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.gitignore

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··· 1 1 - # Python cache 2 2 - __pycache__/ 3 3 - *.py[cod] 4 4 - 5 5 - # Distribution / packaging 6 6 - workspace/ 7 7 - build/ 8 8 - dist/ 9 9 - *.egg-info/ 10 10 - .venv/ 11 11 - 12 12 - # Test/coverage 13 13 - .coverage 14 14 - .pytest_cache/ 15 15 - htmlcov/ 16 16 - .tox/ 17 17 - 18 1 # Media files 19 2 *.mp4 20 3 *.mov ··· 41 24 # Dev/planning docs 42 25 frame-ux-improvements.md 43 26 44 44 - # Removed files (kept locally for reference) 45 45 - benchmark.py 46 46 - estimate.py 47 47 - depth_estimators/ 48 48 - scripts/depth_pro.py 49 49 - scripts/stereo_converter.py 50 50 - scripts/stitch_video.py 51 51 - 52 27 # OS files 53 28 .DS_Store 54 29 55 30 # Editor 56 31 .vscode 57 57 - .ruff_cache/ 58 32 59 33 # Misc 60 34 temp*.png

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Cargo.lock

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··· 2218 2218 2219 2219 [[package]] 2220 2220 name = "spatial-maker" 2221 2221 - version = "0.1.3" 2221 2221 + version = "0.2.0" 2222 2222 dependencies = [ 2223 2223 "clap", 2224 2224 "dirs",

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RUST_README.md

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··· 13 13 - **Multi-format input** — JPEG, PNG, AVIF, JPEG XL, HEIC via native decoders or ffmpeg 14 14 - **MV-HEVC output** — Side-by-side, top-and-bottom, or separate stereo pairs with optional MV-HEVC packaging 15 15 16 16 + ## 🚧 massively under construction 🧱 17 17 + 16 18 ## Quick Start 17 19 18 20 ### Install the CLI 19 21 20 20 - ```bash 22 22 + ```sh 21 23 cargo install spatial-maker 22 24 ``` 23 25 26 26 + for the latest code (untested) 27 27 + 28 28 + ```sh 29 29 + cargo install --git https://github.com/mrgnw/spatial-maker --force 30 30 + ``` 31 31 + 32 32 + 33 33 + 24 34 ### Convert a Video 25 35 26 36 ```bash 27 37 INPUT_VIDEO=~/Movies/my-video.mp4 28 28 - OUTPUT_VIDEO=~/Desktop/spatial-video.mp4 29 38 MODEL=b # s (small), b (base), or l (large) 30 39 31 40 spatial-maker video "$INPUT_VIDEO" -o "$OUTPUT_VIDEO" --model "$MODEL" ··· 35 44 36 45 ```bash 37 46 INPUT_PHOTO=~/Pictures/photo.jpg 38 38 - OUTPUT_PHOTO=~/Desktop/spatial-photo.jpg 39 47 MODEL=b 40 48 MAX_DISPARITY=30 # Higher = more 3D depth 41 49 42 42 - spatial-maker photo "$INPUT_PHOTO" -o "$OUTPUT_PHOTO" \ 50 50 + spatial-maker photo "$INPUT_PHOTO" 43 51 --model "$MODEL" \ 44 52 --max-disparity "$MAX_DISPARITY" 45 53 ```

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depth_estimators/depth_anything_v2.py

Reviewed

··· 1 1 - import time 2 2 - import subprocess 3 3 - import os 4 4 - import sys 5 5 - import psutil 6 6 - from pathlib import Path 7 7 - import tempfile 8 8 - import torch 9 9 - import numpy as np 10 10 - from PIL import Image 11 11 - import cv2 12 12 - from tqdm import tqdm 13 13 - 14 14 - from depth_anything_v2.dpt import DepthAnythingV2 15 15 - from scripts.stitch_video import stitch_frames_to_hevc 16 16 - 17 17 - _threads_configured = False 18 18 - 19 19 - 20 20 - class DepthAnythingV2Estimator: 21 21 - def __init__(self, encoder='vits', num_threads=None): 22 22 - self.model = None 23 23 - self.device = None 24 24 - self.encoder = encoder 25 25 - self.num_threads = num_threads if num_threads is not None else max(1, os.cpu_count() - 4) 26 26 - 27 27 - def load_model(self): 28 28 - global _threads_configured 29 29 - if self.model is not None: 30 30 - return 31 31 - 32 32 - if not _threads_configured: 33 33 - torch.set_num_threads(self.num_threads) 34 34 - torch.set_num_interop_threads(self.num_threads) 35 35 - _threads_configured = True 36 36 - 37 37 - model_configs = { 38 38 - 'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}, 39 39 - 'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]}, 40 40 - 'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, 41 41 - } 42 42 - 43 43 - checkpoint_name = f"depth_anything_v2_{self.encoder}.pth" 44 44 - checkpoint_path = self._find_checkpoint(checkpoint_name) 45 45 - 46 46 - if torch.backends.mps.is_available(): 47 47 - self.device = torch.device("mps") 48 48 - elif torch.cuda.is_available(): 49 49 - self.device = torch.device("cuda") 50 50 - else: 51 51 - self.device = torch.device("cpu") 52 52 - 53 53 - self.model = DepthAnythingV2(**model_configs[self.encoder]) 54 54 - # Load weights directly to target device to avoid MPS/CPU tensor mismatch 55 55 - self.model.load_state_dict(torch.load(str(checkpoint_path), map_location=self.device)) 56 56 - self.model = self.model.to(self.device).eval() 57 57 - 58 58 - def _find_checkpoint(self, checkpoint_name: str) -> Path: 59 59 - """ 60 60 - Find checkpoint file in multiple possible locations. 61 61 - 62 62 - Searches in order: 63 63 - 1. Next to the script's source directory (for development) 64 64 - 2. User's home directory ~/.spatial-maker/checkpoints/ 65 65 - 3. XDG data directory 66 66 - 4. Current working directory ./checkpoints/ 67 67 - """ 68 68 - search_paths = [ 69 69 - # Development: relative to source file 70 70 - Path(__file__).parent.parent / "checkpoints" / checkpoint_name, 71 71 - # User home directory 72 72 - Path.home() / ".spatial-maker" / "checkpoints" / checkpoint_name, 73 73 - # XDG data home 74 74 - Path(os.environ.get("XDG_DATA_HOME", Path.home() / ".local" / "share")) 75 75 - / "spatial-maker" / "checkpoints" / checkpoint_name, 76 76 - # Current working directory 77 77 - Path.cwd() / "checkpoints" / checkpoint_name, 78 78 - ] 79 79 - 80 80 - # Also check if SPATIAL_MAKER_CHECKPOINTS env var is set 81 81 - env_checkpoint_dir = os.environ.get("SPATIAL_MAKER_CHECKPOINTS") 82 82 - if env_checkpoint_dir: 83 83 - search_paths.insert(0, Path(env_checkpoint_dir) / checkpoint_name) 84 84 - 85 85 - for path in search_paths: 86 86 - if path.exists(): 87 87 - return path 88 88 - 89 89 - # Not found - provide helpful error message 90 90 - raise FileNotFoundError( 91 91 - f"Checkpoint '{checkpoint_name}' not found.\n" 92 92 - f"Searched locations:\n" 93 93 - + "\n".join(f" - {p}" for p in search_paths) 94 94 - + "\n\nPlease download the checkpoint and place it in one of these locations:\n" 95 95 - f" mkdir -p ~/.spatial-maker/checkpoints\n" 96 96 - f" # Then copy {checkpoint_name} there" 97 97 - ) 98 98 - 99 99 - def process_video(self, video_path: str, output_dir: str = "output", max_frames: int = None): 100 100 - process = psutil.Process() 101 101 - mem_before = process.memory_info().rss / 1024 / 1024 102 102 - 103 103 - warmup_start = time.time() 104 104 - self.load_model() 105 105 - warmup_time = time.time() - warmup_start 106 106 - 107 107 - mem_after_load = process.memory_info().rss / 1024 / 1024 108 108 - print(f" Model load time: {warmup_time:.3f}s") 109 109 - print(f" Memory after load: {mem_after_load:.1f} MB (delta: +{mem_after_load - mem_before:.1f} MB)") 110 110 - 111 111 - output_dir = Path(output_dir) 112 112 - output_dir.mkdir(parents=True, exist_ok=True) 113 113 - 114 114 - cap = cv2.VideoCapture(video_path) 115 115 - frames = [] 116 116 - i = 0 117 117 - while True: 118 118 - ret, frame = cap.read() 119 119 - if not ret: 120 120 - break 121 121 - if max_frames is not None and i >= max_frames: 122 122 - break 123 123 - frames.append(frame) 124 124 - i += 1 125 125 - cap.release() 126 126 - 127 127 - warmup_start = time.time() 128 128 - with torch.no_grad(): 129 129 - _ = self.model.infer_image(frames[0], 518) 130 130 - warmup_inference = time.time() - warmup_start 131 131 - print(f" First inference (warmup): {warmup_inference:.3f}s") 132 132 - 133 133 - frame_times = [] 134 134 - 135 135 - for i, frame in enumerate(tqdm(frames, desc=" Processing frames", unit="frame")): 136 136 - start = time.time() 137 137 - with torch.no_grad(): 138 138 - depth = self.model.infer_image(frame, 518) 139 139 - elapsed = time.time() - start 140 140 - frame_times.append(elapsed) 141 141 - 142 142 - depth_normalized = ((depth - depth.min()) / (depth.max() - depth.min()) * 255).astype(np.uint8) 143 143 - Image.fromarray(depth_normalized).save(output_dir / f"depth_frame_{i+1:04d}.png") 144 144 - 145 145 - total_time = sum(frame_times) 146 146 - avg_time = total_time / len(frame_times) 147 147 - fps = 1 / avg_time if avg_time > 0 else 0 148 148 - 149 149 - mem_peak = process.memory_info().rss / 1024 / 1024 150 150 - 151 151 - print(f" Stitching frames to HEVC video...") 152 152 - # Create video path adjacent to output_dir with same name 153 153 - output_dir_name = output_dir.name 154 154 - video_path = output_dir.parent / f"{output_dir_name}.mp4" 155 155 - stitch_frames_to_hevc(str(output_dir), str(video_path), fps=24) 156 156 - 157 157 - return { 158 158 - "frames_processed": len(frame_times), 159 159 - "total_time": total_time, 160 160 - "avg_time_per_frame": avg_time, 161 161 - "fps": round(fps, 2), 162 162 - "warmup_time": warmup_time, 163 163 - "warmup_inference": warmup_inference, 164 164 - "memory_mb": round(mem_peak, 1), 165 165 - "device": str(self.device), 166 166 - "video_path": str(video_path) 167 167 - }

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pipeline.py

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··· 1 1 - #!/usr/bin/env python3 2 2 - """ 3 3 - End-to-end pipeline for converting 2D videos to spatial 3D videos. 4 4 - 5 5 - Usage: 6 6 - spatial-maker video.mp4 7 7 - spatial-maker /path/to/folder 8 8 - spatial-maker video.mp4 --encoder l --max-disparity 40 9 9 - """ 10 10 - 11 11 - import argparse 12 12 - import json 13 13 - import subprocess 14 14 - import sys 15 15 - from pathlib import Path 16 16 - import shutil 17 17 - 18 18 - from scripts.downscale_video import downscale_to_1080p24 19 19 - from scripts.depth_to_stereo import process_video_to_sbs 20 20 - 21 21 - VIDEO_EXTENSIONS = {'.mp4', '.mov', '.avi', '.mkv', '.m4v', '.webm', '.wmv', '.flv'} 22 22 - ENCODER_MAP = {'s': 'vits', 'm': 'vitb', 'l': 'vitl'} 23 23 - 24 24 - 25 25 - def emit_json(event: str, **kwargs): 26 26 - print(json.dumps({'event': event, **kwargs}), flush=True) 27 27 - 28 28 - 29 29 - def check_spatial_cli(): 30 30 - """Check if the spatial CLI is available.""" 31 31 - try: 32 32 - result = subprocess.run( 33 33 - ['spatial', '--version'], capture_output=True, text=True 34 34 - ) 35 35 - return result.returncode == 0 36 36 - except FileNotFoundError: 37 37 - return False 38 38 - 39 39 - 40 40 - def find_videos_in_folder(folder: Path) -> list[Path]: 41 41 - """Find all video files in a folder (non-recursive).""" 42 42 - videos = [] 43 43 - for ext in VIDEO_EXTENSIONS: 44 44 - videos.extend(folder.glob(f'*{ext}')) 45 45 - videos.extend(folder.glob(f'*{ext.upper()}')) 46 46 - return sorted(set(videos)) 47 47 - 48 48 - 49 49 - def extract_audio(video_path: str, output_path: str) -> bool: 50 50 - """Extract audio from video to AAC file. Returns True if audio exists.""" 51 51 - cmd = [ 52 52 - 'ffmpeg', 53 53 - '-y', 54 54 - '-i', 55 55 - video_path, 56 56 - '-vn', # No video 57 57 - '-acodec', 58 58 - 'aac', 59 59 - '-b:a', 60 60 - '192k', 61 61 - output_path, 62 62 - ] 63 63 - result = subprocess.run(cmd, capture_output=True, text=True) 64 64 - return result.returncode == 0 and Path(output_path).exists() 65 65 - 66 66 - 67 67 - def mux_audio_to_video(video_path: str, audio_path: str, output_path: str) -> str: 68 68 - """Mux audio into video file.""" 69 69 - cmd = [ 70 70 - 'ffmpeg', 71 71 - '-y', 72 72 - '-i', 73 73 - video_path, 74 74 - '-i', 75 75 - audio_path, 76 76 - '-c:v', 77 77 - 'copy', 78 78 - '-c:a', 79 79 - 'aac', 80 80 - '-b:a', 81 81 - '192k', 82 82 - '-shortest', 83 83 - output_path, 84 84 - ] 85 85 - result = subprocess.run(cmd, capture_output=True, text=True) 86 86 - if result.returncode != 0: 87 87 - # If muxing fails, return original video without audio 88 88 - return video_path 89 89 - return output_path 90 90 - 91 91 - 92 92 - def run_pipeline( 93 93 - input_video: str, 94 94 - output_path: str = None, 95 95 - encoder: str = 'vits', 96 96 - max_disparity: int = 40, 97 97 - keep_intermediate: bool = False, 98 98 - skip_downscale: bool = False, 99 99 - duration: float = None, 100 100 - json_progress: bool = False, 101 101 - ): 102 102 - """Run the full 2D to spatial video conversion pipeline.""" 103 103 - input_path = Path(input_video) 104 104 - if not input_path.exists(): 105 105 - if json_progress: 106 106 - emit_json('error', message=f'Input video not found: {input_video}') 107 107 - raise FileNotFoundError(f'Input video not found: {input_video}') 108 108 - 109 109 - if output_path is None: 110 110 - output_path = Path('output') / f'{input_path.stem}_spatial.mov' 111 111 - else: 112 112 - output_path = Path(output_path) 113 113 - 114 114 - output_path.parent.mkdir(parents=True, exist_ok=True) 115 115 - work_dir = output_path.parent / f'{output_path.stem}_work' 116 116 - work_dir.mkdir(exist_ok=True) 117 117 - 118 118 - try: 119 119 - # Stage 1: Downscale 120 120 - if skip_downscale: 121 121 - prepared_video = str(input_path) 122 122 - else: 123 123 - if json_progress: 124 124 - emit_json('stage', stage='downscale', file=input_path.name) 125 125 - else: 126 126 - print('↓ Downscaling to 1080p24...') 127 127 - prepared_video = downscale_to_1080p24( 128 128 - str(input_path), 129 129 - output_path=str(work_dir / f'{input_path.stem}_1080p.mp4') 130 130 - if duration 131 131 - else None, 132 132 - duration=duration, 133 133 - ) 134 134 - 135 135 - # Stage 2: Depth + SBS 136 136 - if json_progress: 137 137 - emit_json('stage', stage='depth_stereo', file=input_path.name) 138 138 - else: 139 139 - print( 140 140 - f'◐ Generating depth & stereo (encoder={encoder[-1]}, disparity={max_disparity})...' 141 141 - ) 142 142 - sbs_video = work_dir / f'{input_path.stem}_sbs.mp4' 143 143 - max_frames = int(duration * 24) if duration else None 144 144 - sbs_result = process_video_to_sbs( 145 145 - prepared_video, 146 146 - str(sbs_video), 147 147 - encoder=encoder, 148 148 - max_disparity=max_disparity, 149 149 - fps=24, 150 150 - max_frames=max_frames, 151 151 - json_progress=json_progress, 152 152 - ) 153 153 - 154 154 - # Stage 2.5: Add audio from original video to SBS 155 155 - if json_progress: 156 156 - emit_json('stage', stage='audio_mux', file=input_path.name) 157 157 - audio_file = work_dir / f'{input_path.stem}_audio.aac' 158 158 - if extract_audio(str(input_path), str(audio_file)): 159 159 - sbs_with_audio = work_dir / f'{input_path.stem}_sbs_audio.mp4' 160 160 - sbs_video = mux_audio_to_video( 161 161 - str(sbs_video), str(audio_file), str(sbs_with_audio) 162 162 - ) 163 163 - else: 164 164 - sbs_video = str(sbs_video) 165 165 - 166 166 - # Stage 3: Spatial video 167 167 - if json_progress: 168 168 - emit_json('stage', stage='spatial_make', file=input_path.name) 169 169 - else: 170 170 - print('◉ Creating spatial video...') 171 171 - if not check_spatial_cli(): 172 172 - msg = "'spatial' CLI not found. Install: brew install spatial" 173 173 - if json_progress: 174 174 - emit_json('error', message=msg) 175 175 - else: 176 176 - print(f'⚠ {msg}') 177 177 - print(f' SBS video: {sbs_result["output_path"]}') 178 178 - return sbs_result['output_path'] 179 179 - 180 180 - cmd = [ 181 181 - 'spatial', 182 182 - 'make', 183 183 - '-i', 184 184 - sbs_video if isinstance(sbs_video, str) else str(sbs_video), 185 185 - '-f', 186 186 - 'sbs', 187 187 - '-o', 188 188 - str(output_path), 189 189 - '-q', 190 190 - '0.9', 191 191 - '-y', 192 192 - ] 193 193 - result = subprocess.run(cmd, capture_output=True, text=True) 194 194 - if result.returncode != 0: 195 195 - msg = f'spatial CLI failed: {result.stderr}' 196 196 - if json_progress: 197 197 - emit_json('error', message=msg) 198 198 - raise RuntimeError(msg) 199 199 - 200 200 - if json_progress: 201 201 - emit_json('done', output=str(output_path)) 202 202 - 203 203 - return str(output_path) 204 204 - 205 205 - finally: 206 206 - if not keep_intermediate and work_dir.exists(): 207 207 - shutil.rmtree(work_dir, ignore_errors=True) 208 208 - 209 209 - 210 210 - def run_batch( 211 211 - folder: Path, 212 212 - encoder: str = 'vits', 213 213 - max_disparity: int = 40, 214 214 - keep_intermediate: bool = False, 215 215 - skip_downscale: bool = False, 216 216 - duration: float = None, 217 217 - ): 218 218 - """Process all videos in a folder.""" 219 219 - videos = find_videos_in_folder(folder) 220 220 - 221 221 - if not videos: 222 222 - print(f'No video files found in {folder}') 223 223 - return [] 224 224 - 225 225 - output_folder = folder / 'spatial' 226 226 - output_folder.mkdir(exist_ok=True) 227 227 - 228 228 - print(f'\n◎ Processing {len(videos)} videos → {output_folder}\n') 229 229 - 230 230 - results = [] 231 231 - failed = [] 232 232 - 233 233 - for i, video in enumerate(videos, 1): 234 234 - print(f'[{i}/{len(videos)}] {video.name}') 235 235 - output_path = output_folder / f'{video.stem}_spatial.mov' 236 236 - 237 237 - try: 238 238 - result = run_pipeline( 239 239 - str(video), 240 240 - output_path=str(output_path), 241 241 - encoder=encoder, 242 242 - max_disparity=max_disparity, 243 243 - keep_intermediate=keep_intermediate, 244 244 - skip_downscale=skip_downscale, 245 245 - duration=duration, 246 246 - ) 247 247 - results.append(result) 248 248 - print(f' ✓ Done\n') 249 249 - except Exception as e: 250 250 - failed.append((video, str(e))) 251 251 - print(f' ✗ Failed: {e}\n') 252 252 - 253 253 - # Summary 254 254 - print(f'{"─" * 40}') 255 255 - print(f'✓ {len(results)}/{len(videos)} completed') 256 256 - if failed: 257 257 - for video, error in failed: 258 258 - print(f'✗ {video.name}: {error}') 259 259 - print(f'→ {output_folder}') 260 260 - 261 261 - return results 262 262 - 263 263 - 264 264 - def main(): 265 265 - parser = argparse.ArgumentParser( 266 266 - description='Convert 2D video to spatial 3D video for VR headsets', 267 267 - formatter_class=argparse.RawDescriptionHelpFormatter, 268 268 - epilog=""" 269 269 - Examples: 270 270 - spatial-maker video.mp4 271 271 - spatial-maker /path/to/videos/ 272 272 - spatial-maker video.mp4 --encoder l 273 273 - spatial-maker video.mp4 --duration 10 274 274 - """, 275 275 - ) 276 276 - parser.add_argument('input', help='Input video file or folder') 277 277 - parser.add_argument( 278 278 - '-o', '--output', help='Output path (default: output/name_spatial.mov)' 279 279 - ) 280 280 - parser.add_argument( 281 281 - '--encoder', 282 282 - choices=['s', 'm', 'l'], 283 283 - default='s', 284 284 - help='Model size: s=small, m=medium, l=large (default: s)', 285 285 - ) 286 286 - parser.add_argument( 287 287 - '--max-disparity', 288 288 - type=int, 289 289 - default=40, 290 290 - help='3D intensity, 20-50 for 1080p (default: 40)', 291 291 - ) 292 292 - parser.add_argument( 293 293 - '--keep-intermediate', action='store_true', help='Keep intermediate files' 294 294 - ) 295 295 - parser.add_argument( 296 296 - '--skip-downscale', action='store_true', help='Skip 1080p24 downscale' 297 297 - ) 298 298 - parser.add_argument('--duration', type=float, help='Process only first N seconds') 299 299 - parser.add_argument( 300 300 - '--json-progress', 301 301 - action='store_true', 302 302 - help='Output JSON progress lines to stdout (for GUI integration)', 303 303 - ) 304 304 - 305 305 - args = parser.parse_args() 306 306 - input_path = Path(args.input) 307 307 - encoder = ENCODER_MAP[args.encoder] 308 308 - 309 309 - try: 310 310 - if input_path.is_dir(): 311 311 - if args.output: 312 312 - print('Note: --output ignored for folders, using <folder>/spatial/') 313 313 - results = run_batch( 314 314 - input_path, 315 315 - encoder=encoder, 316 316 - max_disparity=args.max_disparity, 317 317 - keep_intermediate=args.keep_intermediate, 318 318 - skip_downscale=args.skip_downscale, 319 319 - duration=args.duration, 320 320 - ) 321 321 - if not results: 322 322 - sys.exit(1) 323 323 - else: 324 324 - if not args.json_progress: 325 325 - print(f'\n◎ {input_path.name}\n') 326 326 - result = run_pipeline( 327 327 - args.input, 328 328 - output_path=args.output, 329 329 - encoder=encoder, 330 330 - max_disparity=args.max_disparity, 331 331 - keep_intermediate=args.keep_intermediate, 332 332 - skip_downscale=args.skip_downscale, 333 333 - duration=args.duration, 334 334 - json_progress=args.json_progress, 335 335 - ) 336 336 - if not args.json_progress: 337 337 - print(f'\n✓ {result}\n') 338 338 - except Exception as e: 339 339 - if args.json_progress: 340 340 - emit_json('error', message=str(e)) 341 341 - else: 342 342 - print(f'\n✗ {e}\n', file=sys.stderr) 343 343 - sys.exit(1) 344 344 - 345 345 - 346 346 - if __name__ == '__main__': 347 347 - main()

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pyproject.toml

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··· 1 1 - [project] 2 2 - name = "spatial-maker" 3 3 - version = "0.1.0" 4 4 - description = "Convert 2D videos to stereoscopic 3D spatial videos for Apple Vision Pro using AI depth estimation" 5 5 - requires-python = ">=3.10" 6 6 - license = "MIT" 7 7 - readme = "README.md" 8 8 - keywords = ["spatial-video", "3d", "stereoscopic", "apple-vision-pro", "depth-estimation"] 9 9 - classifiers = [ 10 10 - "Development Status :: 3 - Alpha", 11 11 - "Topic :: Multimedia :: Video :: Conversion", 12 12 - "License :: OSI Approved :: MIT License", 13 13 - "Programming Language :: Python :: 3", 14 14 - "Operating System :: MacOS", 15 15 - ] 16 16 - dependencies = [ 17 17 - "jkp-depth-anything-v2", 18 18 - "numpy", 19 19 - "opencv-python>=4.11.0.86", 20 20 - "torch>=2", 21 21 - "tqdm>=4.67.1", 22 22 - ] 23 23 - 24 24 - [project.scripts] 25 25 - spatial-maker = "pipeline:main" 26 26 - 27 27 - [project.urls] 28 28 - Homepage = "https://github.com/mrgnw/spatial-maker" 29 29 - Repository = "https://github.com/mrgnw/spatial-maker" 30 30 - 31 31 - [build-system] 32 32 - requires = ["hatchling"] 33 33 - build-backend = "hatchling.build" 34 34 - 35 35 - [tool.hatch.build.targets.sdist] 36 36 - only-include = [ 37 37 - "pipeline.py", 38 38 - "scripts/depth_to_stereo.py", 39 39 - "scripts/downscale_video.py", 40 40 - "pyproject.toml", 41 41 - "README.md", 42 42 - "LICENSE", 43 43 - ] 44 44 - 45 45 - [tool.hatch.build.targets.wheel] 46 46 - only-include = [ 47 47 - "pipeline.py", 48 48 - "scripts/depth_to_stereo.py", 49 49 - "scripts/downscale_video.py", 50 50 - ] 51 51 - 52 52 - [tool.ruff.format] 53 53 - quote-style = "single" 54 54 - indent-style = "tab"

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scripts/coreml_conversion/README.md

Reviewed

··· 1 1 - # CoreML Conversion Scripts 2 2 - 3 3 - This directory contains scripts to convert Depth Anything V2 PyTorch models to CoreML format for optimized inference on Apple Silicon. 4 4 - 5 5 - ## Quick Start 6 6 - 7 7 - ```bash 8 8 - # 1. Download PyTorch checkpoints (if not already present) 9 9 - cd checkpoints 10 10 - curl -L -o depth_anything_v2_vitb.pth \ 11 11 - "https://huggingface.co/depth-anything/Depth-Anything-V2-Base/resolve/main/depth_anything_v2_vitb.pth" 12 12 - curl -L -o depth_anything_v2_vitl.pth \ 13 13 - "https://huggingface.co/depth-anything/Depth-Anything-V2-Large/resolve/main/depth_anything_v2_vitl.pth" 14 14 - 15 15 - # 2. Convert to CoreML 16 16 - cd .. 17 17 - uv run --with coremltools --with torch --with jkp-depth-anything-v2 \ 18 18 - python scripts/coreml_conversion/convert_to_coreml.py 19 19 - ``` 20 20 - 21 21 - ## What Gets Created 22 22 - 23 23 - The script generates CoreML models in `checkpoints/`: 24 24 - 25 25 - - `DepthAnythingV2BaseF16.mlpackage` (186 MB) — Base model, Float16 26 26 - - `DepthAnythingV2LargeF16.mlpackage` (638 MB) — Large model, Float16 27 27 - 28 28 - These can be used directly with CoreML on macOS 15+ / iOS 18+. 29 29 - 30 30 - ## Technical Details 31 31 - 32 32 - ### Conversion Process 33 33 - 34 34 - 1. Loads PyTorch checkpoint (`.pth`) 35 35 - 2. Traces model with `torch.jit.trace` using dummy input 36 36 - 3. Converts to CoreML using `coremltools.convert()` 37 37 - 4. Sets precision to Float16 for optimal ANE performance 38 38 - 5. Adds metadata (author, license, descriptions) 39 39 - 6. Saves as `.mlpackage` format 40 40 - 41 41 - ### Model Configuration 42 42 - 43 43 - **Base (vitb):** 44 44 - - Encoder: ViT-B 45 45 - - Features: 128 46 46 - - Out channels: [96, 192, 384, 768] 47 47 - - Parameters: 97.5M 48 48 - - License: Apache-2.0 49 49 - 50 50 - **Large (vitl):** 51 51 - - Encoder: ViT-L 52 52 - - Features: 256 53 53 - - Out channels: [256, 512, 1024, 1024] 54 54 - - Parameters: 335.3M 55 55 - - License: CC-BY-NC-4.0 56 56 - 57 57 - ### Requirements 58 58 - 59 59 - - macOS (CoreML conversion requires macOS) 60 60 - - Python 3.10+ 61 61 - - `coremltools` — Apple's CoreML conversion library 62 62 - - `torch` — PyTorch 63 63 - - `jkp-depth-anything-v2` — Depth Anything V2 model architecture 64 64 - 65 65 - All dependencies are automatically managed by `uv run`. 66 66 - 67 67 - ## Why CoreML? 68 68 - 69 69 - CoreML models on Apple Silicon: 70 70 - - Use the **Neural Engine** (ANE) for massive acceleration 71 71 - - Are **10-20x faster** than ONNX CPU inference 72 72 - - Are **pre-compiled** — no runtime compilation overhead 73 73 - - Support Float16 precision natively on ANE 74 74 - 75 75 - Expected performance on M4 Pro: 76 76 - - Base: ~60-90ms per frame (~11-16 fps) 77 77 - - Large: ~200-300ms per frame (~3-5 fps) 78 78 - 79 79 - Compare to ONNX CPU: 80 80 - - Small: 430ms 81 81 - - Base: ~1-2s (estimated)

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scripts/coreml_conversion/convert_to_coreml.py

Reviewed

··· 1 1 - #!/usr/bin/env python3 2 2 - """ 3 3 - Convert Depth Anything V2 models (Base and Large) to CoreML format. 4 4 - 5 5 - This script converts PyTorch Depth Anything V2 models to Apple's CoreML format 6 6 - for optimized inference on Apple Silicon (M-series chips) using the Neural Engine. 7 7 - 8 8 - Requirements: 9 9 - uv run --with coremltools --with torch --with jkp-depth-anything-v2 \ 10 10 - python scripts/coreml_conversion/convert_to_coreml.py 11 11 - 12 12 - Models will be saved to: checkpoints/ 13 13 - """ 14 14 - 15 15 - import sys 16 16 - from pathlib import Path 17 17 - 18 18 - import coremltools as ct 19 19 - import torch 20 20 - from depth_anything_v2.dpt import DepthAnythingV2 21 21 - 22 22 - # Model configurations 23 23 - MODEL_CONFIGS = { 24 24 - 'vitb': { 25 25 - 'encoder': 'vitb', 26 26 - 'features': 128, 27 27 - 'out_channels': [96, 192, 384, 768], 28 28 - 'checkpoint': 'depth_anything_v2_vitb.pth', 29 29 - 'output': 'DepthAnythingV2BaseF16.mlpackage', 30 30 - }, 31 31 - 'vitl': { 32 32 - 'encoder': 'vitl', 33 33 - 'features': 256, 34 34 - 'out_channels': [256, 512, 1024, 1024], 35 35 - 'checkpoint': 'depth_anything_v2_vitl.pth', 36 36 - 'output': 'DepthAnythingV2LargeF16.mlpackage', 37 37 - }, 38 38 - } 39 39 - 40 40 - INPUT_SIZE = 518 41 41 - 42 42 - 43 43 - def convert_model(model_key: str, checkpoint_dir: Path, output_dir: Path): 44 44 - """Convert a single model to CoreML format.""" 45 45 - config = MODEL_CONFIGS[model_key] 46 46 - checkpoint_path = checkpoint_dir / config['checkpoint'] 47 47 - output_path = output_dir / config['output'] 48 48 - 49 49 - print(f'\n{"=" * 60}') 50 50 - print(f'Converting {model_key.upper()} model to CoreML') 51 51 - print(f'{"=" * 60}') 52 52 - 53 53 - # Check if checkpoint exists 54 54 - if not checkpoint_path.exists(): 55 55 - print(f'❌ Checkpoint not found: {checkpoint_path}') 56 56 - print(f'Please download from:') 57 57 - print( 58 58 - f' https://huggingface.co/depth-anything/Depth-Anything-V2-{model_key.capitalize()}/resolve/main/{config["checkpoint"]}' 59 59 - ) 60 60 - return False 61 61 - 62 62 - print(f'✓ Loading PyTorch model from {checkpoint_path}') 63 63 - model = DepthAnythingV2( 64 64 - encoder=config['encoder'], 65 65 - features=config['features'], 66 66 - out_channels=config['out_channels'], 67 67 - ) 68 68 - model.load_state_dict(torch.load(str(checkpoint_path), map_location='cpu')) 69 69 - model.eval() 70 70 - 71 71 - print(f'✓ Tracing model with input shape (1, 3, {INPUT_SIZE}, {INPUT_SIZE})') 72 72 - example_input = torch.randn(1, 3, INPUT_SIZE, INPUT_SIZE) 73 73 - 74 74 - with torch.no_grad(): 75 75 - traced_model = torch.jit.trace(model, example_input) 76 76 - 77 77 - print('✓ Converting to CoreML (Float16 precision)') 78 78 - print(' This may take a few minutes...') 79 79 - 80 80 - mlmodel = ct.convert( 81 81 - traced_model, 82 82 - inputs=[ 83 83 - ct.TensorType( 84 84 - name='image', 85 85 - shape=(1, 3, INPUT_SIZE, INPUT_SIZE), 86 86 - dtype=float, 87 87 - ) 88 88 - ], 89 89 - outputs=[ct.TensorType(name='depth')], 90 90 - compute_precision=ct.precision.FLOAT16, 91 91 - minimum_deployment_target=ct.target.macOS15, 92 92 - ) 93 93 - 94 94 - # Add metadata 95 95 - mlmodel.short_description = ( 96 96 - f'Depth Anything V2 {model_key.upper()} - Monocular depth estimation' 97 97 - ) 98 98 - mlmodel.author = 'Depth Anything Team (converted to CoreML)' 99 99 - mlmodel.license = 'CC-BY-NC-4.0' # Base and Large are both non-commercial 100 100 - mlmodel.version = '2.0' 101 101 - 102 102 - mlmodel.input_description['image'] = 'RGB input image (518x518)' 103 103 - mlmodel.output_description['depth'] = 'Depth map (518x518)' 104 104 - 105 105 - print(f'✓ Saving to {output_path}') 106 106 - mlmodel.save(str(output_path)) 107 107 - 108 108 - # Get file size 109 109 - size_mb = output_path.stat().st_size / (1024 * 1024) 110 110 - print(f'✓ Model saved: {size_mb:.1f} MB') 111 111 - print(f'✓ Ready to use with CoreML on Apple Silicon!') 112 112 - 113 113 - return True 114 114 - 115 115 - 116 116 - def main(): 117 117 - # Setup paths 118 118 - repo_root = Path(__file__).parent.parent.parent 119 119 - checkpoint_dir = repo_root / 'checkpoints' 120 120 - output_dir = checkpoint_dir 121 121 - 122 122 - checkpoint_dir.mkdir(exist_ok=True) 123 123 - 124 124 - print('Depth Anything V2 → CoreML Conversion') 125 125 - print('=' * 60) 126 126 - print(f'Checkpoint directory: {checkpoint_dir}') 127 127 - print(f'Output directory: {output_dir}') 128 128 - 129 129 - # Convert both models 130 130 - success = { 131 131 - 'Base': convert_model('vitb', checkpoint_dir, output_dir), 132 132 - 'Large': convert_model('vitl', checkpoint_dir, output_dir), 133 133 - } 134 134 - 135 135 - # Summary 136 136 - print(f'\n{"=" * 60}') 137 137 - print('Conversion Summary') 138 138 - print(f'{"=" * 60}') 139 139 - 140 140 - for name, ok in success.items(): 141 141 - status = '✓ Success' if ok else '✗ Failed' 142 142 - print(f'{name:10} {status}') 143 143 - 144 144 - if all(success.values()): 145 145 - print('\n✓ All conversions completed successfully!') 146 146 - print('\nYou can now use these models with CoreML on Apple Silicon.') 147 147 - print('Expected performance on M4 Pro:') 148 148 - print(' - Base: ~60-90ms per frame') 149 149 - print(' - Large: ~200-300ms per frame') 150 150 - else: 151 151 - print('\n✗ Some conversions failed. See errors above.') 152 152 - sys.exit(1) 153 153 - 154 154 - 155 155 - if __name__ == '__main__': 156 156 - main()

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scripts/depth_to_stereo.py

Reviewed

··· 1 1 - """ 2 2 - Combined depth estimation and stereo conversion - pipes directly to ffmpeg without intermediate frames. 3 3 - 4 4 - This module combines depth estimation (Depth Anything V2) with stereo conversion (DIBR) 5 5 - into a single streaming pipeline that avoids writing intermediate depth frames to disk. 6 6 - """ 7 7 - 8 8 - import json 9 9 - import subprocess 10 10 - import os 11 11 - import sys 12 12 - from pathlib import Path 13 13 - 14 14 - import cv2 15 15 - import numpy as np 16 16 - import torch 17 17 - from tqdm import tqdm 18 18 - 19 19 - from depth_anything_v2.dpt import DepthAnythingV2 20 20 - 21 21 - _threads_configured = False 22 22 - _processor_cache = {} 23 23 - 24 24 - 25 25 - def create_stereo_pair( 26 26 - rgb_frame: np.ndarray, 27 27 - depth_frame: np.ndarray, 28 28 - max_disparity: int = 30, 29 29 - ) -> tuple: 30 30 - """ 31 31 - Create left and right eye views from RGB frame and depth map. 32 32 - 33 33 - Args: 34 34 - rgb_frame: Original RGB image (H, W, 3) 35 35 - depth_frame: Normalized depth map (H, W), values 0-1 36 36 - max_disparity: Maximum pixel shift for closest objects 37 37 - 38 38 - Returns: 39 39 - (left_eye, right_eye) tuple of images 40 40 - """ 41 41 - h, w = rgb_frame.shape[:2] 42 42 - 43 43 - # Calculate disparity map (closer = higher value = more shift) 44 44 - disparity = (depth_frame * max_disparity).astype(np.float32) 45 45 - 46 46 - # Create coordinate grids 47 47 - x_coords = np.arange(w, dtype=np.float32) 48 48 - y_coords = np.arange(h, dtype=np.float32) 49 49 - x_grid, y_grid = np.meshgrid(x_coords, y_coords) 50 50 - 51 51 - # Shift for left eye (shift right for closer objects) 52 52 - x_left = x_grid + disparity / 2 53 53 - # Shift for right eye (shift left for closer objects) 54 54 - x_right = x_grid - disparity / 2 55 55 - 56 56 - # Remap using cv2 (handles interpolation and boundary) 57 57 - left_eye = cv2.remap( 58 58 - rgb_frame, 59 59 - x_left, 60 60 - y_grid, 61 61 - interpolation=cv2.INTER_LINEAR, 62 62 - borderMode=cv2.BORDER_REPLICATE, 63 63 - ) 64 64 - right_eye = cv2.remap( 65 65 - rgb_frame, 66 66 - x_right, 67 67 - y_grid, 68 68 - interpolation=cv2.INTER_LINEAR, 69 69 - borderMode=cv2.BORDER_REPLICATE, 70 70 - ) 71 71 - 72 72 - return left_eye, right_eye 73 73 - 74 74 - 75 75 - class DepthToStereoProcessor: 76 76 - """ 77 77 - Combined depth estimation and stereo conversion processor. 78 78 - 79 79 - Streams RGB frames through depth estimation and stereo conversion, 80 80 - piping output directly to ffmpeg without intermediate files. 81 81 - """ 82 82 - 83 83 - def __init__( 84 84 - self, 85 85 - encoder: str = 'vits', 86 86 - num_threads: int = None, 87 87 - json_progress: bool = False, 88 88 - ): 89 89 - self.encoder = encoder 90 90 - self.num_threads = ( 91 91 - num_threads if num_threads is not None else max(1, os.cpu_count() - 4) 92 92 - ) 93 93 - self.model = None 94 94 - self.device = None 95 95 - self.json_progress = json_progress 96 96 - 97 97 - def load_model(self): 98 98 - """Load the Depth Anything V2 model.""" 99 99 - global _threads_configured 100 100 - if self.model is not None: 101 101 - return 102 102 - 103 103 - if not _threads_configured: 104 104 - torch.set_num_threads(self.num_threads) 105 105 - torch.set_num_interop_threads(self.num_threads) 106 106 - _threads_configured = True 107 107 - 108 108 - model_configs = { 109 109 - 'vits': { 110 110 - 'encoder': 'vits', 111 111 - 'features': 64, 112 112 - 'out_channels': [48, 96, 192, 384], 113 113 - }, 114 114 - 'vitb': { 115 115 - 'encoder': 'vitb', 116 116 - 'features': 128, 117 117 - 'out_channels': [96, 192, 384, 768], 118 118 - }, 119 119 - 'vitl': { 120 120 - 'encoder': 'vitl', 121 121 - 'features': 256, 122 122 - 'out_channels': [256, 512, 1024, 1024], 123 123 - }, 124 124 - } 125 125 - 126 126 - checkpoint_name = f'depth_anything_v2_{self.encoder}.pth' 127 127 - checkpoint_path = self._find_checkpoint(checkpoint_name) 128 128 - 129 129 - if torch.backends.mps.is_available(): 130 130 - self.device = torch.device('mps') 131 131 - elif torch.cuda.is_available(): 132 132 - self.device = torch.device('cuda') 133 133 - else: 134 134 - self.device = torch.device('cpu') 135 135 - 136 136 - self.model = DepthAnythingV2(**model_configs[self.encoder]) 137 137 - # Load weights directly to target device to avoid MPS/CPU tensor mismatch 138 138 - self.model.load_state_dict( 139 139 - torch.load(str(checkpoint_path), map_location=self.device) 140 140 - ) 141 141 - self.model = self.model.to(self.device).eval() 142 142 - 143 143 - def _find_checkpoint(self, checkpoint_name: str) -> Path: 144 144 - """ 145 145 - Find checkpoint file in multiple possible locations. 146 146 - 147 147 - Searches in order: 148 148 - 1. Next to the script's source directory (for development) 149 149 - 2. User's home directory ~/.spatial-maker/checkpoints/ 150 150 - 3. XDG data directory 151 151 - 4. Current working directory ./checkpoints/ 152 152 - """ 153 153 - search_paths = [ 154 154 - # Development: relative to source file 155 155 - Path(__file__).parent.parent / 'checkpoints' / checkpoint_name, 156 156 - # User home directory 157 157 - Path.home() / '.spatial-maker' / 'checkpoints' / checkpoint_name, 158 158 - # XDG data home 159 159 - Path(os.environ.get('XDG_DATA_HOME', Path.home() / '.local' / 'share')) 160 160 - / 'spatial-maker' 161 161 - / 'checkpoints' 162 162 - / checkpoint_name, 163 163 - # Current working directory 164 164 - Path.cwd() / 'checkpoints' / checkpoint_name, 165 165 - ] 166 166 - 167 167 - # Also check if SPATIAL_MAKER_CHECKPOINTS env var is set 168 168 - env_checkpoint_dir = os.environ.get('SPATIAL_MAKER_CHECKPOINTS') 169 169 - if env_checkpoint_dir: 170 170 - search_paths.insert(0, Path(env_checkpoint_dir) / checkpoint_name) 171 171 - 172 172 - for path in search_paths: 173 173 - if path.exists(): 174 174 - return path 175 175 - 176 176 - # Not found - provide helpful error message 177 177 - raise FileNotFoundError( 178 178 - f"Checkpoint '{checkpoint_name}' not found.\n" 179 179 - f'Searched locations:\n' 180 180 - + '\n'.join(f' - {p}' for p in search_paths) 181 181 - + '\n\nPlease download the checkpoint and place it in one of these locations:\n' 182 182 - f' mkdir -p ~/.spatial-maker/checkpoints\n' 183 183 - f' # Then copy {checkpoint_name} there' 184 184 - ) 185 185 - 186 186 - def process_video( 187 187 - self, 188 188 - video_path: str, 189 189 - output_path: str, 190 190 - max_disparity: int = 30, 191 191 - fps: int = 24, 192 192 - max_frames: int = None, 193 193 - ) -> dict: 194 194 - """ 195 195 - Process video: generate depth and create SBS stereo, piping directly to ffmpeg. 196 196 - 197 197 - Args: 198 198 - video_path: Path to input RGB video 199 199 - output_path: Path for output SBS video 200 200 - max_disparity: Maximum pixel disparity for 3D effect 201 201 - fps: Output frame rate 202 202 - max_frames: Optional limit on frames to process 203 203 - 204 204 - Returns: 205 205 - Dict with processing statistics 206 206 - """ 207 207 - self.load_model() 208 208 - 209 209 - # Open input video 210 210 - cap = cv2.VideoCapture(video_path) 211 211 - if not cap.isOpened(): 212 212 - raise ValueError(f'Could not open video: {video_path}') 213 213 - 214 214 - total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) 215 215 - if max_frames is not None: 216 216 - total_frames = min(total_frames, max_frames) 217 217 - 218 218 - # Get dimensions 219 219 - width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) 220 220 - height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) 221 221 - 222 222 - # Output is full-width SBS (double width) 223 223 - out_width = width * 2 224 224 - out_height = height 225 225 - 226 226 - output_path = Path(output_path) 227 227 - output_path.parent.mkdir(parents=True, exist_ok=True) 228 228 - 229 229 - # Start ffmpeg process to receive piped frames 230 230 - ffmpeg_cmd = [ 231 231 - 'ffmpeg', 232 232 - '-y', 233 233 - '-f', 234 234 - 'rawvideo', 235 235 - '-vcodec', 236 236 - 'rawvideo', 237 237 - '-s', 238 238 - f'{out_width}x{out_height}', 239 239 - '-pix_fmt', 240 240 - 'bgr24', 241 241 - '-r', 242 242 - str(fps), 243 243 - '-i', 244 244 - '-', # Read from stdin 245 245 - '-c:v', 246 246 - 'hevc_videotoolbox', 247 247 - '-q:v', 248 248 - '65', # Quality-based encoding (0-100, higher = better) 249 249 - '-tag:v', 250 250 - 'hvc1', 251 251 - str(output_path), 252 252 - ] 253 253 - 254 254 - ffmpeg_proc = subprocess.Popen( 255 255 - ffmpeg_cmd, 256 256 - stdin=subprocess.PIPE, 257 257 - stdout=subprocess.DEVNULL, 258 258 - stderr=subprocess.DEVNULL, 259 259 - ) 260 260 - 261 261 - # Warmup inference 262 262 - ret, first_frame = cap.read() 263 263 - if not ret: 264 264 - raise ValueError('Could not read first frame') 265 265 - cap.set(cv2.CAP_PROP_POS_FRAMES, 0) 266 266 - 267 267 - with torch.no_grad(): 268 268 - _ = self.model.infer_image(first_frame, 518) 269 269 - 270 270 - frames_processed = 0 271 271 - 272 272 - progress_iter = range(total_frames) 273 273 - if not self.json_progress: 274 274 - progress_iter = tqdm(progress_iter, desc=' ', unit='frame', ncols=60) 275 275 - 276 276 - try: 277 277 - for _ in progress_iter: 278 278 - ret, rgb_frame = cap.read() 279 279 - if not ret: 280 280 - break 281 281 - 282 282 - with torch.no_grad(): 283 283 - depth = self.model.infer_image(rgb_frame, 518) 284 284 - 285 285 - depth_normalized = (depth - depth.min()) / (depth.max() - depth.min()) 286 286 - 287 287 - left_eye, right_eye = create_stereo_pair( 288 288 - rgb_frame, depth_normalized, max_disparity 289 289 - ) 290 290 - 291 291 - sbs_frame = np.hstack([left_eye, right_eye]) 292 292 - 293 293 - ffmpeg_proc.stdin.write(sbs_frame.tobytes()) 294 294 - frames_processed += 1 295 295 - 296 296 - if self.json_progress: 297 297 - pct = ( 298 298 - (frames_processed / total_frames * 100) 299 299 - if total_frames > 0 300 300 - else 0 301 301 - ) 302 302 - print( 303 303 - json.dumps( 304 304 - { 305 305 - 'event': 'progress', 306 306 - 'stage': 'depth_stereo', 307 307 - 'frame': frames_processed, 308 308 - 'total': total_frames, 309 309 - 'pct': round(pct, 1), 310 310 - } 311 311 - ), 312 312 - flush=True, 313 313 - ) 314 314 - 315 315 - if max_frames is not None and frames_processed >= max_frames: 316 316 - break 317 317 - 318 318 - finally: 319 319 - cap.release() 320 320 - ffmpeg_proc.stdin.close() 321 321 - ffmpeg_proc.wait() 322 322 - 323 323 - return { 324 324 - 'frames_processed': frames_processed, 325 325 - 'output_path': str(output_path), 326 326 - 'dimensions': f'{out_width}x{out_height}', 327 327 - } 328 328 - 329 329 - 330 330 - def process_video_to_sbs( 331 331 - video_path: str, 332 332 - output_path: str, 333 333 - encoder: str = 'vits', 334 334 - max_disparity: int = 30, 335 335 - fps: int = 24, 336 336 - max_frames: int = None, 337 337 - json_progress: bool = False, 338 338 - ) -> dict: 339 339 - """ 340 340 - Convenience function to process a video to SBS stereo. 341 341 - 342 342 - Uses a cached processor instance to reuse the model across multiple calls 343 343 - (important for batch processing). 344 344 - 345 345 - Args: 346 346 - video_path: Path to input RGB video 347 347 - output_path: Path for output SBS video 348 348 - encoder: Depth Anything V2 encoder (vits, vitb, vitl) 349 349 - max_disparity: Maximum pixel disparity for 3D effect 350 350 - fps: Output frame rate 351 351 - max_frames: Optional limit on frames to process 352 352 - 353 353 - Returns: 354 354 - Dict with processing statistics 355 355 - """ 356 356 - global _processor_cache 357 357 - 358 358 - cache_key = (encoder, json_progress) 359 359 - if cache_key not in _processor_cache: 360 360 - _processor_cache[cache_key] = DepthToStereoProcessor( 361 361 - encoder=encoder, json_progress=json_progress 362 362 - ) 363 363 - 364 364 - processor = _processor_cache[cache_key] 365 365 - return processor.process_video( 366 366 - video_path, 367 367 - output_path, 368 368 - max_disparity=max_disparity, 369 369 - fps=fps, 370 370 - max_frames=max_frames, 371 371 - ) 372 372 - 373 373 - 374 374 - if __name__ == '__main__': 375 375 - import argparse 376 376 - 377 377 - parser = argparse.ArgumentParser( 378 378 - description='Convert 2D video to SBS stereo using depth estimation' 379 379 - ) 380 380 - parser.add_argument('input', help='Input video file') 381 381 - parser.add_argument('output', help='Output SBS video file') 382 382 - parser.add_argument( 383 383 - '--encoder', 384 384 - choices=['vits', 'vitb', 'vitl'], 385 385 - default='vits', 386 386 - help='Depth model encoder (default: vits)', 387 387 - ) 388 388 - parser.add_argument( 389 389 - '--max-disparity', 390 390 - type=int, 391 391 - default=30, 392 392 - help='Maximum pixel disparity (default: 30)', 393 393 - ) 394 394 - parser.add_argument( 395 395 - '--fps', 396 396 - type=int, 397 397 - default=24, 398 398 - help='Output FPS (default: 24)', 399 399 - ) 400 400 - parser.add_argument( 401 401 - '--max-frames', 402 402 - type=int, 403 403 - help='Maximum frames to process', 404 404 - ) 405 405 - 406 406 - args = parser.parse_args() 407 407 - 408 408 - result = process_video_to_sbs( 409 409 - args.input, 410 410 - args.output, 411 411 - encoder=args.encoder, 412 412 - max_disparity=args.max_disparity, 413 413 - fps=args.fps, 414 414 - max_frames=args.max_frames, 415 415 - ) 416 416 - print(f'Done! Output: {result["output_path"]}')

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scripts/download_coreml_models.sh

Reviewed

··· 1 1 - #!/bin/bash 2 2 - set -e 3 3 - 4 4 - # Download CoreML Depth Anything V2 models for Apple Silicon 5 5 - # These are hosted on HuggingFace for easy distribution 6 6 - 7 7 - CHECKPOINT_DIR="checkpoints" 8 8 - HF_REPO="mrgnw/depth-anything-v2-coreml" 9 9 - BASE_URL="https://huggingface.co/${HF_REPO}/resolve/main" 10 10 - 11 11 - mkdir -p "$CHECKPOINT_DIR" 12 12 - cd "$CHECKPOINT_DIR" 13 13 - 14 14 - echo "Downloading Depth Anything V2 CoreML models..." 15 15 - echo "Source: https://huggingface.co/${HF_REPO}" 16 16 - echo "" 17 17 - 18 18 - # Small (Apple's official, fast reference) 19 19 - if [ ! -d "DepthAnythingV2SmallF16.mlpackage" ]; then 20 20 - echo "📥 Downloading Small model (48 MB)..." 21 21 - curl -L -o DepthAnythingV2SmallF16.mlpackage.zip \ 22 22 - "https://ml-assets.apple.com/coreml/models/Image/DepthEstimation/DepthAnything/DepthAnythingV2SmallF16.mlpackage.zip" 23 23 - unzip -q DepthAnythingV2SmallF16.mlpackage.zip 24 24 - rm DepthAnythingV2SmallF16.mlpackage.zip 25 25 - echo "✓ Small model ready" 26 26 - else 27 27 - echo "✓ Small model already exists" 28 28 - fi 29 29 - 30 30 - # Base (converted, sweet spot for quality/speed) 31 31 - if [ ! -d "DepthAnythingV2BaseF16.mlpackage" ]; then 32 32 - echo "📥 Downloading Base model (186 MB)..." 33 33 - curl -L -o DepthAnythingV2BaseF16.mlpackage.tar.gz \ 34 34 - "${BASE_URL}/DepthAnythingV2BaseF16.mlpackage.tar.gz" 35 35 - tar -xzf DepthAnythingV2BaseF16.mlpackage.tar.gz 36 36 - rm DepthAnythingV2BaseF16.mlpackage.tar.gz 37 37 - echo "✓ Base model ready" 38 38 - else 39 39 - echo "✓ Base model already exists" 40 40 - fi 41 41 - 42 42 - # Large (converted, maximum quality) 43 43 - if [ ! -d "DepthAnythingV2LargeF16.mlpackage" ]; then 44 44 - echo "📥 Downloading Large model (638 MB)..." 45 45 - curl -L -o DepthAnythingV2LargeF16.mlpackage.tar.gz \ 46 46 - "${BASE_URL}/DepthAnythingV2LargeF16.mlpackage.tar.gz" 47 47 - tar -xzf DepthAnythingV2LargeF16.mlpackage.tar.gz 48 48 - rm DepthAnythingV2LargeF16.mlpackage.tar.gz 49 49 - echo "✓ Large model ready" 50 50 - else 51 51 - echo "✓ Large model already exists" 52 52 - fi 53 53 - 54 54 - echo "" 55 55 - echo "✅ All CoreML models ready!" 56 56 - echo "" 57 57 - echo "Models:" 58 58 - echo " - Small: 48 MB (~30ms, 30+ fps on M4 Pro)" 59 59 - echo " - Base: 186 MB (~60-90ms, 11-16 fps on M4 Pro)" 60 60 - echo " - Large: 638 MB (~200-300ms, 3-5 fps on M4 Pro)" 61 61 - echo "" 62 62 - echo "These models use the Apple Neural Engine for fast inference."

-55

scripts/downscale_video.py

Reviewed

··· 1 1 - #!/usr/bin/env python3 2 2 - import subprocess 3 3 - import sys 4 4 - import tempfile 5 5 - from pathlib import Path 6 6 - 7 7 - 8 8 - def downscale_to_1080p24(input_path: str, output_path: str = None, duration: float = None): 9 9 - input_p = Path(input_path) 10 10 - 11 11 - cache_dir = Path(tempfile.gettempdir()) / "spatial-maker" / "1080p24fps" 12 12 - cached = cache_dir / f"{input_p.stem}_1080p{input_p.suffix}" 13 13 - 14 14 - if not cached.exists(): 15 15 - cache_dir.mkdir(parents=True, exist_ok=True) 16 16 - cmd = [ 17 17 - "ffmpeg", "-y", "-i", input_path, 18 18 - "-vf", "scale='min(1920,iw)':'min(1080,ih)':force_original_aspect_ratio=decrease:flags=lanczos", 19 19 - "-r", "24", 20 20 - "-c:v", "hevc_videotoolbox", 21 21 - "-q:v", "65", # Quality setting (0-100, higher = better quality) 22 22 - "-tag:v", "hvc1", 23 23 - "-c:a", "aac", 24 24 - "-b:a", "192k", 25 25 - str(cached) 26 26 - ] 27 27 - subprocess.run(cmd, check=True, capture_output=True) 28 28 - 29 29 - if duration is not None: 30 30 - if output_path is None: 31 31 - raise ValueError("output_path required when duration is specified") 32 32 - cmd = [ 33 33 - "ffmpeg", "-y", "-i", str(cached), 34 34 - "-t", str(duration), 35 35 - "-c", "copy", 36 36 - output_path 37 37 - ] 38 38 - subprocess.run(cmd, check=True, capture_output=True) 39 39 - return output_path 40 40 - 41 41 - if output_path is None: 42 42 - return str(cached) 43 43 - 44 44 - Path(cached).replace(output_path) 45 45 - return output_path 46 46 - 47 47 - 48 48 - if __name__ == "__main__": 49 49 - if len(sys.argv) < 2: 50 50 - print("Usage: python downscale_video.py <input> [output]") 51 51 - sys.exit(1) 52 52 - 53 53 - output = sys.argv[2] if len(sys.argv) > 2 else None 54 54 - result = downscale_to_1080p24(sys.argv[1], output) 55 55 - print(f"Created: {result}")

-154

scripts/upload_to_huggingface.md

Reviewed

··· 1 1 - # Upload CoreML Models to HuggingFace 2 2 - 3 3 - Follow these steps to upload the converted CoreML models to HuggingFace for easy distribution. 4 4 - 5 5 - ## 1. Create HuggingFace Repository 6 6 - 7 7 - 1. Go to https://huggingface.co/new 8 8 - 2. Create a new **model** repository 9 9 - 3. Name it: `depth-anything-v2-coreml` (or your preferred name) 10 10 - 4. Make it **public** so users can download without authentication 11 11 - 5. Add a description: "Depth Anything V2 models in CoreML format for Apple Silicon" 12 12 - 13 13 - ## 2. Prepare Models for Upload 14 14 - 15 15 - The models are already in `checkpoints/`. We need to compress them: 16 16 - 17 17 - ```bash 18 18 - cd checkpoints 19 19 - 20 20 - # Compress Base model 21 21 - tar -czf DepthAnythingV2BaseF16.mlpackage.tar.gz DepthAnythingV2BaseF16.mlpackage 22 22 - 23 23 - # Compress Large model 24 24 - tar -czf DepthAnythingV2LargeF16.mlpackage.tar.gz DepthAnythingV2LargeF16.mlpackage 25 25 - 26 26 - # Check sizes 27 27 - ls -lh *.tar.gz 28 28 - ``` 29 29 - 30 30 - This compresses the models from ~870 MB to ~430 MB total. 31 31 - 32 32 - ## 3. Upload via Web Interface (Easiest) 33 33 - 34 34 - 1. Go to your repo: `https://huggingface.co/YOUR_USERNAME/depth-anything-v2-coreml` 35 35 - 2. Click "Files and versions" → "Add file" → "Upload files" 36 36 - 3. Upload: 37 37 - - `DepthAnythingV2BaseF16.mlpackage.tar.gz` 38 38 - - `DepthAnythingV2LargeF16.mlpackage.tar.gz` 39 39 - 4. Commit with message: "Add CoreML models (Base and Large)" 40 40 - 41 41 - **Note**: We don't upload Small because it's available directly from Apple. 42 42 - 43 43 - ## 4. Upload via Git LFS (Alternative) 44 44 - 45 45 - If you prefer command-line: 46 46 - 47 47 - ```bash 48 48 - # Install git-lfs if needed 49 49 - brew install git-lfs 50 50 - git lfs install 51 51 - 52 52 - # Clone your HF repo 53 53 - git clone https://huggingface.co/YOUR_USERNAME/depth-anything-v2-coreml 54 54 - cd depth-anything-v2-coreml 55 55 - 56 56 - # Track tar.gz files with LFS 57 57 - git lfs track "*.tar.gz" 58 58 - git add .gitattributes 59 59 - 60 60 - # Copy and commit models 61 61 - cp ../spatial-maker/checkpoints/*.tar.gz . 62 62 - git add *.tar.gz 63 63 - git commit -m "Add CoreML models (Base and Large)" 64 64 - git push 65 65 - ``` 66 66 - 67 67 - ## 5. Create Model Card (README.md) 68 68 - 69 69 - Add this to your HuggingFace repo's README.md: 70 70 - 71 71 - ````markdown 72 72 - --- 73 73 - license: apache-2.0 74 74 - tags: 75 75 - - depth-estimation 76 76 - - coreml 77 77 - - apple-silicon 78 78 - - vision 79 79 - --- 80 80 - 81 81 - # Depth Anything V2 - CoreML 82 82 - 83 83 - Depth Anything V2 models converted to CoreML format for optimized inference on Apple Silicon (M-series chips). 84 84 - 85 85 - ## Models 86 86 - 87 87 - | Model | Size | Parameters | Performance (M4 Pro) | License | 88 88 - |-------|------|------------|----------------------|---------| 89 89 - | Small F16 | 48 MB | 24.8M | ~30ms (~33 fps) | Apache-2.0 | 90 90 - | Base F16 | 186 MB | 97.5M | ~60-90ms (~14 fps) | Apache-2.0 | 91 91 - | Large F16 | 638 MB | 335.3M | ~200-300ms (~4 fps) | CC-BY-NC-4.0 | 92 92 - 93 93 - All models use Float16 precision and run on Apple's Neural Engine + GPU + CPU. 94 94 - 95 95 - ## Download 96 96 - 97 97 - ```bash 98 98 - # Download from spatial-maker repo 99 99 - git clone https://github.com/YOUR_USERNAME/spatial-maker 100 100 - cd spatial-maker 101 101 - ./scripts/download_coreml_models.sh 102 102 - ``` 103 103 - 104 104 - Or download directly: 105 105 - - [Base model](https://huggingface.co/YOUR_USERNAME/depth-anything-v2-coreml/resolve/main/DepthAnythingV2BaseF16.mlpackage.tar.gz) (186 MB) 106 106 - - [Large model](https://huggingface.co/YOUR_USERNAME/depth-anything-v2-coreml/resolve/main/DepthAnythingV2LargeF16.mlpackage.tar.gz) (638 MB) 107 107 - 108 108 - Small model available from [Apple's CoreML model zoo](https://developer.apple.com/machine-learning/models/). 109 109 - 110 110 - ## Usage 111 111 - 112 112 - ```swift 113 113 - import CoreML 114 114 - 115 115 - let model = try MLModel(contentsOf: URL(fileURLWithPath: "DepthAnythingV2BaseF16.mlpackage")) 116 116 - // ... inference code 117 117 - ``` 118 118 - 119 119 - ## Citation 120 120 - 121 121 - ```bibtex 122 122 - @article{yang2024depth, 123 123 - title={Depth Anything V2}, 124 124 - author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang}, 125 125 - journal={arXiv:2406.09414}, 126 126 - year={2024} 127 127 - } 128 128 - ``` 129 129 - 130 130 - ## Conversion 131 131 - 132 132 - These models were converted from PyTorch using `coremltools`. See [conversion script](https://github.com/YOUR_USERNAME/spatial-maker/tree/main/scripts/coreml_conversion). 133 133 - ```` 134 134 - 135 135 - ## 6. Update Download Script 136 136 - 137 137 - After uploading, update `scripts/download_coreml_models.sh` with your actual HuggingFace username: 138 138 - 139 139 - ```bash 140 140 - HF_REPO="YOUR_ACTUAL_USERNAME/depth-anything-v2-coreml" 141 141 - ``` 142 142 - 143 143 - ## Verification 144 144 - 145 145 - Test the download works: 146 146 - 147 147 - ```bash 148 148 - cd /tmp 149 149 - git clone https://github.com/YOUR_USERNAME/spatial-maker 150 150 - cd spatial-maker 151 151 - ./scripts/download_coreml_models.sh 152 152 - ``` 153 153 - 154 154 - Should download all three models successfully!

+7

src/main.rs

View file Reviewed

··· 9 9 #[command(name = "spatial-maker")] 10 10 #[command(about = "Convert 2D images and videos to stereoscopic 3D spatial content")] 11 11 #[command(version)] 12 12 + #[command(long_version = format!( 13 13 + "{}\nInstalled via: {}\nBuild: {} ({})", 14 14 + env!("CARGO_PKG_VERSION"), 15 15 + option_env!("CARGO_HOME").map(|_| "cargo install").unwrap_or("local build"), 16 16 + env!("CARGO_PKG_NAME"), 17 17 + env!("CARGO_PKG_REPOSITORY") 18 18 + ))] 12 19 struct Cli { 13 20 /// Input image or video file 14 21 input: PathBuf,

-2021

uv.lock

Reviewed

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