Upload biplet_colmap_2dgs_colab_02.ipynb

biplet_colmap_2dgs_colab_02.ipynb

@@ -0,0 +1,1431 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "fb1f1fdc",
+      "metadata": {
+        "papermill": {
+          "duration": 0.002985,
+          "end_time": "2026-01-10T18:17:32.170524",
+          "exception": false,
+          "start_time": "2026-01-10T18:17:32.167539",
+          "status": "completed"
+        },
+        "tags": [],
+        "id": "fb1f1fdc"
+      },
+      "source": [
+        "# **biplet-dino-colmap-2dgs**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#サイズの異なる画像を扱う\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JON4rYSEOzCg",
+        "outputId": "79cbc182-979e-4e50-811f-88a9b8d77160"
+      },
+      "id": "JON4rYSEOzCg",
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "22353010",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-01-10T18:17:32.181455Z",
+          "iopub.status.busy": "2026-01-10T18:17:32.180969Z",
+          "iopub.status.idle": "2026-01-10T18:17:32.355942Z",
+          "shell.execute_reply": "2026-01-10T18:17:32.355229Z"
+        },
+        "papermill": {
+          "duration": 0.179454,
+          "end_time": "2026-01-10T18:17:32.357275",
+          "exception": false,
+          "start_time": "2026-01-10T18:17:32.177821",
+          "status": "completed"
+        },
+        "tags": [],
+        "id": "22353010"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import sys\n",
+        "import subprocess\n",
+        "import shutil\n",
+        "from pathlib import Path\n",
+        "import cv2\n",
+        "from PIL import Image\n",
+        "import glob\n",
+        "\n",
+        "IMAGE_PATH=\"/content/drive/MyDrive/your_folder/fountain100\"\n",
+        "\n",
+        "#WORK_DIR = '/content/gaussian-splatting'\n",
+        "WORK_DIR = \"/content/2d-gaussian-splatting\"\n",
+        "\n",
+        "OUTPUT_DIR = '/content/output'\n",
+        "COLMAP_DIR = '/content/colmap_data'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-01-10T18:22:43.807508Z",
+          "iopub.status.busy": "2026-01-10T18:22:43.807294Z",
+          "iopub.status.idle": "2026-01-11T00:00:17.030890Z",
+          "shell.execute_reply": "2026-01-11T00:00:17.029927Z"
+        },
+        "papermill": {
+          "duration": 20253.434865,
+          "end_time": "2026-01-11T00:00:17.234174",
+          "exception": false,
+          "start_time": "2026-01-10T18:22:43.799309",
+          "status": "completed"
+        },
+        "tags": [],
+        "id": "QXI_UOXaNbgI"
+      },
+      "outputs": [],
+      "source": [
+        "\n"
+      ],
+      "id": "QXI_UOXaNbgI"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "be6df249",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-01-10T18:17:32.363444Z",
+          "iopub.status.busy": "2026-01-10T18:17:32.363175Z",
+          "iopub.status.idle": "2026-01-10T18:22:43.720241Z",
+          "shell.execute_reply": "2026-01-10T18:22:43.719380Z"
+        },
+        "papermill": {
+          "duration": 311.361656,
+          "end_time": "2026-01-10T18:22:43.721610",
+          "exception": false,
+          "start_time": "2026-01-10T18:17:32.359954",
+          "status": "completed"
+        },
+        "tags": [],
+        "id": "be6df249",
+        "outputId": "c6ebe7e1-8e00-46d9-e3c1-09bd1e1f5e60",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "🚀 Setting up COLAB environment (v8 - Python 3.12 compatible)\n",
+            "\n",
+            "======================================================================\n",
+            "STEP 0: Fix NumPy (Python 3.12 compatible)\n",
+            "======================================================================\n",
+            "Found existing installation: torch 2.9.0+cu128\n",
+            "Uninstalling torch-2.9.0+cu128:\n",
+            "  Successfully uninstalled torch-2.9.0+cu128\n",
+            "Found existing installation: torchvision 0.24.0+cu128\n",
+            "Uninstalling torchvision-0.24.0+cu128:\n",
+            "  Successfully uninstalled torchvision-0.24.0+cu128\n",
+            "Found existing installation: torchaudio 2.9.0+cu128\n",
+            "Uninstalling torchaudio-2.9.0+cu128:\n",
+            "  Successfully uninstalled torchaudio-2.9.0+cu128\n",
+            "Looking in indexes: https://download.pytorch.org/whl/cu118\n",
+            "\u001b[31mERROR: Could not find a version that satisfies the requirement torch==2.1.2+cu118 (from versions: 2.2.0+cu118, 2.2.1+cu118, 2.2.2+cu118, 2.3.0+cu118, 2.3.1+cu118, 2.4.0+cu118, 2.4.1+cu118, 2.5.0+cu118, 2.5.1+cu118, 2.6.0+cu118, 2.7.0+cu118, 2.7.1+cu118)\u001b[0m\u001b[31m\n",
+            "\u001b[0m\u001b[31mERROR: No matching distribution found for torch==2.1.2+cu118\u001b[0m\u001b[31m\n",
+            "\u001b[0m\n",
+            "======================================================================\n",
+            "STEP 1: System packages\n",
+            "======================================================================\n",
+            "Running: apt-get update -qq\n",
+            "Running: apt-get install -y -qq colmap build-essential cmake git libopenblas-dev xvfb\n",
+            "\n",
+            "======================================================================\n",
+            "STEP 2: Clone Gaussian Splatting\n",
+            "======================================================================\n",
+            "Running: git clone --recursive https://github.com/hbb1/2d-gaussian-splatting.git 2d-gaussian-splatting\n",
+            "\n",
+            "======================================================================\n",
+            "STEP 3: Python packages (VERBOSE MODE)\n",
+            "======================================================================\n",
+            "\n",
+            "📦 Installing PyTorch...\n",
+            "Running: /usr/bin/python3 -m pip install torch torchvision torchaudio\n",
+            "\n",
+            "📦 Installing core utilities...\n",
+            "Running: /usr/bin/python3 -m pip install opencv-python pillow imageio imageio-ffmpeg plyfile tqdm tensorboard\n",
+            "\n",
+            "📦 Installing transformers (NumPy 1.26 compatible)...\n",
+            "Running: /usr/bin/python3 -m pip install transformers==4.45.0\n",
+            "\n",
+            "📦 Installing LightGlue stack...\n",
+            "Running: /usr/bin/python3 -m pip install kornia\n",
+            "Running: /usr/bin/python3 -m pip install h5py\n",
+            "Running: /usr/bin/python3 -m pip install matplotlib\n",
+            "Running: /usr/bin/python3 -m pip install pycolmap\n",
+            "\n",
+            "======================================================================\n",
+            "STEP 5: Detailed Verification\n",
+            "======================================================================\n",
+            "\n",
+            "🔍 Testing PyTorch...\n",
+            "  ✓ PyTorch: 2.10.0+cu128\n",
+            "  ✓ CUDA available: True\n",
+            "  ✓ CUDA version: 12.8\n",
+            "\n",
+            "🔍 Testing transformers...\n",
+            "  ✓ transformers version: 4.45.0\n",
+            "  ✓ AutoModel import: OK\n",
+            "\n",
+            "🔍 Testing pycolmap...\n",
+            "  ✓ pycolmap: OK\n",
+            "\n",
+            "🔍 Testing kornia...\n",
+            "  ✓ kornia: 0.8.2\n",
+            "\n",
+            "======================================================================\n",
+            "✅ SETUP COMPLETE\n",
+            "======================================================================\n",
+            "Working dir: 2d-gaussian-splatting\n"
+          ]
+        }
+      ],
+      "source": [
+        "def run_cmd(cmd, check=True, capture=False):\n",
+        "    \"\"\"Run command with better error handling\"\"\"\n",
+        "    print(f\"Running: {' '.join(cmd)}\")\n",
+        "    result = subprocess.run(\n",
+        "        cmd,\n",
+        "        capture_output=capture,\n",
+        "        text=True,\n",
+        "        check=False\n",
+        "    )\n",
+        "    if check and result.returncode != 0:\n",
+        "        print(f\"❌ Command failed with code {result.returncode}\")\n",
+        "        if capture:\n",
+        "            print(f\"STDOUT: {result.stdout}\")\n",
+        "            print(f\"STDERR: {result.stderr}\")\n",
+        "    return result\n",
+        "\n",
+        "\n",
+        "def setup_environment():\n",
+        "    \"\"\"\n",
+        "    Colab environment setup for Gaussian Splatting + LightGlue + pycolmap\n",
+        "    Python 3.12 compatible version (v8)\n",
+        "    \"\"\"\n",
+        "\n",
+        "    print(\"🚀 Setting up COLAB environment (v8 - Python 3.12 compatible)\")\n",
+        "\n",
+        "    WORK_DIR = \"gaussian-splatting\"\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 0: NumPy FIX (Python 3.12 compatible)\n",
+        "    # =====================================================================\n",
+        "    print(\"\\n\" + \"=\"*70)\n",
+        "    print(\"STEP 0: Fix NumPy (Python 3.12 compatible)\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    # Python 3.12 requires numpy >= 1.26\n",
+        "    run_cmd([sys.executable, \"-m\", \"pip\", \"uninstall\", \"-y\", \"numpy\"])\n",
+        "    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"numpy==1.26.4\"])\n",
+        "\n",
+        "    # sanity check\n",
+        "    run_cmd([sys.executable, \"-c\", \"import numpy; print('NumPy:', numpy.__version__)\"])\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 1: System packages (Colab)\n",
+        "    # =====================================================================\n",
+        "    print(\"\\n\" + \"=\"*70)\n",
+        "    print(\"STEP 1: System packages\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    run_cmd([\"apt-get\", \"update\", \"-qq\"])\n",
+        "    run_cmd([\n",
+        "        \"apt-get\", \"install\", \"-y\", \"-qq\",\n",
+        "        \"colmap\",\n",
+        "        \"build-essential\",\n",
+        "        \"cmake\",\n",
+        "        \"git\",\n",
+        "        \"libopenblas-dev\",\n",
+        "        \"xvfb\"\n",
+        "    ])\n",
+        "\n",
+        "    # virtual display (COLMAP / OpenCV safety)\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,\n",
+        "        stderr=subprocess.DEVNULL\n",
+        "    )\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 2: Clone 2D Gaussian Splatting\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",
+        "        run_cmd([\n",
+        "            \"git\", \"clone\", \"--recursive\",\n",
+        "            \"https://github.com/hbb1/2d-gaussian-splatting.git\",\n",
+        "            WORK_DIR\n",
+        "        ])\n",
+        "    else:\n",
+        "        print(\"✓ Repository already exists\")\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 3: Python packages (FIXED ORDER & VERSIONS)\n",
+        "    # =====================================================================\n",
+        "    print(\"\\n\" + \"=\"*70)\n",
+        "    print(\"STEP 3: Python packages (VERBOSE MODE)\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    # ---- PyTorch (Colab CUDA対応) ----\n",
+        "    print(\"\\n📦 Installing PyTorch...\")\n",
+        "    run_cmd([\n",
+        "        sys.executable, \"-m\", \"pip\", \"install\",\n",
+        "        \"torch\", \"torchvision\", \"torchaudio\"\n",
+        "    ])\n",
+        "\n",
+        "    # ---- Core utils ----\n",
+        "    print(\"\\n📦 Installing core utilities...\")\n",
+        "    run_cmd([\n",
+        "        sys.executable, \"-m\", \"pip\", \"install\",\n",
+        "        \"opencv-python\",\n",
+        "        \"pillow\",\n",
+        "        \"imageio\",\n",
+        "        \"imageio-ffmpeg\",\n",
+        "        \"plyfile\",\n",
+        "        \"tqdm\",\n",
+        "        \"tensorboard\"\n",
+        "    ])\n",
+        "\n",
+        "    # ---- transformers (NumPy 1.26 compatible) ----\n",
+        "    print(\"\\n📦 Installing transformers (NumPy 1.26 compatible)...\")\n",
+        "    # Install transformers with proper dependencies\n",
+        "    run_cmd([\n",
+        "        sys.executable, \"-m\", \"pip\", \"install\",\n",
+        "        \"transformers==4.40.0\"\n",
+        "    ])\n",
+        "\n",
+        "    # ---- LightGlue stack (GITHUB INSTALL) ----\n",
+        "    print(\"\\n📦 Installing LightGlue stack...\")\n",
+        "\n",
+        "    # Install kornia first\n",
+        "    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"kornia\"])\n",
+        "\n",
+        "    # Install h5py (sometimes needed)\n",
+        "    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"h5py\"])\n",
+        "\n",
+        "    # Install matplotlib (LightGlue dependency)\n",
+        "    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"matplotlib\"])\n",
+        "\n",
+        "    # Install pycolmap\n",
+        "    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"pycolmap\"])\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 4: Build 2D GS submodules\n",
+        "    # =====================================================================\n",
+        "    print(\"\\n\" + \"=\"*70)\n",
+        "    print(\"STEP 4: Build Gaussian Splatting submodules\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    submodules = {\n",
+        "        \"diff-surfel-rasterization\":\n",
+        "            \"https://github.com/hbb1/diff-surfel-rasterization.git\",\n",
+        "        \"simple-knn\":\n",
+        "            \"https://github.com/camenduru/simple-knn.git\"\n",
+        "    }\n",
+        "\n",
+        "    for name, repo in submodules.items():\n",
+        "        print(f\"\\n📦 Installing {name}...\")\n",
+        "        path = os.path.join(WORK_DIR, \"submodules\", name)\n",
+        "        if not os.path.exists(path):\n",
+        "            run_cmd([\"git\", \"clone\", repo, path])\n",
+        "        run_cmd([sys.executable, \"-m\", \"pip\", \"install\", path])\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 5: Detailed Verification\n",
+        "    # =====================================================================\n",
+        "    print(\"\\n\" + \"=\"*70)\n",
+        "    print(\"STEP 5: Detailed Verification\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    # NumPy (verify version first)\n",
+        "    print(\"\\n🔍 Testing NumPy...\")\n",
+        "    try:\n",
+        "        import numpy as np\n",
+        "        print(f\"  ✓ NumPy: {np.__version__}\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"  ❌ NumPy failed: {e}\")\n",
+        "\n",
+        "    # PyTorch\n",
+        "    print(\"\\n🔍 Testing PyTorch...\")\n",
+        "    try:\n",
+        "        import torch\n",
+        "        print(f\"  ✓ PyTorch: {torch.__version__}\")\n",
+        "        print(f\"  ✓ CUDA available: {torch.cuda.is_available()}\")\n",
+        "        if torch.cuda.is_available():\n",
+        "            print(f\"  ✓ CUDA version: {torch.version.cuda}\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"  ❌ PyTorch failed: {e}\")\n",
+        "\n",
+        "    # transformers\n",
+        "    print(\"\\n🔍 Testing transformers...\")\n",
+        "    try:\n",
+        "        import transformers\n",
+        "        print(f\"  ✓ transformers version: {transformers.__version__}\")\n",
+        "        from transformers import AutoModel\n",
+        "        print(f\"  ✓ AutoModel import: OK\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"  ❌ transformers failed: {e}\")\n",
+        "        print(f\"  Attempting detailed diagnosis...\")\n",
+        "        result = run_cmd([\n",
+        "            sys.executable, \"-c\",\n",
+        "            \"import transformers; print(transformers.__version__)\"\n",
+        "        ], capture=True)\n",
+        "        print(f\"  Output: {result.stdout}\")\n",
+        "        print(f\"  Error: {result.stderr}\")\n",
+        "\n",
+        "    '''\n",
+        "    # LightGlue\n",
+        "    print(\"\\n🔍 Testing LightGlue...\")\n",
+        "    try:\n",
+        "        from lightglue import LightGlue, ALIKED\n",
+        "        print(f\"  ✓ LightGlue: OK\")\n",
+        "        print(f\"  ✓ ALIKED: OK\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"  ❌ LightGlue failed: {e}\")\n",
+        "        print(f\"  Attempting detailed diagnosis...\")\n",
+        "        result = run_cmd([\n",
+        "            sys.executable, \"-c\",\n",
+        "            \"from lightglue import LightGlue\"\n",
+        "        ], capture=True)\n",
+        "        print(f\"  Output: {result.stdout}\")\n",
+        "        print(f\"  Error: {result.stderr}\")\n",
+        "   '''\n",
+        "\n",
+        "    # pycolmap\n",
+        "    print(\"\\n🔍 Testing pycolmap...\")\n",
+        "    try:\n",
+        "        import pycolmap\n",
+        "        print(f\"  ✓ pycolmap: OK\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"  ❌ pycolmap failed: {e}\")\n",
+        "\n",
+        "    # kornia\n",
+        "    print(\"\\n🔍 Testing kornia...\")\n",
+        "    try:\n",
+        "        import kornia\n",
+        "        print(f\"  ✓ kornia: {kornia.__version__}\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"  ❌ kornia failed: {e}\")\n",
+        "\n",
+        "    print(\"\\n\" + \"=\"*70)\n",
+        "    print(\"✅ SETUP COMPLETE\")\n",
+        "    print(\"=\"*70)\n",
+        "    print(f\"Working dir: {WORK_DIR}\")\n",
+        "\n",
+        "    return WORK_DIR\n",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    setup_environment()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cgpyET_KmGhe",
+        "outputId": "eaef7613-1658-4621-f9af-c51d5aaa8f5f"
+      },
+      "id": "cgpyET_KmGhe",
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "🚀 Setting up COLAB environment (v8 - Python 3.12 compatible)\n",
+            "\n",
+            "======================================================================\n",
+            "STEP 4: Build Gaussian Splatting submodules\n",
+            "======================================================================\n",
+            "\n",
+            "📦 Installing diff-gaussian-rasterization...\n",
+            "Running: /usr/bin/python3 -m pip install 2d-gaussian-splatting/submodules/diff-gaussian-rasterization\n",
+            "❌ Command failed with code 1\n",
+            "\n",
+            "📦 Installing simple-knn...\n",
+            "Running: /usr/bin/python3 -m pip install 2d-gaussian-splatting/submodules/simple-knn\n",
+            "❌ Command failed with code 1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "break"
+      ],
+      "metadata": {
+        "id": "9rV1ML69tNnt"
+      },
+      "id": "9rV1ML69tNnt",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!nvcc --version\n",
+        "import torch\n",
+        "print(torch.__version__)\n",
+        "print(torch.version.cuda)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ev9PEUdtpEAx",
+        "outputId": "5bfecbbb-f9fc-4bad-af15-f9893c4da7dc"
+      },
+      "id": "Ev9PEUdtpEAx",
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "nvcc: NVIDIA (R) Cuda compiler driver\n",
+            "Copyright (c) 2005-2025 NVIDIA Corporation\n",
+            "Built on Fri_Feb_21_20:23:50_PST_2025\n",
+            "Cuda compilation tools, release 12.8, V12.8.93\n",
+            "Build cuda_12.8.r12.8/compiler.35583870_0\n",
+            "2.10.0+cu128\n",
+            "12.8\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "b8690389",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-01-10T18:22:43.739411Z",
+          "iopub.status.busy": "2026-01-10T18:22:43.738855Z",
+          "iopub.status.idle": "2026-01-10T18:22:43.755664Z",
+          "shell.execute_reply": "2026-01-10T18:22:43.754865Z"
+        },
+        "papermill": {
+          "duration": 0.027297,
+          "end_time": "2026-01-10T18:22:43.756758",
+          "exception": false,
+          "start_time": "2026-01-10T18:22:43.729461",
+          "status": "completed"
+        },
+        "tags": [],
+        "id": "b8690389"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import glob\n",
+        "import cv2\n",
+        "import numpy as np\n",
+        "from PIL import Image\n",
+        "\n",
+        "# =========================================================\n",
+        "# Utility: aspect ratio preserved + black padding\n",
+        "# =========================================================\n",
+        "\n",
+        "def normalize_image_sizes_biplet(input_dir, output_dir=None, size=1024, max_images=None):\n",
+        "    \"\"\"\n",
+        "    Generates two square crops (Left & Right or Top & Bottom)\n",
+        "    from each image in a directory and returns the output directory\n",
+        "    and the list of generated file paths.\n",
+        "\n",
+        "    Args:\n",
+        "        input_dir: Input directory containing source images\n",
+        "        output_dir: Output directory for processed images\n",
+        "        size: Target square size (default: 1024)\n",
+        "        max_images: Maximum number of SOURCE images to process (default: None = all images)\n",
+        "    \"\"\"\n",
+        "    if output_dir is None:\n",
+        "        output_dir = 'output/images_biplet'\n",
+        "    os.makedirs(output_dir, exist_ok=True)\n",
+        "\n",
+        "    print(f\"--- Step 1: Biplet-Square Normalization ---\")\n",
+        "    print(f\"Generating 2 cropped squares (Left/Right or Top/Bottom) for each image...\")\n",
+        "    print()\n",
+        "\n",
+        "    generated_paths = []\n",
+        "    converted_count = 0\n",
+        "    size_stats = {}\n",
+        "\n",
+        "    # Sort for consistent processing order\n",
+        "    image_files = sorted([f for f in os.listdir(input_dir)\n",
+        "                         if f.lower().endswith(('.jpg', '.jpeg', '.png'))])\n",
+        "\n",
+        "    # ★ max_images で元画像数を制限\n",
+        "    if max_images is not None:\n",
+        "        image_files = image_files[:max_images]\n",
+        "        print(f\"Processing limited to {max_images} source images (will generate {max_images * 2} cropped images)\")\n",
+        "\n",
+        "    for img_file in image_files:\n",
+        "        input_path = os.path.join(input_dir, img_file)\n",
+        "        try:\n",
+        "            img = Image.open(input_path)\n",
+        "            original_size = img.size\n",
+        "\n",
+        "            # Tracking original aspect ratios\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 using the helper function\n",
+        "            crops = generate_two_crops(img, size)\n",
+        "            base_name, ext = os.path.splitext(img_file)\n",
+        "\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",
+        "                generated_paths.append(output_path)\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\"Total output images: {len(generated_paths)}\")\n",
+        "    print(f\"Original size distribution: {size_stats}\")\n",
+        "\n",
+        "    return output_dir, generated_paths\n",
+        "\n",
+        "\n",
+        "def 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"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "7acc20b6",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-01-10T18:22:43.772525Z",
+          "iopub.status.busy": "2026-01-10T18:22:43.772303Z",
+          "iopub.status.idle": "2026-01-10T18:22:43.790574Z",
+          "shell.execute_reply": "2026-01-10T18:22:43.789515Z"
+        },
+        "papermill": {
+          "duration": 0.027612,
+          "end_time": "2026-01-10T18:22:43.791681",
+          "exception": false,
+          "start_time": "2026-01-10T18:22:43.764069",
+          "status": "completed"
+        },
+        "tags": [],
+        "id": "7acc20b6"
+      },
+      "outputs": [],
+      "source": [
+        "def run_colmap_reconstruction(image_dir, colmap_dir):\n",
+        "    \"\"\"Estimate camera poses and 3D point cloud with COLMAP\"\"\"\n",
+        "    print(\"Running SfM reconstruction with COLMAP...\")\n",
+        "\n",
+        "    database_path = os.path.join(colmap_dir, \"database.db\")\n",
+        "    sparse_dir = os.path.join(colmap_dir, \"sparse\")\n",
+        "    os.makedirs(sparse_dir, exist_ok=True)\n",
+        "\n",
+        "    # Set environment variable\n",
+        "    env = os.environ.copy()\n",
+        "    env['QT_QPA_PLATFORM'] = 'offscreen'\n",
+        "\n",
+        "    # Feature extraction\n",
+        "    print(\"1/4: Extracting features...\")\n",
+        "    subprocess.run([\n",
+        "        'colmap', 'feature_extractor',\n",
+        "        '--database_path', database_path,\n",
+        "        '--image_path', image_dir,\n",
+        "        '--ImageReader.single_camera', '1',\n",
+        "        '--ImageReader.camera_model', 'OPENCV',\n",
+        "        '--SiftExtraction.use_gpu', '0'  # Use CPU\n",
+        "    ], check=True, env=env)\n",
+        "\n",
+        "    # Feature matching\n",
+        "    print(\"2/4: Matching features...\")\n",
+        "    subprocess.run([\n",
+        "        'colmap', 'exhaustive_matcher',  # Use sequential_matcher instead of exhaustive_matcher\n",
+        "        '--database_path', database_path,\n",
+        "        '--SiftMatching.use_gpu', '0'  # Use CPU\n",
+        "    ], check=True, env=env)\n",
+        "\n",
+        "    # Sparse reconstruction\n",
+        "    print(\"3/4: Sparse reconstruction...\")\n",
+        "    subprocess.run([\n",
+        "        'colmap', 'mapper',\n",
+        "        '--database_path', database_path,\n",
+        "        '--image_path', image_dir,\n",
+        "        '--output_path', sparse_dir,\n",
+        "        '--Mapper.ba_global_max_num_iterations', '20',  # Speed up\n",
+        "        '--Mapper.ba_local_max_num_iterations', '10'\n",
+        "    ], check=True, env=env)\n",
+        "\n",
+        "    # Export to text format\n",
+        "    print(\"4/4: Exporting to text format...\")\n",
+        "    model_dir = os.path.join(sparse_dir, '0')\n",
+        "    if not os.path.exists(model_dir):\n",
+        "        # Use the first model found\n",
+        "        subdirs = [d for d in os.listdir(sparse_dir) if os.path.isdir(os.path.join(sparse_dir, d))]\n",
+        "        if subdirs:\n",
+        "            model_dir = os.path.join(sparse_dir, subdirs[0])\n",
+        "        else:\n",
+        "            raise FileNotFoundError(\"COLMAP reconstruction failed\")\n",
+        "\n",
+        "    subprocess.run([\n",
+        "        'colmap', 'model_converter',\n",
+        "        '--input_path', model_dir,\n",
+        "        '--output_path', model_dir,\n",
+        "        '--output_type', 'TXT'\n",
+        "    ], check=True, env=env)\n",
+        "\n",
+        "    print(f\"COLMAP reconstruction complete: {model_dir}\")\n",
+        "    return model_dir\n",
+        "\n",
+        "\n",
+        "def convert_cameras_to_pinhole(input_file, output_file):\n",
+        "    \"\"\"Convert camera model to PINHOLE format\"\"\"\n",
+        "    print(f\"Reading camera file: {input_file}\")\n",
+        "\n",
+        "    with open(input_file, 'r') as f:\n",
+        "        lines = f.readlines()\n",
+        "\n",
+        "    converted_count = 0\n",
+        "    with open(output_file, 'w') as f:\n",
+        "        for line in lines:\n",
+        "            if line.startswith('#') or line.strip() == '':\n",
+        "                f.write(line)\n",
+        "            else:\n",
+        "                parts = line.strip().split()\n",
+        "                if len(parts) >= 4:\n",
+        "                    cam_id = parts[0]\n",
+        "                    model = parts[1]\n",
+        "                    width = parts[2]\n",
+        "                    height = parts[3]\n",
+        "                    params = parts[4:]\n",
+        "\n",
+        "                    # Convert to PINHOLE format\n",
+        "                    if model == \"PINHOLE\":\n",
+        "                        f.write(line)\n",
+        "                    elif model == \"OPENCV\":\n",
+        "                        # OPENCV: fx, fy, cx, cy, k1, k2, p1, p2\n",
+        "                        fx = params[0]\n",
+        "                        fy = params[1]\n",
+        "                        cx = params[2]\n",
+        "                        cy = params[3]\n",
+        "                        f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
+        "                        converted_count += 1\n",
+        "                    else:\n",
+        "                        # Convert other models too\n",
+        "                        fx = fy = max(float(width), float(height))\n",
+        "                        cx = float(width) / 2\n",
+        "                        cy = float(height) / 2\n",
+        "                        f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
+        "                        converted_count += 1\n",
+        "                else:\n",
+        "                    f.write(line)\n",
+        "\n",
+        "    print(f\"Converted {converted_count} cameras to PINHOLE format\")\n",
+        "\n",
+        "\n",
+        "def prepare_gaussian_splatting_data(image_dir, colmap_model_dir):\n",
+        "    \"\"\"Prepare data for Gaussian Splatting\"\"\"\n",
+        "    print(\"Preparing data for Gaussian Splatting...\")\n",
+        "\n",
+        "    data_dir = f\"{WORK_DIR}/data/video\"\n",
+        "    os.makedirs(f\"{data_dir}/sparse/0\", exist_ok=True)\n",
+        "    os.makedirs(f\"{data_dir}/images\", exist_ok=True)\n",
+        "\n",
+        "    # Copy images\n",
+        "    print(\"Copying images...\")\n",
+        "    img_count = 0\n",
+        "    for img_file in os.listdir(image_dir):\n",
+        "        if img_file.lower().endswith(('.jpg', '.jpeg', '.png')):\n",
+        "            shutil.copy(\n",
+        "                os.path.join(image_dir, img_file),\n",
+        "                f\"{data_dir}/images/{img_file}\"\n",
+        "            )\n",
+        "            img_count += 1\n",
+        "    print(f\"Copied {img_count} images\")\n",
+        "\n",
+        "    # Convert and copy camera file to PINHOLE format\n",
+        "    print(\"Converting camera model to PINHOLE format...\")\n",
+        "    convert_cameras_to_pinhole(\n",
+        "        os.path.join(colmap_model_dir, 'cameras.txt'),\n",
+        "        f\"{data_dir}/sparse/0/cameras.txt\"\n",
+        "    )\n",
+        "\n",
+        "    # Copy other files\n",
+        "    for filename in ['images.txt', 'points3D.txt']:\n",
+        "        src = os.path.join(colmap_model_dir, filename)\n",
+        "        dst = f\"{data_dir}/sparse/0/{filename}\"\n",
+        "        if os.path.exists(src):\n",
+        "            shutil.copy(src, dst)\n",
+        "            print(f\"Copied {filename}\")\n",
+        "        else:\n",
+        "            print(f\"Warning: {filename} not found\")\n",
+        "\n",
+        "    print(f\"Data preparation complete: {data_dir}\")\n",
+        "    return data_dir\n",
+        "\n",
+        "def run_colmap_reconstruction(image_dir, colmap_dir):\n",
+        "    \"\"\"Estimate camera poses and 3D point cloud with COLMAP\"\"\"\n",
+        "    print(\"Running SfM reconstruction with COLMAP...\")\n",
+        "\n",
+        "    database_path = os.path.join(colmap_dir, \"database.db\")\n",
+        "    sparse_dir = os.path.join(colmap_dir, \"sparse\")\n",
+        "    os.makedirs(sparse_dir, exist_ok=True)\n",
+        "\n",
+        "    # Set environment variable\n",
+        "    env = os.environ.copy()\n",
+        "    env['QT_QPA_PLATFORM'] = 'offscreen'\n",
+        "\n",
+        "    # Feature extraction\n",
+        "    print(\"1/4: Extracting features...\")\n",
+        "    subprocess.run([\n",
+        "        'colmap', 'feature_extractor',\n",
+        "        '--database_path', database_path,\n",
+        "        '--image_path', image_dir,\n",
+        "        '--ImageReader.single_camera', '1',\n",
+        "        '--ImageReader.camera_model', 'OPENCV',\n",
+        "        '--SiftExtraction.use_gpu', '0'  # Use CPU\n",
+        "    ], check=True, env=env)\n",
+        "\n",
+        "    # Feature matching\n",
+        "    print(\"2/4: Matching features...\")\n",
+        "    subprocess.run([\n",
+        "        'colmap', 'exhaustive_matcher',  # Use sequential_matcher instead of exhaustive_matcher\n",
+        "        '--database_path', database_path,\n",
+        "        '--SiftMatching.use_gpu', '0'  # Use CPU\n",
+        "    ], check=True, env=env)\n",
+        "\n",
+        "    # Sparse reconstruction\n",
+        "    print(\"3/4: Sparse reconstruction...\")\n",
+        "    subprocess.run([\n",
+        "        'colmap', 'mapper',\n",
+        "        '--database_path', database_path,\n",
+        "        '--image_path', image_dir,\n",
+        "        '--output_path', sparse_dir,\n",
+        "        '--Mapper.ba_global_max_num_iterations', '20',  # Speed up\n",
+        "        '--Mapper.ba_local_max_num_iterations', '10'\n",
+        "    ], check=True, env=env)\n",
+        "\n",
+        "    # Export to text format\n",
+        "    print(\"4/4: Exporting to text format...\")\n",
+        "    model_dir = os.path.join(sparse_dir, '0')\n",
+        "    if not os.path.exists(model_dir):\n",
+        "        # Use the first model found\n",
+        "        subdirs = [d for d in os.listdir(sparse_dir) if os.path.isdir(os.path.join(sparse_dir, d))]\n",
+        "        if subdirs:\n",
+        "            model_dir = os.path.join(sparse_dir, subdirs[0])\n",
+        "        else:\n",
+        "            raise FileNotFoundError(\"COLMAP reconstruction failed\")\n",
+        "\n",
+        "    subprocess.run([\n",
+        "        'colmap', 'model_converter',\n",
+        "        '--input_path', model_dir,\n",
+        "        '--output_path', model_dir,\n",
+        "        '--output_type', 'TXT'\n",
+        "    ], check=True, env=env)\n",
+        "\n",
+        "    print(f\"COLMAP reconstruction complete: {model_dir}\")\n",
+        "    return model_dir\n",
+        "\n",
+        "\n",
+        "def convert_cameras_to_pinhole(input_file, output_file):\n",
+        "    \"\"\"Convert camera model to PINHOLE format\"\"\"\n",
+        "    print(f\"Reading camera file: {input_file}\")\n",
+        "\n",
+        "    with open(input_file, 'r') as f:\n",
+        "        lines = f.readlines()\n",
+        "\n",
+        "    converted_count = 0\n",
+        "    with open(output_file, 'w') as f:\n",
+        "        for line in lines:\n",
+        "            if line.startswith('#') or line.strip() == '':\n",
+        "                f.write(line)\n",
+        "            else:\n",
+        "                parts = line.strip().split()\n",
+        "                if len(parts) >= 4:\n",
+        "                    cam_id = parts[0]\n",
+        "                    model = parts[1]\n",
+        "                    width = parts[2]\n",
+        "                    height = parts[3]\n",
+        "                    params = parts[4:]\n",
+        "\n",
+        "                    # Convert to PINHOLE format\n",
+        "                    if model == \"PINHOLE\":\n",
+        "                        f.write(line)\n",
+        "                    elif model == \"OPENCV\":\n",
+        "                        # OPENCV: fx, fy, cx, cy, k1, k2, p1, p2\n",
+        "                        fx = params[0]\n",
+        "                        fy = params[1]\n",
+        "                        cx = params[2]\n",
+        "                        cy = params[3]\n",
+        "                        f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
+        "                        converted_count += 1\n",
+        "                    else:\n",
+        "                        # Convert other models too\n",
+        "                        fx = fy = max(float(width), float(height))\n",
+        "                        cx = float(width) / 2\n",
+        "                        cy = float(height) / 2\n",
+        "                        f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
+        "                        converted_count += 1\n",
+        "                else:\n",
+        "                    f.write(line)\n",
+        "\n",
+        "    print(f\"Converted {converted_count} cameras to PINHOLE format\")\n",
+        "\n",
+        "\n",
+        "def prepare_gaussian_splatting_data(image_dir, colmap_model_dir):\n",
+        "    \"\"\"Prepare data for Gaussian Splatting\"\"\"\n",
+        "    print(\"Preparing data for Gaussian Splatting...\")\n",
+        "\n",
+        "    data_dir = f\"{WORK_DIR}/data/video\"\n",
+        "    os.makedirs(f\"{data_dir}/sparse/0\", exist_ok=True)\n",
+        "    os.makedirs(f\"{data_dir}/images\", exist_ok=True)\n",
+        "\n",
+        "    # Copy images\n",
+        "    print(\"Copying images...\")\n",
+        "    img_count = 0\n",
+        "    for img_file in os.listdir(image_dir):\n",
+        "        if img_file.lower().endswith(('.jpg', '.jpeg', '.png')):\n",
+        "            shutil.copy(\n",
+        "                os.path.join(image_dir, img_file),\n",
+        "                f\"{data_dir}/images/{img_file}\"\n",
+        "            )\n",
+        "            img_count += 1\n",
+        "    print(f\"Copied {img_count} images\")\n",
+        "\n",
+        "    # Convert and copy camera file to PINHOLE format\n",
+        "    print(\"Converting camera model to PINHOLE format...\")\n",
+        "    convert_cameras_to_pinhole(\n",
+        "        os.path.join(colmap_model_dir, 'cameras.txt'),\n",
+        "        f\"{data_dir}/sparse/0/cameras.txt\"\n",
+        "    )\n",
+        "\n",
+        "    # Copy other files\n",
+        "    for filename in ['images.txt', 'points3D.txt']:\n",
+        "        src = os.path.join(colmap_model_dir, filename)\n",
+        "        dst = f\"{data_dir}/sparse/0/{filename}\"\n",
+        "        if os.path.exists(src):\n",
+        "            shutil.copy(src, dst)\n",
+        "            print(f\"Copied {filename}\")\n",
+        "        else:\n",
+        "            print(f\"Warning: {filename} not found\")\n",
+        "\n",
+        "    print(f\"Data preparation complete: {data_dir}\")\n",
+        "    return data_dir\n",
+        "\n",
+        "\n",
+        "\n",
+        "###############################################################\n",
+        "\n",
+        "# 変更後 (2DGS) - 正則化パラメータを追加\n",
+        "def train_gaussian_splatting(data_dir, iterations=7000,\n",
+        "                            lambda_normal=0.05,\n",
+        "                            lambda_distortion=0,\n",
+        "                            depth_ratio=0):\n",
+        "    \"\"\"\n",
+        "    2DGS用のトレーニング関数\n",
+        "\n",
+        "    Args:\n",
+        "        lambda_normal: 法線一貫性の重み (デフォルト: 0.05)\n",
+        "        lambda_distortion: 深度歪みの重み (デフォルト: 0)\n",
+        "        depth_ratio: 0=平均深度, 1=中央値深度 (デフォルト: 0)\n",
+        "    \"\"\"\n",
+        "    model_path = f\"{WORK_DIR}/output/video\"\n",
+        "    cmd = [\n",
+        "        sys.executable, 'train.py',\n",
+        "        '-s', data_dir,\n",
+        "        '-m', model_path,\n",
+        "        '--iterations', str(iterations),\n",
+        "        '--lambda_normal', str(lambda_normal),\n",
+        "        '--lambda_distortion', str(lambda_distortion),\n",
+        "        '--depth_ratio', str(depth_ratio),\n",
+        "        '--eval'\n",
+        "    ]\n",
+        "    subprocess.run(cmd, cwd=WORK_DIR, check=True)\n",
+        "    return model_path\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 2DGSではメッシュ抽出オプションが追加されています\n",
+        "def render_video_and_mesh(model_path, output_video_path, iteration=7000,\n",
+        "                          extract_mesh=True, unbounded=False, mesh_res=1024):\n",
+        "    \"\"\"\n",
+        "    2DGS用のレンダリングとメッシュ抽出\n",
+        "\n",
+        "    Args:\n",
+        "        extract_mesh: メッシュを抽出するか\n",
+        "        unbounded: 境界なしメッシュ抽出を使用するか\n",
+        "        mesh_res: メッシュ解像度\n",
+        "    \"\"\"\n",
+        "    # 通常のレンダリング\n",
+        "    cmd = [\n",
+        "        sys.executable, 'render.py',\n",
+        "        '-m', model_path,\n",
+        "        '--iteration', str(iteration)\n",
+        "    ]\n",
+        "\n",
+        "    # メッシュ抽出オプション追加\n",
+        "    if extract_mesh:\n",
+        "        if unbounded:\n",
+        "            cmd.extend(['--unbounded', '--mesh_res', str(mesh_res)])\n",
+        "        cmd.extend(['--skip_test', '--skip_train'])\n",
+        "\n",
+        "    subprocess.run(cmd, cwd=WORK_DIR, check=True)\n",
+        "\n",
+        "    # Find the rendering directory\n",
+        "    possible_dirs = [\n",
+        "        f\"{model_path}/test/ours_{iteration}/renders\",\n",
+        "        f\"{model_path}/train/ours_{iteration}/renders\",\n",
+        "    ]\n",
+        "\n",
+        "    render_dir = None\n",
+        "    for test_dir in possible_dirs:\n",
+        "        if os.path.exists(test_dir):\n",
+        "            render_dir = test_dir\n",
+        "            print(f\"Rendering directory found: {render_dir}\")\n",
+        "            break\n",
+        "\n",
+        "    if render_dir and os.path.exists(render_dir):\n",
+        "        render_imgs = sorted([f for f in os.listdir(render_dir) if f.endswith('.png')])\n",
+        "\n",
+        "        if render_imgs:\n",
+        "            print(f\"Found {len(render_imgs)} rendered images\")\n",
+        "\n",
+        "            # Create video with ffmpeg\n",
+        "            subprocess.run([\n",
+        "                'ffmpeg', '-y',\n",
+        "                '-framerate', '30',\n",
+        "                '-pattern_type', 'glob',\n",
+        "                '-i', f\"{render_dir}/*.png\",\n",
+        "                '-c:v', 'libx264',\n",
+        "                '-pix_fmt', 'yuv420p',\n",
+        "                '-crf', '18',\n",
+        "                output_video_path\n",
+        "            ], check=True)\n",
+        "\n",
+        "            print(f\"Video saved: {output_video_path}\")\n",
+        "            return True\n",
+        "\n",
+        "    print(\"Error: Rendering directory not found\")\n",
+        "    return False\n",
+        "\n",
+        "###############################################################\n",
+        "\n",
+        "\n",
+        "def create_gif(video_path, gif_path):\n",
+        "    \"\"\"Create GIF from MP4\"\"\"\n",
+        "    print(\"Creating animated GIF...\")\n",
+        "\n",
+        "    subprocess.run([\n",
+        "        'ffmpeg', '-y',\n",
+        "        '-i', video_path,\n",
+        "        '-vf', 'setpts=8*PTS,fps=10,scale=720:-1:flags=lanczos',\n",
+        "        '-loop', '0',\n",
+        "        gif_path\n",
+        "    ], check=True)\n",
+        "\n",
+        "    if os.path.exists(gif_path):\n",
+        "        size_mb = os.path.getsize(gif_path) / (1024 * 1024)\n",
+        "        print(f\"GIF creation complete: {gif_path} ({size_mb:.2f} MB)\")\n",
+        "        return True\n",
+        "\n",
+        "    return False"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "YtqhBP4T3jEH"
+      },
+      "id": "YtqhBP4T3jEH",
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def main_pipeline(image_dir, output_dir, square_size=1024, max_images=100):\n",
+        "    \"\"\"Main execution function\"\"\"\n",
+        "    try:\n",
+        "        # Step 1: 画像の正規化と前処理\n",
+        "        print(\"=\"*60)\n",
+        "        print(\"Step 1: Normalizing and preprocessing images\")\n",
+        "        print(\"=\"*60)\n",
+        "\n",
+        "        frame_dir = os.path.join(COLMAP_DIR, \"images\")\n",
+        "        os.makedirs(frame_dir, exist_ok=True)\n",
+        "\n",
+        "        # 画像を正規化して直接COLMAPのディレクトリに保存\n",
+        "        num_processed = normalize_image_sizes_biplet(\n",
+        "            input_dir=image_dir,\n",
+        "            output_dir=frame_dir,  # 直接colmap/imagesに保存\n",
+        "            size=square_size,\n",
+        "            max_images=max_images\n",
+        "        )\n",
+        "\n",
+        "        print(f\"Processed {num_processed} images\")\n",
+        "\n",
+        "        # Step 2: Estimate Camera Info with COLMAP\n",
+        "        print(\"=\"*60)\n",
+        "        print(\"Step 2: Running COLMAP reconstruction\")\n",
+        "        print(\"=\"*60)\n",
+        "        colmap_model_dir = run_colmap_reconstruction(frame_dir, COLMAP_DIR)\n",
+        "\n",
+        "        # Step 3: Prepare Data for Gaussian Splatting\n",
+        "        print(\"=\"*60)\n",
+        "        print(\"Step 3: Preparing Gaussian Splatting data\")\n",
+        "        print(\"=\"*60)\n",
+        "        data_dir = prepare_gaussian_splatting_data(frame_dir, colmap_model_dir)\n",
+        "\n",
+        "        # Step 4: Train Model\n",
+        "        print(\"=\"*60)\n",
+        "        print(\"Step 4: Training Gaussian Splatting model\")\n",
+        "        print(\"=\"*60)\n",
+        "        # 修正: frame_dir → data_dir\n",
+        "        model_path = train_gaussian_splatting(\n",
+        "            data_dir,  # ← ここを修正！\n",
+        "            iterations=1000,\n",
+        "            lambda_normal=0.05,\n",
+        "            lambda_distortion=0,\n",
+        "            depth_ratio=0\n",
+        "        )\n",
+        "\n",
+        "        print(f\"Model trained at: {model_path}\")\n",
+        "\n",
+        "        # Step 5: Render Video\n",
+        "        print(\"=\"*60)\n",
+        "        print(\"Step 5: Rendering video\")\n",
+        "        print(\"=\"*60)\n",
+        "        os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
+        "        output_video = os.path.join(OUTPUT_DIR, \"gaussian_splatting_video.mp4\")\n",
+        "\n",
+        "        # 修正: output_video_path → output_video\n",
+        "        success = render_video_and_mesh(\n",
+        "            model_path,\n",
+        "            output_video,  # ← ここを修正！\n",
+        "            iteration=1000,\n",
+        "            extract_mesh=True,  # メッシュ抽出を有効化\n",
+        "            unbounded=True,     # 境界なしメッシュ（推奨）\n",
+        "            mesh_res=1024\n",
+        "        )\n",
+        "\n",
+        "        if success:\n",
+        "            print(\"=\"*60)\n",
+        "            print(f\"Success! Video generation complete: {output_video}\")\n",
+        "            print(\"=\"*60)\n",
+        "\n",
+        "            # Create GIF\n",
+        "            output_gif = os.path.join(OUTPUT_DIR, \"gaussian_splatting_video.gif\")\n",
+        "            create_gif(output_video, output_gif)\n",
+        "\n",
+        "            # Display result\n",
+        "            from IPython.display import Image, display\n",
+        "            display(Image(open(output_gif, 'rb').read()))\n",
+        "\n",
+        "            return output_video, output_gif\n",
+        "        else:\n",
+        "            print(\"Warning: Rendering complete, but video was not generated\")\n",
+        "            return None, None\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        print(f\"Error: {str(e)}\")\n",
+        "        import traceback\n",
+        "        traceback.print_exc()\n",
+        "        return None, None\n",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    IMAGE_DIR = \"/content/drive/MyDrive/your_folder/fountain100\"\n",
+        "    OUTPUT_DIR = \"/content/output\"\n",
+        "    COLMAP_DIR = \"/content/colmap_workspace\"\n",
+        "\n",
+        "    video_path, gif_path = main_pipeline(\n",
+        "        image_dir=IMAGE_DIR,\n",
+        "        output_dir=OUTPUT_DIR,\n",
+        "        square_size=1024,\n",
+        "        max_images=20\n",
+        "    )\n",
+        "\n",
+        "    if video_path:\n",
+        "        print(f\"\\n✅ Success!\")\n",
+        "        print(f\"Video: {video_path}\")\n",
+        "        print(f\"GIF: {gif_path}\")\n",
+        "    else:\n",
+        "        print(\"\\n❌ Pipeline failed\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "fya3kv62NXM-",
+        "outputId": "4c60e0b2-e9fc-49df-d216-7d96fce737ff"
+      },
+      "id": "fya3kv62NXM-",
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "============================================================\n",
+            "Step 1: Normalizing and preprocessing images\n",
+            "============================================================\n",
+            "--- Step 1: Biplet-Square Normalization ---\n",
+            "Generating 2 cropped squares (Left/Right or Top/Bottom) for each image...\n",
+            "\n",
+            "Processing limited to 20 source images (will generate 40 cropped images)\n",
+            "  ✓ image_101.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_102.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_103.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_104.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_105.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_106.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_107.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_108.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_109.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_110.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_111.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_112.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_113.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_114.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_115.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_116.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_117.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_118.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_119.jpeg: (1440, 1920) → 2 square images generated\n",
+            "  ✓ image_120.jpeg: (1440, 1920) → 2 square images generated\n",
+            "\n",
+            "Processing complete: 20 source images processed\n",
+            "Total output images: 40\n",
+            "Original size distribution: {'1440x1920': 20}\n",
+            "Processed ('/content/colmap_workspace/images', ['/content/colmap_workspace/images/image_101_top.jpeg', '/content/colmap_workspace/images/image_101_bottom.jpeg', '/content/colmap_workspace/images/image_102_top.jpeg', '/content/colmap_workspace/images/image_102_bottom.jpeg', '/content/colmap_workspace/images/image_103_top.jpeg', '/content/colmap_workspace/images/image_103_bottom.jpeg', '/content/colmap_workspace/images/image_104_top.jpeg', '/content/colmap_workspace/images/image_104_bottom.jpeg', '/content/colmap_workspace/images/image_105_top.jpeg', '/content/colmap_workspace/images/image_105_bottom.jpeg', '/content/colmap_workspace/images/image_106_top.jpeg', '/content/colmap_workspace/images/image_106_bottom.jpeg', '/content/colmap_workspace/images/image_107_top.jpeg', '/content/colmap_workspace/images/image_107_bottom.jpeg', '/content/colmap_workspace/images/image_108_top.jpeg', '/content/colmap_workspace/images/image_108_bottom.jpeg', '/content/colmap_workspace/images/image_109_top.jpeg', '/content/colmap_workspace/images/image_109_bottom.jpeg', '/content/colmap_workspace/images/image_110_top.jpeg', '/content/colmap_workspace/images/image_110_bottom.jpeg', '/content/colmap_workspace/images/image_111_top.jpeg', '/content/colmap_workspace/images/image_111_bottom.jpeg', '/content/colmap_workspace/images/image_112_top.jpeg', '/content/colmap_workspace/images/image_112_bottom.jpeg', '/content/colmap_workspace/images/image_113_top.jpeg', '/content/colmap_workspace/images/image_113_bottom.jpeg', '/content/colmap_workspace/images/image_114_top.jpeg', '/content/colmap_workspace/images/image_114_bottom.jpeg', '/content/colmap_workspace/images/image_115_top.jpeg', '/content/colmap_workspace/images/image_115_bottom.jpeg', '/content/colmap_workspace/images/image_116_top.jpeg', '/content/colmap_workspace/images/image_116_bottom.jpeg', '/content/colmap_workspace/images/image_117_top.jpeg', '/content/colmap_workspace/images/image_117_bottom.jpeg', '/content/colmap_workspace/images/image_118_top.jpeg', '/content/colmap_workspace/images/image_118_bottom.jpeg', '/content/colmap_workspace/images/image_119_top.jpeg', '/content/colmap_workspace/images/image_119_bottom.jpeg', '/content/colmap_workspace/images/image_120_top.jpeg', '/content/colmap_workspace/images/image_120_bottom.jpeg']) images\n",
+            "============================================================\n",
+            "Step 2: Running COLMAP reconstruction\n",
+            "============================================================\n",
+            "Running SfM reconstruction with COLMAP...\n",
+            "1/4: Extracting features...\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-3712511575.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0mCOLMAP_DIR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/colmap_workspace\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m     video_path, gif_path = main_pipeline(\n\u001b[0m\u001b[1;32m     97\u001b[0m         \u001b[0mimage_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mIMAGE_DIR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m         \u001b[0moutput_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mOUTPUT_DIR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/tmp/ipython-input-3712511575.py\u001b[0m in \u001b[0;36mmain_pipeline\u001b[0;34m(image_dir, output_dir, square_size, max_images)\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Step 2: Running COLMAP reconstruction\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"=\"\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m         \u001b[0mcolmap_model_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_colmap_reconstruction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCOLMAP_DIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0;31m# Step 3: Prepare Data for Gaussian Splatting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/tmp/ipython-input-682645374.py\u001b[0m in \u001b[0;36mrun_colmap_reconstruction\u001b[0;34m(image_dir, colmap_dir)\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;31m# Feature extraction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1/4: Extracting features...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m     subprocess.run([\n\u001b[0m\u001b[1;32m    165\u001b[0m         \u001b[0;34m'colmap'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'feature_extractor'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;34m'--database_path'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatabase_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.12/subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    548\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mPopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m             \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTimeoutExpired\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.12/subprocess.py\u001b[0m in \u001b[0;36mcommunicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m   1199\u001b[0m                 \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1200\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1201\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1202\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1203\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.12/subprocess.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1262\u001b[0m             \u001b[0mendtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1263\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1264\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1265\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1266\u001b[0m             \u001b[0;31m# https://bugs.python.org/issue25942\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.12/subprocess.py\u001b[0m in \u001b[0;36m_wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   2051\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2052\u001b[0m                             \u001b[0;32mbreak\u001b[0m  \u001b[0;31m# Another thread waited.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2053\u001b[0;31m                         \u001b[0;34m(\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msts\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2054\u001b[0m                         \u001b[0;31m# Check the pid and loop as waitpid has been known to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2055\u001b[0m                         \u001b[0;31m# return 0 even without WNOHANG in odd situations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.12/subprocess.py\u001b[0m in \u001b[0;36m_try_wait\u001b[0;34m(self, wait_flags)\u001b[0m\n\u001b[1;32m   2009\u001b[0m             \u001b[0;34m\"\"\"All callers to this function MUST hold self._waitpid_lock.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2010\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2011\u001b[0;31m                 \u001b[0;34m(\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msts\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait_flags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2012\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mChildProcessError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2013\u001b[0m                 \u001b[0;31m# This happens if SIGCLD is set to be ignored or waiting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f75233a8",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-01-10T18:22:43.807508Z",
+          "iopub.status.busy": "2026-01-10T18:22:43.807294Z",
+          "iopub.status.idle": "2026-01-11T00:00:17.030890Z",
+          "shell.execute_reply": "2026-01-11T00:00:17.029927Z"
+        },
+        "papermill": {
+          "duration": 20253.434865,
+          "end_time": "2026-01-11T00:00:17.234174",
+          "exception": false,
+          "start_time": "2026-01-10T18:22:43.799309",
+          "status": "completed"
+        },
+        "tags": [],
+        "id": "f75233a8"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e17ec719",
+      "metadata": {
+        "papermill": {
+          "duration": 0.49801,
+          "end_time": "2026-01-11T00:00:18.165833",
+          "exception": false,
+          "start_time": "2026-01-11T00:00:17.667823",
+          "status": "completed"
+        },
+        "tags": [],
+        "id": "e17ec719"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "id": "38b3974c",
+      "metadata": {
+        "papermill": {
+          "duration": 0.427583,
+          "end_time": "2026-01-11T00:00:19.008387",
+          "exception": false,
+          "start_time": "2026-01-11T00:00:18.580804",
+          "status": "completed"
+        },
+        "tags": [],
+        "id": "38b3974c"
+      },
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kaggle": {
+      "accelerator": "nvidiaTeslaT4",
+      "dataSources": [
+        {
+          "databundleVersionId": 5447706,
+          "sourceId": 49349,
+          "sourceType": "competition"
+        },
+        {
+          "datasetId": 1429416,
+          "sourceId": 14451718,
+          "sourceType": "datasetVersion"
+        }
+      ],
+      "dockerImageVersionId": 31090,
+      "isGpuEnabled": true,
+      "isInternetEnabled": true,
+      "language": "python",
+      "sourceType": "notebook"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.13"
+    },
+    "papermill": {
+      "default_parameters": {},
+      "duration": 20573.990788,
+      "end_time": "2026-01-11T00:00:22.081506",
+      "environment_variables": {},
+      "exception": null,
+      "input_path": "__notebook__.ipynb",
+      "output_path": "__notebook__.ipynb",
+      "parameters": {},
+      "start_time": "2026-01-10T18:17:28.090718",
+      "version": "2.6.0"
+    },
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}