[Pongsatorn K. 2025/09/01] fixing pympta.py #6
2 changed files with 103 additions and 49 deletions
2
.gitignore
vendored
2
.gitignore
vendored
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@ -15,3 +15,5 @@ feeder/
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.venv/
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.vscode/
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dist/
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websocket_comm.log
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temp_debug/
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@ -61,12 +61,27 @@ 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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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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# 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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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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cropped = frame[y1:y2, x1:x2]
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if cropped.size == 0:
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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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return cropped
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def format_action_context(base_context, additional_context=None):
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@ -113,6 +128,7 @@ def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client, db_manage
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"crop": node_config.get("crop", False),
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"cropClass": node_config.get("cropClass"),
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"minConfidence": node_config.get("minConfidence", None),
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"frontalMinConfidence": node_config.get("frontalMinConfidence", None),
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"minBboxAreaRatio": node_config.get("minBboxAreaRatio", 0.0),
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"multiClass": node_config.get("multiClass", False),
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"expectedClasses": node_config.get("expectedClasses", []),
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@ -634,8 +650,7 @@ def run_detection_with_tracking(frame, node, context=None):
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logger.info(f"Camera {camera_id}: 🔄 Reset YOLO tracker - new cars will get fresh track IDs")
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session_state["reset_tracker_on_resume"] = False # Clear the flag
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# Get tracking zone from runtime context (camera-specific)
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tracking_zone = context.get("trackingZone", []) if context else []
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# Tracking zones removed - process all detections
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# Prepare class filtering
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trigger_class_indices = node.get("triggerClassIndices")
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@ -643,19 +658,13 @@ def run_detection_with_tracking(frame, node, context=None):
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logger.debug(f"Running detection for {node['modelId']} - tracking: {tracking_enabled}, stability_threshold: {stability_threshold}, classes: {node.get('triggerClasses', 'all')}")
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if tracking_enabled and tracking_zone:
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# Use tracking with zone validation
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logger.debug(f"Using tracking with ReID config: {reid_config_path}")
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res = node["model"].track(
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frame,
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stream=False,
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persist=True,
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tracker=reid_config_path,
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**class_filter
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)[0]
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elif tracking_enabled:
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# Use tracking without zone restriction
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logger.debug("Using tracking without zone restriction")
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# Use predict for detection-only models (frontal detection), track for main detection models
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model_id = node.get("modelId", "")
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use_tracking = tracking_enabled and not ("frontal" in model_id.lower() or "detection" in model_id.lower())
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if use_tracking:
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# Use tracking for main detection models (yolo11m, etc.)
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logger.debug(f"Using tracking for {model_id}")
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res = node["model"].track(
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frame,
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stream=False,
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@ -663,8 +672,8 @@ def run_detection_with_tracking(frame, node, context=None):
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**class_filter
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)[0]
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else:
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# Use detection only (no tracking)
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logger.debug("Using detection only (tracking disabled)")
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# Use detection only for frontal detection and other detection-only models
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logger.debug(f"Using prediction only for {model_id}")
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res = node["model"].predict(
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frame,
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stream=False,
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@ -673,7 +682,13 @@ def run_detection_with_tracking(frame, node, context=None):
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# Process detection results
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candidate_detections = []
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min_confidence = node.get("minConfidence", 0.0)
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# Use frontalMinConfidence for frontal detection models, otherwise use minConfidence
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model_id = node.get("modelId", "")
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if "frontal" in model_id.lower() and "frontalMinConfidence" in node:
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min_confidence = node.get("frontalMinConfidence", 0.0)
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logger.debug(f"Using frontalMinConfidence={min_confidence} for {model_id}")
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else:
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min_confidence = node.get("minConfidence", 0.0)
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if res.boxes is None or len(res.boxes) == 0:
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logger.debug(f"🚫 Camera {camera_id}: YOLO returned no detections")
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@ -716,15 +731,7 @@ def run_detection_with_tracking(frame, node, context=None):
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logger.debug(f"❌ Camera {camera_id}: Detection {i+1} REJECTED - confidence {conf:.3f} < {min_confidence}")
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continue
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# Apply tracking zone validation if enabled
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if tracking_enabled and tracking_zone:
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bbox_center_x = (x1 + x2) // 2
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bbox_center_y = (y1 + y2) // 2
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# Check if detection center is within tracking zone
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if not _point_in_polygon((bbox_center_x, bbox_center_y), tracking_zone):
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logger.debug(f"❌ Camera {camera_id}: Detection {i+1} REJECTED - outside tracking zone")
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continue
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# Tracking zone validation removed - process all detections
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# Create detection object
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detection = {
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@ -757,6 +764,11 @@ 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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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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# 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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original_class = best_detection["class"]
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@ -808,6 +820,25 @@ 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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# 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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model_id = node.get("modelId", "unknown")
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@ -826,28 +857,6 @@ def run_detection_with_tracking(frame, node, context=None):
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logger.debug(f"Detection error traceback: {traceback.format_exc()}")
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return [], {}, {"validation_complete": False, "stable_tracks": [], "current_tracks": []}
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def _point_in_polygon(point, polygon):
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"""Check if a point is inside a polygon using ray casting algorithm."""
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if not polygon or len(polygon) < 3:
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return True # No zone restriction if invalid polygon
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x, y = point
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n = len(polygon)
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inside = False
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p1x, p1y = polygon[0]
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for i in range(1, n + 1):
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p2x, p2y = polygon[i % n]
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if y > min(p1y, p2y):
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if y <= max(p1y, p2y):
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if x <= max(p1x, p2x):
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if p1y != p2y:
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xinters = (y - p1y) * (p2x - p1x) / (p2y - p1y) + p1x
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if p1x == p2x or x <= xinters:
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inside = not inside
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p1x, p1y = p2x, p2y
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return inside
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def get_camera_stability_data(camera_id, model_id):
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"""Get or create stability tracking data for a specific camera and model."""
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@ -1424,6 +1433,49 @@ 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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if not all_detections:
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logger.debug("No detections from structured detection function - sending 'none' detection")
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none_detection = {
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