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**dino-lightglue-mast3r-gs-colab**\n", "2026/01/15-" ], "metadata": { "id": "qDQLX3PArmh8" } }, { "cell_type": "markdown", "source": [ "# **setup**" ], "metadata": { "id": "vXt8y7QyyRn9" } }, { "cell_type": "code", "source": [], "metadata": { "id": "wsKE_91KY70P" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# 1. NumPyを下げつつ、それと互換性のある ml_dtypes をセットで入れる\n", "!pip install numpy==1.26.4 ml_dtypes==0.5.4\n" ], "metadata": { "id": "zzIlYMf5ozkH", "colab": { "base_uri": "https://localhost:8080/", "height": 544 }, "outputId": "e6b485f4-0d44-4cf2-91f2-a94b3dad46ad" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting numpy==1.26.4\n", " Downloading numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/61.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.0/61.0 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: ml_dtypes==0.5.4 in /usr/local/lib/python3.12/dist-packages (0.5.4)\n", "Downloading numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.0 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.0/18.0 MB\u001b[0m \u001b[31m124.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: numpy\n", " Attempting uninstall: numpy\n", " Found existing installation: numpy 2.0.2\n", " Uninstalling numpy-2.0.2:\n", " Successfully uninstalled numpy-2.0.2\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", "pytensor 2.36.3 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", "shap 0.50.0 requires numpy>=2, but you have numpy 1.26.4 which is incompatible.\n", "tobler 0.13.0 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", "rasterio 1.5.0 requires numpy>=2, but you have numpy 1.26.4 which is incompatible.\n", "jax 0.7.2 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "jaxlib 0.7.2 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed numpy-1.26.4\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "numpy" ] }, "id": "a6998b2335204c1c95c9159b9c99a0d8" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "break" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 110 }, "id": "Dsic4JslI1l4", "outputId": "3fe11c61-a4f0-4f1a-a1fe-b2862090301c" }, "execution_count": 2, "outputs": [ { "output_type": "error", "ename": "SyntaxError", "evalue": "'break' outside loop (ipython-input-668683560.py, line 1)", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"/tmp/ipython-input-668683560.py\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m break\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m 'break' outside loop\n" ] } ] }, { "cell_type": "markdown", "source": [ "#**セッションを再起動する、下のセルを実行する**" ], "metadata": { "id": "n3HClCivHr9W" } }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "37MIfauyj__U", "outputId": "b5b9f954-a904-47dc-e3c5-1d1126e1649a" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "import numpy as np\n", "print(f\"NumPy version: {np.__version__}\")\n", "\n", "import ml_dtypes\n", "print(f\"ML_Dtypes version: {ml_dtypes.__version__}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "inVfYAjA2nQ9", "outputId": "dc23f6f8-6fb0-4f01-a532-78f3cc9af1e5" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "NumPy version: 1.26.4\n", "ML_Dtypes version: 0.5.4\n" ] } ] }, { "cell_type": "code", "source": [ "from transformers import AutoImageProcessor, AutoModel" ], "metadata": { "id": "jTO3dSS5HXrC" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "# manual run this cell\n", "\n", "import os\n", "import sys\n", "import subprocess\n", "import shutil\n", "import glob\n", "\n", "\n", "\n", "def setup_environment():\n", " \"\"\"\n", " Nuclear option: Physically delete ALL numpy installations,\n", " then install clean versions\n", " \"\"\"\n", "\n", " print(\"🚀 Force Delete NumPy 2.0.2 - Nuclear Option\")\n", "\n", " WORK_DIR = \"/content/gaussian-splatting\"\n", "\n", " # =====================================================================\n", " # STEP 1: System packages\n", " # =====================================================================\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 1: System packages\")\n", " print(\"=\"*70)\n", "\n", " subprocess.run([\"apt-get\", \"update\", \"-qq\"], capture_output=True)\n", " subprocess.run([\n", " \"apt-get\", \"install\", \"-y\", \"-qq\",\n", " \"colmap\", \"build-essential\", \"cmake\", \"git\",\n", " \"libopenblas-dev\", \"xvfb\"\n", " ], capture_output=True)\n", "\n", " os.environ[\"QT_QPA_PLATFORM\"] = \"offscreen\"\n", " os.environ[\"DISPLAY\"] = \":99\"\n", " subprocess.Popen(\n", " [\"Xvfb\", \":99\", \"-screen\", \"0\", \"1024x768x24\"],\n", " stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL\n", " )\n", " print(\"✓ System packages installed\")\n", "\n", "\n", " # =====================================================================\n", " # STEP 2: Clone Gaussian Splatting\n", " # =====================================================================\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 2: Clone Gaussian Splatting\")\n", " print(\"=\"*70)\n", "\n", " if not os.path.exists(WORK_DIR):\n", " subprocess.run([\n", " \"git\", \"clone\", \"--recursive\",\n", " \"https://github.com/graphdeco-inria/gaussian-splatting.git\",\n", " WORK_DIR\n", " ], capture_output=True)\n", " print(\"✓ Cloned\")\n", " else:\n", " print(\"✓ Already exists\")\n", "\n", "\n", " # =====================================================================\n", " # STEP 3: NUCLEAR - Physically delete ALL numpy\n", " # =====================================================================\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 3: NUCLEAR - Force delete ALL NumPy installations\")\n", " print(\"=\"*70)\n", "\n", " # Uninstall via pip\n", " print(\"Uninstalling numpy and scipy via pip...\")\n", " subprocess.run(\n", " [sys.executable, \"-m\", \"pip\", \"uninstall\", \"-y\", \"numpy\", \"scipy\"],\n", " capture_output=True\n", " )\n", "\n", " # Find ALL site-packages locations\n", " import site\n", " site_packages = site.getsitepackages() + [site.getusersitepackages()]\n", "\n", " print(f\"Searching {len(site_packages)} site-packages directories...\")\n", "\n", " deleted_count = 0\n", " for sp in site_packages:\n", " if not os.path.exists(sp):\n", " continue\n", "\n", " # Delete numpy directories\n", " numpy_dirs = glob.glob(os.path.join(sp, \"numpy*\"))\n", " for d in numpy_dirs:\n", " try:\n", " if os.path.isdir(d):\n", " shutil.rmtree(d)\n", " else:\n", " os.remove(d)\n", " print(f\" Deleted: {d}\")\n", " deleted_count += 1\n", " except Exception as e:\n", " print(f\" Warning: Could not delete {d}: {e}\")\n", "\n", " # Delete scipy directories\n", " scipy_dirs = glob.glob(os.path.join(sp, \"scipy*\"))\n", " for d in scipy_dirs:\n", " try:\n", " if os.path.isdir(d):\n", " shutil.rmtree(d)\n", " else:\n", " os.remove(d)\n", " print(f\" Deleted: {d}\")\n", " deleted_count += 1\n", " except Exception as e:\n", " print(f\" Warning: Could not delete {d}: {e}\")\n", "\n", " print(f\"✓ Deleted {deleted_count} numpy/scipy installations\")\n", "\n", "\n", " # =====================================================================\n", " # STEP 4: Clean install - SciPy first\n", " # =====================================================================\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 4: Clean install - SciPy first strategy\")\n", " print(\"=\"*70)\n", "\n", " # Install scipy (will install numpy 2.x)\n", " print(\"Installing scipy (with numpy 2.x)...\")\n", " subprocess.run(\n", " [sys.executable, \"-m\", \"pip\", \"install\", \"scipy\"],\n", " capture_output=True,\n", " check=True\n", " )\n", "\n", " # Physically delete numpy 2.x that just got installed\n", " print(\"Deleting numpy 2.x that scipy installed...\")\n", " for sp in site_packages:\n", " if not os.path.exists(sp):\n", " continue\n", " numpy_dirs = glob.glob(os.path.join(sp, \"numpy*\"))\n", " for d in numpy_dirs:\n", " try:\n", " if os.path.isdir(d):\n", " shutil.rmtree(d)\n", " else:\n", " os.remove(d)\n", " print(f\" Deleted: {d}\")\n", " except:\n", " pass\n", "\n", " # Install numpy 1.26.4 cleanly\n", " print(\"Installing numpy 1.26.4...\")\n", " subprocess.run(\n", " [sys.executable, \"-m\", \"pip\", \"install\", \"numpy==1.26.4\"],\n", " capture_output=True,\n", " check=True\n", " )\n", "\n", " print(\"✓ Clean numpy 1.26.4 + scipy installed\")\n", "\n", "\n", " # =====================================================================\n", " # STEP 5: Install other packages\n", " # =====================================================================\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 5: Install other packages\")\n", " print(\"=\"*70)\n", "\n", " packages = [\n", " \"torch torchvision torchaudio\",\n", " \"opencv-python pillow imageio imageio-ffmpeg plyfile tqdm tensorboard psutil\",\n", " \"transformers==4.40.0\",\n", " \"kornia h5py matplotlib\",\n", " \"git+https://github.com/cvg/LightGlue.git\",\n", " \"pycolmap\"\n", " ]\n", "\n", " for pkg in packages:\n", " subprocess.run(\n", " [sys.executable, \"-m\", \"pip\", \"install\"] + pkg.split(),\n", " capture_output=True\n", " )\n", "\n", " print(\"✓ All packages installed\")\n", "\n", "\n", " # =====================================================================\n", " # STEP 6: Build GS submodules\n", " # =====================================================================\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 6: Build Gaussian Splatting submodules\")\n", " print(\"=\"*70)\n", "\n", " for name, repo in {\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", " }.items():\n", " path = os.path.join(WORK_DIR, \"submodules\", name)\n", " if not os.path.exists(path):\n", " subprocess.run([\"git\", \"clone\", repo, path], capture_output=True)\n", " subprocess.run(\n", " [sys.executable, \"-m\", \"pip\", \"install\", path],\n", " capture_output=True\n", " )\n", "\n", " print(\"✓ Submodules built\")\n", "\n", "\n", " # =====================================================================\n", " # STEP 7: Final nuclear strike on numpy 2.x\n", " # =====================================================================\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"STEP 7: Final check and nuclear strike if needed\")\n", " print(\"=\"*70)\n", "\n", " # Check if numpy 2.x snuck back in\n", " for sp in site_packages:\n", " if not os.path.exists(sp):\n", " continue\n", "\n", " # Look for numpy-2.* directories\n", " numpy2_dirs = glob.glob(os.path.join(sp, \"numpy-2.*\"))\n", " if numpy2_dirs:\n", " print(f\"⚠️ Found numpy 2.x installations: {len(numpy2_dirs)}\")\n", " for d in numpy2_dirs:\n", " try:\n", " shutil.rmtree(d)\n", " print(f\" Nuked: {d}\")\n", " except:\n", " pass\n", "\n", " # Also check for numpy/__init__.py with version 2.x\n", " numpy_init = os.path.join(sp, \"numpy\", \"__init__.py\")\n", " if os.path.exists(numpy_init):\n", " try:\n", " with open(numpy_init, 'r') as f:\n", " content = f.read()\n", " if '__version__ = \"2.' in content or \"__version__ = '2.\" in content:\n", " print(f\"⚠️ Found numpy 2.x at {sp}/numpy\")\n", " shutil.rmtree(os.path.join(sp, \"numpy\"))\n", " print(f\" Nuked: {os.path.join(sp, 'numpy')}\")\n", " except:\n", " pass\n", "\n", " # Reinstall numpy 1.26.4 to be absolutely sure\n", " subprocess.run(\n", " [sys.executable, \"-m\", \"pip\", \"install\", \"numpy==1.26.4\", \"--force-reinstall\"],\n", " capture_output=True\n", " )\n", "\n", " print(\"✓ Final numpy cleanup complete\")\n", "\n", " return WORK_DIR\n", "\n", "if __name__ == \"__main__\":\n", " setup_environment()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cN8OS0ZvX018", "outputId": "51e50573-adf8-4a98-9495-0f5d2fe4986c" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🚀 Force Delete NumPy 2.0.2 - Nuclear Option\n", "\n", "======================================================================\n", "STEP 1: System packages\n", "======================================================================\n", "✓ System packages installed\n", "\n", "======================================================================\n", "STEP 2: Clone Gaussian Splatting\n", "======================================================================\n", "✓ Cloned\n", "\n", "======================================================================\n", "STEP 3: NUCLEAR - Force delete ALL NumPy installations\n", "======================================================================\n", "Uninstalling numpy and scipy via pip...\n", "Searching 4 site-packages directories...\n", "✓ Deleted 0 numpy/scipy installations\n", "\n", "======================================================================\n", "STEP 4: Clean install - SciPy first strategy\n", "======================================================================\n", "Installing scipy (with numpy 2.x)...\n", "Deleting numpy 2.x that scipy installed...\n", " Deleted: /usr/local/lib/python3.12/dist-packages/numpy-2.4.1.dist-info\n", " Deleted: /usr/local/lib/python3.12/dist-packages/numpy.libs\n", " Deleted: /usr/local/lib/python3.12/dist-packages/numpy\n", "Installing numpy 1.26.4...\n", "✓ Clean numpy 1.26.4 + scipy installed\n", "\n", "======================================================================\n", "STEP 5: Install other packages\n", "======================================================================\n", "✓ All packages installed\n", "\n", "======================================================================\n", "STEP 6: Build Gaussian Splatting submodules\n", "======================================================================\n", "✓ Submodules built\n", "\n", "======================================================================\n", "STEP 7: Final check and nuclear strike if needed\n", "======================================================================\n", "⚠️ Found numpy 2.x installations: 1\n", " Nuked: /usr/local/lib/python3.12/dist-packages/numpy-2.2.6.dist-info\n", "✓ Final numpy cleanup complete\n" ] } ] }, { "cell_type": "code", "source": [ "import numpy as np\n", "print(f\"✓ NumPy: {np.__version__}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "6d7703a2-7ee6-4025-947d-0b072874205d", "id": "nzzRu5emNQAj" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✓ NumPy: 1.26.4\n" ] } ] }, { "cell_type": "code", "source": [ "import torch\n", "import PIL\n", "\n", "from transformers import AutoConfig\n", "from transformers import AutoImageProcessor" ], "metadata": { "id": "Ib-xRVVIy2PC" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "import os\n", "import sys\n", "\n", "%cd /content/gaussian-splatting\n", "\n", "files = ['database.py', 'h5_to_db.py', 'metric.py']\n", "base_url = 'https://huggingface.co/stpete2/imc25_utils/resolve/main/'\n", "\n", "for file in files:\n", " if not os.path.exists(file):\n", " !wget -q {base_url + file}\n", " print(f\"✓ {file} download complete\")\n", " else:\n", " print(f\"✓ {file} already exists\")\n" ], "metadata": { "id": "eJrkKiCLzt1G", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "b675e1fc-394d-4e02-cc06-4291d8dc4ddc" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content/gaussian-splatting\n", "✓ database.py download complete\n", "✓ h5_to_db.py download complete\n", "✓ metric.py download complete\n" ] } ] }, { "cell_type": "code", "source": [ "from database import COLMAPDatabase, image_ids_to_pair_id\n", "from h5_to_db import add_keypoints, add_matches\n", "from metric import *" ], "metadata": { "id": "bmM_IBUtrEMd" }, "execution_count": 8, "outputs": [] }, { "cell_type": "code", "source": [ "#success\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", "if __name__ == \"__main__\":\n", " setup_mast3r()" ], "metadata": { "id": "3-CN6HJvZ6u2", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "5a602f18-568d-4e76-ff2b-3d578786408a" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\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" ] } ] }, { "cell_type": "code", "source": [ "import torch\n", "import numpy as np\n", "import sys\n", "\n", "# listify関数も必要なので定義\n", "def listify(x):\n", " return list(x) if isinstance(x, (list, tuple)) else [x]" ], "metadata": { "id": "kTPzKB2vYn6b" }, "execution_count": 10, "outputs": [] }, { "cell_type": "code", "source": [ "# /content/mast3r/dust3r/dust3r/utils/device.py の該当関数全体を置き換え\n", "\n", "def collate_with_cat(whatever, lists=False):\n", " if isinstance(whatever, (list, tuple)):\n", " if not whatever:\n", " return whatever\n", " elem = whatever[0]\n", "\n", " T = type(elem)\n", " if T is torch.Tensor or (T is torch.nn.parameter.Parameter):\n", " return listify(whatever) if lists else torch.cat(whatever)\n", "\n", " # numpyの型を確実に処理\n", " elem_type_name = type(elem).__name__\n", " elem_module = type(elem).__module__\n", "\n", " if elem_type_name == 'ndarray' or (elem_module == 'numpy' and elem_type_name == 'ndarray'):\n", " tensors = []\n", " for x in whatever:\n", " # 確実にnumpy配列として扱う\n", " if hasattr(x, '__array__'):\n", " arr = np.asarray(x)\n", " else:\n", " arr = np.array(x)\n", " tensors.append(torch.from_numpy(arr))\n", " return listify(tensors) if lists else torch.cat(tensors)" ], "metadata": { "id": "SpqfW2PDXp-H" }, "execution_count": 11, "outputs": [] }, { "cell_type": "code", "source": [ "# MASt3Rのモジュールをインポート（まだインポートされていない場合）\n", "if 'dust3r.utils.device' not in sys.modules:\n", " from dust3r.utils import device as device_module\n", "else:\n", " device_module = sys.modules['dust3r.utils.device']\n", "\n", "# 関数を置き換え\n", "device_module.collate_with_cat = collate_with_cat\n", "print(\"✓ collate_with_cat関数を置き換えました\")" ], "metadata": { "id": "AUg6a4lEXeS-", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2699743f-c413-4029-9167-dd0c76bbdab2" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✓ collate_with_cat関数を置き換えました\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "oVGIvNWSpzlZ" }, "execution_count": 12, "outputs": [] }, { "cell_type": "code", "source": [ "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", "\n", "\n", "# LightGlue\n", "from lightglue import ALIKED, LightGlue\n", "from lightglue.utils import load_image\n", "\n" ], "metadata": { "id": "GvtwW6aXpzeG" }, "execution_count": 13, "outputs": [] }, { "cell_type": "code", "source": [ "\"\"\"\n", "Gaussian Splatting Pipeline\n", "Simple and robust pipeline: LightGlue → COLMAP → Gaussian Splatting\n", "\"\"\"\n", "\"\"\"\n", "Gaussian Splatting Pipeline\n", "Simple and robust pipeline: LightGlue → MASt3R → Gaussian Splatting\n", "\"\"\"\n", "\n", "# ============================================================================\n", "# Configuration\n", "# ============================================================================\n", "class Config:\n", " # Feature extraction\n", " N_KEYPOINTS = 8192\n", " IMAGE_SIZE = 1024\n", "\n", " # Pair selection\n", " GLOBAL_TOPK = 200\n", " MIN_MATCHES = 10\n", " RATIO_THR = 1.2\n", "\n", " # Paths\n", " DINO_MODEL = \"facebook/dinov2-base\"\n", "\n", " # MASt3R settings (重要: これらが欠けていました!)\n", " MAST3R_MODEL = \"/content/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\"\n", " MAST3R_IMAGE_SIZE = 224 # メモリを節約するため小さめ(224 or 512)\n", "\n", " # Device\n", " DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')" ], "metadata": { "id": "7NfrJdMvrPZn" }, "outputs": [], "execution_count": 14 }, { "cell_type": "code", "source": [], "metadata": { "id": "eFExgZs-k0l9" }, "execution_count": 14, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# Step 0: images_square\n", "# ============================================================================\n", "\n", "def preprocess_images_square(input_dir, output_dir, size=1024, background='black'):\n", " \"\"\"\n", " Standardize all images to a square format (maintaining aspect ratio with padding).\n", "\n", " Args:\n", " input_dir (str): Directory containing input images.\n", " output_dir (str): Directory to save processed images.\n", " size (int): Target square dimension (default: 1024).\n", " background (str): Background style: 'black', 'white', or 'blur'.\n", " \"\"\"\n", " from PIL import Image, ImageFilter\n", " import os\n", " from tqdm import tqdm\n", "\n", " print(f\"\\n=== Preprocessing to {size}x{size} Square Images ===\")\n", "\n", " os.makedirs(output_dir, exist_ok=True)\n", "\n", " image_files = sorted([\n", " f for f in os.listdir(input_dir)\n", " if f.lower().endswith(('.jpg', '.jpeg', '.png'))\n", " ])\n", "\n", " stats = {\n", " 'total': len(image_files),\n", " 'landscape': 0,\n", " 'portrait': 0,\n", " 'square': 0,\n", " 'resized': 0,\n", " }\n", "\n", " for img_file in tqdm(image_files, desc=\"Converting to square\"):\n", " img_path = os.path.join(input_dir, img_file)\n", " img = Image.open(img_path).convert('RGB')\n", "\n", " width, height = img.size\n", "\n", " # Statistics\n", " if width > height:\n", " stats['landscape'] += 1\n", " elif width < height:\n", " stats['portrait'] += 1\n", " else:\n", " stats['square'] += 1\n", "\n", " # Resize based on the longest side\n", " max_dim = max(width, height)\n", " if max_dim != size:\n", " scale = size / max_dim\n", " new_width = int(width * scale)\n", " new_height = int(height * scale)\n", " img = img.resize((new_width, new_height), Image.LANCZOS)\n", " stats['resized'] += 1\n", " else:\n", " new_width, new_height = width, height\n", "\n", " # Create background\n", " if background == 'black':\n", " canvas = Image.new('RGB', (size, size), (0, 0, 0))\n", " elif background == 'white':\n", " canvas = Image.new('RGB', (size, size), (255, 255, 255))\n", " elif background == 'blur':\n", " # Use a blurred version of the image as background for a professional look\n", " canvas = img.resize((size, size), Image.LANCZOS)\n", " canvas = canvas.filter(ImageFilter.GaussianBlur(radius=20))\n", " else:\n", " canvas = Image.new('RGB', (size, size), (0, 0, 0))\n", "\n", " # Center the image\n", " offset_x = (size - new_width) // 2\n", " offset_y = (size - new_height) // 2\n", " canvas.paste(img, (offset_x, offset_y))\n", "\n", " # Save output\n", " output_path = os.path.join(output_dir, img_file)\n", " canvas.save(output_path, quality=95, optimize=True)\n", "\n", " print(f\"\\n✓ Preprocessing complete:\")\n", " print(f\" Total images: {stats['total']}\")\n", " print(f\" Landscape: {stats['landscape']} / Portrait: {stats['portrait']} / Square: {stats['square']}\")\n", " print(f\" Resized: {stats['resized']}\")\n", " print(f\" Output size: {size}x{size}\")\n", "\n", " return output_dir" ], "metadata": { "id": "TkVzKRqsvxFZ" }, "execution_count": 15, "outputs": [] }, { "cell_type": "code", "source": [ "def normalize_image_sizes_biplet(input_dir, output_dir=None, size=1024):\n", " \"\"\"\n", " Generates two square crops (Left & Right or Top & Bottom)\n", " from each image in a directory.\n", " \"\"\"\n", " if output_dir is None:\n", " output_dir = input_dir\n", "\n", " os.makedirs(output_dir, exist_ok=True)\n", "\n", " print(f\"Generating 2 cropped squares (Left/Right or Top/Bottom) for each image...\")\n", " print()\n", "\n", " converted_count = 0\n", " size_stats = {}\n", "\n", " for img_file in sorted(os.listdir(input_dir)):\n", " if not img_file.lower().endswith(('.jpg', '.jpeg', '.png')):\n", " continue\n", "\n", " input_path = os.path.join(input_dir, img_file)\n", "\n", " try:\n", " img = Image.open(input_path)\n", " original_size = img.size\n", "\n", " size_key = f\"{original_size[0]}x{original_size[1]}\"\n", " size_stats[size_key] = size_stats.get(size_key, 0) + 1\n", "\n", " # Generate 2 crops\n", " crops = generate_two_crops(img, size)\n", "\n", " base_name, ext = os.path.splitext(img_file)\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", "\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\"Original size distribution: {size_stats}\")\n", " return converted_count\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" ], "metadata": { "id": "A6smO9X0el3d" }, "execution_count": 16, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# Step 1: Image Pair Selection (DINO + ALIKED local verify)\n", "# ============================================================================\n", "\n", "def load_torch_image(fname, device):\n", " \"\"\"Load image as torch tensor\"\"\"\n", " from PIL import Image\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\"\"\"\n", " print(\"\\n=== Extracting DINO Global Features ===\")\n", "\n", " processor = AutoImageProcessor.from_pretrained(model_path)\n", " model = AutoModel.from_pretrained(model_path).eval().to(device)\n", "\n", " global_descs = []\n", " for img_path in tqdm(image_paths):\n", " img = load_torch_image(img_path, device)\n", " with torch.no_grad():\n", " inputs = processor(images=img, 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", " global_descs = torch.cat(global_descs, dim=0)\n", "\n", " del model\n", " torch.cuda.empty_cache()\n", " gc.collect()\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", " return list(set(pairs))\n", "\n", "def extract_aliked_features(image_paths, device):\n", " \"\"\"Extract ALIKED local features\"\"\"\n", " print(\"\\n=== Extracting ALIKED Local Features ===\")\n", "\n", " extractor = ALIKED(\n", " model_name=\"aliked-n16\",\n", " max_num_keypoints=Config.N_KEYPOINTS,\n", " detection_threshold=0.01,\n", " resize=Config.IMAGE_SIZE\n", " ).eval().to(device)\n", "\n", " features = []\n", " for img_path in tqdm(image_paths):\n", " img = load_torch_image(img_path, device)\n", " with torch.no_grad():\n", " feats = extractor.extract(img)\n", " kpts = feats['keypoints'].reshape(-1, 2).cpu()\n", " descs = feats['descriptors'].reshape(len(kpts), -1).cpu()\n", " features.append({'keypoints': kpts, 'descriptors': descs})\n", "\n", " del extractor\n", " torch.cuda.empty_cache()\n", " gc.collect()\n", "\n", " return features\n", "\n", "def verify_pairs_locally(pairs, features, device, threshold=Config.MIN_MATCHES):\n", " \"\"\"Verify pairs using local descriptor matching\"\"\"\n", " print(\"\\n=== Verifying Pairs with Local Features ===\")\n", "\n", " verified = []\n", " for i, j in tqdm(pairs):\n", " desc1 = features[i]['descriptors'].to(device)\n", " desc2 = features[j]['descriptors'].to(device)\n", "\n", " if len(desc1) == 0 or len(desc2) == 0:\n", " continue\n", "\n", " # Simple mutual nearest neighbor\n", " dist = torch.cdist(desc1, desc2, p=2)\n", " min_dist = dist.min(dim=1)[0]\n", " n_matches = (min_dist < Config.RATIO_THR).sum().item()\n", "\n", " if n_matches >= threshold:\n", " verified.append((i, j))\n", "\n", " return verified\n", "\n", "def get_image_pairs(image_paths):\n", " \"\"\"Main pair selection pipeline\"\"\"\n", " device = Config.DEVICE\n", "\n", " # 1. DINO global\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 global features: {len(pairs)}\")\n", "\n", " # 2. ALIKED local\n", " features = extract_aliked_features(image_paths, device)\n", "\n", " # 3. Local verification\n", " verified_pairs = verify_pairs_locally(pairs, features, device)\n", "\n", " print(f\"Verified pairs: {len(verified_pairs)}\")\n", "\n", " return verified_pairs, features" ], "metadata": { "id": "FNjFURfYmVcL" }, "outputs": [], "execution_count": 17 }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# Step 2: Feature Matching (ALIKED + LightGlue)\n", "# ============================================================================\n", "\n", "def match_pairs_lightglue(image_paths, pairs, features, output_dir):\n", " \"\"\"\n", " Match image pairs using LightGlue\n", " \"\"\"\n", " print(\"\\n=== Matching with LightGlue ===\")\n", "\n", " os.makedirs(output_dir, exist_ok=True)\n", " keypoints_path = os.path.join(output_dir, 'keypoints.h5')\n", " matches_path = os.path.join(output_dir, 'matches.h5')\n", "\n", " if os.path.exists(keypoints_path):\n", " os.remove(keypoints_path)\n", " if os.path.exists(matches_path):\n", " os.remove(matches_path)\n", "\n", " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", " extractor = ALIKED(max_num_keypoints=4096, detection_threshold=0.2, nms_radius=2).eval().to(device)\n", " matcher = LightGlue(features='aliked').eval().to(device)\n", "\n", " if isinstance(features, dict):\n", " all_keypoints = features['keypoints']\n", " all_descriptors = features['descriptors']\n", " elif isinstance(features, list):\n", " all_keypoints = [f['keypoints'] for f in features]\n", " all_descriptors = [f['descriptors'] for f in features]\n", " else:\n", " raise ValueError(f\"Unsupported features type: {type(features)}\")\n", "\n", " with h5py.File(keypoints_path, 'w') as f_kp:\n", " for idx, img_path in enumerate(tqdm(image_paths, desc=\"Saving keypoints\")):\n", " img_name = os.path.splitext(os.path.basename(img_path))[0]\n", "\n", " kp = all_keypoints[idx]\n", " if torch.is_tensor(kp):\n", " kp = kp.cpu().numpy()\n", " f_kp.create_dataset(img_name, data=kp)\n", "\n", " # Match pairs\n", " with h5py.File(matches_path, 'w') as f_match:\n", " for idx1, idx2 in tqdm(pairs, desc=\"Matching\"):\n", " with torch.no_grad():\n", " kp0 = all_keypoints[idx1]\n", " kp1 = all_keypoints[idx2]\n", " desc0 = all_descriptors[idx1]\n", " desc1 = all_descriptors[idx2]\n", "\n", " if isinstance(kp0, np.ndarray):\n", " kp0 = torch.from_numpy(kp0).float().to(device)\n", " kp1 = torch.from_numpy(kp1).float().to(device)\n", " desc0 = torch.from_numpy(desc0).float().to(device)\n", " desc1 = torch.from_numpy(desc1).float().to(device)\n", " else:\n", " kp0 = kp0.float().to(device)\n", " kp1 = kp1.float().to(device)\n", " desc0 = desc0.float().to(device)\n", " desc1 = desc1.float().to(device)\n", "\n", " feats0 = {\n", " 'keypoints': kp0.unsqueeze(0) if kp0.dim() == 2 else kp0,\n", " 'descriptors': desc0.unsqueeze(0) if desc0.dim() == 2 else desc0,\n", " }\n", " feats1 = {\n", " 'keypoints': kp1.unsqueeze(0) if kp1.dim() == 2 else kp1,\n", " 'descriptors': desc1.unsqueeze(0) if desc1.dim() == 2 else desc1,\n", " }\n", "\n", " matches01 = matcher({'image0': feats0, 'image1': feats1})\n", "\n", " if 'matches0' in matches01:\n", " matches0 = matches01['matches0']\n", " if isinstance(matches0, list):\n", " matches0 = matches0[0]\n", "\n", " # CUDAテンソルをCPUに移動\n", " if torch.is_tensor(matches0):\n", " matches0 = matches0.detach().cpu().numpy()\n", "\n", " valid = matches0 > -1\n", " if torch.is_tensor(valid):\n", " valid = valid.cpu().numpy()\n", "\n", " # 標準的なnumpy配列として取得\n", " valid_indices = np.where(valid)[0]\n", " valid_matches = matches0[valid]\n", "\n", " # 手動で2列の配列を構築\n", " n = len(valid_indices)\n", " matches = np.empty((n, 2), dtype=np.int64)\n", " matches[:, 0] = valid_indices\n", " matches[:, 1] = valid_matches\n", "\n", " elif 'matches' in matches01:\n", " m = matches01['matches']\n", " if torch.is_tensor(m):\n", " m = m.detach().cpu().numpy()\n", " matches = m\n", "\n", " else:\n", " continue\n", "\n", " if len(matches) > 0:\n", " img_name1 = os.path.splitext(os.path.basename(image_paths[idx1]))[0]\n", " img_name2 = os.path.splitext(os.path.basename(image_paths[idx2]))[0]\n", " pair_key = f\"{img_name1}_{img_name2}\"\n", " f_match.create_dataset(pair_key, data=matches)\n", "\n", " print(f\"✓ Matches saved to {matches_path}\")\n", "\n" ], "metadata": { "id": "X-PKgmdwmVcL" }, "outputs": [], "execution_count": 18 }, { "cell_type": "code", "source": [ "import numpy as np\n", "print(f\"✓ NumPy: {np.__version__}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Tg_SJYlwkeiD", "outputId": "3d465621-8e4f-43f5-86f9-c5e1fd2b5528" }, "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✓ NumPy: 1.26.4\n" ] } ] }, { "cell_type": "code", "source": [ "import torch\n", "from pathlib import Path\n", "from tqdm import tqdm" ], "metadata": { "id": "7D86wFMan2X8" }, "execution_count": 20, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# 1. まず、すべての通常のインポート\n", "# ============================================================================\n", "from dust3r.image_pairs import make_pairs\n", "from dust3r.inference import inference\n", "from dust3r.utils.image import load_images\n", "from dust3r.cloud_opt import global_aligner, GlobalAlignerMode\n", "\n", "# ============================================================================\n", "# 2. 次に、修正関数を定義してモンキーパッチ\n", "# ============================================================================\n", "import torch\n", "import numpy as np\n", "from dust3r.utils.device import to_cpu\n", "from dust3r.inference import check_if_same_size\n", "import dust3r.inference\n", "import dust3r.utils.misc\n", "\n", "# is_symmetrized関数を修正版に置き換え\n", "def is_symmetrized_fixed(gt1, gt2):\n", " \"\"\"\n", " is_symmetrizedの修正版 - IndexErrorを回避\n", " \"\"\"\n", " # instanceフィールドをチェック\n", " if 'instance' in gt1 and 'instance' in gt2:\n", " x = gt1['instance']\n", " y = gt2['instance']\n", "\n", " # リストの場合\n", " if isinstance(x, list) and isinstance(y, list):\n", " if len(x) != len(y):\n", " return False\n", " if len(x) < 2 or len(y) < 2:\n", " return False\n", " ok = True\n", " for i in range(0, len(x), 2):\n", " if i + 1 >= len(x) or i + 1 >= len(y):\n", " return False\n", " ok = ok and (x[i] == y[i + 1]) and (x[i + 1] == y[i])\n", " return ok\n", "\n", " # 文字列の場合\n", " elif isinstance(x, str) and isinstance(y, str):\n", " if len(x) != len(y):\n", " return False\n", " if len(x) < 2 or len(y) < 2:\n", " return False\n", " ok = True\n", " for i in range(0, len(x), 2):\n", " if i + 1 >= len(x) or i + 1 >= len(y):\n", " return False\n", " ok = ok and (x[i] == y[i + 1]) and (x[i + 1] == y[i])\n", " return ok\n", "\n", " return False\n", "\n", "\n", "def collate_with_cat_fixed(batch, lists=False):\n", " \"\"\"collate_with_catの修正版\"\"\"\n", " if not batch:\n", " return None\n", "\n", " if len(batch) == 1:\n", " elem = batch[0]\n", " if isinstance(elem, (list, tuple)) and len(elem) == 2:\n", " view1, view2 = elem\n", " if isinstance(view1, dict) and isinstance(view2, dict):\n", " view1 = convert_numpy_to_tensor(view1)\n", " view2 = convert_numpy_to_tensor(view2)\n", " return (view1, view2)\n", "\n", " if isinstance(batch[0], (list, tuple)):\n", " view1_list = []\n", " view2_list = []\n", "\n", " for pair in batch:\n", " if len(pair) == 2:\n", " v1 = convert_numpy_to_tensor(pair[0])\n", " v2 = convert_numpy_to_tensor(pair[1])\n", " view1_list.append(v1)\n", " view2_list.append(v2)\n", "\n", " def stack_dicts(dict_list):\n", " if not dict_list:\n", " return {}\n", "\n", " result = {}\n", " for key in dict_list[0].keys():\n", " values = [d[key] for d in dict_list]\n", "\n", " if isinstance(values[0], torch.Tensor):\n", " result[key] = torch.cat(values, dim=0)\n", " elif isinstance(values[0], np.ndarray):\n", " tensors = [torch.from_numpy(v) if isinstance(v, np.ndarray) else v for v in values]\n", " result[key] = torch.cat(tensors, dim=0)\n", " elif isinstance(values[0], (list, tuple)):\n", " result[key] = []\n", " for v in values:\n", " result[key].extend(v if isinstance(v, list) else [v])\n", " else:\n", " result[key] = values\n", "\n", " return result\n", "\n", " view1_batched = stack_dicts(view1_list)\n", " view2_batched = stack_dicts(view2_list)\n", "\n", " return (view1_batched, view2_batched)\n", "\n", " return None\n", "\n", "\n", "def convert_numpy_to_tensor(view_dict):\n", " \"\"\"辞書内のnumpy配列をTensorに変換\"\"\"\n", " result = {}\n", " for key, value in view_dict.items():\n", " if isinstance(value, np.ndarray):\n", " result[key] = torch.from_numpy(value)\n", " else:\n", " result[key] = value\n", " return result\n", "\n", "\n", "def loss_of_one_batch_fixed(batch, model, criterion, device, symmetrize_batch=False, use_amp=False, ret=None):\n", " view1, view2 = batch\n", " ignore_keys = set(['depthmap', 'dataset', 'label', 'instance', 'idx', 'true_shape', 'rng'])\n", " for view in batch:\n", " for name in view.keys():\n", " if name in ignore_keys:\n", " continue\n", " view[name] = view[name].to(device, non_blocking=True)\n", "\n", " with torch.cuda.amp.autocast(enabled=bool(use_amp)):\n", " pred1, pred2 = model(view1, view2)\n", "\n", " with torch.cuda.amp.autocast(enabled=False):\n", " loss = criterion(view1, view2, pred1, pred2) if criterion is not None else None\n", "\n", " result = dict(view1=view1, view2=view2, pred1=pred1, pred2=pred2, loss=loss)\n", " return result[ret] if ret else result\n", "\n", "\n", "@torch.no_grad()\n", "def inference_debug(pairs, model, device, batch_size=8, verbose=True):\n", " \"\"\"\n", " デバッグ機能を追加したinference関数\n", " \"\"\"\n", " if verbose:\n", " print(f'>> Inference with model on {len(pairs)} image pairs')\n", "\n", " result = []\n", "\n", " # Check if all images have the same size\n", " multiple_shapes = not (check_if_same_size(pairs))\n", " if multiple_shapes:\n", " batch_size = 1\n", "\n", " for i in tqdm(range(0, len(pairs), batch_size), disable=not verbose, desc=\"MASt3R inference\"):\n", " batch_pairs = pairs[i:i + batch_size]\n", "\n", " # 修正版のcollate関数を使用\n", " collated = collate_with_cat_fixed(batch_pairs)\n", "\n", " if collated is None:\n", " raise ValueError(f\"collate_with_cat_fixed returned None at batch {i}\")\n", "\n", " # 修正版のloss_of_one_batchを使用\n", " res = loss_of_one_batch_fixed(collated, model, None, device)\n", " result.append(to_cpu(res))\n", "\n", " # ===== ここを修正 =====\n", " # 結果の集約 - multiple_shapesに関わらず辞書形式で結合\n", " if len(result) == 0:\n", " return None\n", "\n", " # 各バッチの結果を結合\n", " combined = {}\n", "\n", " for key in result[0].keys():\n", " if isinstance(result[0][key], dict):\n", " # 辞書の場合：各フィールドを結合\n", " combined[key] = {}\n", " for field in result[0][key].keys():\n", " values = [r[key][field] for r in result]\n", "\n", " if isinstance(values[0], torch.Tensor):\n", " combined[key][field] = torch.cat(values, dim=0)\n", " elif isinstance(values[0], list):\n", " combined[key][field] = []\n", " for v in values:\n", " combined[key][field].extend(v if isinstance(v, list) else [v])\n", " else:\n", " combined[key][field] = values\n", "\n", " elif isinstance(result[0][key], torch.Tensor):\n", " values = [r[key] for r in result]\n", " combined[key] = torch.cat(values, dim=0)\n", "\n", " elif isinstance(result[0][key], list):\n", " combined[key] = []\n", " for r in result:\n", " combined[key].extend(r[key] if isinstance(r[key], list) else [r[key]])\n", "\n", " else:\n", " combined[key] = result[0][key]\n", "\n", " return combined\n", "\n", "\n", "# ============================================================================\n", "# 3. モンキーパッチを適用（これが最も重要！）\n", "# ============================================================================\n", "print(\"Applying monkey patches...\")\n", "dust3r.utils.misc.is_symmetrized = is_symmetrized_fixed\n", "dust3r.inference.inference = inference_debug\n", "inference = dust3r.inference.inference\n", "\n", "print(\"✓ Monkey-patched dust3r.utils.misc.is_symmetrized\")\n", "print(\"✓ Monkey-patched dust3r.inference.inference\")\n", "\n", "\n", "# 確認テスト\n", "print(\"\\n=== Verification ===\")\n", "test_gt1 = {'instance': '12'}\n", "test_gt2 = {'instance': '21'}\n", "try:\n", " result = dust3r.utils.misc.is_symmetrized(test_gt1, test_gt2)\n", " print(f\"✅ Monkey patch working! is_symmetrized test passed\")\n", "except IndexError as e:\n", " print(f\"❌ ERROR: Monkey patch failed! {e}\")\n", " raise\n", "\n", "# ===== すべてのdust3rモジュールでis_symmetrizedを置き換え =====\n", "import sys\n", "\n", "print(\"\\n=== Patching all loaded dust3r modules ===\")\n", "patched_count = 0\n", "\n", "for module_name, module in list(sys.modules.items()):\n", " if module is None:\n", " continue\n", "\n", " # dust3rまたはmast3r関連のモジュール\n", " if 'dust3r' in module_name or 'mast3r' in module_name:\n", " # is_symmetrized属性を持っている場合\n", " if hasattr(module, 'is_symmetrized'):\n", " old_func = module.is_symmetrized\n", " module.is_symmetrized = is_symmetrized_fixed\n", " patched_count += 1\n", " print(f\" ✓ Patched: {module_name}.is_symmetrized\")\n", "\n", " # モジュールの__dict__を直接チェック\n", " if hasattr(module, '__dict__'):\n", " for attr_name in list(module.__dict__.keys()):\n", " attr = getattr(module, attr_name, None)\n", " if callable(attr) and attr_name == 'is_symmetrized':\n", " setattr(module, attr_name, is_symmetrized_fixed)\n", "\n", "print(f\"\\n✓ Patched {patched_count} modules\")\n", "print(\"=\"*70)\n", "\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gVeANB1q0W0p", "outputId": "209f4a60-28b9-4a47-c865-c5d588d0b41e" }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Applying monkey patches...\n", "✓ Monkey-patched dust3r.utils.misc.is_symmetrized\n", "✓ Monkey-patched dust3r.inference.inference\n", "\n", "=== Verification ===\n", "✅ Monkey patch working! is_symmetrized test passed\n", "\n", "=== Patching all loaded dust3r modules ===\n", " ✓ Patched: dust3r.utils.misc.is_symmetrized\n", " ✓ Patched: dust3r.model.is_symmetrized\n", " ✓ Patched: mast3r.model.is_symmetrized\n", "\n", "✓ Patched 3 modules\n", "======================================================================\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" ] } ] }, { "cell_type": "code", "source": [ "def run_mast3r_reconstruction(image_paths, pairs, output_dir, model, device):\n", " \"\"\"MASt3Rで3D再構成を実行\"\"\"\n", " print(\"\\n=== Running MASt3R Reconstruction ===\")\n", "\n", " # メモリ状態を表示\n", " print_memory_status(\"Initial memory state\")\n", "\n", " print(f\"Processing {len(pairs)} pairs...\")\n", "\n", " # 画像サイズを決定（224または512）\n", " img_size = 224 # MASt3Rのデフォルト推論サイズ\n", "\n", " print(f\"Loading {len(image_paths)} images at {img_size}x{img_size}...\")\n", "\n", " # 画像を読み込み\n", " imgs = load_images(image_paths, size=img_size, verbose=True)\n", " print(f\"Loaded {len(imgs)} images\")\n", " print_memory_status(\"After loading images\")\n", "\n", " # ペアを作成\n", " print(f\"Creating {len(pairs)} image pairs...\")\n", " scene_graph = []\n", "\n", " for idx1, idx2 in tqdm(pairs, desc=\"Preparing pairs\"):\n", " # 画像インデックスが有効か確認\n", " if idx1 >= len(imgs) or idx2 >= len(imgs):\n", " print(f\"Warning: Invalid pair ({idx1}, {idx2}), skipping...\")\n", " continue\n", "\n", " # ペアを作成\n", " view1 = imgs[idx1]\n", " view2 = imgs[idx2]\n", "\n", " # viewがNoneでないか確認\n", " if view1 is None or view2 is None:\n", " print(f\"Warning: None view in pair ({idx1}, {idx2}), skipping...\")\n", " continue\n", "\n", " # viewが辞書形式か確認\n", " if not isinstance(view1, dict) or not isinstance(view2, dict):\n", " print(f\"Warning: Invalid view type in pair ({idx1}, {idx2})\")\n", " print(f\" view1 type: {type(view1)}, view2 type: {type(view2)}\")\n", " continue\n", "\n", " scene_graph.append((view1, view2))\n", "\n", " print(f\"Valid pairs: {len(scene_graph)}\")\n", "\n", " if len(scene_graph) == 0:\n", " raise ValueError(\"No valid pairs to process!\")\n", "\n", " # 最初のペアをデバッグ\n", " print(\"\\n=== Debugging first pair ===\")\n", " first_pair = scene_graph[0]\n", " print(f\"Pair type: {type(first_pair)}\")\n", " print(f\"View1 type: {type(first_pair[0])}\")\n", " print(f\"View2 type: {type(first_pair[1])}\")\n", " if isinstance(first_pair[0], dict):\n", " print(f\"View1 keys: {list(first_pair[0].keys())}\")\n", " if isinstance(first_pair[1], dict):\n", " print(f\"View2 keys: {list(first_pair[1].keys())}\")\n", "\n", " # MASt3Rで推論\n", " print(f\"\\nRunning MASt3R inference on {len(scene_graph)} pairs...\")\n", " try:\n", " pairs_output = inference(\n", " scene_graph,\n", " model,\n", " device,\n", " batch_size=1,\n", " verbose=True\n", " )\n", " except Exception as e:\n", " print(f\"Error during inference: {e}\")\n", " print(f\"Error type: {type(e)}\")\n", " import traceback\n", " traceback.print_exc()\n", " raise\n", "\n", " print(f\"Inference complete, got {len(pairs_output)} results\")\n", " print_memory_status(\"After inference\")\n", "\n", " # Global alignmentを実行\n", " print(\"\\n=== Running Global Alignment ===\")\n", " scene = global_aligner(\n", " pairs_output,\n", " device=device,\n", " mode=GlobalAlignerMode.PointCloudOptimizer,\n", " verbose=True\n", " )\n", "\n", " # 最適化\n", " print(\"Optimizing scene...\")\n", " loss = scene.compute_global_alignment(\n", " init='mst',\n", " niter=300,\n", " schedule='cosine',\n", " lr=0.01\n", " )\n", "\n", " print(f\"Optimization complete, final loss: {loss:.4f}\")\n", " print_memory_status(\"After optimization\")\n", "\n", " # COLMAP形式で保存\n", " colmap_dir = Path(output_dir) / \"colmap\"\n", " colmap_dir.mkdir(parents=True, exist_ok=True)\n", "\n", " print(f\"\\n=== Saving to COLMAP format ===\")\n", " save_colmap_format(scene, imgs, colmap_dir)\n", "\n", " print(f\"✓ COLMAP data saved to {colmap_dir}\")\n", "\n", " return scene, colmap_dir\n", "\n", "\n" ], "metadata": { "id": "jC7gd4-ktXiz" }, "execution_count": 22, "outputs": [] }, { "cell_type": "code", "source": [ "def save_colmap_format(scene, imgs, output_dir):\n", " \"\"\"シーンをCOLMAP形式で保存\"\"\"\n", " from dust3r.cloud_opt.base_opt import BasePCOptimizer\n", "\n", " output_dir = Path(output_dir)\n", " output_dir.mkdir(parents=True, exist_ok=True)\n", "\n", " # カメラパラメータを取得\n", " focals = scene.get_focals().cpu().numpy()\n", " principal_points = scene.get_principal_points().cpu().numpy()\n", " poses = scene.get_im_poses().cpu().numpy()\n", " pts3d = scene.get_pts3d().cpu().numpy()\n", "\n", " n_images = len(imgs)\n", "\n", " # cameras.txt\n", " cameras_file = output_dir / \"cameras.txt\"\n", " with open(cameras_file, 'w') as f:\n", " f.write(\"# Camera list with one line of data per camera:\\n\")\n", " f.write(\"# CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\\n\")\n", "\n", " for i in range(n_images):\n", " # SIMPLE_PINHOLE モデル (f, cx, cy)\n", " img_shape = imgs[i]['true_shape']\n", " width, height = img_shape[1], img_shape[0]\n", " fx = focals[i, 0]\n", " cx, cy = principal_points[i]\n", "\n", " f.write(f\"{i+1} SIMPLE_PINHOLE {width} {height} {fx} {cx} {cy}\\n\")\n", "\n", " print(f\"✓ Saved cameras.txt with {n_images} cameras\")\n", "\n", " # images.txt\n", " images_file = output_dir / \"images.txt\"\n", " with open(images_file, 'w') as f:\n", " f.write(\"# Image list with two lines of data per image:\\n\")\n", " f.write(\"# IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\\n\")\n", " f.write(\"# POINTS2D[] as (X, Y, POINT3D_ID)\\n\")\n", "\n", " for i in range(n_images):\n", " # ポーズを回転とtranslationに分解\n", " pose = poses[i]\n", " R = pose[:3, :3]\n", " t = pose[:3, 3]\n", "\n", " # 回転行列をクォータニオンに変換\n", " from scipy.spatial.transform import Rotation\n", " quat = Rotation.from_matrix(R).as_quat() # [x, y, z, w]\n", " qw, qx, qy, qz = quat[3], quat[0], quat[1], quat[2]\n", "\n", " img_name = Path(imgs[i]['filepath']).name\n", "\n", " f.write(f\"{i+1} {qw} {qx} {qy} {qz} {t[0]} {t[1]} {t[2]} {i+1} {img_name}\\n\")\n", " f.write(\"\\n\") # 2D pointsの行（空）\n", "\n", " print(f\"✓ Saved images.txt with {n_images} images\")\n", "\n", " # points3D.txt\n", " points_file = output_dir / \"points3D.txt\"\n", " with open(points_file, 'w') as f:\n", " f.write(\"# 3D point list with one line of data per point:\\n\")\n", " f.write(\"# POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\\n\")\n", "\n", " # 全画像の3Dポイントを集約\n", " point_id = 1\n", " for i in range(n_images):\n", " pts = pts3d[i] # (H, W, 3)\n", "\n", " # 有効なポイントのみ保存\n", " valid_mask = ~np.isnan(pts).any(axis=-1)\n", " valid_pts = pts[valid_mask]\n", "\n", " for pt in valid_pts[:1000]: # 各画像から最大1000点\n", " # デフォルトカラー（グレー）\n", " f.write(f\"{point_id} {pt[0]} {pt[1]} {pt[2]} 128 128 128 0.0\\n\")\n", " point_id += 1\n", "\n", " print(f\"✓ Saved points3D.txt with {point_id-1} points\")\n", "\n", "\n", "def print_memory_status(label=\"\"):\n", " \"\"\"メモリ使用状況を表示\"\"\"\n", " import psutil\n", "\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\"{label}:\")\n", " print(f\"GPU Memory - Allocated: {allocated:.2f}GB, Reserved: {reserved:.2f}GB\")\n", "\n", " cpu_percent = psutil.virtual_memory().percent\n", " print(f\"CPU Memory Usage: {cpu_percent:.1f}%\")" ], "metadata": { "id": "BJDvEDwBnlJm" }, "execution_count": 23, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "# ============================================================================\n", "# Step 3 & 4: MASt3R Reconstruction (COLMAPの代替)\n", "# ============================================================================\n", "\n", "import struct\n", "from pathlib import Path\n", "\n", "def import_to_mast3r_and_save_colmap(\n", " image_dir,\n", " processed_image_dir,\n", " feature_dir,\n", " database_path,\n", " output_dir,\n", " pairs,\n", " single_camera=True\n", "):\n", " \"\"\"\n", " MASt3Rを使用してカメラポーズを推定し、COLMAP形式で保存\n", " \"\"\"\n", " print(\"\\n=== Running MASt3R Reconstruction ===\")\n", " print(\"Initial memory state:\")\n", " get_memory_info()\n", "\n", " # MASt3Rモデルのロード\n", " from mast3r.model import AsymmetricMASt3R\n", " device = Config.DEVICE\n", "\n", " model = AsymmetricMASt3R.from_pretrained(Config.MAST3R_MODEL).to(device)\n", " model.eval()\n", " print(f\"✓ MASt3R model loaded on {device}\")\n", "\n", " # 画像パスの取得\n", " image_paths = sorted([\n", " os.path.join(processed_image_dir, f)\n", " for f in os.listdir(processed_image_dir)\n", " if f.lower().endswith(('.jpg', '.jpeg', '.png'))\n", " ])\n", "\n", " # MASt3Rで再構成\n", " scene, mast3r_images = run_mast3r_pairs(\n", " model, image_paths, pairs, device,\n", " batch_size=1\n", " )\n", "\n", " # モデルをメモリから削除\n", " del model\n", " clear_memory()\n", "\n", " # COLMAP形式のデータを抽出\n", " pts3d, colors, cameras, poses = extract_colmap_data(\n", " scene, image_paths, max_points=1000000\n", " )\n", "\n", " # COLMAP形式で保存\n", " sparse_dir = save_colmap_reconstruction(\n", " pts3d, colors, cameras, poses, image_paths, output_dir\n", " )\n", "\n", " print(f\"\\n✓ MASt3R reconstruction saved in COLMAP format\")\n", " print(f\" Output: {sparse_dir}\")\n", "\n", " return sparse_dir\n", "\n", "\n", "def run_mast3r_mapper(database_path, image_dir, output_dir, pairs, processed_image_dir):\n", " \"\"\"\n", " MASt3Rを使用したマッピング（COLMAPの代替）\n", " \"\"\"\n", " print(\"\\n=== MASt3R Mapper (COLMAP Alternative) ===\")\n", "\n", " # MASt3Rで再構成してCOLMAP形式で保存\n", " sparse_dir = import_to_mast3r_and_save_colmap(\n", " image_dir=image_dir,\n", " processed_image_dir=processed_image_dir,\n", " feature_dir=None,\n", " database_path=database_path,\n", " output_dir=output_dir,\n", " pairs=pairs,\n", " single_camera=True\n", " )\n", "\n", " # sparse/0 ディレクトリが存在することを確認\n", " model_dir = sparse_dir\n", " if not os.path.exists(model_dir):\n", " raise RuntimeError(f\"MASt3R reconstruction failed - directory not found: {model_dir}\")\n", "\n", " # 必要なファイルが存在することを確認\n", " required_files = ['cameras.bin', 'images.bin', 'points3D.bin']\n", " for file in required_files:\n", " file_path = os.path.join(model_dir, file)\n", " if not os.path.exists(file_path):\n", " raise FileNotFoundError(f\"Required file not found: {file}\")\n", "\n", " print(f\"\\n✓ MASt3R reconstruction complete: {model_dir}\")\n", " return model_dir\n", "\n", "\n", "def load_images_for_mast3r(image_paths, size=224):\n", " \"\"\"MASt3R用に画像をロード\"\"\"\n", " print(f\"\\n=== Loading images for MASt3R (size={size}) ===\")\n", "\n", " from dust3r.utils.image import load_images\n", "\n", " images = load_images(image_paths, size=size, verbose=True)\n", "\n", " return images\n", "\n", "\n", "\n", "\n", "\n", "def run_mast3r_pairs(model, image_paths, pairs, device='cuda', batch_size=1, chunk_size=500):\n", " \"\"\"\n", " 選択されたペアでMASt3Rを実行（画像もチャンクごとにロード、結合も最適化）\n", " \"\"\"\n", " print(\"\\n=== Running MASt3R Reconstruction ===\")\n", " print(\"Initial memory state:\")\n", " get_memory_info()\n", "\n", " from dust3r.cloud_opt import global_aligner, GlobalAlignerMode\n", " import dust3r.inference\n", " import pickle\n", " import tempfile\n", "\n", " print(f\"Processing {len(pairs)} pairs in chunks of {chunk_size}...\")\n", " print(\"Note: Images will be loaded on-demand per chunk to save memory\")\n", "\n", " # 一時ディレクトリを作成\n", " temp_dir = tempfile.mkdtemp()\n", " print(f\"Temporary directory: {temp_dir}\")\n", "\n", " # チャンクごとに処理してディスクに保存\n", " chunk_files = []\n", " num_chunks = (len(pairs) + chunk_size - 1) // chunk_size\n", "\n", " for chunk_idx in range(num_chunks):\n", " start_idx = chunk_idx * chunk_size\n", " end_idx = min(start_idx + chunk_size, len(pairs))\n", " chunk_pairs_indices = pairs[start_idx:end_idx]\n", "\n", " print(f\"\\n--- Processing chunk {chunk_idx + 1}/{num_chunks} (pairs {start_idx}-{end_idx}) ---\")\n", "\n", " # このチャンクで必要な画像インデックスを収集\n", " needed_image_indices = set()\n", " for idx1, idx2 in chunk_pairs_indices:\n", " needed_image_indices.add(idx1)\n", " needed_image_indices.add(idx2)\n", "\n", " needed_image_indices = sorted(list(needed_image_indices))\n", " print(f\"Loading {len(needed_image_indices)} unique images for this chunk...\")\n", "\n", " # 必要な画像だけロード\n", " needed_image_paths = [image_paths[i] for i in needed_image_indices]\n", " chunk_images = load_images_for_mast3r(needed_image_paths, size=Config.MAST3R_IMAGE_SIZE)\n", "\n", " # インデックスマッピングを作成（元のインデックス → チャンク内インデックス）\n", " index_mapping = {orig_idx: new_idx for new_idx, orig_idx in enumerate(needed_image_indices)}\n", "\n", " print(f\"Memory after loading chunk images:\")\n", " get_memory_info()\n", "\n", " # 画像ペアを作成（インデックスを変換）\n", " mast3r_pairs = []\n", " for idx1, idx2 in tqdm(chunk_pairs_indices, desc=f\"Preparing chunk {chunk_idx + 1}\"):\n", " new_idx1 = index_mapping[idx1]\n", " new_idx2 = index_mapping[idx2]\n", " mast3r_pairs.append([chunk_images[new_idx1], chunk_images[new_idx2]])\n", "\n", " # 推論を実行\n", " print(f\"Running MASt3R inference on {len(mast3r_pairs)} pairs...\")\n", " output = dust3r.inference.inference(mast3r_pairs, model, device, batch_size=batch_size, verbose=True)\n", "\n", " # ディスクに保存\n", " chunk_file = os.path.join(temp_dir, f'chunk_{chunk_idx}.pkl')\n", " with open(chunk_file, 'wb') as f:\n", " pickle.dump(output, f)\n", " chunk_files.append(chunk_file)\n", "\n", " del mast3r_pairs\n", " del chunk_images # 画像も削除\n", " del output\n", " clear_memory()\n", "\n", " print(f\"Chunk {chunk_idx + 1} saved to disk. Memory state:\")\n", " get_memory_info()\n", "\n", " # ディスクから読み込んで結合（メモリ効率化版）\n", " print(\"\\n=== Combining all chunks from disk ===\")\n", "\n", " # まず最初の2チャンクを結合\n", " print(f\"Loading and combining chunks 1-2...\")\n", " with open(chunk_files[0], 'rb') as f:\n", " combined_output = pickle.load(f)\n", " os.remove(chunk_files[0])\n", "\n", " with open(chunk_files[1], 'rb') as f:\n", " chunk_output = pickle.load(f)\n", "\n", " for key in combined_output.keys():\n", " if isinstance(combined_output[key], dict):\n", " for field in combined_output[key].keys():\n", " if isinstance(combined_output[key][field], torch.Tensor):\n", " combined_output[key][field] = torch.cat([\n", " combined_output[key][field],\n", " chunk_output[key][field]\n", " ], dim=0)\n", " elif isinstance(combined_output[key][field], list):\n", " combined_output[key][field].extend(chunk_output[key][field])\n", " elif isinstance(combined_output[key], torch.Tensor):\n", " combined_output[key] = torch.cat([\n", " combined_output[key],\n", " chunk_output[key]\n", " ], dim=0)\n", " elif isinstance(combined_output[key], list):\n", " combined_output[key].extend(chunk_output[key])\n", "\n", " del chunk_output\n", " os.remove(chunk_files[1])\n", " clear_memory()\n", "\n", " # 残りのチャンクを1つずつ結合\n", " for idx in range(2, len(chunk_files)):\n", " print(f\"Loading and combining chunk {idx + 1}/{len(chunk_files)}...\")\n", "\n", " with open(chunk_files[idx], 'rb') as f:\n", " chunk_output = pickle.load(f)\n", "\n", " for key in combined_output.keys():\n", " if isinstance(combined_output[key], dict):\n", " for field in combined_output[key].keys():\n", " if isinstance(combined_output[key][field], torch.Tensor):\n", " # メモリ効率化: 結合後に元のTensorを削除\n", " old_tensor = combined_output[key][field]\n", " combined_output[key][field] = torch.cat([\n", " old_tensor,\n", " chunk_output[key][field]\n", " ], dim=0)\n", " del old_tensor\n", " elif isinstance(combined_output[key][field], list):\n", " combined_output[key][field].extend(chunk_output[key][field])\n", "\n", " elif isinstance(combined_output[key], torch.Tensor):\n", " old_tensor = combined_output[key]\n", " combined_output[key] = torch.cat([\n", " old_tensor,\n", " chunk_output[key]\n", " ], dim=0)\n", " del old_tensor\n", "\n", " elif isinstance(combined_output[key], list):\n", " combined_output[key].extend(chunk_output[key])\n", "\n", " del chunk_output\n", " os.remove(chunk_files[idx])\n", " clear_memory()\n", "\n", " # 進捗確認\n", " if (idx + 1) % 3 == 0:\n", " print(f\" Memory after combining {idx + 1} chunks:\")\n", " get_memory_info()\n", "\n", " os.rmdir(temp_dir)\n", "\n", " print(f\"✓ Combined output keys: {list(combined_output.keys())}\")\n", " print(\"After combining all chunks:\")\n", " get_memory_info()\n", "\n", " print(\"✓ MASt3R inference complete\")\n", "\n", " # 最後にグローバルアライメント用に全画像をロード\n", " print(\"\\nLoading all images for global alignment...\")\n", " images = load_images_for_mast3r(image_paths, size=Config.MAST3R_IMAGE_SIZE)\n", " print(\"Memory after loading all images:\")\n", " get_memory_info()\n", "\n", " # グローバルアライメント\n", " print(\"\\nRunning global alignment...\")\n", " scene = global_aligner(\n", " combined_output,\n", " device=device,\n", " mode=GlobalAlignerMode.PointCloudOptimizer\n", " )\n", "\n", " del combined_output\n", " clear_memory()\n", "\n", " print(\"Computing global alignment...\")\n", " loss = scene.compute_global_alignment(\n", " init=\"mst\",\n", " niter=150,\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\n", "\n", "\n", "\n", "### revised for mast3r\n", "def extract_colmap_data(scene, image_paths, max_points=1000000):\n", " \"\"\"\n", " Extract COLMAP-compatible camera parameters and 3D points from MASt3R scene\n", "\n", " Args:\n", " scene: MASt3R scene object\n", " image_paths: List of image paths\n", " max_points: Maximum number of 3D points to extract (default: 1M)\n", " \"\"\"\n", " print(\"\\n=== Extracting COLMAP-compatible data ===\")\n", "\n", " # Extract point cloud\n", " pts_all = scene.get_pts3d()\n", " print(f\"pts_all type: {type(pts_all)}\")\n", "\n", " if isinstance(pts_all, list):\n", " print(f\"pts_all is a list with {len(pts_all)} elements\")\n", " if len(pts_all) > 0:\n", " print(f\"First element type: {type(pts_all[0])}\")\n", " if hasattr(pts_all[0], 'shape'):\n", " print(f\"First element shape: {pts_all[0].shape}\")\n", "\n", " pts_all = torch.stack([p if isinstance(p, torch.Tensor) else torch.tensor(p)\n", " for p in pts_all])\n", " print(f\"pts_all shape after conversion: {pts_all.shape}\")\n", "\n", " if len(pts_all.shape) == 4:\n", " print(f\"Found batched point cloud: {pts_all.shape}\")\n", " B, H, W, _ = pts_all.shape\n", " pts3d = pts_all.reshape(-1, 3).detach().cpu().numpy()\n", "\n", " # Extract colors\n", " colors = []\n", " for img_path in image_paths:\n", " img = Image.open(img_path).resize((W, H))\n", " colors.append(np.array(img))\n", " colors = np.stack(colors).reshape(-1, 3) / 255.0\n", " else:\n", " pts3d = pts_all.detach().cpu().numpy() if isinstance(pts_all, torch.Tensor) else pts_all\n", " colors = np.ones((len(pts3d), 3)) * 0.5\n", "\n", " print(f\"✓ Extracted {len(pts3d)} 3D points from {len(image_paths)} images\")\n", "\n", " # **DOWNSAMPLE POINTS TO REDUCE MEMORY USAGE**\n", " if len(pts3d) > max_points:\n", " print(f\"\\n⚠ Downsampling from {len(pts3d)} to {max_points} points to reduce memory usage...\")\n", "\n", " # Remove invalid points first\n", " valid_mask = ~(np.isnan(pts3d).any(axis=1) | np.isinf(pts3d).any(axis=1))\n", " pts3d_valid = pts3d[valid_mask]\n", " colors_valid = colors[valid_mask]\n", "\n", " # Random sampling\n", " indices = np.random.choice(len(pts3d_valid), size=max_points, replace=False)\n", " pts3d = pts3d_valid[indices]\n", " colors = colors_valid[indices]\n", "\n", " print(f\"✓ Downsampled to {len(pts3d)} points\")\n", "\n", " # Extract camera parameters\n", " print(\"Extracting camera parameters...\")\n", "\n", " # 【重要】MASt3Rのポーズはcamera-to-world形式\n", " # COLMAPはworld-to-camera形式を要求するので逆行列が必要\n", " poses_c2w = scene.get_im_poses().detach().cpu().numpy()\n", " print(f\"Retrieved camera-to-world poses: shape {poses_c2w.shape}\")\n", "\n", " # camera-to-world を world-to-camera に変換\n", " poses = []\n", " for i, pose_c2w in enumerate(poses_c2w):\n", " # 4x4行列の逆行列を計算\n", " pose_w2c = np.linalg.inv(pose_c2w)\n", " poses.append(pose_w2c)\n", "\n", " poses = np.array(poses)\n", " print(f\"Converted to world-to-camera poses for COLMAP\")\n", "\n", " # 焦点距離と主点を取得\n", " focals = scene.get_focals().detach().cpu().numpy()\n", " pp = scene.get_principal_points().detach().cpu().numpy()\n", " print(f\"Focals shape: {focals.shape}\")\n", " print(f\"Principal points shape: {pp.shape}\")\n", "\n", " # MASt3Rの処理サイズ（通常224x224）\n", " mast3r_size = 224.0\n", "\n", " cameras = []\n", " for i, img_path in enumerate(image_paths):\n", " img = Image.open(img_path)\n", " W, H = img.size\n", "\n", " # 元画像サイズとのスケール比\n", " scale = W / mast3r_size\n", "\n", " # focalsは[N,1]の形式（fx=fyの等方性カメラ）\n", " if focals.shape[1] == 1:\n", " focal_mast3r = float(focals[i, 0])\n", " fx = fy = focal_mast3r * scale\n", " else:\n", " fx = float(focals[i, 0]) * scale\n", " fy = float(focals[i, 1]) * scale\n", "\n", " # 主点もスケーリング\n", " cx = float(pp[i, 0]) * scale\n", " cy = float(pp[i, 1]) * scale\n", "\n", " camera = {\n", " 'camera_id': i + 1,\n", " 'model': 'PINHOLE',\n", " 'width': W,\n", " 'height': H,\n", " 'params': [fx, fy, cx, cy]\n", " }\n", " cameras.append(camera)\n", "\n", " if i == 0:\n", " print(f\"\\nExample camera 0:\")\n", " print(f\" Image size: {W}x{H}\")\n", " print(f\" MASt3R focal: {focal_mast3r:.2f}, pp: ({pp[i,0]:.2f}, {pp[i,1]:.2f})\")\n", " print(f\" Scaled fx={fx:.2f}, fy={fy:.2f}, cx={cx:.2f}, cy={cy:.2f}\")\n", " print(f\" Pose (first row): {poses[i][0]}\")\n", "\n", " print(f\"\\n✓ Extracted {len(cameras)} cameras and {len(poses)} poses\")\n", "\n", " return pts3d, colors, cameras, poses\n", "\n", "\n", "\n", "\n", "def save_colmap_reconstruction(pts3d, colors, cameras, poses, image_paths, output_dir):\n", " \"\"\"COLMAP形式で再構成を保存\"\"\"\n", " print(\"\\n=== Saving COLMAP reconstruction ===\")\n", "\n", " sparse_dir = Path(output_dir) / 'sparse' / '0'\n", " sparse_dir.mkdir(parents=True, exist_ok=True)\n", "\n", " print(f\" Writing COLMAP files to {sparse_dir}...\")\n", "\n", " write_cameras_binary(cameras, sparse_dir / 'cameras.bin')\n", " print(f\" ✓ Wrote {len(cameras)} cameras\")\n", "\n", " write_images_binary(image_paths, cameras, poses, sparse_dir / 'images.bin')\n", " print(f\" ✓ Wrote {len(image_paths)} images\")\n", "\n", " num_points = write_points3d_binary(pts3d, colors, sparse_dir / 'points3D.bin')\n", " print(f\" ✓ Wrote {num_points} 3D points\")\n", "\n", " print(f\"\\n✓ COLMAP reconstruction saved to {sparse_dir}\")\n", "\n", " return sparse_dir\n", "\n", "\n", "def write_cameras_binary(cameras, output_file):\n", " \"\"\"cameras.binをCOLMAPバイナリ形式で書き込み\"\"\"\n", " with open(output_file, 'wb') as f:\n", " f.write(struct.pack('Q', len(cameras)))\n", "\n", " for i, cam in enumerate(cameras):\n", " camera_id = cam.get('camera_id', i + 1)\n", " model_id = 1\n", " width = cam['width']\n", " height = cam['height']\n", " params = cam['params']\n", "\n", " f.write(struct.pack('i', camera_id))\n", " f.write(struct.pack('i', model_id))\n", " f.write(struct.pack('Q', width))\n", " f.write(struct.pack('Q', height))\n", "\n", " for param in params[:4]:\n", " f.write(struct.pack('d', param))\n", "\n", "\n", "def write_images_binary(image_paths, cameras, poses, output_file):\n", " \"\"\"images.binをCOLMAPバイナリ形式で書き込み\"\"\"\n", " with open(output_file, 'wb') as f:\n", " f.write(struct.pack('Q', len(image_paths)))\n", "\n", " for i, (img_path, pose) in enumerate(zip(image_paths, poses)):\n", " image_id = i + 1\n", " camera_id = cameras[i].get('camera_id', i + 1)\n", " image_name = os.path.basename(img_path)\n", "\n", " R = pose[:3, :3]\n", " t = pose[:3, 3]\n", "\n", " qvec = rotmat2qvec(R)\n", " tvec = t\n", "\n", " f.write(struct.pack('i', image_id))\n", "\n", " for q in qvec:\n", " f.write(struct.pack('d', float(q)))\n", "\n", " for tv in tvec:\n", " f.write(struct.pack('d', float(tv)))\n", "\n", " f.write(struct.pack('i', camera_id))\n", " f.write(image_name.encode('utf-8') + b'\\x00')\n", " f.write(struct.pack('Q', 0))\n", "\n", "\n", "def write_points3d_binary(pts3d, colors, output_file):\n", " \"\"\"points3D.binをCOLMAPバイナリ形式で書き込み\"\"\"\n", " valid_indices = []\n", " for i, pt in enumerate(pts3d):\n", " if not (np.isnan(pt).any() or np.isinf(pt).any()):\n", " valid_indices.append(i)\n", "\n", " with open(output_file, 'wb') as f:\n", " f.write(struct.pack('Q', len(valid_indices)))\n", "\n", " for idx, point_id in enumerate(valid_indices):\n", " pt = pts3d[point_id]\n", " color = colors[point_id]\n", "\n", " f.write(struct.pack('Q', point_id))\n", "\n", " for coord in pt:\n", " f.write(struct.pack('d', float(coord)))\n", "\n", " col_int = (color * 255).astype(np.uint8)\n", " for c in col_int:\n", " f.write(struct.pack('B', int(c)))\n", "\n", " f.write(struct.pack('d', 0.0))\n", " f.write(struct.pack('Q', 0))\n", "\n", " if (idx + 1) % 1000000 == 0:\n", " print(f\" Wrote {idx + 1} / {len(valid_indices)} points...\")\n", "\n", " return len(valid_indices)\n", "\n", "\n", "def rotmat2qvec(R):\n", " \"\"\"回転行列をクォータニオンに変換\"\"\"\n", " R = np.asarray(R, dtype=np.float64)\n", " trace = np.trace(R)\n", "\n", " if trace > 0:\n", " s = 0.5 / np.sqrt(trace + 1.0)\n", " w = 0.25 / s\n", " x = (R[2, 1] - R[1, 2]) * s\n", " y = (R[0, 2] - R[2, 0]) * s\n", " z = (R[1, 0] - R[0, 1]) * s\n", " elif R[0, 0] > R[1, 1] and R[0, 0] > R[2, 2]:\n", " s = 2.0 * np.sqrt(1.0 + R[0, 0] - R[1, 1] - R[2, 2])\n", " w = (R[2, 1] - R[1, 2]) / s\n", " x = 0.25 * s\n", " y = (R[0, 1] + R[1, 0]) / s\n", " z = (R[0, 2] + R[2, 0]) / s\n", " elif R[1, 1] > R[2, 2]:\n", " s = 2.0 * np.sqrt(1.0 + R[1, 1] - R[0, 0] - R[2, 2])\n", " w = (R[0, 2] - R[2, 0]) / s\n", " x = (R[0, 1] + R[1, 0]) / s\n", " y = 0.25 * s\n", " z = (R[1, 2] + R[2, 1]) / s\n", " else:\n", " s = 2.0 * np.sqrt(1.0 + R[2, 2] - R[0, 0] - R[1, 1])\n", " w = (R[1, 0] - R[0, 1]) / s\n", " x = (R[0, 2] + R[2, 0]) / s\n", " y = (R[1, 2] + R[2, 1]) / s\n", " z = 0.25 * s\n", "\n", " qvec = np.array([w, x, y, z], dtype=np.float64)\n", " qvec = qvec / np.linalg.norm(qvec)\n", "\n", " return qvec\n", "\n", "\n", "# メモリ管理ユーティリティ（必要に応じて追加）\n", "def clear_memory():\n", " \"\"\"GPUとCPUメモリを積極的にクリア\"\"\"\n", " gc.collect()\n", " if torch.cuda.is_available():\n", " torch.cuda.empty_cache()\n", " torch.cuda.synchronize()\n", "\n", "\n", "def get_memory_info():\n", " \"\"\"現在のメモリ使用状況を取得\"\"\"\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}%\")" ], "metadata": { "id": "imNgPK2Phwi8" }, "execution_count": 24, "outputs": [] }, { "cell_type": "markdown", "source": [], "metadata": { "id": "0LHFaucWicrB" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# Step 3: Import to COLMAP (too be removed)\n", "# ============================================================================\n", "\n", "def import_to_colmap(image_dir, feature_dir, database_path, single_camera=True):\n", " \"\"\"\n", " Import features and matches to COLMAP database\n", "\n", " Args:\n", " image_dir (str): Directory containing the images.\n", " feature_dir (str): Directory to save/load extracted features.\n", " database_path (str): Path to the database file.\n", " single_camera (bool): Set to True if all images have the same dimensions (e.g., pre-resized).\n", " \"\"\"\n", " print(\"\\n=== Creating COLMAP Database ===\")\n", "\n", " if os.path.exists(database_path):\n", " os.remove(database_path)\n", " print(f\"✓ Removed existing database\")\n", "\n", " db = COLMAPDatabase.connect(database_path)\n", " db.create_tables()\n", "\n", " print(f\"Single camera mode: {single_camera}\")\n", "\n", " image_files = [f for f in os.listdir(image_dir)\n", " if f.lower().endswith(('.jpg', '.jpeg', '.png'))]\n", " if not image_files:\n", " raise ValueError(f\"No images found in {image_dir}\")\n", "\n", " first_image = sorted(image_files)[0]\n", " img_ext = os.path.splitext(first_image)[1]\n", " print(f\"Detected image extension: '{img_ext}'\")\n", "\n", " fname_to_id = add_keypoints(\n", " db,\n", " feature_dir,\n", " image_dir,\n", " img_ext,\n", " 'PINHOLE',\n", " single_camera=single_camera\n", " )\n", "\n", " add_matches(db, feature_dir, fname_to_id)\n", " db.commit()\n", " db.close()\n", "\n", " print(f\"✓ Database created: {database_path}\")\n", "\n", "# ============================================================================\n", "# Step 4: Run COLMAP Mapper\n", "# ============================================================================\n", "\n", "def run_colmap_mapper(database_path, image_dir, output_dir):\n", " \"\"\"\n", " Run COLMAP mapper with verbose output\n", " \"\"\"\n", " print(\"\\n=== Running COLMAP Reconstruction ===\")\n", " os.makedirs(output_dir, exist_ok=True)\n", " cmd = [\n", " 'colmap', 'mapper',\n", " '--database_path', database_path,\n", " '--image_path', image_dir,\n", " '--output_path', output_dir,\n", " '--Mapper.ba_refine_focal_length', '0',\n", " '--Mapper.ba_refine_principal_point', '0',\n", " '--Mapper.ba_refine_extra_params', '0',\n", " '--Mapper.min_num_matches', '15',\n", " '--Mapper.init_min_num_inliers', '50',\n", " '--Mapper.max_num_models', '1',\n", " '--Mapper.num_threads', '16',\n", " ]\n", " print(f\"Command: {' '.join(cmd)}\\n\")\n", "\n", " process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)\n", " for line in process.stdout:\n", " print(line, end='')\n", " process.wait()\n", " if process.returncode == 0:\n", " model_dir = os.path.join(output_dir, '0')\n", " if os.path.exists(model_dir):\n", " print(f\"\\n✓ COLMAP reconstruction complete: {model_dir}\")\n", " return model_dir\n", " raise RuntimeError(\"COLMAP reconstruction failed\")" ], "metadata": { "id": "NJedFruCmVcL" }, "outputs": [], "execution_count": 25 }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# Step 5: Convert to Gaussian Splatting Format (if needed)\n", "# ============================================================================\n", "\n", "def convert_to_gs_format(colmap_model_dir, output_dir):\n", " \"\"\"\n", " Verify COLMAP output and prepare paths for Gaussian Splatting.\n", "\n", " Args:\n", " colmap_model_dir (str): Path to the COLMAP sparse/0 directory.\n", " Example: /content/output/colmap/sparse/0\n", " output_dir (str): Base output directory.\n", "\n", " Returns:\n", " colmap_parent_dir (str): The path to be passed to Gaussian Splatting.\n", " Example: /content/output/colmap (Parent directory containing 'sparse/')\n", " \"\"\"\n", " print(\"\\n=== Verifying COLMAP Model for Gaussian Splatting ===\")\n", "\n", " import pycolmap\n", " reconstruction = pycolmap.Reconstruction(colmap_model_dir)\n", "\n", " print(f\"Registered images: {len(reconstruction.images)}\")\n", " print(f\"3D points: {len(reconstruction.points3D)}\")\n", "\n", " # Check for files required by Gaussian Splatting\n", " required_files = ['cameras.bin', 'images.bin', 'points3D.bin']\n", " for file in required_files:\n", " file_path = os.path.join(colmap_model_dir, file)\n", " if not os.path.exists(file_path):\n", " raise FileNotFoundError(f\"Required file not found: {file}\")\n", " print(f\" ✓ {file}\")\n", "\n", " # Return the grandparent directory of sparse/0\n", " # /content/output/colmap/sparse/0 -> /content/output/colmap\n", " colmap_parent_dir = os.path.dirname(os.path.dirname(colmap_model_dir))\n", "\n", " print(f\"\\n✓ COLMAP model ready for Gaussian Splatting\")\n", " print(f\" Source path: {colmap_parent_dir}\")\n", "\n", " return colmap_parent_dir" ], "metadata": { "id": "4IioqnC1mVcM" }, "outputs": [], "execution_count": 26 }, { "cell_type": "code", "source": [ "def train_gaussian_splatting(colmap_dir, image_dir, output_dir, iterations=6000):\n", " \"\"\"\n", " Gaussian Splattingモデルをトレーニング\n", " \"\"\"\n", " print(\"\\n=== Training Gaussian Splatting ===\")\n", "\n", " # 環境の修正\n", " print(\"Checking and fixing Python environment...\")\n", " import subprocess\n", " subprocess.run([\"pip\", \"install\", \"--upgrade\", \"--force-reinstall\", \"-q\", \"scipy\", \"scikit-learn\"], check=True)\n", " print(\"✓ Environment fixed\")\n", "\n", " # COLMAPのsparseディレクトリを確認\n", " sparse_dir = os.path.join(colmap_dir, 'sparse', '0')\n", " if not os.path.exists(sparse_dir):\n", " raise FileNotFoundError(f\"COLMAP sparse directory not found: {sparse_dir}\")\n", "\n", " print(f\"COLMAP sparse model: {sparse_dir}\")\n", " print(f\"Training images: {image_dir}\")\n", " print(f\"Output: {output_dir}\")\n", " print(f\"Iterations: {iterations}\")\n", "\n", " os.makedirs(output_dir, exist_ok=True)\n", "\n", " # Gaussian Splattingのディレクトリに移動\n", " original_dir = os.getcwd()\n", " os.chdir(\"/content/gaussian-splatting\")\n", "\n", " # トレーニングコマンド\n", " cmd = [\n", " \"python\", \"train.py\",\n", " \"-s\", colmap_dir,\n", " \"--images\", image_dir,\n", " \"-m\", output_dir,\n", " \"--iterations\", str(iterations),\n", " \"--test_iterations\", str(iterations // 2), str(iterations),\n", " \"--save_iterations\", str(iterations // 2), str(iterations)\n", " ]\n", "\n", " print(f\"\\nCommand: {' '.join(cmd)}\\n\")\n", "\n", " # 実行\n", " result = subprocess.run(cmd, capture_output=True, text=True)\n", "\n", " # 元のディレクトリに戻る\n", " os.chdir(original_dir)\n", "\n", " print(\"STDOUT:\", result.stdout)\n", " if result.stderr:\n", " print(\"STDERR:\", result.stderr)\n", "\n", " if result.returncode != 0:\n", " raise RuntimeError(\"Gaussian Splatting training failed\")\n", "\n", " # 生成されたPLYファイルの存在確認\n", " ply_path = os.path.join(output_dir, f\"point_cloud/iteration_{iterations}/point_cloud.ply\")\n", " if not os.path.exists(ply_path):\n", " raise FileNotFoundError(f\"Expected output file not found: {ply_path}\")\n", "\n", " print(f\"\\n✓ Gaussian Splatting training complete!\")\n", " print(f\" Model saved to: {output_dir}\")\n", " print(f\" Point cloud: {ply_path}\")\n", "\n", " return output_dir" ], "metadata": { "id": "EiHoRSfzQ01b" }, "execution_count": 27, "outputs": [] }, { "cell_type": "markdown", "source": [ "# **main**" ], "metadata": { "id": "IqNcsheVywit" } }, { "cell_type": "code", "source": [ "from PIL import Image, ImageFilter" ], "metadata": { "id": "yeP98DO30gNl" }, "execution_count": 28, "outputs": [] }, { "cell_type": "code", "source": [ "import numpy as np\n", "print(f\"✓ NumPy: {np.__version__}\")" ], "metadata": { "id": "D0r5QQNg2GPl", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "28d7347c-70e5-4e62-b158-a38fb7fcb51b" }, "execution_count": 29, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✓ NumPy: 1.26.4\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# メインパイプライン関数（修正版）\n", "# ============================================================================\n", "def main_pipeline(image_dir, output_dir, square_size=512, iterations=6000):\n", " \"\"\"\n", " 完全なパイプライン: Images → Square Processing → MASt3R → Gaussian Splatting\n", " 変更点:\n", " - Step 3: import_to_colmap を import_to_mast3r_and_save_colmap に置き換え\n", " - Step 4: run_colmap_mapper を run_mast3r_mapper に置き換え\n", " \"\"\"\n", " print(\"=\"*70)\n", " print(\"Gaussian Splatting Preparation Pipeline (MASt3R Version)\")\n", " print(\"=\"*70)\n", "\n", " # Step 0: 画像を正方形フォーマットに標準化\n", " processed_image_dir = os.path.join(output_dir, \"processed_images\")\n", " normalize_image_sizes_biplet(\n", " input_dir=image_dir,\n", " output_dir=processed_image_dir,\n", " size=square_size\n", " )\n", "\n", " # パスの設定\n", " feature_dir = os.path.join(output_dir, 'features')\n", " colmap_dir = os.path.join(output_dir, 'colmap')\n", " database_path = os.path.join(colmap_dir, 'database.db')\n", " sparse_dir = os.path.join(colmap_dir, 'sparse')\n", " os.makedirs(output_dir, exist_ok=True)\n", " os.makedirs(colmap_dir, exist_ok=True)\n", "\n", " # 画像パスを取得\n", " image_paths = sorted([\n", " os.path.join(processed_image_dir, f)\n", " for f in os.listdir(processed_image_dir)\n", " if f.lower().endswith(('.jpg', '.jpeg', '.png'))\n", " ])\n", " print(f\"\\n📸 Found {len(image_paths)} images\")\n", "\n", " # Step 1: 画像ペアを生成\n", " pairs, features = get_image_pairs(image_paths)\n", "\n", " # Step 2: LightGlueで特徴マッチング\n", " match_pairs_lightglue(image_paths, pairs, features, feature_dir)\n", "\n", " ######### Step 3 and Step 4: MASt3R for reconstruction (not COLMAP) #########\n", " # Step 3 & 4: MASt3Rでカメラポーズを推定し、COLMAP形式で保存\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"Step 3 & 4: MASt3R Reconstruction → COLMAP Format\")\n", " print(\"=\"*70)\n", "\n", " model_dir = run_mast3r_mapper(\n", " database_path=database_path,\n", " image_dir=image_dir,\n", " output_dir=colmap_dir,\n", " pairs=pairs,\n", " processed_image_dir=processed_image_dir\n", " )\n", " ###############################################################################\n", "\n", " # Step 5: Gaussian Splattingの準備を確認\n", " colmap_parent = convert_to_gs_format(model_dir, output_dir)\n", "\n", " # Step 6: Gaussian Splattingモデルをトレーニング\n", " gs_output = train_gaussian_splatting(\n", " colmap_dir=colmap_parent,\n", " image_dir=processed_image_dir,\n", " output_dir=output_dir,\n", " iterations=iterations\n", " )\n", "\n", " print(\"\\n\" + \"=\"*70)\n", " print(\"✅ Full Pipeline Successfully Completed!\")\n", " print(\"=\"*70)\n", " print(f\"\\nGaussian Splatting model saved at: {gs_output}\")\n", "\n", " return gs_output\n" ], "metadata": { "id": "Ppkm3NVGwtjO" }, "execution_count": 30, "outputs": [] }, { "cell_type": "code", "source": [ "# 使用例\n", "if __name__ == \"__main__\":\n", " # 実際のデータセットで実行する場合の推奨設定\n", " IMAGE_DIR = \"/content/drive/MyDrive/your_folder/fountain\"\n", " OUTPUT_DIR = \"/content/output\"\n", "\n", " # 本番用の設定:\n", " # - square_size: 1024 (高品質) または 512 (バランス)\n", " # - iterations: 6000 (推奨) または 30000 (高品質だが時間がかかる)\n", "\n", " gs_output = main_pipeline(\n", " IMAGE_DIR,\n", " OUTPUT_DIR,\n", " square_size=700,\n", " iterations=2000,\n", " )" ], "metadata": { "id": "66uQIWbs2a1t", "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "d92ebcffd3004188808fa91ae64dc7d5", "e237060b27394ca782bb92c801918f3c", "8f1a658d19aa4b9a97140a3d6bb3fb1b", "f9c53c605a9742b6b0716ec38846a8d7", "077e9108e0054453a6758ac42319307e", "6562c3179f8b49b79d6aea6fa4626125", "dbeb1eac56474b1ca932ac6e8ae0c8a8", "d67f8ed6c159412580a853b6fe00166c", "0b9bcc418f3f4a23b573bdb5712e4cfa", "e741883f45ad40158427487e5a63e851", "d83de3f8a1f940ad978b0859009766a8", "dd04a9daaf7a4490b08510033ab0f108", "06f427eddf474ad69bf0bdf3d23bc7da", "7a2733d032434dc8a9111a37455f1cc8", "b724eed873a34dc9baddd889123ec84d", "70674feb266441e2b516f1932423a165", "7f805bf518d24a559b2648c531144a3f", "2384858d0b9e4a2cb03ac1282d3260f0", "aef493d4c40341b0a40b4eea08450fa6", "7975368cc59a42f796940d030aa1529c", "aa353b865ac6411186fb6dea4dd2c4f5", "d8bf4d2015dc4adcb298ffa8ae5cd49d", "d4c7b381e397468f8a490451cf40371d", "e8edf61206114d3bbd8bd37d3a30272e", "42b39bec196d43f597f49093a435c151", "2e3359e57bd940fe9c95ae3a6049ece0", "4b53319ef59a48259f05359d13cd75b3", "c9f87928d97e4472a1ba1acf3681e44d", "ba7a6929af11428c9a237514d882b3c4", "55caa4b92a394d3eb43132bd455e5760", "f226f14be452477b86c5b265b7912787", "23a064f803d34386987d7a54c74dfed0", "8d79cb6943be4cc1b46b092ac428d99d" ] }, "outputId": "3ceaf7b2-82b2-43bd-bece-1d3828d0bfb4" }, "execution_count": 31, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "======================================================================\n", "Gaussian Splatting Preparation Pipeline (MASt3R Version)\n", "======================================================================\n", "Generating 2 cropped squares (Left/Right or Top/Bottom) for each image...\n", "\n", " ✓ image_001.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_002.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_003.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_004.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_005.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_006.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_007.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_008.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_009.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_010.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_011.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_012.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_013.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_014.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_015.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_016.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_017.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_018.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_019.jpeg: (1440, 1920) → 2 square images generated\n", " ✓ image_020.jpeg: (1440, 1920) → 2 square images generated\n", "\n", "Processing complete: 20 source images processed\n", "Original size distribution: {'1440x1920': 20}\n", "\n", "📸 Found 40 images\n", "\n", "=== Extracting DINO Global Features ===\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: 4.74GB\n", "CPU Memory Usage: 15.3%\n", "Processing 780 pairs in chunks of 500...\n", "Note: Images will be loaded on-demand per chunk to save memory\n", "Temporary directory: /tmp/tmpdk44e9f8\n", "\n", "--- Processing chunk 1/2 (pairs 0-500) ---\n", "Loading 40 unique images for this chunk...\n", "\n", "=== Loading images for MASt3R (size=224) ===\n", ">> Loading a list of 40 images\n", " - adding /content/output/processed_images/image_001_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_001_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_002_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_002_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_003_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_003_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_004_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_004_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_005_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_005_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_006_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_006_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_007_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_007_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_008_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_008_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_009_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_009_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_010_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_010_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_011_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_011_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_012_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_012_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_013_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_013_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_014_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_014_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_015_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_015_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_016_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_016_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_017_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_017_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_018_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_018_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_019_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_019_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_020_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_020_top.jpeg with resolution 700x700 --> 224x224\n", " (Found 40 images)\n", "Memory after loading chunk images:\n", "GPU Memory - Allocated: 2.58GB, Reserved: 4.74GB\n", "CPU Memory Usage: 15.3%\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Preparing chunk 1: 100%|██████████| 500/500 [00:00<00:00, 796185.27it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Running MASt3R inference on 500 pairs...\n", ">> Inference with model on 500 image pairs\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\rMASt3R inference: 0%| | 0/500 [00:00> Loading a list of 40 images\n", " - adding /content/output/processed_images/image_001_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_001_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_002_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_002_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_003_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_003_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_004_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_004_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_005_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_005_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_006_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_006_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_007_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_007_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_008_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_008_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_009_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_009_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_010_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_010_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_011_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_011_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_012_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_012_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_013_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_013_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_014_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_014_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_015_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_015_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_016_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_016_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_017_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_017_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_018_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_018_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_019_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_019_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_020_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_020_top.jpeg with resolution 700x700 --> 224x224\n", " (Found 40 images)\n", "Memory after loading chunk images:\n", "GPU Memory - Allocated: 2.58GB, Reserved: 2.71GB\n", "CPU Memory Usage: 19.0%\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Preparing chunk 2: 100%|██████████| 280/280 [00:00<00:00, 485893.72it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Running MASt3R inference on 280 pairs...\n", ">> Inference with model on 280 image pairs\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "MASt3R inference: 100%|██████████| 280/280 [01:00<00:00, 4.63it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Chunk 2 saved to disk. Memory state:\n", "GPU Memory - Allocated: 2.58GB, Reserved: 2.71GB\n", "CPU Memory Usage: 19.0%\n", "\n", "=== Combining all chunks from disk ===\n", "Loading and combining chunks 1-2...\n", "✓ Combined output keys: ['view1', 'view2', 'pred1', 'pred2', 'loss']\n", "After combining all chunks:\n", "GPU Memory - Allocated: 2.58GB, Reserved: 2.71GB\n", "CPU Memory Usage: 32.8%\n", "✓ MASt3R inference complete\n", "\n", "Loading all images for global alignment...\n", "\n", "=== Loading images for MASt3R (size=224) ===\n", ">> Loading a list of 40 images\n", " - adding /content/output/processed_images/image_001_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_001_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_002_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_002_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_003_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_003_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_004_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_004_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_005_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_005_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_006_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_006_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_007_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_007_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_008_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_008_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_009_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_009_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_010_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_010_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_011_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_011_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_012_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_012_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_013_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_013_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_014_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_014_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_015_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_015_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_016_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_016_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_017_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_017_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_018_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_018_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_019_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_019_top.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_020_bottom.jpeg with resolution 700x700 --> 224x224\n", " - adding /content/output/processed_images/image_020_top.jpeg with resolution 700x700 --> 224x224\n", " (Found 40 images)\n", "Memory after loading all images:\n", "GPU Memory - Allocated: 2.58GB, Reserved: 2.71GB\n", "CPU Memory Usage: 32.8%\n", "\n", "Running global alignment...\n", "Computing global alignment...\n", " init edge (5*,27*) score=38.9428596496582\n", " init edge (21*,27) score=33.0746955871582\n", " init edge (0*,21) score=32.535499572753906\n", " init edge (13*,27) score=32.39470672607422\n", " init edge (5,26*) score=31.844274520874023\n", " init edge (21,38*) score=31.818178176879883\n", " init edge (19*,38) score=31.534162521362305\n", " init edge (0,36*) score=30.71839714050293\n", " init edge (5,31*) score=30.268320083618164\n", " init edge (0,2*) score=29.81885528564453\n", " init edge (30*,31) score=29.185855865478516\n", " init edge (19,29*) score=29.125553131103516\n", " init edge (30,32*) score=28.837690353393555\n", " init edge (4*,21) score=27.3371524810791\n", " init edge (5,24*) score=26.391128540039062\n", " init edge (1*,30) score=23.598636627197266\n", " init edge (16*,29) score=22.02833366394043\n", " init edge (8*,36) score=36.39461135864258\n", " init edge (15*,36) score=33.29084777832031\n", " init edge (18*,36) score=33.115047454833984\n", " init edge (18,22*) score=33.021141052246094\n", " init edge (18,33*) score=32.4524040222168\n", " init edge (18,23*) score=32.13170623779297\n", " init edge (2,37*) score=31.197811126708984\n", " init edge (12*,33) score=30.698335647583008\n", " init edge (18,25*) score=30.463029861450195\n", " init edge (8,14*) score=29.943504333496094\n", " init edge (12,35*) score=29.565750122070312\n", " init edge (20*,35) score=26.465646743774414\n", " init edge (3*,22) score=26.323179244995117\n", " init edge (10*,14) score=25.168712615966797\n", " init edge (18,39*) score=23.882108688354492\n", " init edge (11*,25) score=34.86027145385742\n", " init edge (15,28*) score=34.23221206665039\n", " init edge (9*,25) score=33.08562469482422\n", " init edge (6*,9) score=28.869115829467773\n", " init edge (6,7*) score=28.625152587890625\n", " init edge (7,34*) score=27.892013549804688\n", " init edge (7,17*) score=28.908716201782227\n", " init loss = 0.8638659119606018\n", "Global alignement - optimizing for:\n", "['pw_poses', 'im_depthmaps', 'im_poses', 'im_focals']\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ " 0%| | 0/150 [00:00\n", "pts_all is a list with 40 elements\n", "First element type: \n", "First element shape: torch.Size([224, 224, 3])\n", "pts_all shape after conversion: torch.Size([40, 224, 224, 3])\n", "Found batched point cloud: torch.Size([40, 224, 224, 3])\n", "✓ Extracted 2007040 3D points from 40 images\n", "\n", "⚠ Downsampling from 2007040 to 1000000 points to reduce memory usage...\n", "✓ Downsampled to 1000000 points\n", "Extracting camera parameters...\n", "Retrieved camera-to-world poses: shape (40, 4, 4)\n", "Converted to world-to-camera poses for COLMAP\n", "Focals shape: (40, 1)\n", "Principal points shape: (40, 2)\n", "\n", "Example camera 0:\n", " Image size: 700x700\n", " MASt3R focal: 280.12, pp: (112.00, 112.00)\n", " Scaled fx=875.38, fy=875.38, cx=350.00, cy=350.00\n", " Pose (first row): [ 0.9854731 0.02163128 -0.1684482 0.17588007]\n", "\n", "✓ Extracted 40 cameras and 40 poses\n", "\n", "=== Saving COLMAP reconstruction ===\n", " Writing COLMAP files to /content/output/colmap/sparse/0...\n", " ✓ Wrote 40 cameras\n", " ✓ Wrote 40 images\n", " Wrote 1000000 / 1000000 points...\n", " ✓ Wrote 1000000 3D points\n", "\n", "✓ COLMAP reconstruction saved to /content/output/colmap/sparse/0\n", "\n", "✓ MASt3R reconstruction saved in COLMAP format\n", " Output: /content/output/colmap/sparse/0\n", "\n", "✓ MASt3R reconstruction complete: /content/output/colmap/sparse/0\n", "\n", "=== Verifying COLMAP Model for Gaussian Splatting ===\n", "Registered images: 40\n", "3D points: 1000000\n", " ✓ cameras.bin\n", " ✓ images.bin\n", " ✓ points3D.bin\n", "\n", "✓ COLMAP model ready for Gaussian Splatting\n", " Source path: /content/output/colmap\n", "\n", "=== Training Gaussian Splatting ===\n", "Checking and fixing Python environment...\n", "✓ Environment fixed\n", "COLMAP sparse model: /content/output/colmap/sparse/0\n", "Training images: /content/output/processed_images\n", "Output: /content/output\n", "Iterations: 2000\n", "\n", "Command: python train.py -s /content/output/colmap --images /content/output/processed_images -m /content/output --iterations 2000 --test_iterations 1000 2000 --save_iterations 1000 2000\n", "\n", "STDOUT: Optimizing /content/output\n", "Output folder: /content/output [19/01 16:29:00]\n", "\n", "Reading camera 1/40\n", "Reading camera 2/40\n", "Reading camera 3/40\n", "Reading camera 4/40\n", "Reading camera 5/40\n", "Reading camera 6/40\n", "Reading camera 7/40\n", "Reading camera 8/40\n", "Reading camera 9/40\n", "Reading camera 10/40\n", "Reading camera 11/40\n", "Reading camera 12/40\n", "Reading camera 13/40\n", "Reading camera 14/40\n", "Reading camera 15/40\n", "Reading camera 16/40\n", "Reading camera 17/40\n", "Reading camera 18/40\n", "Reading camera 19/40\n", "Reading camera 20/40\n", "Reading camera 21/40\n", "Reading camera 22/40\n", "Reading camera 23/40\n", "Reading camera 24/40\n", "Reading camera 25/40\n", "Reading camera 26/40\n", "Reading camera 27/40\n", "Reading camera 28/40\n", "Reading camera 29/40\n", "Reading camera 30/40\n", "Reading camera 31/40\n", "Reading camera 32/40\n", "Reading camera 33/40\n", "Reading camera 34/40\n", "Reading camera 35/40\n", "Reading camera 36/40\n", "Reading camera 37/40\n", "Reading camera 38/40\n", "Reading camera 39/40\n", "Reading camera 40/40 [19/01 16:29:00]\n", "Converting point3d.bin to .ply, will happen only the first time you open the scene. [19/01 16:29:00]\n", "Loading Training Cameras [19/01 16:29:06]\n", "Loading Test Cameras [19/01 16:29:07]\n", "Number of points at initialisation : 1000000 [19/01 16:29:07]\n", "\n", "[ITER 1000] Evaluating train: L1 0.13379032462835314 PSNR 14.937109756469727 [19/01 16:31:24]\n", "\n", "[ITER 1000] Saving Gaussians [19/01 16:31:24]\n", "\n", "[ITER 2000] Evaluating train: L1 0.12400420904159547 PSNR 15.522056007385254 [19/01 16:33:29]\n", "\n", "[ITER 2000] Saving Gaussians [19/01 16:33:29]\n", "\n", "Training complete. [19/01 16:33:38]\n", "\n", "STDERR: 2026-01-19 16:28:54.999947: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1768840135.020138 10273 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1768840135.026088 10273 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "W0000 00:00:1768840135.041775 10273 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1768840135.041798 10273 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1768840135.041800 10273 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1768840135.041801 10273 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "\n", "Training progress: 0%| | 0/2000 [00:00