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- "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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- "outputs": [],
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- "execution_count": null
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "# =====================================================================\n",
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- "# biplet_dino_mast3r_ps2_gs_colab_01.ipynb\n",
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- "# ASMK を DINO に置き換えたバージョン\n",
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- "# =====================================================================\n",
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- "\n",
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- "# =====================================================================\n",
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- "# CELL 1: Install Dependencies\n",
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- "# =====================================================================\n",
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- "!pip install roma einops timm huggingface_hub\n",
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- "!pip install opencv-python pillow tqdm pyaml cython plyfile\n",
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- "!pip install pycolmap trimesh\n",
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- "!pip install transformers==4.40.0 # DINOに必要\n",
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- "!pip uninstall -y numpy scipy\n",
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- "!pip install numpy==1.26.4 scipy==1.11.4\n",
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- "break"
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- ],
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- "metadata": {
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- "trusted": true,
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- "id": "6C3QGJD8TKyC",
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- "colab": {
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- "base_uri": "https://localhost:8080/",
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- "height": 1000
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- },
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- "outputId": "b362f97d-fbc1-474f-f2cb-b84b565acdb9"
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- },
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- "outputs": [
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- {
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- "output_type": "stream",
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- "name": "stdout",
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- "text": [
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- "\u001b[?25hDownloading scipy-1.11.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (35.8 MB)\n",
214
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.8/35.8 MB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
215
- "\u001b[?25hInstalling collected packages: numpy, scipy\n",
216
- "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
217
- "mapclassify 2.10.0 requires scipy>=1.12, but you have scipy 1.11.4 which is incompatible.\n",
218
- "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n",
219
- "tsfresh 0.21.1 requires scipy>=1.14.0; python_version >= \"3.10\", but you have scipy 1.11.4 which is incompatible.\n",
220
- "pytensor 2.36.3 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n",
221
- "spopt 0.7.0 requires scipy>=1.12.0, but you have scipy 1.11.4 which is incompatible.\n",
222
- "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n",
223
- "opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n",
224
- "shap 0.50.0 requires numpy>=2, but you have numpy 1.26.4 which is incompatible.\n",
225
- "sentence-transformers 5.2.0 requires transformers<6.0.0,>=4.41.0, but you have transformers 4.40.0 which is incompatible.\n",
226
- "jax 0.7.2 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n",
227
- "jax 0.7.2 requires scipy>=1.13, but you have scipy 1.11.4 which is incompatible.\n",
228
- "libpysal 4.14.1 requires scipy>=1.12.0, but you have scipy 1.11.4 which is incompatible.\n",
229
- "rasterio 1.5.0 requires numpy>=2, but you have numpy 1.26.4 which is incompatible.\n",
230
- "access 1.1.10.post3 requires scipy>=1.14.1, but you have scipy 1.11.4 which is incompatible.\n",
231
- "tobler 0.13.0 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n",
232
- "tobler 0.13.0 requires scipy>=1.13, but you have scipy 1.11.4 which is incompatible.\n",
233
- "esda 2.8.1 requires scipy>=1.12, but you have scipy 1.11.4 which is incompatible.\n",
234
- "inequality 1.1.2 requires scipy>=1.12, but you have scipy 1.11.4 which is incompatible.\n",
235
- "giddy 2.3.8 requires scipy>=1.12, but you have scipy 1.11.4 which is incompatible.\n",
236
- "jaxlib 0.7.2 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n",
237
- "jaxlib 0.7.2 requires scipy>=1.13, but you have scipy 1.11.4 which is incompatible.\u001b[0m\u001b[31m\n",
238
- "\u001b[0mSuccessfully installed numpy-1.26.4 scipy-1.11.4\n"
239
- ]
240
- },
241
- {
242
- "output_type": "display_data",
243
- "data": {
244
- "application/vnd.colab-display-data+json": {
245
- "pip_warning": {
246
- "packages": [
247
- "numpy"
248
- ]
249
- },
250
- "id": "c6df9411f82f41ceb400a90d4bec5f90"
251
- }
252
- },
253
- "metadata": {}
254
- },
255
- {
256
- "output_type": "error",
257
- "ename": "SyntaxError",
258
- "evalue": "'break' outside loop (ipython-input-2150635115.py, line 15)",
259
- "traceback": [
260
- "\u001b[0;36m File \u001b[0;32m\"/tmp/ipython-input-2150635115.py\"\u001b[0;36m, line \u001b[0;32m15\u001b[0m\n\u001b[0;31m break\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m 'break' outside loop\n"
261
- ]
262
- }
263
- ],
264
- "execution_count": 1
265
- },
266
- {
267
- "cell_type": "code",
268
- "source": [],
269
- "metadata": {
270
- "id": "49QM1qVmdm4k"
271
- },
272
- "execution_count": null,
273
- "outputs": []
274
- },
275
- {
276
- "cell_type": "code",
277
- "source": [],
278
- "metadata": {
279
- "id": "bSUbLgHpeeJ4"
280
- },
281
- "execution_count": null,
282
- "outputs": []
283
- },
284
- {
285
- "cell_type": "code",
286
- "source": [],
287
- "metadata": {
288
- "id": "TPcj5qcmedBw"
289
- },
290
- "execution_count": 6,
291
- "outputs": []
292
- },
293
- {
294
- "cell_type": "code",
295
- "source": [
296
- "# restart & run after\n",
297
- "# =====================================================================\n",
298
- "# CELL 2: Mount Drive and Verify\n",
299
- "# =====================================================================\n",
300
- "from google.colab import drive\n",
301
- "drive.mount('/content/drive')\n",
302
- "\n",
303
- "import numpy as np\n",
304
- "print(f\"✓ np: {np.__version__} - {np.__file__}\")\n",
305
- "!pip show numpy | grep Version\n",
306
- "\n",
307
- "try:\n",
308
- " import roma\n",
309
- " print(\"✓ roma is installed\")\n",
310
- "except ModuleNotFoundError:\n",
311
- " print(\"⚠️ roma not found, installing...\")\n",
312
- " !pip install roma\n",
313
- " import roma\n",
314
- " print(\"✓ roma installed\")\n",
315
- "\n",
316
- "# =====================================================================\n",
317
- "# CELL 3: Clone Repositories\n",
318
- "# =====================================================================\n",
319
- "import os\n",
320
- "import sys\n",
321
- "\n",
322
- "# MASt3Rをクローン\n",
323
- "if not os.path.exists('/content/mast3r'):\n",
324
- " print(\"Cloning MASt3R repository...\")\n",
325
- " !git clone --recursive https://github.com/naver/mast3r.git /content/mast3r\n",
326
- " print(\"✓ MASt3R cloned\")\n",
327
- "else:\n",
328
- " print(\"✓ MASt3R already exists\")\n",
329
- "\n",
330
- "# DUSt3Rをクローン（MASt3R内に必要）\n",
331
- "if not os.path.exists('/content/mast3r/dust3r'):\n",
332
- " print(\"Cloning DUSt3R repository...\")\n",
333
- " !git clone --recursive https://github.com/naver/dust3r.git /content/mast3r/dust3r\n",
334
- " print(\"✓ DUSt3R cloned\")\n",
335
- "else:\n",
336
- " print(\"✓ DUSt3R already exists\")\n",
337
- "\n",
338
- "# パスを追加\n",
339
- "sys.path.insert(0, '/content/mast3r')\n",
340
- "sys.path.insert(0, '/content/mast3r/dust3r')\n",
341
- "\n",
342
- "# 確認\n",
343
- "try:\n",
344
- " from dust3r.model import AsymmetricCroCo3DStereo\n",
345
- " print(\"✓ dust3r.model imported successfully\")\n",
346
- "except ImportError as e:\n",
347
- " print(f\"✗ Import error: {e}\")\n",
348
- "\n",
349
- "# croco（MASt3Rの依存関係）もクローン\n",
350
- "if not os.path.exists('/content/mast3r/croco'):\n",
351
- " print(\"Cloning CroCo repository...\")\n",
352
- " !git clone --recursive https://github.com/naver/croco.git /content/mast3r/croco\n",
353
- " print(\"✓ CroCo cloned\")\n",
354
- "\n",
355
- "# =====================================================================\n",
356
- "# CELL 4: Clone and Build Gaussian Splatting\n",
357
- "# =====================================================================\n",
358
- "print(\"\\n\" + \"=\"*70)\n",
359
- "print(\"STEP: Clone Gaussian Splatting\")\n",
360
- "print(\"=\"*70)\n",
361
- "WORK_DIR = \"/content/gaussian-splatting\"\n",
362
- "\n",
363
- "import subprocess\n",
364
- "if not os.path.exists(WORK_DIR):\n",
365
- " subprocess.run([\n",
366
- " \"git\", \"clone\", \"--recursive\",\n",
367
- " \"https://github.com/graphdeco-inria/gaussian-splatting.git\",\n",
368
- " WORK_DIR\n",
369
- " ], capture_output=True)\n",
370
- " print(\"✓ Cloned\")\n",
371
- "else:\n",
372
- " print(\"✓ Already exists\")\n",
373
- "\n",
374
- "# インストールが必要なディレクトリ\n",
375
- "submodules = [\n",
376
- " \"/content/gaussian-splatting/submodules/diff-gaussian-rasterization\",\n",
377
- " \"/content/gaussian-splatting/submodules/simple-knn\"\n",
378
- "]\n",
379
- "\n",
380
- "for path in submodules:\n",
381
- " print(f\"Installing {path}...\")\n",
382
- " subprocess.run([\"pip\", \"install\", path], check=True)\n",
383
- "\n",
384
- "print(\"✓ Custom CUDA modules installed.\")\n",
385
- "\n",
386
- "print(f\"✓ np: {np.__version__} - {np.__file__}\")\n",
387
- "!pip show numpy | grep Version\n",
388
- "\n",
389
- "# =====================================================================\n",
390
- "# CELL 5: Import Core Libraries and Configure Memory\n",
391
- "# =====================================================================\n",
392
- "import os\n",
393
- "import sys\n",
394
- "import gc\n",
395
- "import torch\n",
396
- "import numpy as np\n",
397
- "from pathlib import Path\n",
398
- "from tqdm import tqdm\n",
399
- "import torch.nn.functional as F\n",
400
- "import shutil\n",
401
- "from PIL import Image\n",
402
- "from transformers import AutoImageProcessor, AutoModel\n",
403
- "\n",
404
- "# MEMORY MANAGEMENT\n",
405
- "os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'\n",
406
- "\n",
407
- "def clear_memory():\n",
408
- " \"\"\"メモリクリア関数\"\"\"\n",
409
- " gc.collect()\n",
410
- " if torch.cuda.is_available():\n",
411
- " torch.cuda.empty_cache()\n",
412
- " torch.cuda.synchronize()\n",
413
- "\n",
414
- "def get_memory_info():\n",
415
- " \"\"\"Get current memory usage\"\"\"\n",
416
- " if torch.cuda.is_available():\n",
417
- " allocated = torch.cuda.memory_allocated() / 1024**3\n",
418
- " reserved = torch.cuda.memory_reserved() / 1024**3\n",
419
- " print(f\"GPU Memory - Allocated: {allocated:.2f}GB, Reserved: {reserved:.2f}GB\")\n",
420
- "\n",
421
- " import psutil\n",
422
- " cpu_mem = psutil.virtual_memory().percent\n",
423
- " print(f\"CPU Memory Usage: {cpu_mem:.1f}%\")\n",
424
- "\n",
425
- "# CONFIGURATION\n",
426
- "class Config:\n",
427
- " DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
428
- " MAST3R_WEIGHTS = \"naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric\"\n",
429
- " DUST3R_WEIGHTS = \"naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt\"\n",
430
- "\n",
431
- " # DINO設定\n",
432
- " DINO_MODEL = \"facebook/dinov2-base\"\n",
433
- " GLOBAL_TOPK = 20 # 各画像がペアを組む上位K個\n",
434
- "\n",
435
- " IMAGE_SIZE = 224\n",
436
- "\n",
437
- "# =====================================================================\n",
438
- "# CELL 6: Image Preprocessing Functions (Biplet)\n",
439
- "# =====================================================================\n",
440
- "def normalize_image_sizes_biplet(input_dir, output_dir=None, size=1024):\n",
441
- " \"\"\"\n",
442
- " Generates two square crops (Left & Right or Top & Bottom)\n",
443
- " from each image in a directory.\n",
444
- " \"\"\"\n",
445
- " if output_dir is None:\n",
446
- " output_dir = input_dir + \"_biplet\"\n",
447
- "\n",
448
- " os.makedirs(output_dir, exist_ok=True)\n",
449
- "\n",
450
- " print(f\"\\n=== Generating Biplet Crops ({size}x{size}) ===\")\n",
451
- "\n",
452
- " converted_count = 0\n",
453
- " size_stats = {}\n",
454
- "\n",
455
- " for img_file in tqdm(sorted(os.listdir(input_dir)), desc=\"Creating biplets\"):\n",
456
- " if not img_file.lower().endswith(('.jpg', '.jpeg', '.png')):\n",
457
- " continue\n",
458
- "\n",
459
- " input_path = os.path.join(input_dir, img_file)\n",
460
- "\n",
461
- " try:\n",
462
- " img = Image.open(input_path)\n",
463
- " original_size = img.size\n",
464
- "\n",
465
- " size_key = f\"{original_size[0]}x{original_size[1]}\"\n",
466
- " size_stats[size_key] = size_stats.get(size_key, 0) + 1\n",
467
- "\n",
468
- " # Generate 2 crops\n",
469
- " crops = generate_two_crops(img, size)\n",
470
- "\n",
471
- " base_name, ext = os.path.splitext(img_file)\n",
472
- " for mode, cropped_img in crops.items():\n",
473
- " output_path = os.path.join(output_dir, f\"{base_name}_{mode}{ext}\")\n",
474
- " cropped_img.save(output_path, quality=95)\n",
475
- "\n",
476
- " converted_count += 1\n",
477
- "\n",
478
- " except Exception as e:\n",
479
- " print(f\" ✗ Error processing {img_file}: {e}\")\n",
480
- "\n",
481
- " print(f\"\\n✓ Biplet generation complete:\")\n",
482
- " print(f\" Source images: {converted_count}\")\n",
483
- " print(f\" Biplet crops generated: {converted_count * 2}\")\n",
484
- " print(f\" Original size distribution: {size_stats}\")\n",
485
- "\n",
486
- " return output_dir\n",
487
- "\n",
488
- "\n",
489
- "def generate_two_crops(img, size):\n",
490
- " \"\"\"\n",
491
- " Crops the image into a square and returns 2 variations\n",
492
- " \"\"\"\n",
493
- " width, height = img.size\n",
494
- " crop_size = min(width, height)\n",
495
- " crops = {}\n",
496
- "\n",
497
- " if width > height:\n",
498
- " # Landscape → Left & Right\n",
499
- " positions = {\n",
500
- " 'left': 0,\n",
501
- " 'right': width - crop_size\n",
502
- " }\n",
503
- " for mode, x_offset in positions.items():\n",
504
- " box = (x_offset, 0, x_offset + crop_size, crop_size)\n",
505
- " crops[mode] = img.crop(box).resize(\n",
506
- " (size, size),\n",
507
- " Image.Resampling.LANCZOS\n",
508
- " )\n",
509
- " else:\n",
510
- " # Portrait or Square → Top & Bottom\n",
511
- " positions = {\n",
512
- " 'top': 0,\n",
513
- " 'bottom': height - crop_size\n",
514
- " }\n",
515
- " for mode, y_offset in positions.items():\n",
516
- " box = (0, y_offset, crop_size, y_offset + crop_size)\n",
517
- " crops[mode] = img.crop(box).resize(\n",
518
- " (size, size),\n",
519
- " Image.Resampling.LANCZOS\n",
520
- " )\n",
521
- "\n",
522
- " return crops\n",
523
- "\n",
524
- "# =====================================================================\n",
525
- "# CELL 7: Image Loading Function\n",
526
- "# =====================================================================\n",
527
- "def load_images_from_directory(image_dir, max_images=200):\n",
528
- " \"\"\"ディレクトリから画像をロード\"\"\"\n",
529
- " print(f\"\\nLoading images from: {image_dir}\")\n",
530
- "\n",
531
- " valid_extensions = {'.jpg', '.jpeg', '.png', '.bmp'}\n",
532
- " image_paths = []\n",
533
- "\n",
534
- " for ext in valid_extensions:\n",
535
- " image_paths.extend(sorted(Path(image_dir).glob(f'*{ext}')))\n",
536
- " image_paths.extend(sorted(Path(image_dir).glob(f'*{ext.upper()}')))\n",
537
- "\n",
538
- " image_paths = sorted(set(str(p) for p in image_paths))\n",
539
- "\n",
540
- " if len(image_paths) > max_images:\n",
541
- " print(f\"⚠️ Limiting from {len(image_paths)} to {max_images} images\")\n",
542
- " image_paths = image_paths[:max_images]\n",
543
- "\n",
544
- " print(f\"✓ Found {len(image_paths)} images\")\n",
545
- " return image_paths\n",
546
- "\n",
547
- "# =====================================================================\n",
548
- "# CELL 8: MASt3R Model Loading\n",
549
- "# =====================================================================\n",
550
- "def load_mast3r_model(device):\n",
551
- " \"\"\"MASt3Rモデルをロード\"\"\"\n",
552
- " print(\"\\n=== Loading MASt3R Model ===\")\n",
553
- "\n",
554
- " if '/content/mast3r' not in sys.path:\n",
555
- " sys.path.insert(0, '/content/mast3r')\n",
556
- " if '/content/mast3r/dust3r' not in sys.path:\n",
557
- " sys.path.insert(0, '/content/mast3r/dust3r')\n",
558
- "\n",
559
- " from dust3r.model import AsymmetricCroCo3DStereo\n",
560
- "\n",
561
- " try:\n",
562
- " print(f\"Attempting to load: {Config.MAST3R_WEIGHTS}\")\n",
563
- " model = AsymmetricCroCo3DStereo.from_pretrained(Config.MAST3R_WEIGHTS).to(device)\n",
564
- " print(\"✓ Loaded MASt3R model\")\n",
565
- " except Exception as e:\n",
566
- " print(f\"⚠️ Failed to load MASt3R: {e}\")\n",
567
- " print(f\"Trying DUSt3R instead: {Config.DUST3R_WEIGHTS}\")\n",
568
- " model = AsymmetricCroCo3DStereo.from_pretrained(Config.DUST3R_WEIGHTS).to(device)\n",
569
- " print(\"✓ Loaded DUSt3R model as fallback\")\n",
570
- "\n",
571
- " model.eval()\n",
572
- " print(f\"✓ Model loaded on {device}\")\n",
573
- " return model\n",
574
- "\n",
575
- "# =====================================================================\n",
576
- "# CELL 9: DINO Pair Selection (REPLACES ASMK)\n",
577
- "# =====================================================================\n",
578
- "def load_torch_image(fname, device):\n",
579
- " \"\"\"Load image as torch tensor\"\"\"\n",
580
- " import torchvision.transforms as T\n",
581
- "\n",
582
- " img = Image.open(fname).convert('RGB')\n",
583
- " transform = T.Compose([\n",
584
- " T.ToTensor(),\n",
585
- " ])\n",
586
- " return transform(img).unsqueeze(0).to(device)\n",
587
- "\n",
588
- "def extract_dino_global(image_paths, model_path, device):\n",
589
- " \"\"\"Extract DINO global descriptors with memory management\"\"\"\n",
590
- " print(\"\\n=== Extracting DINO Global Features ===\")\n",
591
- " print(\"Initial memory state:\")\n",
592
- " get_memory_info()\n",
593
- "\n",
594
- " processor = AutoImageProcessor.from_pretrained(model_path)\n",
595
- " model = AutoModel.from_pretrained(model_path).eval().to(device)\n",
596
- "\n",
597
- " global_descs = []\n",
598
- " batch_size = 4 # Small batch to save memory\n",
599
- "\n",
600
- " for i in tqdm(range(0, len(image_paths), batch_size), desc=\"DINO extraction\"):\n",
601
- " batch_paths = image_paths[i:i+batch_size]\n",
602
- " batch_imgs = []\n",
603
- "\n",
604
- " for img_path in batch_paths:\n",
605
- " img = load_torch_image(img_path, device)\n",
606
- " batch_imgs.append(img)\n",
607
- "\n",
608
- " batch_tensor = torch.cat(batch_imgs, dim=0)\n",
609
- "\n",
610
- " with torch.no_grad():\n",
611
- " inputs = processor(images=batch_tensor, return_tensors=\"pt\", do_rescale=False).to(device)\n",
612
- " outputs = model(**inputs)\n",
613
- " desc = F.normalize(outputs.last_hidden_state[:, 1:].max(dim=1)[0], dim=1, p=2)\n",
614
- " global_descs.append(desc.cpu())\n",
615
- "\n",
616
- " # Clear batch memory\n",
617
- " del batch_tensor, inputs, outputs, desc\n",
618
- " clear_memory()\n",
619
- "\n",
620
- " global_descs = torch.cat(global_descs, dim=0)\n",
621
- "\n",
622
- " del model, processor\n",
623
- " clear_memory()\n",
624
- "\n",
625
- " print(\"After DINO extraction:\")\n",
626
- " get_memory_info()\n",
627
- "\n",
628
- " return global_descs\n",
629
- "\n",
630
- "def build_topk_pairs(global_feats, k, device):\n",
631
- " \"\"\"Build top-k similar pairs from global features\"\"\"\n",
632
- " g = global_feats.to(device)\n",
633
- " sim = g @ g.T\n",
634
- " sim.fill_diagonal_(-1)\n",
635
- "\n",
636
- " N = sim.size(0)\n",
637
- " k = min(k, N - 1)\n",
638
- "\n",
639
- " topk_indices = torch.topk(sim, k, dim=1).indices.cpu()\n",
640
- "\n",
641
- " pairs = []\n",
642
- " for i in range(N):\n",
643
- " for j in topk_indices[i]:\n",
644
- " j = j.item()\n",
645
- " if i < j:\n",
646
- " pairs.append((i, j))\n",
647
- "\n",
648
- " # Remove duplicates\n",
649
- " pairs = list(set(pairs))\n",
650
- "\n",
651
- " return pairs\n",
652
- "\n",
653
- "def select_diverse_pairs(pairs, max_pairs, num_images):\n",
654
- " \"\"\"\n",
655
- " Select diverse pairs to ensure good image coverage\n",
656
- " \"\"\"\n",
657
- " import random\n",
658
- " random.seed(42)\n",
659
- "\n",
660
- " if len(pairs) <= max_pairs:\n",
661
- " return pairs\n",
662
- "\n",
663
- " print(f\"Selecting {max_pairs} diverse pairs from {len(pairs)} candidates...\")\n",
664
- "\n",
665
- " # Count how many times each image appears in pairs\n",
666
- " image_counts = {i: 0 for i in range(num_images)}\n",
667
- " for i, j in pairs:\n",
668
- " image_counts[i] += 1\n",
669
- " image_counts[j] += 1\n",
670
- "\n",
671
- " # Sort pairs by: prefer pairs with less-connected images\n",
672
- " def pair_score(pair):\n",
673
- " i, j = pair\n",
674
- " return image_counts[i] + image_counts[j]\n",
675
- "\n",
676
- " pairs_scored = [(pair, pair_score(pair)) for pair in pairs]\n",
677
- " pairs_scored.sort(key=lambda x: x[1])\n",
678
- "\n",
679
- " # Select pairs greedily to maximize coverage\n",
680
- " selected = []\n",
681
- " selected_images = set()\n",
682
- "\n",
683
- " # Phase 1: Select pairs that add new images\n",
684
- " for pair, score in pairs_scored:\n",
685
- " if len(selected) >= max_pairs:\n",
686
- " break\n",
687
- " i, j = pair\n",
688
- " if i not in selected_images or j not in selected_images:\n",
689
- " selected.append(pair)\n",
690
- " selected_images.add(i)\n",
691
- " selected_images.add(j)\n",
692
- "\n",
693
- " # Phase 2: Fill remaining slots\n",
694
- " if len(selected) < max_pairs:\n",
695
- " remaining = [p for p, s in pairs_scored if p not in selected]\n",
696
- " random.shuffle(remaining)\n",
697
- " selected.extend(remaining[:max_pairs - len(selected)])\n",
698
- "\n",
699
- " print(f\"Selected pairs cover {len(selected_images)} / {num_images} images ({100*len(selected_images)/num_images:.1f}%)\")\n",
700
- "\n",
701
- " return selected\n",
702
- "\n",
703
- "def get_image_pairs_dino(image_paths, max_pairs=None):\n",
704
- " \"\"\"DINO-based pair selection\"\"\"\n",
705
- " device = Config.DEVICE\n",
706
- "\n",
707
- " # DINO global features\n",
708
- " global_feats = extract_dino_global(image_paths, Config.DINO_MODEL, device)\n",
709
- " pairs = build_topk_pairs(global_feats, Config.GLOBAL_TOPK, device)\n",
710
- "\n",
711
- " print(f\"Initial pairs from DINO: {len(pairs)}\")\n",
712
- "\n",
713
- " # Apply intelligent pair selection if limit specified\n",
714
- " if max_pairs and len(pairs) > max_pairs:\n",
715
- " pairs = select_diverse_pairs(pairs, max_pairs, len(image_paths))\n",
716
- "\n",
717
- " return pairs\n",
718
- "\n",
719
- "# =====================================================================\n",
720
- "# CELL 10: MASt3R Reconstruction\n",
721
- "# =====================================================================\n",
722
- "def run_mast3r_pairs(model, image_paths, pairs, device, batch_size=1, max_pairs=None):\n",
723
- " \"\"\"Run MASt3R on selected pairs with memory management\"\"\"\n",
724
- " print(\"\\n=== Running MASt3R Reconstruction ===\")\n",
725
- " print(\"Initial memory state:\")\n",
726
- " get_memory_info()\n",
727
- "\n",
728
- " from dust3r.inference import inference\n",
729
- " from dust3r.cloud_opt import global_aligner, GlobalAlignerMode\n",
730
- " from dust3r.utils.image import load_images\n",
731
- "\n",
732
- " # Limit number of pairs if specified\n",
733
- " if max_pairs and len(pairs) > max_pairs:\n",
734
- " print(f\"Limiting pairs from {len(pairs)} to {max_pairs}\")\n",
735
- " step = max(1, len(pairs) // max_pairs)\n",
736
- " pairs = pairs[::step][:max_pairs]\n",
737
- "\n",
738
- " print(f\"Processing {len(pairs)} pairs...\")\n",
739
- "\n",
740
- " # Load images in smaller size\n",
741
- " print(f\"Loading {len(image_paths)} images at {Config.IMAGE_SIZE}x{Config.IMAGE_SIZE}...\")\n",
742
- " images = load_images(image_paths, size=Config.IMAGE_SIZE)\n",
743
- "\n",
744
- " print(f\"Loaded {len(images)} images\")\n",
745
- " print(\"After loading images:\")\n",
746
- " get_memory_info()\n",
747
- "\n",
748
- " # Create all image pairs\n",
749
- " print(f\"Creating {len(pairs)} image pairs...\")\n",
750
- " mast3r_pairs = []\n",
751
- " for idx1, idx2 in tqdm(pairs, desc=\"Preparing pairs\"):\n",
752
- " mast3r_pairs.append((images[idx1], images[idx2]))\n",
753
- "\n",
754
- " print(f\"Running MASt3R inference on {len(mast3r_pairs)} pairs...\")\n",
755
- "\n",
756
- " # Run inference\n",
757
- " output = inference(mast3r_pairs, model, device, batch_size=batch_size, verbose=True)\n",
758
- "\n",
759
- " del mast3r_pairs\n",
760
- " clear_memory()\n",
761
- "\n",
762
- " print(\"✓ MASt3R inference complete\")\n",
763
- " print(\"After inference:\")\n",
764
- " get_memory_info()\n",
765
- "\n",
766
- " # Global alignment\n",
767
- " print(\"Running global alignment...\")\n",
768
- " scene = global_aligner(\n",
769
- " output,\n",
770
- " device=device,\n",
771
- " mode=GlobalAlignerMode.PointCloudOptimizer\n",
772
- " )\n",
773
- "\n",
774
- " del output\n",
775
- " clear_memory()\n",
776
- "\n",
777
- " print(\"Computing global alignment...\")\n",
778
- " loss = scene.compute_global_alignment(\n",
779
- " init=\"mst\",\n",
780
- " niter=50, # Reduced iterations\n",
781
- " schedule='cosine',\n",
782
- " lr=0.01\n",
783
- " )\n",
784
- "\n",
785
- " print(f\"✓ Global alignment complete (final loss: {loss:.6f})\")\n",
786
- " print(\"Final memory state:\")\n",
787
- " get_memory_info()\n",
788
- "\n",
789
- " return scene, images\n",
790
- "\n",
791
- "\n",
792
- "\n"
793
- ],
794
- "metadata": {
795
- "trusted": true,
796
- "id": "OWJEB1oQTKyD",
797
- "colab": {
798
- "base_uri": "https://localhost:8080/"
799
- },
800
- "outputId": "0334296d-b136-45dc-ad4d-e6cc6e3ce9b8"
801
- },
802
- "outputs": [
803
- {
804
- "output_type": "stream",
805
- "name": "stdout",
806
- "text": [
807
- "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
808
- "✓ np: 1.26.4 - /usr/local/lib/python3.12/dist-packages/numpy/__init__.py\n",
809
- "Version: 1.26.4\n",
810
- "Version 3.1, 31 March 2009\n",
811
- " Version 3, 29 June 2007\n",
812
- " 5. Conveying Modified Source Versions.\n",
813
- " 14. Revised Versions of this License.\n",
814
- "✓ roma is installed\n",
815
- "✓ MASt3R already exists\n",
816
- "✓ DUSt3R already exists\n",
817
- "✓ dust3r.model imported successfully\n",
818
- "\n",
819
- "======================================================================\n",
820
- "STEP: Clone Gaussian Splatting\n",
821
- "======================================================================\n",
822
- "✓ Already exists\n",
823
- "Installing /content/gaussian-splatting/submodules/diff-gaussian-rasterization...\n",
824
- "Installing /content/gaussian-splatting/submodules/simple-knn...\n",
825
- "✓ Custom CUDA modules installed.\n",
826
- "✓ np: 1.26.4 - /usr/local/lib/python3.12/dist-packages/numpy/__init__.py\n",
827
- "Version: 1.26.4\n",
828
- "Version 3.1, 31 March 2009\n",
829
- " Version 3, 29 June 2007\n",
830
- " 5. Conveying Modified Source Versions.\n",
831
- " 14. Revised Versions of this License.\n"
832
- ]
833
- }
834
- ],
835
- "execution_count": 7
836
- },
837
- {
838
- "cell_type": "code",
839
- "source": [
840
- "\n",
841
- "# =====================================================================\n",
842
- "# CELL 11: Camera Parameter Extraction\n",
843
- "# =====================================================================\n",
844
- "def extract_camera_params_process2(scene, image_paths, conf_threshold=1.5):\n",
845
- " \"\"\"sceneからカメラパラメータと3D点を抽出\"\"\"\n",
846
- " print(\"\\n=== Extracting Camera Parameters ===\")\n",
847
- "\n",
848
- " cameras_dict = {}\n",
849
- " all_pts3d = []\n",
850
- " all_confidence = []\n",
851
- "\n",
852
- " try:\n",
853
- " if hasattr(scene, 'get_im_poses'):\n",
854
- " poses = scene.get_im_poses()\n",
855
- " elif hasattr(scene, 'im_poses'):\n",
856
- " poses = scene.im_poses\n",
857
- " else:\n",
858
- " poses = None\n",
859
- "\n",
860
- " if hasattr(scene, 'get_focals'):\n",
861
- " focals = scene.get_focals()\n",
862
- " elif hasattr(scene, 'im_focals'):\n",
863
- " focals = scene.im_focals\n",
864
- " else:\n",
865
- " focals = None\n",
866
- "\n",
867
- " if hasattr(scene, 'get_principal_points'):\n",
868
- " pps = scene.get_principal_points()\n",
869
- " elif hasattr(scene, 'im_pp'):\n",
870
- " pps = scene.im_pp\n",
871
- " else:\n",
872
- " pps = None\n",
873
- " except Exception as e:\n",
874
- " print(f\"⚠️ Error getting camera parameters: {e}\")\n",
875
- " poses = None\n",
876
- " focals = None\n",
877
- " pps = None\n",
878
- "\n",
879
- " n_images = min(len(poses) if poses is not None else len(image_paths), len(image_paths))\n",
880
- "\n",
881
- " for idx in range(n_images):\n",
882
- " img_name = os.path.basename(image_paths[idx])\n",
883
- "\n",
884
- " try:\n",
885
- " # Poseを取得\n",
886
- " if poses is not None and idx < len(poses):\n",
887
- " pose = poses[idx]\n",
888
- " if isinstance(pose, torch.Tensor):\n",
889
- " pose = pose.detach().cpu().numpy()\n",
890
- " if not isinstance(pose, np.ndarray) or pose.shape != (4, 4):\n",
891
- " pose = np.eye(4)\n",
892
- " else:\n",
893
- " pose = np.eye(4)\n",
894
- "\n",
895
- " # Focalを取得\n",
896
- " if focals is not None and idx < len(focals):\n",
897
- " focal = focals[idx]\n",
898
- " if isinstance(focal, torch.Tensor):\n",
899
- " focal = focal.detach().cpu().item()\n",
900
- " else:\n",
901
- " focal = float(focal)\n",
902
- " else:\n",
903
- " focal = 1000.0\n",
904
- "\n",
905
- " # Principal pointを取得\n",
906
- " if pps is not None and idx < len(pps):\n",
907
- " pp = pps[idx]\n",
908
- " if isinstance(pp, torch.Tensor):\n",
909
- " pp = pp.detach().cpu().numpy()\n",
910
- " else:\n",
911
- " pp = np.array([112.0, 112.0])\n",
912
- "\n",
913
- " # カメラパラメータを保存\n",
914
- " cameras_dict[img_name] = {\n",
915
- " 'focal': focal,\n",
916
- " 'pp': pp,\n",
917
- " 'pose': pose,\n",
918
- " 'rotation': pose[:3, :3],\n",
919
- " 'translation': pose[:3, 3],\n",
920
- " 'width': Config.IMAGE_SIZE * 4,\n",
921
- " 'height': Config.IMAGE_SIZE * 4\n",
922
- " }\n",
923
- "\n",
924
- " # 3D点を取得\n",
925
- " if hasattr(scene, 'im_pts3d') and idx < len(scene.im_pts3d):\n",
926
- " pts3d_img = scene.im_pts3d[idx]\n",
927
- " elif hasattr(scene, 'get_pts3d'):\n",
928
- " pts3d_all = scene.get_pts3d()\n",
929
- " if idx < len(pts3d_all):\n",
930
- " pts3d_img = pts3d_all[idx]\n",
931
- " else:\n",
932
- " pts3d_img = None\n",
933
- " else:\n",
934
- " pts3d_img = None\n",
935
- "\n",
936
- " # Confidenceを取得\n",
937
- " if hasattr(scene, 'im_conf') and idx < len(scene.im_conf):\n",
938
- " conf_img = scene.im_conf[idx]\n",
939
- " elif hasattr(scene, 'get_conf'):\n",
940
- " conf_all = scene.get_conf()\n",
941
- " if idx < len(conf_all):\n",
942
- " conf_img = conf_all[idx]\n",
943
- " else:\n",
944
- " conf_img = None\n",
945
- " else:\n",
946
- " conf_img = None\n",
947
- "\n",
948
- " # 3D点とconfidenceを処理\n",
949
- " if pts3d_img is not None:\n",
950
- " if isinstance(pts3d_img, torch.Tensor):\n",
951
- " pts3d_img = pts3d_img.detach().cpu().numpy()\n",
952
- "\n",
953
- " if pts3d_img.ndim == 3:\n",
954
- " pts3d_flat = pts3d_img.reshape(-1, 3)\n",
955
- " else:\n",
956
- " pts3d_flat = pts3d_img\n",
957
- "\n",
958
- " all_pts3d.append(pts3d_flat)\n",
959
- "\n",
960
- " # confidenceを処理\n",
961
- " if conf_img is not None:\n",
962
- " if isinstance(conf_img, list):\n",
963
- " conf_img = np.array(conf_img)\n",
964
- " elif isinstance(conf_img, torch.Tensor):\n",
965
- " conf_img = conf_img.detach().cpu().numpy()\n",
966
- "\n",
967
- " if conf_img.ndim > 1:\n",
968
- " conf_flat = conf_img.reshape(-1)\n",
969
- " else:\n",
970
- " conf_flat = conf_img\n",
971
- "\n",
972
- " if len(conf_flat) != len(pts3d_flat):\n",
973
- " conf_flat = np.ones(len(pts3d_flat))\n",
974
- "\n",
975
- " all_confidence.append(conf_flat)\n",
976
- " else:\n",
977
- " all_confidence.append(np.ones(len(pts3d_flat)))\n",
978
- "\n",
979
- " except Exception as e:\n",
980
- " print(f\"⚠️ Error processing image {idx} ({img_name}): {e}\")\n",
981
- " cameras_dict[img_name] = {\n",
982
- " 'focal': 1000.0,\n",
983
- " 'pp': np.array([112.0, 112.0]),\n",
984
- " 'pose': np.eye(4),\n",
985
- " 'rotation': np.eye(3),\n",
986
- " 'translation': np.zeros(3),\n",
987
- " 'width': Config.IMAGE_SIZE * 4,\n",
988
- " 'height': Config.IMAGE_SIZE * 4\n",
989
- " }\n",
990
- " continue\n",
991
- "\n",
992
- " # 全3D点を結合\n",
993
- " if all_pts3d:\n",
994
- " pts3d = np.vstack(all_pts3d)\n",
995
- " confidence = np.concatenate(all_confidence)\n",
996
- " else:\n",
997
- " pts3d = np.zeros((0, 3))\n",
998
- " confidence = np.zeros(0)\n",
999
- "\n",
1000
- " print(f\"✓ Extracted camera parameters for {len(cameras_dict)} images\")\n",
1001
- " print(f\"✓ Total 3D points: {len(pts3d)}\")\n",
1002
- "\n",
1003
- " # Confidenceでフィルタリング\n",
1004
- " if len(confidence) > 0:\n",
1005
- " valid_mask = confidence > conf_threshold\n",
1006
- " pts3d = pts3d[valid_mask]\n",
1007
- " confidence = confidence[valid_mask]\n",
1008
- " print(f\"✓ After confidence filtering (>{conf_threshold}): {len(pts3d)} points\")\n",
1009
- "\n",
1010
- " return cameras_dict, pts3d, confidence\n",
1011
- "\n"
1012
- ],
1013
- "metadata": {
1014
- "id": "YSt2RDqmviUa"
1015
- },
1016
- "execution_count": 8,
1017
- "outputs": []
1018
- },
1019
- {
1020
- "cell_type": "code",
1021
- "source": [
1022
- "# =====================================================================\n",
1023
- "# CELL 12: COLMAP Export Functions\n",
1024
- "# =====================================================================\n",
1025
- "\n",
1026
- "import struct\n",
1027
- "import numpy as np\n",
1028
- "from pathlib import Path\n",
1029
- "\n",
1030
- "def rotmat_to_qvec(R):\n",
1031
- " \"\"\"\n",
1032
- " 回転行列をクォータニオンに変換\n",
1033
- "\n",
1034
- " Args:\n",
1035
- " R: 3x3回転行列\n",
1036
- "\n",
1037
- " Returns:\n",
1038
- " qvec: [qw, qx, qy, qz] クォータニオン\n",
1039
- " \"\"\"\n",
1040
- " # Shepperdの方法\n",
1041
- " trace = np.trace(R)\n",
1042
- "\n",
1043
- " if trace > 0:\n",
1044
- " s = 0.5 / np.sqrt(trace + 1.0)\n",
1045
- " w = 0.25 / s\n",
1046
- " x = (R[2, 1] - R[1, 2]) * s\n",
1047
- " y = (R[0, 2] - R[2, 0]) * s\n",
1048
- " z = (R[1, 0] - R[0, 1]) * s\n",
1049
- " elif R[0, 0] > R[1, 1] and R[0, 0] > R[2, 2]:\n",
1050
- " s = 2.0 * np.sqrt(1.0 + R[0, 0] - R[1, 1] - R[2, 2])\n",
1051
- " w = (R[2, 1] - R[1, 2]) / s\n",
1052
- " x = 0.25 * s\n",
1053
- " y = (R[0, 1] + R[1, 0]) / s\n",
1054
- " z = (R[0, 2] + R[2, 0]) / s\n",
1055
- " elif R[1, 1] > R[2, 2]:\n",
1056
- " s = 2.0 * np.sqrt(1.0 + R[1, 1] - R[0, 0] - R[2, 2])\n",
1057
- " w = (R[0, 2] - R[2, 0]) / s\n",
1058
- " x = (R[0, 1] + R[1, 0]) / s\n",
1059
- " y = 0.25 * s\n",
1060
- " z = (R[1, 2] + R[2, 1]) / s\n",
1061
- " else:\n",
1062
- " s = 2.0 * np.sqrt(1.0 + R[2, 2] - R[0, 0] - R[1, 1])\n",
1063
- " w = (R[1, 0] - R[0, 1]) / s\n",
1064
- " x = (R[0, 2] + R[2, 0]) / s\n",
1065
- " y = (R[1, 2] + R[2, 1]) / s\n",
1066
- " z = 0.25 * s\n",
1067
- "\n",
1068
- " return np.array([w, x, y, z])\n",
1069
- "\n",
1070
- "\n",
1071
- "def write_cameras_binary(cameras_dict, image_size, output_file):\n",
1072
- " \"\"\"\n",
1073
- " COLMAPのcameras.binを出力\n",
1074
- "\n",
1075
- " バイナリ形式:\n",
1076
- " - num_cameras (uint64)\n",
1077
- " - For each camera:\n",
1078
- " - camera_id (uint32)\n",
1079
- " - model_id (int32) # SIMPLE_PINHOLE = 0\n",
1080
- " - width (uint64)\n",
1081
- " - height (uint64)\n",
1082
- " - params (double[]) # focal, cx, cy\n",
1083
- "\n",
1084
- " Args:\n",
1085
- " cameras_dict: カメラパラメータの辞書\n",
1086
- " image_size: (width, height) 画像サイズ\n",
1087
- " output_file: 出力ファイルパス\n",
1088
- " \"\"\"\n",
1089
- " width, height = image_size\n",
1090
- " num_cameras = len(cameras_dict)\n",
1091
- "\n",
1092
- " # COLMAP camera models\n",
1093
- " SIMPLE_PINHOLE = 0\n",
1094
- "\n",
1095
- " with open(output_file, 'wb') as f:\n",
1096
- " # カメラ数\n",
1097
- " f.write(struct.pack('Q', num_cameras))\n",
1098
- "\n",
1099
- " # 各カメラの情報\n",
1100
- " for camera_id, (img_id, cam_params) in enumerate(cameras_dict.items(), start=1):\n",
1101
- " focal = cam_params['focal']\n",
1102
- " cx = width / 2.0\n",
1103
- " cy = height / 2.0\n",
1104
- "\n",
1105
- " # camera_id\n",
1106
- " f.write(struct.pack('I', camera_id))\n",
1107
- " # model_id (SIMPLE_PINHOLE)\n",
1108
- " f.write(struct.pack('i', SIMPLE_PINHOLE))\n",
1109
- " # width\n",
1110
- " f.write(struct.pack('Q', width))\n",
1111
- " # height\n",
1112
- " f.write(struct.pack('Q', height))\n",
1113
- " # params: focal, cx, cy\n",
1114
- " f.write(struct.pack('d', focal))\n",
1115
- " f.write(struct.pack('d', cx))\n",
1116
- " f.write(struct.pack('d', cy))\n",
1117
- "\n",
1118
- " print(f\"COLMAP cameras.bin saved to {output_file}\")\n",
1119
- "\n",
1120
- "\n",
1121
- "def write_images_binary(cameras_dict, output_file):\n",
1122
- " \"\"\"\n",
1123
- " COLMAPのimages.binを出力\n",
1124
- "\n",
1125
- " バイナリ形式:\n",
1126
- " - num_images (uint64)\n",
1127
- " - For each image:\n",
1128
- " - image_id (uint32)\n",
1129
- " - qvec (double[4]) # qw, qx, qy, qz\n",
1130
- " - tvec (double[3]) # tx, ty, tz\n",
1131
- " - camera_id (uint32)\n",
1132
- " - name (string with null terminator)\n",
1133
- " - num_points2D (uint64)\n",
1134
- " - points2D (x, y, point3D_id) * num_points2D\n",
1135
- "\n",
1136
- " Args:\n",
1137
- " cameras_dict: カメラパラメータの辞書\n",
1138
- " output_file: 出力ファイルパス\n",
1139
- " \"\"\"\n",
1140
- " num_images = len(cameras_dict)\n",
1141
- "\n",
1142
- " with open(output_file, 'wb') as f:\n",
1143
- " # 画像数\n",
1144
- " f.write(struct.pack('Q', num_images))\n",
1145
- "\n",
1146
- " # 各画像の情報\n",
1147
- " for image_id, (img_id, cam_params) in enumerate(cameras_dict.items(), start=1):\n",
1148
- " # 回転行列をクォータニオンに変換（自前の関数を使用）\n",
1149
- " R = cam_params['rotation']\n",
1150
- " quat = rotmat_to_qvec(R) # [qw, qx, qy, qz]\n",
1151
- "\n",
1152
- " # 並進ベクトル\n",
1153
- " t = cam_params['translation']\n",
1154
- "\n",
1155
- " # カメラIDは画像IDと同じ\n",
1156
- " camera_id = image_id\n",
1157
- "\n",
1158
- " # image_id\n",
1159
- " f.write(struct.pack('I', image_id))\n",
1160
- "\n",
1161
- " # qvec (qw, qx, qy, qz)\n",
1162
- " for q in quat:\n",
1163
- " f.write(struct.pack('d', q))\n",
1164
- "\n",
1165
- " # tvec (tx, ty, tz)\n",
1166
- " for ti in t:\n",
1167
- " f.write(struct.pack('d', ti))\n",
1168
- "\n",
1169
- " # camera_id\n",
1170
- " f.write(struct.pack('I', camera_id))\n",
1171
- "\n",
1172
- " # name (null-terminated string)\n",
1173
- " name_bytes = img_id.encode('utf-8') + b'\\x00'\n",
1174
- " f.write(name_bytes)\n",
1175
- "\n",
1176
- " # num_points2D (0 for now)\n",
1177
- " f.write(struct.pack('Q', 0))\n",
1178
- "\n",
1179
- " print(f\"COLMAP images.bin saved to {output_file}\")\n",
1180
- "\n",
1181
- "\n",
1182
- "def write_points3D_binary(pts3d, confidence, output_file):\n",
1183
- " \"\"\"\n",
1184
- " COLMAPのpoints3D.binを出力\n",
1185
- "\n",
1186
- " バイナリ形式:\n",
1187
- " - num_points (uint64)\n",
1188
- " - For each point:\n",
1189
- " - point3D_id (uint64)\n",
1190
- " - xyz (double[3])\n",
1191
- " - rgb (uint8[3])\n",
1192
- " - error (double)\n",
1193
- " - track_length (uint64)\n",
1194
- " - track (image_id, point2D_idx) * track_length\n",
1195
- "\n",
1196
- " Args:\n",
1197
- " pts3d: 3D点の配列 [N, 3]\n",
1198
- " confidence: 信頼度の配列 [N]\n",
1199
- " output_file: 出力ファイルパス\n",
1200
- " \"\"\"\n",
1201
- " num_points = len(pts3d)\n",
1202
- "\n",
1203
- " with open(output_file, 'wb') as f:\n",
1204
- " # 点の数\n",
1205
- " f.write(struct.pack('Q', num_points))\n",
1206
- "\n",
1207
- " # 各3D点の情報\n",
1208
- " for point_id, pt in enumerate(pts3d, start=1):\n",
1209
- " x, y, z = pt\n",
1210
- "\n",
1211
- " # point3D_id\n",
1212
- " f.write(struct.pack('Q', point_id))\n",
1213
- "\n",
1214
- " # xyz\n",
1215
- " f.write(struct.pack('d', x))\n",
1216
- " f.write(struct.pack('d', y))\n",
1217
- " f.write(struct.pack('d', z))\n",
1218
- "\n",
1219
- " # rgb (デフォルトはグレー)\n",
1220
- " f.write(struct.pack('B', 128))\n",
1221
- " f.write(struct.pack('B', 128))\n",
1222
- " f.write(struct.pack('B', 128))\n",
1223
- "\n",
1224
- " # error\n",
1225
- " if confidence is not None and point_id <= len(confidence):\n",
1226
- " error = 1.0 / max(confidence[point_id-1], 0.001)\n",
1227
- " else:\n",
1228
- " error = 1.0\n",
1229
- " f.write(struct.pack('d', error))\n",
1230
- "\n",
1231
- " # track_length (0 for now)\n",
1232
- " f.write(struct.pack('Q', 0))\n",
1233
- "\n",
1234
- " print(f\"COLMAP points3D.bin saved to {output_file}\")\n",
1235
- "\n",
1236
- "\n",
1237
- "def export_colmap_binary(cameras_dict, pts3d, confidence, image_size, output_dir):\n",
1238
- " \"\"\"\n",
1239
- " COLMAPバイナリファイル（cameras.bin, images.bin, points3D.bin）を出力\n",
1240
- "\n",
1241
- " Args:\n",
1242
- " cameras_dict: カメラパラメータの辞書\n",
1243
- " pts3d: 3D点の配列 [N, 3]\n",
1244
- " confidence: 信頼度の配列 [N]\n",
1245
- " image_size: (width, height) 画像サイズ\n",
1246
- " output_dir: 出力ディレクトリパス\n",
1247
- " \"\"\"\n",
1248
- " output_path = Path(output_dir)\n",
1249
- " output_path.mkdir(parents=True, exist_ok=True)\n",
1250
- "\n",
1251
- " # cameras.bin\n",
1252
- " write_cameras_binary(\n",
1253
- " cameras_dict,\n",
1254
- " image_size,\n",
1255
- " output_path / 'cameras.bin'\n",
1256
- " )\n",
1257
- "\n",
1258
- " # images.bin\n",
1259
- " write_images_binary(\n",
1260
- " cameras_dict,\n",
1261
- " output_path / 'images.bin'\n",
1262
- " )\n",
1263
- "\n",
1264
- " # points3D.bin\n",
1265
- " write_points3D_binary(\n",
1266
- " pts3d,\n",
1267
- " confidence,\n",
1268
- " output_path / 'points3D.bin'\n",
1269
- " )\n",
1270
- "\n",
1271
- " print(f\"\\nCOLMAP binary files exported to {output_dir}/\")\n",
1272
- " print(f\" - cameras.bin: {len(cameras_dict)} cameras\")\n",
1273
- " print(f\" - images.bin: {len(cameras_dict)} images\")\n",
1274
- " print(f\" - points3D.bin: {len(pts3d)} points\")"
1275
- ],
1276
- "metadata": {
1277
- "id": "jNk5C0k1zkLD"
1278
- },
1279
- "execution_count": 9,
1280
- "outputs": []
1281
- },
1282
- {
1283
- "cell_type": "code",
1284
- "source": [
1285
- "# =====================================================================\n",
1286
- "# CELL 13: Gaussian Splatting Runner\n",
1287
- "# =====================================================================\n",
1288
- "def run_gaussian_splatting(source_dir, output_dir, iterations=30000):\n",
1289
- " \"\"\"Gaussian Splattingを実行\"\"\"\n",
1290
- " print(\"\\n=== Running Gaussian Splatting ===\")\n",
1291
- "\n",
1292
- " os.makedirs(output_dir, exist_ok=True)\n",
1293
- "\n",
1294
- " cmd = [\n",
1295
- " \"python\", \"/content/gaussian-splatting/train.py\",\n",
1296
- " \"-s\", source_dir,\n",
1297
- " \"-m\", output_dir,\n",
1298
- " \"--iterations\", str(iterations),\n",
1299
- " \"--eval\"\n",
1300
- " ]\n",
1301
- "\n",
1302
- " print(f\"Command: {' '.join(cmd)}\")\n",
1303
- " print(f\" Source: {source_dir}\")\n",
1304
- " print(f\" Output: {output_dir}\")\n",
1305
- "\n",
1306
- " result = subprocess.run(cmd, capture_output=False, text=True)\n",
1307
- "\n",
1308
- " if result.returncode == 0:\n",
1309
- " print(f\"\\n✓ Gaussian Splatting complete\")\n",
1310
- "\n",
1311
- " point_cloud_dir = os.path.join(output_dir, \"point_cloud\")\n",
1312
- " if os.path.exists(point_cloud_dir):\n",
1313
- " print(f\"\\n✓ Point cloud directory found: {point_cloud_dir}\")\n",
1314
- "\n",
1315
- " for item in sorted(os.listdir(point_cloud_dir)):\n",
1316
- " item_path = os.path.join(point_cloud_dir, item)\n",
1317
- " if os.path.isdir(item_path) and item.startswith(\"iteration_\"):\n",
1318
- " ply_file = os.path.join(item_path, \"point_cloud.ply\")\n",
1319
- " if os.path.exists(ply_file):\n",
1320
- " file_size = os.path.getsize(ply_file) / (1024 * 1024)\n",
1321
- " print(f\" ✓ {item}/point_cloud.ply ({file_size:.2f} MB)\")\n",
1322
- " else:\n",
1323
- " print(f\"\\n✗ Gaussian Splatting failed with return code {result.returncode}\")\n",
1324
- "\n",
1325
- " return output_dir"
1326
- ],
1327
- "metadata": {
1328
- "id": "o0n2RL3Ep5_Y"
1329
- },
1330
- "execution_count": 10,
1331
- "outputs": []
1332
- },
1333
- {
1334
- "cell_type": "code",
1335
- "source": [
1336
- "# =====================================================================\n",
1337
- "# CELL 14: Main Pipeline\n",
1338
- "# =====================================================================\n",
1339
- "def main_pipeline(image_dir, output_dir, square_size=1024, iterations=30000,\n",
1340
- " max_images=200, max_pairs=100, max_points=500000,\n",
1341
- " conf_threshold=1.001, preprocess_mode='none'):\n",
1342
- " \"\"\"メインパイプライン（DINO + CELL 11/12対応版）\"\"\"\n",
1343
- "\n",
1344
- " # STEP 0: Image Preprocessing\n",
1345
- " if preprocess_mode == 'biplet':\n",
1346
- " print(\"=\"*70)\n",
1347
- " print(\"STEP 0: Image Preprocessing (Biplet Crops)\")\n",
1348
- " print(\"=\"*70)\n",
1349
- "\n",
1350
- " temp_biplet_dir = os.path.join(output_dir, \"temp_biplet\")\n",
1351
- " biplet_dir = normalize_image_sizes_biplet(image_dir, temp_biplet_dir, size=square_size)\n",
1352
- "\n",
1353
- " images_dir = os.path.join(output_dir, \"images\")\n",
1354
- " os.makedirs(images_dir, exist_ok=True)\n",
1355
- "\n",
1356
- " biplet_suffixes = ['_left', '_right', '_top', '_bottom']\n",
1357
- " copied_count = 0\n",
1358
- "\n",
1359
- " for img_file in os.listdir(temp_biplet_dir):\n",
1360
- " if any(suffix in img_file for suffix in biplet_suffixes):\n",
1361
- " src = os.path.join(temp_biplet_dir, img_file)\n",
1362
- " dst = os.path.join(images_dir, img_file)\n",
1363
- " shutil.copy2(src, dst)\n",
1364
- " copied_count += 1\n",
1365
- "\n",
1366
- " print(f\"✓ Copied {copied_count} biplet images to {images_dir}\")\n",
1367
- "\n",
1368
- " original_images_dir = os.path.join(output_dir, \"original_images\")\n",
1369
- " os.makedirs(original_images_dir, exist_ok=True)\n",
1370
- "\n",
1371
- " original_count = 0\n",
1372
- " valid_extensions = ('.jpg', '.jpeg', '.png', '.bmp')\n",
1373
- " for img_file in os.listdir(image_dir):\n",
1374
- " if img_file.lower().endswith(valid_extensions):\n",
1375
- " src = os.path.join(image_dir, img_file)\n",
1376
- " dst = os.path.join(original_images_dir, img_file)\n",
1377
- " shutil.copy2(src, dst)\n",
1378
- " original_count += 1\n",
1379
- "\n",
1380
- " print(f\"✓ Saved {original_count} original images to {original_images_dir}\")\n",
1381
- " shutil.rmtree(temp_biplet_dir)\n",
1382
- " image_dir = images_dir\n",
1383
- " clear_memory()\n",
1384
- " else:\n",
1385
- " images_dir = os.path.join(output_dir, \"images\")\n",
1386
- " if not os.path.exists(images_dir):\n",
1387
- " print(\"=\"*70)\n",
1388
- " print(\"STEP 0: Copying images to output directory\")\n",
1389
- " print(\"=\"*70)\n",
1390
- " shutil.copytree(image_dir, images_dir)\n",
1391
- " print(f\"✓ Copied images to {images_dir}\")\n",
1392
- " image_dir = images_dir\n",
1393
- "\n",
1394
- " # STEP 1: Loading Images\n",
1395
- " print(\"\\n\" + \"=\"*70)\n",
1396
- " print(\"STEP 1: Loading and Preparing Images\")\n",
1397
- " print(\"=\"*70)\n",
1398
- "\n",
1399
- " image_paths = load_images_from_directory(image_dir, max_images=max_images)\n",
1400
- " print(f\"Loaded {len(image_paths)} images\")\n",
1401
- " clear_memory()\n",
1402
- "\n",
1403
- " # STEP 2: Image Pair Selection (DINO)\n",
1404
- " print(\"\\n\" + \"=\"*70)\n",
1405
- " print(\"STEP 2: Image Pair Selection (DINO)\")\n",
1406
- " print(\"=\"*70)\n",
1407
- "\n",
1408
- " max_pairs = min(max_pairs, 50)\n",
1409
- " pairs = get_image_pairs_dino(image_paths, max_pairs=max_pairs)\n",
1410
- " print(f\"Selected {len(pairs)} image pairs\")\n",
1411
- " clear_memory()\n",
1412
- "\n",
1413
- " # STEP 3: MASt3R 3D Reconstruction\n",
1414
- " print(\"\\n\" + \"=\"*70)\n",
1415
- " print(\"STEP 3: MASt3R 3D Reconstruction\")\n",
1416
- " print(\"=\"*70)\n",
1417
- "\n",
1418
- " device = Config.DEVICE\n",
1419
- " model = load_mast3r_model(device)\n",
1420
- " scene, mast3r_images = run_mast3r_pairs(model, image_paths, pairs, device)\n",
1421
- "\n",
1422
- " del model\n",
1423
- " clear_memory()\n",
1424
- "\n",
1425
- "\n",
1426
- "\n",
1427
- " # STEP 4: Converting to COLMAP (CELL 11/12使用)\n",
1428
- " print(\"\\n\" + \"=\"*70)\n",
1429
- " print(\"STEP 4: Converting to COLMAP (PINHOLE)\")\n",
1430
- " print(\"=\"*70)\n",
1431
- "\n",
1432
- " # 画像ファイル名のリストを作成\n",
1433
- " image_names = [os.path.basename(p) for p in image_paths]\n",
1434
- "\n",
1435
- " # CELL 11: カメラパラメータの抽出（修正版関数を使用）\n",
1436
- " cameras_dict, pts3d, confidence = extract_camera_params_process2(\n",
1437
- " scene=scene,\n",
1438
- " image_paths=image_paths,\n",
1439
- " conf_threshold=conf_threshold\n",
1440
- " )\n",
1441
- "\n",
1442
- " print(f\"Extracted {len(cameras_dict)} cameras with conf >= {conf_threshold}\")\n",
1443
- "\n",
1444
- " # 画像サイズを取得（最初の画像から）\n",
1445
- " from PIL import Image\n",
1446
- " first_img = Image.open(image_paths[0])\n",
1447
- " image_size = (first_img.width, first_img.height)\n",
1448
- " first_img.close()\n",
1449
- "\n",
1450
- " # COLMAP出力ディレクトリ\n",
1451
- " colmap_dir = os.path.join(output_dir, \"sparse/0\")\n",
1452
- " os.makedirs(colmap_dir, exist_ok=True)\n",
1453
- "\n",
1454
- " # CELL 12: COLMAPバイナリ形式でエクスポート（修正版関数を使用）\n",
1455
- " export_colmap_binary(\n",
1456
- " cameras_dict=cameras_dict,\n",
1457
- " pts3d=pts3d,\n",
1458
- " confidence=confidence,\n",
1459
- " image_size=image_size,\n",
1460
- " output_dir=colmap_dir\n",
1461
- " )\n",
1462
- "\n",
1463
- " del scene\n",
1464
- " clear_memory()\n",
1465
- "\n",
1466
- "\n",
1467
- "\n",
1468
- " # STEP 5: Running Gaussian Splatting\n",
1469
- " print(\"\\n\" + \"=\"*70)\n",
1470
- " print(\"STEP 5: Running Gaussian Splatting\")\n",
1471
- " print(\"=\"*70)\n",
1472
- "\n",
1473
- " source_dir = output_dir\n",
1474
- " model_output_dir = os.path.join(output_dir, \"gaussian_splatting\")\n",
1475
- "\n",
1476
- " gs_output = run_gaussian_splatting(\n",
1477
- " source_dir=source_dir,\n",
1478
- " output_dir=model_output_dir,\n",
1479
- " iterations=iterations\n",
1480
- " )\n",
1481
- "\n",
1482
- " # STEP 6: Verify Output\n",
1483
- " print(\"\\n\" + \"=\"*70)\n",
1484
- " print(\"PIPELINE COMPLETE\")\n",
1485
- " print(\"=\"*70)\n",
1486
- "\n",
1487
- " ply_path = os.path.join(\n",
1488
- " model_output_dir,\n",
1489
- " \"point_cloud\",\n",
1490
- " f\"iteration_{iterations}\",\n",
1491
- " \"point_cloud.ply\"\n",
1492
- " )\n",
1493
- "\n",
1494
- " if os.path.exists(ply_path):\n",
1495
- " file_size = os.path.getsize(ply_path) / (1024 * 1024)\n",
1496
- " print(f\"✓ Point cloud generated: {ply_path}\")\n",
1497
- " print(f\" Size: {file_size:.2f} MB\")\n",
1498
- " else:\n",
1499
- " print(f\"⚠️ Point cloud not found at: {ply_path}\")\n",
1500
- "\n",
1501
- " print(f\"\\nOutput directory structure:\")\n",
1502
- " print(f\" {output_dir}/\")\n",
1503
- " print(f\" ├── images/ (processed images)\")\n",
1504
- " if preprocess_mode == 'biplet':\n",
1505
- " print(f\" ├── original_images/ (original source images)\")\n",
1506
- " print(f\" ├── sparse/0/ (COLMAP data)\")\n",
1507
- " print(f\" │ ├── cameras.bin\")\n",
1508
- " print(f\" │ ├── images.bin\")\n",
1509
- " print(f\" │ └── points3D.bin\")\n",
1510
- " print(f\" └── gaussian_splatting/ (GS output)\")\n",
1511
- "\n",
1512
- " return gs_output"
1513
- ],
1514
- "metadata": {
1515
- "trusted": true,
1516
- "id": "U7Lk41hLTKyF"
1517
- },
1518
- "outputs": [],
1519
- "execution_count": 11
1520
- },
1521
- {
1522
- "cell_type": "code",
1523
- "source": [
1524
- "# =====================================================================\n",
1525
- "# CELL 15: Run Pipeline\n",
1526
- "# =====================================================================\n",
1527
- "if __name__ == \"__main__\":\n",
1528
- " IMAGE_DIR = \"/content/drive/MyDrive/your_folder/fountain\"\n",
1529
- " OUTPUT_DIR = \"/content/output\"\n",
1530
- "\n",
1531
- "\n",
1532
- " gs_output = main_pipeline(\n",
1533
- " image_dir=IMAGE_DIR,\n",
1534
- " output_dir=OUTPUT_DIR,\n",
1535
- " square_size=512,\n",
1536
- " iterations=1000,\n",
1537
- " max_images=15,\n",
1538
- " max_pairs=150,\n",
1539
- " max_points=300000,\n",
1540
- " conf_threshold=0.5,\n",
1541
- " preprocess_mode='biplet' # or 'none'\n",
1542
- " )\n",
1543
- "\n",
1544
- " print(\"\\n\" + \"=\"*70)\n",
1545
- " print(\"PIPELINE COMPLETE\")\n",
1546
- " print(\"=\"*70)\n",
1547
- " print(f\"Output directory: {gs_output}\")"
1548
- ],
1549
- "metadata": {
1550
- "trusted": true,
1551
- "id": "_-8kDLieTKyG",
1552
- "colab": {
1553
- "base_uri": "https://localhost:8080/"
1554
- },
1555
- "outputId": "253d0e53-e356-4ada-8793-916fa0174a06"
1556
- },
1557
- "outputs": [
1558
- {
1559
- "output_type": "stream",
1560
- "name": "stdout",
1561
- "text": [
1562
- "======================================================================\n",
1563
- "STEP 0: Image Preprocessing (Biplet Crops)\n",
1564
- "======================================================================\n",
1565
- "\n",
1566
- "=== Generating Biplet Crops (512x512) ===\n"
1567
- ]
1568
- },
1569
- {
1570
- "output_type": "stream",
1571
- "name": "stderr",
1572
- "text": [
1573
- "Creating biplets: 100%|██████████| 30/30 [00:02<00:00, 11.14it/s]\n"
1574
- ]
1575
- },
1576
- {
1577
- "output_type": "stream",
1578
- "name": "stdout",
1579
- "text": [
1580
- "\n",
1581
- "✓ Biplet generation complete:\n",
1582
- " Source images: 30\n",
1583
- " Biplet crops generated: 60\n",
1584
- " Original size distribution: {'1440x1920': 30}\n",
1585
- "✓ Copied 60 biplet images to /content/output/images\n",
1586
- "✓ Saved 30 original images to /content/output/original_images\n",
1587
- "\n",
1588
- "======================================================================\n",
1589
- "STEP 1: Loading and Preparing Images\n",
1590
- "======================================================================\n",
1591
- "\n",
1592
- "Loading images from: /content/output/images\n",
1593
- "⚠️ Limiting from 60 to 15 images\n",
1594
- "✓ Found 15 images\n",
1595
- "Loaded 15 images\n",
1596
- "\n",
1597
- "======================================================================\n",
1598
- "STEP 2: Image Pair Selection (DINO)\n",
1599
- "======================================================================\n",
1600
- "\n",
1601
- "=== Extracting DINO Global Features ===\n",
1602
- "Initial memory state:\n",
1603
- "GPU Memory - Allocated: 0.16GB, Reserved: 0.23GB\n",
1604
- "CPU Memory Usage: 42.5%\n"
1605
- ]
1606
- },
1607
- {
1608
- "output_type": "stream",
1609
- "name": "stderr",
1610
- "text": [
1611
- "/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:942: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
1612
- " warnings.warn(\n",
1613
- "DINO extraction: 100%|██████████| 4/4 [00:02<00:00, 1.83it/s]\n"
1614
- ]
1615
- },
1616
- {
1617
- "output_type": "stream",
1618
- "name": "stdout",
1619
- "text": [
1620
- "After DINO extraction:\n",
1621
- "GPU Memory - Allocated: 0.17GB, Reserved: 0.25GB\n",
1622
- "CPU Memory Usage: 42.6%\n",
1623
- "Initial pairs from DINO: 105\n",
1624
- "Selecting 50 diverse pairs from 105 candidates...\n",
1625
- "Selected pairs cover 15 / 15 images (100.0%)\n",
1626
- "Selected 50 image pairs\n",
1627
- "\n",
1628
- "======================================================================\n",
1629
- "STEP 3: MASt3R 3D Reconstruction\n",
1630
- "======================================================================\n",
1631
- "\n",
1632
- "=== Loading MASt3R Model ===\n",
1633
- "Attempting to load: naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric\n",
1634
- "⚠️ Failed to load MASt3R: tried to load naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric from huggingface, but failed\n",
1635
- "Trying DUSt3R instead: naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt\n",
1636
- "✓ Loaded DUSt3R model as fallback\n",
1637
- "✓ Model loaded on cuda\n",
1638
- "\n",
1639
- "=== Running MASt3R Reconstruction ===\n",
1640
- "Initial memory state:\n",
1641
- "GPU Memory - Allocated: 2.29GB, Reserved: 2.31GB\n",
1642
- "CPU Memory Usage: 42.5%\n",
1643
- "Processing 50 pairs...\n",
1644
- "Loading 15 images at 224x224...\n",
1645
- ">> Loading a list of 15 images\n",
1646
- " - adding /content/output/images/image_001_bottom.jpeg with resolution 512x512 --> 224x224\n",
1647
- " - adding /content/output/images/image_001_top.jpeg with resolution 512x512 --> 224x224\n",
1648
- " - adding /content/output/images/image_002_bottom.jpeg with resolution 512x512 --> 224x224\n",
1649
- " - adding /content/output/images/image_002_top.jpeg with resolution 512x512 --> 224x224\n",
1650
- " - adding /content/output/images/image_003_bottom.jpeg with resolution 512x512 --> 224x224\n",
1651
- " - adding /content/output/images/image_003_top.jpeg with resolution 512x512 --> 224x224\n",
1652
- " - adding /content/output/images/image_004_bottom.jpeg with resolution 512x512 --> 224x224\n",
1653
- " - adding /content/output/images/image_004_top.jpeg with resolution 512x512 --> 224x224\n",
1654
- " - adding /content/output/images/image_005_bottom.jpeg with resolution 512x512 --> 224x224\n",
1655
- " - adding /content/output/images/image_005_top.jpeg with resolution 512x512 --> 224x224\n",
1656
- " - adding /content/output/images/image_006_bottom.jpeg with resolution 512x512 --> 224x224\n",
1657
- " - adding /content/output/images/image_006_top.jpeg with resolution 512x512 --> 224x224\n",
1658
- " - adding /content/output/images/image_007_bottom.jpeg with resolution 512x512 --> 224x224\n",
1659
- " - adding /content/output/images/image_007_top.jpeg with resolution 512x512 --> 224x224\n",
1660
- " - adding /content/output/images/image_008_bottom.jpeg with resolution 512x512 --> 224x224\n",
1661
- " (Found 15 images)\n",
1662
- "Loaded 15 images\n",
1663
- "After loading images:\n",
1664
- "GPU Memory - Allocated: 2.29GB, Reserved: 2.31GB\n",
1665
- "CPU Memory Usage: 42.5%\n",
1666
- "Creating 50 image pairs...\n"
1667
- ]
1668
- },
1669
- {
1670
- "output_type": "stream",
1671
- "name": "stderr",
1672
- "text": [
1673
- "Preparing pairs: 100%|██████████| 50/50 [00:00<00:00, 377185.61it/s]\n"
1674
- ]
1675
- },
1676
- {
1677
- "output_type": "stream",
1678
- "name": "stdout",
1679
- "text": [
1680
- "Running MASt3R inference on 50 pairs...\n",
1681
- ">> Inference with model on 50 image pairs\n"
1682
- ]
1683
- },
1684
- {
1685
- "output_type": "stream",
1686
- "name": "stderr",
1687
- "text": [
1688
- "\r 0%| | 0/50 [00:00<?, ?it/s]/content/mast3r/dust3r/dust3r/inference.py:44: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
1689
- " with torch.cuda.amp.autocast(enabled=bool(use_amp)):\n",
1690
- "/content/mast3r/dust3r/dust3r/model.py:206: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
1691
- " with torch.cuda.amp.autocast(enabled=False):\n",
1692
- "/content/mast3r/dust3r/dust3r/inference.py:48: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
1693
- " with torch.cuda.amp.autocast(enabled=False):\n",
1694
- "100%|██████████| 50/50 [00:11<00:00, 4.44it/s]\n"
1695
- ]
1696
- },
1697
- {
1698
- "output_type": "stream",
1699
- "name": "stdout",
1700
- "text": [
1701
- "✓ MASt3R inference complete\n",
1702
- "After inference:\n",
1703
- "GPU Memory - Allocated: 2.29GB, Reserved: 2.31GB\n",
1704
- "CPU Memory Usage: 42.8%\n",
1705
- "Running global alignment...\n",
1706
- "Computing global alignment...\n",
1707
- " init edge (0*,14*) score=32.19751739501953\n",
1708
- " init edge (0,6*) score=23.234012603759766\n",
1709
- " init edge (6,10*) score=22.925432205200195\n",
1710
- " init edge (0,8*) score=21.67432975769043\n",
1711
- " init edge (5*,10) score=16.97602081298828\n",
1712
- " init edge (2*,14) score=16.639034271240234\n",
1713
- " init edge (7*,10) score=24.173402786254883\n",
1714
- " init edge (3*,7) score=19.910110473632812\n",
1715
- " init edge (3,4*) score=18.220979690551758\n",
1716
- " init edge (5,12*) score=17.51984405517578\n",
1717
- " init edge (3,13*) score=15.624801635742188\n",
1718
- " init edge (3,9*) score=15.045086860656738\n",
1719
- " init edge (1*,9) score=14.921503067016602\n",
1720
- " init edge (1,11*) score=15.762734413146973\n",
1721
- " init loss = 0.023337900638580322\n",
1722
- "Global alignement - optimizing for:\n",
1723
- "['pw_poses', 'im_depthmaps', 'im_poses', 'im_focals']\n"
1724
- ]
1725
- },
1726
- {
1727
- "output_type": "stream",
1728
- "name": "stderr",
1729
- "text": [
1730
- "100%|██████████| 50/50 [00:02<00:00, 21.67it/s, lr=1.08654e-05 loss=0.0153168]\n"
1731
- ]
1732
- },
1733
- {
1734
- "output_type": "stream",
1735
- "name": "stdout",
1736
- "text": [
1737
- "✓ Global alignment complete (final loss: 0.015317)\n",
1738
- "Final memory state:\n",
1739
- "GPU Memory - Allocated: 2.45GB, Reserved: 2.77GB\n",
1740
- "CPU Memory Usage: 42.8%\n",
1741
- "\n",
1742
- "======================================================================\n",
1743
- "STEP 4: Converting to COLMAP (PINHOLE)\n",
1744
- "======================================================================\n",
1745
- "\n",
1746
- "=== Extracting Camera Parameters ===\n",
1747
- "✓ Extracted camera parameters for 15 images\n",
1748
- "✓ Total 3D points: 752640\n",
1749
- "✓ After confidence filtering (>0.5): 752640 points\n",
1750
- "Extracted 15 cameras with conf >= 0.5\n",
1751
- "COLMAP cameras.bin saved to /content/output/sparse/0/cameras.bin\n",
1752
- "COLMAP images.bin saved to /content/output/sparse/0/images.bin\n",
1753
- "COLMAP points3D.bin saved to /content/output/sparse/0/points3D.bin\n",
1754
- "\n",
1755
- "COLMAP binary files exported to /content/output/sparse/0/\n",
1756
- " - cameras.bin: 15 cameras\n",
1757
- " - images.bin: 15 images\n",
1758
- " - points3D.bin: 752640 points\n",
1759
- "\n",
1760
- "======================================================================\n",
1761
- "STEP 5: Running Gaussian Splatting\n",
1762
- "======================================================================\n",
1763
- "\n",
1764
- "=== Running Gaussian Splatting ===\n",
1765
- "Command: python /content/gaussian-splatting/train.py -s /content/output -m /content/output/gaussian_splatting --iterations 1000 --eval\n",
1766
- " Source: /content/output\n",
1767
- " Output: /content/output/gaussian_splatting\n",
1768
- "\n",
1769
- "✓ Gaussian Splatting complete\n",
1770
- "\n",
1771
- "✓ Point cloud directory found: /content/output/gaussian_splatting/point_cloud\n",
1772
- " ✓ iteration_1000/point_cloud.ply (118.56 MB)\n",
1773
- "\n",
1774
- "======================================================================\n",
1775
- "PIPELINE COMPLETE\n",
1776
- "======================================================================\n",
1777
- "✓ Point cloud generated: /content/output/gaussian_splatting/point_cloud/iteration_1000/point_cloud.ply\n",
1778
- " Size: 118.56 MB\n",
1779
- "\n",
1780
- "Output directory structure:\n",
1781
- " /content/output/\n",
1782
- " ├── images/ (processed images)\n",
1783
- " ├── original_images/ (original source images)\n",
1784
- " ├── sparse/0/ (COLMAP data)\n",
1785
- " │ ├── cameras.bin\n",
1786
- " │ ├── images.bin\n",
1787
- " │ └── points3D.bin\n",
1788
- " └── gaussian_splatting/ (GS output)\n",
1789
- "\n",
1790
- "======================================================================\n",
1791
- "PIPELINE COMPLETE\n",
1792
- "======================================================================\n",
1793
- "Output directory: /content/output/gaussian_splatting\n"
1794
- ]
1795
- }
1796
- ],
1797
- "execution_count": 14
1798
- },
1799
- {
1800
- "cell_type": "code",
1801
- "source": [],
1802
- "metadata": {
1803
- "trusted": true,
1804
- "id": "vVlwllleTKyG"
1805
- },
1806
- "outputs": [],
1807
- "execution_count": 12
1808
- }
1809
- ]
1810
- }