172 lines
4.2 KiB
Markdown
172 lines
4.2 KiB
Markdown
# VR180 Streaming Matting
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True streaming implementation for VR180 human matting with constant memory usage.
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## Key Features
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- **True Streaming**: Process frames one at a time without accumulation
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- **Constant Memory**: No memory buildup regardless of video length
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- **Stereo Consistency**: Master-slave processing ensures matched detection
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- **2-3x Faster**: Eliminates chunking overhead from original implementation
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- **Direct FFmpeg Pipe**: Zero-copy frame writing
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## Architecture
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```
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Input Video → Frame Reader → SAM2 Streaming → Frame Writer → Output Video
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↓ ↓ ↓ ↓
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(no chunks) (one frame) (propagate) (immediate write)
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```
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### Components
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1. **StreamingFrameReader** (`frame_reader.py`)
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- Reads frames one at a time
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- Supports seeking for resume/recovery
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- Constant memory footprint
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2. **StreamingFrameWriter** (`frame_writer.py`)
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- Direct pipe to ffmpeg encoder
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- GPU-accelerated encoding (H.264/H.265)
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- Preserves audio from source
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3. **StereoConsistencyManager** (`stereo_manager.py`)
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- Master-slave eye processing
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- Disparity-aware detection transfer
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- Automatic consistency validation
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4. **SAM2StreamingProcessor** (`sam2_streaming.py`)
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- Integrates with SAM2's native video predictor
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- Memory-efficient state management
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- Continuous correction support
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5. **VR180StreamingProcessor** (`streaming_processor.py`)
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- Main orchestrator
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- Adaptive GPU scaling
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- Checkpoint/resume support
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## Usage
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### Quick Start
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```bash
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# Generate example config
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python -m vr180_streaming --generate-config my_config.yaml
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# Edit config with your paths
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vim my_config.yaml
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# Run processing
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python -m vr180_streaming my_config.yaml
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```
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### Command Line Options
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```bash
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# Override output path
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python -m vr180_streaming config.yaml --output /path/to/output.mp4
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# Process specific frame range
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python -m vr180_streaming config.yaml --start-frame 1000 --max-frames 5000
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# Override scale factor
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python -m vr180_streaming config.yaml --scale 0.25
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# Dry run to validate config
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python -m vr180_streaming config.yaml --dry-run
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```
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## Configuration
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Key configuration options:
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```yaml
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streaming:
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mode: true # Enable streaming mode
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buffer_frames: 10 # Lookahead buffer
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processing:
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scale_factor: 0.5 # Resolution scaling
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adaptive_scaling: true # Dynamic GPU optimization
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stereo:
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mode: "master_slave" # Stereo consistency mode
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master_eye: "left" # Which eye leads detection
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recovery:
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enable_checkpoints: true # Save progress
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auto_resume: true # Resume from checkpoint
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```
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## Performance
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Compared to chunked implementation:
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| Metric | Chunked | Streaming | Improvement |
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|--------|---------|-----------|-------------|
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| Speed | ~0.54s/frame | ~0.18s/frame | 3x faster |
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| Memory | 100GB+ peak | <50GB constant | 2x lower |
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| VRAM | 2.5% usage | 70%+ usage | 28x better |
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| Consistency | Variable | Guaranteed | ✓ |
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## Requirements
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- Python 3.10+
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- PyTorch 2.0+
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- CUDA GPU (8GB+ VRAM recommended)
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- FFmpeg with GPU encoding support
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- SAM2 (segment-anything-2)
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## Troubleshooting
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### Out of Memory
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- Reduce `scale_factor` in config
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- Enable `adaptive_scaling`
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- Ensure `memory_offload: true`
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### Stereo Mismatch
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- Adjust `consistency_threshold`
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- Enable `disparity_correction`
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- Check `baseline` and `focal_length` settings
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### Slow Processing
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- Use GPU video codec (`h264_nvenc`)
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- Reduce `correction_interval`
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- Lower output quality (`crf: 23`)
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## Advanced Features
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### Adaptive Scaling
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Automatically adjusts processing resolution based on GPU load:
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```yaml
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processing:
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adaptive_scaling: true
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target_gpu_usage: 0.7
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min_scale: 0.25
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max_scale: 1.0
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```
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### Continuous Correction
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Periodically refines tracking for long videos:
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```yaml
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matting:
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continuous_correction: true
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correction_interval: 300 # Every 5 seconds at 60fps
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```
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### Checkpoint Recovery
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Automatically resume from interruptions:
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```yaml
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recovery:
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enable_checkpoints: true
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checkpoint_interval: 1000
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auto_resume: true
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```
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## Contributing
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Please ensure your code follows the streaming architecture principles:
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- No frame accumulation in memory
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- Immediate processing and writing
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- Proper resource cleanup
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- Checkpoint support for long videos |