Upload biplet-asmk-mast3r-ps2-gs-kg-31oq.ipynb

biplet-asmk-mast3r-ps2-gs-kg-31oq.ipynb

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+{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU","kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":14571475,"sourceType":"datasetVersion","datasetId":1429416}],"dockerImageVersionId":31260,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **biplet-asmk-mast3r-ps2-gs-kg** \n\n","metadata":{"id":"qDQLX3PArmh8"}},{"cell_type":"markdown","source":"https://www.kaggle.com/code/stpeteishii/dino-mast3r-gs-kg-34","metadata":{}},{"cell_type":"code","source":"!pip install roma einops timm huggingface_hub\n!pip install opencv-python pillow tqdm pyaml cython plyfile\n!pip install pycolmap trimesh\n!pip uninstall -y numpy scipy\n!pip install numpy==1.26.4 scipy==1.11.4\n\nbreak","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# restart, then run after","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:23:40.805863Z","iopub.execute_input":"2026-01-22T06:23:40.806197Z","iopub.status.idle":"2026-01-22T06:23:40.810507Z","shell.execute_reply.started":"2026-01-22T06:23:40.806162Z","shell.execute_reply":"2026-01-22T06:23:40.809602Z"}},"outputs":[],"execution_count":32},{"cell_type":"code","source":"#!rm output/images/*","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:23:40.811883Z","iopub.execute_input":"2026-01-22T06:23:40.812195Z","iopub.status.idle":"2026-01-22T06:23:40.822747Z","shell.execute_reply.started":"2026-01-22T06:23:40.812169Z","shell.execute_reply":"2026-01-22T06:23:40.821989Z"}},"outputs":[],"execution_count":33},{"cell_type":"code","source":"#!ls output/images","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:23:40.824420Z","iopub.execute_input":"2026-01-22T06:23:40.824887Z","iopub.status.idle":"2026-01-22T06:23:40.837106Z","shell.execute_reply.started":"2026-01-22T06:23:40.824860Z","shell.execute_reply":"2026-01-22T06:23:40.836172Z"}},"outputs":[],"execution_count":34},{"cell_type":"code","source":"import numpy as np\nprint(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# romaが必要な時点で確認・インストール\ntry:\n    import roma\n    print(\"✓ roma is installed\")\nexcept ModuleNotFoundError:\n    print(\"⚠️ roma not found, installing...\")\n    !pip install roma\n    import roma\n    print(\"✓ roma installed\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:23:43.332455Z","iopub.execute_input":"2026-01-22T06:23:43.332775Z","iopub.status.idle":"2026-01-22T06:23:43.338489Z","shell.execute_reply.started":"2026-01-22T06:23:43.332716Z","shell.execute_reply":"2026-01-22T06:23:43.337637Z"}},"outputs":[{"name":"stdout","text":"✓ roma is installed\n","output_type":"stream"}],"execution_count":36},{"cell_type":"code","source":"import os\nimport sys\n\n# MASt3Rをクローン\nif not os.path.exists('/kaggle/working/mast3r'):\n    print(\"Cloning MASt3R repository...\")\n    !git clone --recursive https://github.com/naver/mast3r.git /kaggle/working/mast3r\n    print(\"✓ MASt3R cloned\")\nelse:\n    print(\"✓ MASt3R already exists\")\n\n# DUSt3Rをクローン（MASt3R内に必要）\nif not os.path.exists('/kaggle/working/mast3r/dust3r'):\n    print(\"Cloning DUSt3R repository...\")\n    !git clone --recursive https://github.com/naver/dust3r.git /kaggle/working/mast3r/dust3r\n    print(\"✓ DUSt3R cloned\")\nelse:\n    print(\"✓ DUSt3R already exists\")\n\n# ASMKをクローン\nif not os.path.exists('/kaggle/working/asmk'):\n    print(\"Cloning ASMK repository...\")\n    !git clone https://github.com/jenicek/asmk.git /kaggle/working/asmk\n    print(\"✓ ASMK cloned\")\nelse:\n    print(\"✓ ASMK already exists\")\n\n# パスを追加\nsys.path.insert(0, '/kaggle/working/mast3r')\nsys.path.insert(0, '/kaggle/working/mast3r/dust3r')\nsys.path.insert(0, '/kaggle/working/asmk')\n\n# 確認\ntry:\n    from dust3r.model import AsymmetricCroCo3DStereo\n    print(\"✓ dust3r.model imported successfully\")\nexcept ImportError as e:\n    print(f\"✗ Import error: {e}\")\n\n# croco（MASt3Rの依存関係）もクローン\nif not os.path.exists('/kaggle/working/mast3r/croco'):\n    print(\"Cloning CroCo repository...\")\n    !git clone --recursive https://github.com/naver/croco.git /kaggle/working/mast3r/croco\n    print(\"✓ CroCo cloned\")\n\n# CroCo v2の依存関係\nif not os.path.exists('/kaggle/working/mast3r/croco/models/curope'):\n    print(\"Cloning CuRoPe...\")\n    !git clone --recursive https://github.com/naver/curope.git /kaggle/working/mast3r/croco/models/curope\n    print(\"✓ CuRoPe cloned\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:23:43.339716Z","iopub.execute_input":"2026-01-22T06:23:43.340102Z","iopub.status.idle":"2026-01-22T06:23:43.361015Z","shell.execute_reply.started":"2026-01-22T06:23:43.340069Z","shell.execute_reply":"2026-01-22T06:23:43.360304Z"}},"outputs":[{"name":"stdout","text":"✓ MASt3R already exists\n✓ DUSt3R already exists\n✓ ASMK already exists\n✓ dust3r.model imported successfully\n","output_type":"stream"}],"execution_count":37},{"cell_type":"code","source":"# =====================================================================\n# STEP 2: Clone Gaussian Splatting\n# =====================================================================\nprint(\"\\n\" + \"=\"*70)\nprint(\"STEP 2: Clone Gaussian Splatting\")\nprint(\"=\"*70)\nWORK_DIR = \"/kaggle/working/gaussian-splatting\"\n\nimport subprocess\nif not os.path.exists(WORK_DIR):\n    subprocess.run([\n        \"git\", \"clone\", \"--recursive\",\n        \"https://github.com/graphdeco-inria/gaussian-splatting.git\",\n        WORK_DIR\n    ], capture_output=True)\n    print(\"✓ Cloned\")\nelse:\n    print(\"✓ Already exists\")\n\n# インストールが必要なディレクトリ\nsubmodules = [\n    \"/kaggle/working/gaussian-splatting/submodules/diff-gaussian-rasterization\",\n    \"/kaggle/working/gaussian-splatting/submodules/simple-knn\"\n]\n\nfor path in submodules:\n    print(f\"Installing {path}...\")\n    # -e は編集可能モード、不要なら外してもOKです\n    subprocess.run([\"pip\", \"install\", path], check=True)\n\nprint(\"✓ Custom CUDA modules installed.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:23:43.362110Z","iopub.execute_input":"2026-01-22T06:23:43.362388Z","iopub.status.idle":"2026-01-22T06:24:06.980493Z","shell.execute_reply.started":"2026-01-22T06:23:43.362360Z","shell.execute_reply":"2026-01-22T06:24:06.979818Z"}},"outputs":[{"name":"stdout","text":"\n======================================================================\nSTEP 2: Clone Gaussian Splatting\n======================================================================\n✓ Already exists\nInstalling /kaggle/working/gaussian-splatting/submodules/diff-gaussian-rasterization...\nProcessing ./gaussian-splatting/submodules/diff-gaussian-rasterization\n  Preparing metadata (setup.py): started\n  Preparing metadata (setup.py): finished with status 'done'\nBuilding wheels for collected packages: diff_gaussian_rasterization\n  Building wheel for diff_gaussian_rasterization (setup.py): started\n  Building wheel for diff_gaussian_rasterization (setup.py): finished with status 'done'\n  Created wheel for diff_gaussian_rasterization: filename=diff_gaussian_rasterization-0.0.0-cp312-cp312-linux_x86_64.whl size=3455607 sha256=b162a676512ef536704512c471b001d63aa2bd086183ec432689a63f0ed8a56e\n  Stored in directory: /root/.cache/pip/wheels/ba/99/d3/014520068aca8c2e8bdc358ca774581380cadb65788559b3ea\nSuccessfully built diff_gaussian_rasterization\nInstalling collected packages: diff_gaussian_rasterization\n  Attempting uninstall: diff_gaussian_rasterization\n    Found existing installation: diff_gaussian_rasterization 0.0.0\n    Uninstalling diff_gaussian_rasterization-0.0.0:\n      Successfully uninstalled diff_gaussian_rasterization-0.0.0\nSuccessfully installed diff_gaussian_rasterization-0.0.0\nInstalling /kaggle/working/gaussian-splatting/submodules/simple-knn...\nProcessing ./gaussian-splatting/submodules/simple-knn\n  Preparing metadata (setup.py): started\n  Preparing metadata (setup.py): finished with status 'done'\nBuilding wheels for collected packages: simple_knn\n  Building wheel for simple_knn (setup.py): started\n  Building wheel for simple_knn (setup.py): finished with status 'done'\n  Created wheel for simple_knn: filename=simple_knn-0.0.0-cp312-cp312-linux_x86_64.whl size=3212333 sha256=9d5dcc21a6c9e7635c0aa47924d5021730c2c30d93b97359cb5c486cc730f258\n  Stored in directory: /root/.cache/pip/wheels/ca/30/df/7f4f362d12edead48c699acde5962cbb06ca05033b9d970934\nSuccessfully built simple_knn\nInstalling collected packages: simple_knn\n  Attempting uninstall: simple_knn\n    Found existing installation: simple_knn 0.0.0\n    Uninstalling simple_knn-0.0.0:\n      Successfully uninstalled simple_knn-0.0.0\nSuccessfully installed simple_knn-0.0.0\n✓ Custom CUDA modules installed.\n","output_type":"stream"}],"execution_count":38},{"cell_type":"code","source":"import numpy as np\nprint(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:24:06.981524Z","iopub.execute_input":"2026-01-22T06:24:06.981865Z","iopub.status.idle":"2026-01-22T06:24:09.315567Z","shell.execute_reply.started":"2026-01-22T06:24:06.981830Z","shell.execute_reply":"2026-01-22T06:24:09.314660Z"}},"outputs":[{"name":"stdout","text":"✓ np: 1.26.4 - /usr/local/lib/python3.12/dist-packages/numpy/__init__.py\nVersion: 1.26.4\nVersion 3.1, 31 March 2009\n                       Version 3, 29 June 2007\n  5. Conveying Modified Source Versions.\n  14. Revised Versions of this License.\n","output_type":"stream"}],"execution_count":39},{"cell_type":"code","source":"import os\nimport sys\nimport gc\nimport torch\nimport numpy as np\nfrom pathlib import Path\nfrom tqdm import tqdm\nimport torch.nn.functional as F\n\n# ======================================================================\n# MEMORY MANAGEMENT\n# ======================================================================\n\nos.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'\n\ndef clear_memory():\n    \"\"\"メモリクリア関数\"\"\"\n    gc.collect()\n    if torch.cuda.is_available():\n        torch.cuda.empty_cache()\n        torch.cuda.synchronize()\n\n\n# ======================================================================\n# CONFIGURATION\n# ======================================================================\n\nclass Config:\n    DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    # 正しいMASt3Rモデル名\n    MAST3R_WEIGHTS = \"naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric\"\n    DUST3R_WEIGHTS = \"naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt\"  # フォールバック用\n    RETRIEVAL_TOPK = 10\n    IMAGE_SIZE = 224  # メモリ節約のため224に設定\n\n\n# ======================================================================\n# IMAGE PREPROCESSING\n# ======================================================================\n\nimport os\nimport shutil\nfrom PIL import Image\nfrom tqdm import tqdm","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:24:09.318395Z","iopub.execute_input":"2026-01-22T06:24:09.318749Z","iopub.status.idle":"2026-01-22T06:24:09.326128Z","shell.execute_reply.started":"2026-01-22T06:24:09.318703Z","shell.execute_reply":"2026-01-22T06:24:09.325288Z"}},"outputs":[],"execution_count":40},{"cell_type":"code","source":"# ======================================================================\n# IMAGE PREPROCESSING\n# ======================================================================\n\nimport os\nimport shutil\nfrom PIL import Image\nfrom tqdm import tqdm\n\n# ======================================================================\n# IMAGE PREPROCESSING\n# ======================================================================\n\ndef normalize_image_sizes_biplet(input_dir, output_dir=None, size=1024):\n    \"\"\"\n    Generates two square crops (Left & Right or Top & Bottom)\n    from each image in a directory.\n    Bipletクロップのみ生成（元画像はコピーしない）\n    \"\"\"\n    if output_dir is None:\n        output_dir = input_dir + \"_biplet\"\n    \n    os.makedirs(output_dir, exist_ok=True)\n    \n    print(f\"\\n=== Generating Biplet Crops ({size}x{size}) ===\")\n    \n    converted_count = 0\n    size_stats = {}\n    \n    for img_file in tqdm(sorted(os.listdir(input_dir)), desc=\"Creating biplets\"):\n        if not img_file.lower().endswith(('.jpg', '.jpeg', '.png')):\n            continue\n        \n        input_path = os.path.join(input_dir, img_file)\n        \n        try:\n            img = Image.open(input_path)\n            original_size = img.size\n            \n            size_key = f\"{original_size[0]}x{original_size[1]}\"\n            size_stats[size_key] = size_stats.get(size_key, 0) + 1\n            \n            # Generate 2 crops (元画像のコピーは削除)\n            crops = generate_two_crops(img, size)\n            \n            base_name, ext = os.path.splitext(img_file)\n            for mode, cropped_img in crops.items():\n                output_path = os.path.join(output_dir, f\"{base_name}_{mode}{ext}\")\n                cropped_img.save(output_path, quality=95)\n            \n            converted_count += 1\n        \n        except Exception as e:\n            print(f\"  ✗ Error processing {img_file}: {e}\")\n    \n    print(f\"\\n✓ Biplet generation complete:\")\n    print(f\"  Source images: {converted_count}\")\n    print(f\"  Biplet crops generated: {converted_count * 2}\")\n    print(f\"  Original size distribution: {size_stats}\")\n    \n    return output_dir\n\n\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\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:24:09.327035Z","iopub.execute_input":"2026-01-22T06:24:09.327313Z","iopub.status.idle":"2026-01-22T06:24:09.343475Z","shell.execute_reply.started":"2026-01-22T06:24:09.327279Z","shell.execute_reply":"2026-01-22T06:24:09.342929Z"}},"outputs":[],"execution_count":41},{"cell_type":"code","source":"# ======================================================================\n# IMAGE LOADING\n# ======================================================================\n\ndef load_images_from_directory(image_dir, max_images=200):\n    \"\"\"ディレクトリから画像をロード\"\"\"\n    print(f\"\\nLoading images from: {image_dir}\")\n    \n    valid_extensions = {'.jpg', '.jpeg', '.png', '.bmp'}\n    image_paths = []\n    \n    for ext in valid_extensions:\n        image_paths.extend(sorted(Path(image_dir).glob(f'*{ext}')))\n        image_paths.extend(sorted(Path(image_dir).glob(f'*{ext.upper()}')))\n    \n    image_paths = sorted(set(str(p) for p in image_paths))\n    \n    if len(image_paths) > max_images:\n        print(f\"⚠️  Limiting from {len(image_paths)} to {max_images} images\")\n        image_paths = image_paths[:max_images]\n    \n    print(f\"✓ Found {len(image_paths)} images\")\n    return image_paths\n\n\n# ======================================================================\n# MAST3R MODEL\n# ======================================================================\n\ndef load_mast3r_model(device):\n    \"\"\"MASt3Rモデルをロード\"\"\"\n    print(\"\\n=== Loading MASt3R Model ===\")\n    \n    # mast3rのパスを追加\n    if '/kaggle/working/mast3r' not in sys.path:\n        sys.path.insert(0, '/kaggle/working/mast3r')\n    if '/kaggle/working/mast3r/dust3r' not in sys.path:\n        sys.path.insert(0, '/kaggle/working/mast3r/dust3r')\n    \n    from dust3r.model import AsymmetricCroCo3DStereo\n    \n    try:\n        # MASt3Rモデルを試す\n        print(f\"Attempting to load: {Config.MAST3R_WEIGHTS}\")\n        model = AsymmetricCroCo3DStereo.from_pretrained(Config.MAST3R_WEIGHTS).to(device)\n        print(\"✓ Loaded MASt3R model\")\n    except Exception as e:\n        print(f\"⚠️  Failed to load MASt3R: {e}\")\n        print(f\"Trying DUSt3R instead: {Config.DUST3R_WEIGHTS}\")\n        try:\n            model = AsymmetricCroCo3DStereo.from_pretrained(Config.DUST3R_WEIGHTS).to(device)\n            print(\"✓ Loaded DUSt3R model as fallback\")\n        except Exception as e2:\n            print(f\"⚠️  Failed to load DUSt3R: {e2}\")\n            raise Exception(\"Could not load any model. Please check model names and internet connection.\")\n    \n    model.eval()\n    \n    print(f\"✓ Model loaded on {device}\")\n    return model\n\n\n# ======================================================================\n# FEATURE EXTRACTION & PAIR SELECTION\n# ======================================================================\n\ndef load_asmk_retrieval_model(device):\n    \"\"\"ASMKリトリーバルモデルをロード\"\"\"\n    print(\"\\n=== Loading ASMK Retrieval Model ===\")\n    \n    # mast3rとasmkのパスを追加\n    if '/kaggle/working/mast3r' not in sys.path:\n        sys.path.insert(0, '/kaggle/working/mast3r')\n    if '/kaggle/working/mast3r/dust3r' not in sys.path:\n        sys.path.insert(0, '/kaggle/working/mast3r/dust3r')\n    if '/kaggle/working/asmk' not in sys.path:\n        sys.path.insert(0, '/kaggle/working/asmk')\n    \n    from dust3r.model import AsymmetricCroCo3DStereo\n    \n    try:\n        # MASt3Rモデルを試す\n        model = AsymmetricCroCo3DStereo.from_pretrained(Config.MAST3R_WEIGHTS).to(device)\n        print(\"✓ Loaded MASt3R model for retrieval\")\n    except Exception as e:\n        print(f\"⚠️  Failed to load MASt3R: {e}\")\n        print(f\"Trying DUSt3R instead\")\n        model = AsymmetricCroCo3DStereo.from_pretrained(Config.DUST3R_WEIGHTS).to(device)\n        print(\"✓ Loaded DUSt3R model for retrieval\")\n    \n    model.eval()\n    \n    # Codebookの初期化（簡易版）\n    codebook = np.random.randn(1024, 24).astype(np.float32)\n    \n    print(\"✓ ASMK model loaded\")\n    return model, codebook\n\n\ndef extract_mast3r_features(model, image_paths, device, batch_size=1):\n    \"\"\"MASt3Rモデルを使用して特徴量を抽出（ペア画像として処理）\"\"\"\n    print(\"\\n=== Extracting MASt3R Features ===\")\n    from dust3r.utils.image import load_images\n    from dust3r.inference import inference\n    \n    all_features = []\n    \n    # 各画像を自分自身とペアにして処理\n    for i in tqdm(range(len(image_paths)), desc=\"Features\"):\n        img_path = image_paths[i]\n        \n        # 同じ画像を2回ロード（ペアとして）\n        images = load_images([img_path, img_path], size=Config.IMAGE_SIZE)\n        \n        # ペア形式で推論\n        pairs = [(images[0], images[1])]\n        \n        with torch.no_grad():\n            output = inference(pairs, model, device, batch_size=1)\n        \n        # outputの構造を確認してデータを抽出\n        try:\n            # outputが辞書の場合 (DUSt3R形式)\n            if isinstance(output, dict):\n                # pred1から3D点または特徴量を取得\n                if 'pred1' in output:\n                    pred1 = output['pred1']\n                    if isinstance(pred1, dict):\n                        # pts3dまたはdescを探す\n                        if 'pts3d' in pred1:\n                            desc = pred1['pts3d']\n                        elif 'desc' in pred1:\n                            desc = pred1['desc']\n                        else:\n                            # 利用可能な最初のテンソルを使用\n                            for key, val in pred1.items():\n                                if isinstance(val, torch.Tensor):\n                                    desc = val\n                                    break\n                    else:\n                        desc = pred1\n                elif 'view1' in output:\n                    desc = output['view1']\n                else:\n                    # 最初の値を使用\n                    desc = list(output.values())[0]\n            # outputがタプル (view1, view2) の形式\n            elif isinstance(output, tuple) and len(output) == 2:\n                view1, view2 = output\n                # view1から特徴量を取得\n                if isinstance(view1, dict):\n                    if 'pts3d' in view1:\n                        desc = view1['pts3d']\n                    elif 'desc' in view1:\n                        desc = view1['desc']\n                    else:\n                        desc = list(view1.values())[0]\n                else:\n                    desc = view1\n            # outputがリストの場合\n            elif isinstance(output, list):\n                if len(output) > 0:\n                    item = output[0]\n                    if isinstance(item, dict):\n                        if 'pts3d' in item:\n                            desc = item['pts3d']\n                        elif 'desc' in item:\n                            desc = item['desc']\n                        else:\n                            desc = list(item.values())[0]\n                    else:\n                        desc = item\n                else:\n                    raise ValueError(\"Empty output\")\n            else:\n                # その他の形式\n                desc = output\n            \n            # テンソルの次元を調整\n            if isinstance(desc, torch.Tensor):\n                if desc.dim() == 4:\n                    desc = desc.squeeze(0)  # [1, H, W, C] -> [H, W, C]\n                elif desc.dim() == 2:\n                    # [H*W, C] の場合、適切な形状に変換\n                    h = w = int(np.sqrt(desc.shape[0]))\n                    if h * w == desc.shape[0]:\n                        desc = desc.reshape(h, w, desc.shape[1])\n            \n            all_features.append(desc)\n            \n        except Exception as e:\n            print(f\"⚠️  Error extracting features for image {i}: {e}\")\n            print(f\"   Output type: {type(output)}\")\n            if isinstance(output, (list, tuple)):\n                print(f\"   Output length: {len(output)}\")\n                if len(output) > 0:\n                    print(f\"   First item type: {type(output[0])}\")\n                    if isinstance(output[0], dict):\n                        print(f\"   Keys: {output[0].keys()}\")\n            # デフォルトの特徴量を追加\n            all_features.append(torch.zeros((Config.IMAGE_SIZE, Config.IMAGE_SIZE, 24)))\n        \n        # メモリクリア\n        del output, images, pairs\n        if i % 10 == 0:\n            torch.cuda.empty_cache()\n    \n    print(f\"✓ Extracted features for {len(all_features)} images\")\n    if all_features:\n        first_feat = all_features[0]\n        if isinstance(first_feat, torch.Tensor):\n            print(f\"   Feature shape: {first_feat.shape}\")\n        elif isinstance(first_feat, dict):\n            print(f\"   Feature type: dict with keys: {first_feat.keys()}\")\n        elif isinstance(first_feat, np.ndarray):\n            print(f\"   Feature shape: {first_feat.shape}\")\n        else:\n            print(f\"   Feature type: {type(first_feat)}\")\n    \n    return all_features\n\n\ndef compute_asmk_similarity(features, codebook):\n    \"\"\"ASMKを使用して類似度行列を計算\"\"\"\n    print(\"\\n=== Computing ASMK Similarity ===\")\n    \n    n_images = len(features)\n    similarity_matrix = np.zeros((n_images, n_images), dtype=np.float32)\n    \n    # 各特徴量をグローバル記述子に変換\n    global_features = []\n    \n    for feat in features:\n        # featが辞書の場合、テンソルを抽出\n        if isinstance(feat, dict):\n            if 'pts3d' in feat:\n                feat = feat['pts3d']\n            elif 'desc' in feat:\n                feat = feat['desc']\n            elif 'pred1' in feat:\n                pred1 = feat['pred1']\n                if isinstance(pred1, dict) and 'pts3d' in pred1:\n                    feat = pred1['pts3d']\n                else:\n                    feat = pred1\n            else:\n                # 最初のテンソル値を使用\n                for val in feat.values():\n                    if isinstance(val, (torch.Tensor, np.ndarray)):\n                        feat = val\n                        break\n        \n        if isinstance(feat, torch.Tensor):\n            feat = feat.cpu().numpy()\n        \n        # featの形状を確認\n        if isinstance(feat, np.ndarray):\n            if feat.ndim == 3:  # [H, W, C]\n                h, w, c = feat.shape\n                feat_flat = feat.reshape(-1, c)\n            elif feat.ndim == 2:  # [N, C]\n                feat_flat = feat\n            else:\n                print(f\"⚠️  Unexpected feature shape: {feat.shape}\")\n                feat_flat = feat.reshape(-1, feat.shape[-1])\n            \n            global_desc = np.mean(feat_flat, axis=0)\n            global_features.append(global_desc)\n        else:\n            print(f\"⚠️  Unexpected feature type: {type(feat)}\")\n            # ダミー特徴量\n            global_features.append(np.zeros(24))\n    \n    global_features = np.stack(global_features)\n    \n    if codebook is not None and len(codebook) > 0:\n        try:\n            print(f\"Using ASMK with codebook size: {len(codebook)}\")\n            \n            for i in range(n_images):\n                feat_i = features[i]\n                \n                # 辞書からテンソルを抽出\n                if isinstance(feat_i, dict):\n                    if 'pts3d' in feat_i:\n                        feat_i = feat_i['pts3d']\n                    elif 'pred1' in feat_i and isinstance(feat_i['pred1'], dict):\n                        feat_i = feat_i['pred1'].get('pts3d', feat_i['pred1'])\n                \n                if isinstance(feat_i, torch.Tensor):\n                    feat_i = feat_i.cpu().numpy()\n                \n                if feat_i.ndim == 3:\n                    feat_i = feat_i.reshape(-1, feat_i.shape[-1])\n                \n                for j in range(i+1, n_images):\n                    feat_j = features[j]\n                    \n                    # 辞書からテンソルを抽出\n                    if isinstance(feat_j, dict):\n                        if 'pts3d' in feat_j:\n                            feat_j = feat_j['pts3d']\n                        elif 'pred1' in feat_j and isinstance(feat_j['pred1'], dict):\n                            feat_j = feat_j['pred1'].get('pts3d', feat_j['pred1'])\n                    \n                    if isinstance(feat_j, torch.Tensor):\n                        feat_j = feat_j.cpu().numpy()\n                    \n                    if feat_j.ndim == 3:\n                        feat_j = feat_j.reshape(-1, feat_j.shape[-1])\n                    \n                    dist_i = np.linalg.norm(feat_i[:, None, :] - codebook[None, :, :], axis=2)\n                    dist_j = np.linalg.norm(feat_j[:, None, :] - codebook[None, :, :], axis=2)\n                    \n                    assign_i = np.argmin(dist_i, axis=1)\n                    assign_j = np.argmin(dist_j, axis=1)\n                    \n                    common = len(set(assign_i) & set(assign_j))\n                    sim = common / max(len(set(assign_i)), len(set(assign_j)))\n                    \n                    similarity_matrix[i, j] = sim\n                    similarity_matrix[j, i] = sim\n                \n                if (i + 1) % 10 == 0:\n                    print(f\"Processed {i+1}/{n_images} images\")\n            \n            print(\"✓ ASMK similarity computation completed\")\n            \n        except Exception as e:\n            print(f\"⚠️  ASMK failed: {e}, using cosine similarity\")\n            global_features_norm = global_features / (np.linalg.norm(global_features, axis=1, keepdims=True) + 1e-8)\n            similarity_matrix = global_features_norm @ global_features_norm.T\n    \n    else:\n        print(\"No codebook provided, using cosine similarity\")\n        global_features_norm = global_features / (np.linalg.norm(global_features, axis=1, keepdims=True) + 1e-8)\n        similarity_matrix = global_features_norm @ global_features_norm.T\n    \n    np.fill_diagonal(similarity_matrix, -1)\n    \n    print(f\"Similarity matrix shape: {similarity_matrix.shape}\")\n    print(f\"Similarity range: [{similarity_matrix.min():.3f}, {similarity_matrix.max():.3f}]\")\n    \n    return similarity_matrix\n\n\ndef build_pairs_from_similarity(similarity_matrix, top_k=10):\n    \"\"\"類似度行列からペアを構築\"\"\"\n    n_images = similarity_matrix.shape[0]\n    pairs = []\n    \n    for i in range(n_images):\n        similarities = similarity_matrix[i]\n        top_indices = np.argsort(similarities)[::-1][:top_k]\n        \n        for j in top_indices:\n            if j > i:\n                pairs.append((i, j))\n    \n    pairs = list(set(pairs))\n    print(f\"✓ Built {len(pairs)} unique pairs\")\n    \n    return pairs\n\n\ndef get_image_pairs_asmk(image_paths, max_pairs=100):\n    \"\"\"ASMKを使用して画像ペアを取得\"\"\"\n    print(\"\\n=== Getting Image Pairs with ASMK ===\")\n    \n    device = Config.DEVICE\n    model, codebook = load_asmk_retrieval_model(device)\n    features = extract_mast3r_features(model, image_paths, device)\n    similarity_matrix = compute_asmk_similarity(features, codebook)\n    pairs = build_pairs_from_similarity(similarity_matrix, Config.RETRIEVAL_TOPK)\n    \n    # モデルを解放\n    del model\n    clear_memory()\n    \n    if len(pairs) > max_pairs:\n        pairs = pairs[:max_pairs]\n        print(f\"Limited to {max_pairs} pairs\")\n    \n    return pairs\n\n\n# ======================================================================\n# MAST3R RECONSTRUCTION\n# ======================================================================\n\ndef run_mast3r_pairs(model, image_paths, pairs, device, batch_size=1):\n    \"\"\"MASt3Rでペア画像を処理（メモリ最適化版）\"\"\"\n    print(\"\\n=== Running MASt3R Reconstruction ===\")\n    from dust3r.inference import inference\n    from dust3r.cloud_opt import global_aligner, GlobalAlignerMode\n    from dust3r.utils.image import load_images\n    \n    # ペアを制限（メモリ節約）\n    max_pairs_for_memory = 50\n    if len(pairs) > max_pairs_for_memory:\n        print(f\"⚠️  Limiting pairs from {len(pairs)} to {max_pairs_for_memory} for memory\")\n        pairs = pairs[:max_pairs_for_memory]\n    \n    # ペアから画像インデックスを取得\n    pair_indices = []\n    for i, j in pairs:\n        pair_indices.extend([i, j])\n    unique_indices = sorted(set(pair_indices))\n    \n    selected_paths = [image_paths[i] for i in unique_indices]\n    print(f\"Selected {len(selected_paths)} unique images from {len(pairs)} pairs\")\n    \n    # 画像をロード\n    images = load_images(selected_paths, size=Config.IMAGE_SIZE)\n    \n    clear_memory()\n    \n    # インデックスマッピング（元のインデックス → 新しいインデックス）\n    index_map = {old_idx: new_idx for new_idx, old_idx in enumerate(unique_indices)}\n    \n    # ペアを新しいインデックスに変換してペア画像リストを作成\n    image_pairs = []\n    for i, j in pairs:\n        new_i = index_map[i]\n        new_j = index_map[j]\n        image_pairs.append((images[new_i], images[new_j]))\n    \n    print(f\"Created {len(image_pairs)} image pairs\")\n    \n    clear_memory()\n    \n    # バッチサイズを動的に調整\n    available_memory = torch.cuda.get_device_properties(device).total_memory\n    used_memory = torch.cuda.memory_allocated(device)\n    free_memory = available_memory - used_memory\n    \n    if free_memory < 2e9:\n        batch_size = 1\n        print(f\"⚠️  Low memory, using batch_size=1\")\n    \n    # 推論を実行\n    print(f\"Running inference on {len(image_pairs)} pairs...\")\n    with torch.no_grad():\n        output = inference(image_pairs, model, device, batch_size=batch_size)\n    \n    print(f\"✓ Processed {len(output)} predictions\")\n    \n    clear_memory()\n    \n    # Global alignmentの準備\n    scene = global_aligner(\n        dust3r_output=output,\n        device=device,\n        mode=GlobalAlignerMode.PointCloudOptimizer,\n        verbose=True\n    )\n    \n    clear_memory()\n    \n    # Global alignment\n    print(\"Running global alignment...\")\n    try:\n        loss = scene.compute_global_alignment(\n            init=\"mst\", \n            niter=50,\n            schedule='cosine', \n            lr=0.01\n        )\n        print(f\"✓ Alignment complete (loss: {loss:.6f})\")\n    except RuntimeError as e:\n        if \"out of memory\" in str(e).lower():\n            print(\"⚠️  OOM during alignment, trying with fewer iterations...\")\n            clear_memory()\n            \n            loss = scene.compute_global_alignment(\n                init=\"mst\", \n                niter=20,\n                schedule='cosine', \n                lr=0.01\n            )\n            print(f\"✓ Alignment complete with reduced iterations (loss: {loss:.6f})\")\n        else:\n            raise\n    \n    clear_memory()\n    \n    return scene, images\n\n\n# ======================================================================\n# CAMERA PARAMETER EXTRACTION (PROCESS2)\n# ======================================================================\n\ndef extract_camera_params_process2(scene, image_paths, conf_threshold=1.5):\n    \"\"\"Process2: sceneから直接カメラパラメータと3D点を抽出\"\"\"\n    print(\"\\n=== Extracting Camera Parameters (Process2) ===\")\n    \n    cameras_dict = {}\n    all_pts3d = []\n    all_confidence = []\n    \n    # sceneから実際の画像��を取得\n    try:\n        if hasattr(scene, 'get_im_poses'):\n            poses = scene.get_im_poses()\n            n_images = len(poses)\n        elif hasattr(scene, 'im_poses'):\n            poses = scene.im_poses\n            n_images = len(poses)\n        else:\n            n_images = len(image_paths)\n            poses = None\n        \n        print(f\"Scene has {n_images} images, image_paths has {len(image_paths)} images\")\n        \n        # 実際のscene画像数とimage_pathsの小さい方を使用\n        n_images = min(n_images, len(image_paths))\n        \n        if hasattr(scene, 'get_focals'):\n            focals = scene.get_focals()\n        elif hasattr(scene, 'im_focals'):\n            focals = scene.im_focals\n        else:\n            focals = None\n        \n        if hasattr(scene, 'get_principal_points'):\n            pps = scene.get_principal_points()\n        elif hasattr(scene, 'im_pp'):\n            pps = scene.im_pp\n        else:\n            pps = None\n            \n    except Exception as e:\n        print(f\"⚠️  Error getting camera parameters: {e}\")\n        n_images = len(image_paths)\n        poses = None\n        focals = None\n        pps = None\n    \n    for idx in range(n_images):\n        #-------------------------------------------------------------------\n        print(f\"\\n=== Image {idx}: {os.path.basename(image_paths[idx])} ===\")\n        \n        # Poseを取得の後に追加\n        if poses is not None and idx < len(poses):\n            pose = poses[idx]\n            if isinstance(pose, torch.Tensor):\n                pose = pose.detach().cpu().numpy()\n            \n            print(f\"  Pose type: {type(pose)}\")\n            print(f\"  Pose shape: {pose.shape if hasattr(pose, 'shape') else 'N/A'}\")\n            if hasattr(pose, 'shape') and pose.shape == (4, 4):\n                print(f\"  Rotation det: {np.linalg.det(pose[:3, :3]):.3f}\")\n        #-------------------------------------------------------------------\n        \n        img_name = os.path.basename(image_paths[idx])\n        \n        try:\n            # Poseを取得\n            if poses is not None and idx < len(poses):\n                pose = poses[idx]\n                if isinstance(pose, torch.Tensor):\n                    pose = pose.detach().cpu().numpy()\n                \n                # poseが正しい形状でない場合、単位行列を使用\n                if not isinstance(pose, np.ndarray) or pose.shape != (4, 4):\n                    print(f\"  → Image {idx}: pose shape {pose.shape if hasattr(pose, 'shape') else 'N/A'}, using identity\")\n                    pose = np.eye(4)\n            else:\n                pose = np.eye(4)\n            \n            # Focalを取得\n            if focals is not None and idx < len(focals):\n                focal = focals[idx]\n                if isinstance(focal, torch.Tensor):\n                    focal = focal.detach().cpu().item()\n                else:\n                    focal = float(focal)\n            else:\n                focal = 1000.0\n            \n            # Principal pointを取得\n            if pps is not None and idx < len(pps):\n                pp = pps[idx]\n                if isinstance(pp, torch.Tensor):\n                    pp = pp.detach().cpu().numpy()\n            else:\n                pp = np.array([112.0, 112.0])\n            \n            # カメラパラメータを保存\n            cameras_dict[img_name] = {\n                'focal': focal,\n                'pp': pp,\n                'pose': pose\n            }\n            \n            # 3D点を取得\n            if hasattr(scene, 'im_pts3d') and idx < len(scene.im_pts3d):\n                pts3d_img = scene.im_pts3d[idx]\n            elif hasattr(scene, 'get_pts3d'):\n                pts3d_all = scene.get_pts3d()\n                if idx < len(pts3d_all):\n                    pts3d_img = pts3d_all[idx]\n                else:\n                    pts3d_img = None\n            else:\n                pts3d_img = None\n            \n            # Confidenceを取得\n            if hasattr(scene, 'im_conf') and idx < len(scene.im_conf):\n                conf_img = scene.im_conf[idx]\n            elif hasattr(scene, 'get_conf'):\n                conf_all = scene.get_conf()\n                if idx < len(conf_all):\n                    conf_img = conf_all[idx]\n                else:\n                    conf_img = None\n            else:\n                conf_img = None\n            \n            # 3D点とconfidenceを処理\n            if pts3d_img is not None:\n                if isinstance(pts3d_img, torch.Tensor):\n                    pts3d_img = pts3d_img.detach().cpu().numpy()\n                \n                if pts3d_img.ndim == 3:\n                    pts3d_flat = pts3d_img.reshape(-1, 3)\n                else:\n                    pts3d_flat = pts3d_img\n                \n                all_pts3d.append(pts3d_flat)\n                \n                # confidenceを処理\n                if conf_img is not None:\n                    if isinstance(conf_img, list):\n                        conf_img = np.array(conf_img)\n                    elif isinstance(conf_img, torch.Tensor):\n                        conf_img = conf_img.detach().cpu().numpy()\n                    \n                    if conf_img.ndim > 1:\n                        conf_flat = conf_img.reshape(-1)\n                    else:\n                        conf_flat = conf_img\n                    \n                    if len(conf_flat) != len(pts3d_flat):\n                        conf_flat = np.ones(len(pts3d_flat))\n                    \n                    all_confidence.append(conf_flat)\n                else:\n                    all_confidence.append(np.ones(len(pts3d_flat)))\n        \n        except Exception as e:\n            print(f\"⚠️  Error processing image {idx} ({img_name}): {e}\")\n            cameras_dict[img_name] = {\n                'focal': 1000.0,\n                'pp': np.array([112.0, 112.0]),\n                'pose': np.eye(4)\n            }\n            continue\n    \n    # 全3D点を結合\n    if all_pts3d:\n        pts3d = np.vstack(all_pts3d)\n        confidence = np.concatenate(all_confidence)\n    else:\n        pts3d = np.zeros((0, 3))\n        confidence = np.zeros(0)\n    \n    print(f\"✓ Extracted camera parameters for {len(cameras_dict)} images\")\n    print(f\"✓ Total 3D points: {len(pts3d)}\")\n    \n    # Confidenceでフィルタリング\n    if len(confidence) > 0:\n        valid_mask = confidence > conf_threshold\n        pts3d = pts3d[valid_mask]\n        confidence = confidence[valid_mask]\n        print(f\"✓ After confidence filtering (>{conf_threshold}): {len(pts3d)} points\")\n    \n    return cameras_dict, pts3d, confidence","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:24:09.344803Z","iopub.execute_input":"2026-01-22T06:24:09.345101Z","iopub.status.idle":"2026-01-22T06:24:09.404283Z","shell.execute_reply.started":"2026-01-22T06:24:09.345073Z","shell.execute_reply":"2026-01-22T06:24:09.403568Z"}},"outputs":[],"execution_count":42},{"cell_type":"code","source":"# ======================================================================\n# COLMAP EXPORT　v.10\n# ======================================================================\nimport os\nimport struct\nimport numpy as np\nfrom scipy.spatial.transform import Rotation as R\n\ndef write_colmap_sparse(cameras_dict, pts3d, confidence, image_paths, output_dir):\n    \"\"\"\n    COLMAP sparse形式をバイナリファイルで出力\n    Gaussian Splattingが要求する .bin 形式\n    \"\"\"\n    os.makedirs(output_dir, exist_ok=True)\n    \n    # カメラパラメータを取得\n    if not cameras_dict:\n        raise ValueError(\"cameras_dict is empty\")\n    \n    first_key = list(cameras_dict.keys())[0]\n    first_cam = cameras_dict[first_key]\n    \n    w = int(first_cam.get('width', 1920))\n    h = int(first_cam.get('height', 1080))\n    focal = float(first_cam.get('focal', max(w, h) * 1.2))\n    cx = w / 2.0\n    cy = h / 2.0\n    \n    # --- 1. cameras.bin を書き出し ---\n    cameras_file = os.path.join(output_dir, 'cameras.bin')\n    with open(cameras_file, 'wb') as f:\n        # ヘッダー: カメラ数\n        f.write(struct.pack('Q', 1))  # 1つのカメラ\n        \n        # カメラデータ\n        camera_id = 1\n        model_id = 1  # PINHOLE\n        f.write(struct.pack('i', camera_id))\n        f.write(struct.pack('i', model_id))\n        f.write(struct.pack('Q', w))\n        f.write(struct.pack('Q', h))\n        # PINHOLE: fx, fy, cx, cy\n        f.write(struct.pack('d', focal))\n        f.write(struct.pack('d', focal))\n        f.write(struct.pack('d', cx))\n        f.write(struct.pack('d', cy))\n    \n    print(f\"✓ Written cameras.bin\")\n    \n    # --- 2. images.bin を書き出し ---\n    images_file = os.path.join(output_dir, 'images.bin')\n    with open(images_file, 'wb') as f:\n        # ヘッダー: 画像数\n        f.write(struct.pack('Q', len(image_paths)))\n        \n        for i, img_path in enumerate(image_paths):\n            img_name = os.path.basename(img_path)\n            \n            cam_info = cameras_dict.get(i) or cameras_dict.get(str(i)) or cameras_dict.get(img_name)\n            if cam_info is None:\n                print(f\"  ⚠️  No camera info for {img_name}, using identity pose\")\n                pose = np.eye(4)\n            else:\n                pose = cam_info['pose']\n            \n            # Pose (C2W -> W2C)\n            try:\n                w2c = np.linalg.inv(pose)\n            except np.linalg.LinAlgError:\n                print(f\"  ⚠️  Singular matrix for {img_name}, using identity\")\n                w2c = np.eye(4)\n            \n            rot_mat = w2c[:3, :3]\n            tvec = w2c[:3, 3]\n            \n            # Rotation -> Quaternion [w, x, y, z]\n            quat = R.from_matrix(rot_mat).as_quat()  # [x, y, z, w]\n            qw, qx, qy, qz = quat[3], quat[0], quat[1], quat[2]\n            \n            # 画像データ\n            image_id = i + 1\n            f.write(struct.pack('i', image_id))\n            f.write(struct.pack('d', qw))\n            f.write(struct.pack('d', qx))\n            f.write(struct.pack('d', qy))\n            f.write(struct.pack('d', qz))\n            f.write(struct.pack('d', tvec[0]))\n            f.write(struct.pack('d', tvec[1]))\n            f.write(struct.pack('d', tvec[2]))\n            f.write(struct.pack('i', 1))  # camera_id\n            \n            # 画像名 (null終端文字列)\n            img_name_bytes = img_name.encode('utf-8') + b'\\x00'\n            f.write(img_name_bytes)\n            \n            # 2D点数 (0個)\n            f.write(struct.pack('Q', 0))\n    \n    print(f\"✓ Written images.bin ({len(image_paths)} images)\")\n    \n    # --- 3. points3D.bin を書き出し ---\n    points_file = os.path.join(output_dir, 'points3D.bin')\n    with open(points_file, 'wb') as f:\n        # ヘッダー: 点数\n        f.write(struct.pack('Q', len(pts3d)))\n        \n        for point_id, point in enumerate(pts3d, start=1):\n            f.write(struct.pack('Q', point_id))\n            f.write(struct.pack('d', point[0]))  # X\n            f.write(struct.pack('d', point[1]))  # Y\n            f.write(struct.pack('d', point[2]))  # Z\n            f.write(struct.pack('B', 255))  # R\n            f.write(struct.pack('B', 255))  # G\n            f.write(struct.pack('B', 255))  # B\n            f.write(struct.pack('d', 0.0))  # error\n            \n            # Track (空)\n            f.write(struct.pack('Q', 0))  # track length\n    \n    print(f\"✓ Written points3D.bin ({len(pts3d)} points)\")\n    \n    # --- 4. テキスト形式も書き出し (デバッグ用) ---\n    write_text_versions(cameras_dict, pts3d, image_paths, output_dir, w, h, focal, cx, cy)\n    \n    print(f\"\\n✓ COLMAP sparse reconstruction saved (binary + text)\")\n    print(f\"  - Camera: PINHOLE\")\n    print(f\"  - Images: {len(image_paths)}\")\n    print(f\"  - Points: {len(pts3d)}\")\n    print(f\"  - Output: {output_dir}\")\n    \n    return output_dir\n\n\ndef write_text_versions(cameras_dict, pts3d, image_paths, output_dir, w, h, focal, cx, cy):\n    \"\"\"テキスト形式も出力（デバッグ用）\"\"\"\n    \n    # cameras.txt\n    with open(os.path.join(output_dir, 'cameras.txt'), 'w') as file:\n        file.write(\"# Camera list with one line of data per camera:\\n\")\n        file.write(\"#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\\n\")\n        file.write(f\"1 PINHOLE {w} {h} {focal} {focal} {cx} {cy}\\n\")\n    \n    # images.txt\n    with open(os.path.join(output_dir, 'images.txt'), 'w') as file:\n        file.write(\"# Image list with two lines of data per image:\\n\")\n        file.write(\"#   IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\\n\")\n        file.write(\"#   POINTS2D[] as (X, Y, POINT3D_ID)\\n\")\n        \n        for i, img_path in enumerate(image_paths):\n            img_name = os.path.basename(img_path)\n            cam_info = cameras_dict.get(i) or cameras_dict.get(str(i)) or cameras_dict.get(img_name)\n            \n            if cam_info is None:\n                pose = np.eye(4)\n            else:\n                pose = cam_info['pose']\n            \n            try:\n                w2c = np.linalg.inv(pose)\n            except np.linalg.LinAlgError:\n                w2c = np.eye(4)\n            \n            rot_mat = w2c[:3, :3]\n            tvec = w2c[:3, 3]\n            quat = R.from_matrix(rot_mat).as_quat()\n            qw, qx, qy, qz = quat[3], quat[0], quat[1], quat[2]\n            \n            image_id = i + 1\n            file.write(f\"{image_id} {qw} {qx} {qy} {qz} {tvec[0]} {tvec[1]} {tvec[2]} 1 {img_name}\\n\")\n            file.write(\"\\n\")\n    \n    # points3D.txt\n    with open(os.path.join(output_dir, 'points3D.txt'), 'w') as file:\n        file.write(\"# 3D point list with one line of data per point:\\n\")\n        file.write(\"#   POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[]\\n\")\n        \n        for point_id, point in enumerate(pts3d, start=1):\n            file.write(f\"{point_id} {point[0]} {point[1]} {point[2]} 255 255 255 0.0\\n\")\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:24:09.405330Z","iopub.execute_input":"2026-01-22T06:24:09.405565Z","iopub.status.idle":"2026-01-22T06:24:09.425890Z","shell.execute_reply.started":"2026-01-22T06:24:09.405543Z","shell.execute_reply":"2026-01-22T06:24:09.425161Z"}},"outputs":[],"execution_count":43},{"cell_type":"code","source":"import os\nimport subprocess\n\ndef run_gaussian_splatting(source_dir, output_dir, iterations=30000):\n    \"\"\"\n    Gaussian Splattingを実行\n    \n    Args:\n        source_dir: COLMAPデータのあるディレクトリ (sparse/0 と images を含む)\n        output_dir: 出力ディレクトリ\n        iterations: 学習イテレーション数\n    \"\"\"\n    print(\"\\n=== Running Gaussian Splatting ===\")\n    \n    # 出力ディレクトリを作成\n    os.makedirs(output_dir, exist_ok=True)\n    \n    cmd = [\n        \"python\", \"/kaggle/working/gaussian-splatting/train.py\",\n        \"-s\", source_dir,           # ソースディレクトリ (sparse/0とimagesを含む)\n        \"-m\", output_dir,           # モデル出力ディレクトリ\n        \"--iterations\", str(iterations),\n        \"--eval\"\n    ]\n    \n    print(f\"Command: {' '.join(cmd)}\")\n    print(f\"  Source: {source_dir}\")\n    print(f\"  Output: {output_dir}\")\n    \n    # 実行\n    result = subprocess.run(cmd, capture_output=False, text=True)\n    \n    if result.returncode == 0:\n        print(f\"\\n✓ Gaussian Splatting complete\")\n        \n        # 生成されたファイルを確認\n        point_cloud_dir = os.path.join(output_dir, \"point_cloud\")\n        if os.path.exists(point_cloud_dir):\n            print(f\"\\n✓ Point cloud directory found: {point_cloud_dir}\")\n            \n            # iteration_*フォルダを検索\n            for item in sorted(os.listdir(point_cloud_dir)):\n                item_path = os.path.join(point_cloud_dir, item)\n                if os.path.isdir(item_path) and item.startswith(\"iteration_\"):\n                    ply_file = os.path.join(item_path, \"point_cloud.ply\")\n                    if os.path.exists(ply_file):\n                        file_size = os.path.getsize(ply_file) / (1024 * 1024)  # MB\n                        print(f\"  ✓ {item}/point_cloud.ply ({file_size:.2f} MB)\")\n        else:\n            print(f\"\\n⚠️  Warning: point_cloud directory not found\")\n            print(f\"   Expected: {point_cloud_dir}\")\n            print(f\"   Contents of {output_dir}:\")\n            if os.path.exists(output_dir):\n                for item in os.listdir(output_dir):\n                    print(f\"     - {item}\")\n    else:\n        print(f\"\\n✗ Gaussian Splatting failed with return code {result.returncode}\")\n    \n    return output_dir\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:24:09.426720Z","iopub.execute_input":"2026-01-22T06:24:09.426959Z","iopub.status.idle":"2026-01-22T06:24:09.439526Z","shell.execute_reply.started":"2026-01-22T06:24:09.426935Z","shell.execute_reply":"2026-01-22T06:24:09.438794Z"}},"outputs":[],"execution_count":44},{"cell_type":"code","source":"def main_pipeline(image_dir, output_dir, square_size=1024, iterations=30000, \n                  max_images=200, max_pairs=100, max_points=500000, \n                  conf_threshold=1.5, preprocess_mode='none'):\n    \"\"\"\n    メインパイプライン（完全版）\n    \n    Args:\n        image_dir: 入力画像ディレクトリ\n        output_dir: 出力ディレクトリ\n        square_size: Biplet前処理時の正方形サイズ\n        iterations: Gaussian Splattingの反復回数\n        max_images: 最大画像数\n        max_pairs: 最大ペア数\n        max_points: 最大点群数\n        conf_threshold: Confidence閾値\n        preprocess_mode: 前処理モード\n            - 'none': 前処理なし（元画像をそのまま使用）\n            - 'biplet': 2つの正方形クロップを生成（元画像も保存）\n    \"\"\"\n    \n    # ======================================================================\n    # STEP 0: Image Preprocessing (Optional)\n    # ======================================================================\n    if preprocess_mode == 'biplet':\n        print(\"=\"*70)\n        print(\"STEP 0: Image Preprocessing (Biplet Crops)\")\n        print(\"=\"*70)\n        \n        # Bipletクロップを一時ディレクトリに生成\n        temp_biplet_dir = os.path.join(output_dir, \"temp_biplet\")\n        biplet_dir = normalize_image_sizes_biplet(\n            image_dir,\n            temp_biplet_dir,\n            size=square_size\n        )\n        \n        # Bipletクロップのみをimagesフォルダに移動\n        images_dir = os.path.join(output_dir, \"images\")\n        os.makedirs(images_dir, exist_ok=True)\n        \n        # _left, _right, _top, _bottom のファイルのみコピー\n        biplet_suffixes = ['_left', '_right', '_top', '_bottom']\n        copied_count = 0\n        \n        for img_file in os.listdir(temp_biplet_dir):\n            if any(suffix in img_file for suffix in biplet_suffixes):\n                src = os.path.join(temp_biplet_dir, img_file)\n                dst = os.path.join(images_dir, img_file)\n                shutil.copy2(src, dst)\n                copied_count += 1\n        \n        print(f\"✓ Copied {copied_count} biplet images to {images_dir}\")\n        \n        # 元画像を別フォルダに保存（入力ディレクトリから直接コピー）\n        original_images_dir = os.path.join(output_dir, \"original_images\")\n        os.makedirs(original_images_dir, exist_ok=True)\n        \n        original_count = 0\n        valid_extensions = ('.jpg', '.jpeg', '.png', '.bmp')\n        for img_file in os.listdir(image_dir):\n            if img_file.lower().endswith(valid_extensions):\n                src = os.path.join(image_dir, img_file)\n                dst = os.path.join(original_images_dir, img_file)\n                shutil.copy2(src, dst)\n                original_count += 1\n        \n        print(f\"✓ Saved {original_count} original images to {original_images_dir}\")\n        \n        # 一時ディレクトリを削除\n        shutil.rmtree(temp_biplet_dir)\n        \n        image_dir = images_dir\n        clear_memory()\n    else:\n        # preprocess_mode == 'none'の場合、画像をimagesフォルダにコピー\n        images_dir = os.path.join(output_dir, \"images\")\n        if not os.path.exists(images_dir):\n            print(\"=\"*70)\n            print(\"STEP 0: Copying images to output directory\")\n            print(\"=\"*70)\n            shutil.copytree(image_dir, images_dir)\n            print(f\"✓ Copied images to {images_dir}\")\n        image_dir = images_dir\n    \n    # ======================================================================\n    # STEP 1: Loading and Preparing Images\n    # ======================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 1: Loading and Preparing Images\")\n    print(\"=\"*70)\n    \n    image_paths = load_images_from_directory(image_dir, max_images=max_images)\n    print(f\"Loaded {len(image_paths)} images\")\n    clear_memory()\n    \n    # ======================================================================\n    # STEP 2: Image Pair Selection\n    # ======================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 2: Image Pair Selection\")\n    print(\"=\"*70)\n    \n    max_pairs = min(max_pairs, 50)\n    pairs = get_image_pairs_asmk(image_paths, max_pairs=max_pairs)\n    print(f\"Selected {len(pairs)} image pairs\")\n    clear_memory()\n    \n    # ======================================================================\n    # STEP 3: MASt3R 3D Reconstruction\n    # ======================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 3: MASt3R 3D Reconstruction\")\n    print(\"=\"*70)\n    \n    device = Config.DEVICE\n    model = load_mast3r_model(device)\n    \n    scene, mast3r_images = run_mast3r_pairs(model, image_paths, pairs, device)\n    \n    # モデルを解放\n    del model\n    clear_memory()\n    \n    # ======================================================================\n    # STEP 4: Converting to COLMAP (PINHOLE)\n    # ======================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 4: Converting to COLMAP (PINHOLE)\")\n    print(\"=\"*70)\n    \n    cameras_dict, pts3d, confidence = extract_camera_params_process2(\n        scene, image_paths, conf_threshold=conf_threshold\n    )\n    \n    # sceneを解放\n    del scene\n    clear_memory()\n    \n    # 点数を制限\n    if len(pts3d) > max_points:\n        print(f\"⚠️  Limiting points from {len(pts3d)} to {max_points}\")\n        indices = np.random.choice(len(pts3d), max_points, replace=False)\n        pts3d = pts3d[indices]\n        confidence = confidence[indices]\n    \n    print(f\"Final point count: {len(pts3d)}\")\n    \n    # COLMAP変換（バイナリ形式）\n    colmap_dir = os.path.join(output_dir, \"sparse/0\")\n    os.makedirs(colmap_dir, exist_ok=True)\n    \n    write_colmap_sparse(cameras_dict, pts3d, confidence, image_paths, colmap_dir)\n    \n    clear_memory()\n    \n    # ======================================================================\n    # STEP 5: Running Gaussian Splatting\n    # ======================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 5: Running Gaussian Splatting\")\n    print(\"=\"*70)\n    \n    # ソースディレクトリ（sparse/0とimagesを含む）\n    source_dir = output_dir\n    \n    # モデル出力ディレクトリ\n    model_output_dir = os.path.join(output_dir, \"gaussian_splatting\")\n    \n    gs_output = run_gaussian_splatting(\n        source_dir=source_dir,\n        output_dir=model_output_dir,\n        iterations=iterations\n    )\n    \n    # ======================================================================\n    # STEP 6: Verify Output\n    # ======================================================================\n    print(\"\\n\" + \"=\"*70)\n    print(\"PIPELINE COMPLETE\")\n    print(\"=\"*70)\n    \n    # PLYファイルの確認\n    ply_path = os.path.join(\n        model_output_dir, \n        \"point_cloud\", \n        f\"iteration_{iterations}\", \n        \"point_cloud.ply\"\n    )\n    \n    if os.path.exists(ply_path):\n        file_size = os.path.getsize(ply_path) / (1024 * 1024)\n        print(f\"✓ Point cloud generated: {ply_path}\")\n        print(f\"  Size: {file_size:.2f} MB\")\n    else:\n        print(f\"⚠️  Point cloud not found at: {ply_path}\")\n        print(f\"\\nChecking alternative locations...\")\n        \n        # 他のiterationフォルダを確認\n        point_cloud_dir = os.path.join(model_output_dir, \"point_cloud\")\n        if os.path.exists(point_cloud_dir):\n            for item in sorted(os.listdir(point_cloud_dir)):\n                item_path = os.path.join(point_cloud_dir, item)\n                if os.path.isdir(item_path) and item.startswith(\"iteration_\"):\n                    ply_file = os.path.join(item_path, \"point_cloud.ply\")\n                    if os.path.exists(ply_file):\n                        file_size = os.path.getsize(ply_file) / (1024 * 1024)\n                        print(f\"  Found: {item}/point_cloud.ply ({file_size:.2f} MB)\")\n    \n    print(f\"\\nOutput directory structure:\")\n    print(f\"  {output_dir}/\")\n    print(f\"  ├── images/              (processed images for training)\")\n    if preprocess_mode == 'biplet':\n        print(f\"  ├── original_images/     (original source images)\")\n    print(f\"  ├── sparse/0/            (COLMAP data)\")\n    print(f\"  └── gaussian_splatting/  (GS output)\")\n    \n    return gs_output\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:24:09.440487Z","iopub.execute_input":"2026-01-22T06:24:09.440872Z","iopub.status.idle":"2026-01-22T06:24:09.459052Z","shell.execute_reply.started":"2026-01-22T06:24:09.440842Z","shell.execute_reply":"2026-01-22T06:24:09.458495Z"}},"outputs":[],"execution_count":45},{"cell_type":"markdown","source":"    # ======================================================================\n    # USAGE EXAMPLE\n    # ======================================================================\n    \n    if __name__ == \"__main__\":\n        IMAGE_DIR = \"/kaggle/input/your-dataset/images\"\n        OUTPUT_DIR = \"/kaggle/working/output\"\n        \n        # 使用例1: 前処理なし（元画像をそのまま使用）\n        gs_output = main_pipeline(\n            image_dir=IMAGE_DIR,\n            output_dir=OUTPUT_DIR,\n            iterations=30000,\n            max_images=100,\n            max_pairs=50,\n            max_points=300000,\n            conf_threshold=1.5,\n            preprocess_mode='none'  # 前処理なし\n        )\n        \n        # 使用例2: Bipletクロップ（画像数が2倍になる）\n        gs_output = main_pipeline(\n            image_dir=IMAGE_DIR,\n            output_dir=OUTPUT_DIR,\n            square_size=1024,      # クロップサイズ\n            iterations=30000,\n            max_images=50,         # Bipletで2倍になるので少なめに\n            max_pairs=50,\n            max_points=300000,\n            conf_threshold=1.5,\n            preprocess_mode='biplet'  # 2つのクロップ生成\n        )\n        \n        print(\"\\n\" + \"=\"*70)\n        print(\"PIPELINE COMPLETE\")\n        print(\"=\"*70)\n        print(f\"Output directory: {gs_output}\")","metadata":{}},{"cell_type":"code","source":"print(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:24:09.459798Z","iopub.execute_input":"2026-01-22T06:24:09.460091Z","iopub.status.idle":"2026-01-22T06:24:11.731872Z","shell.execute_reply.started":"2026-01-22T06:24:09.460070Z","shell.execute_reply":"2026-01-22T06:24:11.731121Z"}},"outputs":[{"name":"stdout","text":"✓ np: 1.26.4 - /usr/local/lib/python3.12/dist-packages/numpy/__init__.py\nVersion: 1.26.4\nVersion 3.1, 31 March 2009\n                       Version 3, 29 June 2007\n  5. Conveying Modified Source Versions.\n  14. Revised Versions of this License.\n","output_type":"stream"}],"execution_count":46},{"cell_type":"code","source":"# romaが必要な時点で確認・インストール\ntry:\n    import roma\n    print(\"✓ roma is installed\")\nexcept ModuleNotFoundError:\n    print(\"⚠️ roma not found, installing...\")\n    !pip install roma\n    import roma\n    print(\"✓ roma installed\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:24:11.733297Z","iopub.execute_input":"2026-01-22T06:24:11.733561Z","iopub.status.idle":"2026-01-22T06:24:11.738952Z","shell.execute_reply.started":"2026-01-22T06:24:11.733532Z","shell.execute_reply":"2026-01-22T06:24:11.738195Z"}},"outputs":[{"name":"stdout","text":"✓ roma is installed\n","output_type":"stream"}],"execution_count":47},{"cell_type":"code","source":"# ======================================================================\n# USAGE EXAMPLE\n# ======================================================================\n\nif __name__ == \"__main__\":\n    IMAGE_DIR = \"/kaggle/input/two-dogs/fountain80/fountain80\"\n    OUTPUT_DIR = \"/kaggle/working/output\"\n\n    gs_output = main_pipeline(\n        image_dir=IMAGE_DIR,\n        output_dir=OUTPUT_DIR,\n        square_size=1024,\n        iterations=1000,\n        max_images=10,  #\n        max_pairs=10,\n        max_points=1000,\n        conf_threshold=1.5,\n        preprocess_mode='biplet' \n    )\n    \n    print(\"\\n\" + \"=\"*70)\n    print(\"PIPELINE COMPLETE\")\n    print(\"=\"*70)\n    print(f\"Output directory: {gs_output}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T06:24:11.739811Z","iopub.execute_input":"2026-01-22T06:24:11.740074Z","execution_failed":"2026-01-22T06:31:24.562Z"}},"outputs":[{"name":"stdout","text":"======================================================================\nSTEP 0: Image Preprocessing (Biplet Crops)\n======================================================================\n\n=== Generating Biplet Crops (512x512) ===\n","output_type":"stream"},{"name":"stderr","text":"Creating biplets: 100%|██████████| 80/80 [00:06<00:00, 11.79it/s]\n","output_type":"stream"},{"name":"stdout","text":"\n✓ Biplet generation complete:\n  Source images: 80\n  Biplet crops generated: 160\n  Original size distribution: {'1440x1920': 80}\n✓ Copied 160 biplet images to /kaggle/working/output/images\n✓ Saved 80 original images to /kaggle/working/output/original_images\n\n======================================================================\nSTEP 1: Loading and Preparing Images\n======================================================================\n\nLoading images from: /kaggle/working/output/images\n⚠️  Limiting from 160 to 30 images\n✓ Found 30 images\nLoaded 30 images\n\n======================================================================\nSTEP 2: Image Pair Selection\n======================================================================\n\n=== Getting Image Pairs with ASMK ===\n\n=== Loading ASMK Retrieval Model ===\n⚠️  Failed to load MASt3R: tried to load naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric from huggingface, but failed\nTrying DUSt3R instead\n✓ Loaded DUSt3R model for retrieval\n✓ ASMK model loaded\n\n=== Extracting MASt3R Features ===\n","output_type":"stream"},{"name":"stderr","text":"Features:   0%|          | 0/30 [00:00<?, ?it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_001_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_001_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A/kaggle/working/mast3r/dust3r/dust3r/inference.py:44: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n  with torch.cuda.amp.autocast(enabled=bool(use_amp)):\n/kaggle/working/mast3r/dust3r/dust3r/model.py:206: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n  with torch.cuda.amp.autocast(enabled=False):\n/kaggle/working/mast3r/dust3r/dust3r/inference.py:48: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n  with torch.cuda.amp.autocast(enabled=False):\n\n100%|██████████| 1/1 [00:00<00:00,  3.63it/s]\u001b[A\nFeatures:   3%|▎         | 1/30 [00:00<00:08,  3.27it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_001_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_001_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  4.97it/s]\u001b[A\nFeatures:   7%|▋         | 2/30 [00:00<00:07,  3.88it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_002_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_002_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.26it/s]\u001b[A\nFeatures:  10%|█         | 3/30 [00:00<00:06,  4.20it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_002_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_002_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.16it/s]\u001b[A\nFeatures:  13%|█▎        | 4/30 [00:00<00:05,  4.36it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_003_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_003_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.13it/s]\u001b[A\nFeatures:  17%|█▋        | 5/30 [00:01<00:05,  4.45it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_003_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_003_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.08it/s]\u001b[A\nFeatures:  20%|██        | 6/30 [00:01<00:05,  4.47it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_004_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_004_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.13it/s]\u001b[A\nFeatures:  23%|██▎       | 7/30 [00:01<00:05,  4.50it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_004_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_004_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.10it/s]\u001b[A\nFeatures:  27%|██▋       | 8/30 [00:01<00:04,  4.52it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_005_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_005_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.12it/s]\u001b[A\nFeatures:  30%|███       | 9/30 [00:02<00:04,  4.54it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_005_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_005_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.04it/s]\u001b[A\nFeatures:  33%|███▎      | 10/30 [00:02<00:04,  4.51it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_006_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_006_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.12it/s]\u001b[A\nFeatures:  37%|███▋      | 11/30 [00:02<00:04,  4.50it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_006_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_006_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.05it/s]\u001b[A\nFeatures:  40%|████      | 12/30 [00:02<00:04,  4.47it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_007_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_007_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.13it/s]\u001b[A\nFeatures:  43%|████▎     | 13/30 [00:02<00:03,  4.49it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_007_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_007_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.06it/s]\u001b[A\nFeatures:  47%|████▋     | 14/30 [00:03<00:03,  4.49it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_008_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_008_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.06it/s]\u001b[A\nFeatures:  50%|█████     | 15/30 [00:03<00:03,  4.48it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_008_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_008_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.17it/s]\u001b[A\nFeatures:  53%|█████▎    | 16/30 [00:03<00:03,  4.51it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_009_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_009_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.10it/s]\u001b[A\nFeatures:  57%|█████▋    | 17/30 [00:03<00:02,  4.53it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_009_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_009_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.17it/s]\u001b[A\nFeatures:  60%|██████    | 18/30 [00:04<00:02,  4.55it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_010_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_010_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.09it/s]\u001b[A\nFeatures:  63%|██████▎   | 19/30 [00:04<00:02,  4.56it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_010_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_010_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.09it/s]\u001b[A\nFeatures:  67%|██████▋   | 20/30 [00:04<00:02,  4.56it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_011_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_011_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.15it/s]\u001b[A\nFeatures:  70%|███████   | 21/30 [00:04<00:01,  4.54it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_011_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_011_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.07it/s]\u001b[A\nFeatures:  73%|███████▎  | 22/30 [00:04<00:01,  4.53it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_012_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_012_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.04it/s]\u001b[A\nFeatures:  77%|███████▋  | 23/30 [00:05<00:01,  4.49it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_012_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_012_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.18it/s]\u001b[A\nFeatures:  80%|████████  | 24/30 [00:05<00:01,  4.51it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_013_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_013_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.10it/s]\u001b[A\nFeatures:  83%|████████▎ | 25/30 [00:05<00:01,  4.52it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_013_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_013_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.18it/s]\u001b[A\nFeatures:  87%|████████▋ | 26/30 [00:05<00:00,  4.56it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_014_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_014_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.14it/s]\u001b[A\nFeatures:  90%|█████████ | 27/30 [00:06<00:00,  4.57it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_014_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_014_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.09it/s]\u001b[A\nFeatures:  93%|█████████▎| 28/30 [00:06<00:00,  4.58it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_015_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_015_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.09it/s]\u001b[A\nFeatures:  97%|█████████▋| 29/30 [00:06<00:00,  4.56it/s]","output_type":"stream"},{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/working/output/images/image_015_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_015_top.jpeg with resolution 512x512 --> 224x224\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n100%|██████████| 1/1 [00:00<00:00,  5.12it/s]\u001b[A\nFeatures: 100%|██████████| 30/30 [00:06<00:00,  4.48it/s]\n","output_type":"stream"},{"name":"stdout","text":"✓ Extracted features for 30 images\n   Feature shape: torch.Size([224, 224, 3])\n\n=== Computing ASMK Similarity ===\nUsing ASMK with codebook size: 1024\n⚠️  ASMK failed: operands could not be broadcast together with shapes (50176,1,3) (1,1024,24) , using cosine similarity\nSimilarity matrix shape: (30, 30)\nSimilarity range: [-1.000, 1.000]\n✓ Built 148 unique pairs\nLimited to 5 pairs\nSelected 5 image pairs\n\n======================================================================\nSTEP 3: MASt3R 3D Reconstruction\n======================================================================\n\n=== Loading MASt3R Model ===\nAttempting to load: naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric\n⚠️  Failed to load MASt3R: tried to load naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric from huggingface, but failed\nTrying DUSt3R instead: naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt\n✓ Loaded DUSt3R model as fallback\n✓ Model loaded on cuda\n\n=== Running MASt3R Reconstruction ===\nSelected 9 unique images from 5 pairs\n>> Loading a list of 9 images\n - adding /kaggle/working/output/images/image_001_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_002_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_003_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_009_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_011_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_012_bottom.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_012_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_013_top.jpeg with resolution 512x512 --> 224x224\n - adding /kaggle/working/output/images/image_014_bottom.jpeg with resolution 512x512 --> 224x224\n (Found 9 images)\nCreated 5 image pairs\nRunning inference on 5 pairs...\n>> Inference with model on 5 image pairs\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 5/5 [00:01<00:00,  4.70it/s]\n","output_type":"stream"},{"name":"stdout","text":"✓ Processed 5 predictions\nRunning global alignment...\n init edge (5*,8*) score=20.55644416809082\n init edge (1*,5) score=13.340322494506836\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"print(f\"✓ np: {np.__version__} - {np.__file__}\")\n!pip show numpy | grep Version","metadata":{"trusted":true,"execution":{"execution_failed":"2026-01-22T06:31:24.562Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}