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"cells": [
{
"cell_type": "markdown",
"id": "fb1f1fdc",
"metadata": {
"papermill": {
"duration": 0.002985,
"end_time": "2026-01-10T18:17:32.170524",
"exception": false,
"start_time": "2026-01-10T18:17:32.167539",
"status": "completed"
},
"tags": [],
"id": "fb1f1fdc"
},
"source": [
"# **biplet-dino-colmap-2dgs**"
]
},
{
"cell_type": "markdown",
"source": [
"# 新しいセクション"
],
"metadata": {
"id": "jK0ja9PfddVA"
},
"id": "jK0ja9PfddVA"
},
{
"cell_type": "code",
"source": [
"#サイズの異なる画像を扱う\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JON4rYSEOzCg",
"outputId": "458cec38-282c-48a0-a836-832559e5acf1"
},
"id": "JON4rYSEOzCg",
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "22353010",
"metadata": {
"execution": {
"iopub.execute_input": "2026-01-10T18:17:32.181455Z",
"iopub.status.busy": "2026-01-10T18:17:32.180969Z",
"iopub.status.idle": "2026-01-10T18:17:32.355942Z",
"shell.execute_reply": "2026-01-10T18:17:32.355229Z"
},
"papermill": {
"duration": 0.179454,
"end_time": "2026-01-10T18:17:32.357275",
"exception": false,
"start_time": "2026-01-10T18:17:32.177821",
"status": "completed"
},
"tags": [],
"id": "22353010"
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import subprocess\n",
"import shutil\n",
"from pathlib import Path\n",
"import cv2\n",
"from PIL import Image\n",
"import glob\n",
"\n",
"IMAGE_PATH=\"/content/drive/MyDrive/your_folder/fountain100\"\n",
"\n",
"#WORK_DIR = '/content/gaussian-splatting'\n",
"WORK_DIR = \"/content/2d-gaussian-splatting\"\n",
"\n",
"OUTPUT_DIR = '/content/output'\n",
"COLMAP_DIR = '/content/colmap_data'"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "be6df249",
"metadata": {
"execution": {
"iopub.execute_input": "2026-01-10T18:17:32.363444Z",
"iopub.status.busy": "2026-01-10T18:17:32.363175Z",
"iopub.status.idle": "2026-01-10T18:22:43.720241Z",
"shell.execute_reply": "2026-01-10T18:22:43.719380Z"
},
"papermill": {
"duration": 311.361656,
"end_time": "2026-01-10T18:22:43.721610",
"exception": false,
"start_time": "2026-01-10T18:17:32.359954",
"status": "completed"
},
"tags": [],
"id": "be6df249",
"outputId": "4d17052f-2c01-4f3e-ebd6-cb864bc264a5",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"🚀 Setting up COLAB environment (v8 - Python 3.12 compatible)\n",
"\n",
"======================================================================\n",
"STEP 0: Fix NumPy (Python 3.12 compatible)\n",
"======================================================================\n",
"Running: /usr/bin/python3 -m pip uninstall -y numpy\n",
"Running: /usr/bin/python3 -m pip install numpy==1.26.4\n",
"Running: /usr/bin/python3 -c import numpy; print('NumPy:', numpy.__version__)\n",
"\n",
"======================================================================\n",
"STEP 1: System packages\n",
"======================================================================\n",
"Running: apt-get update -qq\n",
"Running: apt-get install -y -qq colmap build-essential cmake git libopenblas-dev xvfb\n",
"\n",
"======================================================================\n",
"STEP 2: Clone Gaussian Splatting\n",
"======================================================================\n",
"✓ Repository already exists\n",
"\n",
"======================================================================\n",
"STEP 3: Python packages (VERBOSE MODE)\n",
"======================================================================\n",
"\n",
"📦 Installing PyTorch...\n",
"Running: /usr/bin/python3 -m pip install torch torchvision torchaudio\n",
"\n",
"📦 Installing core utilities...\n",
"Running: /usr/bin/python3 -m pip install opencv-python pillow imageio imageio-ffmpeg plyfile tqdm tensorboard\n",
"\n",
"📦 Installing transformers (NumPy 1.26 compatible)...\n",
"Running: /usr/bin/python3 -m pip install transformers==4.40.0\n",
"\n",
"📦 Installing LightGlue stack...\n",
"Running: /usr/bin/python3 -m pip install kornia\n",
"Running: /usr/bin/python3 -m pip install h5py\n",
"Running: /usr/bin/python3 -m pip install matplotlib\n",
"Running: /usr/bin/python3 -m pip install pycolmap\n",
"\n",
"======================================================================\n",
"STEP 4: Detailed Verification\n",
"======================================================================\n",
"\n",
"🔍 Testing NumPy...\n",
" ✓ NumPy: 2.0.2\n",
"\n",
"🔍 Testing PyTorch...\n",
" ✓ PyTorch: 2.9.0+cu128\n",
" ✓ CUDA available: True\n",
" ✓ CUDA version: 12.8\n",
"\n",
"🔍 Testing transformers...\n",
" ✓ transformers version: 4.40.0\n",
" ✓ AutoModel import: OK\n",
"\n",
"🔍 Testing pycolmap...\n",
" ✓ pycolmap: OK\n",
"\n",
"🔍 Testing kornia...\n",
" ✓ kornia: 0.8.2\n"
]
}
],
"source": [
"def run_cmd(cmd, check=True, capture=False, cwd=None): # ← cwd=None を追加\n",
" \"\"\"Run command with better error handling\"\"\"\n",
" print(f\"Running: {' '.join(cmd)}\")\n",
" result = subprocess.run(\n",
" cmd,\n",
" capture_output=capture,\n",
" text=True,\n",
" check=False,\n",
" cwd=cwd # ← ここに渡す\n",
" )\n",
" if check and result.returncode != 0:\n",
" print(f\"❌ Command failed with code {result.returncode}\")\n",
" if capture:\n",
" print(f\"STDOUT: {result.stdout}\")\n",
" print(f\"STDERR: {result.stderr}\")\n",
" return result\n",
"\n",
"\n",
"def setup_environment():\n",
" \"\"\"\n",
" Colab environment setup for Gaussian Splatting + LightGlue + pycolmap\n",
" Python 3.12 compatible version (v8)\n",
" \"\"\"\n",
"\n",
" print(\"🚀 Setting up COLAB environment (v8 - Python 3.12 compatible)\")\n",
"\n",
" WORK_DIR = \"2d-gaussian-splatting\"\n",
"\n",
" # =====================================================================\n",
" # STEP 0: NumPy FIX (Python 3.12 compatible)\n",
" # =====================================================================\n",
" print(\"\\n\" + \"=\"*70)\n",
" print(\"STEP 0: Fix NumPy (Python 3.12 compatible)\")\n",
" print(\"=\"*70)\n",
"\n",
" # Python 3.12 requires numpy >= 1.26\n",
" run_cmd([sys.executable, \"-m\", \"pip\", \"uninstall\", \"-y\", \"numpy\"])\n",
" run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"numpy==1.26.4\"])\n",
"\n",
" # sanity check\n",
" run_cmd([sys.executable, \"-c\", \"import numpy; print('NumPy:', numpy.__version__)\"])\n",
"\n",
" # =====================================================================\n",
" # STEP 1: System packages (Colab)\n",
" # =====================================================================\n",
" print(\"\\n\" + \"=\"*70)\n",
" print(\"STEP 1: System packages\")\n",
" print(\"=\"*70)\n",
"\n",
" run_cmd([\"apt-get\", \"update\", \"-qq\"])\n",
" run_cmd([\n",
" \"apt-get\", \"install\", \"-y\", \"-qq\",\n",
" \"colmap\",\n",
" \"build-essential\",\n",
" \"cmake\",\n",
" \"git\",\n",
" \"libopenblas-dev\",\n",
" \"xvfb\"\n",
" ])\n",
"\n",
" # virtual display (COLMAP / OpenCV safety)\n",
" os.environ[\"QT_QPA_PLATFORM\"] = \"offscreen\"\n",
" os.environ[\"DISPLAY\"] = \":99\"\n",
" subprocess.Popen(\n",
" [\"Xvfb\", \":99\", \"-screen\", \"0\", \"1024x768x24\"],\n",
" stdout=subprocess.DEVNULL,\n",
" stderr=subprocess.DEVNULL\n",
" )\n",
"\n",
" # =====================================================================\n",
" # STEP 2: Clone 2D 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",
" run_cmd([\n",
" \"git\", \"clone\", \"--recursive\",\n",
" \"https://github.com/hbb1/2d-gaussian-splatting.git\",\n",
" WORK_DIR\n",
" ])\n",
" else:\n",
" print(\"✓ Repository already exists\")\n",
"\n",
" # =====================================================================\n",
" # STEP 3: Python packages (FIXED ORDER & VERSIONS)\n",
" # =====================================================================\n",
" print(\"\\n\" + \"=\"*70)\n",
" print(\"STEP 3: Python packages (VERBOSE MODE)\")\n",
" print(\"=\"*70)\n",
"\n",
" # ---- PyTorch (Colab CUDA対応) ----\n",
" print(\"\\n📦 Installing PyTorch...\")\n",
" run_cmd([\n",
" sys.executable, \"-m\", \"pip\", \"install\",\n",
" \"torch\", \"torchvision\", \"torchaudio\"\n",
" ])\n",
"\n",
" # ---- Core utils ----\n",
" print(\"\\n📦 Installing core utilities...\")\n",
" run_cmd([\n",
" sys.executable, \"-m\", \"pip\", \"install\",\n",
" \"opencv-python\",\n",
" \"pillow\",\n",
" \"imageio\",\n",
" \"imageio-ffmpeg\",\n",
" \"plyfile\",\n",
" \"tqdm\",\n",
" \"tensorboard\"\n",
" ])\n",
"\n",
" # ---- transformers (NumPy 1.26 compatible) ----\n",
" print(\"\\n📦 Installing transformers (NumPy 1.26 compatible)...\")\n",
" # Install transformers with proper dependencies\n",
" run_cmd([\n",
" sys.executable, \"-m\", \"pip\", \"install\",\n",
" \"transformers==4.40.0\"\n",
" ])\n",
"\n",
" # ---- LightGlue stack (GITHUB INSTALL) ----\n",
" print(\"\\n📦 Installing LightGlue stack...\")\n",
"\n",
" # Install kornia first\n",
" run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"kornia\"])\n",
"\n",
" # Install h5py (sometimes needed)\n",
" run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"h5py\"])\n",
"\n",
" # Install matplotlib (LightGlue dependency)\n",
" run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"matplotlib\"])\n",
"\n",
" # Install pycolmap\n",
" run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"pycolmap\"])\n",
"\n",
"\n",
"\n",
" # =====================================================================\n",
" # STEP 4: Detailed Verification\n",
" # =====================================================================\n",
" print(\"\\n\" + \"=\"*70)\n",
" print(\"STEP 4: Detailed Verification\")\n",
" print(\"=\"*70)\n",
"\n",
" # NumPy (verify version first)\n",
" print(\"\\n🔍 Testing NumPy...\")\n",
" try:\n",
" import numpy as np\n",
" print(f\" ✓ NumPy: {np.__version__}\")\n",
" except Exception as e:\n",
" print(f\" ❌ NumPy failed: {e}\")\n",
"\n",
" # PyTorch\n",
" print(\"\\n🔍 Testing PyTorch...\")\n",
" try:\n",
" import torch\n",
" print(f\" ✓ PyTorch: {torch.__version__}\")\n",
" print(f\" ✓ CUDA available: {torch.cuda.is_available()}\")\n",
" if torch.cuda.is_available():\n",
" print(f\" ✓ CUDA version: {torch.version.cuda}\")\n",
" except Exception as e:\n",
" print(f\" ❌ PyTorch failed: {e}\")\n",
"\n",
" # transformers\n",
" print(\"\\n🔍 Testing transformers...\")\n",
" try:\n",
" import transformers\n",
" print(f\" ✓ transformers version: {transformers.__version__}\")\n",
" from transformers import AutoModel\n",
" print(f\" ✓ AutoModel import: OK\")\n",
" except Exception as e:\n",
" print(f\" ❌ transformers failed: {e}\")\n",
" print(f\" Attempting detailed diagnosis...\")\n",
" result = run_cmd([\n",
" sys.executable, \"-c\",\n",
" \"import transformers; print(transformers.__version__)\"\n",
" ], capture=True)\n",
" print(f\" Output: {result.stdout}\")\n",
" print(f\" Error: {result.stderr}\")\n",
"\n",
" # pycolmap\n",
" print(\"\\n🔍 Testing pycolmap...\")\n",
" try:\n",
" import pycolmap\n",
" print(f\" ✓ pycolmap: OK\")\n",
" except Exception as e:\n",
" print(f\" ❌ pycolmap failed: {e}\")\n",
"\n",
" # kornia\n",
" print(\"\\n🔍 Testing kornia...\")\n",
" try:\n",
" import kornia\n",
" print(f\" ✓ kornia: {kornia.__version__}\")\n",
" except Exception as e:\n",
" print(f\" ❌ kornia failed: {e}\")\n",
"\n",
" return WORK_DIR\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" setup_environment()"
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "3UEcAPBILz6Z"
},
"id": "3UEcAPBILz6Z",
"execution_count": 34,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# =====================================================================\n",
"# STEP 4: Build 2D GS submodules (確実な方法)\n",
"# =====================================================================\n",
"print(\"\\n\" + \"=\"*70)\n",
"print(\"STEP 5: Build Gaussian Splatting submodules\")\n",
"print(\"=\"*70)\n",
"\n",
"# diff-surfel-rasterization\n",
"\n",
"path = os.path.join(WORK_DIR, \"submodules\", \"diff-surfel-rasterization\")\n",
"url = \"https://github.com/hbb1/diff-surfel-rasterization.git\"\n",
"name = os.path.basename(path)\n",
"print(f\"\\n📦 Processing {name}...\")\n",
"if not os.path.exists(path):\n",
" print(f\" > Cloning {url}...\")\n",
" # 親ディレクトリが存在することを確認\n",
" os.makedirs(os.path.dirname(path), exist_ok=True)\n",
" run_cmd([\"git\", \"clone\", url, path])\n",
"else:\n",
" print(f\" ✓ {name} already exists.\")\n",
"# 2. setup.py install (コンパイル)\n",
"print(f\" > Compiling and Installing {name}...\")\n",
"result = run_cmd(\n",
" [sys.executable, \"setup.py\", \"install\"],\n",
" cwd=path,\n",
" check=False, # エラーでも止めない\n",
" capture=True\n",
")\n",
"if result.returncode != 0:\n",
" print(f\"❌ Failed to build {name}\")\n",
" print(\"--- STDERR ---\")\n",
" print(result.stderr)\n",
"else:\n",
" print(f\"✅ Successfully built {name}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kLdJ-FeT-kQc",
"outputId": "1366deca-2c20-49f1-a540-6528b2827efd"
},
"id": "kLdJ-FeT-kQc",
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"======================================================================\n",
"STEP 5: Build Gaussian Splatting submodules\n",
"======================================================================\n",
"\n",
"📦 Processing diff-surfel-rasterization...\n",
" ✓ diff-surfel-rasterization already exists.\n",
" > Compiling and Installing diff-surfel-rasterization...\n",
"Running: /usr/bin/python3 setup.py install\n",
"✅ Successfully built diff-surfel-rasterization\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import sys\n",
"import shutil\n",
"import subprocess\n",
"\n",
"# --- 前準備: 環境の整備 ---\n",
"print(\"Configuring build environment...\")\n",
"# 1. CUDAコンパイラの確認\n",
"!nvcc --version\n",
"\n",
"# 2. 必須ツールのインストール (ninjaはビルドを安定・高速化させます)\n",
"!pip install setuptools wheel ninja\n",
"\n",
"# 3. 環境変数のセットアップ (CUDAのパスを明示的に指定)\n",
"os.environ[\"CUDA_HOME\"] = \"/usr/local/cuda\"\n",
"os.environ[\"PATH\"] = f'{os.environ[\"CUDA_HOME\"]}/bin:{os.environ[\"PATH\"]}'\n",
"os.environ[\"LD_LIBRARY_PATH\"] = f'{os.environ[\"CUDA_HOME\"]}/lib64:{os.environ[\"LD_LIBRARY_PATH\"]}'\n",
"# メモリ不足によるクラッシュを防ぐため、並列ビルド数を制限\n",
"os.environ[\"MAX_JOBS\"] = \"2\"\n",
"\n",
"def run_cmd(cmd, cwd=None, check=True):\n",
" \"\"\"コマンド実行用のヘルパー関数\"\"\"\n",
" return subprocess.run(cmd, cwd=cwd, capture_output=True, text=True, check=check)\n",
"\n",
"def install_submodule(name, url, base_dir):\n",
" \"\"\"個別のサブモジュールをインストール\"\"\"\n",
" print(f\"\\n{'='*70}\")\n",
" print(f\"Installing {name}\")\n",
" print(f\"{'='*70}\")\n",
"\n",
" # 絶対パスを使用\n",
" path = os.path.abspath(os.path.join(base_dir, \"submodules\", name))\n",
" print(f\" > Target path: {path}\")\n",
"\n",
" # Step 1: 既存を削除\n",
" if os.path.exists(path):\n",
" print(f\" > Removing old {name}...\")\n",
" shutil.rmtree(path)\n",
"\n",
" # Step 2: クローン\n",
" print(f\" > Cloning from {url}...\")\n",
" os.makedirs(os.path.dirname(path), exist_ok=True)\n",
" try:\n",
" run_cmd([\"git\", \"clone\", url, path])\n",
" except subprocess.CalledProcessError as e:\n",
" print(f\"❌ Failed to clone {name}\")\n",
" print(e.stderr)\n",
" return False\n",
"\n",
" # Step 3: ファイル確認 (spatial.cu 等の存在をチェック)\n",
" print(f\" > Checking cloned files...\")\n",
" files = os.listdir(path)\n",
" print(f\" > Files in {name}: {files[:10]}...\")\n",
"\n",
" # Step 4: 特定モジュールのサブモジュール初期化\n",
" if name == \"diff-surfel-rasterization\":\n",
" print(f\" > Initializing GLM submodule...\")\n",
" run_cmd([\"git\", \"submodule\", \"update\", \"--init\", \"--recursive\"], cwd=path)\n",
"\n",
" # Step 5: ビルドキャッシュ削除\n",
" build_dir = os.path.join(path, \"build\")\n",
" if os.path.exists(build_dir):\n",
" print(f\" > Cleaning build cache...\")\n",
" shutil.rmtree(build_dir)\n",
"\n",
" # Step 6: インストール\n",
" print(f\" > Installing {name} (This may take a few minutes)...\")\n",
" # 環境変数を明示的に引き継ぐ\n",
" current_env = os.environ.copy()\n",
"\n",
" result = subprocess.run(\n",
" [sys.executable, \"-m\", \"pip\", \"install\", \"-e\", \".\", \"--no-build-isolation\", \"-v\"],\n",
" cwd=path,\n",
" env=current_env,\n",
" capture_output=True,\n",
" text=True\n",
" )\n",
"\n",
" if result.returncode != 0:\n",
" print(f\"❌ Failed to install {name}\")\n",
" # C++/CUDAのビルドエラーは stdout に出ることが多いため、両方出力\n",
" print(\"\\n--- STDOUT (Build Logs) ---\")\n",
" stdout_lines = result.stdout.split('\\n')\n",
" print('\\n'.join(stdout_lines[-60:])) # 最後の60行を表示\n",
"\n",
" print(\"\\n--- STDERR (Error Details) ---\")\n",
" print(result.stderr)\n",
" return False\n",
"\n",
" print(f\"✅ Successfully installed {name}\")\n",
" return True\n",
"\n",
"# =====================================================================\n",
"# STEP 4: Build 2D GS submodules\n",
"# =====================================================================\n",
"print(\"\\n\" + \"=\"*70)\n",
"print(\"STEP 4: Build Gaussian Splatting submodules\")\n",
"print(\"=\"*70)\n",
"\n",
"# Colabの場合は絶対パス\n",
"WORK_DIR = \"/content/2d-gaussian-splatting\"\n",
"\n",
"# 各サブモジュールのインストール\n",
"# simple-knn\n",
"success_knn = install_submodule(\n",
" \"simple-knn\",\n",
" \"https://github.com/tztechno/simple-knn.git\",\n",
" WORK_DIR\n",
")\n",
"\n",
"\n",
"# 結果表示\n",
"print(\"\\n\" + \"=\"*70)\n",
"print(\"Installation Summary\")\n",
"print(\"=\"*70)\n",
"print(f\"simple-knn: {'✅ Success' if success_knn else '❌ Failed'}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qYgJl2Fw_Phk",
"outputId": "58d7c749-fe3c-44b5-a64a-214f57dda063"
},
"id": "qYgJl2Fw_Phk",
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Configuring build environment...\n",
"nvcc: NVIDIA (R) Cuda compiler driver\n",
"Copyright (c) 2005-2025 NVIDIA Corporation\n",
"Built on Fri_Feb_21_20:23:50_PST_2025\n",
"Cuda compilation tools, release 12.8, V12.8.93\n",
"Build cuda_12.8.r12.8/compiler.35583870_0\n",
"\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/diff_surfel_rasterization-0.0.1-py3.12-linux-x86_64.egg is deprecated. pip 24.3 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
"\u001b[0mRequirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (75.2.0)\n",
"Requirement already satisfied: wheel in /usr/local/lib/python3.12/dist-packages (0.46.3)\n",
"Requirement already satisfied: ninja in /usr/local/lib/python3.12/dist-packages (1.13.0)\n",
"Requirement already satisfied: packaging>=24.0 in /usr/local/lib/python3.12/dist-packages (from wheel) (26.0)\n",
"\n",
"======================================================================\n",
"STEP 4: Build Gaussian Splatting submodules\n",
"======================================================================\n",
"\n",
"======================================================================\n",
"Installing simple-knn\n",
"======================================================================\n",
" > Target path: /content/2d-gaussian-splatting/submodules/simple-knn\n",
" > Removing old simple-knn...\n",
" > Cloning from https://github.com/tztechno/simple-knn.git...\n",
" > Checking cloned files...\n",
" > Files in simple-knn: ['.git', 'setup.py', 'simple_knn.h', 'simple_knn', 'README.md', 'spatial.h', 'simple_knn0.cu', 'spatial.cu', '.gitignore', 'ext.cpp']...\n",
" > Installing simple-knn (This may take a few minutes)...\n",
"✅ Successfully installed simple-knn\n",
"\n",
"======================================================================\n",
"Installation Summary\n",
"======================================================================\n",
"simple-knn: ✅ Success\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install trimesh"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-ZfMABILvydS",
"outputId": "07356f72-9a60-4103-ee76-ee2da6f3d542"
},
"id": "-ZfMABILvydS",
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/diff_surfel_rasterization-0.0.1-py3.12-linux-x86_64.egg is deprecated. pip 24.3 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
"\u001b[0mCollecting trimesh\n",
" Downloading trimesh-4.11.2-py3-none-any.whl.metadata (13 kB)\n",
"Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.12/dist-packages (from trimesh) (2.4.2)\n",
"Downloading trimesh-4.11.2-py3-none-any.whl (740 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m740.3/740.3 kB\u001b[0m \u001b[31m39.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: trimesh\n",
"Successfully installed trimesh-4.11.2\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def setup_2dgs_environment():\n",
" \"\"\"2DGS環境のセットアップ（完全版）\"\"\"\n",
" print(\"Setting up 2DGS environment...\")\n",
"\n",
" # 必要なパッケージをすべてインストール\n",
" packages = [\n",
" 'plyfile',\n",
" 'mediapy',\n",
" 'open3d', # ← これを追加\n",
" ]\n",
"\n",
" for pkg in packages:\n",
" print(f\"Installing {pkg}...\")\n",
" subprocess.run(['pip', 'install', pkg], check=True)\n",
"\n",
" # 2DGSリポジトリのクローン\n",
" if not os.path.exists(WORK_DIR):\n",
" subprocess.run([\n",
" 'git', 'clone', '--recursive',\n",
" 'https://github.com/hbb1/2d-gaussian-splatting.git',\n",
" WORK_DIR\n",
" ], check=True)\n",
"\n",
" subprocess.run(['git', 'submodule', 'update', '--init', '--recursive'],\n",
" cwd=WORK_DIR, check=True)\n",
"\n",
" build_2dgs_submodules()\n",
"\n",
" print(\"✅ 2DGS environment setup complete\")"
],
"metadata": {
"id": "kXPLG7byqFlr"
},
"id": "kXPLG7byqFlr",
"execution_count": 37,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install open3d"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "55dtC6ByqJRY",
"outputId": "11171aa0-0dc5-4235-c39b-6e19985a5632"
},
"id": "55dtC6ByqJRY",
"execution_count": 38,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/diff_surfel_rasterization-0.0.1-py3.12-linux-x86_64.egg is deprecated. pip 24.3 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
"\u001b[0mRequirement already satisfied: open3d in /usr/local/lib/python3.12/dist-packages (0.19.0)\n",
"Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.12/dist-packages (from open3d) (2.4.2)\n",
"Requirement already satisfied: dash>=2.6.0 in /usr/local/lib/python3.12/dist-packages (from open3d) (4.0.0)\n",
"Requirement already satisfied: werkzeug>=3.0.0 in /usr/local/lib/python3.12/dist-packages (from open3d) (3.1.5)\n",
"Requirement already satisfied: flask>=3.0.0 in /usr/local/lib/python3.12/dist-packages (from open3d) (3.1.2)\n",
"Requirement already satisfied: nbformat>=5.7.0 in /usr/local/lib/python3.12/dist-packages (from open3d) (5.10.4)\n",
"Requirement already satisfied: configargparse in /usr/local/lib/python3.12/dist-packages (from open3d) (1.7.1)\n",
"Requirement already satisfied: ipywidgets>=8.0.4 in /usr/local/lib/python3.12/dist-packages (from open3d) (8.1.8)\n",
"Requirement already satisfied: addict in /usr/local/lib/python3.12/dist-packages (from open3d) (2.4.0)\n",
"Requirement already satisfied: pillow>=9.3.0 in /usr/local/lib/python3.12/dist-packages (from open3d) (11.3.0)\n",
"Requirement already satisfied: matplotlib>=3 in /usr/local/lib/python3.12/dist-packages (from open3d) (3.10.0)\n",
"Requirement already satisfied: pandas>=1.0 in /usr/local/lib/python3.12/dist-packages (from open3d) (2.2.2)\n",
"Requirement already satisfied: pyyaml>=5.4.1 in /usr/local/lib/python3.12/dist-packages (from open3d) (6.0.3)\n",
"Requirement already satisfied: scikit-learn>=0.21 in /usr/local/lib/python3.12/dist-packages (from open3d) (1.6.1)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from open3d) (4.67.3)\n",
"Requirement already satisfied: pyquaternion in /usr/local/lib/python3.12/dist-packages (from open3d) (0.9.9)\n",
"Requirement already satisfied: plotly>=5.0.0 in /usr/local/lib/python3.12/dist-packages (from dash>=2.6.0->open3d) (5.24.1)\n",
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"Requirement already satisfied: typing_extensions>=4.1.1 in /usr/local/lib/python3.12/dist-packages (from dash>=2.6.0->open3d) (4.15.0)\n",
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"Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from dash>=2.6.0->open3d) (75.2.0)\n",
"Requirement already satisfied: blinker>=1.9.0 in /usr/local/lib/python3.12/dist-packages (from flask>=3.0.0->open3d) (1.9.0)\n",
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"Requirement already satisfied: rpds-py>=0.25.0 in /usr/local/lib/python3.12/dist-packages (from jsonschema>=2.6->nbformat>=5.7.0->open3d) (0.30.0)\n",
"Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.12/dist-packages (from jupyter-core!=5.0.*,>=4.12->nbformat>=5.7.0->open3d) (4.5.1)\n",
"Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.12/dist-packages (from plotly>=5.0.0->dash>=2.6.0->open3d) (9.1.3)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib>=3->open3d) (1.17.0)\n",
"Requirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.12/dist-packages (from importlib-metadata->dash>=2.6.0->open3d) (3.23.0)\n",
"Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->dash>=2.6.0->open3d) (3.4.4)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->dash>=2.6.0->open3d) (3.11)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->dash>=2.6.0->open3d) (2.5.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->dash>=2.6.0->open3d) (2026.1.4)\n",
"Requirement already satisfied: parso<0.9.0,>=0.8.4 in /usr/local/lib/python3.12/dist-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets>=8.0.4->open3d) (0.8.5)\n",
"Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.12/dist-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets>=8.0.4->open3d) (0.7.0)\n",
"Requirement already satisfied: wcwidth in /usr/local/lib/python3.12/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=6.1.0->ipywidgets>=8.0.4->open3d) (0.5.3)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"\n",
"\n",
"# 再度レンダリング実行\n",
"import subprocess\n",
"result = subprocess.run(\n",
" ['/usr/bin/python3', 'render.py',\n",
" '-m', '/content/2d-gaussian-splatting/output/video',\n",
" '--iteration', '1000',\n",
" '--skip_test',\n",
" '--skip_train'],\n",
" cwd='/content/2d-gaussian-splatting',\n",
" capture_output=True,\n",
" text=True\n",
")\n",
"\n",
"print(\"=== STDOUT ===\")\n",
"print(result.stdout)\n",
"print(\"\\n=== STDERR ===\")\n",
"print(result.stderr)\n",
"print(f\"\\n=== EXIT CODE: {result.returncode} ===\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vRxNgRnypv0l",
"outputId": "80d48d2c-7fe5-42af-932c-10ef9e9dffb2"
},
"id": "vRxNgRnypv0l",
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"=== STDOUT ===\n",
"\n",
"\n",
"=== STDERR ===\n",
"Traceback (most recent call last):\n",
" File \"/content/2d-gaussian-splatting/render.py\", line 23, in <module>\n",
" from utils.mesh_utils import GaussianExtractor, to_cam_open3d, post_process_mesh\n",
" File \"/content/2d-gaussian-splatting/utils/mesh_utils.py\", line 20, in <module>\n",
" import trimesh\n",
"ModuleNotFoundError: No module named 'trimesh'\n",
"\n",
"\n",
"=== EXIT CODE: 1 ===\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "1W62vlfhe9TS"
},
"id": "1W62vlfhe9TS",
"execution_count": 39,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!nvcc --version\n",
"import torch\n",
"print(torch.__version__)\n",
"print(torch.version.cuda)"
],
"metadata": {
"id": "Ev9PEUdtpEAx",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e858fa2e-eb9a-4814-f5f5-de8173bf8cdb"
},
"id": "Ev9PEUdtpEAx",
"execution_count": 40,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"nvcc: NVIDIA (R) Cuda compiler driver\n",
"Copyright (c) 2005-2025 NVIDIA Corporation\n",
"Built on Fri_Feb_21_20:23:50_PST_2025\n",
"Cuda compilation tools, release 12.8, V12.8.93\n",
"Build cuda_12.8.r12.8/compiler.35583870_0\n",
"2.9.0+cu128\n",
"12.8\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "b8690389",
"metadata": {
"execution": {
"iopub.execute_input": "2026-01-10T18:22:43.739411Z",
"iopub.status.busy": "2026-01-10T18:22:43.738855Z",
"iopub.status.idle": "2026-01-10T18:22:43.755664Z",
"shell.execute_reply": "2026-01-10T18:22:43.754865Z"
},
"papermill": {
"duration": 0.027297,
"end_time": "2026-01-10T18:22:43.756758",
"exception": false,
"start_time": "2026-01-10T18:22:43.729461",
"status": "completed"
},
"tags": [],
"id": "b8690389"
},
"outputs": [],
"source": [
"import os\n",
"import glob\n",
"import cv2\n",
"import numpy as np\n",
"from PIL import Image\n",
"\n",
"# =========================================================\n",
"# Utility: aspect ratio preserved + black padding\n",
"# =========================================================\n",
"\n",
"def normalize_image_sizes_biplet(input_dir, output_dir=None, size=1024, max_images=None):\n",
" \"\"\"\n",
" Generates two square crops (Left & Right or Top & Bottom)\n",
" from each image in a directory and returns the output directory\n",
" and the list of generated file paths.\n",
"\n",
" Args:\n",
" input_dir: Input directory containing source images\n",
" output_dir: Output directory for processed images\n",
" size: Target square size (default: 1024)\n",
" max_images: Maximum number of SOURCE images to process (default: None = all images)\n",
" \"\"\"\n",
" if output_dir is None:\n",
" output_dir = 'output/images_biplet'\n",
" os.makedirs(output_dir, exist_ok=True)\n",
"\n",
" print(f\"--- Step 1: Biplet-Square Normalization ---\")\n",
" print(f\"Generating 2 cropped squares (Left/Right or Top/Bottom) for each image...\")\n",
" print()\n",
"\n",
" generated_paths = []\n",
" converted_count = 0\n",
" size_stats = {}\n",
"\n",
" # Sort for consistent processing order\n",
" image_files = sorted([f for f in os.listdir(input_dir)\n",
" if f.lower().endswith(('.jpg', '.jpeg', '.png'))])\n",
"\n",
" # ★ max_images で元画像数を制限\n",
" if max_images is not None:\n",
" image_files = image_files[:max_images]\n",
" print(f\"Processing limited to {max_images} source images (will generate {max_images * 2} cropped images)\")\n",
"\n",
" for img_file in image_files:\n",
" input_path = os.path.join(input_dir, img_file)\n",
" try:\n",
" img = Image.open(input_path)\n",
" original_size = img.size\n",
"\n",
" # Tracking original aspect ratios\n",
" size_key = f\"{original_size[0]}x{original_size[1]}\"\n",
" size_stats[size_key] = size_stats.get(size_key, 0) + 1\n",
"\n",
" # Generate 2 crops using the helper function\n",
" crops = generate_two_crops(img, size)\n",
" base_name, ext = os.path.splitext(img_file)\n",
"\n",
" for mode, cropped_img in crops.items():\n",
" output_path = os.path.join(output_dir, f\"{base_name}_{mode}{ext}\")\n",
" cropped_img.save(output_path, quality=95)\n",
" generated_paths.append(output_path)\n",
"\n",
" converted_count += 1\n",
" print(f\" ✓ {img_file}: {original_size} → 2 square images generated\")\n",
"\n",
" except Exception as e:\n",
" print(f\" ✗ Error processing {img_file}: {e}\")\n",
"\n",
" print(f\"\\nProcessing complete: {converted_count} source images processed\")\n",
" print(f\"Total output images: {len(generated_paths)}\")\n",
" print(f\"Original size distribution: {size_stats}\")\n",
"\n",
" return output_dir, generated_paths\n",
"\n",
"\n",
"def generate_two_crops(img, size):\n",
" \"\"\"\n",
" Crops the image into a square and returns 2 variations\n",
" (Left/Right for landscape, Top/Bottom for portrait).\n",
" \"\"\"\n",
" width, height = img.size\n",
" crop_size = min(width, height)\n",
" crops = {}\n",
"\n",
" if width > height:\n",
" # Landscape → Left & Right\n",
" positions = {\n",
" 'left': 0,\n",
" 'right': width - crop_size\n",
" }\n",
" for mode, x_offset in positions.items():\n",
" box = (x_offset, 0, x_offset + crop_size, crop_size)\n",
" crops[mode] = img.crop(box).resize(\n",
" (size, size),\n",
" Image.Resampling.LANCZOS\n",
" )\n",
"\n",
" else:\n",
" # Portrait or Square → Top & Bottom\n",
" positions = {\n",
" 'top': 0,\n",
" 'bottom': height - crop_size\n",
" }\n",
" for mode, y_offset in positions.items():\n",
" box = (0, y_offset, crop_size, y_offset + crop_size)\n",
" crops[mode] = img.crop(box).resize(\n",
" (size, size),\n",
" Image.Resampling.LANCZOS\n",
" )\n",
"\n",
" return crops\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "7acc20b6",
"metadata": {
"execution": {
"iopub.execute_input": "2026-01-10T18:22:43.772525Z",
"iopub.status.busy": "2026-01-10T18:22:43.772303Z",
"iopub.status.idle": "2026-01-10T18:22:43.790574Z",
"shell.execute_reply": "2026-01-10T18:22:43.789515Z"
},
"papermill": {
"duration": 0.027612,
"end_time": "2026-01-10T18:22:43.791681",
"exception": false,
"start_time": "2026-01-10T18:22:43.764069",
"status": "completed"
},
"tags": [],
"id": "7acc20b6"
},
"outputs": [],
"source": [
"def run_colmap_reconstruction(image_dir, colmap_dir):\n",
" \"\"\"Estimate camera poses and 3D point cloud with COLMAP\"\"\"\n",
" print(\"Running SfM reconstruction with COLMAP...\")\n",
"\n",
" database_path = os.path.join(colmap_dir, \"database.db\")\n",
" sparse_dir = os.path.join(colmap_dir, \"sparse\")\n",
" os.makedirs(sparse_dir, exist_ok=True)\n",
"\n",
" # Set environment variable\n",
" env = os.environ.copy()\n",
" env['QT_QPA_PLATFORM'] = 'offscreen'\n",
"\n",
" # Feature extraction\n",
" print(\"1/4: Extracting features...\")\n",
" subprocess.run([\n",
" 'colmap', 'feature_extractor',\n",
" '--database_path', database_path,\n",
" '--image_path', image_dir,\n",
" '--ImageReader.single_camera', '1',\n",
" '--ImageReader.camera_model', 'OPENCV',\n",
" '--SiftExtraction.use_gpu', '0' # Use CPU\n",
" ], check=True, env=env)\n",
"\n",
" # Feature matching\n",
" print(\"2/4: Matching features...\")\n",
" subprocess.run([\n",
" 'colmap', 'exhaustive_matcher', # Use sequential_matcher instead of exhaustive_matcher\n",
" '--database_path', database_path,\n",
" '--SiftMatching.use_gpu', '0' # Use CPU\n",
" ], check=True, env=env)\n",
"\n",
" # Sparse reconstruction\n",
" print(\"3/4: Sparse reconstruction...\")\n",
" subprocess.run([\n",
" 'colmap', 'mapper',\n",
" '--database_path', database_path,\n",
" '--image_path', image_dir,\n",
" '--output_path', sparse_dir,\n",
" '--Mapper.ba_global_max_num_iterations', '20', # Speed up\n",
" '--Mapper.ba_local_max_num_iterations', '10'\n",
" ], check=True, env=env)\n",
"\n",
" # Export to text format\n",
" print(\"4/4: Exporting to text format...\")\n",
" model_dir = os.path.join(sparse_dir, '0')\n",
" if not os.path.exists(model_dir):\n",
" # Use the first model found\n",
" subdirs = [d for d in os.listdir(sparse_dir) if os.path.isdir(os.path.join(sparse_dir, d))]\n",
" if subdirs:\n",
" model_dir = os.path.join(sparse_dir, subdirs[0])\n",
" else:\n",
" raise FileNotFoundError(\"COLMAP reconstruction failed\")\n",
"\n",
" subprocess.run([\n",
" 'colmap', 'model_converter',\n",
" '--input_path', model_dir,\n",
" '--output_path', model_dir,\n",
" '--output_type', 'TXT'\n",
" ], check=True, env=env)\n",
"\n",
" print(f\"COLMAP reconstruction complete: {model_dir}\")\n",
" return model_dir\n",
"\n",
"\n",
"def convert_cameras_to_pinhole(input_file, output_file):\n",
" \"\"\"Convert camera model to PINHOLE format\"\"\"\n",
" print(f\"Reading camera file: {input_file}\")\n",
"\n",
" with open(input_file, 'r') as f:\n",
" lines = f.readlines()\n",
"\n",
" converted_count = 0\n",
" with open(output_file, 'w') as f:\n",
" for line in lines:\n",
" if line.startswith('#') or line.strip() == '':\n",
" f.write(line)\n",
" else:\n",
" parts = line.strip().split()\n",
" if len(parts) >= 4:\n",
" cam_id = parts[0]\n",
" model = parts[1]\n",
" width = parts[2]\n",
" height = parts[3]\n",
" params = parts[4:]\n",
"\n",
" # Convert to PINHOLE format\n",
" if model == \"PINHOLE\":\n",
" f.write(line)\n",
" elif model == \"OPENCV\":\n",
" # OPENCV: fx, fy, cx, cy, k1, k2, p1, p2\n",
" fx = params[0]\n",
" fy = params[1]\n",
" cx = params[2]\n",
" cy = params[3]\n",
" f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
" converted_count += 1\n",
" else:\n",
" # Convert other models too\n",
" fx = fy = max(float(width), float(height))\n",
" cx = float(width) / 2\n",
" cy = float(height) / 2\n",
" f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
" converted_count += 1\n",
" else:\n",
" f.write(line)\n",
"\n",
" print(f\"Converted {converted_count} cameras to PINHOLE format\")\n",
"\n",
"\n",
"def prepare_gaussian_splatting_data(image_dir, colmap_model_dir):\n",
" \"\"\"Prepare data for Gaussian Splatting\"\"\"\n",
" print(\"Preparing data for Gaussian Splatting...\")\n",
"\n",
" data_dir = f\"{WORK_DIR}/data/video\"\n",
" os.makedirs(f\"{data_dir}/sparse/0\", exist_ok=True)\n",
" os.makedirs(f\"{data_dir}/images\", exist_ok=True)\n",
"\n",
" # Copy images\n",
" print(\"Copying images...\")\n",
" img_count = 0\n",
" for img_file in os.listdir(image_dir):\n",
" if img_file.lower().endswith(('.jpg', '.jpeg', '.png')):\n",
" shutil.copy(\n",
" os.path.join(image_dir, img_file),\n",
" f\"{data_dir}/images/{img_file}\"\n",
" )\n",
" img_count += 1\n",
" print(f\"Copied {img_count} images\")\n",
"\n",
" # Convert and copy camera file to PINHOLE format\n",
" print(\"Converting camera model to PINHOLE format...\")\n",
" convert_cameras_to_pinhole(\n",
" os.path.join(colmap_model_dir, 'cameras.txt'),\n",
" f\"{data_dir}/sparse/0/cameras.txt\"\n",
" )\n",
"\n",
" # Copy other files\n",
" for filename in ['images.txt', 'points3D.txt']:\n",
" src = os.path.join(colmap_model_dir, filename)\n",
" dst = f\"{data_dir}/sparse/0/{filename}\"\n",
" if os.path.exists(src):\n",
" shutil.copy(src, dst)\n",
" print(f\"Copied {filename}\")\n",
" else:\n",
" print(f\"Warning: {filename} not found\")\n",
"\n",
" print(f\"Data preparation complete: {data_dir}\")\n",
" return data_dir\n",
"\n",
"def run_colmap_reconstruction(image_dir, colmap_dir):\n",
" \"\"\"Estimate camera poses and 3D point cloud with COLMAP\"\"\"\n",
" print(\"Running SfM reconstruction with COLMAP...\")\n",
"\n",
" database_path = os.path.join(colmap_dir, \"database.db\")\n",
" sparse_dir = os.path.join(colmap_dir, \"sparse\")\n",
" os.makedirs(sparse_dir, exist_ok=True)\n",
"\n",
" # Set environment variable\n",
" env = os.environ.copy()\n",
" env['QT_QPA_PLATFORM'] = 'offscreen'\n",
"\n",
" # Feature extraction\n",
" print(\"1/4: Extracting features...\")\n",
" subprocess.run([\n",
" 'colmap', 'feature_extractor',\n",
" '--database_path', database_path,\n",
" '--image_path', image_dir,\n",
" '--ImageReader.single_camera', '1',\n",
" '--ImageReader.camera_model', 'OPENCV',\n",
" '--SiftExtraction.use_gpu', '0' # Use CPU\n",
" ], check=True, env=env)\n",
"\n",
" # Feature matching\n",
" print(\"2/4: Matching features...\")\n",
" subprocess.run([\n",
" 'colmap', 'exhaustive_matcher', # Use sequential_matcher instead of exhaustive_matcher\n",
" '--database_path', database_path,\n",
" '--SiftMatching.use_gpu', '0' # Use CPU\n",
" ], check=True, env=env)\n",
"\n",
" # Sparse reconstruction\n",
" print(\"3/4: Sparse reconstruction...\")\n",
" subprocess.run([\n",
" 'colmap', 'mapper',\n",
" '--database_path', database_path,\n",
" '--image_path', image_dir,\n",
" '--output_path', sparse_dir,\n",
" '--Mapper.ba_global_max_num_iterations', '20', # Speed up\n",
" '--Mapper.ba_local_max_num_iterations', '10'\n",
" ], check=True, env=env)\n",
"\n",
" # Export to text format\n",
" print(\"4/4: Exporting to text format...\")\n",
" model_dir = os.path.join(sparse_dir, '0')\n",
" if not os.path.exists(model_dir):\n",
" # Use the first model found\n",
" subdirs = [d for d in os.listdir(sparse_dir) if os.path.isdir(os.path.join(sparse_dir, d))]\n",
" if subdirs:\n",
" model_dir = os.path.join(sparse_dir, subdirs[0])\n",
" else:\n",
" raise FileNotFoundError(\"COLMAP reconstruction failed\")\n",
"\n",
" subprocess.run([\n",
" 'colmap', 'model_converter',\n",
" '--input_path', model_dir,\n",
" '--output_path', model_dir,\n",
" '--output_type', 'TXT'\n",
" ], check=True, env=env)\n",
"\n",
" print(f\"COLMAP reconstruction complete: {model_dir}\")\n",
" return model_dir\n",
"\n",
"\n",
"def convert_cameras_to_pinhole(input_file, output_file):\n",
" \"\"\"Convert camera model to PINHOLE format\"\"\"\n",
" print(f\"Reading camera file: {input_file}\")\n",
"\n",
" with open(input_file, 'r') as f:\n",
" lines = f.readlines()\n",
"\n",
" converted_count = 0\n",
" with open(output_file, 'w') as f:\n",
" for line in lines:\n",
" if line.startswith('#') or line.strip() == '':\n",
" f.write(line)\n",
" else:\n",
" parts = line.strip().split()\n",
" if len(parts) >= 4:\n",
" cam_id = parts[0]\n",
" model = parts[1]\n",
" width = parts[2]\n",
" height = parts[3]\n",
" params = parts[4:]\n",
"\n",
" # Convert to PINHOLE format\n",
" if model == \"PINHOLE\":\n",
" f.write(line)\n",
" elif model == \"OPENCV\":\n",
" # OPENCV: fx, fy, cx, cy, k1, k2, p1, p2\n",
" fx = params[0]\n",
" fy = params[1]\n",
" cx = params[2]\n",
" cy = params[3]\n",
" f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
" converted_count += 1\n",
" else:\n",
" # Convert other models too\n",
" fx = fy = max(float(width), float(height))\n",
" cx = float(width) / 2\n",
" cy = float(height) / 2\n",
" f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
" converted_count += 1\n",
" else:\n",
" f.write(line)\n",
"\n",
" print(f\"Converted {converted_count} cameras to PINHOLE format\")\n",
"\n",
"\n",
"def prepare_gaussian_splatting_data(image_dir, colmap_model_dir):\n",
" \"\"\"Prepare data for Gaussian Splatting\"\"\"\n",
" print(\"Preparing data for Gaussian Splatting...\")\n",
"\n",
" data_dir = f\"{WORK_DIR}/data/video\"\n",
" os.makedirs(f\"{data_dir}/sparse/0\", exist_ok=True)\n",
" os.makedirs(f\"{data_dir}/images\", exist_ok=True)\n",
"\n",
" # Copy images\n",
" print(\"Copying images...\")\n",
" img_count = 0\n",
" for img_file in os.listdir(image_dir):\n",
" if img_file.lower().endswith(('.jpg', '.jpeg', '.png')):\n",
" shutil.copy(\n",
" os.path.join(image_dir, img_file),\n",
" f\"{data_dir}/images/{img_file}\"\n",
" )\n",
" img_count += 1\n",
" print(f\"Copied {img_count} images\")\n",
"\n",
" # Convert and copy camera file to PINHOLE format\n",
" print(\"Converting camera model to PINHOLE format...\")\n",
" convert_cameras_to_pinhole(\n",
" os.path.join(colmap_model_dir, 'cameras.txt'),\n",
" f\"{data_dir}/sparse/0/cameras.txt\"\n",
" )\n",
"\n",
" # Copy other files\n",
" for filename in ['images.txt', 'points3D.txt']:\n",
" src = os.path.join(colmap_model_dir, filename)\n",
" dst = f\"{data_dir}/sparse/0/{filename}\"\n",
" if os.path.exists(src):\n",
" shutil.copy(src, dst)\n",
" print(f\"Copied {filename}\")\n",
" else:\n",
" print(f\"Warning: {filename} not found\")\n",
"\n",
" print(f\"Data preparation complete: {data_dir}\")\n",
" return data_dir\n",
"\n",
"\n",
"\n",
"###############################################################\n",
"\n",
"# 変更後 (2DGS) - 正則化パラメータを追加\n",
"def train_gaussian_splatting(data_dir, iterations=7000,\n",
" lambda_normal=0.05,\n",
" lambda_dist=0, # ← distortion → dist に修正\n",
" depth_ratio=0):\n",
" \"\"\"\n",
" 2DGS用のトレーニング関数\n",
" Args:\n",
" lambda_normal: 法線一貫性の重み (デフォルト: 0.05)\n",
" lambda_dist: 深度歪みの重み (デフォルト: 0) # ← 名前修正\n",
" depth_ratio: 0=平均深度, 1=中央値深度 (デフォルト: 0)\n",
" \"\"\"\n",
" model_path = f\"{WORK_DIR}/output/video\"\n",
" cmd = [\n",
" sys.executable, 'train.py',\n",
" '-s', data_dir,\n",
" '-m', model_path,\n",
" '--iterations', str(iterations),\n",
" '--lambda_normal', str(lambda_normal),\n",
" '--lambda_dist', str(lambda_dist), # ← ここを修正！\n",
" '--depth_ratio', str(depth_ratio),\n",
" '--eval'\n",
" ]\n",
" subprocess.run(cmd, cwd=WORK_DIR, check=True)\n",
" return model_path\n",
"\n",
"\n",
"\n",
"# 2DGSではメッシュ抽出オプションが追加されています\n",
"def render_video_and_mesh(model_path, output_video_path, iteration=1000,\n",
" extract_mesh=False, unbounded=False, mesh_res=1024):\n",
" \"\"\"\n",
" 2DGS用のレンダリングとメッシュ抽出\n",
" Args:\n",
" extract_mesh: メッシュを抽出するか (デフォルト: False、動画のみ)\n",
" unbounded: 境界なしメッシュ抽出を使用するか\n",
" mesh_res: メッシュ解像度\n",
" \"\"\"\n",
" # 通常のレンダリング\n",
" cmd = [\n",
" sys.executable, 'render.py',\n",
" '-m', model_path,\n",
" '--iteration', str(iteration),\n",
" '--skip_test',\n",
" '--skip_train'\n",
" ]\n",
"\n",
" # メッシュ抽出オプション（必要な場合のみ）\n",
" if extract_mesh:\n",
" if unbounded:\n",
" cmd.extend(['--unbounded'])\n",
" cmd.extend(['--mesh_res', str(mesh_res)])\n",
"\n",
" # エラー詳細をキャプチャ\n",
" result = subprocess.run(\n",
" cmd,\n",
" cwd=WORK_DIR,\n",
" capture_output=True,\n",
" text=True\n",
" )\n",
"\n",
" if result.returncode != 0:\n",
" print(\"❌ STDOUT:\", result.stdout)\n",
" print(\"❌ STDERR:\", result.stderr)\n",
" raise subprocess.CalledProcessError(\n",
" result.returncode, cmd, result.stdout, result.stderr\n",
" )\n",
"\n",
" # レンダリング結果からビデオ作成\n",
" possible_dirs = [\n",
" f\"{model_path}/test/ours_{iteration}/renders\",\n",
" f\"{model_path}/train/ours_{iteration}/renders\",\n",
" ]\n",
"\n",
" render_dir = None\n",
" for test_dir in possible_dirs:\n",
" if os.path.exists(test_dir):\n",
" render_dir = test_dir\n",
" print(f\"✅ Rendering directory found: {render_dir}\")\n",
" break\n",
"\n",
" if render_dir and os.path.exists(render_dir):\n",
" render_imgs = sorted([f for f in os.listdir(render_dir)\n",
" if f.endswith('.png')])\n",
" if render_imgs:\n",
" print(f\"Found {len(render_imgs)} rendered images\")\n",
" # ffmpegでビデオ作成\n",
" subprocess.run([\n",
" 'ffmpeg', '-y',\n",
" '-framerate', '30',\n",
" '-pattern_type', 'glob',\n",
" '-i', f\"{render_dir}/*.png\",\n",
" '-c:v', 'libx264',\n",
" '-pix_fmt', 'yuv420p',\n",
" '-crf', '18',\n",
" output_video_path\n",
" ], check=True)\n",
" print(f\"✅ Video saved: {output_video_path}\")\n",
" return True\n",
"\n",
" print(\"❌ Error: Rendering directory not found\")\n",
" return False\n",
"\n",
"\n",
"\n",
"###############################################################\n",
"\n",
"\n",
"def create_gif(video_path, gif_path):\n",
" \"\"\"Create GIF from MP4\"\"\"\n",
" print(\"Creating animated GIF...\")\n",
"\n",
" subprocess.run([\n",
" 'ffmpeg', '-y',\n",
" '-i', video_path,\n",
" '-vf', 'setpts=8*PTS,fps=10,scale=720:-1:flags=lanczos',\n",
" '-loop', '0',\n",
" gif_path\n",
" ], check=True)\n",
"\n",
" if os.path.exists(gif_path):\n",
" size_mb = os.path.getsize(gif_path) / (1024 * 1024)\n",
" print(f\"GIF creation complete: {gif_path} ({size_mb:.2f} MB)\")\n",
" return True\n",
"\n",
" return False"
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "YtqhBP4T3jEH"
},
"id": "YtqhBP4T3jEH",
"execution_count": 42,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def main_pipeline(image_dir, output_dir, square_size=1024, max_images=100):\n",
" \"\"\"Main execution function\"\"\"\n",
" try:\n",
" # Step 1: 画像の正規化と前処理\n",
" print(\"=\"*60)\n",
" print(\"Step 1: Normalizing and preprocessing images\")\n",
" print(\"=\"*60)\n",
"\n",
" frame_dir = os.path.join(COLMAP_DIR, \"images\")\n",
" os.makedirs(frame_dir, exist_ok=True)\n",
"\n",
" # 画像を正規化して直接COLMAPのディレクトリに保存\n",
" num_processed = normalize_image_sizes_biplet(\n",
" input_dir=image_dir,\n",
" output_dir=frame_dir, # 直接colmap/imagesに保存\n",
" size=square_size,\n",
" max_images=max_images\n",
" )\n",
"\n",
" print(f\"Processed {num_processed} images\")\n",
"\n",
" # Step 2: Estimate Camera Info with COLMAP\n",
" print(\"=\"*60)\n",
" print(\"Step 2: Running COLMAP reconstruction\")\n",
" print(\"=\"*60)\n",
" colmap_model_dir = run_colmap_reconstruction(frame_dir, COLMAP_DIR)\n",
"\n",
" # Step 3: Prepare Data for Gaussian Splatting\n",
" print(\"=\"*60)\n",
" print(\"Step 3: Preparing Gaussian Splatting data\")\n",
" print(\"=\"*60)\n",
" data_dir = prepare_gaussian_splatting_data(frame_dir, colmap_model_dir)\n",
"\n",
" # Step 4: Train Model\n",
" print(\"=\"*60)\n",
" print(\"Step 4: Training Gaussian Splatting model\")\n",
" print(\"=\"*60)\n",
" # 修正: frame_dir → data_dir\n",
"\n",
" # main_pipeline内で呼び出す部分\n",
" model_path = train_gaussian_splatting(\n",
" data_dir,\n",
" iterations=1000,\n",
" lambda_normal=0.05,\n",
" lambda_dist=0, # ← distortion → dist に修正\n",
" depth_ratio=0\n",
" )\n",
"\n",
" print(f\"Model trained at: {model_path}\")\n",
"\n",
" ############################################\n",
"\n",
" except Exception as e: # ← これを追加\n",
" print(f\"❌ Pipeline failed: {e}\")\n",
" import traceback\n",
" traceback.print_exc()\n",
" return None\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" IMAGE_DIR = \"/content/drive/MyDrive/your_folder/fountain100\"\n",
" OUTPUT_DIR = \"/content/output\"\n",
" COLMAP_DIR = \"/content/colmap_workspace\"\n",
"\n",
" # シンプルに1つの戻り値だけ\n",
" ply_path = main_pipeline(\n",
" image_dir=IMAGE_DIR,\n",
" output_dir=OUTPUT_DIR,\n",
" square_size=1024,\n",
" max_images=20\n",
" )\n",
"\n",
"\n"
],
"metadata": {
"id": "fya3kv62NXM-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5f562d50-ffba-43df-b33d-c9a92d0383e7"
},
"id": "fya3kv62NXM-",
"execution_count": 54,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"============================================================\n",
"Step 1: Normalizing and preprocessing images\n",
"============================================================\n",
"--- Step 1: Biplet-Square Normalization ---\n",
"Generating 2 cropped squares (Left/Right or Top/Bottom) for each image...\n",
"\n",
"Processing limited to 20 source images (will generate 40 cropped images)\n",
" ✓ image_101.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_102.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_103.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_104.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_105.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_106.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_107.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_108.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_109.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_110.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_111.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_112.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_113.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_114.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_115.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_116.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_117.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_118.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_119.jpeg: (1440, 1920) → 2 square images generated\n",
" ✓ image_120.jpeg: (1440, 1920) → 2 square images generated\n",
"\n",
"Processing complete: 20 source images processed\n",
"Total output images: 40\n",
"Original size distribution: {'1440x1920': 20}\n",
"Processed ('/content/colmap_workspace/images', ['/content/colmap_workspace/images/image_101_top.jpeg', '/content/colmap_workspace/images/image_101_bottom.jpeg', '/content/colmap_workspace/images/image_102_top.jpeg', '/content/colmap_workspace/images/image_102_bottom.jpeg', '/content/colmap_workspace/images/image_103_top.jpeg', '/content/colmap_workspace/images/image_103_bottom.jpeg', '/content/colmap_workspace/images/image_104_top.jpeg', '/content/colmap_workspace/images/image_104_bottom.jpeg', '/content/colmap_workspace/images/image_105_top.jpeg', '/content/colmap_workspace/images/image_105_bottom.jpeg', '/content/colmap_workspace/images/image_106_top.jpeg', '/content/colmap_workspace/images/image_106_bottom.jpeg', '/content/colmap_workspace/images/image_107_top.jpeg', '/content/colmap_workspace/images/image_107_bottom.jpeg', '/content/colmap_workspace/images/image_108_top.jpeg', '/content/colmap_workspace/images/image_108_bottom.jpeg', '/content/colmap_workspace/images/image_109_top.jpeg', '/content/colmap_workspace/images/image_109_bottom.jpeg', '/content/colmap_workspace/images/image_110_top.jpeg', '/content/colmap_workspace/images/image_110_bottom.jpeg', '/content/colmap_workspace/images/image_111_top.jpeg', '/content/colmap_workspace/images/image_111_bottom.jpeg', '/content/colmap_workspace/images/image_112_top.jpeg', '/content/colmap_workspace/images/image_112_bottom.jpeg', '/content/colmap_workspace/images/image_113_top.jpeg', '/content/colmap_workspace/images/image_113_bottom.jpeg', '/content/colmap_workspace/images/image_114_top.jpeg', '/content/colmap_workspace/images/image_114_bottom.jpeg', '/content/colmap_workspace/images/image_115_top.jpeg', '/content/colmap_workspace/images/image_115_bottom.jpeg', '/content/colmap_workspace/images/image_116_top.jpeg', '/content/colmap_workspace/images/image_116_bottom.jpeg', '/content/colmap_workspace/images/image_117_top.jpeg', '/content/colmap_workspace/images/image_117_bottom.jpeg', '/content/colmap_workspace/images/image_118_top.jpeg', '/content/colmap_workspace/images/image_118_bottom.jpeg', '/content/colmap_workspace/images/image_119_top.jpeg', '/content/colmap_workspace/images/image_119_bottom.jpeg', '/content/colmap_workspace/images/image_120_top.jpeg', '/content/colmap_workspace/images/image_120_bottom.jpeg']) images\n",
"============================================================\n",
"Step 2: Running COLMAP reconstruction\n",
"============================================================\n",
"Running SfM reconstruction with COLMAP...\n",
"1/4: Extracting features...\n",
"2/4: Matching features...\n",
"3/4: Sparse reconstruction...\n",
"4/4: Exporting to text format...\n",
"COLMAP reconstruction complete: /content/colmap_workspace/sparse/0\n",
"============================================================\n",
"Step 3: Preparing Gaussian Splatting data\n",
"============================================================\n",
"Preparing data for Gaussian Splatting...\n",
"Copying images...\n",
"Copied 40 images\n",
"Converting camera model to PINHOLE format...\n",
"Reading camera file: /content/colmap_workspace/sparse/0/cameras.txt\n",
"Converted 1 cameras to PINHOLE format\n",
"Copied images.txt\n",
"Copied points3D.txt\n",
"Data preparation complete: /content/2d-gaussian-splatting/data/video\n",
"============================================================\n",
"Step 4: Training Gaussian Splatting model\n",
"============================================================\n",
"Model trained at: /content/2d-gaussian-splatting/output/video\n"
]
}
]
},
{
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