[Pongsatorn K. 2025/09/01] fixing pympta.py #6
1 changed files with 67 additions and 70 deletions
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@ -62,18 +62,18 @@ def crop_region_by_class(frame, regions_dict, class_name):
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bbox = regions_dict[class_name]['bbox']
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x1, y1, x2, y2 = bbox
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# TEMP DEBUG: Diagnostic logging for crop issues
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# Diagnostic logging for crop issues
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frame_h, frame_w = frame.shape[:2]
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logger.info(f"🔍 CROP DEBUG: Frame dimensions: {frame_w}x{frame_h}")
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logger.info(f"🔍 CROP DEBUG: Original bbox: {bbox}")
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logger.info(f"🔍 CROP DEBUG: Bbox dimensions: {x2-x1}x{y2-y1}")
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logger.debug(f"CROP DEBUG: Frame dimensions: {frame_w}x{frame_h}")
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logger.debug(f"CROP DEBUG: Original bbox: {bbox}")
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logger.debug(f"CROP DEBUG: Bbox dimensions: {x2-x1}x{y2-y1}")
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# Check if bbox is within frame bounds
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if x1 < 0 or y1 < 0 or x2 > frame_w or y2 > frame_h:
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logger.warning(f"🔍 CROP DEBUG: Bbox extends beyond frame! Clipping...")
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logger.warning(f"CROP DEBUG: Bbox extends beyond frame! Clipping...")
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(frame_w, x2), min(frame_h, y2)
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logger.info(f"🔍 CROP DEBUG: Clipped bbox: ({x1}, {y1}, {x2}, {y2})")
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logger.debug(f"CROP DEBUG: Clipped bbox: ({x1}, {y1}, {x2}, {y2})")
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cropped = frame[y1:y2, x1:x2]
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@ -81,7 +81,7 @@ def crop_region_by_class(frame, regions_dict, class_name):
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logger.warning(f"Empty crop for class '{class_name}' with bbox {bbox}")
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return None
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logger.info(f"🔍 CROP DEBUG: Successful crop shape: {cropped.shape}")
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logger.debug(f"CROP DEBUG: Successful crop shape: {cropped.shape}")
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return cropped
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def format_action_context(base_context, additional_context=None):
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@ -764,10 +764,10 @@ def run_detection_with_tracking(frame, node, context=None):
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for i, detection in enumerate(candidate_detections):
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logger.debug(f"🏆 Camera {camera_id}: Candidate {i+1}: {detection['class']} conf={detection['confidence']:.3f} track_id={detection['id']}")
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# TEMP DEBUG: Show all candidate detections before selection
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logger.info(f"🔍 TEMP DEBUG: Found {len(candidate_detections)} candidate detections:")
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# Show all candidate detections before selection
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logger.debug(f"Found {len(candidate_detections)} candidate detections:")
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for i, det in enumerate(candidate_detections):
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logger.info(f"🔍 TEMP DEBUG: Candidate {i+1}: {det['class']} conf={det['confidence']:.3f} bbox={det['bbox']}")
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logger.debug(f"Candidate {i+1}: {det['class']} conf={det['confidence']:.3f} bbox={det['bbox']}")
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# Find the single highest confidence detection across all detected classes
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best_detection = max(candidate_detections, key=lambda x: x["confidence"])
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@ -820,24 +820,24 @@ def run_detection_with_tracking(frame, node, context=None):
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logger.info(f"✅ Camera {camera_id}: DETECTION COMPLETE - tracking single car: track_id={track_id}, conf={best_detection['confidence']:.3f}")
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logger.debug(f"📊 Camera {camera_id}: Detection summary: {len(res.boxes)} raw → {len(candidate_detections)} candidates → 1 selected")
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# TEMP DEBUG: Save vehicle crop immediately after yolo detection
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if node.get("modelId") in ["yolo11n", "yolo11m"] and regions_dict:
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try:
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import datetime
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os.makedirs("temp_debug", exist_ok=True)
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")[:-3]
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for class_name, region_data in regions_dict.items():
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bbox = region_data['bbox']
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x1, y1, x2, y2 = bbox
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cropped = frame[y1:y2, x1:x2]
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if cropped.size > 0:
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model_name = node.get("modelId", "yolo")
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debug_path = f"temp_debug/{model_name}_{class_name}_crop_{timestamp}.jpg"
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cv2.imwrite(debug_path, cropped)
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logger.info(f"🚗 TEMP DEBUG: Saved {model_name} {class_name} crop to {debug_path}")
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except Exception as e:
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logger.error(f"🚗 TEMP DEBUG: Failed to save {node.get('modelId', 'yolo')} crop: {e}")
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# Debug: Save vehicle crop for debugging (disabled for production)
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# if node.get("modelId") in ["yolo11n", "yolo11m"] and regions_dict:
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# try:
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# import datetime
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# os.makedirs("temp_debug", exist_ok=True)
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# timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")[:-3]
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#
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# for class_name, region_data in regions_dict.items():
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# bbox = region_data['bbox']
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# x1, y1, x2, y2 = bbox
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# cropped = frame[y1:y2, x1:x2]
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# if cropped.size > 0:
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# model_name = node.get("modelId", "yolo")
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# debug_path = f"temp_debug/{model_name}_{class_name}_crop_{timestamp}.jpg"
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# cv2.imwrite(debug_path, cropped)
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# logger.debug(f"Saved {model_name} {class_name} crop to {debug_path}")
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# except Exception as e:
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# logger.error(f"Failed to save {node.get('modelId', 'yolo')} crop: {e}")
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# Update track-based stability tracking for the single selected car
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camera_id = context.get("camera_id", "unknown") if context else "unknown"
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@ -1433,48 +1433,45 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None, valid
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# Normal detection stage - Using structured detection function
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all_detections, regions_dict, track_validation_result = run_detection_with_tracking(frame, node, context)
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# TEMP DEBUG: Save only specific crops
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if regions_dict:
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try:
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import datetime
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os.makedirs("temp_debug", exist_ok=True)
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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model_id = node.get("modelId", "unknown")
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# Save vehicle crop from yolo model (any vehicle: car, truck, bus, motorcycle)
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if model_id in ["yolo11n", "yolo11m"]:
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# Look for any vehicle class in regions_dict
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vehicle_classes = ["car", "truck", "bus", "motorcycle"]
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found_vehicle = None
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for vehicle_class in vehicle_classes:
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if vehicle_class in regions_dict:
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found_vehicle = vehicle_class
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break
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bbox = regions_dict[found_vehicle]['bbox']
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x1, y1, x2, y2 = bbox
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cropped = frame[y1:y2, x1:x2]
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if cropped.size > 0:
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debug_path = f"temp_debug/{found_vehicle}_crop_{timestamp}.jpg"
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cv2.imwrite(debug_path, cropped)
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logger.info(f"🚗 TEMP DEBUG: Saved {found_vehicle} crop to {debug_path}")
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else:
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logger.warning(f"🚗 TEMP DEBUG: Empty {found_vehicle} crop with bbox {bbox}")
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else:
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logger.warning(f"🚗 TEMP DEBUG: {model_id} detected but no vehicle classes found. Available: {list(regions_dict.keys())}")
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# Save frontal crop from frontal_detection_v1
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elif model_id == "frontal_detection_v1" and "frontal" in regions_dict:
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bbox = regions_dict["frontal"]['bbox']
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x1, y1, x2, y2 = bbox
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cropped = frame[y1:y2, x1:x2]
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if cropped.size > 0:
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debug_path = f"temp_debug/frontal_crop_{timestamp}.jpg"
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cv2.imwrite(debug_path, cropped)
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logger.info(f"🔍 TEMP DEBUG: Saved frontal crop to {debug_path}")
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except Exception as e:
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logger.error(f"🔍 TEMP DEBUG: Failed to save crops: {e}")
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# Debug: Save crops for debugging (disabled for production)
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# if regions_dict:
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# try:
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# import datetime
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# os.makedirs("temp_debug", exist_ok=True)
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# timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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# model_id = node.get("modelId", "unknown")
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#
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# # Save vehicle crop from yolo model (any vehicle: car, truck, bus, motorcycle)
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# if model_id in ["yolo11n", "yolo11m"]:
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# # Look for any vehicle class in regions_dict
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# vehicle_classes = ["car", "truck", "bus", "motorcycle"]
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# found_vehicle = None
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# for vehicle_class in vehicle_classes:
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# if vehicle_class in regions_dict:
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# found_vehicle = vehicle_class
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# break
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#
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# if found_vehicle:
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# bbox = regions_dict[found_vehicle]['bbox']
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# x1, y1, x2, y2 = bbox
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# cropped = frame[y1:y2, x1:x2]
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# if cropped.size > 0:
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# debug_path = f"temp_debug/{found_vehicle}_crop_{timestamp}.jpg"
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# cv2.imwrite(debug_path, cropped)
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# logger.debug(f"Saved {found_vehicle} crop to {debug_path}")
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#
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# # Save frontal crop from frontal_detection_v1
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# elif model_id == "frontal_detection_v1" and "frontal" in regions_dict:
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# bbox = regions_dict["frontal"]['bbox']
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# x1, y1, x2, y2 = bbox
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# cropped = frame[y1:y2, x1:x2]
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# if cropped.size > 0:
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# debug_path = f"temp_debug/frontal_crop_{timestamp}.jpg"
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# cv2.imwrite(debug_path, cropped)
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# logger.debug(f"Saved frontal crop to {debug_path}")
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#
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# except Exception as e:
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# logger.error(f"Failed to save crops: {e}")
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if not all_detections:
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logger.debug("No detections from structured detection function - sending 'none' detection")
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