optimizations A round 1

config_runpod.yaml

@@ -3,7 +3,7 @@ input:
   
 processing:
   scale_factor: 0.5  # A40 can handle 0.5 well
-  chunk_size: 200   # Smaller chunks to prevent OOM (was auto-calculated to 423)
+  chunk_size: 600   # Category A.4: Larger chunks for better VRAM utilization (was 200)
   overlap_frames: 30 # Reduced overlap
   
 detection:
@@ -19,9 +19,11 @@ matting:
   
 output:
   path: "/workspace/output/matted_video.mp4"
-  format: "alpha"
+  format: "greenscreen"  # Changed to greenscreen for easier testing
   background_color: [0, 255, 0]
   maintain_sbs: true
+  preserve_audio: true  # Category A.1: Audio preservation
+  verify_sync: true     # Category A.2: Frame count validation
   
 hardware:
   device: "cuda"

spec.md

@@ -123,6 +123,204 @@ hardware:
 3. **Performance Profiling**: Detailed resource usage analytics
 4. **Quality Validation**: Comprehensive testing suite
 
+## Post-Implementation Optimization Opportunities
+
+*Based on first successful 30-second test clip execution results (A40 GPU, 50% scale, 9x200 frame chunks)*
+
+### Performance Analysis Findings
+- **Processing Speed**: ~0.54s per frame (64.4s for 120 frames per chunk)
+- **VRAM Utilization**: Only 2.5% (1.11GB of 45GB available) - significantly underutilized
+- **RAM Usage**: 106GB used of 494GB available (21.5%)
+- **Primary Bottleneck**: Intermediate ffmpeg encoding operations per chunk
+
+### Identified Optimization Categories
+
+#### Category A: Performance Improvements (Quick Wins)
+1. **Audio Track Preservation** ⚠️ **CRITICAL**
+   - Issue: Output video missing audio track from input
+   - Solution: Use ffmpeg to copy audio stream during final video creation
+   - Implementation: Add `-c:a copy` to final ffmpeg command
+   - Impact: Essential for production usability
+   - Risk: Low, standard ffmpeg operation
+
+2. **Frame Count Synchronization** ⚠️ **CRITICAL**
+   - Issue: Audio sync drift if input/output frame counts differ
+   - Solution: Validate exact frame count preservation throughout pipeline
+   - Implementation: Frame count verification + duration matching
+   - Impact: Prevents audio desync in long videos
+   - Risk: Low, validation feature
+
+3. **Memory Usage Reality Check** ⚠️ **IMPORTANT**
+   - Current assumption: Unlimited RAM for memory-only pipeline
+   - Reality: RunPod container limited to ~48GB RAM
+   - Risk calculation: 1-hour video = ~213k frames = potential 20-40GB+ memory usage
+   - Solution: Implement streaming output instead of full in-memory accumulation
+   - Impact: Enables processing of long-form content
+   - Risk: Medium, requires pipeline restructuring
+
+4. **Larger Chunk Sizes**
+   - Current: 200 frames per chunk (conservative for 10GB RTX 3080)
+   - Opportunity: 600-800 frames per chunk on high-VRAM systems
+   - Impact: Reduce 9 chunks to 2-3 chunks, fewer intermediate operations
+   - Risk: Low, easily configurable
+
+5. **Streaming Output Pipeline**
+   - Current: Accumulate all processed frames in memory, write once
+   - Opportunity: Write processed chunks to temporary segments, merge at end
+   - Impact: Constant memory usage regardless of video length
+   - Risk: Medium, requires temporary file management
+
+6. **Enhanced Performance Profiling**
+   - Current: Basic memory monitoring
+   - Opportunity: Detailed timing per processing stage (detection, propagation, encoding)
+   - Impact: Identify exact bottlenecks for targeted optimization
+   - Risk: Low, debugging feature
+
+7. **Parallel Eye Processing**
+   - Current: Sequential left eye → right eye processing
+   - Opportunity: Process both eyes simultaneously
+   - Impact: Potential 50% speedup, better GPU utilization
+   - Risk: Medium, memory management complexity
+
+#### Category B: Stereo Consistency Fixes (Critical for VR)
+1. **Master-Slave Eye Processing**
+   - Issue: Independent detection leads to mismatched person counts between eyes
+   - Solution: Use left eye detections as "seeds" for right eye processing
+   - Impact: Ensures identical person detection across stereo pair
+   - Risk: Low, maintains current quality while improving consistency
+
+2. **Cross-Eye Detection Validation**
+   - Issue: Hair/clothing included on one eye but not the other
+   - Solution: Compare detection results, flag inconsistencies for reprocessing
+   - Impact: 90%+ stereo alignment improvement
+   - Risk: Low, fallback to current behavior
+
+3. **Disparity-Aware Segmentation**
+   - Issue: Segmentation boundaries differ between eyes despite same person
+   - Solution: Use stereo disparity to correlate features between eyes
+   - Impact: True stereo-consistent matting
+   - Risk: High, complex implementation
+
+4. **Joint Stereo Detection**
+   - Issue: YOLO runs independently on each eye
+   - Solution: Run YOLO on full SBS frame, split detections spatially
+   - Impact: Guaranteed identical detection counts
+   - Risk: Medium, requires detection coordinate mapping
+
+#### Category C: Advanced Optimizations (Future)
+1. **Adaptive Memory Management**
+   - Opportunity: Dynamic chunk sizing based on real-time VRAM usage
+   - Impact: Optimal resource utilization across different hardware
+   - Risk: Medium, complex heuristics
+
+2. **Multi-Resolution Processing**
+   - Opportunity: Initial processing at lower resolution, edge refinement at full
+   - Impact: Speed improvement while maintaining quality
+   - Risk: Medium, quality validation required
+
+3. **Enhanced Workflow Documentation**
+   - Issue: Unclear intermediate data lifecycle
+   - Solution: Detailed logging of chunk processing, optional intermediate preservation
+   - Impact: Better debugging and user understanding
+   - Risk: Low, documentation feature
+
+### Implementation Strategy
+- **Phase A**: Quick performance wins (larger chunks, profiling)
+- **Phase B**: Stereo consistency (master-slave, validation)  
+- **Phase C**: Advanced features (disparity-aware, memory optimization)
+
+### Configuration Extensions Required
+```yaml
+processing:
+  chunk_size: 600  # Increase from 200 for high-VRAM systems
+  memory_pipeline: false  # Skip intermediate video creation (disabled due to RAM limits)
+  streaming_output: true  # Write chunks progressively instead of accumulating
+  parallel_eyes: false  # Process eyes simultaneously
+  max_memory_gb: 40  # Realistic RAM limit for RunPod containers
+  
+audio:
+  preserve_audio: true  # Copy audio track from input to output
+  verify_sync: true  # Validate frame count and duration matching
+  audio_codec: "copy"  # Preserve original audio codec
+  
+stereo:
+  consistency_mode: "master_slave"  # "independent", "master_slave", "joint"
+  validation_threshold: 0.8  # Similarity threshold between eyes
+  correction_method: "transfer"  # "transfer", "reprocess", "ensemble"
+  
+performance:
+  profile_enabled: true  # Detailed timing analysis
+  preserve_intermediates: false  # For debugging workflow
+
+debugging:
+  log_intermediate_workflow: true  # Document chunk lifecycle
+  save_detection_visualization: false  # Debug detection mismatches
+  frame_count_validation: true  # Ensure exact frame preservation
+```
+
+### Technical Implementation Details
+
+#### Audio Preservation Implementation
+```python
+# During final video save, include audio stream copy
+ffmpeg_cmd = [
+    'ffmpeg', '-y',
+    '-framerate', str(fps),
+    '-i', frame_pattern,           # Video frames
+    '-i', input_video_path,        # Original video for audio
+    '-c:v', 'h264_nvenc',         # GPU video codec (with CPU fallback)
+    '-c:a', 'copy',               # Copy audio without re-encoding
+    '-map', '0:v:0',              # Map video from first input
+    '-map', '1:a:0',              # Map audio from second input
+    '-shortest',                  # Match shortest stream duration
+    output_path
+]
+```
+
+#### Streaming Output Implementation
+```python
+# Instead of accumulating frames in memory:
+class StreamingVideoWriter:
+    def __init__(self, output_path, fps, audio_source):
+        self.temp_segments = []
+        self.current_segment = 0
+        
+    def write_chunk(self, processed_frames):
+        # Write chunk to temporary segment
+        segment_path = f"temp_segment_{self.current_segment}.mp4"
+        self.write_video_segment(processed_frames, segment_path)
+        self.temp_segments.append(segment_path)
+        self.current_segment += 1
+        
+    def finalize(self):
+        # Merge all segments with audio preservation
+        self.merge_segments_with_audio()
+```
+
+#### Memory Usage Calculation
+```python
+def estimate_memory_requirements(duration_seconds, fps, resolution_scale=0.5):
+    """Calculate memory usage for different video lengths"""
+    frames = duration_seconds * fps
+    
+    # Per-frame memory (rough estimates for VR180 at 50% scale)
+    frame_size_mb = (3072 * 1536 * 3 * 4) / (1024 * 1024)  # ~18MB per frame
+    
+    total_memory_gb = (frames * frame_size_mb) / 1024
+    
+    return {
+        'duration': duration_seconds,
+        'total_frames': frames,
+        'estimated_memory_gb': total_memory_gb,
+        'safe_for_48gb': total_memory_gb < 40
+    }
+
+# Example outputs:
+# 30 seconds: ~2.7GB (safe)
+# 5 minutes: ~27GB (borderline) 
+# 1 hour: ~324GB (requires streaming)
+```
+
 ## Success Criteria
 
 ### Technical Feasibility

vr180_matting/config.py

@@ -37,6 +37,8 @@ class OutputConfig:
     format: str = "alpha"
     background_color: List[int] = None
     maintain_sbs: bool = True
+    preserve_audio: bool = True
+    verify_sync: bool = True
 
     def __post_init__(self):
         if self.background_color is None:
@@ -99,7 +101,9 @@ class VR180Config:
                 'path': self.output.path,
                 'format': self.output.format,
                 'background_color': self.output.background_color,
-                'maintain_sbs': self.output.maintain_sbs
+                'maintain_sbs': self.output.maintain_sbs,
+                'preserve_audio': self.output.preserve_audio,
+                'verify_sync': self.output.verify_sync
             },
             'hardware': {
                 'device': self.hardware.device,

vr180_matting/video_processor.py

@@ -7,6 +7,8 @@ import tempfile
 import shutil
 from tqdm import tqdm
 import warnings
+import time
+import subprocess
 
 from .config import VR180Config
 from .detector import YOLODetector
@@ -35,6 +37,16 @@ class VideoProcessor:
         self.frame_width = 0
         self.frame_height = 0
         
+        # Processing statistics
+        self.processing_stats = {
+            'start_time': None,
+            'end_time': None,
+            'total_duration': 0,
+            'processing_fps': 0,
+            'chunks_processed': 0,
+            'frames_processed': 0
+        }
+        
         self._initialize_models()
     
     def _initialize_models(self):
@@ -348,25 +360,109 @@ class VideoProcessor:
         print(f"Saved {len(frames)} PNG frames to {output_dir}")
     
     def _save_mp4_video(self, frames: List[np.ndarray], output_path: str):
-        """Save frames as MP4 video"""
+        """Save frames as MP4 video with audio preservation"""
         if not frames:
             return
         
-        height, width = frames[0].shape[:2]
+        output_path = Path(output_path)
+        temp_frames_dir = output_path.parent / f"temp_frames_{output_path.stem}"
+        temp_frames_dir.mkdir(exist_ok=True)
         
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        try:
-        writer = cv2.VideoWriter(output_path, fourcc, self.fps, (width, height))
+            # Save frames as images
+            print("Saving frames as images...")
+            for i, frame in enumerate(tqdm(frames, desc="Saving frames")):
+                if frame.shape[2] == 4:  # Convert RGBA to BGR
+                    frame = cv2.cvtColor(frame, cv2.COLOR_RGBA2BGR)
+                
+                frame_path = temp_frames_dir / f"frame_{i:06d}.jpg"
+                cv2.imwrite(str(frame_path), frame, [cv2.IMWRITE_JPEG_QUALITY, 95])
+            
+            # Create video with ffmpeg
+            self._create_video_with_ffmpeg(temp_frames_dir, output_path, len(frames))
+            
+        finally:
+            # Cleanup temporary frames
+            if temp_frames_dir.exists():
+                shutil.rmtree(temp_frames_dir)
+    
+    def _create_video_with_ffmpeg(self, frames_dir: Path, output_path: Path, frame_count: int):
+        """Create video using ffmpeg with audio preservation"""
+        frame_pattern = str(frames_dir / "frame_%06d.jpg")
         
-        for frame in tqdm(frames, desc="Writing video"):
+        if self.config.output.preserve_audio:
-            if frame.shape[2] == 4:  # Convert RGBA to BGR
+            # Create video with audio from input
-                frame = cv2.cvtColor(frame, cv2.COLOR_RGBA2BGR)
+            cmd = [
-            writer.write(frame)
+                'ffmpeg', '-y',
+                '-framerate', str(self.fps),
+                '-i', frame_pattern,
+                '-i', str(self.config.input.video_path),  # Input video for audio
+                '-c:v', 'h264_nvenc',  # Try GPU encoding first
+                '-preset', 'fast',
+                '-cq', '18',
+                '-c:a', 'copy',  # Copy audio without re-encoding
+                '-map', '0:v:0',  # Map video from frames
+                '-map', '1:a:0',  # Map audio from input video
+                '-shortest',     # Match shortest stream duration
+                '-pix_fmt', 'yuv420p',
+                str(output_path)
+            ]
+        else:
+            # Create video without audio
+            cmd = [
+                'ffmpeg', '-y',
+                '-framerate', str(self.fps),
+                '-i', frame_pattern,
+                '-c:v', 'h264_nvenc',
+                '-preset', 'fast',
+                '-cq', '18',
+                '-pix_fmt', 'yuv420p',
+                str(output_path)
+            ]
+        
+        print(f"Creating video with ffmpeg...")
+        result = subprocess.run(cmd, capture_output=True, text=True)
+        
+        if result.returncode != 0:
+            # Try CPU encoding as fallback
+            print("GPU encoding failed, trying CPU encoding...")
+            cmd[cmd.index('h264_nvenc')] = 'libx264'
+            cmd[cmd.index('-cq')] = '-crf'  # Change quality parameter for CPU
+            
+            result = subprocess.run(cmd, capture_output=True, text=True)
+            
+            if result.returncode != 0:
+                print(f"FFmpeg stdout: {result.stdout}")
+                print(f"FFmpeg stderr: {result.stderr}")
+                raise RuntimeError(f"FFmpeg failed with return code {result.returncode}")
+        
+        # Verify frame count if sync verification is enabled
+        if self.config.output.verify_sync:
+            self._verify_frame_count(output_path, frame_count)
         
-        writer.release()
         print(f"Saved video to {output_path}")
     
+    def _verify_frame_count(self, video_path: Path, expected_frames: int):
+        """Verify output video has correct frame count"""
+        try:
+            probe = ffmpeg.probe(str(video_path))
+            video_stream = next(
+                (stream for stream in probe['streams'] if stream['codec_type'] == 'video'), 
+                None
+            )
+            
+            if video_stream:
+                actual_frames = int(video_stream.get('nb_frames', 0))
+                if actual_frames != expected_frames:
+                    print(f"⚠️  Frame count mismatch: expected {expected_frames}, got {actual_frames}")
+                else:
+                    print(f"✅ Frame count verified: {actual_frames} frames")
+        except Exception as e:
+            print(f"⚠️  Could not verify frame count: {e}")
+    
     def process_video(self) -> None:
         """Main video processing pipeline"""
+        self.processing_stats['start_time'] = time.time()
         print("Starting VR180 video processing...")
         
         # Load video info
@@ -397,6 +493,10 @@ class VideoProcessor:
             matted_frames = self.process_chunk(frames, chunk_idx)
             chunk_results.append(matted_frames)
             
+            # Update statistics
+            self.processing_stats['chunks_processed'] += 1
+            self.processing_stats['frames_processed'] += len(frames)
+            
             # Memory cleanup
             self.memory_manager.cleanup_memory()
             
@@ -411,7 +511,43 @@ class VideoProcessor:
         print(f"Saving {len(final_frames)} processed frames...")
         self.save_video(final_frames, self.config.output.path)
         
+        # Calculate final statistics
+        self.processing_stats['end_time'] = time.time()
+        self.processing_stats['total_duration'] = self.processing_stats['end_time'] - self.processing_stats['start_time']
+        if self.processing_stats['total_duration'] > 0:
+            self.processing_stats['processing_fps'] = self.processing_stats['frames_processed'] / self.processing_stats['total_duration']
+        
+        # Print processing statistics
+        self._print_processing_statistics()
+        
         # Print final memory report
         self.memory_manager.print_memory_report()
         
-        print("Video processing completed!")
+        print("Video processing completed!")
+    
+    def _print_processing_statistics(self):
+        """Print detailed processing statistics"""
+        stats = self.processing_stats
+        video_duration = self.total_frames / self.fps if self.fps > 0 else 0
+        
+        print("\n" + "="*60)
+        print("PROCESSING STATISTICS")
+        print("="*60)
+        print(f"Input video duration: {video_duration:.1f} seconds ({self.total_frames} frames @ {self.fps:.2f} fps)")
+        print(f"Total processing time: {stats['total_duration']:.1f} seconds")
+        print(f"Processing speed: {stats['processing_fps']:.2f} fps")
+        print(f"Speedup factor: {self.fps / stats['processing_fps']:.1f}x slower than realtime")
+        print(f"Chunks processed: {stats['chunks_processed']}")
+        print(f"Frames processed: {stats['frames_processed']}")
+        
+        if video_duration > 0:
+            efficiency = video_duration / stats['total_duration']
+            print(f"Processing efficiency: {efficiency:.3f} (1.0 = realtime)")
+            
+            # Estimate time for different video lengths
+            print(f"\nEstimated processing times:")
+            print(f"  5 minutes: {(5 * 60) / efficiency / 60:.1f} minutes")
+            print(f"  30 minutes: {(30 * 60) / efficiency / 60:.1f} minutes") 
+            print(f"  1 hour: {(60 * 60) / efficiency / 60:.1f} minutes")
+        
+        print("="*60 + "\n")