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ipynb

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- {
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- "cells": [
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- {
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- "cell_type": "markdown",
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- "id": "fb1f1fdc",
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- "metadata": {
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- "papermill": {
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- "duration": 0.002985,
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- "end_time": "2026-01-10T18:17:32.170524",
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- "exception": false,
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- "start_time": "2026-01-10T18:17:32.167539",
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- "status": "completed"
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- },
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- "tags": [],
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- "id": "fb1f1fdc"
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- },
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- "source": [
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- "# **biplet-dino-colmap-2dgs**"
19
- ]
20
- },
21
- {
22
- "cell_type": "code",
23
- "source": [
24
- "#サイズの異なる画像を扱う\n",
25
- "from google.colab import drive\n",
26
- "drive.mount('/content/drive')"
27
- ],
28
- "metadata": {
29
- "colab": {
30
- "base_uri": "https://localhost:8080/"
31
- },
32
- "id": "JON4rYSEOzCg",
33
- "outputId": "79cbc182-979e-4e50-811f-88a9b8d77160"
34
- },
35
- "id": "JON4rYSEOzCg",
36
- "execution_count": 1,
37
- "outputs": [
38
- {
39
- "output_type": "stream",
40
- "name": "stdout",
41
- "text": [
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- "Mounted at /content/drive\n"
43
- ]
44
- }
45
- ]
46
- },
47
- {
48
- "cell_type": "code",
49
- "execution_count": 2,
50
- "id": "22353010",
51
- "metadata": {
52
- "execution": {
53
- "iopub.execute_input": "2026-01-10T18:17:32.181455Z",
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- "iopub.status.busy": "2026-01-10T18:17:32.180969Z",
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- "iopub.status.idle": "2026-01-10T18:17:32.355942Z",
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- "shell.execute_reply": "2026-01-10T18:17:32.355229Z"
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- },
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- "papermill": {
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- "duration": 0.179454,
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- "end_time": "2026-01-10T18:17:32.357275",
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- "exception": false,
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- "start_time": "2026-01-10T18:17:32.177821",
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- "status": "completed"
64
- },
65
- "tags": [],
66
- "id": "22353010"
67
- },
68
- "outputs": [],
69
- "source": [
70
- "import os\n",
71
- "import sys\n",
72
- "import subprocess\n",
73
- "import shutil\n",
74
- "from pathlib import Path\n",
75
- "import cv2\n",
76
- "from PIL import Image\n",
77
- "import glob\n",
78
- "\n",
79
- "IMAGE_PATH=\"/content/drive/MyDrive/your_folder/fountain100\"\n",
80
- "\n",
81
- "#WORK_DIR = '/content/gaussian-splatting'\n",
82
- "WORK_DIR = \"/content/2d-gaussian-splatting\"\n",
83
- "\n",
84
- "OUTPUT_DIR = '/content/output'\n",
85
- "COLMAP_DIR = '/content/colmap_data'"
86
- ]
87
- },
88
- {
89
- "cell_type": "code",
90
- "execution_count": 2,
91
- "metadata": {
92
- "execution": {
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- "iopub.execute_input": "2026-01-10T18:22:43.807508Z",
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- "iopub.status.busy": "2026-01-10T18:22:43.807294Z",
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- "iopub.status.idle": "2026-01-11T00:00:17.030890Z",
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- "shell.execute_reply": "2026-01-11T00:00:17.029927Z"
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- },
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- "papermill": {
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- "duration": 20253.434865,
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- "end_time": "2026-01-11T00:00:17.234174",
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- "exception": false,
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- "start_time": "2026-01-10T18:22:43.799309",
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- "status": "completed"
104
- },
105
- "tags": [],
106
- "id": "QXI_UOXaNbgI"
107
- },
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- "outputs": [],
109
- "source": [
110
- "\n"
111
- ],
112
- "id": "QXI_UOXaNbgI"
113
- },
114
- {
115
- "cell_type": "code",
116
- "execution_count": 3,
117
- "id": "be6df249",
118
- "metadata": {
119
- "execution": {
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- "iopub.execute_input": "2026-01-10T18:17:32.363444Z",
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- "iopub.status.busy": "2026-01-10T18:17:32.363175Z",
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- "iopub.status.idle": "2026-01-10T18:22:43.720241Z",
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- "shell.execute_reply": "2026-01-10T18:22:43.719380Z"
124
- },
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- "papermill": {
126
- "duration": 311.361656,
127
- "end_time": "2026-01-10T18:22:43.721610",
128
- "exception": false,
129
- "start_time": "2026-01-10T18:17:32.359954",
130
- "status": "completed"
131
- },
132
- "tags": [],
133
- "id": "be6df249",
134
- "outputId": "c6ebe7e1-8e00-46d9-e3c1-09bd1e1f5e60",
135
- "colab": {
136
- "base_uri": "https://localhost:8080/"
137
- }
138
- },
139
- "outputs": [
140
- {
141
- "output_type": "stream",
142
- "name": "stdout",
143
- "text": [
144
- "🚀 Setting up COLAB environment (v8 - Python 3.12 compatible)\n",
145
- "\n",
146
- "======================================================================\n",
147
- "STEP 0: Fix NumPy (Python 3.12 compatible)\n",
148
- "======================================================================\n",
149
- "Found existing installation: torch 2.9.0+cu128\n",
150
- "Uninstalling torch-2.9.0+cu128:\n",
151
- " Successfully uninstalled torch-2.9.0+cu128\n",
152
- "Found existing installation: torchvision 0.24.0+cu128\n",
153
- "Uninstalling torchvision-0.24.0+cu128:\n",
154
- " Successfully uninstalled torchvision-0.24.0+cu128\n",
155
- "Found existing installation: torchaudio 2.9.0+cu128\n",
156
- "Uninstalling torchaudio-2.9.0+cu128:\n",
157
- " Successfully uninstalled torchaudio-2.9.0+cu128\n",
158
- "Looking in indexes: https://download.pytorch.org/whl/cu118\n",
159
- "\u001b[31mERROR: Could not find a version that satisfies the requirement torch==2.1.2+cu118 (from versions: 2.2.0+cu118, 2.2.1+cu118, 2.2.2+cu118, 2.3.0+cu118, 2.3.1+cu118, 2.4.0+cu118, 2.4.1+cu118, 2.5.0+cu118, 2.5.1+cu118, 2.6.0+cu118, 2.7.0+cu118, 2.7.1+cu118)\u001b[0m\u001b[31m\n",
160
- "\u001b[0m\u001b[31mERROR: No matching distribution found for torch==2.1.2+cu118\u001b[0m\u001b[31m\n",
161
- "\u001b[0m\n",
162
- "======================================================================\n",
163
- "STEP 1: System packages\n",
164
- "======================================================================\n",
165
- "Running: apt-get update -qq\n",
166
- "Running: apt-get install -y -qq colmap build-essential cmake git libopenblas-dev xvfb\n",
167
- "\n",
168
- "======================================================================\n",
169
- "STEP 2: Clone Gaussian Splatting\n",
170
- "======================================================================\n",
171
- "Running: git clone --recursive https://github.com/hbb1/2d-gaussian-splatting.git 2d-gaussian-splatting\n",
172
- "\n",
173
- "======================================================================\n",
174
- "STEP 3: Python packages (VERBOSE MODE)\n",
175
- "======================================================================\n",
176
- "\n",
177
- "📦 Installing PyTorch...\n",
178
- "Running: /usr/bin/python3 -m pip install torch torchvision torchaudio\n",
179
- "\n",
180
- "📦 Installing core utilities...\n",
181
- "Running: /usr/bin/python3 -m pip install opencv-python pillow imageio imageio-ffmpeg plyfile tqdm tensorboard\n",
182
- "\n",
183
- "📦 Installing transformers (NumPy 1.26 compatible)...\n",
184
- "Running: /usr/bin/python3 -m pip install transformers==4.45.0\n",
185
- "\n",
186
- "📦 Installing LightGlue stack...\n",
187
- "Running: /usr/bin/python3 -m pip install kornia\n",
188
- "Running: /usr/bin/python3 -m pip install h5py\n",
189
- "Running: /usr/bin/python3 -m pip install matplotlib\n",
190
- "Running: /usr/bin/python3 -m pip install pycolmap\n",
191
- "\n",
192
- "======================================================================\n",
193
- "STEP 5: Detailed Verification\n",
194
- "======================================================================\n",
195
- "\n",
196
- "🔍 Testing PyTorch...\n",
197
- " ✓ PyTorch: 2.10.0+cu128\n",
198
- " ✓ CUDA available: True\n",
199
- " ✓ CUDA version: 12.8\n",
200
- "\n",
201
- "🔍 Testing transformers...\n",
202
- " ✓ transformers version: 4.45.0\n",
203
- " ✓ AutoModel import: OK\n",
204
- "\n",
205
- "🔍 Testing pycolmap...\n",
206
- " ✓ pycolmap: OK\n",
207
- "\n",
208
- "🔍 Testing kornia...\n",
209
- " ✓ kornia: 0.8.2\n",
210
- "\n",
211
- "======================================================================\n",
212
- "✅ SETUP COMPLETE\n",
213
- "======================================================================\n",
214
- "Working dir: 2d-gaussian-splatting\n"
215
- ]
216
- }
217
- ],
218
- "source": [
219
- "def run_cmd(cmd, check=True, capture=False):\n",
220
- " \"\"\"Run command with better error handling\"\"\"\n",
221
- " print(f\"Running: {' '.join(cmd)}\")\n",
222
- " result = subprocess.run(\n",
223
- " cmd,\n",
224
- " capture_output=capture,\n",
225
- " text=True,\n",
226
- " check=False\n",
227
- " )\n",
228
- " if check and result.returncode != 0:\n",
229
- " print(f\"❌ Command failed with code {result.returncode}\")\n",
230
- " if capture:\n",
231
- " print(f\"STDOUT: {result.stdout}\")\n",
232
- " print(f\"STDERR: {result.stderr}\")\n",
233
- " return result\n",
234
- "\n",
235
- "\n",
236
- "def setup_environment():\n",
237
- " \"\"\"\n",
238
- " Colab environment setup for Gaussian Splatting + LightGlue + pycolmap\n",
239
- " Python 3.12 compatible version (v8)\n",
240
- " \"\"\"\n",
241
- "\n",
242
- " print(\"🚀 Setting up COLAB environment (v8 - Python 3.12 compatible)\")\n",
243
- "\n",
244
- " WORK_DIR = \"gaussian-splatting\"\n",
245
- "\n",
246
- " # =====================================================================\n",
247
- " # STEP 0: NumPy FIX (Python 3.12 compatible)\n",
248
- " # =====================================================================\n",
249
- " print(\"\\n\" + \"=\"*70)\n",
250
- " print(\"STEP 0: Fix NumPy (Python 3.12 compatible)\")\n",
251
- " print(\"=\"*70)\n",
252
- "\n",
253
- " # Python 3.12 requires numpy >= 1.26\n",
254
- " run_cmd([sys.executable, \"-m\", \"pip\", \"uninstall\", \"-y\", \"numpy\"])\n",
255
- " run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"numpy==1.26.4\"])\n",
256
- "\n",
257
- " # sanity check\n",
258
- " run_cmd([sys.executable, \"-c\", \"import numpy; print('NumPy:', numpy.__version__)\"])\n",
259
- "\n",
260
- " # =====================================================================\n",
261
- " # STEP 1: System packages (Colab)\n",
262
- " # =====================================================================\n",
263
- " print(\"\\n\" + \"=\"*70)\n",
264
- " print(\"STEP 1: System packages\")\n",
265
- " print(\"=\"*70)\n",
266
- "\n",
267
- " run_cmd([\"apt-get\", \"update\", \"-qq\"])\n",
268
- " run_cmd([\n",
269
- " \"apt-get\", \"install\", \"-y\", \"-qq\",\n",
270
- " \"colmap\",\n",
271
- " \"build-essential\",\n",
272
- " \"cmake\",\n",
273
- " \"git\",\n",
274
- " \"libopenblas-dev\",\n",
275
- " \"xvfb\"\n",
276
- " ])\n",
277
- "\n",
278
- " # virtual display (COLMAP / OpenCV safety)\n",
279
- " os.environ[\"QT_QPA_PLATFORM\"] = \"offscreen\"\n",
280
- " os.environ[\"DISPLAY\"] = \":99\"\n",
281
- " subprocess.Popen(\n",
282
- " [\"Xvfb\", \":99\", \"-screen\", \"0\", \"1024x768x24\"],\n",
283
- " stdout=subprocess.DEVNULL,\n",
284
- " stderr=subprocess.DEVNULL\n",
285
- " )\n",
286
- "\n",
287
- " # =====================================================================\n",
288
- " # STEP 2: Clone 2D Gaussian Splatting\n",
289
- " # =====================================================================\n",
290
- " print(\"\\n\" + \"=\"*70)\n",
291
- " print(\"STEP 2: Clone Gaussian Splatting\")\n",
292
- " print(\"=\"*70)\n",
293
- "\n",
294
- " if not os.path.exists(WORK_DIR):\n",
295
- " run_cmd([\n",
296
- " \"git\", \"clone\", \"--recursive\",\n",
297
- " \"https://github.com/hbb1/2d-gaussian-splatting.git\",\n",
298
- " WORK_DIR\n",
299
- " ])\n",
300
- " else:\n",
301
- " print(\"✓ Repository already exists\")\n",
302
- "\n",
303
- " # =====================================================================\n",
304
- " # STEP 3: Python packages (FIXED ORDER & VERSIONS)\n",
305
- " # =====================================================================\n",
306
- " print(\"\\n\" + \"=\"*70)\n",
307
- " print(\"STEP 3: Python packages (VERBOSE MODE)\")\n",
308
- " print(\"=\"*70)\n",
309
- "\n",
310
- " # ---- PyTorch (Colab CUDA対応) ----\n",
311
- " print(\"\\n📦 Installing PyTorch...\")\n",
312
- " run_cmd([\n",
313
- " sys.executable, \"-m\", \"pip\", \"install\",\n",
314
- " \"torch\", \"torchvision\", \"torchaudio\"\n",
315
- " ])\n",
316
- "\n",
317
- " # ---- Core utils ----\n",
318
- " print(\"\\n📦 Installing core utilities...\")\n",
319
- " run_cmd([\n",
320
- " sys.executable, \"-m\", \"pip\", \"install\",\n",
321
- " \"opencv-python\",\n",
322
- " \"pillow\",\n",
323
- " \"imageio\",\n",
324
- " \"imageio-ffmpeg\",\n",
325
- " \"plyfile\",\n",
326
- " \"tqdm\",\n",
327
- " \"tensorboard\"\n",
328
- " ])\n",
329
- "\n",
330
- " # ---- transformers (NumPy 1.26 compatible) ----\n",
331
- " print(\"\\n📦 Installing transformers (NumPy 1.26 compatible)...\")\n",
332
- " # Install transformers with proper dependencies\n",
333
- " run_cmd([\n",
334
- " sys.executable, \"-m\", \"pip\", \"install\",\n",
335
- " \"transformers==4.40.0\"\n",
336
- " ])\n",
337
- "\n",
338
- " # ---- LightGlue stack (GITHUB INSTALL) ----\n",
339
- " print(\"\\n📦 Installing LightGlue stack...\")\n",
340
- "\n",
341
- " # Install kornia first\n",
342
- " run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"kornia\"])\n",
343
- "\n",
344
- " # Install h5py (sometimes needed)\n",
345
- " run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"h5py\"])\n",
346
- "\n",
347
- " # Install matplotlib (LightGlue dependency)\n",
348
- " run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"matplotlib\"])\n",
349
- "\n",
350
- " # Install pycolmap\n",
351
- " run_cmd([sys.executable, \"-m\", \"pip\", \"install\", \"pycolmap\"])\n",
352
- "\n",
353
- " # =====================================================================\n",
354
- " # STEP 4: Build 2D GS submodules\n",
355
- " # =====================================================================\n",
356
- " print(\"\\n\" + \"=\"*70)\n",
357
- " print(\"STEP 4: Build Gaussian Splatting submodules\")\n",
358
- " print(\"=\"*70)\n",
359
- "\n",
360
- " submodules = {\n",
361
- " \"diff-surfel-rasterization\":\n",
362
- " \"https://github.com/hbb1/diff-surfel-rasterization.git\",\n",
363
- " \"simple-knn\":\n",
364
- " \"https://github.com/camenduru/simple-knn.git\"\n",
365
- " }\n",
366
- "\n",
367
- " for name, repo in submodules.items():\n",
368
- " print(f\"\\n📦 Installing {name}...\")\n",
369
- " path = os.path.join(WORK_DIR, \"submodules\", name)\n",
370
- " if not os.path.exists(path):\n",
371
- " run_cmd([\"git\", \"clone\", repo, path])\n",
372
- " run_cmd([sys.executable, \"-m\", \"pip\", \"install\", path])\n",
373
- "\n",
374
- " # =====================================================================\n",
375
- " # STEP 5: Detailed Verification\n",
376
- " # =====================================================================\n",
377
- " print(\"\\n\" + \"=\"*70)\n",
378
- " print(\"STEP 5: Detailed Verification\")\n",
379
- " print(\"=\"*70)\n",
380
- "\n",
381
- " # NumPy (verify version first)\n",
382
- " print(\"\\n🔍 Testing NumPy...\")\n",
383
- " try:\n",
384
- " import numpy as np\n",
385
- " print(f\" ✓ NumPy: {np.__version__}\")\n",
386
- " except Exception as e:\n",
387
- " print(f\" ❌ NumPy failed: {e}\")\n",
388
- "\n",
389
- " # PyTorch\n",
390
- " print(\"\\n🔍 Testing PyTorch...\")\n",
391
- " try:\n",
392
- " import torch\n",
393
- " print(f\" ✓ PyTorch: {torch.__version__}\")\n",
394
- " print(f\" ✓ CUDA available: {torch.cuda.is_available()}\")\n",
395
- " if torch.cuda.is_available():\n",
396
- " print(f\" ✓ CUDA version: {torch.version.cuda}\")\n",
397
- " except Exception as e:\n",
398
- " print(f\" ❌ PyTorch failed: {e}\")\n",
399
- "\n",
400
- " # transformers\n",
401
- " print(\"\\n🔍 Testing transformers...\")\n",
402
- " try:\n",
403
- " import transformers\n",
404
- " print(f\" ✓ transformers version: {transformers.__version__}\")\n",
405
- " from transformers import AutoModel\n",
406
- " print(f\" ✓ AutoModel import: OK\")\n",
407
- " except Exception as e:\n",
408
- " print(f\" ❌ transformers failed: {e}\")\n",
409
- " print(f\" Attempting detailed diagnosis...\")\n",
410
- " result = run_cmd([\n",
411
- " sys.executable, \"-c\",\n",
412
- " \"import transformers; print(transformers.__version__)\"\n",
413
- " ], capture=True)\n",
414
- " print(f\" Output: {result.stdout}\")\n",
415
- " print(f\" Error: {result.stderr}\")\n",
416
- "\n",
417
- " '''\n",
418
- " # LightGlue\n",
419
- " print(\"\\n🔍 Testing LightGlue...\")\n",
420
- " try:\n",
421
- " from lightglue import LightGlue, ALIKED\n",
422
- " print(f\" ✓ LightGlue: OK\")\n",
423
- " print(f\" ✓ ALIKED: OK\")\n",
424
- " except Exception as e:\n",
425
- " print(f\" ❌ LightGlue failed: {e}\")\n",
426
- " print(f\" Attempting detailed diagnosis...\")\n",
427
- " result = run_cmd([\n",
428
- " sys.executable, \"-c\",\n",
429
- " \"from lightglue import LightGlue\"\n",
430
- " ], capture=True)\n",
431
- " print(f\" Output: {result.stdout}\")\n",
432
- " print(f\" Error: {result.stderr}\")\n",
433
- " '''\n",
434
- "\n",
435
- " # pycolmap\n",
436
- " print(\"\\n🔍 Testing pycolmap...\")\n",
437
- " try:\n",
438
- " import pycolmap\n",
439
- " print(f\" ✓ pycolmap: OK\")\n",
440
- " except Exception as e:\n",
441
- " print(f\" ❌ pycolmap failed: {e}\")\n",
442
- "\n",
443
- " # kornia\n",
444
- " print(\"\\n🔍 Testing kornia...\")\n",
445
- " try:\n",
446
- " import kornia\n",
447
- " print(f\" ✓ kornia: {kornia.__version__}\")\n",
448
- " except Exception as e:\n",
449
- " print(f\" ❌ kornia failed: {e}\")\n",
450
- "\n",
451
- " print(\"\\n\" + \"=\"*70)\n",
452
- " print(\"✅ SETUP COMPLETE\")\n",
453
- " print(\"=\"*70)\n",
454
- " print(f\"Working dir: {WORK_DIR}\")\n",
455
- "\n",
456
- " return WORK_DIR\n",
457
- "\n",
458
- "\n",
459
- "if __name__ == \"__main__\":\n",
460
- " setup_environment()"
461
- ]
462
- },
463
- {
464
- "cell_type": "code",
465
- "source": [],
466
- "metadata": {
467
- "colab": {
468
- "base_uri": "https://localhost:8080/"
469
- },
470
- "id": "cgpyET_KmGhe",
471
- "outputId": "eaef7613-1658-4621-f9af-c51d5aaa8f5f"
472
- },
473
- "id": "cgpyET_KmGhe",
474
- "execution_count": 9,
475
- "outputs": [
476
- {
477
- "output_type": "stream",
478
- "name": "stdout",
479
- "text": [
480
- "🚀 Setting up COLAB environment (v8 - Python 3.12 compatible)\n",
481
- "\n",
482
- "======================================================================\n",
483
- "STEP 4: Build Gaussian Splatting submodules\n",
484
- "======================================================================\n",
485
- "\n",
486
- "📦 Installing diff-gaussian-rasterization...\n",
487
- "Running: /usr/bin/python3 -m pip install 2d-gaussian-splatting/submodules/diff-gaussian-rasterization\n",
488
- "❌ Command failed with code 1\n",
489
- "\n",
490
- "📦 Installing simple-knn...\n",
491
- "Running: /usr/bin/python3 -m pip install 2d-gaussian-splatting/submodules/simple-knn\n",
492
- "❌ Command failed with code 1\n"
493
- ]
494
- }
495
- ]
496
- },
497
- {
498
- "cell_type": "code",
499
- "source": [
500
- "break"
501
- ],
502
- "metadata": {
503
- "id": "9rV1ML69tNnt"
504
- },
505
- "id": "9rV1ML69tNnt",
506
- "execution_count": null,
507
- "outputs": []
508
- },
509
- {
510
- "cell_type": "code",
511
- "source": [
512
- "!nvcc --version\n",
513
- "import torch\n",
514
- "print(torch.__version__)\n",
515
- "print(torch.version.cuda)"
516
- ],
517
- "metadata": {
518
- "colab": {
519
- "base_uri": "https://localhost:8080/"
520
- },
521
- "id": "Ev9PEUdtpEAx",
522
- "outputId": "5bfecbbb-f9fc-4bad-af15-f9893c4da7dc"
523
- },
524
- "id": "Ev9PEUdtpEAx",
525
- "execution_count": 5,
526
- "outputs": [
527
- {
528
- "output_type": "stream",
529
- "name": "stdout",
530
- "text": [
531
- "nvcc: NVIDIA (R) Cuda compiler driver\n",
532
- "Copyright (c) 2005-2025 NVIDIA Corporation\n",
533
- "Built on Fri_Feb_21_20:23:50_PST_2025\n",
534
- "Cuda compilation tools, release 12.8, V12.8.93\n",
535
- "Build cuda_12.8.r12.8/compiler.35583870_0\n",
536
- "2.10.0+cu128\n",
537
- "12.8\n"
538
- ]
539
- }
540
- ]
541
- },
542
- {
543
- "cell_type": "code",
544
- "execution_count": 6,
545
- "id": "b8690389",
546
- "metadata": {
547
- "execution": {
548
- "iopub.execute_input": "2026-01-10T18:22:43.739411Z",
549
- "iopub.status.busy": "2026-01-10T18:22:43.738855Z",
550
- "iopub.status.idle": "2026-01-10T18:22:43.755664Z",
551
- "shell.execute_reply": "2026-01-10T18:22:43.754865Z"
552
- },
553
- "papermill": {
554
- "duration": 0.027297,
555
- "end_time": "2026-01-10T18:22:43.756758",
556
- "exception": false,
557
- "start_time": "2026-01-10T18:22:43.729461",
558
- "status": "completed"
559
- },
560
- "tags": [],
561
- "id": "b8690389"
562
- },
563
- "outputs": [],
564
- "source": [
565
- "import os\n",
566
- "import glob\n",
567
- "import cv2\n",
568
- "import numpy as np\n",
569
- "from PIL import Image\n",
570
- "\n",
571
- "# =========================================================\n",
572
- "# Utility: aspect ratio preserved + black padding\n",
573
- "# =========================================================\n",
574
- "\n",
575
- "def normalize_image_sizes_biplet(input_dir, output_dir=None, size=1024, max_images=None):\n",
576
- " \"\"\"\n",
577
- " Generates two square crops (Left & Right or Top & Bottom)\n",
578
- " from each image in a directory and returns the output directory\n",
579
- " and the list of generated file paths.\n",
580
- "\n",
581
- " Args:\n",
582
- " input_dir: Input directory containing source images\n",
583
- " output_dir: Output directory for processed images\n",
584
- " size: Target square size (default: 1024)\n",
585
- " max_images: Maximum number of SOURCE images to process (default: None = all images)\n",
586
- " \"\"\"\n",
587
- " if output_dir is None:\n",
588
- " output_dir = 'output/images_biplet'\n",
589
- " os.makedirs(output_dir, exist_ok=True)\n",
590
- "\n",
591
- " print(f\"--- Step 1: Biplet-Square Normalization ---\")\n",
592
- " print(f\"Generating 2 cropped squares (Left/Right or Top/Bottom) for each image...\")\n",
593
- " print()\n",
594
- "\n",
595
- " generated_paths = []\n",
596
- " converted_count = 0\n",
597
- " size_stats = {}\n",
598
- "\n",
599
- " # Sort for consistent processing order\n",
600
- " image_files = sorted([f for f in os.listdir(input_dir)\n",
601
- " if f.lower().endswith(('.jpg', '.jpeg', '.png'))])\n",
602
- "\n",
603
- " # ★ max_images で元画像数を制限\n",
604
- " if max_images is not None:\n",
605
- " image_files = image_files[:max_images]\n",
606
- " print(f\"Processing limited to {max_images} source images (will generate {max_images * 2} cropped images)\")\n",
607
- "\n",
608
- " for img_file in image_files:\n",
609
- " input_path = os.path.join(input_dir, img_file)\n",
610
- " try:\n",
611
- " img = Image.open(input_path)\n",
612
- " original_size = img.size\n",
613
- "\n",
614
- " # Tracking original aspect ratios\n",
615
- " size_key = f\"{original_size[0]}x{original_size[1]}\"\n",
616
- " size_stats[size_key] = size_stats.get(size_key, 0) + 1\n",
617
- "\n",
618
- " # Generate 2 crops using the helper function\n",
619
- " crops = generate_two_crops(img, size)\n",
620
- " base_name, ext = os.path.splitext(img_file)\n",
621
- "\n",
622
- " for mode, cropped_img in crops.items():\n",
623
- " output_path = os.path.join(output_dir, f\"{base_name}_{mode}{ext}\")\n",
624
- " cropped_img.save(output_path, quality=95)\n",
625
- " generated_paths.append(output_path)\n",
626
- "\n",
627
- " converted_count += 1\n",
628
- " print(f\" ✓ {img_file}: {original_size} → 2 square images generated\")\n",
629
- "\n",
630
- " except Exception as e:\n",
631
- " print(f\" ✗ Error processing {img_file}: {e}\")\n",
632
- "\n",
633
- " print(f\"\\nProcessing complete: {converted_count} source images processed\")\n",
634
- " print(f\"Total output images: {len(generated_paths)}\")\n",
635
- " print(f\"Original size distribution: {size_stats}\")\n",
636
- "\n",
637
- " return output_dir, generated_paths\n",
638
- "\n",
639
- "\n",
640
- "def generate_two_crops(img, size):\n",
641
- " \"\"\"\n",
642
- " Crops the image into a square and returns 2 variations\n",
643
- " (Left/Right for landscape, Top/Bottom for portrait).\n",
644
- " \"\"\"\n",
645
- " width, height = img.size\n",
646
- " crop_size = min(width, height)\n",
647
- " crops = {}\n",
648
- "\n",
649
- " if width > height:\n",
650
- " # Landscape → Left & Right\n",
651
- " positions = {\n",
652
- " 'left': 0,\n",
653
- " 'right': width - crop_size\n",
654
- " }\n",
655
- " for mode, x_offset in positions.items():\n",
656
- " box = (x_offset, 0, x_offset + crop_size, crop_size)\n",
657
- " crops[mode] = img.crop(box).resize(\n",
658
- " (size, size),\n",
659
- " Image.Resampling.LANCZOS\n",
660
- " )\n",
661
- "\n",
662
- " else:\n",
663
- " # Portrait or Square → Top & Bottom\n",
664
- " positions = {\n",
665
- " 'top': 0,\n",
666
- " 'bottom': height - crop_size\n",
667
- " }\n",
668
- " for mode, y_offset in positions.items():\n",
669
- " box = (0, y_offset, crop_size, y_offset + crop_size)\n",
670
- " crops[mode] = img.crop(box).resize(\n",
671
- " (size, size),\n",
672
- " Image.Resampling.LANCZOS\n",
673
- " )\n",
674
- "\n",
675
- " return crops\n"
676
- ]
677
- },
678
- {
679
- "cell_type": "code",
680
- "execution_count": 7,
681
- "id": "7acc20b6",
682
- "metadata": {
683
- "execution": {
684
- "iopub.execute_input": "2026-01-10T18:22:43.772525Z",
685
- "iopub.status.busy": "2026-01-10T18:22:43.772303Z",
686
- "iopub.status.idle": "2026-01-10T18:22:43.790574Z",
687
- "shell.execute_reply": "2026-01-10T18:22:43.789515Z"
688
- },
689
- "papermill": {
690
- "duration": 0.027612,
691
- "end_time": "2026-01-10T18:22:43.791681",
692
- "exception": false,
693
- "start_time": "2026-01-10T18:22:43.764069",
694
- "status": "completed"
695
- },
696
- "tags": [],
697
- "id": "7acc20b6"
698
- },
699
- "outputs": [],
700
- "source": [
701
- "def run_colmap_reconstruction(image_dir, colmap_dir):\n",
702
- " \"\"\"Estimate camera poses and 3D point cloud with COLMAP\"\"\"\n",
703
- " print(\"Running SfM reconstruction with COLMAP...\")\n",
704
- "\n",
705
- " database_path = os.path.join(colmap_dir, \"database.db\")\n",
706
- " sparse_dir = os.path.join(colmap_dir, \"sparse\")\n",
707
- " os.makedirs(sparse_dir, exist_ok=True)\n",
708
- "\n",
709
- " # Set environment variable\n",
710
- " env = os.environ.copy()\n",
711
- " env['QT_QPA_PLATFORM'] = 'offscreen'\n",
712
- "\n",
713
- " # Feature extraction\n",
714
- " print(\"1/4: Extracting features...\")\n",
715
- " subprocess.run([\n",
716
- " 'colmap', 'feature_extractor',\n",
717
- " '--database_path', database_path,\n",
718
- " '--image_path', image_dir,\n",
719
- " '--ImageReader.single_camera', '1',\n",
720
- " '--ImageReader.camera_model', 'OPENCV',\n",
721
- " '--SiftExtraction.use_gpu', '0' # Use CPU\n",
722
- " ], check=True, env=env)\n",
723
- "\n",
724
- " # Feature matching\n",
725
- " print(\"2/4: Matching features...\")\n",
726
- " subprocess.run([\n",
727
- " 'colmap', 'exhaustive_matcher', # Use sequential_matcher instead of exhaustive_matcher\n",
728
- " '--database_path', database_path,\n",
729
- " '--SiftMatching.use_gpu', '0' # Use CPU\n",
730
- " ], check=True, env=env)\n",
731
- "\n",
732
- " # Sparse reconstruction\n",
733
- " print(\"3/4: Sparse reconstruction...\")\n",
734
- " subprocess.run([\n",
735
- " 'colmap', 'mapper',\n",
736
- " '--database_path', database_path,\n",
737
- " '--image_path', image_dir,\n",
738
- " '--output_path', sparse_dir,\n",
739
- " '--Mapper.ba_global_max_num_iterations', '20', # Speed up\n",
740
- " '--Mapper.ba_local_max_num_iterations', '10'\n",
741
- " ], check=True, env=env)\n",
742
- "\n",
743
- " # Export to text format\n",
744
- " print(\"4/4: Exporting to text format...\")\n",
745
- " model_dir = os.path.join(sparse_dir, '0')\n",
746
- " if not os.path.exists(model_dir):\n",
747
- " # Use the first model found\n",
748
- " subdirs = [d for d in os.listdir(sparse_dir) if os.path.isdir(os.path.join(sparse_dir, d))]\n",
749
- " if subdirs:\n",
750
- " model_dir = os.path.join(sparse_dir, subdirs[0])\n",
751
- " else:\n",
752
- " raise FileNotFoundError(\"COLMAP reconstruction failed\")\n",
753
- "\n",
754
- " subprocess.run([\n",
755
- " 'colmap', 'model_converter',\n",
756
- " '--input_path', model_dir,\n",
757
- " '--output_path', model_dir,\n",
758
- " '--output_type', 'TXT'\n",
759
- " ], check=True, env=env)\n",
760
- "\n",
761
- " print(f\"COLMAP reconstruction complete: {model_dir}\")\n",
762
- " return model_dir\n",
763
- "\n",
764
- "\n",
765
- "def convert_cameras_to_pinhole(input_file, output_file):\n",
766
- " \"\"\"Convert camera model to PINHOLE format\"\"\"\n",
767
- " print(f\"Reading camera file: {input_file}\")\n",
768
- "\n",
769
- " with open(input_file, 'r') as f:\n",
770
- " lines = f.readlines()\n",
771
- "\n",
772
- " converted_count = 0\n",
773
- " with open(output_file, 'w') as f:\n",
774
- " for line in lines:\n",
775
- " if line.startswith('#') or line.strip() == '':\n",
776
- " f.write(line)\n",
777
- " else:\n",
778
- " parts = line.strip().split()\n",
779
- " if len(parts) >= 4:\n",
780
- " cam_id = parts[0]\n",
781
- " model = parts[1]\n",
782
- " width = parts[2]\n",
783
- " height = parts[3]\n",
784
- " params = parts[4:]\n",
785
- "\n",
786
- " # Convert to PINHOLE format\n",
787
- " if model == \"PINHOLE\":\n",
788
- " f.write(line)\n",
789
- " elif model == \"OPENCV\":\n",
790
- " # OPENCV: fx, fy, cx, cy, k1, k2, p1, p2\n",
791
- " fx = params[0]\n",
792
- " fy = params[1]\n",
793
- " cx = params[2]\n",
794
- " cy = params[3]\n",
795
- " f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
796
- " converted_count += 1\n",
797
- " else:\n",
798
- " # Convert other models too\n",
799
- " fx = fy = max(float(width), float(height))\n",
800
- " cx = float(width) / 2\n",
801
- " cy = float(height) / 2\n",
802
- " f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
803
- " converted_count += 1\n",
804
- " else:\n",
805
- " f.write(line)\n",
806
- "\n",
807
- " print(f\"Converted {converted_count} cameras to PINHOLE format\")\n",
808
- "\n",
809
- "\n",
810
- "def prepare_gaussian_splatting_data(image_dir, colmap_model_dir):\n",
811
- " \"\"\"Prepare data for Gaussian Splatting\"\"\"\n",
812
- " print(\"Preparing data for Gaussian Splatting...\")\n",
813
- "\n",
814
- " data_dir = f\"{WORK_DIR}/data/video\"\n",
815
- " os.makedirs(f\"{data_dir}/sparse/0\", exist_ok=True)\n",
816
- " os.makedirs(f\"{data_dir}/images\", exist_ok=True)\n",
817
- "\n",
818
- " # Copy images\n",
819
- " print(\"Copying images...\")\n",
820
- " img_count = 0\n",
821
- " for img_file in os.listdir(image_dir):\n",
822
- " if img_file.lower().endswith(('.jpg', '.jpeg', '.png')):\n",
823
- " shutil.copy(\n",
824
- " os.path.join(image_dir, img_file),\n",
825
- " f\"{data_dir}/images/{img_file}\"\n",
826
- " )\n",
827
- " img_count += 1\n",
828
- " print(f\"Copied {img_count} images\")\n",
829
- "\n",
830
- " # Convert and copy camera file to PINHOLE format\n",
831
- " print(\"Converting camera model to PINHOLE format...\")\n",
832
- " convert_cameras_to_pinhole(\n",
833
- " os.path.join(colmap_model_dir, 'cameras.txt'),\n",
834
- " f\"{data_dir}/sparse/0/cameras.txt\"\n",
835
- " )\n",
836
- "\n",
837
- " # Copy other files\n",
838
- " for filename in ['images.txt', 'points3D.txt']:\n",
839
- " src = os.path.join(colmap_model_dir, filename)\n",
840
- " dst = f\"{data_dir}/sparse/0/{filename}\"\n",
841
- " if os.path.exists(src):\n",
842
- " shutil.copy(src, dst)\n",
843
- " print(f\"Copied {filename}\")\n",
844
- " else:\n",
845
- " print(f\"Warning: {filename} not found\")\n",
846
- "\n",
847
- " print(f\"Data preparation complete: {data_dir}\")\n",
848
- " return data_dir\n",
849
- "\n",
850
- "def run_colmap_reconstruction(image_dir, colmap_dir):\n",
851
- " \"\"\"Estimate camera poses and 3D point cloud with COLMAP\"\"\"\n",
852
- " print(\"Running SfM reconstruction with COLMAP...\")\n",
853
- "\n",
854
- " database_path = os.path.join(colmap_dir, \"database.db\")\n",
855
- " sparse_dir = os.path.join(colmap_dir, \"sparse\")\n",
856
- " os.makedirs(sparse_dir, exist_ok=True)\n",
857
- "\n",
858
- " # Set environment variable\n",
859
- " env = os.environ.copy()\n",
860
- " env['QT_QPA_PLATFORM'] = 'offscreen'\n",
861
- "\n",
862
- " # Feature extraction\n",
863
- " print(\"1/4: Extracting features...\")\n",
864
- " subprocess.run([\n",
865
- " 'colmap', 'feature_extractor',\n",
866
- " '--database_path', database_path,\n",
867
- " '--image_path', image_dir,\n",
868
- " '--ImageReader.single_camera', '1',\n",
869
- " '--ImageReader.camera_model', 'OPENCV',\n",
870
- " '--SiftExtraction.use_gpu', '0' # Use CPU\n",
871
- " ], check=True, env=env)\n",
872
- "\n",
873
- " # Feature matching\n",
874
- " print(\"2/4: Matching features...\")\n",
875
- " subprocess.run([\n",
876
- " 'colmap', 'exhaustive_matcher', # Use sequential_matcher instead of exhaustive_matcher\n",
877
- " '--database_path', database_path,\n",
878
- " '--SiftMatching.use_gpu', '0' # Use CPU\n",
879
- " ], check=True, env=env)\n",
880
- "\n",
881
- " # Sparse reconstruction\n",
882
- " print(\"3/4: Sparse reconstruction...\")\n",
883
- " subprocess.run([\n",
884
- " 'colmap', 'mapper',\n",
885
- " '--database_path', database_path,\n",
886
- " '--image_path', image_dir,\n",
887
- " '--output_path', sparse_dir,\n",
888
- " '--Mapper.ba_global_max_num_iterations', '20', # Speed up\n",
889
- " '--Mapper.ba_local_max_num_iterations', '10'\n",
890
- " ], check=True, env=env)\n",
891
- "\n",
892
- " # Export to text format\n",
893
- " print(\"4/4: Exporting to text format...\")\n",
894
- " model_dir = os.path.join(sparse_dir, '0')\n",
895
- " if not os.path.exists(model_dir):\n",
896
- " # Use the first model found\n",
897
- " subdirs = [d for d in os.listdir(sparse_dir) if os.path.isdir(os.path.join(sparse_dir, d))]\n",
898
- " if subdirs:\n",
899
- " model_dir = os.path.join(sparse_dir, subdirs[0])\n",
900
- " else:\n",
901
- " raise FileNotFoundError(\"COLMAP reconstruction failed\")\n",
902
- "\n",
903
- " subprocess.run([\n",
904
- " 'colmap', 'model_converter',\n",
905
- " '--input_path', model_dir,\n",
906
- " '--output_path', model_dir,\n",
907
- " '--output_type', 'TXT'\n",
908
- " ], check=True, env=env)\n",
909
- "\n",
910
- " print(f\"COLMAP reconstruction complete: {model_dir}\")\n",
911
- " return model_dir\n",
912
- "\n",
913
- "\n",
914
- "def convert_cameras_to_pinhole(input_file, output_file):\n",
915
- " \"\"\"Convert camera model to PINHOLE format\"\"\"\n",
916
- " print(f\"Reading camera file: {input_file}\")\n",
917
- "\n",
918
- " with open(input_file, 'r') as f:\n",
919
- " lines = f.readlines()\n",
920
- "\n",
921
- " converted_count = 0\n",
922
- " with open(output_file, 'w') as f:\n",
923
- " for line in lines:\n",
924
- " if line.startswith('#') or line.strip() == '':\n",
925
- " f.write(line)\n",
926
- " else:\n",
927
- " parts = line.strip().split()\n",
928
- " if len(parts) >= 4:\n",
929
- " cam_id = parts[0]\n",
930
- " model = parts[1]\n",
931
- " width = parts[2]\n",
932
- " height = parts[3]\n",
933
- " params = parts[4:]\n",
934
- "\n",
935
- " # Convert to PINHOLE format\n",
936
- " if model == \"PINHOLE\":\n",
937
- " f.write(line)\n",
938
- " elif model == \"OPENCV\":\n",
939
- " # OPENCV: fx, fy, cx, cy, k1, k2, p1, p2\n",
940
- " fx = params[0]\n",
941
- " fy = params[1]\n",
942
- " cx = params[2]\n",
943
- " cy = params[3]\n",
944
- " f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
945
- " converted_count += 1\n",
946
- " else:\n",
947
- " # Convert other models too\n",
948
- " fx = fy = max(float(width), float(height))\n",
949
- " cx = float(width) / 2\n",
950
- " cy = float(height) / 2\n",
951
- " f.write(f\"{cam_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\\n\")\n",
952
- " converted_count += 1\n",
953
- " else:\n",
954
- " f.write(line)\n",
955
- "\n",
956
- " print(f\"Converted {converted_count} cameras to PINHOLE format\")\n",
957
- "\n",
958
- "\n",
959
- "def prepare_gaussian_splatting_data(image_dir, colmap_model_dir):\n",
960
- " \"\"\"Prepare data for Gaussian Splatting\"\"\"\n",
961
- " print(\"Preparing data for Gaussian Splatting...\")\n",
962
- "\n",
963
- " data_dir = f\"{WORK_DIR}/data/video\"\n",
964
- " os.makedirs(f\"{data_dir}/sparse/0\", exist_ok=True)\n",
965
- " os.makedirs(f\"{data_dir}/images\", exist_ok=True)\n",
966
- "\n",
967
- " # Copy images\n",
968
- " print(\"Copying images...\")\n",
969
- " img_count = 0\n",
970
- " for img_file in os.listdir(image_dir):\n",
971
- " if img_file.lower().endswith(('.jpg', '.jpeg', '.png')):\n",
972
- " shutil.copy(\n",
973
- " os.path.join(image_dir, img_file),\n",
974
- " f\"{data_dir}/images/{img_file}\"\n",
975
- " )\n",
976
- " img_count += 1\n",
977
- " print(f\"Copied {img_count} images\")\n",
978
- "\n",
979
- " # Convert and copy camera file to PINHOLE format\n",
980
- " print(\"Converting camera model to PINHOLE format...\")\n",
981
- " convert_cameras_to_pinhole(\n",
982
- " os.path.join(colmap_model_dir, 'cameras.txt'),\n",
983
- " f\"{data_dir}/sparse/0/cameras.txt\"\n",
984
- " )\n",
985
- "\n",
986
- " # Copy other files\n",
987
- " for filename in ['images.txt', 'points3D.txt']:\n",
988
- " src = os.path.join(colmap_model_dir, filename)\n",
989
- " dst = f\"{data_dir}/sparse/0/{filename}\"\n",
990
- " if os.path.exists(src):\n",
991
- " shutil.copy(src, dst)\n",
992
- " print(f\"Copied {filename}\")\n",
993
- " else:\n",
994
- " print(f\"Warning: {filename} not found\")\n",
995
- "\n",
996
- " print(f\"Data preparation complete: {data_dir}\")\n",
997
- " return data_dir\n",
998
- "\n",
999
- "\n",
1000
- "\n",
1001
- "###############################################################\n",
1002
- "\n",
1003
- "# 変更後 (2DGS) - 正則化パラメータを追加\n",
1004
- "def train_gaussian_splatting(data_dir, iterations=7000,\n",
1005
- " lambda_normal=0.05,\n",
1006
- " lambda_distortion=0,\n",
1007
- " depth_ratio=0):\n",
1008
- " \"\"\"\n",
1009
- " 2DGS用のトレーニング関数\n",
1010
- "\n",
1011
- " Args:\n",
1012
- " lambda_normal: 法線一貫性の重み (デフォルト: 0.05)\n",
1013
- " lambda_distortion: 深度歪みの重み (デフォルト: 0)\n",
1014
- " depth_ratio: 0=平均深度, 1=中央値深度 (デフォルト: 0)\n",
1015
- " \"\"\"\n",
1016
- " model_path = f\"{WORK_DIR}/output/video\"\n",
1017
- " cmd = [\n",
1018
- " sys.executable, 'train.py',\n",
1019
- " '-s', data_dir,\n",
1020
- " '-m', model_path,\n",
1021
- " '--iterations', str(iterations),\n",
1022
- " '--lambda_normal', str(lambda_normal),\n",
1023
- " '--lambda_distortion', str(lambda_distortion),\n",
1024
- " '--depth_ratio', str(depth_ratio),\n",
1025
- " '--eval'\n",
1026
- " ]\n",
1027
- " subprocess.run(cmd, cwd=WORK_DIR, check=True)\n",
1028
- " return model_path\n",
1029
- "\n",
1030
- "\n",
1031
- "\n",
1032
- "# 2DGSではメッシュ抽出オプションが追加されています\n",
1033
- "def render_video_and_mesh(model_path, output_video_path, iteration=7000,\n",
1034
- " extract_mesh=True, unbounded=False, mesh_res=1024):\n",
1035
- " \"\"\"\n",
1036
- " 2DGS用のレンダリングとメッシュ抽出\n",
1037
- "\n",
1038
- " Args:\n",
1039
- " extract_mesh: メッシュを抽出するか\n",
1040
- " unbounded: 境界なしメッシュ抽出を使用するか\n",
1041
- " mesh_res: メッシュ解像度\n",
1042
- " \"\"\"\n",
1043
- " # 通常のレンダリング\n",
1044
- " cmd = [\n",
1045
- " sys.executable, 'render.py',\n",
1046
- " '-m', model_path,\n",
1047
- " '--iteration', str(iteration)\n",
1048
- " ]\n",
1049
- "\n",
1050
- " # メッシュ抽出オプション追加\n",
1051
- " if extract_mesh:\n",
1052
- " if unbounded:\n",
1053
- " cmd.extend(['--unbounded', '--mesh_res', str(mesh_res)])\n",
1054
- " cmd.extend(['--skip_test', '--skip_train'])\n",
1055
- "\n",
1056
- " subprocess.run(cmd, cwd=WORK_DIR, check=True)\n",
1057
- "\n",
1058
- " # Find the rendering directory\n",
1059
- " possible_dirs = [\n",
1060
- " f\"{model_path}/test/ours_{iteration}/renders\",\n",
1061
- " f\"{model_path}/train/ours_{iteration}/renders\",\n",
1062
- " ]\n",
1063
- "\n",
1064
- " render_dir = None\n",
1065
- " for test_dir in possible_dirs:\n",
1066
- " if os.path.exists(test_dir):\n",
1067
- " render_dir = test_dir\n",
1068
- " print(f\"Rendering directory found: {render_dir}\")\n",
1069
- " break\n",
1070
- "\n",
1071
- " if render_dir and os.path.exists(render_dir):\n",
1072
- " render_imgs = sorted([f for f in os.listdir(render_dir) if f.endswith('.png')])\n",
1073
- "\n",
1074
- " if render_imgs:\n",
1075
- " print(f\"Found {len(render_imgs)} rendered images\")\n",
1076
- "\n",
1077
- " # Create video with ffmpeg\n",
1078
- " subprocess.run([\n",
1079
- " 'ffmpeg', '-y',\n",
1080
- " '-framerate', '30',\n",
1081
- " '-pattern_type', 'glob',\n",
1082
- " '-i', f\"{render_dir}/*.png\",\n",
1083
- " '-c:v', 'libx264',\n",
1084
- " '-pix_fmt', 'yuv420p',\n",
1085
- " '-crf', '18',\n",
1086
- " output_video_path\n",
1087
- " ], check=True)\n",
1088
- "\n",
1089
- " print(f\"Video saved: {output_video_path}\")\n",
1090
- " return True\n",
1091
- "\n",
1092
- " print(\"Error: Rendering directory not found\")\n",
1093
- " return False\n",
1094
- "\n",
1095
- "###############################################################\n",
1096
- "\n",
1097
- "\n",
1098
- "def create_gif(video_path, gif_path):\n",
1099
- " \"\"\"Create GIF from MP4\"\"\"\n",
1100
- " print(\"Creating animated GIF...\")\n",
1101
- "\n",
1102
- " subprocess.run([\n",
1103
- " 'ffmpeg', '-y',\n",
1104
- " '-i', video_path,\n",
1105
- " '-vf', 'setpts=8*PTS,fps=10,scale=720:-1:flags=lanczos',\n",
1106
- " '-loop', '0',\n",
1107
- " gif_path\n",
1108
- " ], check=True)\n",
1109
- "\n",
1110
- " if os.path.exists(gif_path):\n",
1111
- " size_mb = os.path.getsize(gif_path) / (1024 * 1024)\n",
1112
- " print(f\"GIF creation complete: {gif_path} ({size_mb:.2f} MB)\")\n",
1113
- " return True\n",
1114
- "\n",
1115
- " return False"
1116
- ]
1117
- },
1118
- {
1119
- "cell_type": "code",
1120
- "source": [],
1121
- "metadata": {
1122
- "id": "YtqhBP4T3jEH"
1123
- },
1124
- "id": "YtqhBP4T3jEH",
1125
- "execution_count": 7,
1126
- "outputs": []
1127
- },
1128
- {
1129
- "cell_type": "code",
1130
- "source": [
1131
- "def main_pipeline(image_dir, output_dir, square_size=1024, max_images=100):\n",
1132
- " \"\"\"Main execution function\"\"\"\n",
1133
- " try:\n",
1134
- " # Step 1: 画像の正規化と前処理\n",
1135
- " print(\"=\"*60)\n",
1136
- " print(\"Step 1: Normalizing and preprocessing images\")\n",
1137
- " print(\"=\"*60)\n",
1138
- "\n",
1139
- " frame_dir = os.path.join(COLMAP_DIR, \"images\")\n",
1140
- " os.makedirs(frame_dir, exist_ok=True)\n",
1141
- "\n",
1142
- " # 画像を正規化して直接COLMAPのディレクトリに保存\n",
1143
- " num_processed = normalize_image_sizes_biplet(\n",
1144
- " input_dir=image_dir,\n",
1145
- " output_dir=frame_dir, # 直接colmap/imagesに保存\n",
1146
- " size=square_size,\n",
1147
- " max_images=max_images\n",
1148
- " )\n",
1149
- "\n",
1150
- " print(f\"Processed {num_processed} images\")\n",
1151
- "\n",
1152
- " # Step 2: Estimate Camera Info with COLMAP\n",
1153
- " print(\"=\"*60)\n",
1154
- " print(\"Step 2: Running COLMAP reconstruction\")\n",
1155
- " print(\"=\"*60)\n",
1156
- " colmap_model_dir = run_colmap_reconstruction(frame_dir, COLMAP_DIR)\n",
1157
- "\n",
1158
- " # Step 3: Prepare Data for Gaussian Splatting\n",
1159
- " print(\"=\"*60)\n",
1160
- " print(\"Step 3: Preparing Gaussian Splatting data\")\n",
1161
- " print(\"=\"*60)\n",
1162
- " data_dir = prepare_gaussian_splatting_data(frame_dir, colmap_model_dir)\n",
1163
- "\n",
1164
- " # Step 4: Train Model\n",
1165
- " print(\"=\"*60)\n",
1166
- " print(\"Step 4: Training Gaussian Splatting model\")\n",
1167
- " print(\"=\"*60)\n",
1168
- " # 修正: frame_dir → data_dir\n",
1169
- " model_path = train_gaussian_splatting(\n",
1170
- " data_dir, # ← ここを修正！\n",
1171
- " iterations=1000,\n",
1172
- " lambda_normal=0.05,\n",
1173
- " lambda_distortion=0,\n",
1174
- " depth_ratio=0\n",
1175
- " )\n",
1176
- "\n",
1177
- " print(f\"Model trained at: {model_path}\")\n",
1178
- "\n",
1179
- " # Step 5: Render Video\n",
1180
- " print(\"=\"*60)\n",
1181
- " print(\"Step 5: Rendering video\")\n",
1182
- " print(\"=\"*60)\n",
1183
- " os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
1184
- " output_video = os.path.join(OUTPUT_DIR, \"gaussian_splatting_video.mp4\")\n",
1185
- "\n",
1186
- " # 修正: output_video_path → output_video\n",
1187
- " success = render_video_and_mesh(\n",
1188
- " model_path,\n",
1189
- " output_video, # ← ここを修正！\n",
1190
- " iteration=1000,\n",
1191
- " extract_mesh=True, # メッシュ抽出を有効化\n",
1192
- " unbounded=True, # 境界なしメッシュ（推奨）\n",
1193
- " mesh_res=1024\n",
1194
- " )\n",
1195
- "\n",
1196
- " if success:\n",
1197
- " print(\"=\"*60)\n",
1198
- " print(f\"Success! Video generation complete: {output_video}\")\n",
1199
- " print(\"=\"*60)\n",
1200
- "\n",
1201
- " # Create GIF\n",
1202
- " output_gif = os.path.join(OUTPUT_DIR, \"gaussian_splatting_video.gif\")\n",
1203
- " create_gif(output_video, output_gif)\n",
1204
- "\n",
1205
- " # Display result\n",
1206
- " from IPython.display import Image, display\n",
1207
- " display(Image(open(output_gif, 'rb').read()))\n",
1208
- "\n",
1209
- " return output_video, output_gif\n",
1210
- " else:\n",
1211
- " print(\"Warning: Rendering complete, but video was not generated\")\n",
1212
- " return None, None\n",
1213
- "\n",
1214
- " except Exception as e:\n",
1215
- " print(f\"Error: {str(e)}\")\n",
1216
- " import traceback\n",
1217
- " traceback.print_exc()\n",
1218
- " return None, None\n",
1219
- "\n",
1220
- "\n",
1221
- "if __name__ == \"__main__\":\n",
1222
- " IMAGE_DIR = \"/content/drive/MyDrive/your_folder/fountain100\"\n",
1223
- " OUTPUT_DIR = \"/content/output\"\n",
1224
- " COLMAP_DIR = \"/content/colmap_workspace\"\n",
1225
- "\n",
1226
- " video_path, gif_path = main_pipeline(\n",
1227
- " image_dir=IMAGE_DIR,\n",
1228
- " output_dir=OUTPUT_DIR,\n",
1229
- " square_size=1024,\n",
1230
- " max_images=20\n",
1231
- " )\n",
1232
- "\n",
1233
- " if video_path:\n",
1234
- " print(f\"\\n✅ Success!\")\n",
1235
- " print(f\"Video: {video_path}\")\n",
1236
- " print(f\"GIF: {gif_path}\")\n",
1237
- " else:\n",
1238
- " print(\"\\n❌ Pipeline failed\")"
1239
- ],
1240
- "metadata": {
1241
- "colab": {
1242
- "base_uri": "https://localhost:8080/",
1243
- "height": 1000
1244
- },
1245
- "id": "fya3kv62NXM-",
1246
- "outputId": "4c60e0b2-e9fc-49df-d216-7d96fce737ff"
1247
- },
1248
- "id": "fya3kv62NXM-",
1249
- "execution_count": 8,
1250
- "outputs": [
1251
- {
1252
- "output_type": "stream",
1253
- "name": "stdout",
1254
- "text": [
1255
- "============================================================\n",
1256
- "Step 1: Normalizing and preprocessing images\n",
1257
- "============================================================\n",
1258
- "--- Step 1: Biplet-Square Normalization ---\n",
1259
- "Generating 2 cropped squares (Left/Right or Top/Bottom) for each image...\n",
1260
- "\n",
1261
- "Processing limited to 20 source images (will generate 40 cropped images)\n",
1262
- " ✓ image_101.jpeg: (1440, 1920) → 2 square images generated\n",
1263
- " ✓ image_102.jpeg: (1440, 1920) → 2 square images generated\n",
1264
- " ✓ image_103.jpeg: (1440, 1920) → 2 square images generated\n",
1265
- " ✓ image_104.jpeg: (1440, 1920) → 2 square images generated\n",
1266
- " ✓ image_105.jpeg: (1440, 1920) → 2 square images generated\n",
1267
- " ✓ image_106.jpeg: (1440, 1920) → 2 square images generated\n",
1268
- " ✓ image_107.jpeg: (1440, 1920) → 2 square images generated\n",
1269
- " ✓ image_108.jpeg: (1440, 1920) → 2 square images generated\n",
1270
- " ✓ image_109.jpeg: (1440, 1920) → 2 square images generated\n",
1271
- " ✓ image_110.jpeg: (1440, 1920) → 2 square images generated\n",
1272
- " ✓ image_111.jpeg: (1440, 1920) → 2 square images generated\n",
1273
- " ✓ image_112.jpeg: (1440, 1920) → 2 square images generated\n",
1274
- " ✓ image_113.jpeg: (1440, 1920) → 2 square images generated\n",
1275
- " ✓ image_114.jpeg: (1440, 1920) → 2 square images generated\n",
1276
- " ✓ image_115.jpeg: (1440, 1920) → 2 square images generated\n",
1277
- " ✓ image_116.jpeg: (1440, 1920) → 2 square images generated\n",
1278
- " ✓ image_117.jpeg: (1440, 1920) → 2 square images generated\n",
1279
- " ✓ image_118.jpeg: (1440, 1920) → 2 square images generated\n",
1280
- " ✓ image_119.jpeg: (1440, 1920) → 2 square images generated\n",
1281
- " ✓ image_120.jpeg: (1440, 1920) → 2 square images generated\n",
1282
- "\n",
1283
- "Processing complete: 20 source images processed\n",
1284
- "Total output images: 40\n",
1285
- "Original size distribution: {'1440x1920': 20}\n",
1286
- "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",
1287
- "============================================================\n",
1288
- "Step 2: Running COLMAP reconstruction\n",
1289
- "============================================================\n",
1290
- "Running SfM reconstruction with COLMAP...\n",
1291
- "1/4: Extracting features...\n"
1292
- ]
1293
- },
1294
- {
1295
- "output_type": "error",
1296
- "ename": "KeyboardInterrupt",
1297
- "evalue": "",
1298
- "traceback": [
1299
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1300
- "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
1301
- "\u001b[0;32m/tmp/ipython-input-3712511575.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0mCOLMAP_DIR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/colmap_workspace\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m video_path, gif_path = main_pipeline(\n\u001b[0m\u001b[1;32m 97\u001b[0m \u001b[0mimage_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mIMAGE_DIR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0moutput_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mOUTPUT_DIR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1302
- "\u001b[0;32m/tmp/ipython-input-3712511575.py\u001b[0m in \u001b[0;36mmain_pipeline\u001b[0;34m(image_dir, output_dir, square_size, max_images)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Step 2: Running COLMAP reconstruction\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"=\"\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mcolmap_model_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_colmap_reconstruction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCOLMAP_DIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;31m# Step 3: Prepare Data for Gaussian Splatting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1303
- "\u001b[0;32m/tmp/ipython-input-682645374.py\u001b[0m in \u001b[0;36mrun_colmap_reconstruction\u001b[0;34m(image_dir, colmap_dir)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0;31m# Feature extraction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1/4: Extracting features...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m subprocess.run([\n\u001b[0m\u001b[1;32m 165\u001b[0m \u001b[0;34m'colmap'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'feature_extractor'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;34m'--database_path'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatabase_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1304
- "\u001b[0;32m/usr/lib/python3.12/subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mPopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTimeoutExpired\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1305
- "\u001b[0;32m/usr/lib/python3.12/subprocess.py\u001b[0m in \u001b[0;36mcommunicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m 1199\u001b[0m \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1200\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1201\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1202\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1203\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1306
- "\u001b[0;32m/usr/lib/python3.12/subprocess.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1262\u001b[0m \u001b[0mendtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1263\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1264\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1265\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1266\u001b[0m \u001b[0;31m# https://bugs.python.org/issue25942\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1307
- "\u001b[0;32m/usr/lib/python3.12/subprocess.py\u001b[0m in \u001b[0;36m_wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 2051\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2052\u001b[0m \u001b[0;32mbreak\u001b[0m \u001b[0;31m# Another thread waited.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2053\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msts\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2054\u001b[0m \u001b[0;31m# Check the pid and loop as waitpid has been known to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2055\u001b[0m \u001b[0;31m# return 0 even without WNOHANG in odd situations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1308
- "\u001b[0;32m/usr/lib/python3.12/subprocess.py\u001b[0m in \u001b[0;36m_try_wait\u001b[0;34m(self, wait_flags)\u001b[0m\n\u001b[1;32m 2009\u001b[0m \u001b[0;34m\"\"\"All callers to this function MUST hold self._waitpid_lock.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2010\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2011\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msts\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait_flags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2012\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mChildProcessError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2013\u001b[0m \u001b[0;31m# This happens if SIGCLD is set to be ignored or waiting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1309
- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
1310
- ]
1311
- }
1312
- ]
1313
- },
1314
- {
1315
- "cell_type": "code",
1316
- "execution_count": null,
1317
- "id": "f75233a8",
1318
- "metadata": {
1319
- "execution": {
1320
- "iopub.execute_input": "2026-01-10T18:22:43.807508Z",
1321
- "iopub.status.busy": "2026-01-10T18:22:43.807294Z",
1322
- "iopub.status.idle": "2026-01-11T00:00:17.030890Z",
1323
- "shell.execute_reply": "2026-01-11T00:00:17.029927Z"
1324
- },
1325
- "papermill": {
1326
- "duration": 20253.434865,
1327
- "end_time": "2026-01-11T00:00:17.234174",
1328
- "exception": false,
1329
- "start_time": "2026-01-10T18:22:43.799309",
1330
- "status": "completed"
1331
- },
1332
- "tags": [],
1333
- "id": "f75233a8"
1334
- },
1335
- "outputs": [],
1336
- "source": [
1337
- "\n",
1338
- "\n"
1339
- ]
1340
- },
1341
- {
1342
- "cell_type": "markdown",
1343
- "id": "e17ec719",
1344
- "metadata": {
1345
- "papermill": {
1346
- "duration": 0.49801,
1347
- "end_time": "2026-01-11T00:00:18.165833",
1348
- "exception": false,
1349
- "start_time": "2026-01-11T00:00:17.667823",
1350
- "status": "completed"
1351
- },
1352
- "tags": [],
1353
- "id": "e17ec719"
1354
- },
1355
- "source": []
1356
- },
1357
- {
1358
- "cell_type": "markdown",
1359
- "id": "38b3974c",
1360
- "metadata": {
1361
- "papermill": {
1362
- "duration": 0.427583,
1363
- "end_time": "2026-01-11T00:00:19.008387",
1364
- "exception": false,
1365
- "start_time": "2026-01-11T00:00:18.580804",
1366
- "status": "completed"
1367
- },
1368
- "tags": [],
1369
- "id": "38b3974c"
1370
- },
1371
- "source": []
1372
- }
1373
- ],
1374
- "metadata": {
1375
- "kaggle": {
1376
- "accelerator": "nvidiaTeslaT4",
1377
- "dataSources": [
1378
- {
1379
- "databundleVersionId": 5447706,
1380
- "sourceId": 49349,
1381
- "sourceType": "competition"
1382
- },
1383
- {
1384
- "datasetId": 1429416,
1385
- "sourceId": 14451718,
1386
- "sourceType": "datasetVersion"
1387
- }
1388
- ],
1389
- "dockerImageVersionId": 31090,
1390
- "isGpuEnabled": true,
1391
- "isInternetEnabled": true,
1392
- "language": "python",
1393
- "sourceType": "notebook"
1394
- },
1395
- "kernelspec": {
1396
- "display_name": "Python 3",
1397
- "name": "python3"
1398
- },
1399
- "language_info": {
1400
- "codemirror_mode": {
1401
- "name": "ipython",
1402
- "version": 3
1403
- },
1404
- "file_extension": ".py",
1405
- "mimetype": "text/x-python",
1406
- "name": "python",
1407
- "nbconvert_exporter": "python",
1408
- "pygments_lexer": "ipython3",
1409
- "version": "3.11.13"
1410
- },
1411
- "papermill": {
1412
- "default_parameters": {},
1413
- "duration": 20573.990788,
1414
- "end_time": "2026-01-11T00:00:22.081506",
1415
- "environment_variables": {},
1416
- "exception": null,
1417
- "input_path": "__notebook__.ipynb",
1418
- "output_path": "__notebook__.ipynb",
1419
- "parameters": {},
1420
- "start_time": "2026-01-10T18:17:28.090718",
1421
- "version": "2.6.0"
1422
- },
1423
- "colab": {
1424
- "provenance": [],
1425
- "gpuType": "T4"
1426
- },
1427
- "accelerator": "GPU"
1428
- },
1429
- "nbformat": 4,
1430
- "nbformat_minor": 5
1431
- }