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"base_uri": "https://localhost:8080/" }, "id": "vfqsbwoqlO7r", "outputId": "da254904-082f-472d-8bf6-2117530cdd7f" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "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", "\n", "import os\n", "import sys\n", "import gc\n", "import h5py\n", "import numpy as np\n", "import torch\n", "import torch.nn.functional as F\n", "from tqdm import tqdm\n", "from pathlib import Path\n", "import subprocess\n", "from PIL import Image, ImageFilter\n", "import struct\n", "\n", "# Transformers for DINO\n", "from transformers import AutoImageProcessor, AutoModel\n", "\n", "# ============================================================================\n", "# Configuration\n", "# ============================================================================\n", "class 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", "\n", "def 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", "\n", "def 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", "\n", "def run_cmd(cmd, check=True, capture=False):\n", " \"\"\"Run command with better error handling\"\"\"\n", " print(f\"Running: {' '.join(cmd)}\")\n", " result = subprocess.run(\n", " cmd,\n", " capture_output=capture,\n", " text=True,\n", " check=False\n", " )\n", " if check and result.returncode != 0:\n", " print(f\"❌ Command failed with code {result.returncode}\")\n", " if capture:\n", " print(f\"STDOUT: {result.stdout}\")\n", " print(f\"STDERR: {result.stderr}\")\n", " return result\n", "\n", "\n", "def setup_base_environment():\n", " \"\"\"Setup base Python environment\"\"\"\n", " print(\"\\n=== Setting up Base Environment ===\")\n", "\n", " # NumPy fix for Python 3.12\n", " print(\"\\n📦 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(\"\\n📦 Installing PyTorch...\")\n", " run_cmd([\n", " sys.executable, \"-m\", \"pip\", \"install\",\n", " \"torch\", \"torchvision\", \"torchaudio\"\n", " ])\n", "\n", " # Core utilities\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", " \"scipy\", # for rotation conversions and image resizing\n", " \"psutil\" # for memory monitoring\n", " ])\n", "\n", " # Transformers for DINO\n", " print(\"\\n📦 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(\"\\n📦 Installing pycolmap...\")\n", " run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"pycolmap\"])\n", "\n", " print(\"✓ Base environment setup complete!\")\n", "\n", "\n", "def 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(\"\\n🔍 Verifying MASt3R installation...\")\n", " try:\n", " from mast3r.model import AsymmetricMASt3R\n", " print(\" ✓ MASt3R import: OK\")\n", " except Exception as e:\n", " print(f\" ❌ MASt3R import failed: {e}\")\n", " raise\n", "\n", " print(\"✓ MASt3R setup complete!\")\n", "\n", "def 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(\"✓ 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(\"\\n📦 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\"\\n📦 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(\"✓ 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": 2 }, { "cell_type": "code", "source": [ "\n", "setup_base_environment()\n", "clear_memory()\n", "\n", "setup_mast3r()\n", "clear_memory()\n", "\n", "setup_gaussian_splatting()\n", "clear_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", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "0a1eb888-aba1-4990-ab52-28584e9104ec" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=== Setting up Base Environment ===\n", "\n", "📦 Fixing NumPy...\n", "Running: /usr/bin/python3 -m pip uninstall -y numpy\n", "Running: /usr/bin/python3 -m pip install numpy==1.26.4\n", "\n", "📦 Installing PyTorch...\n", "Running: /usr/bin/python3 -m pip install torch torchvision torchaudio\n", "\n", "📦 Installing core utilities...\n", "Running: /usr/bin/python3 -m pip install opencv-python pillow imageio imageio-ffmpeg plyfile tqdm tensorboard scipy psutil\n", "\n", "📦 Installing transformers...\n", "Running: /usr/bin/python3 -m pip install transformers>=4.45.0\n", "\n", "📦 Installing pycolmap...\n", "Running: /usr/bin/python3 -m pip install pycolmap\n", "✓ Base environment setup complete!\n", "\n", "=== Setting up MASt3R ===\n", "Cloning MASt3R repository...\n", "Checking dust3r structure...\n", "Installing dust3r...\n", "Installing croco...\n", "Installing MASt3R requirements...\n", "Downloading model weights...\n", "Installing additional dependencies...\n", "\n", "🔍 Verifying MASt3R installation...\n", "Warning, cannot find cuda-compiled version of RoPE2D, using a slow pytorch version instead\n", " ✓ MASt3R import: OK\n", "✓ MASt3R setup complete!\n", "\n", "=== Setting up Gaussian Splatting ===\n", "Cloning Gaussian Splatting repository...\n", "Running: git clone --recursive https://github.com/graphdeco-inria/gaussian-splatting.git gaussian-splatting\n", "Installing Gaussian Splatting requirements...\n", "Running: /usr/bin/python3 -m pip install -r requirements.txt\n", "❌ Command failed with code 1\n", "\n", "📦 Building Gaussian Splatting submodules...\n", "\n", "📦 Installing diff-gaussian-rasterization...\n", "Running: /usr/bin/python3 -m pip install submodules/diff-gaussian-rasterization\n", "\n", "📦 Installing simple-knn...\n", "Running: /usr/bin/python3 -m pip install submodules/simple-knn\n", "✓ Gaussian Splatting setup complete!\n" ] } ], "execution_count": 3 }, { "cell_type": "code", "source": [], "metadata": { "trusted": true, "id": "38nn_QqcGpBa" }, "outputs": [], "execution_count": 3 }, { "cell_type": "markdown", "source": [ "# dino & mast3r" ], "metadata": { "id": "L6OBEO0zGpBa" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# Step 0: Biplet-Square Normalization (PRESERVED FROM ORIGINAL)\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", "\n", "# ============================================================================\n", "# Step 1: DINO-based Pair Selection (PRESERVED FROM ORIGINAL)\n", "# ============================================================================\n", "\n", "def 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", "\n", "def 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", "\n", "def 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", "\n", "def 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", "\n", "def 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", "\n", "def 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\"✓ MASt3R model loaded on {device}\")\n", " return model\n", "\n", "def 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", "\n", "def 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(\"✓ 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\"✓ 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": 4 }, { "cell_type": "code", "source": [], "metadata": { "trusted": true, "id": "_tTGwERoGpBb" }, "outputs": [], "execution_count": 4 }, { "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", "\n", "import numpy as np\n", "import cv2\n", "from pathlib import Path\n", "import struct\n", "from scipy.spatial.transform import Rotation\n", "import torch\n", "from PIL import Image\n", "\n", "\n", "def 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", "\n", "def 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", "\n", "def 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", "\n", "def 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", "\n", "def 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", "\n", "def 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", "\n", "def 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", "\n", "def 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\" ✓ cameras.bin ({len(cameras)} cameras)\")\n", "\n", " write_images_binary(images_data, sparse_dir / \"images.bin\")\n", " if verbose:\n", " print(f\" ✓ images.bin ({len(images_data)} images)\")\n", "\n", " write_points3d_binary(points3D, sparse_dir / \"points3D.bin\")\n", " if verbose:\n", " print(f\" ✓ points3D.bin ({len(points3D)} points)\")\n", "\n", " if verbose:\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"✓ COLMAP conversion complete!\")\n", " print(\"=\"*70)\n", "\n", " return output_dir" ], "metadata": { "id": "X2ZCB9Kt3C1w" }, "execution_count": 5, "outputs": [] }, { "cell_type": "markdown", "source": [ "# gaussian splat" ], "metadata": { "id": "KUHtLjgsGpBc" } }, { "cell_type": "code", "source": [ "# ==========================================\n", "# Gaussian Splatting Training Function\n", "# ==========================================\n", "def 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\" ✓ 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", " # ├── images/\n", " # └── 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\" ✓ Created {images_dst_dir}\")\n", " print(f\" ✓ 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\" ✓ Copied {copied_count} images from {processed_images_src}\")\n", " print(f\" ✓ 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\" ✓ Copied {filename} ({file_size:,} bytes)\")\n", "\n", " print(f\" ✓ 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(\"❌ 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(\"✓ 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": 6 }, { "cell_type": "code", "source": [], "metadata": { "trusted": true, "id": "ElzSgpJ6GpBc" }, "outputs": [], "execution_count": 6 }, { "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 → DINO → MASt3R → COLMAP → 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": 7 }, { "cell_type": "code", "source": [], "metadata": { "trusted": true, "id": "JmsYT2hVGpBc" }, "outputs": [], "execution_count": 7 }, { "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", "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "71659c5eb8704c428eb984e9dd6fca41", "157b8fcb5f564a1f83feb30af2412dbc", "ab27d947283b4dda80bc0267ac0950d1", "dfb970745845470892fcef2a793fa722", "f2bb371554334d4bbee1839c7b3c5b6e", "b9643bbd15964e4a90b496fab872f754", "817e2eaa5eb341f29c86a73c715c476a", "335528a65ea74708bee26d778aac70b5", "2912e19431b845a988f6617668ceecf8", "5db5e4aeb52848728293e9d082e4940c", "d78328ca525d4bceab3890d57fa34ae5", "627c1695bcb04598a36dd72c8a69e7f5", "c6ffcf6df8764f3faf59420f3373939d", "00cf6f11d4314130a64df95396b892ec", "ea5e39d79d7f4acfb8f3063d72311462", "4fce934bd4bf46baa7be4fb33e74d16f", "bd527001905b4329bbe4f58446b72d37", "b807318a18134af7ae1ae18a4c9f8a13", "3781299ab1244523931232163602cb44", "932b36d8d6324e13ad1bfad07f93e57e", "37cb90e7e8d7424fb02e171ad3350c7c", "9732ccda34774836a11d81da1520ac99", "69af90d36da248978f28892084155d27", "789dc5db84b84e7299d7cd6a513500a3", "acc6ca7ddbba4ca6bf2a6a2c217d566b", "b7e774e19f264f738e8b365d93423b3d", "d1cb7996322e4b1e8124857839e305db", "90a22edc704c4768ac3b1511d2e629ea", "392c7092c9214557b0f26209ad32fcf1", "74c7eb4966a14d65916259589ff1dc2e", "45e0866962b0460e87c8ce180a96115f", "4b9c40e6d61d4b3aaaa6df96e695e831", "732b1fca8bab4e3e98e5b80317fa9198", "fc6e0d42af74464da586a89397b74752", "f00b37c9901a4dde85ca44bf93aad7c4", "777478352a84439eb393cb5b6ea3fa1a", "81fd0c78a76244b48c99983a9cf66660", "fe164865bb9d49ecb4caf9389baa7975", "b3ba20d7739847a8ba8a7a19150cab5f", "f22284b9edc04fb8937ae8c434f98cd7", "dff1208989d34ad88b517c4ca788c6b2", "bfdd2325fc234667980928f3871d04ca", "bccce2558d5a4e01840a388a39e0104e", "e867a871e17c4f46a4959900d066f540" ] }, "outputId": "f3637d4f-b7f6-4325-806e-39326a1ba02a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "======================================================================\n", "Step 1: Biplet-Square Normalization\n", "======================================================================\n", "--- Step 1: Biplet-Square Normalization ---\n", "Generating 2 cropped squares (Left/Right or Top/Bottom) for each image...\n", "\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", " ✓ image_121.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_122.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_123.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_124.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_125.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_126.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_127.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_128.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_129.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_130.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_131.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_132.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_133.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_134.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_135.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_136.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_137.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_138.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_139.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_140.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_141.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_142.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_143.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_144.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_145.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_146.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_147.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_148.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_149.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_150.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_151.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_152.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_153.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_154.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_155.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_156.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_157.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_158.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_159.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_160.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_161.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_162.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_163.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_164.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_165.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_166.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_167.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_168.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_169.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_170.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_171.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_172.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_173.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_174.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_175.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_176.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_177.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_178.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_179.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_180.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_181.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_182.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_183.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_184.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_185.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_186.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_187.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_188.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_189.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_190.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_191.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_192.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_193.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_194.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_195.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_196.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_197.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_198.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_199.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_200.jpeg: (1440, 1920) → 2 square images generated\n", "\n", "Processing complete: 100 source images processed\n", "Total output images: 200\n", "Original size distribution: {'1440x1920': 100}\n", "\n", "======================================================================\n", "Step 2: DINO Pair Selection\n", "======================================================================\n", "\n", "=== Extracting DINO Global Features ===\n", "Initial memory state:\n", "GPU Memory - Allocated: 0.00GB, Reserved: 0.00GB\n", "CPU Memory Usage: 7.0%\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/436 [00:00\n", "✓ MASt3R model loaded on cuda\n", "\n", "=== Running MASt3R Reconstruction ===\n", "Initial memory state:\n", "GPU Memory - Allocated: 2.58GB, Reserved: 2.69GB\n", "CPU Memory Usage: 16.2%\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/content/mast3r/dust3r/dust3r/cloud_opt/base_opt.py:275: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", " @torch.cuda.amp.autocast(enabled=False)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Processing 1000 pairs...\n", "Loading 200 images at 224x224...\n", "\n", "=== Loading images for MASt3R (size=224) ===\n", ">> Loading a list of 200 images\n", " - adding /content/output/processed_images/image_101_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_101_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_102_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_102_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_103_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_103_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_104_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_104_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_105_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_105_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_106_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_106_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_107_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_107_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_108_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_108_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_109_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_109_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_110_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_110_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_111_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_111_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_112_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_112_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_113_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_113_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_114_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_114_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_115_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_115_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_116_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_116_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_117_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_117_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_118_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_118_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_119_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_119_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_120_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_120_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_121_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_121_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_122_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_122_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_123_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_123_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_124_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_124_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_125_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_125_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_126_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_126_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_127_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_127_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_128_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_128_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_129_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_129_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_130_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_130_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding 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/content/output/processed_images/image_185_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_185_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_186_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_186_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_187_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_187_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_188_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_188_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_189_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_189_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_190_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_190_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_191_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_191_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_192_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_192_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_193_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_193_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_194_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_194_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_195_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_195_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_196_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_196_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_197_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_197_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_198_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_198_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_199_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_199_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_200_top.jpeg with resolution 1024x1024 --> 224x224\n", " - adding /content/output/processed_images/image_200_bottom.jpeg with resolution 1024x1024 --> 224x224\n", " (Found 200 images)\n", "Loaded 200 images\n", "After loading images:\n", "GPU Memory - Allocated: 2.58GB, Reserved: 2.69GB\n", "CPU Memory Usage: 16.3%\n", "Creating 1000 image pairs...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Preparing pairs: 100%|██████████| 1000/1000 [00:00<00:00, 1155137.43it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Running MASt3R inference on 1000 pairs...\n", ">> Inference with model on 1000 image pairs\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\r 0%| | 0/1000 [00:00 /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": 9 }, { "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", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "814a6a56-23ca-4138-b69b-bbef1f9e262e" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[01;34m/content/output\u001b[0m\n", "├── \u001b[01;34mcolmap\u001b[0m\n", "│   ├── \u001b[01;34mdepth\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0000.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0001.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0002.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0003.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0004.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0005.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0006.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0007.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0008.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0009.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0010.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0011.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0012.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0013.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0014.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0015.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0016.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0017.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0018.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0019.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0020.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0021.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0022.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0023.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0024.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0025.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0026.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0027.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0028.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0029.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0030.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0031.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0032.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0033.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0034.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0035.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0036.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0037.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0038.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0039.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0040.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0041.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0042.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0043.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0044.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0045.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0046.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0047.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0048.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0049.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0050.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0051.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0052.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0053.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0054.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0055.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0056.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0057.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0058.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0059.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0060.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0061.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0062.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0063.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0064.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0065.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0066.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0067.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0068.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0069.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0070.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0071.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0072.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0073.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0074.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0075.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0076.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0077.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0078.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0079.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0080.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0081.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0082.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0083.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0084.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0085.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0086.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0087.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0088.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0089.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0090.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0091.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0092.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0093.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0094.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0095.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0096.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0097.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0098.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0099.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0100.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0101.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0102.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0103.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0104.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0105.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0106.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0107.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0108.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0109.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0110.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0111.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0112.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0113.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0114.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0115.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0116.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0117.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0118.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0119.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0120.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0121.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0122.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0123.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0124.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0125.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0126.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0127.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0128.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0129.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0130.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0131.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0132.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0133.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0134.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0135.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0136.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0137.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0138.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0139.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0140.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0141.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0142.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0143.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0144.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0145.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0146.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0147.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0148.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0149.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0150.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0151.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0152.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0153.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0154.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0155.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0156.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0157.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0158.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0159.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0160.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0161.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0162.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0163.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0164.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0165.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0166.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0167.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0168.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0169.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0170.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0171.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0172.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0173.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0174.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0175.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0176.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0177.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0178.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0179.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0180.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0181.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0182.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0183.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0184.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0185.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0186.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0187.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0188.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0189.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0190.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0191.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0192.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0193.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0194.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0195.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0196.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0197.npy\u001b[0m\n", "│   │   ├── \u001b[00mdepth_0198.npy\u001b[0m\n", "│   │   └── \u001b[00mdepth_0199.npy\u001b[0m\n", "│   ├── \u001b[01;34mimages\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0000.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0001.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0002.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0003.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0004.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0005.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0006.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0007.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0008.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0009.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0010.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0011.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0012.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0013.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0014.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0015.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0016.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0017.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0018.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0019.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0020.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0021.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0022.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0023.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0024.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0025.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0026.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0027.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0028.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0029.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0030.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0031.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0032.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0033.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0034.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0035.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0036.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0037.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0038.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0039.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0040.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0041.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0042.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0043.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0044.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0045.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0046.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0047.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0048.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0049.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0050.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0051.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0052.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0053.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0054.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0055.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0056.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0057.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0058.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0059.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0060.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0061.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0062.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0063.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0064.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0065.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0066.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0067.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0068.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0069.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0070.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0071.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0072.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0073.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0074.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0075.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0076.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0077.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0078.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0079.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0080.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0081.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0082.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0083.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0084.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0085.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0086.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0087.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0088.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0089.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0090.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0091.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0092.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0093.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0094.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0095.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0096.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0097.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0098.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0099.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0100.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0101.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0102.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0103.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0104.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0105.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0106.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0107.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0108.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0109.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0110.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0111.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0112.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0113.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0114.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0115.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0116.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0117.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0118.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0119.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0120.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0121.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0122.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0123.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0124.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0125.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0126.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0127.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0128.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0129.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0130.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0131.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0132.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0133.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0134.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0135.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0136.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0137.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0138.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0139.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0140.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0141.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0142.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0143.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0144.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0145.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0146.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0147.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0148.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0149.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0150.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0151.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0152.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0153.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0154.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0155.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0156.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0157.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0158.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0159.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0160.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0161.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0162.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0163.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0164.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0165.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0166.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0167.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0168.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0169.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0170.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0171.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0172.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0173.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0174.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0175.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0176.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0177.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0178.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0179.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0180.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0181.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0182.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0183.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0184.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0185.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0186.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0187.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0188.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0189.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0190.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0191.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0192.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0193.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0194.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0195.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0196.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0197.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0198.jpg\u001b[0m\n", "│   │   └── \u001b[01;35mimage_0199.jpg\u001b[0m\n", "│   ├── \u001b[01;34mmask\u001b[0m\n", "│   │   ├── \u001b[00mmask_0000.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0001.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0002.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0003.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0004.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0005.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0006.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0007.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0008.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0009.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0010.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0011.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0012.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0013.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0014.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0015.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0016.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0017.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0018.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0019.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0020.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0021.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0022.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0023.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0024.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0025.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0026.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0027.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0028.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0029.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0030.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0031.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0032.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0033.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0034.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0035.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0036.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0037.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0038.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0039.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0040.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0041.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0042.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0043.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0044.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0045.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0046.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0047.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0048.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0049.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0050.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0051.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0052.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0053.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0054.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0055.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0056.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0057.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0058.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0059.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0060.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0061.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0062.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0063.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0064.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0065.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0066.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0067.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0068.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0069.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0070.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0071.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0072.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0073.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0074.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0075.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0076.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0077.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0078.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0079.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0080.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0081.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0082.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0083.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0084.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0085.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0086.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0087.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0088.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0089.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0090.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0091.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0092.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0093.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0094.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0095.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0096.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0097.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0098.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0099.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0100.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0101.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0102.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0103.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0104.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0105.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0106.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0107.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0108.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0109.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0110.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0111.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0112.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0113.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0114.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0115.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0116.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0117.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0118.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0119.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0120.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0121.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0122.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0123.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0124.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0125.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0126.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0127.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0128.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0129.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0130.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0131.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0132.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0133.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0134.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0135.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0136.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0137.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0138.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0139.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0140.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0141.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0142.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0143.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0144.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0145.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0146.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0147.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0148.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0149.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0150.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0151.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0152.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0153.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0154.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0155.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0156.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0157.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0158.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0159.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0160.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0161.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0162.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0163.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0164.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0165.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0166.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0167.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0168.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0169.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0170.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0171.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0172.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0173.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0174.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0175.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0176.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0177.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0178.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0179.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0180.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0181.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0182.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0183.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0184.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0185.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0186.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0187.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0188.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0189.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0190.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0191.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0192.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0193.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0194.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0195.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0196.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0197.png\u001b[0m\n", "│   │   ├── \u001b[00mmask_0198.png\u001b[0m\n", "│   │   └── \u001b[00mmask_0199.png\u001b[0m\n", "│   ├── \u001b[01;34mnormal\u001b[0m\n", "│   └── \u001b[01;34msparse\u001b[0m\n", "│   └── \u001b[01;34m0\u001b[0m\n", "│   ├── \u001b[00mcameras.bin\u001b[0m\n", "│   ├── \u001b[00mimages.bin\u001b[0m\n", "│   └── \u001b[00mpoints3D.bin\u001b[0m\n", "├── \u001b[01;34mgaussian_splatting\u001b[0m\n", "│   ├── \u001b[00mcameras.json\u001b[0m\n", "│   ├── \u001b[00mcfg_args\u001b[0m\n", "│   ├── \u001b[00mevents.out.tfevents.1770385581.e0c21a542c81.8920.0\u001b[0m\n", "│   ├── \u001b[00mexposure.json\u001b[0m\n", "│   ├── \u001b[01;34mimages\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0000.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0001.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0002.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0003.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0004.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0005.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0006.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0007.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0008.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0009.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0010.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0011.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0012.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0013.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0014.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0015.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0016.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0017.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0018.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0019.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0020.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0021.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0022.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0023.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0024.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0025.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0026.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0027.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0028.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0029.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0030.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0031.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0032.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0033.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0034.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0035.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0036.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0037.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0038.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0039.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0040.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0041.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0042.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0043.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0044.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0045.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0046.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0047.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0048.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0049.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0050.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0051.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0052.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0053.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0054.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0055.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0056.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0057.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0058.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0059.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0060.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0061.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0062.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0063.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0064.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0065.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0066.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0067.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0068.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0069.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0070.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0071.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0072.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0073.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0074.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0075.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0076.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0077.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0078.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0079.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0080.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0081.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0082.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0083.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0084.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0085.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0086.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0087.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0088.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0089.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0090.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0091.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0092.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0093.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0094.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0095.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0096.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0097.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0098.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0099.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0100.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0101.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0102.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0103.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0104.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0105.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0106.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0107.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0108.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0109.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0110.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0111.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0112.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0113.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0114.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0115.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0116.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0117.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0118.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0119.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0120.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0121.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0122.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0123.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0124.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0125.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0126.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0127.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0128.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0129.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0130.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0131.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0132.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0133.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0134.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0135.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0136.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0137.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0138.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0139.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0140.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0141.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0142.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0143.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0144.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0145.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0146.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0147.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0148.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0149.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0150.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0151.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0152.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0153.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0154.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0155.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0156.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0157.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0158.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0159.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0160.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0161.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0162.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0163.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0164.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0165.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0166.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0167.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0168.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0169.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0170.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0171.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0172.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0173.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0174.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0175.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0176.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0177.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0178.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0179.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0180.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0181.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0182.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0183.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0184.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0185.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0186.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0187.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0188.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0189.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0190.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0191.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0192.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0193.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0194.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0195.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0196.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0197.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0198.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_0199.jpg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_101_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_101_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_102_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_102_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_103_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_103_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_104_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_104_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_105_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_105_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_106_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_106_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_107_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_107_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_108_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_108_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_109_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_109_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_110_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_110_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_111_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_111_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_112_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_112_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_113_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_113_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_114_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_114_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_115_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_115_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_116_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_116_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_117_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_117_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_118_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_118_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_119_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_119_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_120_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_120_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_121_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_121_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_122_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_122_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_123_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_123_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_124_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_124_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_125_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_125_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_126_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_126_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_127_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_127_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_128_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_128_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_129_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_129_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_130_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_130_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_131_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_131_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_132_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_132_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_133_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_133_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_134_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_134_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_135_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_135_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_136_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_136_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_137_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_137_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_138_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_138_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_139_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_139_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_140_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_140_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_141_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_141_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_142_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_142_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_143_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_143_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_144_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_144_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_145_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_145_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_146_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_146_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_147_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_147_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_148_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_148_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_149_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_149_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_150_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_150_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_151_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_151_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_152_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_152_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_153_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_153_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_154_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_154_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_155_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_155_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_156_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_156_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_157_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_157_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_158_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_158_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_159_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_159_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_160_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_160_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_161_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_161_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_162_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_162_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_163_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_163_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_164_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_164_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_165_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_165_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_166_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_166_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_167_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_167_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_168_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_168_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_169_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_169_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_170_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_170_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_171_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_171_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_172_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_172_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_173_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_173_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_174_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_174_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_175_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_175_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_176_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_176_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_177_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_177_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_178_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_178_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_179_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_179_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_180_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_180_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_181_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_181_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_182_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_182_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_183_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_183_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_184_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_184_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_185_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_185_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_186_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_186_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_187_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_187_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_188_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_188_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_189_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_189_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_190_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_190_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_191_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_191_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_192_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_192_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_193_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_193_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_194_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_194_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_195_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_195_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_196_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_196_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_197_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_197_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_198_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_198_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_199_bottom.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_199_top.jpeg\u001b[0m\n", "│   │   ├── \u001b[01;35mimage_200_bottom.jpeg\u001b[0m\n", "│   │   └── \u001b[01;35mimage_200_top.jpeg\u001b[0m\n", "│   ├── \u001b[00minput.ply\u001b[0m\n", "│   ├── \u001b[01;34mpoint_cloud\u001b[0m\n", "│   │   └── \u001b[01;34miteration_3000\u001b[0m\n", "│   │   └── \u001b[00mpoint_cloud.ply\u001b[0m\n", "│   └── \u001b[01;34msparse\u001b[0m\n", "│   └── \u001b[01;34m0\u001b[0m\n", "│   ├── \u001b[00mcameras.bin\u001b[0m\n", "│   ├── \u001b[00mimages.bin\u001b[0m\n", "│   ├── \u001b[00mpoints3D.bin\u001b[0m\n", "│   └── \u001b[00mpoints3D.ply\u001b[0m\n", "└── \u001b[01;34mprocessed_images\u001b[0m\n", " ├── \u001b[01;35mimage_101_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_101_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_102_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_102_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_103_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_103_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_104_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_104_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_105_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_105_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_106_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_106_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_107_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_107_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_108_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_108_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_109_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_109_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_110_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_110_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_111_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_111_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_112_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_112_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_113_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_113_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_114_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_114_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_115_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_115_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_116_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_116_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_117_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_117_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_118_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_118_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_119_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_119_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_120_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_120_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_121_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_121_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_122_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_122_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_123_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_123_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_124_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_124_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_125_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_125_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_126_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_126_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_127_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_127_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_128_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_128_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_129_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_129_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_130_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_130_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_131_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_131_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_132_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_132_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_133_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_133_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_134_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_134_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_135_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_135_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_136_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_136_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_137_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_137_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_138_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_138_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_139_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_139_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_140_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_140_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_141_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_141_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_142_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_142_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_143_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_143_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_144_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_144_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_145_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_145_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_146_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_146_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_147_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_147_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_148_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_148_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_149_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_149_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_150_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_150_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_151_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_151_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_152_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_152_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_153_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_153_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_154_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_154_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_155_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_155_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_156_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_156_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_157_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_157_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_158_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_158_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_159_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_159_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_160_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_160_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_161_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_161_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_162_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_162_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_163_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_163_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_164_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_164_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_165_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_165_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_166_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_166_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_167_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_167_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_168_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_168_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_169_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_169_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_170_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_170_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_171_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_171_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_172_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_172_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_173_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_173_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_174_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_174_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_175_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_175_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_176_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_176_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_177_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_177_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_178_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_178_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_179_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_179_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_180_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_180_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_181_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_181_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_182_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_182_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_183_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_183_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_184_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_184_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_185_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_185_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_186_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_186_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_187_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_187_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_188_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_188_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_189_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_189_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_190_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_190_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_191_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_191_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_192_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_192_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_193_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_193_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_194_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_194_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_195_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_195_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_196_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_196_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_197_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_197_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_198_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_198_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_199_bottom.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_199_top.jpeg\u001b[0m\n", " ├── \u001b[01;35mimage_200_bottom.jpeg\u001b[0m\n", " └── \u001b[01;35mimage_200_top.jpeg\u001b[0m\n", "\n", "14 directories, 1213 files\n" ] } ], "execution_count": 10 }, { "cell_type": "code", "source": [], "metadata": { "trusted": true, "id": "W0Y_jaLoGpBd" }, "outputs": [], "execution_count": 10 } ] }