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fountain-scafford-gaussian-splat-w-biplet-colmap.ipynb

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+{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":49349,"databundleVersionId":5447706,"sourceType":"competition"}],"dockerImageVersionId":31260,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **Fountain: Scaffold Gaussian Splatting w/biplet,colmap**\n### **biplet-colmap-scaffold-gs**\n\n","metadata":{}},{"cell_type":"markdown","source":"\n\n---\n\n## Overview: What is 3D Gaussian Splatting (3DGS)?\n\nTraditional methods like NeRF use a continuous neural network to represent a scene. In contrast, **3D Gaussian Splatting** is a point-based rendering technique. It represents a 3D scene using millions of overlapping, semi-transparent \"blobs\" (Gaussians).\n\nEach Gaussian is defined by:\n\n* **Position (Mean):** Where it is in space ().\n* **Covariance:** Its shape, size, and orientation (defined via scaling and rotation).\n* **Opacity ():** How transparent it is.\n* **Color (Spherical Harmonics):** How its color changes based on the viewing angle.\n\n**Why it’s a big deal:** It allows for **real-time rendering** (100+ FPS) and faster training times compared to NeRF because it uses \"splatting\"—rasterizing the Gaussians onto the 2D image plane using high-speed GPU kernels.\n\n---\n\n## Comparison of 3DGS Variants\n\nHere is the breakdown of the three specific variants you mentioned and how they differ from the original 3DGS.\n\n### 1. Scafford (Scaffold-GS)\n\n**Focus:** Efficiency and Sparse Data.\n\n* **The Difference:** While 3DGS places Gaussians somewhat randomly, Scaffold-GS uses a **sparse voxel grid (a \"scaffold\")** to guide the distribution.\n* **Key Features:**\n* **Anchor Points:** Gaussians are \"attached\" to anchor points within the grid.\n* **Neural Weighting:** It uses a small MLP to predict the properties of the Gaussians based on the local neighborhood.\n* **Storage:** It is significantly more storage-efficient than 3DGS because it doesn't need to save every attribute for every single Gaussian independently.\n\n\n* **Best For:** Large-scale scenes where memory usage is a concern.\n\n### 2. 2D Gaussian Splatting (2DGS)\n\n**Focus:** Surface Reconstruction and Consistency.\n\n* **The Difference:** Instead of 3D \"volumes,\" it uses **2D oriented disks (flat Gaussians)**.\n* **Key Features:**\n* **Thin Geometry:** By flattening the Gaussians, it forces the model to represent surfaces more accurately. Standard 3DGS often struggles with \"popping\" artifacts or fuzzy surfaces.\n* **Normal Mapping:** It provides well-defined surface normals, which makes it much easier to export the splat into a standard 3D mesh (like a .obj or .ply file).\n\n\n* **Best For:** When you need to turn your splat into a high-quality 3D mesh for gaming or CAD.\n\n### 3. Mip-Splatting\n\n**Focus:** Anti-aliasing and Multi-scale Rendering.\n\n* **The Difference:** 3DGS suffers from \"aliasing\" (jagged edges or flickering) when you zoom out or change resolutions. Mip-Splatting introduces **3D smoothing filters**.\n* **Key Features:**\n* **Frequency Constraint:** It limits the frequency of the Gaussians to match the pixel size of the image.\n* **Scale-Adaptive:** When you move the camera further away, the Gaussians are effectively \"blurred\" or filtered so they don't become smaller than a single pixel, preventing the \"salt-and-pepper\" noise seen in original 3DGS.\n\n\n* **Best For:** High-quality cinematics where the camera moves across different distances.\n\n---\n\n## Summary Table\n\n| Feature | 3DGS (Original) | Scaffold-GS | 2DGS | Mip-Splatting |\n| --- | --- | --- | --- | --- |\n| **Primitive** | 3D Ellipsoid | Anchored 3D Ellipsoid | 2D Thin Disk | Filtered 3D Ellipsoid |\n| **Main Advantage** | Speed & Simplicity | Memory Efficiency | Better Surfaces/Meshing | No Aliasing/Blurring |\n| **VRAM Usage** | High | **Low** | Medium | Medium |\n| **Visual Quality** | Great (except zoom) | Great | Sharp Surfaces | **Best** (consistent) |\n\n---\n\n","metadata":{}},{"cell_type":"code","source":"import os\nimport sys\nimport subprocess\nimport shutil\nfrom pathlib import Path\nimport cv2\nfrom PIL import Image\nimport glob\n\nIMAGE_PATH=\"/kaggle/input/competitions/image-matching-challenge-2023/train/haiper/fountain/images\"\nWORK_DIR = '/kaggle/working/scaffold-gs' \nOUTPUT_DIR = '/kaggle/working/output'\nCOLMAP_DIR = '/kaggle/working/colmap'","metadata":{"trusted":true,"execution":{"execution_failed":"2026-02-14T08:31:25.061Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install torch_scatter -f https://data.pyg.org/whl/torch-2.8.0+cu126.html","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def install_submodule(name, url, base_dir):\n\n    print(f\"\\n{'='*70}\")\n    print(f\"Installing {name}\")\n    print(f\"{'='*70}\")\n\n    # Use absolute path\n    path = os.path.abspath(os.path.join(base_dir, \"submodules\", name))\n    print(f\"  > Target path: {path}\")\n\n    # Step 1: Remove existing directory\n    if os.path.exists(path):\n        print(f\"  > Removing old {name}...\")\n        shutil.rmtree(path)\n\n    # Step 2: Clone repository\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: Verify files\n    print(f\"  > Checking cloned files...\")\n    files = os.listdir(path)\n    print(f\"  > Files in {name}: {files[:10]}...\")\n\n    # Step 4: Clear build cache\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: Install\n    print(f\"  > Installing {name} (This may take a few minutes)...\")\n\n    # Explicitly pass environment variables\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 build errors often appear in stdout, so output both\n        print(\"\\n--- STDOUT (Build Logs) ---\")\n        stdout_lines = result.stdout.split('\\n')\n        print('\\n'.join(stdout_lines[-60:])) # Show last 60 lines\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\ndef install_mipsplatting_submodule(name, base_dir):\n    \"\"\"Install submodules included in mip-splatting (no cloning required)\"\"\"\n    print(f\"\\n{'='*70}\")\n    print(f\"Installing {name} (from mip-splatting submodules)\")\n    print(f\"{'='*70}\")\n\n    # Submodule path\n    path = os.path.abspath(os.path.join(base_dir, \"submodules\", name))\n    print(f\"  > Target path: {path}\")\n\n    # Verify path existence\n    if not os.path.exists(path):\n        print(f\"❌ Path not found: {path}\")\n        return False\n\n    # Verify setup.py existence\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    # Clear build cache\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    # Install\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\n\ndef run_cmd(cmd, check=True, capture=False):\n    \"\"\"Run command with better error handling\"\"\"\n    print(f\"Running: {' '.join(cmd)}\")\n    result = subprocess.run(\n        cmd,\n        capture_output=capture,\n        text=True,\n        check=False\n    )\n    if check and result.returncode != 0:\n        print(f\"❌ Command failed with code {result.returncode}\")\n        if capture:\n            print(f\"STDOUT: {result.stdout}\")\n            print(f\"STDERR: {result.stderr}\")\n    return result\n\n\ndef setup_environment():\n    print(\"🚀 Setting up environment for Scaffold-GS (FIXED VERSION)\")\n\n    WORK_DIR = \"scaffold-gs\"\n    os.environ[\"CUDA_HOME\"] = \"/usr/local/cuda\"\n\n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 0: Fix NumPy\")\n    print(\"=\"*70)\n\n    run_cmd([sys.executable, \"-m\", \"pip\", \"uninstall\", \"-y\", \"numpy\"])\n    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"numpy==1.26.4\"])\n\n\n    print('------numpy version----------------------')\n    !pip show numpy | grep Version\n    print('----------------------------')\n\n\n\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    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    print('------numpy version----------------------')\n    !pip show numpy | grep Version\n    print('----------------------------')\n\n\n    \n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 2: Clone Scaffold-GS\")\n    print(\"=\"*70)\n\n    if not os.path.exists(WORK_DIR):\n        run_cmd([\n            \"git\", \"clone\", \"--recursive\",\n            \"https://github.com/city-super/Scaffold-GS.git\",\n            WORK_DIR\n        ])\n    else:\n        print(\"✓ Repository already exists\")\n\n    print('------numpy version----------------------')\n    !pip show numpy | grep Version\n    print('----------------------------')\n\n\n    \n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 3: Python packages\")\n    print(\"=\"*70)\n\n    print(\"\\n📦 Installing PyTorch...\")\n    run_cmd([\n        sys.executable, \"-m\", \"pip\", \"install\",\n        \"torch\", \"torchvision\", \"torchaudio\"\n    ])\n\n    print(\"\\n📦 Installing core utilities...\")\n    run_cmd([\n        sys.executable, \"-m\", \"pip\", \"install\",\n        \"opencv-python\",\n        \"pillow\",\n        \"imageio\",\n        \"imageio-ffmpeg\",\n        \"plyfile\",\n        \"tqdm\",\n        \"tensorboard\"\n    ])\n\n    print(\"\\n📦 Installing additional dependencies...\")\n    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"kornia\"])\n    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"h5py\"])\n    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"matplotlib\"])\n    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"pycolmap\"])\n    \n    # CRITICAL: Scaffold-GS specific dependencies\n    print(\"\\n📦 Installing Scaffold-GS specific dependencies...\")\n    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"lpips\"])  # LPIPS for perceptual loss\n\n\n    print('------numpy version----------------------')\n    !pip show numpy | grep Version\n    print('----------------------------')\n\n\n    run_cmd([sys.executable, \"-m\", \"pip\", \"uninstall\", \"-y\", \"numpy\"])\n    run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"numpy==1.26.4\"])\n\n\n\n    \n    print(\"\\n\" + \"=\"*70)\n    print(\"STEP 4: Build Scaffold-GS submodules\")\n    print(\"=\"*70)\n\n    \n    # simple-knn: Using a proven fixed version (re-cloning)\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: Use the version included in mip-splatting\n    # (Do not re-clone as this version supports kernel_size)\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    print('------numpy version----------------------')\n    !pip show numpy | grep Version\n    print('----------------------------')\n\n\n    return WORK_DIR\n\n\nif __name__ == \"__main__\":\n    setup_environment()","metadata":{"trusted":true,"execution":{"execution_failed":"2026-02-14T08:31:25.061Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport glob\nimport cv2\nimport numpy as np\nfrom PIL import Image\n\ndef 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    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    image_files = sorted([f for f in os.listdir(input_dir)\n                         if f.lower().endswith(('.jpg', '.jpeg', '.png'))])\n\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            size_key = f\"{original_size[0]}x{original_size[1]}\"\n            size_stats[size_key] = size_stats.get(size_key, 0) + 1\n\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\ndef generate_two_crops(img, size):\n    \"\"\"\n    Crops the image into a square and returns 2 variations\n    (Left/Right for landscape, Top/Bottom for portrait).\n    \"\"\"\n    width, height = img.size\n    crop_size = min(width, height)\n    crops = {}\n\n    if width > height:\n        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        positions = {\n            'top': 0,\n            'bottom': height - crop_size\n        }\n        for mode, y_offset in positions.items():\n            box = (0, y_offset, crop_size, y_offset + crop_size)\n            crops[mode] = img.crop(box).resize(\n                (size, size),\n                Image.Resampling.LANCZOS\n            )\n\n    return crops","metadata":{"trusted":true,"execution":{"execution_failed":"2026-02-14T08:31:25.062Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","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    env = os.environ.copy()\n    env['QT_QPA_PLATFORM'] = 'offscreen'\n\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'\n    ], check=True, env=env)\n\n    print(\"2/4: Matching features...\")\n    subprocess.run([\n        'colmap', 'exhaustive_matcher',\n        '--database_path', database_path,\n        '--SiftMatching.use_gpu', '0'\n    ], check=True, env=env)\n\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',\n        '--Mapper.ba_local_max_num_iterations', '10'\n    ], check=True, env=env)\n\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        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\ndef 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                    if model == \"PINHOLE\":\n                        f.write(line)\n                    elif model == \"OPENCV\":\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                        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\ndef prepare_scaffold_gs_data(image_dir, colmap_model_dir):\n    \"\"\"Prepare data for Scaffold-GS (same as 3DGS)\"\"\"\n    print(\"Preparing data for Scaffold-GS...\")\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    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    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    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\ndef train_scaffold_gs(data_dir, iterations=30000, voxel_size=0, update_init_factor=16):\n    \"\"\"Train the Scaffold-GS model with specific parameters\"\"\"\n    print(f\"Training Scaffold-GS model for {iterations} iterations...\")\n    print(f\"Parameters: voxel_size={voxel_size}, update_init_factor={update_init_factor}\")\n\n    model_path = f\"{WORK_DIR}/output/video\"\n\n    cmd = [\n        sys.executable, 'train.py',\n        '-s', data_dir,\n        '-m', model_path,\n        '--iterations', str(iterations),\n        '--voxel_size', str(voxel_size),  \n        '--update_init_factor', str(update_init_factor),  \n        '--eval'\n    ]\n\n    subprocess.run(cmd, cwd=WORK_DIR, check=True)\n\n    return model_path\n\n\ndef render_video(model_path, output_video_path, iteration=30000):\n    \"\"\"Generate video from the trained Scaffold-GS model\"\"\"\n    print(\"Rendering video with Scaffold-GS...\")\n\n    cmd = [\n        sys.executable, 'render.py',\n        '-m', model_path,\n        '--iteration', str(iteration)\n    ]\n\n    subprocess.run(cmd, cwd=WORK_DIR, check=True)\n\n    possible_dirs = [\n        f\"{model_path}/test/ours_{iteration}/renders\",\n        f\"{model_path}/train/ours_{iteration}/renders\",\n    ]\n\n    render_dir = None\n    for test_dir in possible_dirs:\n        if os.path.exists(test_dir):\n            render_dir = test_dir\n            print(f\"Rendering directory found: {render_dir}\")\n            break\n\n    if render_dir and os.path.exists(render_dir):\n        render_imgs = sorted([f for f in os.listdir(render_dir) if f.endswith('.png')])\n\n        if render_imgs:\n            print(f\"Found {len(render_imgs)} rendered images\")\n\n            subprocess.run([\n                'ffmpeg', '-y',\n                '-framerate', '30',\n                '-pattern_type', 'glob',\n                '-i', f\"{render_dir}/*.png\",\n                '-c:v', 'libx264',\n                '-pix_fmt', 'yuv420p',\n                '-crf', '18',\n                output_video_path\n            ], check=True)\n\n            print(f\"Video saved: {output_video_path}\")\n            return True\n\n    print(\"Error: Rendering directory not found\")\n    return False\n\n\ndef create_gif(video_path, gif_path):\n    \"\"\"Create GIF from MP4\"\"\"\n    print(\"Creating animated GIF...\")\n\n    subprocess.run([\n        'ffmpeg', '-y',\n        '-i', video_path,\n        '-vf', 'setpts=8*PTS,fps=10,scale=720:-1:flags=lanczos',\n        '-loop', '0',\n        gif_path\n    ], check=True)\n\n    if os.path.exists(gif_path):\n        size_mb = os.path.getsize(gif_path) / (1024 * 1024)\n        print(f\"GIF creation complete: {gif_path} ({size_mb:.2f} MB)\")\n        return True\n\n    return False","metadata":{"trusted":true,"execution":{"execution_failed":"2026-02-14T08:31:25.062Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def main_pipeline(image_dir, output_dir, square_size=1024, max_images=100, \n                 iterations=30000, voxel_size=0, update_init_factor=16):\n    \"\"\"\n    Main pipeline for Scaffold-GS training and rendering\n    \n    Args:\n        image_dir: Input image directory\n        output_dir: Output directory for results\n        square_size: Image size for preprocessing\n        max_images: Maximum number of images to process\n        iterations: Training iterations (default: 30000 for Scaffold-GS)\n        voxel_size: Voxel size for anchor placement (0 = auto)\n        update_init_factor: Initial resolution for anchor growing\n    \"\"\"\n\n    try:\n        print(\"=\"*60)\n        print(\"Scaffold-GS Pipeline\")\n        print(\"=\"*60)\n        \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        num_processed = normalize_image_sizes_biplet(\n            input_dir=image_dir,\n            output_dir=frame_dir,  \n            size=square_size,\n            max_images=max_images\n        )\n\n        print(f\"Processed images: {num_processed}\")\n\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(\"=\"*60)\n        print(\"Step 3: Preparing Scaffold-GS data\")\n        print(\"=\"*60)\n        data_dir = prepare_scaffold_gs_data(frame_dir, colmap_model_dir)\n\n        print(\"=\"*60)\n        print(\"Step 4: Training Scaffold-GS model\")\n        print(f\"  - Iterations: {iterations}\")\n        print(f\"  - Voxel size: {voxel_size} (0=auto)\")\n        print(f\"  - Update init factor: {update_init_factor}\")\n        print(\"=\"*60)\n        model_path = train_scaffold_gs(\n            data_dir, \n            iterations=iterations,\n            voxel_size=voxel_size,\n            update_init_factor=update_init_factor\n        )\n\n        print(\"=\"*60)\n        print(\"Step 5: Rendering video\")\n        print(\"=\"*60)\n        os.makedirs(OUTPUT_DIR, exist_ok=True)\n        output_video = os.path.join(OUTPUT_DIR, \"scaffold_gs_video.mp4\")\n\n        success = render_video(model_path, output_video, iteration=iterations)\n\n        if success:\n            print(\"=\"*60)\n            print(f\"Success! Scaffold-GS video generation complete: {output_video}\")\n            print(\"=\"*60)\n\n            output_gif = os.path.join(OUTPUT_DIR, \"scaffold_gs_video.gif\")\n            create_gif(output_video, output_gif)\n\n            from IPython.display import Image, display\n            display(Image(open(output_gif, 'rb').read()))\n\n            return output_video, output_gif\n        else:\n            print(\"Warning: Rendering complete, but video was not generated\")\n            return None, None\n\n    except Exception as e:\n        print(f\"Error: {str(e)}\")\n        import traceback\n        traceback.print_exc()\n        return None, None\n\n\nif __name__ == \"__main__\":\n    IMAGE_DIR = \"/kaggle/input/competitions/image-matching-challenge-2023/train/haiper/fountain/images\"\n    OUTPUT_DIR = \"/kaggle/working/output\"\n    COLMAP_DIR = \"/kaggle/working/colmap\"\n\n    # Scaffold-GS specific parameters\n    video_path, gif_path = main_pipeline(\n        image_dir=IMAGE_DIR,\n        output_dir=OUTPUT_DIR,\n        square_size=1024,\n        max_images=20,\n        iterations=3000,  \n        voxel_size=0,  \n        update_init_factor=16  \n    )","metadata":{"trusted":true,"execution":{"execution_failed":"2026-02-14T08:31:25.062Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}