.gitignore

@@ -17,10 +17,3 @@ feeder/
 dist/
 websocket_comm.log
 temp_debug/
-.claude
-
-# Video Test
-video_rtsp/
-multi_stream_viewer.py
-multi_camera_simulator.py
-start_4_cameras.bat

app.py

@@ -1049,34 +1049,10 @@ async def detect(websocket: WebSocket):
                     from siwatsystem.pympta import run_lightweight_detection
                     basic_detection = run_lightweight_detection(cropped_frame, model_tree)
                     
-                    # Enhanced car detection: requires both confidence pass AND bbox >= 50% of frame
+                    any_car_detected = basic_detection and basic_detection.get("car_detected", False)
-                    car_detected_confidence = basic_detection and basic_detection.get("car_detected", False)
+                    logger.debug(f"🔍 Camera {camera_id}: LIGHTWEIGHT - simple car presence check: {any_car_detected}")
-                    car_detected_with_bbox_validation = False
                     
-                    if car_detected_confidence:
+                    if any_car_detected:
-                        # Car passed confidence - now check bbox area
-                        best_detection = basic_detection.get("best_detection")
-                        if best_detection and best_detection.get("bbox"):
-                            bbox = best_detection["bbox"]
-                            x1, y1, x2, y2 = bbox
-                            bbox_area = (x2 - x1) * (y2 - y1)
-                            frame_height, frame_width = cropped_frame.shape[:2]
-                            frame_area = frame_height * frame_width
-                            bbox_area_ratio = bbox_area / frame_area if frame_area > 0 else 0
-                            
-                            min_area_ratio = 0.2  # 20% of frame
-                            car_detected_with_bbox_validation = bbox_area_ratio >= min_area_ratio
-                            
-                            if not car_detected_with_bbox_validation:
-                                logger.info(f"🚫 Camera {camera_id}: LIGHTWEIGHT - car detected but bbox {bbox_area_ratio:.1%} < {min_area_ratio:.0%} (too distant) - counting as absent")
-                            else:
-                                logger.debug(f"✅ Camera {camera_id}: LIGHTWEIGHT - car detected with valid bbox {bbox_area_ratio:.1%} >= {min_area_ratio:.0%}")
-                        else:
-                            logger.debug(f"⚠️ Camera {camera_id}: LIGHTWEIGHT - car detected but no bbox info available")
-                    
-                    logger.debug(f"🔍 Camera {camera_id}: LIGHTWEIGHT - enhanced car presence check: confidence={car_detected_confidence}, bbox_valid={car_detected_with_bbox_validation}")
-                    
-                    if car_detected_with_bbox_validation:
                         # Car detected - reset absence counter, continue sending cached detection dict
                         pipeline_state["absence_counter"] = 0  # Reset absence since cars are present
                         

siwatsystem/pympta.py

@@ -732,17 +732,12 @@ def run_detection_with_tracking(frame, node, context=None):
             
             # Tracking zone validation removed - process all detections
             
-            # Calculate bbox area
-            x1, y1, x2, y2 = bbox
-            bbox_area = (x2 - x1) * (y2 - y1)
-            
             # Create detection object
             detection = {
                 "class": class_name,
                 "confidence": conf,
                 "id": track_id,
                 "bbox": bbox,
-                "bbox_area": bbox_area,
                 "class_id": cls_id
             }
             
@@ -931,31 +926,6 @@ def update_single_track_stability(node, detection, camera_id, frame_shape=None,
     
     current_track_id = detection.get("id") if detection else None
     
-    # ─── Bbox Area Validation for Stability ───
-    # Only count detections where bbox area is >=20% of frame (close cars only)
-    if detection and frame_shape is not None:
-        bbox = detection.get("bbox", [0, 0, 0, 0])
-        if bbox and len(bbox) >= 4:
-            x1, y1, x2, y2 = bbox
-            bbox_area = (x2 - x1) * (y2 - y1)
-            frame_height, frame_width = frame_shape[:2]
-            frame_area = frame_height * frame_width
-            bbox_area_ratio = bbox_area / frame_area if frame_area > 0 else 0
-            
-            # Require bbox to be at least 20% of frame area
-            min_area_ratio = 0.2
-            
-            if bbox_area_ratio < min_area_ratio:
-                logger.info(f"🚫 Camera {camera_id}: Track {current_track_id} REJECTED for stability - bbox area {bbox_area_ratio:.1%} < {min_area_ratio:.0%} (too small/distant)")
-                # Completely reset - remove track entirely (same as trackId change)
-                if current_track_id and current_track_id in track_counters:
-                    old_count = track_counters.pop(current_track_id, 0)  # Remove completely
-                    stable_tracks.discard(current_track_id)  # Remove from stable
-                    logger.info(f"🔄 Camera {camera_id}: COMPLETELY RESET track {current_track_id} counter from {old_count} to 0 (reason: bbox too small)")
-                return {"validation_complete": False, "stable_tracks": list(stable_tracks), "current_tracks": [], "bbox_too_small": True}
-            else:
-                logger.debug(f"✅ Camera {camera_id}: Track {current_track_id} bbox area {bbox_area_ratio:.1%} >= {min_area_ratio:.0%} - acceptable for stability")
-    
     # ═══ MODE-AWARE TRACK VALIDATION ═══
     logger.debug(f"📋 Camera {camera_id}: === TRACK VALIDATION ANALYSIS ===")
     logger.debug(f"📋 Camera {camera_id}: Current mode: {current_mode} (validation_mode={is_validation_mode})")
@@ -1473,35 +1443,6 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None, valid
             # Normal detection stage - Using structured detection function
             all_detections, regions_dict, track_validation_result = run_detection_with_tracking(frame, node, context)
         
-            # ─── Apply largest bbox area selection for full pipeline mode ───
-            # When we have stable tracks and multiple detections, select the largest bbox area one
-            stable_tracks = track_validation_result.get("stable_tracks", [])
-            if stable_tracks and len(all_detections) > 1:
-                logger.info(f"🔍 PIPELINE: Full pipeline mode - selecting largest bbox area from {len(all_detections)} detections")
-                
-                # Select detection with largest bbox area
-                largest_detection = max(all_detections, key=lambda x: x.get("bbox_area", 0))
-                logger.info(f"🎯 PIPELINE: Selected largest bbox area detection: conf={largest_detection.get('confidence', 0):.3f}, area={largest_detection.get('bbox_area', 0):.0f}")
-                
-                # Update all_detections to only contain the largest bbox area detection
-                all_detections = [largest_detection]
-                
-                # Update regions_dict to reflect the selected detection
-                class_name = largest_detection.get("class", "car")
-                regions_dict = {
-                    class_name: {
-                        "confidence": largest_detection.get("confidence"),
-                        "bbox": largest_detection.get("bbox", [0, 0, 0, 0]),
-                        "detection": largest_detection
-                    }
-                }
-                
-                logger.debug(f"🔄 PIPELINE: Updated regions_dict for largest bbox area selection: {list(regions_dict.keys())}")
-            elif stable_tracks:
-                logger.debug(f"🔄 PIPELINE: Full pipeline mode - single detection, no area selection needed")
-            else:
-                logger.debug(f"🔄 PIPELINE: No stable tracks yet, proceeding with confidence-based detection")
-        
         # Debug: Save crops for debugging (disabled for production)
         # if regions_dict:
         #     try: