Delete dino-lightglue-mast3r-gs-kg-07.ipynb

dino-lightglue-mast3r-gs-kg-07.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","machine_shape":"hm"},"accelerator":"GPU","kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":14554378,"sourceType":"datasetVersion","datasetId":1429416}],"dockerImageVersionId":31260,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **dino-lightglue-mast3r-gs-kg**\n2026/01/20","metadata":{"id":"qDQLX3PArmh8"}},{"cell_type":"markdown","source":"https://huggingface.co/datasets/stpete2/ipynb/resolve/main/dino_lightglue_mast3r_gs_colab_48oo.ipynb","metadata":{}},{"cell_type":"markdown","source":"# **setup**","metadata":{"id":"vXt8y7QyyRn9"}},{"cell_type":"code","source":"# 1. NumPyを下げつつ、それと互換性のある ml_dtypes をセットで入れる\n!pip install numpy==1.26.4 ml_dtypes==0.5.4","metadata":{"id":"zzIlYMf5ozkH","outputId":"1b6b080d-a90c-4612-fe9b-7c285e4d6f28","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"break","metadata":{"id":"Dsic4JslI1l4","outputId":"25ee6ab0-42b1-42aa-a626-ed9331dfccbd","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#**セッションを再起動する、下のセルを実行する**","metadata":{"id":"n3HClCivHr9W"}},{"cell_type":"code","source":"import numpy as np\nprint(f\"NumPy version: {np.__version__}\")\n\nimport ml_dtypes\nprint(f\"ML_Dtypes version: {ml_dtypes.__version__}\")","metadata":{"id":"inVfYAjA2nQ9","outputId":"4c59bb48-324e-4d17-dc69-9d53d55db806","trusted":true,"execution":{"iopub.status.busy":"2026-01-20T05:55:23.854494Z","iopub.execute_input":"2026-01-20T05:55:23.854847Z","iopub.status.idle":"2026-01-20T05:55:23.860433Z","shell.execute_reply.started":"2026-01-20T05:55:23.854820Z","shell.execute_reply":"2026-01-20T05:55:23.859337Z"}},"outputs":[{"name":"stdout","text":"NumPy version: 1.26.4\nML_Dtypes version: 0.5.4\n","output_type":"stream"}],"execution_count":35},{"cell_type":"code","source":"from transformers import AutoImageProcessor, AutoModel","metadata":{"id":"jTO3dSS5HXrC","trusted":true,"execution":{"iopub.status.busy":"2026-01-20T05:55:23.862134Z","iopub.execute_input":"2026-01-20T05:55:23.862436Z","iopub.status.idle":"2026-01-20T05:55:23.879724Z","shell.execute_reply.started":"2026-01-20T05:55:23.862403Z","shell.execute_reply":"2026-01-20T05:55:23.878800Z"}},"outputs":[],"execution_count":36},{"cell_type":"code","source":"import os\nimport sys\nimport glob\nimport shutil\nimport subprocess\nimport site\n\ndef setup_environment():\n    \"\"\"\n    Gaussian Splatting環境セットアップ（改善版）\n    diff-gaussian-rasterization と simple-knn のインストールを確実に行う\n    \"\"\"\n\n    print(\"🚀 Gaussian Splatting Environment Setup\")\n\n    WORK_DIR = \"/content/gaussian-splatting\"\n\n    # =====================================================================\n    # STEP 1: System packages\n    # =====================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 1: System packages\")\n    print(\"=\"*70)\n\n    subprocess.run([\"apt-get\", \"update\", \"-qq\"], capture_output=True)\n    subprocess.run([\n        \"apt-get\", \"install\", \"-y\", \"-qq\",\n        \"colmap\", \"build-essential\", \"cmake\", \"git\",\n        \"libopenblas-dev\", \"xvfb\"\n    ], capture_output=True)\n\n    os.environ[\"QT_QPA_PLATFORM\"] = \"offscreen\"\n    os.environ[\"DISPLAY\"] = \":99\"\n    subprocess.Popen(\n        [\"Xvfb\", \":99\", \"-screen\", \"0\", \"1024x768x24\"],\n        stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL\n    )\n    print(\"✓ System packages installed\")\n\n\n    # =====================================================================\n    # STEP 2: Clone Gaussian Splatting with submodules\n    # =====================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 2: Clone Gaussian Splatting\")\n    print(\"=\"*70)\n\n    if not os.path.exists(WORK_DIR):\n        result = subprocess.run([\n            \"git\", \"clone\", \"--recursive\",\n            \"https://github.com/graphdeco-inria/gaussian-splatting.git\",\n            WORK_DIR\n        ], capture_output=True, text=True)\n        \n        if result.returncode != 0:\n            print(f\"ERROR cloning repo: {result.stderr}\")\n            return None\n        print(\"✓ Cloned with submodules\")\n    else:\n        print(\"✓ Already exists\")\n        # 既存の場合でもサブモジュールを確認・更新\n        print(\"  Updating submodules...\")\n        subprocess.run(\n            [\"git\", \"submodule\", \"update\", \"--init\", \"--recursive\"],\n            cwd=WORK_DIR,\n            capture_output=True\n        )\n\n\n    # =====================================================================\n    # STEP 3: NUCLEAR - Physically delete ALL numpy\n    # =====================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 3: NUCLEAR - Force delete ALL NumPy installations\")\n    print(\"=\"*70)\n\n    subprocess.run(\n        [sys.executable, \"-m\", \"pip\", \"uninstall\", \"-y\", \"numpy\", \"scipy\"],\n        capture_output=True\n    )\n\n    site_packages = site.getsitepackages() + [site.getusersitepackages()]\n    print(f\"Searching {len(site_packages)} site-packages directories...\")\n\n    deleted_count = 0\n    for sp in site_packages:\n        if not os.path.exists(sp):\n            continue\n\n        for pattern in [\"numpy*\", \"scipy*\"]:\n            dirs = glob.glob(os.path.join(sp, pattern))\n            for d in dirs:\n                try:\n                    if os.path.isdir(d):\n                        shutil.rmtree(d)\n                    else:\n                        os.remove(d)\n                    print(f\"  Deleted: {d}\")\n                    deleted_count += 1\n                except Exception as e:\n                    print(f\"  Warning: Could not delete {d}: {e}\")\n\n    print(f\"✓ Deleted {deleted_count} numpy/scipy installations\")\n\n\n    # =====================================================================\n    # STEP 4: Clean install - SciPy first\n    # =====================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 4: Clean install - SciPy first strategy\")\n    print(\"=\"*70)\n\n    subprocess.run(\n        [sys.executable, \"-m\", \"pip\", \"install\", \"scipy\"],\n        capture_output=True,\n        check=True\n    )\n\n    for sp in site_packages:\n        if not os.path.exists(sp):\n            continue\n        numpy_dirs = glob.glob(os.path.join(sp, \"numpy*\"))\n        for d in numpy_dirs:\n            try:\n                if os.path.isdir(d):\n                    shutil.rmtree(d)\n                else:\n                    os.remove(d)\n                print(f\"  Deleted: {d}\")\n            except:\n                pass\n\n    subprocess.run(\n        [sys.executable, \"-m\", \"pip\", \"install\", \"numpy==1.26.4\"],\n        capture_output=True,\n        check=True\n    )\n\n    print(\"✓ Clean numpy 1.26.4 + scipy installed\")\n\n\n    # =====================================================================\n    # STEP 5: Install other packages\n    # =====================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 5: Install other packages\")\n    print(\"=\"*70)\n\n    packages = [\n        \"torch torchvision torchaudio\",\n        \"opencv-python pillow imageio imageio-ffmpeg plyfile tqdm tensorboard psutil\",\n        \"transformers==4.40.0\",\n        \"kornia h5py matplotlib\",\n        \"git+https://github.com/cvg/LightGlue.git\",\n        \"pycolmap\"\n    ]\n\n    for pkg in packages:\n        subprocess.run(\n            [sys.executable, \"-m\", \"pip\", \"install\"] + pkg.split(),\n            capture_output=True\n        )\n\n    print(\"✓ All packages installed\")\n\n\n    # =====================================================================\n    # STEP 6: Build GS submodules (改善版)\n    # =====================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 6: Build Gaussian Splatting submodules (IMPROVED)\")\n    print(\"=\"*70)\n\n    # サブモジュールの設定\n    submodules = {\n        \"diff-gaussian-rasterization\": {\n            \"path\": os.path.join(WORK_DIR, \"submodules\", \"diff-gaussian-rasterization\"),\n            \"repo\": \"https://github.com/graphdeco-inria/diff-gaussian-rasterization.git\"\n        },\n        \"simple-knn\": {\n            \"path\": os.path.join(WORK_DIR, \"submodules\", \"simple-knn\"),\n            \"repo\": \"https://github.com/camenduru/simple-knn.git\"\n        }\n    }\n\n    for name, info in submodules.items():\n        print(f\"\\n--- Installing {name} ---\")\n        path = info[\"path\"]\n        repo = info[\"repo\"]\n        \n        # ディレクトリが存在しない場合はクローン\n        if not os.path.exists(path):\n            print(f\"Cloning {name}...\")\n            result = subprocess.run(\n                [\"git\", \"clone\", repo, path],\n                capture_output=True,\n                text=True\n            )\n            if result.returncode != 0:\n                print(f\"ERROR cloning {name}: {result.stderr}\")\n                continue\n        else:\n            print(f\"✓ {name} directory exists\")\n        \n        # ディレクトリ内容の確認\n        if os.path.exists(path):\n            files = os.listdir(path)\n            print(f\"  Files in {name}: {len(files)} items\")\n            if len(files) == 0:\n                print(f\"  WARNING: {name} directory is empty!\")\n                continue\n            \n            # setup.py の存在確認\n            setup_py = os.path.join(path, \"setup.py\")\n            if not os.path.exists(setup_py):\n                print(f\"  WARNING: setup.py not found in {name}!\")\n                continue\n            print(f\"  ✓ setup.py found\")\n        \n        # インストール実行（詳細なログ付き）\n        print(f\"Installing {name}...\")\n        result = subprocess.run(\n            [sys.executable, \"-m\", \"pip\", \"install\", path, \"-v\"],\n            capture_output=True,\n            text=True,\n            cwd=WORK_DIR\n        )\n        \n        if result.returncode != 0:\n            print(f\"ERROR installing {name}:\")\n            print(\"STDOUT:\", result.stdout[-500:] if len(result.stdout) > 500 else result.stdout)\n            print(\"STDERR:\", result.stderr[-500:] if len(result.stderr) > 500 else result.stderr)\n            \n            # 代替インストール方法を試す\n            print(f\"Trying alternative installation method for {name}...\")\n            result2 = subprocess.run(\n                [sys.executable, \"setup.py\", \"install\"],\n                capture_output=True,\n                text=True,\n                cwd=path\n            )\n            \n            if result2.returncode != 0:\n                print(f\"Alternative method also failed:\")\n                print(\"STDERR:\", result2.stderr[-500:] if len(result2.stderr) > 500 else result2.stderr)\n            else:\n                print(f\"✓ {name} installed (alternative method)\")\n        else:\n            print(f\"✓ {name} installed successfully\")\n        \n        # インストール確認\n        try:\n            if name == \"diff-gaussian-rasterization\":\n                import diff_gaussian_rasterization\n                print(f\"  ✓ {name} import successful\")\n            elif name == \"simple-knn\":\n                import simple_knn\n                print(f\"  ✓ {name} import successful\")\n        except ImportError as e:\n            print(f\"  ✗ {name} import failed: {e}\")\n\n    print(\"\\n✓ Submodules installation complete\")\n\n\n    # =====================================================================\n    # STEP 7: Final verification\n    # =====================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 7: Final verification\")\n    print(\"=\"*70)\n\n    # NumPy 2.x の最終チェック\n    for sp in site_packages:\n        if not os.path.exists(sp):\n            continue\n\n        numpy2_dirs = glob.glob(os.path.join(sp, \"numpy-2.*\"))\n        if numpy2_dirs:\n            print(f\"⚠️  Found numpy 2.x installations: {len(numpy2_dirs)}\")\n            for d in numpy2_dirs:\n                try:\n                    shutil.rmtree(d)\n                    print(f\"  Nuked: {d}\")\n                except:\n                    pass\n\n    subprocess.run(\n        [sys.executable, \"-m\", \"pip\", \"install\", \"numpy==1.26.4\", \"--force-reinstall\"],\n        capture_output=True\n    )\n\n    # 最終的なインポートテスト\n    print(\"\\n--- Final Import Tests ---\")\n    \n    test_imports = [\n        (\"numpy\", \"NumPy\"),\n        (\"torch\", \"PyTorch\"),\n        (\"diff_gaussian_rasterization\", \"Diff Gaussian Rasterization\"),\n        (\"simple_knn\", \"Simple KNN\")\n    ]\n    \n    all_success = True\n    for module_name, display_name in test_imports:\n        try:\n            __import__(module_name)\n            print(f\"✓ {display_name}: OK\")\n        except ImportError as e:\n            print(f\"✗ {display_name}: FAILED - {e}\")\n            all_success = False\n    \n    print(\"\\n\" + \"=\"*70)\n    if all_success:\n        print(\"🎉 ALL CHECKS PASSED - Ready for Gaussian Splatting!\")\n    else:\n        print(\"⚠️  SOME CHECKS FAILED - Please review errors above\")\n    print(\"=\"*70)\n\n    return WORK_DIR\n\n\nif __name__ == \"__main__\":\n    work_dir = setup_environment()\n    if work_dir:\n        print(f\"\\nGaussian Splatting directory: {work_dir}\")\n        print(\"You can now run: %cd {work_dir}\")\n    else:\n        print(\"\\nSetup failed. Please review errors above.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-20T05:55:23.972474Z","iopub.execute_input":"2026-01-20T05:55:23.973028Z","execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[{"name":"stdout","text":"🚀 Gaussian Splatting Environment Setup\n\n======================================================================\nSTEP 1: System packages\n======================================================================\n✓ System packages installed\n\n======================================================================\nSTEP 2: Clone Gaussian Splatting\n======================================================================\n✓ Already exists\n  Updating submodules...\n\n======================================================================\nSTEP 3: NUCLEAR - Force delete ALL NumPy installations\n======================================================================\nSearching 4 site-packages directories...\n  Deleted: /usr/local/lib/python3.12/dist-packages/numpy.libs\n  Deleted: /usr/local/lib/python3.12/dist-packages/numpy\n✓ Deleted 2 numpy/scipy installations\n\n======================================================================\nSTEP 4: Clean install - SciPy first strategy\n======================================================================\n  Deleted: /usr/local/lib/python3.12/dist-packages/numpy.libs\n  Deleted: /usr/local/lib/python3.12/dist-packages/numpy-2.4.1.dist-info\n  Deleted: /usr/local/lib/python3.12/dist-packages/numpy\n✓ Clean numpy 1.26.4 + scipy installed\n\n======================================================================\nSTEP 5: Install other packages\n======================================================================\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nprint(f\"✓ NumPy: {np.__version__}\")","metadata":{"outputId":"e2e1402f-e3f2-4260-aa05-b572c9ac2c74","id":"nzzRu5emNQAj","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch\nimport PIL\n\nfrom transformers import AutoConfig\nfrom transformers import AutoImageProcessor","metadata":{"id":"Ib-xRVVIy2PC","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport sys\n\n%cd /content/gaussian-splatting\n\nfiles = ['database.py', 'h5_to_db.py', 'metric.py']\nbase_url = 'https://huggingface.co/stpete2/imc25_utils/resolve/main/'\n\nfor file in files:\n    if not os.path.exists(file):\n        !wget -q {base_url + file}\n        print(f\"✓ {file} download complete\")\n    else:\n        print(f\"✓ {file} already exists\")\n","metadata":{"id":"eJrkKiCLzt1G","outputId":"11f9b0b8-c08b-44f0-c50a-f480047b0363","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from database import COLMAPDatabase, image_ids_to_pair_id\nfrom h5_to_db import add_keypoints, add_matches\nfrom metric import *","metadata":{"id":"bmM_IBUtrEMd","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#success\n\ndef setup_mast3r():\n    \"\"\"Install and setup MASt3R\"\"\"\n    print(\"\\n=== Setting up MASt3R ===\")\n\n    os.chdir('/content')\n\n    # Remove existing installation\n    if os.path.exists('mast3r'):\n        print(\"Removing existing MASt3R installation...\")\n        os.system('rm -rf mast3r')\n\n    # Clone repository\n    print(\"Cloning MASt3R repository...\")\n    os.system('git clone --recursive https://github.com/naver/mast3r')\n    os.chdir('/content/mast3r')\n\n    # Check dust3r directory\n    print(\"Checking dust3r structure...\")\n    os.system('ls -la dust3r/')\n\n    # Install dust3r\n    print(\"Installing dust3r...\")\n    os.system('cd dust3r && python -m pip install -e .')\n\n    # Install croco\n    print(\"Installing croco...\")\n    os.system('cd dust3r/croco && python -m pip install -e .')\n\n    # Install requirements\n    print(\"Installing MASt3R requirements...\")\n    os.system('pip install -r requirements.txt')\n\n    # Download model weights\n    print(\"Downloading model weights...\")\n    os.system('mkdir -p checkpoints')\n    os.system('wget -P checkpoints/ https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth')\n\n    # Install additional dependencies\n    print(\"Installing additional dependencies...\")\n    os.system('pip install trimesh matplotlib roma')\n\n    # Add to path\n    sys.path.insert(0, '/content/mast3r')\n    sys.path.insert(0, '/content/mast3r/dust3r')\n\n    # Verification\n    print(\"\\n🔍 Verifying MASt3R installation...\")\n    try:\n        from mast3r.model import AsymmetricMASt3R\n        print(\"  ✓ MASt3R import: OK\")\n    except Exception as e:\n        print(f\"  ❌ MASt3R import failed: {e}\")\n        raise\n\n    print(\"✓ MASt3R setup complete!\")\n\nif __name__ == \"__main__\":\n    setup_mast3r()","metadata":{"id":"3-CN6HJvZ6u2","outputId":"04501f64-ad96-448e-aecb-8caceda0b04e","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch\nimport numpy as np\nimport sys\n\n# listify関数も必要なので定義\ndef listify(x):\n    return list(x) if isinstance(x, (list, tuple)) else [x]","metadata":{"id":"kTPzKB2vYn6b","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# /content/mast3r/dust3r/dust3r/utils/device.py の該当関数全体を置き換え\n\ndef collate_with_cat(whatever, lists=False):\n    if isinstance(whatever, (list, tuple)):\n        if not whatever:\n            return whatever\n        elem = whatever[0]\n\n        T = type(elem)\n        if T is torch.Tensor or (T is torch.nn.parameter.Parameter):\n            return listify(whatever) if lists else torch.cat(whatever)\n\n        # numpyの型を確実に処理\n        elem_type_name = type(elem).__name__\n        elem_module = type(elem).__module__\n\n        if elem_type_name == 'ndarray' or (elem_module == 'numpy' and elem_type_name == 'ndarray'):\n            tensors = []\n            for x in whatever:\n                # 確実にnumpy配列として扱う\n                if hasattr(x, '__array__'):\n                    arr = np.asarray(x)\n                else:\n                    arr = np.array(x)\n                tensors.append(torch.from_numpy(arr))\n            return listify(tensors) if lists else torch.cat(tensors)","metadata":{"id":"SpqfW2PDXp-H","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# MASt3Rのモジュールをインポート（まだインポートされていない場合）\nif 'dust3r.utils.device' not in sys.modules:\n    from dust3r.utils import device as device_module\nelse:\n    device_module = sys.modules['dust3r.utils.device']\n\n# 関数を置き換え\ndevice_module.collate_with_cat = collate_with_cat\nprint(\"✓ collate_with_cat関数を置き換えました\")","metadata":{"id":"AUg6a4lEXeS-","outputId":"5b5873d6-e7ca-40ea-b023-6a52d646f437","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"id":"oVGIvNWSpzlZ","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport sys\nimport gc\nimport h5py\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom tqdm import tqdm\nfrom pathlib import Path\nimport subprocess\n\n\n# LightGlue\nfrom lightglue import ALIKED, LightGlue\nfrom lightglue.utils import load_image\n\n","metadata":{"id":"GvtwW6aXpzeG","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\"\"\"\nGaussian Splatting Pipeline\nSimple and robust pipeline: LightGlue → COLMAP → Gaussian Splatting\n\"\"\"\n\"\"\"\nGaussian Splatting Pipeline\nSimple and robust pipeline: LightGlue → MASt3R → Gaussian Splatting\n\"\"\"\n\n# ============================================================================\n# Configuration\n# ============================================================================\nclass Config:\n    # Feature extraction\n    N_KEYPOINTS = 8192\n    IMAGE_SIZE = 1024\n\n    # Pair selection\n    GLOBAL_TOPK = 80  #全画像数の半分程度\n    MIN_MATCHES = 10\n    RATIO_THR = 1.2\n\n    # Paths\n    DINO_MODEL = \"facebook/dinov2-base\"\n\n    # MASt3R settings (重要: これらが欠けていました!)\n    MAST3R_MODEL = \"/content/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\"\n    MAST3R_IMAGE_SIZE = 224  # メモリを節約するため小さめ(224 or 512)\n\n    # Device\n    DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')","metadata":{"id":"7NfrJdMvrPZn","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"id":"eFExgZs-k0l9","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# Step 0: images_square\n# ============================================================================\n\ndef preprocess_images_square(input_dir, output_dir, size=1024, background='black'):\n    \"\"\"\n    Standardize all images to a square format (maintaining aspect ratio with padding).\n\n    Args:\n        input_dir (str): Directory containing input images.\n        output_dir (str): Directory to save processed images.\n        size (int): Target square dimension (default: 1024).\n        background (str): Background style: 'black', 'white', or 'blur'.\n    \"\"\"\n    from PIL import Image, ImageFilter\n    import os\n    from tqdm import tqdm\n\n    print(f\"\\n=== Preprocessing to {size}x{size} Square Images ===\")\n\n    os.makedirs(output_dir, exist_ok=True)\n\n    image_files = sorted([\n        f for f in os.listdir(input_dir)\n        if f.lower().endswith(('.jpg', '.jpeg', '.png'))\n    ])\n\n    stats = {\n        'total': len(image_files),\n        'landscape': 0,\n        'portrait': 0,\n        'square': 0,\n        'resized': 0,\n    }\n\n    for img_file in tqdm(image_files, desc=\"Converting to square\"):\n        img_path = os.path.join(input_dir, img_file)\n        img = Image.open(img_path).convert('RGB')\n\n        width, height = img.size\n\n        # Statistics\n        if width > height:\n            stats['landscape'] += 1\n        elif width < height:\n            stats['portrait'] += 1\n        else:\n            stats['square'] += 1\n\n        # Resize based on the longest side\n        max_dim = max(width, height)\n        if max_dim != size:\n            scale = size / max_dim\n            new_width = int(width * scale)\n            new_height = int(height * scale)\n            img = img.resize((new_width, new_height), Image.LANCZOS)\n            stats['resized'] += 1\n        else:\n            new_width, new_height = width, height\n\n        # Create background\n        if background == 'black':\n            canvas = Image.new('RGB', (size, size), (0, 0, 0))\n        elif background == 'white':\n            canvas = Image.new('RGB', (size, size), (255, 255, 255))\n        elif background == 'blur':\n            # Use a blurred version of the image as background for a professional look\n            canvas = img.resize((size, size), Image.LANCZOS)\n            canvas = canvas.filter(ImageFilter.GaussianBlur(radius=20))\n        else:\n            canvas = Image.new('RGB', (size, size), (0, 0, 0))\n\n        # Center the image\n        offset_x = (size - new_width) // 2\n        offset_y = (size - new_height) // 2\n        canvas.paste(img, (offset_x, offset_y))\n\n        # Save output\n        output_path = os.path.join(output_dir, img_file)\n        canvas.save(output_path, quality=95, optimize=True)\n\n    print(f\"\\n✓ Preprocessing complete:\")\n    print(f\"  Total images: {stats['total']}\")\n    print(f\"  Landscape: {stats['landscape']} / Portrait: {stats['portrait']} / Square: {stats['square']}\")\n    print(f\"  Resized: {stats['resized']}\")\n    print(f\"  Output size: {size}x{size}\")\n\n    return output_dir","metadata":{"id":"TkVzKRqsvxFZ","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def normalize_image_sizes_biplet(input_dir, output_dir=None, size=1024):\n    \"\"\"\n    Generates two square crops (Left & Right or Top & Bottom)\n    from each image in a directory.\n    \"\"\"\n    if output_dir is None:\n        output_dir = input_dir\n\n    os.makedirs(output_dir, exist_ok=True)\n\n    print(f\"Generating 2 cropped squares (Left/Right or Top/Bottom) for each image...\")\n    print()\n\n    converted_count = 0\n    size_stats = {}\n\n    for img_file in sorted(os.listdir(input_dir)):\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            print(f\"  ✓ {img_file}: {original_size} → 2 square images generated\")\n\n        except Exception as e:\n            print(f\"  ✗ Error processing {img_file}: {e}\")\n\n    print(f\"\\nProcessing complete: {converted_count} source images processed\")\n    print(f\"Original size distribution: {size_stats}\")\n    return converted_count\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    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","metadata":{"id":"A6smO9X0el3d","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# Step 1: Image Pair Selection (DINO + ALIKED local verify)\n# ============================================================================\n\ndef load_torch_image(fname, device):\n    \"\"\"Load image as torch tensor\"\"\"\n    from PIL import Image\n    import torchvision.transforms as T\n\n    img = Image.open(fname).convert('RGB')\n    transform = T.Compose([\n        T.ToTensor(),\n    ])\n    return transform(img).unsqueeze(0).to(device)\n\ndef extract_dino_global(image_paths, model_path, device):\n    \"\"\"Extract DINO global descriptors\"\"\"\n    print(\"\\n=== Extracting DINO Global Features ===\")\n\n    processor = AutoImageProcessor.from_pretrained(model_path)\n    model = AutoModel.from_pretrained(model_path).eval().to(device)\n\n    global_descs = []\n    for img_path in tqdm(image_paths):\n        img = load_torch_image(img_path, device)\n        with torch.no_grad():\n            inputs = processor(images=img, return_tensors=\"pt\", do_rescale=False).to(device)\n            outputs = model(**inputs)\n            desc = F.normalize(outputs.last_hidden_state[:, 1:].max(dim=1)[0], dim=1, p=2)\n            global_descs.append(desc.cpu())\n\n    global_descs = torch.cat(global_descs, dim=0)\n\n    del model\n    torch.cuda.empty_cache()\n    gc.collect()\n\n    return global_descs\n\ndef build_topk_pairs(global_feats, k, device):\n    \"\"\"Build top-k similar pairs from global features\"\"\"\n    g = global_feats.to(device)\n    sim = g @ g.T\n    sim.fill_diagonal_(-1)\n\n    N = sim.size(0)\n    k = min(k, N - 1)\n\n    topk_indices = torch.topk(sim, k, dim=1).indices.cpu()\n\n    pairs = []\n    for i in range(N):\n        for j in topk_indices[i]:\n            j = j.item()\n            if i < j:\n                pairs.append((i, j))\n\n    return list(set(pairs))\n\ndef extract_aliked_features(image_paths, device):\n    \"\"\"Extract ALIKED local features\"\"\"\n    print(\"\\n=== Extracting ALIKED Local Features ===\")\n\n    extractor = ALIKED(\n        model_name=\"aliked-n16\",\n        max_num_keypoints=Config.N_KEYPOINTS,\n        detection_threshold=0.01,\n        resize=Config.IMAGE_SIZE\n    ).eval().to(device)\n\n    features = []\n    for img_path in tqdm(image_paths):\n        img = load_torch_image(img_path, device)\n        with torch.no_grad():\n            feats = extractor.extract(img)\n            kpts = feats['keypoints'].reshape(-1, 2).cpu()\n            descs = feats['descriptors'].reshape(len(kpts), -1).cpu()\n            features.append({'keypoints': kpts, 'descriptors': descs})\n\n    del extractor\n    torch.cuda.empty_cache()\n    gc.collect()\n\n    return features\n\ndef verify_pairs_locally(pairs, features, device, threshold=Config.MIN_MATCHES):\n    \"\"\"Verify pairs using local descriptor matching\"\"\"\n    print(\"\\n=== Verifying Pairs with Local Features ===\")\n\n    verified = []\n    for i, j in tqdm(pairs):\n        desc1 = features[i]['descriptors'].to(device)\n        desc2 = features[j]['descriptors'].to(device)\n\n        if len(desc1) == 0 or len(desc2) == 0:\n            continue\n\n        # Simple mutual nearest neighbor\n        dist = torch.cdist(desc1, desc2, p=2)\n        min_dist = dist.min(dim=1)[0]\n        n_matches = (min_dist < Config.RATIO_THR).sum().item()\n\n        if n_matches >= threshold:\n            verified.append((i, j))\n\n    return verified\n\ndef get_image_pairs(image_paths, max_pairs=None):\n    \"\"\"Main pair selection pipeline\"\"\"\n    device = Config.DEVICE\n\n    # 1. DINO global\n    global_feats = extract_dino_global(image_paths, Config.DINO_MODEL, device)\n    pairs = build_topk_pairs(global_feats, Config.GLOBAL_TOPK, device)\n\n    print(f\"Initial pairs from global features: {len(pairs)}\")\n\n    # 2. ALIKED local\n    features = extract_aliked_features(image_paths, device)\n\n    # 3. Local verification\n    verified_pairs = verify_pairs_locally(pairs, features, device)\n\n    print(f\"Verified pairs: {len(verified_pairs)}\")\n    \n    # ===== ここに追加 =====\n    if max_pairs and len(verified_pairs) > max_pairs:\n        print(f\"⚠️  Limiting pairs from {len(verified_pairs)} to {max_pairs}\")\n        verified_pairs = verified_pairs[:max_pairs]\n    # =====================\n\n    return verified_pairs, features","metadata":{"id":"FNjFURfYmVcL","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# Step 2: Feature Matching (ALIKED + LightGlue)\n# ============================================================================\n\ndef match_pairs_lightglue(image_paths, pairs, features, output_dir):\n    \"\"\"\n    Match image pairs using LightGlue\n    \"\"\"\n    print(\"\\n=== Matching with LightGlue ===\")\n\n    os.makedirs(output_dir, exist_ok=True)\n    keypoints_path = os.path.join(output_dir, 'keypoints.h5')\n    matches_path = os.path.join(output_dir, 'matches.h5')\n\n    if os.path.exists(keypoints_path):\n        os.remove(keypoints_path)\n    if os.path.exists(matches_path):\n        os.remove(matches_path)\n\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    extractor = ALIKED(max_num_keypoints=4096, detection_threshold=0.2, nms_radius=2).eval().to(device)\n    matcher = LightGlue(features='aliked').eval().to(device)\n\n    if isinstance(features, dict):\n        all_keypoints = features['keypoints']\n        all_descriptors = features['descriptors']\n    elif isinstance(features, list):\n        all_keypoints = [f['keypoints'] for f in features]\n        all_descriptors = [f['descriptors'] for f in features]\n    else:\n        raise ValueError(f\"Unsupported features type: {type(features)}\")\n\n    with h5py.File(keypoints_path, 'w') as f_kp:\n        for idx, img_path in enumerate(tqdm(image_paths, desc=\"Saving keypoints\")):\n            img_name = os.path.splitext(os.path.basename(img_path))[0]\n\n            kp = all_keypoints[idx]\n            if torch.is_tensor(kp):\n                kp = kp.cpu().numpy()\n            f_kp.create_dataset(img_name, data=kp)\n\n    # Match pairs\n    with h5py.File(matches_path, 'w') as f_match:\n        for idx1, idx2 in tqdm(pairs, desc=\"Matching\"):\n            with torch.no_grad():\n                kp0 = all_keypoints[idx1]\n                kp1 = all_keypoints[idx2]\n                desc0 = all_descriptors[idx1]\n                desc1 = all_descriptors[idx2]\n\n                if isinstance(kp0, np.ndarray):\n                    kp0 = torch.from_numpy(kp0).float().to(device)\n                    kp1 = torch.from_numpy(kp1).float().to(device)\n                    desc0 = torch.from_numpy(desc0).float().to(device)\n                    desc1 = torch.from_numpy(desc1).float().to(device)\n                else:\n                    kp0 = kp0.float().to(device)\n                    kp1 = kp1.float().to(device)\n                    desc0 = desc0.float().to(device)\n                    desc1 = desc1.float().to(device)\n\n                feats0 = {\n                    'keypoints': kp0.unsqueeze(0) if kp0.dim() == 2 else kp0,\n                    'descriptors': desc0.unsqueeze(0) if desc0.dim() == 2 else desc0,\n                }\n                feats1 = {\n                    'keypoints': kp1.unsqueeze(0) if kp1.dim() == 2 else kp1,\n                    'descriptors': desc1.unsqueeze(0) if desc1.dim() == 2 else desc1,\n                }\n\n                matches01 = matcher({'image0': feats0, 'image1': feats1})\n\n                if 'matches0' in matches01:\n                    matches0 = matches01['matches0']\n                    if isinstance(matches0, list):\n                        matches0 = matches0[0]\n\n                    # CUDAテンソルをCPUに移動\n                    if torch.is_tensor(matches0):\n                        matches0 = matches0.detach().cpu().numpy()\n\n                    valid = matches0 > -1\n                    if torch.is_tensor(valid):\n                        valid = valid.cpu().numpy()\n\n                    # 標準的なnumpy配列として取得\n                    valid_indices = np.where(valid)[0]\n                    valid_matches = matches0[valid]\n\n                    # 手動で2列の配列を構築\n                    n = len(valid_indices)\n                    matches = np.empty((n, 2), dtype=np.int64)\n                    matches[:, 0] = valid_indices\n                    matches[:, 1] = valid_matches\n\n                elif 'matches' in matches01:\n                    m = matches01['matches']\n                    if torch.is_tensor(m):\n                        m = m.detach().cpu().numpy()\n                    matches = m\n\n                else:\n                    continue\n\n                if len(matches) > 0:\n                    img_name1 = os.path.splitext(os.path.basename(image_paths[idx1]))[0]\n                    img_name2 = os.path.splitext(os.path.basename(image_paths[idx2]))[0]\n                    pair_key = f\"{img_name1}_{img_name2}\"\n                    f_match.create_dataset(pair_key, data=matches)\n\n    print(f\"✓ Matches saved to {matches_path}\")\n\n","metadata":{"id":"X-PKgmdwmVcL","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nprint(f\"✓ NumPy: {np.__version__}\")","metadata":{"id":"Tg_SJYlwkeiD","outputId":"9cc8b0cd-a2bf-4020-94e0-9739d4567770","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch\nfrom pathlib import Path\nfrom tqdm import tqdm","metadata":{"id":"7D86wFMan2X8","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# 1. まず、すべての通常のインポート\n# ============================================================================\nfrom dust3r.image_pairs import make_pairs\nfrom dust3r.inference import inference\nfrom dust3r.utils.image import load_images\nfrom dust3r.cloud_opt import global_aligner, GlobalAlignerMode\n\n# ============================================================================\n# 2. 次に、修正関数を定義してモンキーパッチ\n# ============================================================================\nimport torch\nimport numpy as np\nfrom dust3r.utils.device import to_cpu\nfrom dust3r.inference import check_if_same_size\nimport dust3r.inference\nimport dust3r.utils.misc\n\n# is_symmetrized関数を修正版に置き換え\ndef is_symmetrized_fixed(gt1, gt2):\n    \"\"\"\n    is_symmetrizedの修正版 - IndexErrorを回避\n    \"\"\"\n    # instanceフィールドをチェック\n    if 'instance' in gt1 and 'instance' in gt2:\n        x = gt1['instance']\n        y = gt2['instance']\n\n        # リストの場合\n        if isinstance(x, list) and isinstance(y, list):\n            if len(x) != len(y):\n                return False\n            if len(x) < 2 or len(y) < 2:\n                return False\n            ok = True\n            for i in range(0, len(x), 2):\n                if i + 1 >= len(x) or i + 1 >= len(y):\n                    return False\n                ok = ok and (x[i] == y[i + 1]) and (x[i + 1] == y[i])\n            return ok\n\n        # 文字列の場合\n        elif isinstance(x, str) and isinstance(y, str):\n            if len(x) != len(y):\n                return False\n            if len(x) < 2 or len(y) < 2:\n                return False\n            ok = True\n            for i in range(0, len(x), 2):\n                if i + 1 >= len(x) or i + 1 >= len(y):\n                    return False\n                ok = ok and (x[i] == y[i + 1]) and (x[i + 1] == y[i])\n            return ok\n\n    return False\n\n\ndef collate_with_cat_fixed(batch, lists=False):\n    \"\"\"collate_with_catの修正版\"\"\"\n    if not batch:\n        return None\n\n    if len(batch) == 1:\n        elem = batch[0]\n        if isinstance(elem, (list, tuple)) and len(elem) == 2:\n            view1, view2 = elem\n            if isinstance(view1, dict) and isinstance(view2, dict):\n                view1 = convert_numpy_to_tensor(view1)\n                view2 = convert_numpy_to_tensor(view2)\n                return (view1, view2)\n\n    if isinstance(batch[0], (list, tuple)):\n        view1_list = []\n        view2_list = []\n\n        for pair in batch:\n            if len(pair) == 2:\n                v1 = convert_numpy_to_tensor(pair[0])\n                v2 = convert_numpy_to_tensor(pair[1])\n                view1_list.append(v1)\n                view2_list.append(v2)\n\n        def stack_dicts(dict_list):\n            if not dict_list:\n                return {}\n\n            result = {}\n            for key in dict_list[0].keys():\n                values = [d[key] for d in dict_list]\n\n                if isinstance(values[0], torch.Tensor):\n                    result[key] = torch.cat(values, dim=0)\n                elif isinstance(values[0], np.ndarray):\n                    tensors = [torch.from_numpy(v) if isinstance(v, np.ndarray) else v for v in values]\n                    result[key] = torch.cat(tensors, dim=0)\n                elif isinstance(values[0], (list, tuple)):\n                    result[key] = []\n                    for v in values:\n                        result[key].extend(v if isinstance(v, list) else [v])\n                else:\n                    result[key] = values\n\n            return result\n\n        view1_batched = stack_dicts(view1_list)\n        view2_batched = stack_dicts(view2_list)\n\n        return (view1_batched, view2_batched)\n\n    return None\n\n\ndef convert_numpy_to_tensor(view_dict):\n    \"\"\"辞書内のnumpy配列をTensorに変換\"\"\"\n    result = {}\n    for key, value in view_dict.items():\n        if isinstance(value, np.ndarray):\n            result[key] = torch.from_numpy(value)\n        else:\n            result[key] = value\n    return result\n\n\ndef loss_of_one_batch_fixed(batch, model, criterion, device, symmetrize_batch=False, use_amp=False, ret=None):\n    view1, view2 = batch\n    ignore_keys = set(['depthmap', 'dataset', 'label', 'instance', 'idx', 'true_shape', 'rng'])\n    for view in batch:\n        for name in view.keys():\n            if name in ignore_keys:\n                continue\n            view[name] = view[name].to(device, non_blocking=True)\n\n    with torch.cuda.amp.autocast(enabled=bool(use_amp)):\n        pred1, pred2 = model(view1, view2)\n\n        with torch.cuda.amp.autocast(enabled=False):\n            loss = criterion(view1, view2, pred1, pred2) if criterion is not None else None\n\n    result = dict(view1=view1, view2=view2, pred1=pred1, pred2=pred2, loss=loss)\n    return result[ret] if ret else result\n\n\n@torch.no_grad()\ndef inference_debug(pairs, model, device, batch_size=8, verbose=True):\n    \"\"\"\n    デバッグ機能を追加したinference関数\n    \"\"\"\n    if verbose:\n        print(f'>> Inference with model on {len(pairs)} image pairs')\n\n    result = []\n\n    # Check if all images have the same size\n    multiple_shapes = not (check_if_same_size(pairs))\n    if multiple_shapes:\n        batch_size = 1\n\n    for i in tqdm(range(0, len(pairs), batch_size), disable=not verbose, desc=\"MASt3R inference\"):\n        batch_pairs = pairs[i:i + batch_size]\n\n        # 修正版のcollate関数��使用\n        collated = collate_with_cat_fixed(batch_pairs)\n\n        if collated is None:\n            raise ValueError(f\"collate_with_cat_fixed returned None at batch {i}\")\n\n        # 修正版のloss_of_one_batchを使用\n        res = loss_of_one_batch_fixed(collated, model, None, device)\n        result.append(to_cpu(res))\n\n    # ===== ここを修正 =====\n    # 結果の集約 - multiple_shapesに関わらず辞書形式で結合\n    if len(result) == 0:\n        return None\n\n    # 各バッチの結果を結合\n    combined = {}\n\n    for key in result[0].keys():\n        if isinstance(result[0][key], dict):\n            # 辞書の場合：各フィールドを結合\n            combined[key] = {}\n            for field in result[0][key].keys():\n                values = [r[key][field] for r in result]\n\n                if isinstance(values[0], torch.Tensor):\n                    combined[key][field] = torch.cat(values, dim=0)\n                elif isinstance(values[0], list):\n                    combined[key][field] = []\n                    for v in values:\n                        combined[key][field].extend(v if isinstance(v, list) else [v])\n                else:\n                    combined[key][field] = values\n\n        elif isinstance(result[0][key], torch.Tensor):\n            values = [r[key] for r in result]\n            combined[key] = torch.cat(values, dim=0)\n\n        elif isinstance(result[0][key], list):\n            combined[key] = []\n            for r in result:\n                combined[key].extend(r[key] if isinstance(r[key], list) else [r[key]])\n\n        else:\n            combined[key] = result[0][key]\n\n    return combined\n\n\n# ============================================================================\n# 3. モンキーパッチを適用（これが最も重要！）\n# ============================================================================\nprint(\"Applying monkey patches...\")\ndust3r.utils.misc.is_symmetrized = is_symmetrized_fixed\ndust3r.inference.inference = inference_debug\ninference = dust3r.inference.inference\n\nprint(\"✓ Monkey-patched dust3r.utils.misc.is_symmetrized\")\nprint(\"✓ Monkey-patched dust3r.inference.inference\")\n\n\n# 確認テスト\nprint(\"\\n=== Verification ===\")\ntest_gt1 = {'instance': '12'}\ntest_gt2 = {'instance': '21'}\ntry:\n    result = dust3r.utils.misc.is_symmetrized(test_gt1, test_gt2)\n    print(f\"✅ Monkey patch working! is_symmetrized test passed\")\nexcept IndexError as e:\n    print(f\"❌ ERROR: Monkey patch failed! {e}\")\n    raise\n\n# ===== すべてのdust3rモジュールでis_symmetrizedを置き換え =====\nimport sys\n\nprint(\"\\n=== Patching all loaded dust3r modules ===\")\npatched_count = 0\n\nfor module_name, module in list(sys.modules.items()):\n    if module is None:\n        continue\n\n    # dust3rまたはmast3r関連のモジュール\n    if 'dust3r' in module_name or 'mast3r' in module_name:\n        # is_symmetrized属性を持っている場合\n        if hasattr(module, 'is_symmetrized'):\n            old_func = module.is_symmetrized\n            module.is_symmetrized = is_symmetrized_fixed\n            patched_count += 1\n            print(f\"  ✓ Patched: {module_name}.is_symmetrized\")\n\n        # モジュールの__dict__を直接チェック\n        if hasattr(module, '__dict__'):\n            for attr_name in list(module.__dict__.keys()):\n                attr = getattr(module, attr_name, None)\n                if callable(attr) and attr_name == 'is_symmetrized':\n                    setattr(module, attr_name, is_symmetrized_fixed)\n\nprint(f\"\\n✓ Patched {patched_count} modules\")\nprint(\"=\"*70)\n\n\n","metadata":{"id":"gVeANB1q0W0p","outputId":"c60c2b08-ee64-4d5a-ae65-06ff02ef812b","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def run_mast3r_reconstruction(image_paths, pairs, output_dir, model, device):\n    \"\"\"MASt3Rで3D再構成を実行\"\"\"\n    print(\"\\n=== Running MASt3R Reconstruction ===\")\n\n    # メモリ状態を表示\n    print_memory_status(\"Initial memory state\")\n\n    print(f\"Processing {len(pairs)} pairs...\")\n\n    # 画像サイズを決定（224または512）\n    img_size = 224  # MASt3Rのデフォルト推論サイズ\n\n    print(f\"Loading {len(image_paths)} images at {img_size}x{img_size}...\")\n\n    # 画像を読み込み\n    imgs = load_images(image_paths, size=img_size, verbose=True)\n    print(f\"Loaded {len(imgs)} images\")\n    print_memory_status(\"After loading images\")\n\n    # ペアを作成\n    print(f\"Creating {len(pairs)} image pairs...\")\n    scene_graph = []\n\n    for idx1, idx2 in tqdm(pairs, desc=\"Preparing pairs\"):\n        # 画像インデックスが有効か確認\n        if idx1 >= len(imgs) or idx2 >= len(imgs):\n            print(f\"Warning: Invalid pair ({idx1}, {idx2}), skipping...\")\n            continue\n\n        # ペアを作成\n        view1 = imgs[idx1]\n        view2 = imgs[idx2]\n\n        # viewがNoneでないか確認\n        if view1 is None or view2 is None:\n            print(f\"Warning: None view in pair ({idx1}, {idx2}), skipping...\")\n            continue\n\n        # viewが辞書形式か確認\n        if not isinstance(view1, dict) or not isinstance(view2, dict):\n            print(f\"Warning: Invalid view type in pair ({idx1}, {idx2})\")\n            print(f\"  view1 type: {type(view1)}, view2 type: {type(view2)}\")\n            continue\n\n        scene_graph.append((view1, view2))\n\n    print(f\"Valid pairs: {len(scene_graph)}\")\n\n    if len(scene_graph) == 0:\n        raise ValueError(\"No valid pairs to process!\")\n\n    # 最初のペアをデバッグ\n    print(\"\\n=== Debugging first pair ===\")\n    first_pair = scene_graph[0]\n    print(f\"Pair type: {type(first_pair)}\")\n    print(f\"View1 type: {type(first_pair[0])}\")\n    print(f\"View2 type: {type(first_pair[1])}\")\n    if isinstance(first_pair[0], dict):\n        print(f\"View1 keys: {list(first_pair[0].keys())}\")\n    if isinstance(first_pair[1], dict):\n        print(f\"View2 keys: {list(first_pair[1].keys())}\")\n\n    # MASt3Rで推論\n    print(f\"\\nRunning MASt3R inference on {len(scene_graph)} pairs...\")\n    try:\n        pairs_output = inference(\n            scene_graph,\n            model,\n            device,\n            batch_size=1,\n            verbose=True\n        )\n    except Exception as e:\n        print(f\"Error during inference: {e}\")\n        print(f\"Error type: {type(e)}\")\n        import traceback\n        traceback.print_exc()\n        raise\n\n    print(f\"Inference complete, got {len(pairs_output)} results\")\n    print_memory_status(\"After inference\")\n\n    # Global alignmentを実行\n    print(\"\\n=== Running Global Alignment ===\")\n    scene = global_aligner(\n        pairs_output,\n        device=device,\n        mode=GlobalAlignerMode.PointCloudOptimizer,\n        verbose=True\n    )\n\n    # 最適化\n    print(\"Optimizing scene...\")\n    loss = scene.compute_global_alignment(\n        init='mst',\n        niter=300,\n        schedule='cosine',\n        lr=0.01\n    )\n\n    print(f\"Optimization complete, final loss: {loss:.4f}\")\n    print_memory_status(\"After optimization\")\n\n    # COLMAP形式で保存\n    colmap_dir = Path(output_dir) / \"colmap\"\n    colmap_dir.mkdir(parents=True, exist_ok=True)\n\n    print(f\"\\n=== Saving to COLMAP format ===\")\n    save_colmap_format(scene, imgs, colmap_dir)\n\n    print(f\"✓ COLMAP data saved to {colmap_dir}\")\n\n    return scene, colmap_dir\n\n\n","metadata":{"id":"jC7gd4-ktXiz","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def save_colmap_format(scene, imgs, output_dir):\n    \"\"\"シーンをCOLMAP形式で保存\"\"\"\n    from dust3r.cloud_opt.base_opt import BasePCOptimizer\n\n    output_dir = Path(output_dir)\n    output_dir.mkdir(parents=True, exist_ok=True)\n\n    # カメラパラメータを取得\n    focals = scene.get_focals().cpu().numpy()\n    principal_points = scene.get_principal_points().cpu().numpy()\n    poses = scene.get_im_poses().cpu().numpy()\n    pts3d = scene.get_pts3d().cpu().numpy()\n\n    n_images = len(imgs)\n\n    # cameras.txt\n    cameras_file = output_dir / \"cameras.txt\"\n    with open(cameras_file, 'w') as f:\n        f.write(\"# Camera list with one line of data per camera:\\n\")\n        f.write(\"#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\\n\")\n\n        for i in range(n_images):\n            # SIMPLE_PINHOLE モデル (f, cx, cy)\n            img_shape = imgs[i]['true_shape']\n            width, height = img_shape[1], img_shape[0]\n            fx = focals[i, 0]\n            cx, cy = principal_points[i]\n\n            f.write(f\"{i+1} SIMPLE_PINHOLE {width} {height} {fx} {cx} {cy}\\n\")\n\n    print(f\"✓ Saved cameras.txt with {n_images} cameras\")\n\n    # images.txt\n    images_file = output_dir / \"images.txt\"\n    with open(images_file, 'w') as f:\n        f.write(\"# Image list with two lines of data per image:\\n\")\n        f.write(\"#   IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\\n\")\n        f.write(\"#   POINTS2D[] as (X, Y, POINT3D_ID)\\n\")\n\n        for i in range(n_images):\n            # ポーズを回転とtranslationに分解\n            pose = poses[i]\n            R = pose[:3, :3]\n            t = pose[:3, 3]\n\n            # 回転行列をクォータニオンに変換\n            from scipy.spatial.transform import Rotation\n            quat = Rotation.from_matrix(R).as_quat()  # [x, y, z, w]\n            qw, qx, qy, qz = quat[3], quat[0], quat[1], quat[2]\n\n            img_name = Path(imgs[i]['filepath']).name\n\n            f.write(f\"{i+1} {qw} {qx} {qy} {qz} {t[0]} {t[1]} {t[2]} {i+1} {img_name}\\n\")\n            f.write(\"\\n\")  # 2D pointsの行（空）\n\n    print(f\"✓ Saved images.txt with {n_images} images\")\n\n    # points3D.txt\n    points_file = output_dir / \"points3D.txt\"\n    with open(points_file, 'w') as f:\n        f.write(\"# 3D point list with one line of data per point:\\n\")\n        f.write(\"#   POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\\n\")\n\n        # 全画像の3Dポイントを集約\n        point_id = 1\n        for i in range(n_images):\n            pts = pts3d[i]  # (H, W, 3)\n\n            # 有効なポイントのみ保存\n            valid_mask = ~np.isnan(pts).any(axis=-1)\n            valid_pts = pts[valid_mask]\n\n            for pt in valid_pts[:1000]:  # 各画像から最大1000点\n                # デフォルトカラー（グレー）\n                f.write(f\"{point_id} {pt[0]} {pt[1]} {pt[2]} 128 128 128 0.0\\n\")\n                point_id += 1\n\n    print(f\"✓ Saved points3D.txt with {point_id-1} points\")\n\n\ndef print_memory_status(label=\"\"):\n    \"\"\"メモリ使用状況を表示\"\"\"\n    import psutil\n\n    if torch.cuda.is_available():\n        allocated = torch.cuda.memory_allocated() / 1024**3\n        reserved = torch.cuda.memory_reserved() / 1024**3\n        print(f\"{label}:\")\n        print(f\"GPU Memory - Allocated: {allocated:.2f}GB, Reserved: {reserved:.2f}GB\")\n\n    cpu_percent = psutil.virtual_memory().percent\n    print(f\"CPU Memory Usage: {cpu_percent:.1f}%\")","metadata":{"id":"BJDvEDwBnlJm","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#v26\ndef extract_colmap_data(scene, image_paths, max_points=1000000):\n    \"\"\"\n    Extract COLMAP-compatible camera parameters and 3D points from MASt3R scene\n    \n    Args:\n        scene: MASt3R scene object\n        image_paths: List of image paths\n        max_points: Maximum number of 3D points to extract (default: 1M)\n    \"\"\"\n    print(\"\\n=== Extracting COLMAP-compatible data ===\")\n    \n    # Extract point cloud\n    pts_all = scene.get_pts3d()\n    print(f\"pts_all type: {type(pts_all)}\")\n    \n    if isinstance(pts_all, list):\n        print(f\"pts_all is a list with {len(pts_all)} elements\")\n        if len(pts_all) > 0:\n            print(f\"First element type: {type(pts_all[0])}\")\n            if hasattr(pts_all[0], 'shape'):\n                print(f\"First element shape: {pts_all[0].shape}\")\n        \n        pts_all = torch.stack([p if isinstance(p, torch.Tensor) else torch.tensor(p) \n                              for p in pts_all])\n        print(f\"pts_all shape after conversion: {pts_all.shape}\")\n    \n    if len(pts_all.shape) == 4:\n        print(f\"Found batched point cloud: {pts_all.shape}\")\n        B, H, W, _ = pts_all.shape\n        pts3d = pts_all.reshape(-1, 3).detach().cpu().numpy()  \n        \n        # Extract colors\n        colors = []\n        for img_path in image_paths:\n            img = Image.open(img_path).resize((W, H))\n            colors.append(np.array(img))\n        colors = np.stack(colors).reshape(-1, 3) / 255.0\n    else:\n        pts3d = pts_all.detach().cpu().numpy() if isinstance(pts_all, torch.Tensor) else pts_all\n        colors = np.ones((len(pts3d), 3)) * 0.5\n    \n    print(f\"✓ Extracted {len(pts3d)} 3D points from {len(image_paths)} images\")\n    \n    # **DOWNSAMPLE POINTS TO REDUCE MEMORY USAGE**\n    if len(pts3d) > max_points:\n        print(f\"\\n⚠ Downsampling from {len(pts3d)} to {max_points} points to reduce memory usage...\")\n        \n        # Remove invalid points first\n        valid_mask = ~(np.isnan(pts3d).any(axis=1) | np.isinf(pts3d).any(axis=1))\n        pts3d_valid = pts3d[valid_mask]\n        colors_valid = colors[valid_mask]\n        \n        # Random sampling\n        indices = np.random.choice(len(pts3d_valid), size=max_points, replace=False)\n        pts3d = pts3d_valid[indices]\n        colors = colors_valid[indices]\n        \n        print(f\"✓ Downsampled to {len(pts3d)} points\")\n    \n    # Extract camera parameters\n    print(\"Extracting camera parameters...\")\n    \n    # 【重要】MASt3Rのポーズはcamera-to-world形式\n    # COLMAPはworld-to-camera形式を要求するので逆行列が必要\n    poses_c2w = scene.get_im_poses().detach().cpu().numpy()\n    print(f\"Retrieved camera-to-world poses: shape {poses_c2w.shape}\")\n    \n    # camera-to-world を world-to-camera に変換\n    poses = []\n    for i, pose_c2w in enumerate(poses_c2w):\n        # 4x4行列の逆行列を計算\n        pose_w2c = np.linalg.inv(pose_c2w)\n        poses.append(pose_w2c)\n    \n    poses = np.array(poses)\n    print(f\"Converted to world-to-camera poses for COLMAP\")\n    \n    # 焦点距離と主点を取得\n    focals = scene.get_focals().detach().cpu().numpy()\n    pp = scene.get_principal_points().detach().cpu().numpy()\n    print(f\"Focals shape: {focals.shape}\")\n    print(f\"Principal points shape: {pp.shape}\")\n    \n    # MASt3Rの処理サイズ（通常224x224）\n    mast3r_size = 224.0\n    \n    cameras = []\n    for i, img_path in enumerate(image_paths):\n        img = Image.open(img_path)\n        W, H = img.size\n        \n        # 元画像サイズとのスケール比\n        scale = W / mast3r_size\n        \n        # focalsは[N,1]の形式（fx=fyの等方性カメラ）\n        if focals.shape[1] == 1:\n            focal_mast3r = float(focals[i, 0])\n            fx = fy = focal_mast3r * scale\n        else:\n            fx = float(focals[i, 0]) * scale\n            fy = float(focals[i, 1]) * scale\n        \n        # 主点もスケーリング\n        cx = float(pp[i, 0]) * scale\n        cy = float(pp[i, 1]) * scale\n        \n        camera = {\n            'camera_id': i + 1,\n            'model': 'PINHOLE',\n            'width': W,\n            'height': H,\n            'params': [fx, fy, cx, cy]\n        }\n        cameras.append(camera)\n        \n        if i == 0:\n            print(f\"\\nExample camera 0:\")\n            print(f\"  Image size: {W}x{H}\")\n            print(f\"  MASt3R focal: {focal_mast3r:.2f}, pp: ({pp[i,0]:.2f}, {pp[i,1]:.2f})\")\n            print(f\"  Scaled fx={fx:.2f}, fy={fy:.2f}, cx={cx:.2f}, cy={cy:.2f}\")\n            print(f\"  Pose (first row): {poses[i][0]}\")\n    \n    print(f\"\\n✓ Extracted {len(cameras)} cameras and {len(poses)} poses\")\n    \n    return pts3d, colors, cameras, poses","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# Step 3 & 4: MASt3R Reconstruction (COLMAPの代替)\n# ============================================================================\n\nimport struct\nfrom pathlib import Path\n\ndef import_to_mast3r_and_save_colmap(\n    image_dir,\n    processed_image_dir,\n    feature_dir,\n    database_path,\n    output_dir,\n    pairs,\n    single_camera=True\n):\n    \"\"\"\n    MASt3Rを使用してカメラポーズを推定し、COLMAP形式で保存\n    \"\"\"\n    print(\"\\n=== Running MASt3R Reconstruction ===\")\n    print(\"Initial memory state:\")\n    get_memory_info()\n\n    # MASt3Rモデルのロード\n    from mast3r.model import AsymmetricMASt3R\n    device = Config.DEVICE\n\n    model = AsymmetricMASt3R.from_pretrained(Config.MAST3R_MODEL).to(device)\n    model.eval()\n    print(f\"✓ MASt3R model loaded on {device}\")\n\n    # 画像パスの取得\n    image_paths = sorted([\n        os.path.join(processed_image_dir, f)\n        for f in os.listdir(processed_image_dir)\n        if f.lower().endswith(('.jpg', '.jpeg', '.png'))\n    ])\n\n    # MASt3Rで再構成\n    scene, mast3r_images = run_mast3r_pairs(\n        model, image_paths, pairs, device,\n        batch_size=1\n    )\n\n    # モデルをメモリから削除\n    del model\n    clear_memory()\n\n    # COLMAP形式のデータを抽出\n    pts3d, colors, cameras, poses = extract_colmap_data(\n        scene, image_paths, max_points=1000000\n    )\n\n    # COLMAP形式で保存\n    sparse_dir = save_colmap_reconstruction(\n        pts3d, colors, cameras, poses, image_paths, output_dir\n    )\n\n    print(f\"\\n✓ MASt3R reconstruction saved in COLMAP format\")\n    print(f\"  Output: {sparse_dir}\")\n\n    return sparse_dir","metadata":{"id":"imNgPK2Phwi8","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.066Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def run_mast3r_mapper(database_path, image_dir, output_dir, pairs, processed_image_dir, max_points=1000000):\n    \"\"\"\n    MASt3Rを使用したマッピング（COLMAPの代替）\n    \n    Args:\n        database_path: Path to database\n        image_dir: Original image directory\n        output_dir: Output directory\n        pairs: Image pairs\n        processed_image_dir: Processed image directory\n        max_points: Maximum number of 3D points to extract\n    \"\"\"\n    print(\"\\n=== MASt3R Mapper (COLMAP Alternative) ===\")\n    print(\"Initial memory state:\")\n    get_memory_info()\n\n    # MASt3Rモデルのロード\n    from mast3r.model import AsymmetricMASt3R\n    device = Config.DEVICE\n\n    model = AsymmetricMASt3R.from_pretrained(Config.MAST3R_MODEL).to(device)\n    model.eval()\n    print(f\"✓ MASt3R model loaded on {device}\")\n\n    # 画像パスの取得\n    image_paths = sorted([\n        os.path.join(processed_image_dir, f)\n        for f in os.listdir(processed_image_dir)\n        if f.lower().endswith(('.jpg', '.jpeg', '.png'))\n    ])\n\n    # MASt3Rで再構成\n    scene, mast3r_images = run_mast3r_pairs(\n        model, image_paths, pairs, device,\n        batch_size=1\n    )\n\n    # モデルをメモリから削除\n    del model\n    clear_memory()\n\n    # COLMAP形式のデータを抽出\n    pts3d, colors, cameras, poses = extract_colmap_data(\n        scene, image_paths, max_points=max_points  # max_pointsを渡す\n    )\n\n    # COLMAP形式で保存\n    sparse_dir = save_colmap_reconstruction(\n        pts3d, colors, cameras, poses, image_paths, output_dir\n    )\n\n    print(f\"\\n✓ MASt3R reconstruction saved in COLMAP format\")\n    print(f\"  Output: {sparse_dir}\")\n\n    # sparse/0 ディレクトリが存在することを確���\n    model_dir = sparse_dir\n    if not os.path.exists(model_dir):\n        raise RuntimeError(f\"MASt3R reconstruction failed - directory not found: {model_dir}\")\n\n    # 必要なファイルが存在することを確認\n    required_files = ['cameras.bin', 'images.bin', 'points3D.bin']\n    for file in required_files:\n        file_path = os.path.join(model_dir, file)\n        if not os.path.exists(file_path):\n            raise FileNotFoundError(f\"Required file not found: {file}\")\n\n    print(f\"\\n✓ MASt3R reconstruction complete: {model_dir}\")\n    return model_dir","metadata":{"trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def load_images_for_mast3r(image_paths, size=224):\n    \"\"\"MASt3R用に画像をロード\"\"\"\n    print(f\"\\n=== Loading images for MASt3R (size={size}) ===\")\n\n    from dust3r.utils.image import load_images\n\n    images = load_images(image_paths, size=size, verbose=True)\n\n    return images\n\n\n\n\n\ndef run_mast3r_pairs(model, image_paths, pairs, device='cuda', batch_size=1, chunk_size=500):\n    \"\"\"\n    選択されたペアでMASt3Rを実行（画像もチャンクごとにロード、結合も最適化）\n    \"\"\"\n    print(\"\\n=== Running MASt3R Reconstruction ===\")\n    print(\"Initial memory state:\")\n    get_memory_info()\n\n    from dust3r.cloud_opt import global_aligner, GlobalAlignerMode\n    import dust3r.inference\n    import pickle\n    import tempfile\n\n    print(f\"Processing {len(pairs)} pairs in chunks of {chunk_size}...\")\n    print(\"Note: Images will be loaded on-demand per chunk to save memory\")\n\n    # 一時ディレクトリを作成\n    temp_dir = tempfile.mkdtemp()\n    print(f\"Temporary directory: {temp_dir}\")\n\n    # チャンクごとに処理してディスクに保存\n    chunk_files = []\n    num_chunks = (len(pairs) + chunk_size - 1) // chunk_size\n\n    for chunk_idx in range(num_chunks):\n        start_idx = chunk_idx * chunk_size\n        end_idx = min(start_idx + chunk_size, len(pairs))\n        chunk_pairs_indices = pairs[start_idx:end_idx]\n\n        print(f\"\\n--- Processing chunk {chunk_idx + 1}/{num_chunks} (pairs {start_idx}-{end_idx}) ---\")\n\n        # このチャンクで必要な画像インデックスを収集\n        needed_image_indices = set()\n        for idx1, idx2 in chunk_pairs_indices:\n            needed_image_indices.add(idx1)\n            needed_image_indices.add(idx2)\n\n        needed_image_indices = sorted(list(needed_image_indices))\n        print(f\"Loading {len(needed_image_indices)} unique images for this chunk...\")\n\n        # 必要な画像だけロード\n        needed_image_paths = [image_paths[i] for i in needed_image_indices]\n        chunk_images = load_images_for_mast3r(needed_image_paths, size=Config.MAST3R_IMAGE_SIZE)\n\n        # インデックスマッピングを作成（元のインデックス → チャンク内インデックス）\n        index_mapping = {orig_idx: new_idx for new_idx, orig_idx in enumerate(needed_image_indices)}\n\n        print(f\"Memory after loading chunk images:\")\n        get_memory_info()\n\n        # 画像ペアを作成（インデックスを変換）\n        mast3r_pairs = []\n        for idx1, idx2 in tqdm(chunk_pairs_indices, desc=f\"Preparing chunk {chunk_idx + 1}\"):\n            new_idx1 = index_mapping[idx1]\n            new_idx2 = index_mapping[idx2]\n            mast3r_pairs.append([chunk_images[new_idx1], chunk_images[new_idx2]])\n\n        # 推論を実行\n        print(f\"Running MASt3R inference on {len(mast3r_pairs)} pairs...\")\n        output = dust3r.inference.inference(mast3r_pairs, model, device, batch_size=batch_size, verbose=True)\n\n        # ディスクに保存\n        chunk_file = os.path.join(temp_dir, f'chunk_{chunk_idx}.pkl')\n        with open(chunk_file, 'wb') as f:\n            pickle.dump(output, f)\n        chunk_files.append(chunk_file)\n\n        del mast3r_pairs\n        del chunk_images  # 画像も削除\n        del output\n        clear_memory()\n\n        print(f\"Chunk {chunk_idx + 1} saved to disk. Memory state:\")\n        get_memory_info()\n\n    # ディスクから読み込んで結合（メモリ効率化版）\n    print(\"\\n=== Combining all chunks from disk ===\")\n\n    # まず最初の2チャンクを結合\n    print(f\"Loading and combining chunks 1-2...\")\n    with open(chunk_files[0], 'rb') as f:\n        combined_output = pickle.load(f)\n    os.remove(chunk_files[0])\n\n    with open(chunk_files[1], 'rb') as f:\n        chunk_output = pickle.load(f)\n\n    for key in combined_output.keys():\n        if isinstance(combined_output[key], dict):\n            for field in combined_output[key].keys():\n                if isinstance(combined_output[key][field], torch.Tensor):\n                    combined_output[key][field] = torch.cat([\n                        combined_output[key][field],\n                        chunk_output[key][field]\n                    ], dim=0)\n                elif isinstance(combined_output[key][field], list):\n                    combined_output[key][field].extend(chunk_output[key][field])\n        elif isinstance(combined_output[key], torch.Tensor):\n            combined_output[key] = torch.cat([\n                combined_output[key],\n                chunk_output[key]\n            ], dim=0)\n        elif isinstance(combined_output[key], list):\n            combined_output[key].extend(chunk_output[key])\n\n    del chunk_output\n    os.remove(chunk_files[1])\n    clear_memory()\n\n    # 残りのチャンクを1つずつ結合\n    for idx in range(2, len(chunk_files)):\n        print(f\"Loading and combining chunk {idx + 1}/{len(chunk_files)}...\")\n\n        with open(chunk_files[idx], 'rb') as f:\n            chunk_output = pickle.load(f)\n\n        for key in combined_output.keys():\n            if isinstance(combined_output[key], dict):\n                for field in combined_output[key].keys():\n                    if isinstance(combined_output[key][field], torch.Tensor):\n                        # メモリ効率化: 結合後に元のTensorを削除\n                        old_tensor = combined_output[key][field]\n                        combined_output[key][field] = torch.cat([\n                            old_tensor,\n                            chunk_output[key][field]\n                        ], dim=0)\n                        del old_tensor\n                    elif isinstance(combined_output[key][field], list):\n                        combined_output[key][field].extend(chunk_output[key][field])\n\n            elif isinstance(combined_output[key], torch.Tensor):\n                old_tensor = combined_output[key]\n                combined_output[key] = torch.cat([\n                    old_tensor,\n                    chunk_output[key]\n                ], dim=0)\n                del old_tensor\n\n            elif isinstance(combined_output[key], list):\n                combined_output[key].extend(chunk_output[key])\n\n        del chunk_output\n        os.remove(chunk_files[idx])\n        clear_memory()\n\n        # 進捗確認\n        if (idx + 1) % 3 == 0:\n            print(f\"  Memory after combining {idx + 1} chunks:\")\n            get_memory_info()\n\n    os.rmdir(temp_dir)\n\n    print(f\"✓ Combined output keys: {list(combined_output.keys())}\")\n    print(\"After combining all chunks:\")\n    get_memory_info()\n\n    print(\"✓ MASt3R inference complete\")\n\n    # 最後にグローバルアライメント用に全画像をロード\n    print(\"\\nLoading all images for global alignment...\")\n    images = load_images_for_mast3r(image_paths, size=Config.MAST3R_IMAGE_SIZE)\n    print(\"Memory after loading all images:\")\n    get_memory_info()\n\n    # グローバルアライメント\n    print(\"\\nRunning global alignment...\")\n    scene = global_aligner(\n        combined_output,\n        device=device,\n        mode=GlobalAlignerMode.PointCloudOptimizer\n    )\n\n    del combined_output\n    clear_memory()\n\n    print(\"Computing global alignment...\")\n    loss = scene.compute_global_alignment(\n        init=\"mst\",\n        niter=150,\n        schedule='cosine',\n        lr=0.01\n    )\n\n    print(f\"✓ Global alignment complete (final loss: {loss:.6f})\")\n    print(\"Final memory state:\")\n    get_memory_info()\n\n    return scene, images\n\n\n\n\n\n\n\ndef extract_colmap_data(scene, image_paths, max_points=1000000):\n    \"\"\"\n    MASt3RシーンからCOLMAP互換のデータを抽出\n    \"\"\"\n    print(\"\\n=== Extracting COLMAP-compatible data ===\")\n\n    pts_all = scene.get_pts3d()\n    print(f\"pts_all type: {type(pts_all)}\")\n\n    if isinstance(pts_all, list):\n        print(f\"pts_all is a list with {len(pts_all)} elements\")\n        if len(pts_all) > 0:\n            print(f\"First element type: {type(pts_all[0])}\")\n            if hasattr(pts_all[0], 'shape'):\n                print(f\"First element shape: {pts_all[0].shape}\")\n\n        pts_all = torch.stack([p if isinstance(p, torch.Tensor) else torch.tensor(p)\n                              for p in pts_all])\n        print(f\"pts_all shape after conversion: {pts_all.shape}\")\n\n    if len(pts_all.shape) == 4:\n        print(f\"Found batched point cloud: {pts_all.shape}\")\n        B, H, W, _ = pts_all.shape\n        pts3d = pts_all.reshape(-1, 3).detach().cpu().numpy()\n\n        colors = []\n        for img_path in image_paths:\n            img = Image.open(img_path).resize((W, H))\n            colors.append(np.array(img))\n        colors = np.stack(colors).reshape(-1, 3) / 255.0\n    else:\n        pts3d = pts_all.detach().cpu().numpy() if isinstance(pts_all, torch.Tensor) else pts_all\n        colors = np.ones((len(pts3d), 3)) * 0.5\n\n    print(f\"✓ Extracted {len(pts3d)} 3D points from {len(image_paths)} images\")\n\n    if len(pts3d) > max_points:\n        print(f\"\\n⚠ Downsampling from {len(pts3d)} to {max_points} points...\")\n\n        valid_mask = ~(np.isnan(pts3d).any(axis=1) | np.isinf(pts3d).any(axis=1))\n        pts3d_valid = pts3d[valid_mask]\n        colors_valid = colors[valid_mask]\n\n        indices = np.random.choice(len(pts3d_valid), size=max_points, replace=False)\n        pts3d = pts3d_valid[indices]\n        colors = colors_valid[indices]\n\n        print(f\"✓ Downsampled to {len(pts3d)} points\")\n\n    print(\"Extracting camera parameters...\")\n\n    poses_tensor = scene.get_im_poses()\n    print(f\"Retrieved all poses at once: shape {poses_tensor.shape}\")\n    poses = poses_tensor.detach().cpu().numpy()\n\n    cameras = []\n    for i, img_path in enumerate(image_paths):\n        img = Image.open(img_path)\n        W, H = img.size\n\n        K = scene.get_intrinsics()[i].detach().cpu().numpy()\n\n        camera = {\n            'camera_id': i + 1,\n            'model': 'PINHOLE',\n            'width': W,\n            'height': H,\n            'params': [K[0, 0], K[1, 1], K[0, 2], K[1, 2]]\n        }\n        cameras.append(camera)\n\n    print(f\"✓ Extracted {len(cameras)} cameras and {len(poses)} poses\")\n\n    return pts3d, colors, cameras, poses\n\n\ndef save_colmap_reconstruction(pts3d, colors, cameras, poses, image_paths, output_dir):\n    \"\"\"COLMAP形式で再構成を保存\"\"\"\n    print(\"\\n=== Saving COLMAP reconstruction ===\")\n\n    sparse_dir = Path(output_dir) / 'sparse' / '0'\n    sparse_dir.mkdir(parents=True, exist_ok=True)\n\n    print(f\"  Writing COLMAP files to {sparse_dir}...\")\n\n    write_cameras_binary(cameras, sparse_dir / 'cameras.bin')\n    print(f\"  ✓ Wrote {len(cameras)} cameras\")\n\n    write_images_binary(image_paths, cameras, poses, sparse_dir / 'images.bin')\n    print(f\"  ✓ Wrote {len(image_paths)} images\")\n\n    num_points = write_points3d_binary(pts3d, colors, sparse_dir / 'points3D.bin')\n    print(f\"  ✓ Wrote {num_points} 3D points\")\n\n    print(f\"\\n✓ COLMAP reconstruction saved to {sparse_dir}\")\n\n    return sparse_dir\n\n\ndef write_cameras_binary(cameras, output_file):\n    \"\"\"cameras.binをCOLMAPバイナリ形式で書き込み\"\"\"\n    with open(output_file, 'wb') as f:\n        f.write(struct.pack('Q', len(cameras)))\n\n        for i, cam in enumerate(cameras):\n            camera_id = cam.get('camera_id', i + 1)\n            model_id = 1\n            width = cam['width']\n            height = cam['height']\n            params = cam['params']\n\n            f.write(struct.pack('i', camera_id))\n            f.write(struct.pack('i', model_id))\n            f.write(struct.pack('Q', width))\n            f.write(struct.pack('Q', height))\n\n            for param in params[:4]:\n                f.write(struct.pack('d', param))\n\n\ndef write_images_binary(image_paths, cameras, poses, output_file):\n    \"\"\"images.binをCOLMAPバイナリ形式で書き込み\"\"\"\n    with open(output_file, 'wb') as f:\n        f.write(struct.pack('Q', len(image_paths)))\n\n        for i, (img_path, pose) in enumerate(zip(image_paths, poses)):\n            image_id = i + 1\n            camera_id = cameras[i].get('camera_id', i + 1)\n            image_name = os.path.basename(img_path)\n\n            R = pose[:3, :3]\n            t = pose[:3, 3]\n\n            qvec = rotmat2qvec(R)\n            tvec = t\n\n            f.write(struct.pack('i', image_id))\n\n            for q in qvec:\n                f.write(struct.pack('d', float(q)))\n\n            for tv in tvec:\n                f.write(struct.pack('d', float(tv)))\n\n            f.write(struct.pack('i', camera_id))\n            f.write(image_name.encode('utf-8') + b'\\x00')\n            f.write(struct.pack('Q', 0))\n\n\ndef write_points3d_binary(pts3d, colors, output_file):\n    \"\"\"points3D.binをCOLMAPバイナリ形式で書き込み\"\"\"\n    valid_indices = []\n    for i, pt in enumerate(pts3d):\n        if not (np.isnan(pt).any() or np.isinf(pt).any()):\n            valid_indices.append(i)\n\n    with open(output_file, 'wb') as f:\n        f.write(struct.pack('Q', len(valid_indices)))\n\n        for idx, point_id in enumerate(valid_indices):\n            pt = pts3d[point_id]\n            color = colors[point_id]\n\n            f.write(struct.pack('Q', point_id))\n\n            for coord in pt:\n                f.write(struct.pack('d', float(coord)))\n\n            col_int = (color * 255).astype(np.uint8)\n            for c in col_int:\n                f.write(struct.pack('B', int(c)))\n\n            f.write(struct.pack('d', 0.0))\n            f.write(struct.pack('Q', 0))\n\n            if (idx + 1) % 1000000 == 0:\n                print(f\"    Wrote {idx + 1} / {len(valid_indices)} points...\")\n\n    return len(valid_indices)\n\n\ndef rotmat2qvec(R):\n    \"\"\"回転行列をクォータニオンに変換\"\"\"\n    R = np.asarray(R, dtype=np.float64)\n    trace = np.trace(R)\n\n    if trace > 0:\n        s = 0.5 / np.sqrt(trace + 1.0)\n        w = 0.25 / s\n        x = (R[2, 1] - R[1, 2]) * s\n        y = (R[0, 2] - R[2, 0]) * s\n        z = (R[1, 0] - R[0, 1]) * s\n    elif R[0, 0] > R[1, 1] and R[0, 0] > R[2, 2]:\n        s = 2.0 * np.sqrt(1.0 + R[0, 0] - R[1, 1] - R[2, 2])\n        w = (R[2, 1] - R[1, 2]) / s\n        x = 0.25 * s\n        y = (R[0, 1] + R[1, 0]) / s\n        z = (R[0, 2] + R[2, 0]) / s\n    elif R[1, 1] > R[2, 2]:\n        s = 2.0 * np.sqrt(1.0 + R[1, 1] - R[0, 0] - R[2, 2])\n        w = (R[0, 2] - R[2, 0]) / s\n        x = (R[0, 1] + R[1, 0]) / s\n        y = 0.25 * s\n        z = (R[1, 2] + R[2, 1]) / s\n    else:\n        s = 2.0 * np.sqrt(1.0 + R[2, 2] - R[0, 0] - R[1, 1])\n        w = (R[1, 0] - R[0, 1]) / s\n        x = (R[0, 2] + R[2, 0]) / s\n        y = (R[1, 2] + R[2, 1]) / s\n        z = 0.25 * s\n\n    qvec = np.array([w, x, y, z], dtype=np.float64)\n    qvec = qvec / np.linalg.norm(qvec)\n\n    return qvec\n\n\n# メモリ管理ユーティリティ（必要に応じて追加）\ndef clear_memory():\n    \"\"\"GPUとCPUメモリを積極的にクリア\"\"\"\n    gc.collect()\n    if torch.cuda.is_available():\n        torch.cuda.empty_cache()\n        torch.cuda.synchronize()\n\n\ndef get_memory_info():\n    \"\"\"現在のメモリ使用状況を取得\"\"\"\n    if torch.cuda.is_available():\n        allocated = torch.cuda.memory_allocated() / 1024**3\n        reserved = torch.cuda.memory_reserved() / 1024**3\n        print(f\"GPU Memory - Allocated: {allocated:.2f}GB, Reserved: {reserved:.2f}GB\")\n\n    import psutil\n    cpu_mem = psutil.virtual_memory().percent\n    print(f\"CPU Memory Usage: {cpu_mem:.1f}%\")","metadata":{"trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.067Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"","metadata":{"id":"0LHFaucWicrB"}},{"cell_type":"code","source":"# ============================================================================\n# Step 3: Import to COLMAP (too be removed)\n# ============================================================================\n\ndef import_to_colmap(image_dir, feature_dir, database_path, single_camera=True):\n    \"\"\"\n    Import features and matches to COLMAP database\n\n    Args:\n            image_dir (str): Directory containing the images.\n            feature_dir (str): Directory to save/load extracted features.\n            database_path (str): Path to the database file.\n            single_camera (bool): Set to True if all images have the same dimensions (e.g., pre-resized).\n    \"\"\"\n    print(\"\\n=== Creating COLMAP Database ===\")\n\n    if os.path.exists(database_path):\n        os.remove(database_path)\n        print(f\"✓ Removed existing database\")\n\n    db = COLMAPDatabase.connect(database_path)\n    db.create_tables()\n\n    print(f\"Single camera mode: {single_camera}\")\n\n    image_files = [f for f in os.listdir(image_dir)\n                   if f.lower().endswith(('.jpg', '.jpeg', '.png'))]\n    if not image_files:\n        raise ValueError(f\"No images found in {image_dir}\")\n\n    first_image = sorted(image_files)[0]\n    img_ext = os.path.splitext(first_image)[1]\n    print(f\"Detected image extension: '{img_ext}'\")\n\n    fname_to_id = add_keypoints(\n        db,\n        feature_dir,\n        image_dir,\n        img_ext,\n        'PINHOLE',\n        single_camera=single_camera\n    )\n\n    add_matches(db, feature_dir, fname_to_id)\n    db.commit()\n    db.close()\n\n    print(f\"✓ Database created: {database_path}\")\n\n# ============================================================================\n# Step 4: Run COLMAP Mapper\n# ============================================================================\n\ndef run_colmap_mapper(database_path, image_dir, output_dir):\n    \"\"\"\n    Run COLMAP mapper with verbose output\n    \"\"\"\n    print(\"\\n=== Running COLMAP Reconstruction ===\")\n    os.makedirs(output_dir, exist_ok=True)\n    cmd = [\n        'colmap', 'mapper',\n        '--database_path', database_path,\n        '--image_path', image_dir,\n        '--output_path', output_dir,\n        '--Mapper.ba_refine_focal_length', '0',\n        '--Mapper.ba_refine_principal_point', '0',\n        '--Mapper.ba_refine_extra_params', '0',\n        '--Mapper.min_num_matches', '15',\n        '--Mapper.init_min_num_inliers', '50',\n        '--Mapper.max_num_models', '1',\n        '--Mapper.num_threads', '16',\n    ]\n    print(f\"Command: {' '.join(cmd)}\\n\")\n\n    process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)\n    for line in process.stdout:\n        print(line, end='')\n    process.wait()\n    if process.returncode == 0:\n        model_dir = os.path.join(output_dir, '0')\n        if os.path.exists(model_dir):\n            print(f\"\\n✓ COLMAP reconstruction complete: {model_dir}\")\n            return model_dir\n    raise RuntimeError(\"COLMAP reconstruction failed\")","metadata":{"id":"NJedFruCmVcL","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# Step 5: Convert to Gaussian Splatting Format (if needed)\n# ============================================================================\n\ndef convert_to_gs_format(colmap_model_dir, output_dir):\n    \"\"\"\n    Verify COLMAP output and prepare paths for Gaussian Splatting.\n\n    Args:\n        colmap_model_dir (str): Path to the COLMAP sparse/0 directory.\n                                Example: /content/output/colmap/sparse/0\n        output_dir (str): Base output directory.\n\n    Returns:\n        colmap_parent_dir (str): The path to be passed to Gaussian Splatting.\n                                 Example: /content/output/colmap (Parent directory containing 'sparse/')\n    \"\"\"\n    print(\"\\n=== Verifying COLMAP Model for Gaussian Splatting ===\")\n\n    import pycolmap\n    reconstruction = pycolmap.Reconstruction(colmap_model_dir)\n\n    print(f\"Registered images: {len(reconstruction.images)}\")\n    print(f\"3D points: {len(reconstruction.points3D)}\")\n\n    # Check for files required by Gaussian Splatting\n    required_files = ['cameras.bin', 'images.bin', 'points3D.bin']\n    for file in required_files:\n        file_path = os.path.join(colmap_model_dir, file)\n        if not os.path.exists(file_path):\n            raise FileNotFoundError(f\"Required file not found: {file}\")\n        print(f\"  ✓ {file}\")\n\n    # Return the grandparent directory of sparse/0\n    # /content/output/colmap/sparse/0 -> /content/output/colmap\n    colmap_parent_dir = os.path.dirname(os.path.dirname(colmap_model_dir))\n\n    print(f\"\\n✓ COLMAP model ready for Gaussian Splatting\")\n    print(f\"  Source path: {colmap_parent_dir}\")\n\n    return colmap_parent_dir","metadata":{"id":"4IioqnC1mVcM","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def train_gaussian_splatting(colmap_dir, image_dir, output_dir, iterations=6000):\n    \"\"\"\n    Gaussian Splattingモデルをトレーニング\n    \"\"\"\n    print(\"\\n=== Training Gaussian Splatting ===\")\n\n    # 環境の修正\n    print(\"Checking and fixing Python environment...\")\n    import subprocess\n    subprocess.run([\"pip\", \"install\", \"--upgrade\", \"--force-reinstall\", \"-q\", \"scipy\", \"scikit-learn\"], check=True)\n    print(\"✓ Environment fixed\")\n\n    # COLMAPのsparseディレクトリを確認\n    sparse_dir = os.path.join(colmap_dir, 'sparse', '0')\n    if not os.path.exists(sparse_dir):\n        raise FileNotFoundError(f\"COLMAP sparse directory not found: {sparse_dir}\")\n\n    print(f\"COLMAP sparse model: {sparse_dir}\")\n    print(f\"Training images: {image_dir}\")\n    print(f\"Output: {output_dir}\")\n    print(f\"Iterations: {iterations}\")\n\n    os.makedirs(output_dir, exist_ok=True)\n\n    # Gaussian Splattingのディレクトリに移動\n    original_dir = os.getcwd()\n    os.chdir(\"/content/gaussian-splatting\")\n\n    # トレーニングコマンド\n    cmd = [\n        \"python\", \"train.py\",\n        \"-s\", colmap_dir,\n        \"--images\", image_dir,\n        \"-m\", output_dir,\n        \"--iterations\", str(iterations),\n        \"--test_iterations\", str(iterations // 2), str(iterations),\n        \"--save_iterations\", str(iterations // 2), str(iterations)\n    ]\n\n    print(f\"\\nCommand: {' '.join(cmd)}\\n\")\n\n    # 実行\n    result = subprocess.run(cmd, capture_output=True, text=True)\n\n    # 元のディレクトリに戻る\n    os.chdir(original_dir)\n\n    print(\"STDOUT:\", result.stdout)\n    if result.stderr:\n        print(\"STDERR:\", result.stderr)\n\n    if result.returncode != 0:\n        raise RuntimeError(\"Gaussian Splatting training failed\")\n\n    # 生成されたPLYファイルの存在確認\n    ply_path = os.path.join(output_dir, f\"point_cloud/iteration_{iterations}/point_cloud.ply\")\n    if not os.path.exists(ply_path):\n        raise FileNotFoundError(f\"Expected output file not found: {ply_path}\")\n\n    print(f\"\\n✓ Gaussian Splatting training complete!\")\n    print(f\"  Model saved to: {output_dir}\")\n    print(f\"  Point cloud: {ply_path}\")\n\n    return output_dir","metadata":{"id":"EiHoRSfzQ01b","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from PIL import Image, ImageFilter","metadata":{"id":"yeP98DO30gNl","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nprint(f\"✓ NumPy: {np.__version__}\")","metadata":{"id":"D0r5QQNg2GPl","outputId":"74932505-08da-43f9-d0d3-8831e52569f7","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# メインパイプライン関数（修正版）\n# ============================================================================\ndef main_pipeline(IMAGE_DIR, OUTPUT_DIR, square_size=1024, iterations=2000,\n                 max_images=None, max_pairs=1000, max_points=100000):\n    \"\"\"\n    Main pipeline for DINO + LightGlue -> MASt3R -> Gaussian Splatting\n    \n    Args:\n        IMAGE_DIR: Directory containing input images\n        OUTPUT_DIR: Directory for output files\n        square_size: Size to resize images for processing\n        iterations: Number of training iterations\n        max_images: Maximum number of images to process (None = all)\n        max_pairs: Maximum number of image pairs for matching\n        max_points: Maximum number of 3D points to extract\n    \"\"\"\n    print(\"=\"*70)\n    print(\"Gaussian Splatting Preparation Pipeline (MASt3R Version)\")\n    print(\"=\"*70)\n    \n    # パスの設定\n    feature_dir = os.path.join(OUTPUT_DIR, 'features')\n    colmap_dir = os.path.join(OUTPUT_DIR, 'colmap')\n    database_path = os.path.join(colmap_dir, 'database.db')\n    sparse_dir = os.path.join(colmap_dir, 'sparse')\n    os.makedirs(OUTPUT_DIR, exist_ok=True)\n    os.makedirs(colmap_dir, exist_ok=True)\n    \n    # Step 0: 画像の前処理（max_images適用前の元画像を対象）\n    print(\"\\n\" + \"=\"*70)\n    print(\"Step 0: Image Preprocessing\")\n    print(\"=\"*70)\n    \n    # 元画像のパスを取得\n    original_image_paths = sorted([\n        os.path.join(IMAGE_DIR, f)\n        for f in os.listdir(IMAGE_DIR)\n        if f.lower().endswith(('.jpg', '.jpeg', '.png'))\n    ])\n    \n    # max_images制限を元画像に適用\n    if max_images and len(original_image_paths) > max_images:\n        print(f\"\\n⚠️  Limiting to {max_images} original images (from {len(original_image_paths)})\")\n        original_image_paths = original_image_paths[:max_images]\n    \n    print(f\"Processing {len(original_image_paths)} original images → ~{len(original_image_paths)*2} after biplet-square\")\n    \n    # 選択された画像を一時ディレクトリにコピー\n    temp_dir = os.path.join(OUTPUT_DIR, \"temp_originals\")\n    os.makedirs(temp_dir, exist_ok=True)\n    \n    import shutil\n    for img_path in original_image_paths:\n        shutil.copy(img_path, temp_dir)\n    \n    # 正方形フォーマットに変換\n    processed_image_dir = os.path.join(OUTPUT_DIR, \"processed_images\")\n    normalize_image_sizes_biplet(\n        input_dir=temp_dir,\n        output_dir=processed_image_dir,\n        size=square_size\n    )\n    \n    # 一時ディレクトリをクリーンアップ\n    shutil.rmtree(temp_dir)\n    \n    # 処理後の画像パスを取得\n    image_paths = sorted([\n        os.path.join(processed_image_dir, f)\n        for f in os.listdir(processed_image_dir)\n        if f.lower().endswith(('.jpg', '.jpeg', '.png'))\n    ])\n    \n    print(f\"\\n📸 Processing {len(image_paths)} images (after biplet-square)\")\n    print(f\"⚠️  Will use maximum {max_pairs} pairs to save memory\")\n    print(f\"⚠️  Will extract maximum {max_points} 3D points\")\n    \n    # Step 1: 画像ペアを生成（max_pairs制限付き）\n    print(\"\\n\" + \"=\"*70)\n    print(\"Step 1: Image Pair Selection\")\n    print(\"=\"*70)\n    \n    pairs, features = get_image_pairs(image_paths, max_pairs=max_pairs)\n    \n    print(f\"✓ Selected {len(pairs)} pairs for matching\")\n    \n    # Step 2: LightGlueで特徴マッチング\n    print(\"\\n\" + \"=\"*70)\n    print(\"Step 2: LightGlue Feature Matching\")\n    print(\"=\"*70)\n    \n    match_pairs_lightglue(image_paths, pairs, features, feature_dir)\n    \n    ######### Step 3 and Step 4: MASt3R for reconstruction (not COLMAP) #########\n    # Step 3 & 4: MASt3Rでカメラポーズを推定し、COLMAP形式で保存\n    print(\"\\n\" + \"=\"*70)\n    print(\"Step 3 & 4: MASt3R Reconstruction → COLMAP Format\")\n    print(\"=\"*70)\n    \n    model_dir = run_mast3r_mapper(\n        database_path=database_path,\n        image_dir=IMAGE_DIR,\n        output_dir=colmap_dir,\n        pairs=pairs,\n        processed_image_dir=processed_image_dir,\n        max_points=max_points  # max_points制限を追加\n    )\n    ###############################################################################\n    \n    # Step 5: Gaussian Splattingの準備を確認\n    print(\"\\n\" + \"=\"*70)\n    print(\"Step 5: Convert to Gaussian Splatting Format\")\n    print(\"=\"*70)\n    \n    colmap_parent = convert_to_gs_format(model_dir, OUTPUT_DIR)\n    \n    # Step 6: Gaussian Splattingモデルをトレーニング\n    print(\"\\n\" + \"=\"*70)\n    print(\"Step 6: Training Gaussian Splatting\")\n    print(\"=\"*70)\n    \n    gs_output = train_gaussian_splatting(\n        colmap_dir=colmap_parent,\n        image_dir=processed_image_dir,\n        output_dir=OUTPUT_DIR,\n        iterations=iterations\n    )\n    \n    print(\"\\n\" + \"=\"*70)\n    print(\"✅ Full Pipeline Successfully Completed!\")\n    print(\"=\"*70)\n    print(f\"\\nGaussian Splatting model saved at: {gs_output}\")\n    print(f\"\\nPipeline Summary:\")\n    print(f\"  - Original images: {len(original_image_paths)}\")\n    print(f\"  - Processed images: {len(image_paths)}\")\n    print(f\"  - Image pairs: {len(pairs)}\")\n    print(f\"  - Max 3D points: {max_points}\")\n    \n    return gs_output","metadata":{"id":"Ppkm3NVGwtjO","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 使用例\nif __name__ == \"__main__\":\n    # 実際のデータセットで実行する場合の推奨設定\n    IMAGE_DIR = \"/kaggle/input/two-dogs/fountain80/fountain80\"\n    OUTPUT_DIR = \"/kaggle/working/output\"\n\n    # 本番用の設定:\n    # - square_size: 1024 (高品質) または 512 (バランス)\n    # - iterations: 6000 (推奨) または 30000 (高品質だが時間がかかる)\n\n    gs_output = main_pipeline(\n        IMAGE_DIR,\n        OUTPUT_DIR,\n        square_size=1024,\n        iterations=2000,\n        max_images=None,      # 最大50枚の画像\n        max_pairs=2000,     # 最大1000ペア\n        max_points=100000   # 最大10万ポイント\n    )","metadata":{"id":"66uQIWbs2a1t","outputId":"481e53ad-6a09-4ed1-b5ac-0d071350e1d4","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch\ntorch.cuda.empty_cache()","metadata":{"id":"SZzD2-K6islN","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print('Congratulation! Successsfully Completed!')","metadata":{"id":"8jhKKtTqwv7O","outputId":"961a2f66-099e-4efa-ec0f-4fad620f6e09","trusted":true,"execution":{"execution_failed":"2026-01-20T05:55:54.067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"id":"VQsLeKY8Rl8Y","trusted":true},"outputs":[],"execution_count":null}]}