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**biplet_dino_mast3r_ps3_gs_kg**\n\n","metadata":{"id":"qDQLX3PArmh8","papermill":{"duration":0.003504,"end_time":"2026-01-20T01:06:31.022336","exception":false,"start_time":"2026-01-20T01:06:31.018832","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"# setup","metadata":{"id":"M4iuU0WnGpBY"}},{"cell_type":"code","source":"from google.colab import drive\ndrive.mount('/content/drive')","metadata":{"id":"vfqsbwoqlO7r","outputId":"da254904-082f-472d-8bf6-2117530cdd7f"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# MASt3R-based Gaussian Splatting Pipeline\n# Preserves: DINO pair selection + Biplet-Square Normalization\n# Replaces: ALIKED/LightGlue/COLMAP with MASt3R\n\nimport os\nimport sys\nimport gc\nimport h5py\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom tqdm import tqdm\nfrom pathlib import Path\nimport subprocess\nfrom PIL import Image, ImageFilter\nimport struct\n\n# Transformers for DINO\nfrom transformers import AutoImageProcessor, AutoModel\n\n# ============================================================================\n# Configuration\n# ============================================================================\nclass Config:\n # Feature extraction\n N_KEYPOINTS = 8192\n IMAGE_SIZE = 1024\n\n # Pair selection - CRITICAL for memory\n GLOBAL_TOPK = 20 # Reduced from 50 - each image pairs with top 20\n MIN_MATCHES = 10\n RATIO_THR = 1.2\n\n # Paths\n DINO_MODEL = \"facebook/dinov2-base\"\n\n # MASt3R - Reduced size for memory\n MAST3R_MODEL = \"/content/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\"\n MAST3R_IMAGE_SIZE = 224 # Small size to save memory\n\n # Device\n DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# ============================================================================\n# Memory Management Utilities\n# ============================================================================\n\ndef clear_memory():\n \"\"\"Aggressively clear GPU and CPU memory\"\"\"\n gc.collect()\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n torch.cuda.synchronize()\n\ndef get_memory_info():\n \"\"\"Get current memory usage\"\"\"\n if torch.cuda.is_available():\n allocated = torch.cuda.memory_allocated() / 1024**3\n reserved = torch.cuda.memory_reserved() / 1024**3\n print(f\"GPU Memory - Allocated: {allocated:.2f}GB, Reserved: {reserved:.2f}GB\")\n\n import psutil\n cpu_mem = psutil.virtual_memory().percent\n print(f\"CPU Memory Usage: {cpu_mem:.1f}%\")\n\n# ============================================================================\n# Environment Setup\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\"β 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_base_environment():\n \"\"\"Setup base Python environment\"\"\"\n print(\"\\n=== Setting up Base Environment ===\")\n\n # NumPy fix for Python 3.12\n print(\"\\nπ¦ Fixing NumPy...\")\n run_cmd([sys.executable, \"-m\", \"pip\", \"uninstall\", \"-y\", \"numpy\"])\n run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"numpy==1.26.4\"])\n\n # PyTorch\n print(\"\\nπ¦ Installing PyTorch...\")\n run_cmd([\n sys.executable, \"-m\", \"pip\", \"install\",\n \"torch\", \"torchvision\", \"torchaudio\"\n ])\n\n # Core utilities\n print(\"\\nπ¦ 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 \"scipy\", # for rotation conversions and image resizing\n \"psutil\" # for memory monitoring\n ])\n\n # Transformers for DINO\n print(\"\\nπ¦ Installing transformers...\")\n run_cmd([\n sys.executable, \"-m\", \"pip\", \"install\",\n \"transformers>=4.45.0\"\n ])\n\n # pycolmap for COLMAP format\n print(\"\\nπ¦ Installing pycolmap...\")\n run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"pycolmap\"])\n\n print(\"β Base environment setup complete!\")\n\n\ndef setup_mast3r():\n \"\"\"Install and setup MASt3R\"\"\"\n print(\"\\n=== Setting up MASt3R ===\")\n\n os.chdir('/content')\n\n # Remove existing installation\n if os.path.exists('mast3r'):\n print(\"Removing existing MASt3R installation...\")\n os.system('rm -rf mast3r')\n\n # Clone repository\n print(\"Cloning MASt3R repository...\")\n os.system('git clone --recursive https://github.com/naver/mast3r')\n os.chdir('/content/mast3r')\n\n # Check dust3r directory\n print(\"Checking dust3r structure...\")\n os.system('ls -la dust3r/')\n\n # Install dust3r\n print(\"Installing dust3r...\")\n os.system('cd dust3r && python -m pip install -e .')\n\n # Install croco\n print(\"Installing croco...\")\n os.system('cd dust3r/croco && python -m pip install -e .')\n\n # Install requirements\n print(\"Installing MASt3R requirements...\")\n os.system('pip install -r requirements.txt')\n\n # Download model weights\n print(\"Downloading model weights...\")\n os.system('mkdir -p checkpoints')\n os.system('wget -P checkpoints/ https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth')\n\n # Install additional dependencies\n print(\"Installing additional dependencies...\")\n os.system('pip install trimesh matplotlib roma')\n\n # Add to path\n sys.path.insert(0, '/content/mast3r')\n sys.path.insert(0, '/content/mast3r/dust3r')\n\n # Verification\n print(\"\\nπ Verifying MASt3R installation...\")\n try:\n from mast3r.model import AsymmetricMASt3R\n print(\" β MASt3R import: OK\")\n except Exception as e:\n print(f\" β MASt3R import failed: {e}\")\n raise\n\n print(\"β MASt3R setup complete!\")\n\ndef setup_gaussian_splatting():\n \"\"\"Setup Gaussian Splatting\"\"\"\n print(\"\\n=== Setting up Gaussian Splatting ===\")\n\n os.chdir('/content')\n\n WORK_DIR = \"gaussian-splatting\"\n\n if not os.path.exists(WORK_DIR):\n print(\"Cloning Gaussian Splatting repository...\")\n run_cmd([\n \"git\", \"clone\", \"--recursive\",\n \"https://github.com/graphdeco-inria/gaussian-splatting.git\",\n WORK_DIR\n ])\n else:\n print(\"β Repository already exists\")\n\n os.chdir(WORK_DIR)\n\n # Install requirements\n print(\"Installing Gaussian Splatting requirements...\")\n run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"-r\", \"requirements.txt\"])\n\n # Build submodules\n print(\"\\nπ¦ Building Gaussian Splatting submodules...\")\n\n submodules = {\n \"diff-gaussian-rasterization\":\n \"https://github.com/graphdeco-inria/diff-gaussian-rasterization.git\",\n \"simple-knn\":\n \"https://github.com/camenduru/simple-knn.git\"\n }\n\n for name, repo in submodules.items():\n print(f\"\\nπ¦ Installing {name}...\")\n path = os.path.join(\"submodules\", name)\n if not os.path.exists(path):\n run_cmd([\"git\", \"clone\", repo, path])\n run_cmd([sys.executable, \"-m\", \"pip\", \"install\", path])\n\n print(\"β Gaussian Splatting setup complete!\")\n","metadata":{"execution":{"iopub.status.busy":"2026-02-02T08:53:09.950151Z","iopub.execute_input":"2026-02-02T08:53:09.950445Z","iopub.status.idle":"2026-02-02T08:53:09.967045Z","shell.execute_reply.started":"2026-02-02T08:53:09.95042Z","shell.execute_reply":"2026-02-02T08:53:09.966479Z"},"papermill":{"duration":46.280727,"end_time":"2026-01-20T01:07:23.641872","exception":false,"start_time":"2026-01-20T01:06:37.361145","status":"completed"},"tags":[],"trusted":true,"_kg_hide-output":true,"id":"hGX7IYJ6GpBZ"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\nsetup_base_environment()\nclear_memory()\n\nsetup_mast3r()\nclear_memory()\n\nsetup_gaussian_splatting()\nclear_memory()","metadata":{"trusted":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2026-02-02T08:53:09.968021Z","iopub.execute_input":"2026-02-02T08:53:09.968253Z","iopub.status.idle":"2026-02-02T08:56:35.635976Z","shell.execute_reply.started":"2026-02-02T08:53:09.968233Z","shell.execute_reply":"2026-02-02T08:56:35.635328Z"},"id":"sIf3UgDZGpBa","outputId":"0a1eb888-aba1-4990-ab52-28584e9104ec"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true,"id":"38nn_QqcGpBa"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# dino & mast3r","metadata":{"id":"L6OBEO0zGpBa"}},{"cell_type":"code","source":"# ============================================================================\n# Step 0: Biplet-Square Normalization (PRESERVED FROM ORIGINAL)\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 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 # β
max_images γ§ε
η»εζ°γεΆι\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\" β {img_file}: {original_size} β 2 square images generated\")\n\n except Exception as e:\n print(f\" β 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 # Landscape β 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 β 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\n# ============================================================================\n# Step 1: DINO-based Pair Selection (PRESERVED FROM ORIGINAL)\n# ============================================================================\n\ndef load_torch_image(fname, device):\n \"\"\"Load image as torch tensor\"\"\"\n import torchvision.transforms as T\n\n img = Image.open(fname).convert('RGB')\n transform = T.Compose([\n T.ToTensor(),\n ])\n return transform(img).unsqueeze(0).to(device)\n\ndef extract_dino_global(image_paths, model_path, device):\n \"\"\"Extract DINO global descriptors with memory management\"\"\"\n print(\"\\n=== Extracting DINO Global Features ===\")\n print(\"Initial memory state:\")\n get_memory_info()\n\n processor = AutoImageProcessor.from_pretrained(model_path)\n model = AutoModel.from_pretrained(model_path).eval().to(device)\n\n global_descs = []\n batch_size = 1 # Small batch to save memory\n\n for i in tqdm(range(0, len(image_paths), batch_size)):\n batch_paths = image_paths[i:i+batch_size]\n batch_imgs = []\n\n for img_path in batch_paths:\n img = load_torch_image(img_path, device)\n batch_imgs.append(img)\n\n batch_tensor = torch.cat(batch_imgs, dim=0)\n\n with torch.no_grad():\n inputs = processor(images=batch_tensor, return_tensors=\"pt\", do_rescale=False).to(device)\n outputs = model(**inputs)\n desc = F.normalize(outputs.last_hidden_state[:, 1:].max(dim=1)[0], dim=1, p=2)\n global_descs.append(desc.cpu())\n\n # Clear batch memory\n del batch_tensor, inputs, outputs, desc\n clear_memory()\n\n global_descs = torch.cat(global_descs, dim=0)\n\n del model, processor\n clear_memory()\n\n print(\"After DINO extraction:\")\n get_memory_info()\n\n return global_descs\n\ndef build_topk_pairs(global_feats, k, device):\n \"\"\"Build top-k similar pairs from global features\"\"\"\n g = global_feats.to(device)\n sim = g @ g.T\n sim.fill_diagonal_(-1)\n\n N = sim.size(0)\n k = min(k, N - 1)\n\n topk_indices = torch.topk(sim, k, dim=1).indices.cpu()\n\n pairs = []\n for i in range(N):\n for j in topk_indices[i]:\n j = j.item()\n if i < j:\n pairs.append((i, j))\n\n # Remove duplicates\n pairs = list(set(pairs))\n\n return pairs\n\ndef select_diverse_pairs(pairs, max_pairs, num_images):\n \"\"\"\n Select diverse pairs to ensure good image coverage\n Strategy: Select pairs that maximize image coverage\n \"\"\"\n import random\n random.seed(42)\n\n if len(pairs) <= max_pairs:\n return pairs\n\n print(f\"Selecting {max_pairs} diverse pairs from {len(pairs)} candidates...\")\n\n # Count how many times each image appears in pairs\n image_counts = {i: 0 for i in range(num_images)}\n for i, j in pairs:\n image_counts[i] += 1\n image_counts[j] += 1\n\n # Sort pairs by: prefer pairs with less-connected images\n def pair_score(pair):\n i, j = pair\n # Lower score = images appear in fewer pairs = more diverse\n return image_counts[i] + image_counts[j]\n\n pairs_scored = [(pair, pair_score(pair)) for pair in pairs]\n pairs_scored.sort(key=lambda x: x[1])\n\n # Select pairs greedily to maximize coverage\n selected = []\n selected_images = set()\n\n # Phase 1: Select pairs that add new images (greedy coverage)\n for pair, score in pairs_scored:\n if len(selected) >= max_pairs:\n break\n i, j = pair\n # Prefer pairs that include new images\n if i not in selected_images or j not in selected_images:\n selected.append(pair)\n selected_images.add(i)\n selected_images.add(j)\n\n # Phase 2: Fill remaining slots with high-similarity pairs\n if len(selected) < max_pairs:\n remaining = [p for p, s in pairs_scored if p not in selected]\n random.shuffle(remaining)\n selected.extend(remaining[:max_pairs - len(selected)])\n\n print(f\"Selected pairs cover {len(selected_images)} / {num_images} images ({100*len(selected_images)/num_images:.1f}%)\")\n\n return selected\n\ndef get_image_pairs_dino(image_paths, max_pairs=None):\n \"\"\"DINO-based pair selection with intelligent limiting\"\"\"\n device = Config.DEVICE\n\n # DINO global features\n global_feats = extract_dino_global(image_paths, Config.DINO_MODEL, device)\n pairs = build_topk_pairs(global_feats, Config.GLOBAL_TOPK, device)\n\n print(f\"Initial pairs from DINO: {len(pairs)}\")\n\n # Apply intelligent pair selection if limit specified\n if max_pairs and len(pairs) > max_pairs:\n pairs = select_diverse_pairs(pairs, max_pairs, len(image_paths))\n\n return pairs\n\n# ============================================================================\n# Step 2: MASt3R Reconstruction (REPLACES ALIKED/LIGHTGLUE/COLMAP)\n# ============================================================================\n\ndef load_mast3r_model(device='cuda'):\n \"\"\"Load MASt3R model\"\"\"\n from mast3r.model import AsymmetricMASt3R\n\n model = AsymmetricMASt3R.from_pretrained(Config.MAST3R_MODEL).to(device)\n model.eval()\n\n print(f\"β MASt3R model loaded on {device}\")\n return model\n\ndef load_images_for_mast3r(image_paths, size=224):\n \"\"\"Load images using DUSt3R's format with reduced size\"\"\"\n print(f\"\\n=== Loading images for MASt3R (size={size}) ===\")\n\n from dust3r.utils.image import load_images\n\n # Load images using DUSt3R's loader with reduced size\n images = load_images(image_paths, size=size, verbose=True)\n\n return images\n\ndef run_mast3r_pairs(model, image_paths, pairs, device='cuda', batch_size=1, max_pairs=None):\n \"\"\"Run MASt3R on selected pairs with memory management\"\"\"\n print(\"\\n=== Running MASt3R Reconstruction ===\")\n print(\"Initial memory state:\")\n get_memory_info()\n\n from dust3r.inference import inference\n from dust3r.cloud_opt import global_aligner, GlobalAlignerMode\n\n # Limit number of pairs if specified\n if max_pairs and len(pairs) > max_pairs:\n print(f\"Limiting pairs from {len(pairs)} to {max_pairs}\")\n # Select pairs more evenly distributed\n step = max(1, len(pairs) // max_pairs)\n pairs = pairs[::step][:max_pairs]\n\n print(f\"Processing {len(pairs)} pairs...\")\n\n # Load images in smaller size\n print(f\"Loading {len(image_paths)} images at {Config.MAST3R_IMAGE_SIZE}x{Config.MAST3R_IMAGE_SIZE}...\")\n images = load_images_for_mast3r(image_paths, size=Config.MAST3R_IMAGE_SIZE)\n\n print(f\"Loaded {len(images)} images\")\n print(\"After loading images:\")\n get_memory_info()\n\n # Create all image pairs at once\n print(f\"Creating {len(pairs)} image pairs...\")\n mast3r_pairs = []\n for idx1, idx2 in tqdm(pairs, desc=\"Preparing pairs\"):\n mast3r_pairs.append((images[idx1], images[idx2]))\n\n print(f\"Running MASt3R inference on {len(mast3r_pairs)} pairs...\")\n\n # Run inference (this returns the dict format we need)\n output = inference(mast3r_pairs, model, device, batch_size=batch_size, verbose=True)\n\n # Clear pairs from memory\n del mast3r_pairs\n clear_memory()\n\n print(\"β MASt3R inference complete\")\n print(\"After inference:\")\n get_memory_info()\n\n # Global alignment\n print(\"Running global alignment...\")\n scene = global_aligner(\n output,\n device=device,\n mode=GlobalAlignerMode.PointCloudOptimizer\n )\n\n # Clear output after creating scene\n del output\n clear_memory()\n\n print(\"Computing global alignment...\")\n loss = scene.compute_global_alignment(\n init=\"mst\",\n niter=150, # Reduced from 300\n schedule='cosine',\n lr=0.01\n )\n\n print(f\"β Global alignment complete (final loss: {loss:.6f})\")\n print(\"Final memory state:\")\n get_memory_info()\n\n return scene, images","metadata":{"execution":{"iopub.status.busy":"2026-02-02T08:56:35.637478Z","iopub.execute_input":"2026-02-02T08:56:35.637807Z","iopub.status.idle":"2026-02-02T08:56:35.663238Z","shell.execute_reply.started":"2026-02-02T08:56:35.637784Z","shell.execute_reply":"2026-02-02T08:56:35.66271Z"},"papermill":{"duration":46.280727,"end_time":"2026-01-20T01:07:23.641872","exception":false,"start_time":"2026-01-20T01:06:37.361145","status":"completed"},"tags":[],"trusted":true,"id":"uMBRydm7GpBa"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true,"id":"_tTGwERoGpBb"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# process3","metadata":{"id":"1FcMgB48GpBb"}},{"cell_type":"code","source":"# ============================================================================\n# COLMAP Conversion (process3_11.py) - COMPLETE FIXED VERSION - ply success\n# ============================================================================\n\nimport numpy as np\nimport cv2\nfrom pathlib import Path\nimport struct\nfrom scipy.spatial.transform import Rotation\nimport torch\nfrom PIL import Image\n\n\ndef write_next_bytes(fid, data, format_str):\n \"\"\"Helper function to write bytes to file\"\"\"\n if isinstance(data, (list, tuple, np.ndarray)):\n fid.write(struct.pack(\"<\" + format_str, *data))\n else:\n fid.write(struct.pack(\"<\" + format_str, data))\n\n\ndef matrix_to_quaternion_translation(matrix: np.ndarray):\n \"\"\"Robust conversion of 4x4 transformation matrix to quaternion and translation.\"\"\"\n R = matrix[:3, :3]\n t = matrix[:3, 3]\n\n # Use scipy for robust quaternion conversion\n rot = Rotation.from_matrix(R)\n quat = rot.as_quat() # Returns [x, y, z, w]\n\n # COLMAP format is [w, x, y, z]\n qvec = np.array([quat[3], quat[0], quat[1], quat[2]])\n\n return qvec, t\n\n\ndef write_cameras_binary(cameras, path_to_model_file):\n \"\"\"Write COLMAP cameras.bin file\"\"\"\n with open(path_to_model_file, \"wb\") as fid:\n write_next_bytes(fid, len(cameras), \"Q\")\n for camera_id, cam in cameras.items():\n model_id = 1 # PINHOLE\n write_next_bytes(fid, camera_id, \"I\")\n write_next_bytes(fid, model_id, \"I\")\n write_next_bytes(fid, cam['width'], \"Q\")\n write_next_bytes(fid, cam['height'], \"Q\")\n for p in cam['params']:\n write_next_bytes(fid, float(p), \"d\")\n\n\ndef write_images_binary(images, path_to_model_file):\n \"\"\"Write COLMAP images.bin file\"\"\"\n with open(path_to_model_file, \"wb\") as fid:\n write_next_bytes(fid, len(images), \"Q\")\n for image_id, img in images.items():\n write_next_bytes(fid, image_id, \"I\")\n write_next_bytes(fid, img['qvec'], \"dddd\")\n write_next_bytes(fid, img['tvec'], \"ddd\")\n write_next_bytes(fid, img['camera_id'], \"I\")\n\n # Write image name\n for char in img['name']:\n write_next_bytes(fid, char.encode(\"utf-8\"), \"c\")\n write_next_bytes(fid, b\"\\x00\", \"c\")\n\n # Write 2D points\n write_next_bytes(fid, len(img['xys']), \"Q\")\n for xy, point3D_id in zip(img['xys'], img['point3D_ids']):\n write_next_bytes(fid, xy, \"dd\")\n write_next_bytes(fid, point3D_id, \"Q\")\n\n\ndef write_points3d_binary(points3D, path_to_model_file):\n \"\"\"\n Write COLMAP points3D.bin file\n\n Args:\n points3D: list or dict of 3D point data\n path_to_model_file: path to points3D.bin\n \"\"\"\n with open(path_to_model_file, \"wb\") as fid:\n # Write number of points\n if isinstance(points3D, dict):\n write_next_bytes(fid, len(points3D), \"Q\")\n points_iter = points3D.values()\n else:\n write_next_bytes(fid, len(points3D), \"Q\")\n points_iter = points3D\n\n # Write each point\n for point_id, point in enumerate(points_iter):\n # Handle both dict with 'id' key and list with index\n if isinstance(point, dict) and 'id' in point:\n pid = point['id']\n else:\n pid = point_id\n\n write_next_bytes(fid, pid, \"Q\")\n write_next_bytes(fid, point['xyz'], \"ddd\")\n write_next_bytes(fid, point['rgb'], \"BBB\")\n write_next_bytes(fid, point['error'], \"d\")\n\n # Write track\n track_length = len(point['image_ids'])\n write_next_bytes(fid, track_length, \"Q\")\n for image_id, point2D_idx in zip(point['image_ids'], point['point2D_idxs']):\n write_next_bytes(fid, int(image_id), \"I\")\n write_next_bytes(fid, int(point2D_idx), \"I\")\n\n\ndef save_image_data(scene, images_dir, depth_dir, normal_dir, mask_dir, min_conf_thr, verbose, processed_image_paths=None):\n \"\"\"Save RGB images, depth maps, normal maps, and masks\"\"\"\n if verbose:\n print(\"\\nSaving image data...\")\n\n # Ensure directories exist\n images_dir.mkdir(parents=True, exist_ok=True)\n depth_dir.mkdir(parents=True, exist_ok=True)\n normal_dir.mkdir(parents=True, exist_ok=True)\n mask_dir.mkdir(parents=True, exist_ok=True)\n\n # Get the number of views\n if hasattr(scene, 'imgs'):\n num_views = len(scene.imgs)\n imgs = scene.imgs\n elif hasattr(scene, 'views'):\n num_views = len(scene.views)\n imgs = scene.views\n else:\n if verbose:\n print(\" Warning: Cannot access views\")\n return\n\n # Use processed images if provided\n if processed_image_paths is not None and len(processed_image_paths) > 0:\n if verbose:\n print(f\" Using {len(processed_image_paths)} processed images\")\n\n import shutil\n for idx, src_path in enumerate(processed_image_paths):\n if idx >= num_views:\n break\n\n try:\n # Copy processed images\n dst_path = images_dir / f'image_{idx:04d}.jpg'\n shutil.copy2(src_path, dst_path)\n\n if verbose and idx < 3:\n print(f\" Copied image {idx}: {Path(src_path).name}\")\n except Exception as e:\n if verbose:\n print(f\" Error copying image {idx}: {e}\")\n else:\n # If no processed images, extract images from the scene\n if verbose:\n print(\" No processed images provided, extracting from scene...\")\n\n for idx in range(num_views):\n try:\n # Save RGB images\n img_path = images_dir / f'image_{idx:04d}.jpg'\n\n # Retrieve image data\n if hasattr(imgs[idx], 'img'):\n img = imgs[idx].img\n elif hasattr(imgs[idx], 'image'):\n img = imgs[idx].image\n else:\n img = imgs[idx]\n\n # Convert tensor to numpy array\n if isinstance(img, torch.Tensor):\n img = img.detach().cpu().numpy()\n\n # Convert image to correct format\n if isinstance(img, np.ndarray):\n # Convert (C, H, W) -> (H, W, C)\n if img.ndim == 3 and img.shape[0] in [1, 3, 4]:\n img = np.transpose(img, (1, 2, 0))\n\n # Normalize values to [0, 255] range\n if img.max() <= 1.0:\n img = (img * 255).astype(np.uint8)\n else:\n img = img.astype(np.uint8)\n\n # Convert grayscale to RGB\n if img.ndim == 2:\n img = np.stack([img, img, img], axis=-1)\n elif img.shape[-1] == 1:\n img = np.repeat(img, 3, axis=-1)\n\n # Save the image\n Image.fromarray(img).save(img_path)\n\n if verbose and idx < 3:\n print(f\" Saved image {idx}: {img_path}\")\n except Exception as e:\n if verbose:\n print(f\" Error saving image {idx}: {e}\")\n\n # Save depth maps\n try:\n if hasattr(scene, 'get_depthmaps'):\n depthmaps = scene.get_depthmaps()\n if depthmaps is not None:\n for idx in range(min(num_views, len(depthmaps))):\n depth = depthmaps[idx]\n if isinstance(depth, torch.Tensor):\n depth = depth.detach().cpu().numpy()\n\n if isinstance(depth, np.ndarray):\n depth_path = depth_dir / f'depth_{idx:04d}.npy'\n np.save(depth_path, depth)\n\n if verbose and idx < 3:\n print(f\" Saved depth {idx}: {depth_path}\")\n except Exception as e:\n if verbose:\n print(f\" Note: Could not save depth maps: {e}\")\n\n # Save masks\n try:\n if hasattr(scene, 'get_masks'):\n masks = scene.get_masks()\n if masks is not None:\n for idx in range(min(num_views, len(masks))):\n mask = masks[idx]\n if isinstance(mask, torch.Tensor):\n mask = mask.detach().cpu().numpy()\n\n if isinstance(mask, np.ndarray):\n mask_path = mask_dir / f'mask_{idx:04d}.png'\n mask_img = (mask * 255).astype(np.uint8)\n Image.fromarray(mask_img).save(mask_path)\n\n if verbose and idx < 3:\n print(f\" Saved mask {idx}: {mask_path}\")\n except Exception as e:\n if verbose:\n print(f\" Note: Could not save masks: {e}\")\n\n if verbose:\n print(f\" Completed saving {num_views} images\")\n\n\ndef extract_scene_data(scene, min_conf_thr, verbose):\n \"\"\"Extract cameras, images, and 3D points from MASt3R scene\"\"\"\n cameras = {}\n images_data = {}\n points3D = []\n\n if verbose:\n print(\"\\nExtracting scene data...\")\n\n # Check scene structure\n if hasattr(scene, 'imgs'):\n num_views = len(scene.imgs)\n imgs = scene.imgs\n elif hasattr(scene, 'views'):\n num_views = len(scene.views)\n imgs = scene.views\n else:\n num_views = 0\n imgs = []\n\n if verbose:\n print(f\"Number of views: {num_views}\")\n\n # Extract camera parameters and poses\n for idx in range(num_views):\n # Get image size\n if hasattr(scene, 'imshapes') and idx < len(scene.imshapes):\n height, width = scene.imshapes[idx]\n else:\n height, width = 192, 256\n\n # Get intrinsics\n fx = fy = 260.0\n cx = width / 2.0\n cy = height / 2.0\n\n try:\n if hasattr(scene, 'get_intrinsics'):\n K = scene.get_intrinsics()\n if K is not None:\n if isinstance(K, torch.Tensor):\n K = K.detach().cpu().numpy()\n if K.ndim >= 2:\n K_view = K[idx] if K.ndim == 3 else K\n if K_view.shape[0] >= 3 and K_view.shape[1] >= 3:\n fx = float(K_view[0, 0])\n fy = float(K_view[1, 1])\n cx = float(K_view[0, 2])\n cy = float(K_view[1, 2])\n except:\n pass\n\n cameras[idx] = {\n 'model': 'PINHOLE',\n 'width': int(width),\n 'height': int(height),\n 'params': [fx, fy, cx, cy]\n }\n\n # Get pose\n qvec = np.array([1.0, 0.0, 0.0, 0.0])\n tvec = np.array([0.0, 0.0, 0.0])\n\n try:\n if hasattr(scene, 'get_im_poses'):\n poses = scene.get_im_poses()\n if poses is not None and idx < len(poses):\n pose = poses[idx]\n if isinstance(pose, torch.Tensor):\n pose = pose.detach().cpu().numpy()\n\n if isinstance(pose, np.ndarray) and pose.ndim == 2 and pose.shape == (4, 4):\n det = np.linalg.det(pose)\n if abs(det) > 1e-10:\n pose_inv = np.linalg.inv(pose)\n qvec, tvec = matrix_to_quaternion_translation(pose_inv)\n except:\n pass\n\n images_data[idx + 1] = {\n 'qvec': qvec,\n 'tvec': tvec,\n 'camera_id': idx,\n 'name': f'image_{idx:04d}.jpg',\n 'xys': np.array([]),\n 'point3D_ids': np.array([])\n }\n\n # Extract 3D points WITH COLORS\n if verbose:\n print(\"\\nExtracting 3D points with colors...\")\n\n try:\n if hasattr(scene, 'get_pts3d'):\n pts3d = scene.get_pts3d()\n\n if pts3d is not None:\n # Handle list of arrays\n if isinstance(pts3d, list):\n all_points = []\n all_colors = []\n\n for view_idx, pts in enumerate(pts3d):\n if isinstance(pts, torch.Tensor):\n pts = pts.detach().cpu().numpy()\n if isinstance(pts, np.ndarray):\n all_points.append(pts.reshape(-1, 3))\n\n # Extract colors from corresponding image\n if view_idx < len(imgs):\n img = imgs[view_idx]\n if isinstance(img, torch.Tensor):\n img = img.detach().cpu().numpy()\n\n # Convert image format\n if img.ndim == 3:\n # (C, H, W) -> (H, W, C)\n if img.shape[0] in [1, 3, 4]:\n img = np.transpose(img, (1, 2, 0))\n\n # Normalize to 0-255\n if img.max() <= 1.0:\n img = (img * 255).astype(np.uint8)\n else:\n img = img.astype(np.uint8)\n\n # Handle grayscale\n if img.ndim == 2 or img.shape[-1] == 1:\n img = np.stack([img.squeeze()] * 3, axis=-1)\n\n # Reshape to match points\n img_flat = img.reshape(-1, 3)\n all_colors.append(img_flat)\n else:\n # Default gray if no image available\n n_pts = pts.reshape(-1, 3).shape[0]\n all_colors.append(np.full((n_pts, 3), 128, dtype=np.uint8))\n\n pts3d_combined = np.vstack(all_points) if all_points else None\n colors_combined = np.vstack(all_colors) if all_colors else None\n\n elif isinstance(pts3d, torch.Tensor):\n pts3d_combined = pts3d.detach().cpu().numpy().reshape(-1, 3)\n\n # Extract colors from first image\n if len(imgs) > 0:\n img = imgs[0]\n if isinstance(img, torch.Tensor):\n img = img.detach().cpu().numpy()\n\n if img.ndim == 3 and img.shape[0] in [1, 3, 4]:\n img = np.transpose(img, (1, 2, 0))\n\n if img.max() <= 1.0:\n img = (img * 255).astype(np.uint8)\n else:\n img = img.astype(np.uint8)\n\n if img.ndim == 2 or img.shape[-1] == 1:\n img = np.stack([img.squeeze()] * 3, axis=-1)\n\n colors_combined = img.reshape(-1, 3)\n else:\n colors_combined = None\n\n elif isinstance(pts3d, np.ndarray):\n pts3d_combined = pts3d.reshape(-1, 3)\n\n # Extract colors from first image\n if len(imgs) > 0:\n img = imgs[0]\n if isinstance(img, torch.Tensor):\n img = img.detach().cpu().numpy()\n\n if img.ndim == 3 and img.shape[0] in [1, 3, 4]:\n img = np.transpose(img, (1, 2, 0))\n\n if img.max() <= 1.0:\n img = (img * 255).astype(np.uint8)\n else:\n img = img.astype(np.uint8)\n\n if img.ndim == 2 or img.shape[-1] == 1:\n img = np.stack([img.squeeze()] * 3, axis=-1)\n\n colors_combined = img.reshape(-1, 3)\n else:\n colors_combined = None\n else:\n pts3d_combined = None\n colors_combined = None\n\n if pts3d_combined is not None and len(pts3d_combined) > 0:\n # Get confidence\n conf_combined = None\n if hasattr(scene, 'get_conf'):\n conf = scene.get_conf()\n if conf is not None:\n if isinstance(conf, list):\n all_conf = []\n for c in conf:\n if isinstance(c, torch.Tensor):\n c = c.detach().cpu().numpy()\n all_conf.append(c.flatten())\n conf_combined = np.concatenate(all_conf) if all_conf else None\n elif isinstance(conf, torch.Tensor):\n conf_combined = conf.detach().cpu().numpy().flatten()\n elif isinstance(conf, np.ndarray):\n conf_combined = conf.flatten()\n\n # Ensure all arrays have the same size\n min_size = len(pts3d_combined)\n if colors_combined is not None:\n min_size = min(min_size, len(colors_combined))\n if conf_combined is not None:\n min_size = min(min_size, len(conf_combined))\n\n pts3d_combined = pts3d_combined[:min_size]\n if colors_combined is not None:\n colors_combined = colors_combined[:min_size]\n else:\n colors_combined = np.full((min_size, 3), 128, dtype=np.uint8)\n\n # Filter by confidence\n if conf_combined is not None and len(conf_combined) > 0:\n conf_combined = conf_combined[:min_size]\n mask = conf_combined >= min_conf_thr\n pts3d_filtered = pts3d_combined[mask]\n colors_filtered = colors_combined[mask]\n else:\n pts3d_filtered = pts3d_combined\n colors_filtered = colors_combined\n\n # Create point cloud with colors\n for pt, color in zip(pts3d_filtered, colors_filtered):\n if np.all(np.isfinite(pt)):\n points3D.append({\n 'xyz': pt,\n 'rgb': color.astype(np.uint8), #use actual color\n 'error': 0.0,\n 'image_ids': np.array([]),\n 'point2D_idxs': np.array([])\n })\n\n if verbose:\n print(f\" Extracted {len(points3D)} 3D points with colors\")\n print(f\" Sample colors: {[p['rgb'].tolist() for p in points3D[:3]]}\")\n except Exception as e:\n if verbose:\n print(f\" Error extracting 3D points: {e}\")\n import traceback\n traceback.print_exc()\n\n if verbose:\n print(f\"\\nTotal: {len(cameras)} cameras, {len(images_data)} images, {len(points3D)} points\")\n\n return cameras, images_data, points3D\n\n\n\n\ndef convert_mast3r_to_colmap(scene, output_dir, min_conf_thr=1.5, clean_depth=True,\n mask_images=True, verbose=True, processed_image_paths=None,\n max_points=100000):\n \"\"\"\n Convert MASt3R scene to COLMAP format\n\n Args:\n scene: MASt3R optimized scene\n output_dir: Output directory path\n min_conf_thr: Minimum confidence threshold for 3D points\n clean_depth: Whether to clean depth maps\n mask_images: Whether to apply masks\n verbose: Print verbose output\n processed_image_paths: List of paths to processed (square) images\n \"\"\"\n\n output_dir = Path(output_dir)\n sparse_dir = output_dir / \"sparse\" / \"0\"\n images_dir = output_dir / \"images\"\n depth_dir = output_dir / \"depth\"\n normal_dir = output_dir / \"normal\"\n mask_dir = output_dir / \"mask\"\n\n # Create directories\n sparse_dir.mkdir(parents=True, exist_ok=True)\n images_dir.mkdir(parents=True, exist_ok=True)\n depth_dir.mkdir(parents=True, exist_ok=True)\n normal_dir.mkdir(parents=True, exist_ok=True)\n mask_dir.mkdir(parents=True, exist_ok=True)\n\n if verbose:\n print(\"\\n\" + \"=\"*70)\n print(\"Converting MASt3R scene to COLMAP format\")\n print(\"=\"*70)\n print(f\"Output directory: {output_dir}\")\n\n cameras, images_data, points3D = extract_scene_data(scene, min_conf_thr, verbose)\n\n #----------------------------down sampling\n if max_points is not None and len(points3D) > max_points:\n print(f\"\\nDownsampling 3D points from {len(points3D)} to {max_points}...\")\n\n if isinstance(points3D, dict):\n all_ids = list(points3D.keys())\n sampled_ids = np.random.choice(all_ids, max_points, replace=False)\n points3D = {idx: points3D[idx] for idx in sampled_ids}\n elif isinstance(points3D, list):\n sampled_indices = np.random.choice(len(points3D), max_points, replace=False)\n points3D = [points3D[i] for i in sampled_indices]\n else:\n raise TypeError(f\"points3D must be dict or list, got {type(points3D)}\")\n #----------------------------down sampling\n\n save_image_data(scene, images_dir, depth_dir, normal_dir, mask_dir,\n min_conf_thr, verbose, processed_image_paths=processed_image_paths)\n\n if verbose:\n print(\"\\nWriting COLMAP binary files...\")\n\n write_cameras_binary(cameras, sparse_dir / \"cameras.bin\")\n if verbose:\n print(f\" β cameras.bin ({len(cameras)} cameras)\")\n\n write_images_binary(images_data, sparse_dir / \"images.bin\")\n if verbose:\n print(f\" β images.bin ({len(images_data)} images)\")\n\n write_points3d_binary(points3D, sparse_dir / \"points3D.bin\")\n if verbose:\n print(f\" β points3D.bin ({len(points3D)} points)\")\n\n if verbose:\n print(\"\\n\" + \"=\"*70)\n print(\"β COLMAP conversion complete!\")\n print(\"=\"*70)\n\n return output_dir","metadata":{"id":"X2ZCB9Kt3C1w"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# gaussian splat","metadata":{"id":"KUHtLjgsGpBc"}},{"cell_type":"code","source":"# ==========================================\n# Gaussian Splatting Training Function\n# ==========================================\ndef train_gaussian_splatting(colmap_dir, output_dir, iterations=7000):\n \"\"\"\n Train a Gaussian Splatting model using COLMAP data.\n\n Args:\n colmap_dir: Root directory of COLMAP data (contains sparse/0/*.bin)\n output_dir: Target directory for Gaussian Splatting output\n iterations: Number of training iterations\n\n Returns:\n output_dir: The path where the trained model is saved\n \"\"\"\n import subprocess\n import os\n import shutil\n from pathlib import Path\n\n print(\"======================================================================\")\n print(\"Step 5: Gaussian Splatting Training\")\n print(\"======================================================================\")\n print(f\"Input COLMAP directory (Root): {colmap_dir}\")\n print(f\"Output directory: {output_dir}\")\n print(f\"Iterations: {iterations}\")\n\n # --- Fix: Set correct search path for COLMAP binaries ---\n # MASt3R output is located in colmap_dir/sparse/0/*.bin\n colmap_sparse_src = os.path.join(colmap_dir, \"sparse\", \"0\")\n required_files = ['cameras.bin', 'images.bin', 'points3D.bin']\n\n # Pre-flight check\n print(\"\\n[1/4] Checking COLMAP files...\")\n for filename in required_files:\n filepath = os.path.join(colmap_sparse_src, filename)\n if not os.path.exists(filepath):\n raise FileNotFoundError(\n f\"Required COLMAP file not found: {filepath}\\n\"\n f\"Verify if Step 4 correctly saved files to {colmap_sparse_src}\"\n )\n print(f\" β Found {filename}\")\n\n # Verify Gaussian Splatting repository\n gs_repo = \"/content/gaussian-splatting\"\n if not os.path.exists(gs_repo):\n raise FileNotFoundError(f\"Gaussian Splatting repository not found: {gs_repo}\")\n\n # --- Prepare Directory Structure ---\n # The GS train.py expects the following structure:\n # output_dir/\n # βββ images/\n # βββ sparse/0/*.bin\n\n print(\"\\n[2/4] Preparing directory structure...\")\n images_dst_dir = os.path.join(output_dir, 'images')\n sparse_dst_dir = os.path.join(output_dir, 'sparse', '0')\n os.makedirs(images_dst_dir, exist_ok=True)\n os.makedirs(sparse_dst_dir, exist_ok=True)\n print(f\" β Created {images_dst_dir}\")\n print(f\" β Created {sparse_dst_dir}\")\n\n # --- Copy Images (Processed/Split images) ---\n # Retrieve images from 'processed_images' located alongside the colmap_dir\n print(\"\\n[3/4] Copying processed images...\")\n processed_images_src = os.path.join(os.path.dirname(colmap_dir), 'processed_images')\n\n if not os.path.exists(processed_images_src):\n raise FileNotFoundError(\n f\"Processed images directory not found: {processed_images_src}\\n\"\n f\"Expected location: {os.path.dirname(colmap_dir)}/processed_images\"\n )\n\n # Copy image files and keep a count\n copied_count = 0\n image_extensions = ('.jpg', '.jpeg', '.png', '.JPG', '.JPEG', '.PNG')\n\n for img in sorted(os.listdir(processed_images_src)):\n if img.lower().endswith(image_extensions):\n src = os.path.join(processed_images_src, img)\n dst = os.path.join(images_dst_dir, img)\n shutil.copy2(src, dst)\n copied_count += 1\n\n if copied_count == 0:\n raise RuntimeError(f\"No images found in {processed_images_src}\")\n\n print(f\" β Copied {copied_count} images from {processed_images_src}\")\n print(f\" β Images prepared in {images_dst_dir}\")\n\n # --- Copy COLMAP Binaries ---\n print(\"\\n[4/4] Copying COLMAP sparse reconstruction...\")\n for filename in required_files:\n src = os.path.join(colmap_sparse_src, filename)\n dst = os.path.join(sparse_dst_dir, filename)\n # Avoid error if src and dst are the same path\n if os.path.abspath(src) != os.path.abspath(dst):\n shutil.copy2(src, dst)\n file_size = os.path.getsize(dst)\n print(f\" β Copied {filename} ({file_size:,} bytes)\")\n\n print(f\" β COLMAP files prepared in {sparse_dst_dir}\")\n\n # --- Construct Execution Command ---\n # Set the parent directory (containing 'images' and 'sparse/0') as the source (-s)\n print(\"\\n\" + \"=\"*70)\n print(\"Starting Gaussian Splatting Training...\")\n print(\"=\"*70)\n\n cmd = [\n \"python\", os.path.join(gs_repo, \"train.py\"),\n \"-s\", output_dir, # Use prepared directory as source\n \"-m\", output_dir, # Output training results to the same directory\n \"--iterations\", str(iterations),\n \"--test_iterations\", \"-1\",\n \"--save_iterations\", str(iterations), # Save only the final result\n \"--checkpoint_iterations\", \"-1\",\n \"--quiet\"\n ]\n\n print(f\"Command: {' '.join(cmd)}\\n\")\n\n # Execute training\n result = subprocess.run(cmd, capture_output=True, text=True)\n\n if result.returncode != 0:\n print(\"\\n\" + \"=\"*70)\n print(\"β Training failed!\")\n print(\"=\"*70)\n print(\"\\n--- STDOUT ---\")\n print(result.stdout)\n print(\"\\n--- STDERR ---\")\n print(result.stderr)\n print(\"=\"*70)\n raise RuntimeError(\"Gaussian Splatting training failed\")\n\n print(\"\\n\" + \"=\"*70)\n print(\"β Training complete!\")\n print(\"=\"*70)\n print(f\"Model saved to: {output_dir}\")\n print(f\"Point cloud: {os.path.join(output_dir, 'point_cloud', f'iteration_{iterations}')}\")\n\n return output_dir","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-02T08:56:35.733855Z","iopub.execute_input":"2026-02-02T08:56:35.734036Z","iopub.status.idle":"2026-02-02T08:56:35.751392Z","shell.execute_reply.started":"2026-02-02T08:56:35.734019Z","shell.execute_reply":"2026-02-02T08:56:35.75091Z"},"id":"HTSHOx23GpBc"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true,"id":"ElzSgpJ6GpBc"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# main_pipeline","metadata":{"id":"wnNtrHf7GpBc"}},{"cell_type":"code","source":"def main_pipeline(image_dir, output_dir, square_size=1024, iterations=7000,\n max_images=None, max_pairs=None, max_points=1000000):\n \"\"\"\n Complete Process3 Pipeline:\n Biplet β DINO β MASt3R β COLMAP β Gaussian Splatting\n \"\"\"\n import os\n import torch\n\n os.makedirs(output_dir, exist_ok=True)\n\n # ==========================================\n # Step 1: Biplet-Square Normalization\n # ==========================================\n print(\"\\n\" + \"=\"*70)\n print(\"Step 1: Biplet-Square Normalization\")\n print(\"=\"*70)\n\n processed_dir, image_paths = normalize_image_sizes_biplet(\n input_dir=image_dir,\n output_dir=os.path.join(output_dir, 'processed_images'),\n size=square_size,\n )\n\n # ==========================================\n # Step 2: DINO Pair Selection\n # ==========================================\n print(\"\\n\" + \"=\"*70)\n print(\"Step 2: DINO Pair Selection\")\n print(\"=\"*70)\n\n pairs = get_image_pairs_dino(\n image_paths=image_paths,\n max_pairs=max_pairs\n )\n\n # ==========================================\n # Step 3: MASt3R Reconstruction\n # ==========================================\n print(\"\\n\" + \"=\"*70)\n print(\"Step 3: MASt3R Reconstruction\")\n print(\"=\"*70)\n\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n model = load_mast3r_model(device)\n\n scene, mast3r_images = run_mast3r_pairs(\n model=model,\n image_paths=image_paths,\n pairs=pairs, device=device,\n max_pairs=max_pairs\n )\n\n # Clean up model\n del model\n clear_memory()\n\n # ==========================================\n # Step 4: Convert to COLMAP Format\n # ==========================================\n print(\"\\n\" + \"=\"*70)\n print(\"Step 4: COLMAP Conversion\")\n print(\"=\"*70)\n\n colmap_dir = convert_mast3r_to_colmap(\n scene=scene,\n output_dir=os.path.join(output_dir, 'colmap'),\n min_conf_thr=1.5,max_points=max_points\n )\n\n #---------------\n\n import shutil\n\n src_dir = '/content/output/colmap/images'\n dst_dir = '/content/output/gaussian_splatting/images'\n\n os.makedirs(dst_dir, exist_ok=True)\n\n files = os.listdir(src_dir)\n for f in files:\n if f.startswith('image_') and f.endswith('.jpg'):\n src_path = os.path.join(src_dir, f)\n dst_path = os.path.join(dst_dir, f)\n\n if not os.path.exists(dst_path):\n shutil.copy2(src_path, dst_path)\n\n print(f\"Copied {len(files)} files to {dst_dir}\")\n\n #-----------------\n\n # ==========================================\n # Step 5: Gaussian Splatting Training\n # ==========================================\n print(\"\\n\" + \"=\"*70)\n print(\"Step 5: Gaussian Splatting Training\")\n print(\"=\"*70)\n\n # 'colmap_output' is a Path object pointing to 'output_dir/colmap'.\n # This directory contains the generated 'sparse/0/*.bin' files.\n colmap_root = '/content/output/colmap'#str(colmap_output)\n\n # Define the output directory for Gaussian Splatting\n gs_output_dir = os.path.join(output_dir, 'gaussian_splatting')\n\n # Call the existing 'train_gaussian_splatting' function.\n # Standard GS practice is to pass the parent directory containing the 'sparse' folder.\n gs_output = train_gaussian_splatting(\n colmap_dir=colmap_root, # This is the crucial path\n output_dir=gs_output_dir,\n iterations=iterations\n )\n\n return gs_output","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-02T09:19:47.375104Z","iopub.execute_input":"2026-02-02T09:19:47.375866Z","iopub.status.idle":"2026-02-02T09:19:47.38652Z","shell.execute_reply.started":"2026-02-02T09:19:47.375831Z","shell.execute_reply":"2026-02-02T09:19:47.38583Z"},"id":"GcNTYU67GpBc"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true,"id":"JmsYT2hVGpBc"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# execute","metadata":{"id":"Ce5fNJCEGpBc"}},{"cell_type":"code","source":"if __name__ == \"__main__\":\n\n IMAGE_DIR = \"/content/drive/MyDrive/your_folder/fountain100\"\n OUTPUT_DIR = \"/content/output\"\n\n gs_output = main_pipeline(\n image_dir=IMAGE_DIR,\n output_dir=OUTPUT_DIR,\n square_size=1024,\n iterations=3000,\n max_images=30,\n max_pairs=1000,\n max_points=100000\n )\n\n print(f\"\\n{'='*70}\")\n print(\"Pipeline completed successfully!\")\n print(f\"{'='*70}\")\n print(f\"Gaussian Splatting output: {gs_output}\")","metadata":{"execution":{"iopub.status.busy":"2026-02-02T09:19:51.510722Z","iopub.execute_input":"2026-02-02T09:19:51.51143Z","iopub.status.idle":"2026-02-02T09:23:11.510434Z","shell.execute_reply.started":"2026-02-02T09:19:51.511399Z","shell.execute_reply":"2026-02-02T09:23:11.509646Z"},"papermill":{"duration":905.62414,"end_time":"2026-01-20T01:22:29.355023","exception":false,"start_time":"2026-01-20T01:07:23.730883","status":"completed"},"tags":[],"_kg_hide-output":true,"trusted":true,"id":"xgMLHPPpGpBd","outputId":"f3637d4f-b7f6-4325-806e-39326a1ba02a"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true,"id":"DPfmJdNvGpBd"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!apt-get install -y tree > /dev/null","metadata":{"id":"VQsLeKY8Rl8Y","papermill":{"duration":0.154679,"end_time":"2026-01-20T01:22:29.976313","exception":false,"start_time":"2026-01-20T01:22:29.821634","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-02-02T09:10:56.433222Z","iopub.execute_input":"2026-02-02T09:10:56.433994Z","iopub.status.idle":"2026-02-02T09:11:06.952416Z","shell.execute_reply.started":"2026-02-02T09:10:56.433963Z","shell.execute_reply":"2026-02-02T09:11:06.95163Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!tree /content/output","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-02T09:17:58.224254Z","iopub.execute_input":"2026-02-02T09:17:58.225096Z","iopub.status.idle":"2026-02-02T09:17:58.5013Z","shell.execute_reply.started":"2026-02-02T09:17:58.225063Z","shell.execute_reply":"2026-02-02T09:17:58.500415Z"},"id":"O3F9m-VjGpBd","outputId":"814a6a56-23ca-4138-b69b-bbef1f9e262e"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true,"id":"W0Y_jaLoGpBd"},"outputs":[],"execution_count":null}]} |