n            conf_threshold=1.5,\n            preprocess_mode='biplet'  # 2つのクロップ生成\n        )\n        \n        print(\"\\n\" + \"=\"*70)\n        print(\"PIPELINE COMPLETE\")\n        print(\"=\"*70)\n        print(f\"Output directory: {gs_output}\")","metadata":{}},{"cell_type":"code","source":"print(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T17:03:18.806437Z","iopub.execute_input":"2026-01-21T17:03:18.807034Z","iopub.status.idle":"2026-01-21T17:03:20.958524Z","shell.execute_reply.started":"2026-01-21T17:03:18.807012Z","shell.execute_reply":"2026-01-21T17:03:20.957787Z"}},"outputs":[{"name":"stdout","text":"✓ np: 2.0.2 - /usr/local/lib/python3.12/dist-packages/numpy/__init__.py\nVersion: 1.26.4\nVersion 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","output_type":"stream"}],"execution_count":25},{"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    gs_output = main_pipeline(\n        image_dir=IMAGE_DIR,\n        output_dir=OUTPUT_DIR,\n        square_size=512,\n        iterations=1000,\n        max_images=5,  #\n        max_pairs=5,\n        max_points=300,\n        conf_threshold=1.5,\n        preprocess_mode='biplet' \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":"2026-01-21T17:03:20.959906Z","iopub.execute_input":"2026-01-21T17:03:20.960191Z","iopub.status.idle":"2026-01-21T17:03:44.499255Z","shell.execute_reply.started":"2026-01-21T17:03:20.960162Z","shell.execute_reply":"2026-01-21T17:03:44.498176Z"}},"outputs":[{"name":"stdout","text":"======================================================================\nSTEP 0: Image Preprocessing (Biplet Crops)\n======================================================================\n\n=== Generating Biplet Crops (512x512) ===\n","output_type":"stream"},{"name":"stderr","text":"Creating biplets: 100%|██████████| 80/80 [00:08<00:00,  9.79it/s]\n","output_type":"stream"},{"name":"stdout","text":"\n✓ Biplet generation complete:\n  Source images: 80\n  Output images: 160\n  Original size distribution: {'1440x1920': 80}\n======================================================================\nSTEP 1: Loading and Preparing Images\n======================================================================\n\nLoading images from: /kaggle/working/output/preprocessed_biplet\n⚠️  Limiting from 160 to 5 images\n✓ Found 5 images\nLoaded 5 images\n\n======================================================================\nSTEP 2: Image Pair Selection\n======================================================================\n\n=== Getting Image Pairs with ASMK ===\n\n=== Loading ASMK Retrieval Model ===\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/546 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cd76344df11448d2828193513ea651fb"}},"metadata":{}},{"name":"stdout","text":"⚠️  Failed to load MASt3R: tried to load naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric from huggingface, but failed\nTrying DUSt3R instead\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/450 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b5b24c5931c44e108a4f42aa51b0059d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors:   0%|          | 0.00/2.28G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"15ede083ad6a4ac7a5706c3a28c3bfdc"}},"metadata":{}},{"name":"stdout","text":"✓ Loaded DUSt3R model for retrieval\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_55/3249206571.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mOUTPUT_DIR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/kaggle/working/output\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     gs_output = main_pipeline(\n\u001b[0m\u001b[1;32m     10\u001b[0m         \u001b[0mimage_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mIMAGE_DIR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0moutput_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mOUTPUT_DIR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/tmp/ipykernel_55/3103415528.py\u001b[0m in \u001b[0;36mmain_pipeline\u001b[0;34m(image_dir, output_dir, square_size, iterations, max_images, max_pairs, max_points, conf_threshold, preprocess_mode)\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0mmax_pairs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_pairs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m     \u001b[0mpairs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_image_pairs_asmk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_paths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_pairs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_pairs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Selected {len(pairs)} image pairs\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/tmp/ipykernel_55/1291864485.py\u001b[0m in \u001b[0;36mget_image_pairs_asmk\u001b[0;34m(image_paths, max_pairs)\u001b[0m\n\u001b[1;32m    500\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m     \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcodebook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_asmk_retrieval_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    503\u001b[0m     \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_mast3r_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage_paths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m     \u001b[0msimilarity_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_asmk_similarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcodebook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/tmp/ipykernel_55/1291864485.py\u001b[0m in \u001b[0;36mload_asmk_retrieval_model\u001b[0;34m(device)\u001b[0m\n\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m     \u001b[0;31m# Codebookの初期化（簡易版）\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m     \u001b[0mcodebook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1024\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m24\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"✓ ASMK model loaded\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/numpy/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mpublic_symbols\u001b[0m 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Expected 96 from C header, got 88 from PyObject"],"ename":"ValueError","evalue":"numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject","output_type":"error"}],"execution_count":26},{"cell_type":"code","source":"print(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T17:03:44.499847Z","iopub.status.idle":"2026-01-21T17:03:44.500078Z","shell.execute_reply.started":"2026-01-21T17:03:44.499966Z","shell.execute_reply":"2026-01-21T17:03:44.499980Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pycolmap\n\n# 確認\nrec = pycolmap.Reconstruction()\ncamera = pycolmap.Camera(\n    model='SIMPLE_PINHOLE',\n    width=224,\n    height=224,\n    params=[1000.0, 112.0, 112.0]\n)\n\nprint(f\"Camera before add: {camera}\")\nprint(f\"Camera attributes: {[a for a in dir(camera) if not a.startswith('_')]}\")\n\nresult = rec.add_camera(camera)\nprint(f\"add_camera returned: {result}, type: {type(result)}\")\nprint(f\"Cameras in reconstruction: {rec.cameras}\")\nprint(f\"Camera IDs: {list(rec.cameras.keys())}\")\n\n# カメラIDの取得方法\nif len(rec.cameras) > 0:\n    camera_id = list(rec.cameras.keys())[0]\n    print(f\"Actual camera_id: {camera_id}\")\n    actual_camera = rec.cameras[camera_id]\n    print(f\"Camera object: {actual_camera}\")\n    print(f\"Camera camera_id attribute: {actual_camera.camera_id if hasattr(actual_camera, 'camera_id') else 'N/A'}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T17:03:44.501735Z","iopub.status.idle":"2026-01-21T17:03:44.502510Z","shell.execute_reply.started":"2026-01-21T17:03:44.502305Z","shell.execute_reply":"2026-01-21T17:03:44.502331Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}