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biplet_colmap_mipgs_colab_07.ipynb

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+{
+  "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-colmap-mipgs-colab-00**"
+      ]
+    },
+    {
+      "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": "26faa569-d3de-41d3-be2f-fbf38d363f7a"
+      },
+      "id": "JON4rYSEOzCg",
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "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",
+        "WORK_DIR = '/content/mip-splatting'\n",
+        "OUTPUT_DIR = '/content/output'\n",
+        "COLMAP_DIR = '/content/colmap_data'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "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": null,
+      "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": "9f6895a6-db59-4d18-bddd-b2e42d047cf9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "======================================================================\n",
+            "Setting up mip-splatting environment\n",
+            "======================================================================\n",
+            "\n",
+            "STEP 1: Clone mip-splatting repository\n",
+            "======================================================================\n",
+            "  > Cloning mip-splatting with submodules...\n",
+            "Running: git clone --recursive https://github.com/autonomousvision/mip-splatting.git /content/mip-splatting\n",
+            "✅ Repository cloned with submodules\n",
+            "\n",
+            "  > Verifying submodules...\n",
+            "  > Found submodules: ['diff-gaussian-rasterization', 'simple-knn']\n",
+            "\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"
+          ]
+        }
+      ],
+      "source": [
+        "def run_cmd(cmd, check=True, capture=False, cwd=None): # ← cwd=None を追加\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",
+        "        cwd=cwd # ← ここに渡す\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 install_submodule(name, url, base_dir):\n",
+        "    \"\"\"個別のサブモジュールをインストール\"\"\"\n",
+        "    print(f\"\\n{'='*70}\")\n",
+        "    print(f\"Installing {name}\")\n",
+        "    print(f\"{'='*70}\")\n",
+        "\n",
+        "    # 絶対パスを使用\n",
+        "    path = os.path.abspath(os.path.join(base_dir, \"submodules\", name))\n",
+        "    print(f\"  > Target path: {path}\")\n",
+        "\n",
+        "    # Step 1: 既存を削除\n",
+        "    if os.path.exists(path):\n",
+        "        print(f\"  > Removing old {name}...\")\n",
+        "        shutil.rmtree(path)\n",
+        "\n",
+        "    # Step 2: クローン\n",
+        "    print(f\"  > Cloning from {url}...\")\n",
+        "    os.makedirs(os.path.dirname(path), exist_ok=True)\n",
+        "    try:\n",
+        "        run_cmd([\"git\", \"clone\", url, path])\n",
+        "    except subprocess.CalledProcessError as e:\n",
+        "        print(f\"❌ Failed to clone {name}\")\n",
+        "        print(e.stderr)\n",
+        "        return False\n",
+        "\n",
+        "    # Step 3: ファイル確認\n",
+        "    print(f\"  > Checking cloned files...\")\n",
+        "    files = os.listdir(path)\n",
+        "    print(f\"  > Files in {name}: {files[:10]}...\")\n",
+        "\n",
+        "    # Step 4: ビルドキャッシュ削除\n",
+        "    build_dir = os.path.join(path, \"build\")\n",
+        "    if os.path.exists(build_dir):\n",
+        "        print(f\"  > Cleaning build cache...\")\n",
+        "        shutil.rmtree(build_dir)\n",
+        "\n",
+        "    # Step 5: インストール\n",
+        "    print(f\"  > Installing {name} (This may take a few minutes)...\")\n",
+        "\n",
+        "    # 環境変数を明示的に引き継ぐ\n",
+        "    current_env = os.environ.copy()\n",
+        "    result = subprocess.run(\n",
+        "        [sys.executable, \"-m\", \"pip\", \"install\", \"-e\", \".\", \"--no-build-isolation\", \"-v\"],\n",
+        "        cwd=path,\n",
+        "        env=current_env,\n",
+        "        capture_output=True,\n",
+        "        text=True\n",
+        "    )\n",
+        "\n",
+        "    if result.returncode != 0:\n",
+        "        print(f\"❌ Failed to install {name}\")\n",
+        "        # C++/CUDAのビルドエラーは stdout に出ることが多いため、両方出力\n",
+        "        print(\"\\n--- STDOUT (Build Logs) ---\")\n",
+        "        stdout_lines = result.stdout.split('\\n')\n",
+        "        print('\\n'.join(stdout_lines[-60:])) # 最後の60行を表示\n",
+        "        print(\"\\n--- STDERR (Error Details) ---\")\n",
+        "        print(result.stderr)\n",
+        "        return False\n",
+        "\n",
+        "    print(f\"✅ Successfully installed {name}\")\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "def install_mipsplatting_submodule(name, base_dir):\n",
+        "    \"\"\"mip-splattingに含まれるsubmoduleをインストール（クローン不要）\"\"\"\n",
+        "    print(f\"\\n{'='*70}\")\n",
+        "    print(f\"Installing {name} (from mip-splatting submodules)\")\n",
+        "    print(f\"{'='*70}\")\n",
+        "\n",
+        "    # submoduleのパス\n",
+        "    path = os.path.abspath(os.path.join(base_dir, \"submodules\", name))\n",
+        "    print(f\"  > Target path: {path}\")\n",
+        "\n",
+        "    # ファイルの存在確認\n",
+        "    if not os.path.exists(path):\n",
+        "        print(f\"❌ Path not found: {path}\")\n",
+        "        return False\n",
+        "\n",
+        "    # setup.pyの存在確認\n",
+        "    setup_py = os.path.join(path, \"setup.py\")\n",
+        "    if not os.path.exists(setup_py):\n",
+        "        print(f\"❌ setup.py not found: {setup_py}\")\n",
+        "        return False\n",
+        "\n",
+        "    print(f\"  > Checking files...\")\n",
+        "    files = os.listdir(path)\n",
+        "    print(f\"  > Files in {name}: {files[:10]}...\")\n",
+        "\n",
+        "    # ビルドキャッシュ削除\n",
+        "    build_dir = os.path.join(path, \"build\")\n",
+        "    if os.path.exists(build_dir):\n",
+        "        print(f\"  > Cleaning build cache...\")\n",
+        "        shutil.rmtree(build_dir)\n",
+        "\n",
+        "    # インストール\n",
+        "    print(f\"  > Installing {name} (This may take a few minutes)...\")\n",
+        "\n",
+        "    current_env = os.environ.copy()\n",
+        "    result = subprocess.run(\n",
+        "        [sys.executable, \"-m\", \"pip\", \"install\", \"-e\", \".\", \"--no-build-isolation\", \"-v\"],\n",
+        "        cwd=path,\n",
+        "        env=current_env,\n",
+        "        capture_output=True,\n",
+        "        text=True\n",
+        "    )\n",
+        "\n",
+        "    if result.returncode != 0:\n",
+        "        print(f\"❌ Failed to install {name}\")\n",
+        "        print(\"\\n--- STDOUT (Build Logs) ---\")\n",
+        "        stdout_lines = result.stdout.split('\\n')\n",
+        "        print('\\n'.join(stdout_lines[-60:]))\n",
+        "        print(\"\\n--- STDERR (Error Details) ---\")\n",
+        "        print(result.stderr)\n",
+        "        return False\n",
+        "\n",
+        "    print(f\"✅ Successfully installed {name}\")\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "def setup_environment():\n",
+        "    \"\"\"Setup mip-splatting environment with correct submodules\"\"\"\n",
+        "    print(\"=\"*70)\n",
+        "    print(\"Setting up mip-splatting environment\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    WORK_DIR = \"/content/mip-splatting\"\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 1: Clone main repository with submodules\n",
+        "    # =====================================================================\n",
+        "    print(\"\\nSTEP 1: Clone mip-splatting repository\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    if os.path.exists(WORK_DIR):\n",
+        "        print(f\"  > {WORK_DIR} already exists, removing...\")\n",
+        "        shutil.rmtree(WORK_DIR)\n",
+        "\n",
+        "    print(f\"  > Cloning mip-splatting with submodules...\")\n",
+        "    # --recursive で submodules も一緒にクローン\n",
+        "    run_cmd([\n",
+        "        \"git\", \"clone\", \"--recursive\",\n",
+        "        \"https://github.com/autonomousvision/mip-splatting.git\",\n",
+        "        WORK_DIR\n",
+        "    ])\n",
+        "    print(\"✅ Repository cloned with submodules\")\n",
+        "\n",
+        "    # submodulesが正しくクローンされたか確認\n",
+        "    print(\"\\n  > Verifying submodules...\")\n",
+        "    submodules_dir = os.path.join(WORK_DIR, \"submodules\")\n",
+        "    if os.path.exists(submodules_dir):\n",
+        "        items = os.listdir(submodules_dir)\n",
+        "        print(f\"  > Found submodules: {items}\")\n",
+        "\n",
+        "        # 空のsubmoduleディレクトリがある場合は初期化\n",
+        "        for item in items:\n",
+        "            item_path = os.path.join(submodules_dir, item)\n",
+        "            if os.path.isdir(item_path):\n",
+        "                item_files = os.listdir(item_path)\n",
+        "                if not item_files or len(item_files) == 0:\n",
+        "                    print(f\"  > {item} is empty, initializing...\")\n",
+        "                    run_cmd([\"git\", \"submodule\", \"update\", \"--init\", \"--recursive\"], cwd=WORK_DIR)\n",
+        "                    break\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: Fix numpy compatibility\n",
+        "    # =====================================================================\n",
+        "    print(\"\\nSTEP 2: Fix numpy compatibility\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    print(\"  > Uninstalling numpy 2.x...\")\n",
+        "    run_cmd([sys.executable, \"-m\", \"pip\", \"uninstall\", \"numpy\", \"-y\"], check=False)\n",
+        "\n",
+        "    print(\"  > Installing numpy<2.0...\")\n",
+        "    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"numpy<2.0\"])\n",
+        "    print(\"✅ numpy<2.0 installed\")\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 3: Install core dependencies\n",
+        "    # =====================================================================\n",
+        "    print(\"\\nSTEP 3: Install core dependencies\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    core_packages = [\n",
+        "        \"open3d\",\n",
+        "        \"plyfile\",\n",
+        "        \"tqdm\",\n",
+        "        \"Pillow\",\n",
+        "        \"opencv-python\"\n",
+        "    ]\n",
+        "\n",
+        "    for package in core_packages:\n",
+        "        print(f\"  > Installing {package}...\")\n",
+        "        run_cmd([sys.executable, \"-m\", \"pip\", \"install\", package])\n",
+        "    print(\"✅ Core dependencies installed\")\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 4: Build mip-splatting submodules\n",
+        "    # =====================================================================\n",
+        "    print(\"\\nSTEP 4: Build mip-splatting submodules\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    # simple-knn: 実績のあるfixed版を使用（クローンし直す）\n",
+        "    success_knn = install_submodule(\n",
+        "        \"simple-knn\",\n",
+        "        \"https://github.com/tztechno/simple-knn.git\",\n",
+        "        WORK_DIR\n",
+        "    )\n",
+        "\n",
+        "    if not success_knn:\n",
+        "        print(\"❌ Failed to install simple-knn\")\n",
+        "        return None\n",
+        "\n",
+        "    # diff-gaussian-rasterization: mip-splattingに含まれているものを使用\n",
+        "    # （kernel_size対応版なのでクローンし直さない）\n",
+        "    success_rast = install_mipsplatting_submodule(\n",
+        "        \"diff-gaussian-rasterization\",\n",
+        "        WORK_DIR\n",
+        "    )\n",
+        "\n",
+        "    if not success_rast:\n",
+        "        print(\"❌ Failed to install diff-gaussian-rasterization\")\n",
+        "        return None\n",
+        "\n",
+        "\n",
+        "    return WORK_DIR\n",
+        "\n",
+        "\n",
+        "\n",
+        "work_dir = setup_environment()\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "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": null,
+      "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",
+        "\n",
+        "\n",
+        "\n",
+        "# After (mipGS) - Added Kernel Size and Multi-Scale Support\n",
+        "def train_gaussian_splatting(data_dir, work_dir, iterations=3000):\n",
+        "    \"\"\"Training function for mipGS with comprehensive error handling\"\"\"\n",
+        "\n",
+        "    # 入力検証\n",
+        "    if not work_dir:\n",
+        "        raise ValueError(\"work_dir cannot be None or empty\")\n",
+        "\n",
+        "    if not os.path.exists(work_dir):\n",
+        "        raise FileNotFoundError(f\"Work directory not found: {work_dir}\")\n",
+        "\n",
+        "    if not os.path.exists(data_dir):\n",
+        "        raise FileNotFoundError(f\"Data directory not found: {data_dir}\")\n",
+        "\n",
+        "    train_py_path = os.path.join(work_dir, \"train.py\")\n",
+        "    if not os.path.exists(train_py_path):\n",
+        "        raise FileNotFoundError(f\"train.py not found: {train_py_path}\")\n",
+        "\n",
+        "    # モデル保存パス\n",
+        "    model_path = os.path.join(work_dir, \"output\", \"video\")\n",
+        "    os.makedirs(model_path, exist_ok=True)\n",
+        "\n",
+        "    # コマンド構築\n",
+        "    cmd = [\n",
+        "        sys.executable, 'train.py',\n",
+        "        '-s', data_dir,\n",
+        "        '-m', model_path,\n",
+        "        '--iterations', str(iterations),\n",
+        "        '--eval'\n",
+        "    ]\n",
+        "\n",
+        "    print(f\"Training configuration:\")\n",
+        "    print(f\"  Work dir: {work_dir}\")\n",
+        "    print(f\"  Data dir: {data_dir}\")\n",
+        "    print(f\"  Model path: {model_path}\")\n",
+        "    print(f\"  Command: {' '.join(cmd)}\")\n",
+        "\n",
+        "    # 実行\n",
+        "    result = subprocess.run(\n",
+        "        cmd,\n",
+        "        cwd=work_dir,\n",
+        "        capture_output=True,\n",
+        "        text=True\n",
+        "    )\n",
+        "\n",
+        "    # エラーチェック\n",
+        "    if result.returncode != 0:\n",
+        "        print(f\"\\n❌ Training failed with exit code {result.returncode}\")\n",
+        "        print(\"\\n--- STDOUT ---\")\n",
+        "        print(result.stdout)\n",
+        "        print(\"\\n--- STDERR ---\")\n",
+        "        print(result.stderr)\n",
+        "        raise subprocess.CalledProcessError(result.returncode, cmd)\n",
+        "\n",
+        "    print(\"\\n✅ Training completed successfully\")\n",
+        "    return model_path\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# New function for mipGS - Fuse 3D filter into Gaussian parameters\n",
+        "def create_fused_ply(model_path, scene_name, output_dir=\"fused\"):\n",
+        "    \"\"\"\n",
+        "    Fuse the 3D smoothing filter to Gaussian parameters for deployment\n",
+        "    This creates a .ply file that can be used in online viewers\n",
+        "\n",
+        "    Args:\n",
+        "        model_path: Path to trained model\n",
+        "        scene_name: Name of the scene\n",
+        "        output_dir: Directory to save fused .ply file\n",
+        "    \"\"\"\n",
+        "    os.makedirs(output_dir, exist_ok=True)\n",
+        "    output_ply = f\"{output_dir}/{scene_name}_fused.ply\"\n",
+        "\n",
+        "    cmd = [\n",
+        "        sys.executable, 'create_fused_ply.py',\n",
+        "        '-m', f\"{model_path}/{scene_name}\",\n",
+        "        '--output_ply', output_ply\n",
+        "    ]\n",
+        "    subprocess.run(cmd, cwd=WORK_DIR, check=True)\n",
+        "    return output_ply\n",
+        ""
+      ],
+      "metadata": {
+        "id": "-Cwgr3I0b57O"
+      },
+      "id": "-Cwgr3I0b57O",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "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": [
+        "def main_pipeline(image_dir, output_dir,\n",
+        "                  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",
+        "        print(frame_dir)\n",
+        "        print(colmap_model_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",
+        "        model_path = train_gaussian_splatting(\n",
+        "            data_dir=data_dir,\n",
+        "            work_dir=work_dir,  # 明示的に渡す\n",
+        "            iterations=3000\n",
+        "        )\n",
+        "\n",
+        "        return ply_path\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",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    IMAGE_DIR = \"/content/drive/MyDrive/your_folder/fountain100\"\n",
+        "    OUTPUT_DIR = \"/content/output\"\n",
+        "    COLMAP_DIR = \"/content/colmap\"\n",
+        "\n",
+        "    ply_path = main_pipeline(\n",
+        "        image_dir=IMAGE_DIR,\n",
+        "        output_dir=OUTPUT_DIR,\n",
+        "        square_size=1024,\n",
+        "        max_images=20\n",
+        "    )\n",
+        "\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
+}

biplet_colmap_mipgs_colab_07oo.ipynb

@@ -0,0 +1,1150 @@
+{
+  "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-colmap-mipgs-colab-00**"
+      ]
+    },
+    {
+      "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": "26faa569-d3de-41d3-be2f-fbf38d363f7a"
+      },
+      "id": "JON4rYSEOzCg",
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "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",
+        "WORK_DIR = '/content/mip-splatting'\n",
+        "OUTPUT_DIR = '/content/output'\n",
+        "COLMAP_DIR = '/content/colmap_data'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "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": 5,
+      "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": "9f6895a6-db59-4d18-bddd-b2e42d047cf9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "======================================================================\n",
+            "Setting up mip-splatting environment\n",
+            "======================================================================\n",
+            "\n",
+            "STEP 1: Clone mip-splatting repository\n",
+            "======================================================================\n",
+            "  > Cloning mip-splatting with submodules...\n",
+            "Running: git clone --recursive https://github.com/autonomousvision/mip-splatting.git /content/mip-splatting\n",
+            "✅ Repository cloned with submodules\n",
+            "\n",
+            "  > Verifying submodules...\n",
+            "  > Found submodules: ['diff-gaussian-rasterization', 'simple-knn']\n",
+            "\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",
+            "STEP 2: Fix numpy compatibility\n",
+            "======================================================================\n",
+            "  > Uninstalling numpy 2.x...\n",
+            "Running: /usr/bin/python3 -m pip uninstall numpy -y\n",
+            "  > Installing numpy<2.0...\n",
+            "Running: /usr/bin/python3 -m pip install numpy<2.0\n",
+            "✅ numpy<2.0 installed\n",
+            "\n",
+            "STEP 3: Install core dependencies\n",
+            "======================================================================\n",
+            "  > Installing open3d...\n",
+            "Running: /usr/bin/python3 -m pip install open3d\n",
+            "  > Installing plyfile...\n",
+            "Running: /usr/bin/python3 -m pip install plyfile\n",
+            "  > Installing tqdm...\n",
+            "Running: /usr/bin/python3 -m pip install tqdm\n",
+            "  > Installing Pillow...\n",
+            "Running: /usr/bin/python3 -m pip install Pillow\n",
+            "  > Installing opencv-python...\n",
+            "Running: /usr/bin/python3 -m pip install opencv-python\n",
+            "✅ Core dependencies installed\n",
+            "\n",
+            "STEP 4: Build mip-splatting submodules\n",
+            "======================================================================\n",
+            "\n",
+            "======================================================================\n",
+            "Installing simple-knn\n",
+            "======================================================================\n",
+            "  > Target path: /content/mip-splatting/submodules/simple-knn\n",
+            "  > Removing old simple-knn...\n",
+            "  > Cloning from https://github.com/tztechno/simple-knn.git...\n",
+            "Running: git clone https://github.com/tztechno/simple-knn.git /content/mip-splatting/submodules/simple-knn\n",
+            "  > Checking cloned files...\n",
+            "  > Files in simple-knn: ['spatial.cu', '.gitignore', '.git', 'setup.py', 'simple_knn.h', 'simple_knn0.cu', 'ext.cpp', 'simple_knn', 'spatial.h', 'simple_knn.cu']...\n",
+            "  > Installing simple-knn (This may take a few minutes)...\n",
+            "✅ Successfully installed simple-knn\n",
+            "\n",
+            "======================================================================\n",
+            "Installing diff-gaussian-rasterization (from mip-splatting submodules)\n",
+            "======================================================================\n",
+            "  > Target path: /content/mip-splatting/submodules/diff-gaussian-rasterization\n",
+            "  > Checking files...\n",
+            "  > Files in diff-gaussian-rasterization: ['LICENSE.md', 'rasterize_points.h', 'cuda_rasterizer', 'diff_gaussian_rasterization', 'setup.py', 'ext.cpp', 'rasterize_points.cu', 'diff_gaussian_rasterization.egg-info', 'CMakeLists.txt', 'third_party']...\n",
+            "  > Installing diff-gaussian-rasterization (This may take a few minutes)...\n",
+            "✅ Successfully installed diff-gaussian-rasterization\n"
+          ]
+        }
+      ],
+      "source": [
+        "def run_cmd(cmd, check=True, capture=False, cwd=None): # ← cwd=None を追加\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",
+        "        cwd=cwd # ← ここに渡す\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 install_submodule(name, url, base_dir):\n",
+        "    \"\"\"個別のサブモジュールをインストール\"\"\"\n",
+        "    print(f\"\\n{'='*70}\")\n",
+        "    print(f\"Installing {name}\")\n",
+        "    print(f\"{'='*70}\")\n",
+        "\n",
+        "    # 絶対パスを使用\n",
+        "    path = os.path.abspath(os.path.join(base_dir, \"submodules\", name))\n",
+        "    print(f\"  > Target path: {path}\")\n",
+        "\n",
+        "    # Step 1: 既存を削除\n",
+        "    if os.path.exists(path):\n",
+        "        print(f\"  > Removing old {name}...\")\n",
+        "        shutil.rmtree(path)\n",
+        "\n",
+        "    # Step 2: クローン\n",
+        "    print(f\"  > Cloning from {url}...\")\n",
+        "    os.makedirs(os.path.dirname(path), exist_ok=True)\n",
+        "    try:\n",
+        "        run_cmd([\"git\", \"clone\", url, path])\n",
+        "    except subprocess.CalledProcessError as e:\n",
+        "        print(f\"❌ Failed to clone {name}\")\n",
+        "        print(e.stderr)\n",
+        "        return False\n",
+        "\n",
+        "    # Step 3: ファイル確認\n",
+        "    print(f\"  > Checking cloned files...\")\n",
+        "    files = os.listdir(path)\n",
+        "    print(f\"  > Files in {name}: {files[:10]}...\")\n",
+        "\n",
+        "    # Step 4: ビルドキャッシュ削除\n",
+        "    build_dir = os.path.join(path, \"build\")\n",
+        "    if os.path.exists(build_dir):\n",
+        "        print(f\"  > Cleaning build cache...\")\n",
+        "        shutil.rmtree(build_dir)\n",
+        "\n",
+        "    # Step 5: インストール\n",
+        "    print(f\"  > Installing {name} (This may take a few minutes)...\")\n",
+        "\n",
+        "    # 環境変数を明示的に引き継ぐ\n",
+        "    current_env = os.environ.copy()\n",
+        "    result = subprocess.run(\n",
+        "        [sys.executable, \"-m\", \"pip\", \"install\", \"-e\", \".\", \"--no-build-isolation\", \"-v\"],\n",
+        "        cwd=path,\n",
+        "        env=current_env,\n",
+        "        capture_output=True,\n",
+        "        text=True\n",
+        "    )\n",
+        "\n",
+        "    if result.returncode != 0:\n",
+        "        print(f\"❌ Failed to install {name}\")\n",
+        "        # C++/CUDAのビルドエラーは stdout に出ることが多いため、両方出力\n",
+        "        print(\"\\n--- STDOUT (Build Logs) ---\")\n",
+        "        stdout_lines = result.stdout.split('\\n')\n",
+        "        print('\\n'.join(stdout_lines[-60:])) # 最後の60行を表示\n",
+        "        print(\"\\n--- STDERR (Error Details) ---\")\n",
+        "        print(result.stderr)\n",
+        "        return False\n",
+        "\n",
+        "    print(f\"✅ Successfully installed {name}\")\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "def install_mipsplatting_submodule(name, base_dir):\n",
+        "    \"\"\"mip-splattingに含まれるsubmoduleをインストール（クローン不要）\"\"\"\n",
+        "    print(f\"\\n{'='*70}\")\n",
+        "    print(f\"Installing {name} (from mip-splatting submodules)\")\n",
+        "    print(f\"{'='*70}\")\n",
+        "\n",
+        "    # submoduleのパス\n",
+        "    path = os.path.abspath(os.path.join(base_dir, \"submodules\", name))\n",
+        "    print(f\"  > Target path: {path}\")\n",
+        "\n",
+        "    # ファイルの存在確認\n",
+        "    if not os.path.exists(path):\n",
+        "        print(f\"❌ Path not found: {path}\")\n",
+        "        return False\n",
+        "\n",
+        "    # setup.pyの存在確認\n",
+        "    setup_py = os.path.join(path, \"setup.py\")\n",
+        "    if not os.path.exists(setup_py):\n",
+        "        print(f\"❌ setup.py not found: {setup_py}\")\n",
+        "        return False\n",
+        "\n",
+        "    print(f\"  > Checking files...\")\n",
+        "    files = os.listdir(path)\n",
+        "    print(f\"  > Files in {name}: {files[:10]}...\")\n",
+        "\n",
+        "    # ビルドキャッシュ削除\n",
+        "    build_dir = os.path.join(path, \"build\")\n",
+        "    if os.path.exists(build_dir):\n",
+        "        print(f\"  > Cleaning build cache...\")\n",
+        "        shutil.rmtree(build_dir)\n",
+        "\n",
+        "    # インストール\n",
+        "    print(f\"  > Installing {name} (This may take a few minutes)...\")\n",
+        "\n",
+        "    current_env = os.environ.copy()\n",
+        "    result = subprocess.run(\n",
+        "        [sys.executable, \"-m\", \"pip\", \"install\", \"-e\", \".\", \"--no-build-isolation\", \"-v\"],\n",
+        "        cwd=path,\n",
+        "        env=current_env,\n",
+        "        capture_output=True,\n",
+        "        text=True\n",
+        "    )\n",
+        "\n",
+        "    if result.returncode != 0:\n",
+        "        print(f\"❌ Failed to install {name}\")\n",
+        "        print(\"\\n--- STDOUT (Build Logs) ---\")\n",
+        "        stdout_lines = result.stdout.split('\\n')\n",
+        "        print('\\n'.join(stdout_lines[-60:]))\n",
+        "        print(\"\\n--- STDERR (Error Details) ---\")\n",
+        "        print(result.stderr)\n",
+        "        return False\n",
+        "\n",
+        "    print(f\"✅ Successfully installed {name}\")\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "def setup_environment():\n",
+        "    \"\"\"Setup mip-splatting environment with correct submodules\"\"\"\n",
+        "    print(\"=\"*70)\n",
+        "    print(\"Setting up mip-splatting environment\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    WORK_DIR = \"/content/mip-splatting\"\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 1: Clone main repository with submodules\n",
+        "    # =====================================================================\n",
+        "    print(\"\\nSTEP 1: Clone mip-splatting repository\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    if os.path.exists(WORK_DIR):\n",
+        "        print(f\"  > {WORK_DIR} already exists, removing...\")\n",
+        "        shutil.rmtree(WORK_DIR)\n",
+        "\n",
+        "    print(f\"  > Cloning mip-splatting with submodules...\")\n",
+        "    # --recursive で submodules も一緒にクローン\n",
+        "    run_cmd([\n",
+        "        \"git\", \"clone\", \"--recursive\",\n",
+        "        \"https://github.com/autonomousvision/mip-splatting.git\",\n",
+        "        WORK_DIR\n",
+        "    ])\n",
+        "    print(\"✅ Repository cloned with submodules\")\n",
+        "\n",
+        "    # submodulesが正しくクローンされたか確認\n",
+        "    print(\"\\n  > Verifying submodules...\")\n",
+        "    submodules_dir = os.path.join(WORK_DIR, \"submodules\")\n",
+        "    if os.path.exists(submodules_dir):\n",
+        "        items = os.listdir(submodules_dir)\n",
+        "        print(f\"  > Found submodules: {items}\")\n",
+        "\n",
+        "        # 空のsubmoduleディレクトリがある場合は初期化\n",
+        "        for item in items:\n",
+        "            item_path = os.path.join(submodules_dir, item)\n",
+        "            if os.path.isdir(item_path):\n",
+        "                item_files = os.listdir(item_path)\n",
+        "                if not item_files or len(item_files) == 0:\n",
+        "                    print(f\"  > {item} is empty, initializing...\")\n",
+        "                    run_cmd([\"git\", \"submodule\", \"update\", \"--init\", \"--recursive\"], cwd=WORK_DIR)\n",
+        "                    break\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: Fix numpy compatibility\n",
+        "    # =====================================================================\n",
+        "    print(\"\\nSTEP 2: Fix numpy compatibility\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    print(\"  > Uninstalling numpy 2.x...\")\n",
+        "    run_cmd([sys.executable, \"-m\", \"pip\", \"uninstall\", \"numpy\", \"-y\"], check=False)\n",
+        "\n",
+        "    print(\"  > Installing numpy<2.0...\")\n",
+        "    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"numpy<2.0\"])\n",
+        "    print(\"✅ numpy<2.0 installed\")\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 3: Install core dependencies\n",
+        "    # =====================================================================\n",
+        "    print(\"\\nSTEP 3: Install core dependencies\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    core_packages = [\n",
+        "        \"open3d\",\n",
+        "        \"plyfile\",\n",
+        "        \"tqdm\",\n",
+        "        \"Pillow\",\n",
+        "        \"opencv-python\"\n",
+        "    ]\n",
+        "\n",
+        "    for package in core_packages:\n",
+        "        print(f\"  > Installing {package}...\")\n",
+        "        run_cmd([sys.executable, \"-m\", \"pip\", \"install\", package])\n",
+        "    print(\"✅ Core dependencies installed\")\n",
+        "\n",
+        "    # =====================================================================\n",
+        "    # STEP 4: Build mip-splatting submodules\n",
+        "    # =====================================================================\n",
+        "    print(\"\\nSTEP 4: Build mip-splatting submodules\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    # simple-knn: 実績のあるfixed版を使用（クローンし直す）\n",
+        "    success_knn = install_submodule(\n",
+        "        \"simple-knn\",\n",
+        "        \"https://github.com/tztechno/simple-knn.git\",\n",
+        "        WORK_DIR\n",
+        "    )\n",
+        "\n",
+        "    if not success_knn:\n",
+        "        print(\"❌ Failed to install simple-knn\")\n",
+        "        return None\n",
+        "\n",
+        "    # diff-gaussian-rasterization: mip-splattingに含まれているものを使用\n",
+        "    # （kernel_size対応版なのでクローンし直さない）\n",
+        "    success_rast = install_mipsplatting_submodule(\n",
+        "        \"diff-gaussian-rasterization\",\n",
+        "        WORK_DIR\n",
+        "    )\n",
+        "\n",
+        "    if not success_rast:\n",
+        "        print(\"❌ Failed to install diff-gaussian-rasterization\")\n",
+        "        return None\n",
+        "\n",
+        "\n",
+        "    return WORK_DIR\n",
+        "\n",
+        "\n",
+        "\n",
+        "work_dir = setup_environment()\n",
+        "\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",
+        "\n",
+        "\n",
+        "\n",
+        "# After (mipGS) - Added Kernel Size and Multi-Scale Support\n",
+        "def train_gaussian_splatting(data_dir, work_dir, iterations=3000):\n",
+        "    \"\"\"Training function for mipGS with comprehensive error handling\"\"\"\n",
+        "\n",
+        "    # 入力検証\n",
+        "    if not work_dir:\n",
+        "        raise ValueError(\"work_dir cannot be None or empty\")\n",
+        "\n",
+        "    if not os.path.exists(work_dir):\n",
+        "        raise FileNotFoundError(f\"Work directory not found: {work_dir}\")\n",
+        "\n",
+        "    if not os.path.exists(data_dir):\n",
+        "        raise FileNotFoundError(f\"Data directory not found: {data_dir}\")\n",
+        "\n",
+        "    train_py_path = os.path.join(work_dir, \"train.py\")\n",
+        "    if not os.path.exists(train_py_path):\n",
+        "        raise FileNotFoundError(f\"train.py not found: {train_py_path}\")\n",
+        "\n",
+        "    # モデル保存パス\n",
+        "    model_path = os.path.join(work_dir, \"output\", \"video\")\n",
+        "    os.makedirs(model_path, exist_ok=True)\n",
+        "\n",
+        "    # コマンド構築\n",
+        "    cmd = [\n",
+        "        sys.executable, 'train.py',\n",
+        "        '-s', data_dir,\n",
+        "        '-m', model_path,\n",
+        "        '--iterations', str(iterations),\n",
+        "        '--eval'\n",
+        "    ]\n",
+        "\n",
+        "    print(f\"Training configuration:\")\n",
+        "    print(f\"  Work dir: {work_dir}\")\n",
+        "    print(f\"  Data dir: {data_dir}\")\n",
+        "    print(f\"  Model path: {model_path}\")\n",
+        "    print(f\"  Command: {' '.join(cmd)}\")\n",
+        "\n",
+        "    # 実行\n",
+        "    result = subprocess.run(\n",
+        "        cmd,\n",
+        "        cwd=work_dir,\n",
+        "        capture_output=True,\n",
+        "        text=True\n",
+        "    )\n",
+        "\n",
+        "    # エラーチェック\n",
+        "    if result.returncode != 0:\n",
+        "        print(f\"\\n❌ Training failed with exit code {result.returncode}\")\n",
+        "        print(\"\\n--- STDOUT ---\")\n",
+        "        print(result.stdout)\n",
+        "        print(\"\\n--- STDERR ---\")\n",
+        "        print(result.stderr)\n",
+        "        raise subprocess.CalledProcessError(result.returncode, cmd)\n",
+        "\n",
+        "    print(\"\\n✅ Training completed successfully\")\n",
+        "    return model_path\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# New function for mipGS - Fuse 3D filter into Gaussian parameters\n",
+        "def create_fused_ply(model_path, scene_name, output_dir=\"fused\"):\n",
+        "    \"\"\"\n",
+        "    Fuse the 3D smoothing filter to Gaussian parameters for deployment\n",
+        "    This creates a .ply file that can be used in online viewers\n",
+        "\n",
+        "    Args:\n",
+        "        model_path: Path to trained model\n",
+        "        scene_name: Name of the scene\n",
+        "        output_dir: Directory to save fused .ply file\n",
+        "    \"\"\"\n",
+        "    os.makedirs(output_dir, exist_ok=True)\n",
+        "    output_ply = f\"{output_dir}/{scene_name}_fused.ply\"\n",
+        "\n",
+        "    cmd = [\n",
+        "        sys.executable, 'create_fused_ply.py',\n",
+        "        '-m', f\"{model_path}/{scene_name}\",\n",
+        "        '--output_ply', output_ply\n",
+        "    ]\n",
+        "    subprocess.run(cmd, cwd=WORK_DIR, check=True)\n",
+        "    return output_ply\n",
+        ""
+      ],
+      "metadata": {
+        "id": "-Cwgr3I0b57O"
+      },
+      "id": "-Cwgr3I0b57O",
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "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",
+        "outputId": "4d068a8d-5a0b-4fb5-dbc7-c89fe2422fd6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "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/images', ['/content/colmap/images/image_101_top.jpeg', '/content/colmap/images/image_101_bottom.jpeg', '/content/colmap/images/image_102_top.jpeg', '/content/colmap/images/image_102_bottom.jpeg', '/content/colmap/images/image_103_top.jpeg', '/content/colmap/images/image_103_bottom.jpeg', '/content/colmap/images/image_104_top.jpeg', '/content/colmap/images/image_104_bottom.jpeg', '/content/colmap/images/image_105_top.jpeg', '/content/colmap/images/image_105_bottom.jpeg', '/content/colmap/images/image_106_top.jpeg', '/content/colmap/images/image_106_bottom.jpeg', '/content/colmap/images/image_107_top.jpeg', '/content/colmap/images/image_107_bottom.jpeg', '/content/colmap/images/image_108_top.jpeg', '/content/colmap/images/image_108_bottom.jpeg', '/content/colmap/images/image_109_top.jpeg', '/content/colmap/images/image_109_bottom.jpeg', '/content/colmap/images/image_110_top.jpeg', '/content/colmap/images/image_110_bottom.jpeg', '/content/colmap/images/image_111_top.jpeg', '/content/colmap/images/image_111_bottom.jpeg', '/content/colmap/images/image_112_top.jpeg', '/content/colmap/images/image_112_bottom.jpeg', '/content/colmap/images/image_113_top.jpeg', '/content/colmap/images/image_113_bottom.jpeg', '/content/colmap/images/image_114_top.jpeg', '/content/colmap/images/image_114_bottom.jpeg', '/content/colmap/images/image_115_top.jpeg', '/content/colmap/images/image_115_bottom.jpeg', '/content/colmap/images/image_116_top.jpeg', '/content/colmap/images/image_116_bottom.jpeg', '/content/colmap/images/image_117_top.jpeg', '/content/colmap/images/image_117_bottom.jpeg', '/content/colmap/images/image_118_top.jpeg', '/content/colmap/images/image_118_bottom.jpeg', '/content/colmap/images/image_119_top.jpeg', '/content/colmap/images/image_119_bottom.jpeg', '/content/colmap/images/image_120_top.jpeg', '/content/colmap/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",
+            "2/4: Matching features...\n",
+            "3/4: Sparse reconstruction...\n",
+            "4/4: Exporting to text format...\n",
+            "COLMAP reconstruction complete: /content/colmap/sparse/0\n",
+            "/content/colmap/images\n",
+            "/content/colmap/sparse/0\n",
+            "============================================================\n",
+            "Step 3: Preparing Gaussian Splatting data\n",
+            "============================================================\n",
+            "Preparing data for Gaussian Splatting...\n",
+            "Copying images...\n",
+            "Copied 40 images\n",
+            "Converting camera model to PINHOLE format...\n",
+            "Reading camera file: /content/colmap/sparse/0/cameras.txt\n",
+            "Converted 1 cameras to PINHOLE format\n",
+            "Copied images.txt\n",
+            "Copied points3D.txt\n",
+            "Data preparation complete: /content/mip-splatting/data/video\n",
+            "============================================================\n",
+            "Step 4: Training Gaussian Splatting model\n",
+            "============================================================\n",
+            "Training configuration:\n",
+            "  Work dir: /content/mip-splatting\n",
+            "  Data dir: /content/mip-splatting/data/video\n",
+            "  Model path: /content/mip-splatting/output/video\n",
+            "  Command: /usr/bin/python3 train.py -s /content/mip-splatting/data/video -m /content/mip-splatting/output/video --iterations 3000 --eval\n",
+            "\n",
+            "✅ Training completed successfully\n"
+          ]
+        }
+      ],
+      "source": [
+        "def main_pipeline(image_dir, output_dir,\n",
+        "                  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",
+        "        print(frame_dir)\n",
+        "        print(colmap_model_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",
+        "        model_path = train_gaussian_splatting(\n",
+        "            data_dir=data_dir,\n",
+        "            work_dir=work_dir,  # 明示的に渡す\n",
+        "            iterations=3000\n",
+        "        )\n",
+        "\n",
+        "        return ply_path\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",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    IMAGE_DIR = \"/content/drive/MyDrive/your_folder/fountain100\"\n",
+        "    OUTPUT_DIR = \"/content/output\"\n",
+        "    COLMAP_DIR = \"/content/colmap\"\n",
+        "\n",
+        "    ply_path = main_pipeline(\n",
+        "        image_dir=IMAGE_DIR,\n",
+        "        output_dir=OUTPUT_DIR,\n",
+        "        square_size=1024,\n",
+        "        max_images=20\n",
+        "    )\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e17ec719",
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+          "status": "completed"
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+      "accelerator": "nvidiaTeslaT4",
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+        {
+          "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",
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+      "environment_variables": {},
+      "exception": null,
+      "input_path": "__notebook__.ipynb",
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+  "nbformat_minor": 5
+}