Update app.py

app.py

@@ -63,144 +63,89 @@ class DatasetCollectionApp:
         print(f"Database has {len(self.db.get_all_dogs())} dogs")
 
     def create_visualization_video(self, video_path: str, sample_rate: int) -> str:
-        """Create visualization video with boxes matching ReID temp_ids with ID labels"""
+        """Create visualization video with bold boxes from tracking.py only (no ReID, no labels)"""
         try:
             cap = cv2.VideoCapture(video_path)
             if not cap.isOpened():
                 print("ERROR: Cannot open input video")
                 return None
-            
+    
             fps = cap.get(cv2.CAP_PROP_FPS) or 30
             width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
             height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-            
+    
             output_path = f"visualization_{datetime.now().strftime('%Y%m%d_%H%M%S')}.mp4"
-            
-            # Try multiple codecs
+    
+            # Try codecs
             codecs = ['mp4v', 'XVID', 'MJPG']
             out = None
-            
             for codec in codecs:
                 fourcc = cv2.VideoWriter_fourcc(*codec)
                 out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
-                
                 if out.isOpened():
                     print(f"Using codec: {codec}")
                     break
                 else:
                     out.release()
                     out = None
-            
+    
             if out is None or not out.isOpened():
                 print("ERROR: Could not initialize VideoWriter with any codec")
                 cap.release()
                 return None
-            
-            # Create separate tracker for visualization
+    
+            # Tracker only (no ReID)
             viz_tracker = DeepSORTTracker(
                 max_iou_distance=0.5,
                 max_age=90,
                 n_init=1,
-                use_appearance=True
+                use_appearance=False
             )
-            
-            # Create separate ReID instance for visualization
-            viz_reid = SimplifiedReID(device=self.device)
-            viz_reid.set_threshold(self.reid.threshold)
-            viz_reid.set_video_source(video_path)
-            
-            # Color mapping for temp_ids
+    
             track_colors = {}
-            
             frame_num = 0
-            
-            print("\nCreating visualization video with ReID matching and ID labels...")
-            
+    
+            print("\nCreating visualization video (tracking IDs only)...")
+    
             while cap.isOpened():
                 ret, frame = cap.read()
                 if not ret:
                     break
-                
+    
                 if frame_num % sample_rate == 0:
-                    # Detect dogs
                     detections = self.detector.detect(frame)
-                    
-                    # Track dogs
                     tracks = viz_tracker.update(detections)
-                    
-                    # Match with ReID to get temp_ids
+    
                     for track in tracks:
-                        # Get ReID temp_id (matches gallery)
-                        result = viz_reid.match_or_register(track)
-                        temp_id = result['temp_id']
-                        
-                        if temp_id == 0:
-                            continue
-                        
-                        # Assign color based on temp_id
-                        if temp_id not in track_colors:
-                            track_colors[temp_id] = (
+                        tid = track.track_id
+                        if tid not in track_colors:
+                            track_colors[tid] = (
                                 np.random.randint(50, 255),
                                 np.random.randint(50, 255),
                                 np.random.randint(50, 255)
                             )
-                        
+                        color = track_colors[tid]
                         x1, y1, x2, y2 = map(int, track.bbox)
-                        color = track_colors[temp_id]
-                        
-                        # Draw bold box
+    
+                        # Bold box only
                         cv2.rectangle(frame, (x1, y1), (x2, y2), color, 6)
-                        
-                        # Add ID label
-                        label = f"#{temp_id}"
-                        font = cv2.FONT_HERSHEY_SIMPLEX
-                        font_scale = 1.5
-                        font_thickness = 3
-                        
-                        # Get text size
-                        (text_width, text_height), baseline = cv2.getTextSize(
-                            label, font, font_scale, font_thickness
-                        )
-                        
-                        # Draw filled rectangle background for text
-                        cv2.rectangle(
-                            frame,
-                            (x1, y1 - text_height - 15),
-                            (x1 + text_width + 15, y1),
-                            color,
-                            -1
-                        )
-                        
-                        # Draw white text
-                        cv2.putText(
-                            frame,
-                            label,
-                            (x1 + 7, y1 - 7),
-                            font,
-                            font_scale,
-                            (255, 255, 255),
-                            font_thickness,
-                            cv2.LINE_AA
-                        )
-                
+    
                 out.write(frame)
                 frame_num += 1
-                
+    
                 if frame_num % 30 == 0:
                     print(f"Visualization progress: {frame_num} frames")
-            
+    
             cap.release()
             out.release()
-            
-            # Verify file
+    
             if os.path.exists(output_path) and os.path.getsize(output_path) > 1000:
                 print(f"Visualization video saved: {output_path}")
-                print(f"Gallery temp_ids shown: {sorted(track_colors.keys())}")
                 return output_path
             else:
                 print("ERROR: Video file not created or is empty")
                 return None
-            
+    
         except Exception as e:
             print(f"Visualization video error: {e}")
             import traceback