det core

vr180_matting/config.py

@@ -29,6 +29,11 @@ class MattingConfig:
     fp16: bool = True
     sam2_model_cfg: str = "sam2.1_hiera_l"
     sam2_checkpoint: str = "segment-anything-2/checkpoints/sam2.1_hiera_large.pt"
+    # Det-SAM2 optimizations
+    continuous_correction: bool = True
+    correction_interval: int = 60  # Add correction prompts every N frames
+    frame_release_interval: int = 50  # Release old frames every N frames
+    frame_window_size: int = 30  # Keep N frames in memory
 
 
 @dataclass

vr180_matting/sam2_wrapper.py

@@ -152,13 +152,16 @@ class SAM2VideoMatting:
         
         return object_ids
     
-    def propagate_masks(self, start_frame: int = 0, max_frames: Optional[int] = None) -> Dict[int, Dict[int, np.ndarray]]:
+    def propagate_masks(self, start_frame: int = 0, max_frames: Optional[int] = None, 
+                       frame_release_interval: int = 50, frame_window_size: int = 30) -> Dict[int, Dict[int, np.ndarray]]:
         """
-        Propagate masks through video
+        Propagate masks through video with Det-SAM2 style memory management
         
         Args:
             start_frame: Starting frame index
             max_frames: Maximum number of frames to process
+            frame_release_interval: Release old frames every N frames
+            frame_window_size: Keep N frames in memory
             
         Returns:
             Dictionary mapping frame_idx -> {obj_id: mask}
@@ -182,9 +185,108 @@ class SAM2VideoMatting:
             
             video_segments[out_frame_idx] = frame_masks
             
-            # Memory management: release old frames periodically
+            # Det-SAM2 style memory management: more aggressive frame release
-            if self.memory_offload and out_frame_idx % 100 == 0:
+            if self.memory_offload and out_frame_idx % frame_release_interval == 0:
-                self._release_old_frames(out_frame_idx - 50)
+                self._release_old_frames(out_frame_idx - frame_window_size)
+                # Optional: Log frame release for monitoring
+                if out_frame_idx % (frame_release_interval * 4) == 0:  # Log every 4x release interval
+                    print(f"Det-SAM2: Released frames before {out_frame_idx - frame_window_size}, keeping {frame_window_size} frames")
+        
+        return video_segments
+    
+    def propagate_masks_with_continuous_correction(self, 
+                                                 detector,
+                                                 temp_video_path: str,
+                                                 start_frame: int = 0, 
+                                                 max_frames: Optional[int] = None,
+                                                 correction_interval: int = 60,
+                                                 frame_release_interval: int = 50,
+                                                 frame_window_size: int = 30) -> Dict[int, Dict[int, np.ndarray]]:
+        """
+        Det-SAM2 style: Propagate masks with continuous prompt correction
+        
+        Args:
+            detector: YOLODetector instance for generating correction prompts
+            temp_video_path: Path to video file for frame access
+            start_frame: Starting frame index
+            max_frames: Maximum number of frames to process
+            correction_interval: Add correction prompts every N frames
+            frame_release_interval: Release old frames every N frames
+            frame_window_size: Keep N frames in memory
+            
+        Returns:
+            Dictionary mapping frame_idx -> {obj_id: mask}
+        """
+        if self.inference_state is None:
+            raise RuntimeError("Video state not initialized")
+        
+        video_segments = {}
+        max_frames = max_frames or 10000  # Default limit
+        
+        # Open video for accessing frames during propagation
+        cap = cv2.VideoCapture(str(temp_video_path))
+        
+        try:
+            for out_frame_idx, out_obj_ids, out_mask_logits in self.predictor.propagate_in_video(
+                self.inference_state, 
+                start_frame_idx=start_frame,
+                max_frame_num_to_track=max_frames,
+                reverse=False
+            ):
+                frame_masks = {}
+                
+                for i, out_obj_id in enumerate(out_obj_ids):
+                    mask = (out_mask_logits[i] > 0.0).cpu().numpy()
+                    frame_masks[out_obj_id] = mask
+                
+                video_segments[out_frame_idx] = frame_masks
+                
+                # Det-SAM2 optimization: Add correction prompts at keyframes
+                if (out_frame_idx % correction_interval == 0 and 
+                    out_frame_idx > start_frame and 
+                    out_frame_idx < max_frames - 1):
+                    
+                    # Read frame for detection
+                    cap.set(cv2.CAP_PROP_POS_FRAMES, out_frame_idx)
+                    ret, correction_frame = cap.read()
+                    
+                    if ret:
+                        # Run detection on this keyframe
+                        detections = detector.detect_persons(correction_frame)
+                        
+                        if detections:
+                            # Convert to prompts and add as corrections
+                            box_prompts, labels = detector.convert_to_sam_prompts(detections)
+                            
+                            # Add correction prompts (SAM2 will propagate backward)
+                            correction_count = 0
+                            try:
+                                for i, (box, label) in enumerate(zip(box_prompts, labels)):
+                                    # Use existing object IDs if available, otherwise create new ones
+                                    obj_id = out_obj_ids[i] if i < len(out_obj_ids) else len(out_obj_ids) + i + 1
+                                    
+                                    self.predictor.add_new_points_or_box(
+                                        inference_state=self.inference_state,
+                                        frame_idx=out_frame_idx,
+                                        obj_id=obj_id,
+                                        box=box,
+                                    )
+                                    correction_count += 1
+                                
+                                print(f"Det-SAM2: Added {correction_count} correction prompts at frame {out_frame_idx}")
+                                
+                            except Exception as e:
+                                warnings.warn(f"Failed to add correction prompt at frame {out_frame_idx}: {e}")
+                
+                # Memory management: More aggressive frame release (Det-SAM2 style)
+                if self.memory_offload and out_frame_idx % frame_release_interval == 0:
+                    self._release_old_frames(out_frame_idx - frame_window_size)
+                    # Optional: Log frame release for monitoring
+                    if out_frame_idx % (frame_release_interval * 4) == 0:  # Log every 4x release interval
+                        print(f"Det-SAM2: Released frames before {out_frame_idx - frame_window_size}, keeping {frame_window_size} frames")
+            
+        finally:
+            cap.release()
         
         return video_segments
     

vr180_matting/vr180_processor.py

@@ -375,10 +375,27 @@ class VR180Processor(VideoProcessor):
             
             # Propagate masks (most expensive operation)
             self._print_memory_step(f"Before SAM2 propagation ({eye_name} eye, {num_frames} frames)")
-            video_segments = self.sam2_model.propagate_masks(
+            
-                start_frame=0, 
+            # Use Det-SAM2 continuous correction if enabled
-                max_frames=num_frames
+            if self.config.matting.continuous_correction:
-            )
+                video_segments = self.sam2_model.propagate_masks_with_continuous_correction(
+                    detector=self.detector,
+                    temp_video_path=str(temp_video_path),
+                    start_frame=0, 
+                    max_frames=num_frames,
+                    correction_interval=self.config.matting.correction_interval,
+                    frame_release_interval=self.config.matting.frame_release_interval,
+                    frame_window_size=self.config.matting.frame_window_size
+                )
+                print(f"Used Det-SAM2 continuous correction (interval: {self.config.matting.correction_interval} frames)")
+            else:
+                video_segments = self.sam2_model.propagate_masks(
+                    start_frame=0, 
+                    max_frames=num_frames,
+                    frame_release_interval=self.config.matting.frame_release_interval,
+                    frame_window_size=self.config.matting.frame_window_size
+                )
+            
             self._print_memory_step(f"After SAM2 propagation ({eye_name} eye)")
             
             # Apply masks - need to reload frames from temp video since we freed the original frames