# from PySide6.QtCore import QObject, Signal, QThread, Qt, QMutex, QWaitCondition, QTimer, Slot # from PySide6.QtGui import QImage, QPixmap # import cv2 # import time # import numpy as np # from datetime import datetime # from collections import deque # from typing import Dict, List, Optional # import os # import sys # import math # # Add parent directory to path for imports # sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) # # Import utilities # from utils.annotation_utils import ( # draw_detections, # draw_performance_metrics, # resize_frame_for_display, # convert_cv_to_qimage, # convert_cv_to_pixmap, # pipeline_with_violation_line # ) # # Import enhanced annotation utilities # from utils.enhanced_annotation_utils import ( # enhanced_draw_detections, # draw_performance_overlay, # enhanced_cv_to_qimage, # enhanced_cv_to_pixmap # ) # # Import traffic light color detection utilities # from red_light_violation_pipeline import RedLightViolationPipeline # from utils.traffic_light_utils import detect_traffic_light_color, draw_traffic_light_status, ensure_traffic_light_color # from utils.crosswalk_utils2 import detect_crosswalk_and_violation_line, draw_violation_line, get_violation_line_y # from controllers.bytetrack_tracker import ByteTrackVehicleTracker # TRAFFIC_LIGHT_CLASSES = ["traffic light", "trafficlight", "tl"] # TRAFFIC_LIGHT_NAMES = ['trafficlight', 'traffic light', 'tl', 'signal'] # def normalize_class_name(class_name): # """Normalizes class names from different models/formats to a standard name""" # if not class_name: # return "" # name_lower = class_name.lower() # # Traffic light variants # if name_lower in ['traffic light', 'trafficlight', 'traffic_light', 'tl', 'signal']: # return 'traffic light' # # Keep specific vehicle classes (car, truck, bus) separate # # Just normalize naming variations within each class # if name_lower in ['car', 'auto', 'automobile']: # return 'car' # elif name_lower in ['truck']: # return 'truck' # elif name_lower in ['bus']: # return 'bus' # elif name_lower in ['motorcycle', 'scooter', 'motorbike', 'bike']: # return 'motorcycle' # # Person variants # if name_lower in ['person', 'pedestrian', 'human']: # return 'person' # # Other common classes can be added here # return class_name # def is_traffic_light(class_name): # """Helper function to check if a class name is a traffic light with normalization""" # if not class_name: # return False # normalized = normalize_class_name(class_name) # return normalized == 'traffic light' # class VideoController(QObject): # frame_ready = Signal(object, object, dict) # QPixmap, detections, metrics # raw_frame_ready = Signal(np.ndarray, list, float) # frame, detections, fps # frame_np_ready = Signal(np.ndarray) # Direct NumPy frame signal for display # stats_ready = Signal(dict) # Dictionary with stats (fps, detection_time, traffic_light) # violation_detected = Signal(dict) # Signal emitted when a violation is detected # progress_ready = Signal(int, int, float) # value, max_value, timestamp # device_info_ready = Signal(dict) # Signal to emit device info to the UI # auto_select_model_device = Signal() # Signal for UI to request auto model/device selection # performance_stats_ready = Signal(dict) # NEW: Signal for performance tab (fps, inference, device, res) # violations_batch_ready = Signal(list) # NEW: Signal to emit a batch of violations # pause_state_changed = Signal(bool) # Signal emitted when pause state changes (True=paused, False=playing) # def __init__(self, model_manager=None): # """ # Initialize video controller. # Args: # model_manager: Model manager instance for detection and violation # """ # super().__init__() # print("Loaded advanced VideoController from video_controller_new.py") # DEBUG: Confirm correct controller # self._running = False # self._paused = False # Add pause state # self._last_frame = None # Store last frame for VLM analysis during pause # self._last_analysis_data = {} # Store last analysis data for VLM # self.source = None # self.source_type = None # self.source_fps = 0 # self.performance_metrics = {} # self.mutex = QMutex() # self.pause_condition = QWaitCondition() # Add wait condition for pause # # Performance tracking # self.processing_times = deque(maxlen=100) # Store last 100 processing times # self.fps_history = deque(maxlen=100) # Store last 100 FPS values # self.start_time = time.time() # self.frame_count = 0 # self.actual_fps = 0.0 # self.model_manager = model_manager # self.inference_model = None # self.tracker = None # # Initialize device tracking # if self.model_manager and hasattr(self.model_manager, 'config'): # self.current_device = self.model_manager.config.get("detection", {}).get("device", "CPU") # else: # self.current_device = "CPU" # print(f"🔧 Video Controller: Initialized with device: {self.current_device}") # self.current_frame = None # self.current_detections = [] # # Traffic light state tracking # self.latest_traffic_light = {"color": "unknown", "confidence": 0.0} # # Vehicle tracking settings # self.vehicle_history = {} # Dictionary to store vehicle position history # self.vehicle_statuses = {} # Track stable movement status # self.movement_threshold = 1.5 # ADJUSTED: More balanced movement detection (was 0.8) # self.min_confidence_threshold = 0.3 # FIXED: Lower threshold for better detection (was 0.5) # # Enhanced violation detection settings # self.position_history_size = 20 # Increased from 10 to track longer history # self.crossing_check_window = 8 # Check for crossings over the last 8 frames instead of just 2 # self.max_position_jump = 50 # Maximum allowed position jump between frames (detect ID switches) # # Set up violation detection # try: # from controllers.red_light_violation_detector import RedLightViolationDetector # self.violation_detector = RedLightViolationDetector() # print("✅ Red light violation detector initialized") # except Exception as e: # self.violation_detector = None # print(f"❌ Could not initialize violation detector: {e}") # # Import crosswalk detection # try: # self.detect_crosswalk_and_violation_line = detect_crosswalk_and_violation_line # # self.draw_violation_line = draw_violation_line # print("✅ Crosswalk detection utilities imported") # except Exception as e: # print(f"❌ Could not import crosswalk detection: {e}") # self.detect_crosswalk_and_violation_line = lambda frame, *args: (None, None, {}) # # self.draw_violation_line = lambda frame, *args, **kwargs: frame # # Configure thread # self.thread = QThread() # self.moveToThread(self.thread) # self.thread.started.connect(self._run) # # Performance measurement # self.mutex = QMutex() # self.condition = QWaitCondition() # self.performance_metrics = { # 'FPS': 0.0, # 'Detection (ms)': 0.0, # 'Total (ms)': 0.0 # } # # Setup render timer with more aggressive settings for UI updates # self.render_timer = QTimer() # self.render_timer.timeout.connect(self._process_frame) # # Frame buffer # self.current_frame = None # self.current_detections = [] # self.current_violations = [] # # Debug counter for monitoring frame processing # self.debug_counter = 0 # self.violation_frame_counter = 0 # Add counter for violation processing # # Initialize the traffic light color detection pipeline # self.cv_violation_pipeline = RedLightViolationPipeline(debug=True) # # Initialize vehicle tracker # self.vehicle_tracker = ByteTrackVehicleTracker() # # Add red light violation system # # self.red_light_violation_system = RedLightViolationSystem() # # Initialize scene analytics adapter # try: # from utils.scene_analytics import SceneAnalyticsAdapter # self.scene_analytics = SceneAnalyticsAdapter(camera_id="desktop_main") # self.scene_analytics.object_detected.connect(self._on_scene_object_detected) # self.scene_analytics.scene_analytics_updated.connect(self._on_scene_analytics_updated) # self.scene_analytics.roi_event_detected.connect(self._on_roi_event_detected) # print("✅ Scene analytics adapter initialized") # except Exception as e: # self.scene_analytics = None # print(f"❌ Could not initialize scene analytics: {e}") # def refresh_model_info(self): # """Force refresh of model information for performance display""" # if hasattr(self, 'model_manager') and self.model_manager: # print("🔄 Refreshing model information in video controller") # # The model info will be refreshed in the next stats update # # Force current device update from config # if hasattr(self.model_manager, 'config') and 'detection' in self.model_manager.config: # self.current_device = self.model_manager.config['detection'].get('device', 'CPU') # print(f"🔄 Updated current device to: {self.current_device}") # def set_source(self, source): # """ # Set video source (file path, camera index, or URL) # Args: # source: Video source - can be a camera index (int), file path (str), # or URL (str). If None, defaults to camera 0. # Returns: # bool: True if source was set successfully, False otherwise # """ # print(f"đŸŽŦ VideoController.set_source called with: {source} (type: {type(source)})") # # Store current state # was_running = self._running # # Stop current processing if running # if self._running: # print("⏚ī¸ Stopping current video processing") # self.stop() # try: # # Handle source based on type with better error messages # if source is None: # print("⚠ī¸ Received None source, defaulting to camera 0") # self.source = 0 # self.source_type = "camera" # elif isinstance(source, str) and source.strip(): # if os.path.exists(source): # # Valid file path # self.source = source # self.source_type = "file" # print(f"📄 Source set to file: {self.source}") # elif source.lower().startswith(("http://", "https://", "rtsp://", "rtmp://")): # # URL stream # self.source = source # self.source_type = "url" # print(f"🌐 Source set to URL stream: {self.source}") # elif source.isdigit(): # # String camera index (convert to int) # self.source = int(source) # self.source_type = "camera" # print(f"📹 Source set to camera index: {self.source}") # else: # # Try as device path or special string # self.source = source # self.source_type = "device" # print(f"📱 Source set to device path: {self.source}") # elif isinstance(source, int): # # Camera index # self.source = source # self.source_type = "camera" # print(f"📹 Source set to camera index: {self.source}") # else: # # Unrecognized - default to camera 0 with warning # print(f"⚠ī¸ Unrecognized source type: {type(source)}, defaulting to camera 0") # self.source = 0 # self.source_type = "camera" # except Exception as e: # print(f"❌ Error setting source: {e}") # self.source = 0 # self.source_type = "camera" # return False # # Get properties of the source (fps, dimensions, etc) # print(f"🔍 Getting properties for source: {self.source}") # success = self._get_source_properties() # if success: # print(f"✅ Successfully configured source: {self.source} ({self.source_type})") # # Reset ByteTrack tracker for new source to ensure IDs start from 1 # if hasattr(self, 'vehicle_tracker') and self.vehicle_tracker is not None: # try: # print("🔄 Resetting vehicle tracker for new source") # self.vehicle_tracker.reset() # except Exception as e: # print(f"⚠ī¸ Could not reset vehicle tracker: {e}") # # Emit successful source change # self.stats_ready.emit({ # 'source_changed': True, # 'source_type': self.source_type, # 'fps': self.source_fps if hasattr(self, 'source_fps') else 0, # 'dimensions': f"{self.frame_width}x{self.frame_height}" if hasattr(self, 'frame_width') else "unknown" # }) # # Restart if previously running # if was_running: # print("â–ļī¸ Restarting video processing with new source") # self.start() # else: # print(f"❌ Failed to configure source: {self.source}") # # Notify UI about the error # self.stats_ready.emit({ # 'source_changed': False, # 'error': f"Invalid video source: {self.source}", # 'source_type': self.source_type, # 'fps': 0, # 'detection_time_ms': "0", # 'traffic_light_color': {"color": "unknown", "confidence": 0.0} # }) # return False # # Return success status # return success # def _get_source_properties(self): # try: # print(f"🔍 Opening video source for properties check: {self.source}") # cap = cv2.VideoCapture(self.source) # if not cap.isOpened(): # print(f"❌ Failed to open video source: {self.source}") # return False # self.source_fps = cap.get(cv2.CAP_PROP_FPS) # self.frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) # self.frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # self.frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # ret, test_frame = cap.read() # if not ret or test_frame is None: # print("⚠ī¸ Could not read test frame from source") # if self.source_type == "camera": # print("🔄 Retrying camera initialization...") # time.sleep(1.0) # ret, test_frame = cap.read() # if not ret or test_frame is None: # print("❌ Camera initialization failed after retry") # cap.release() # return False # else: # print("❌ Could not read frames from video source") # cap.release() # return False # # Release the capture # cap.release() # print(f"✅ Video source properties: {self.frame_width}x{self.frame_height}, {self.source_fps} FPS") # return True # except Exception as e: # print(f"❌ Error getting source properties: {e}") # return False # return False # def start(self): # """Start video processing""" # if not self._running: # self._running = True # self.start_time = time.time() # self.frame_count = 0 # self.debug_counter = 0 # print("DEBUG: Starting video processing thread") # # Reset notification flags for new session # if hasattr(self, '_no_traffic_light_notified'): # delattr(self, '_no_traffic_light_notified') # # Reset ByteTrack tracker to ensure IDs start from 1 # if hasattr(self, 'vehicle_tracker') and self.vehicle_tracker is not None: # try: # print("🔄 Resetting vehicle tracker for new session") # self.vehicle_tracker.reset() # except Exception as e: # print(f"⚠ī¸ Could not reset vehicle tracker: {e}") # # Start the processing thread - add more detailed debugging # if not self.thread.isRunning(): # print("🚀 Thread not running, starting now...") # try: # self.thread.start() # print("✅ Thread started successfully") # print(f"🔄 Thread state: running={self.thread.isRunning()}, finished={self.thread.isFinished()}") # except Exception as e: # print(f"❌ Failed to start thread: {e}") # import traceback # traceback.print_exc() # else: # print("⚠ī¸ Thread is already running!") # print(f"🔄 Thread state: running={self.thread.isRunning()}, finished={self.thread.isFinished()}") # # Start the render timer with a very aggressive interval (10ms = 100fps) # # This ensures we can process frames as quickly as possible # print("⏱ī¸ Starting render timer...") # self.render_timer.start(10) # print("✅ Render timer started at 100Hz") # def stop(self): # """Stop video processing""" # if self._running: # print("DEBUG: Stopping video processing") # self._running = False # # If paused, wake up the thread so it can exit # self.mutex.lock() # self._paused = False # self.pause_condition.wakeAll() # self.mutex.unlock() # self.render_timer.stop() # # Properly terminate the thread # if self.thread.isRunning(): # self.thread.quit() # if not self.thread.wait(3000): # Wait 3 seconds max # self.thread.terminate() # print("WARNING: Thread termination forced") # # Clear the current frame # self.mutex.lock() # self.current_frame = None # self.mutex.unlock() # print("DEBUG: Video processing stopped") # def __del__(self): # print("[VideoController] __del__ called. Cleaning up thread and timer.") # self.stop() # if self.thread.isRunning(): # self.thread.quit() # self.thread.wait(1000) # self.render_timer.stop() # def capture_snapshot(self) -> np.ndarray: # """Capture current frame""" # if self.current_frame is not None: # return self.current_frame.copy() # return None # def _run(self): # """Main processing loop (runs in thread)""" # try: # # Print the source we're trying to open # print(f"DEBUG: Opening video source: {self.source} (type: {type(self.source)})") # cap = None # Initialize capture variable # # Try to open source with more robust error handling # max_retries = 3 # retry_delay = 1.0 # seconds # # Function to attempt opening the source with multiple retries # def try_open_source(src, retries=max_retries, delay=retry_delay): # for attempt in range(1, retries + 1): # print(f"đŸŽĨ Opening source (attempt {attempt}/{retries}): {src}") # try: # capture = cv2.VideoCapture(src) # if capture.isOpened(): # # Try to read a test frame to confirm it's working # ret, test_frame = capture.read() # if ret and test_frame is not None: # print(f"✅ Source opened successfully: {src}") # # Reset capture position for file sources # if isinstance(src, str) and os.path.exists(src): # capture.set(cv2.CAP_PROP_POS_FRAMES, 0) # return capture # else: # print(f"⚠ī¸ Source opened but couldn't read frame: {src}") # capture.release() # else: # print(f"⚠ī¸ Failed to open source: {src}") # # Retry after delay # if attempt < retries: # print(f"Retrying in {delay:.1f} seconds...") # time.sleep(delay) # except Exception as e: # print(f"❌ Error opening source {src}: {e}") # if attempt < retries: # print(f"Retrying in {delay:.1f} seconds...") # time.sleep(delay) # print(f"❌ Failed to open source after {retries} attempts: {src}") # return None # # Handle different source types # if isinstance(self.source, str) and os.path.exists(self.source): # # It's a valid file path # print(f"📄 Opening video file: {self.source}") # cap = try_open_source(self.source) # elif isinstance(self.source, int) or (isinstance(self.source, str) and self.source.isdigit()): # # It's a camera index # camera_idx = int(self.source) if isinstance(self.source, str) else self.source # print(f"📹 Opening camera with index: {camera_idx}") # # For cameras, try with different backend options if it fails # cap = try_open_source(camera_idx) # # If failed, try with DirectShow backend on Windows # if cap is None and os.name == 'nt': # print("🔄 Trying camera with DirectShow backend...") # cap = try_open_source(camera_idx + cv2.CAP_DSHOW) # else: # # Try as a string source (URL or device path) # print(f"🌐 Opening source as string: {self.source}") # cap = try_open_source(str(self.source)) # # Check if we successfully opened the source # if cap is None: # print(f"❌ Failed to open video source after all attempts: {self.source}") # # Notify UI about the error # self.stats_ready.emit({ # 'error': f"Could not open video source: {self.source}", # 'fps': "0", # 'detection_time_ms': "0", # 'traffic_light_color': {"color": "unknown", "confidence": 0.0} # }) # return # # Check again to ensure capture is valid # if not cap or not cap.isOpened(): # print(f"ERROR: Could not open video source {self.source}") # # Emit a signal to notify UI about the error # self.stats_ready.emit({ # 'error': f"Failed to open video source: {self.source}", # 'fps': "0", # 'detection_time_ms': "0", # 'traffic_light_color': {"color": "unknown", "confidence": 0.0} # }) # return # # Configure frame timing based on source FPS # frame_time = 1.0 / self.source_fps if self.source_fps > 0 else 0.033 # prev_time = time.time() # # Log successful opening # print(f"SUCCESS: Video source opened: {self.source}") # print(f"Source info - FPS: {self.source_fps}, Size: {self.frame_width}x{self.frame_height}") # # Main processing loop # frame_error_count = 0 # max_consecutive_errors = 10 # # --- Violation Rule Functions --- # def point_in_polygon(point, polygon): # # Simple point-in-rect for now; replace with polygon logic if needed # x, y = point # x1, y1, w, h = polygon # return x1 <= x <= x1 + w and y1 <= y <= y1 + h # def calculate_speed(track, history_dict): # # Use last two positions for speed # hist = history_dict.get(track['id'], []) # if len(hist) < 2: # return 0.0 # (x1, y1), t1 = hist[-2] # (x2, y2), t2 = hist[-1] # dist = ((x2-x1)**2 + (y2-y1)**2)**0.5 # dt = max(t2-t1, 1e-3) # return dist / dt # def check_vehicle_pedestrian_conflict(vehicle_track, pedestrian_tracks, crosswalk_poly, light_state): # if light_state != 'green': # return False # if not point_in_polygon(vehicle_track['center'], crosswalk_poly): # return False # for ped in pedestrian_tracks: # if point_in_polygon(ped['center'], crosswalk_poly): # return True # return False # def check_stop_on_crosswalk(vehicle_track, crosswalk_poly, light_state, history_dict): # if light_state != 'red': # return False # is_inside = point_in_polygon(vehicle_track['center'], crosswalk_poly) # speed = calculate_speed(vehicle_track, history_dict) # return is_inside and speed < 0.5 # def check_amber_overspeed(vehicle_track, light_state, amber_start_time, stopline_poly, history_dict, speed_limit_px_per_sec): # if light_state != 'amber': # return False # if not point_in_polygon(vehicle_track['center'], stopline_poly): # return False # current_time = time.time() # speed = calculate_speed(vehicle_track, history_dict) # if current_time > amber_start_time and speed > speed_limit_px_per_sec: # return True # return False # # --- End Violation Rule Functions --- # while self._running and cap.isOpened(): # # Handle pause state # self.mutex.lock() # if self._paused: # print("[VideoController] Video paused, waiting...") # self.pause_condition.wait(self.mutex) # print("[VideoController] Video resumed") # self.mutex.unlock() # # Exit if we're no longer running (could have stopped while paused) # if not self._running: # break # try: # ret, frame = cap.read() # # Add critical frame debugging # print(f"🟡 Frame read attempt: ret={ret}, frame={None if frame is None else frame.shape}") # if not ret or frame is None: # frame_error_count += 1 # print(f"⚠ī¸ Frame read error ({frame_error_count}/{max_consecutive_errors})") # if frame_error_count >= max_consecutive_errors: # print("❌ Too many consecutive frame errors, stopping video thread") # break # # Skip this iteration and try again # time.sleep(0.1) # Wait a bit before trying again # continue # # Reset the error counter if we successfully got a frame # frame_error_count = 0 # # Store the last frame for VLM analysis during pause # self._last_frame = frame.copy() # print(f"đŸŸĸ Last frame stored for VLM: {frame.shape}") # except Exception as e: # print(f"❌ Critical error reading frame: {e}") # frame_error_count += 1 # if frame_error_count >= max_consecutive_errors: # print("❌ Too many errors, stopping video thread") # break # continue # # Detection and violation processing # process_start = time.time() # # Process detections # detection_start = time.time() # detections = [] # if self.model_manager: # detections = self.model_manager.detect(frame) # # Normalize class names for consistency and check for traffic lights # traffic_light_indices = [] # for i, det in enumerate(detections): # if 'class_name' in det: # original_name = det['class_name'] # normalized_name = normalize_class_name(original_name) # # Keep track of traffic light indices # if normalized_name == 'traffic light' or original_name == 'traffic light': # traffic_light_indices.append(i) # if original_name != normalized_name: # print(f"📊 Normalized class name: '{original_name}' -> '{normalized_name}'") # det['class_name'] = normalized_name # # Ensure we have at least one traffic light for debugging # if not traffic_light_indices and self.source_type == 'video': # print("⚠ī¸ No traffic lights detected, checking for objects that might be traffic lights...") # # Try lowering the confidence threshold specifically for traffic lights # # This is only for debugging purposes # if self.model_manager and hasattr(self.model_manager, 'detect'): # try: # low_conf_detections = self.model_manager.detect(frame, conf_threshold=0.2) # for det in low_conf_detections: # if 'class_name' in det and det['class_name'] == 'traffic light': # if det not in detections: # print(f"đŸšĻ Found low confidence traffic light: {det['confidence']:.2f}") # detections.append(det) # except: # pass # detection_time = (time.time() - detection_start) * 1000 # # Violation detection is disabled # violation_start = time.time() # violations = [] # # if self.model_manager and detections: # # violations = self.model_manager.detect_violations( # # detections, frame, time.time() # # ) # violation_time = (time.time() - violation_start) * 1000 # # Update tracking if available # if self.model_manager: # detections = self.model_manager.update_tracking(detections, frame) # # If detections are returned as tuples, convert to dicts for downstream code # if detections and isinstance(detections[0], tuple): # # Convert (id, bbox, conf, class_id) to dict # detections = [ # {'id': d[0], 'bbox': d[1], 'confidence': d[2], 'class_id': d[3]} # for d in detections # ] # # Calculate timing metrics # process_time = (time.time() - process_start) * 1000 # self.processing_times.append(process_time) # # Update FPS # now = time.time() # self.frame_count += 1 # elapsed = now - self.start_time # if elapsed > 0: # self.actual_fps = self.frame_count / elapsed # fps_smoothed = 1.0 / (now - prev_time) if now > prev_time else 0 # prev_time = now # # Update metrics # self.performance_metrics = { # 'FPS': f"{fps_smoothed:.1f}", # 'Detection (ms)': f"{detection_time:.1f}", # 'Total (ms)': f"{process_time:.1f}" # } # # Store current frame data (thread-safe) # self.mutex.lock() # self.current_frame = frame.copy() # self.current_detections = detections # self.mutex.unlock() # # --- SCENE ANALYTICS PROCESSING --- # # Process detections through scene analytics if available # if self.scene_analytics: # try: # scene_analytics_data = self.scene_analytics.process_frame(frame, detections) # # Scene analytics automatically emit signals that we handle above # except Exception as e: # print(f"Error in scene analytics processing: {e}") # # Process frame with annotations before sending to UI # annotated_frame = frame.copy() # # --- VIOLATION DETECTION LOGIC (Run BEFORE drawing boxes) --- # # First get violation information so we can color boxes appropriately # violating_vehicle_ids = set() # Track which vehicles are violating # violations = [] # # Initialize traffic light variables # traffic_lights = [] # has_traffic_lights = False # # Handle multiple traffic lights with consensus approach # traffic_light_count = 0 # for det in detections: # if is_traffic_light(det.get('class_name')): # has_traffic_lights = True # traffic_light_count += 1 # if 'traffic_light_color' in det: # light_info = det['traffic_light_color'] # traffic_lights.append({'bbox': det['bbox'], 'color': light_info.get('color', 'unknown'), 'confidence': light_info.get('confidence', 0.0)}) # print(f"[TRAFFIC LIGHT] Detected {traffic_light_count} traffic light(s), has_traffic_lights={has_traffic_lights}") # if has_traffic_lights: # print(f"[TRAFFIC LIGHT] Traffic light colors: {[tl.get('color', 'unknown') for tl in traffic_lights]}") # # Get traffic light position for crosswalk detection # traffic_light_position = None # if has_traffic_lights: # for det in detections: # if is_traffic_light(det.get('class_name')) and 'bbox' in det: # traffic_light_bbox = det['bbox'] # # Extract center point from bbox for crosswalk utils # x1, y1, x2, y2 = traffic_light_bbox # traffic_light_position = ((x1 + x2) // 2, (y1 + y2) // 2) # break # # Run crosswalk detection ONLY if traffic light is detected # crosswalk_bbox, violation_line_y, debug_info = None, None, {} # if has_traffic_lights and traffic_light_position is not None: # try: # print(f"[CROSSWALK] Traffic light detected at {traffic_light_position}, running crosswalk detection") # # Use new crosswalk_utils2 logic only when traffic light exists # annotated_frame, crosswalk_bbox, violation_line_y, debug_info = detect_crosswalk_and_violation_line( # annotated_frame, # traffic_light_position=traffic_light_position # ) # print(f"[CROSSWALK] Detection result: crosswalk_bbox={crosswalk_bbox is not None}, violation_line_y={violation_line_y}") # # --- Draw crosswalk region if detected and close to traffic light --- # # (REMOVED: Do not draw crosswalk box or label) # # if crosswalk_bbox is not None: # # x, y, w, h = map(int, crosswalk_bbox) # # tl_x, tl_y = traffic_light_position # # crosswalk_center_y = y + h // 2 # # distance = abs(crosswalk_center_y - tl_y) # # print(f"[CROSSWALK DEBUG] Crosswalk bbox: {crosswalk_bbox}, Traffic light: {traffic_light_position}, vertical distance: {distance}") # # if distance < 120: # # cv2.rectangle(annotated_frame, (x, y), (x + w, y + h), (0, 255, 0), 3) # # cv2.putText(annotated_frame, "Crosswalk", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2) # # # Top and bottom edge of crosswalk # # top_edge = y # # bottom_edge = y + h # # if abs(tl_y - top_edge) < abs(tl_y - bottom_edge): # # crosswalk_edge_y = top_edge # # else: # # crosswalk_edge_y = bottom_edge # if crosswalk_bbox is not None: # x, y, w, h = map(int, crosswalk_bbox) # tl_x, tl_y = traffic_light_position # crosswalk_center_y = y + h // 2 # distance = abs(crosswalk_center_y - tl_y) # print(f"[CROSSWALK DEBUG] Crosswalk bbox: {crosswalk_bbox}, Traffic light: {traffic_light_position}, vertical distance: {distance}") # # Top and bottom edge of crosswalk # top_edge = y # bottom_edge = y + h # if abs(tl_y - top_edge) < abs(tl_y - bottom_edge): # crosswalk_edge_y = top_edge # else: # crosswalk_edge_y = bottom_edge # except Exception as e: # print(f"[ERROR] Crosswalk detection failed: {e}") # crosswalk_bbox, violation_line_y, debug_info = None, None, {} # else: # print(f"[CROSSWALK] No traffic light detected (has_traffic_lights={has_traffic_lights}), skipping crosswalk detection") # # NO crosswalk detection without traffic light # violation_line_y = None # # Check if crosswalk is detected # crosswalk_detected = crosswalk_bbox is not None # stop_line_detected = debug_info.get('stop_line') is not None # # ALWAYS process vehicle tracking (moved outside violation logic) # tracked_vehicles = [] # if hasattr(self, 'vehicle_tracker') and self.vehicle_tracker is not None: # try: # # Filter vehicle detections # vehicle_classes = ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle'] # vehicle_dets = [] # h, w = frame.shape[:2] # print(f"[TRACK DEBUG] Processing {len(detections)} total detections") # for det in detections: # if (det.get('class_name') in vehicle_classes and # 'bbox' in det and # det.get('confidence', 0) > self.min_confidence_threshold): # # Check bbox dimensions # bbox = det['bbox'] # x1, y1, x2, y2 = bbox # box_w, box_h = x2-x1, y2-y1 # box_area = box_w * box_h # area_ratio = box_area / (w * h) # print(f"[TRACK DEBUG] Vehicle {det.get('class_name')} conf={det.get('confidence'):.2f}, area_ratio={area_ratio:.4f}") # if 0.001 <= area_ratio <= 0.25: # vehicle_dets.append(det) # print(f"[TRACK DEBUG] Added vehicle: {det.get('class_name')} conf={det.get('confidence'):.2f}") # else: # print(f"[TRACK DEBUG] Rejected vehicle: area_ratio={area_ratio:.4f} not in range [0.001, 0.25]") # print(f"[TRACK DEBUG] Filtered to {len(vehicle_dets)} vehicle detections") # # Update tracker # if len(vehicle_dets) > 0: # print(f"[TRACK DEBUG] Updating tracker with {len(vehicle_dets)} vehicles...") # tracks = self.vehicle_tracker.update(vehicle_dets, frame) # # Filter out tracks without bbox to avoid warnings # valid_tracks = [] # for track in tracks: # bbox = None # if isinstance(track, dict): # bbox = track.get('bbox', None) # else: # bbox = getattr(track, 'bbox', None) # if bbox is not None: # valid_tracks.append(track) # else: # print(f"Warning: Track has no bbox, skipping: {track}") # tracks = valid_tracks # print(f"[TRACK DEBUG] Tracker returned {len(tracks)} tracks (after bbox filter)") # else: # print(f"[TRACK DEBUG] No vehicles to track, skipping tracker update") # tracks = [] # # Process each tracked vehicle # tracked_vehicles = [] # track_ids_seen = [] # for track in tracks: # track_id = track['id'] # bbox = track['bbox'] # x1, y1, x2, y2 = map(float, bbox) # center_y = (y1 + y2) / 2 # # Check for duplicate IDs # if track_id in track_ids_seen: # print(f"[TRACK ERROR] Duplicate ID detected: {track_id}") # track_ids_seen.append(track_id) # print(f"[TRACK DEBUG] Processing track ID={track_id} bbox={bbox}") # # Initialize or update vehicle history # if track_id not in self.vehicle_history: # from collections import deque # self.vehicle_history[track_id] = deque(maxlen=self.position_history_size) # # Initialize vehicle status if not exists # if track_id not in self.vehicle_statuses: # self.vehicle_statuses[track_id] = { # 'recent_movement': [], # 'violation_history': [], # 'crossed_during_red': False, # 'last_position': None, # Track last position for jump detection # 'suspicious_jumps': 0 # Count suspicious position jumps # } # # Detect suspicious position jumps (potential ID switches) # if self.vehicle_statuses[track_id]['last_position'] is not None: # last_y = self.vehicle_statuses[track_id]['last_position'] # center_y = (y1 + y2) / 2 # position_jump = abs(center_y - last_y) # if position_jump > self.max_position_jump: # self.vehicle_statuses[track_id]['suspicious_jumps'] += 1 # print(f"[TRACK WARNING] Vehicle ID={track_id} suspicious position jump: {last_y:.1f} -> {center_y:.1f} (jump={position_jump:.1f})") # # If too many suspicious jumps, reset violation status to be safe # if self.vehicle_statuses[track_id]['suspicious_jumps'] > 2: # print(f"[TRACK RESET] Vehicle ID={track_id} has too many suspicious jumps, resetting violation status") # self.vehicle_statuses[track_id]['crossed_during_red'] = False # self.vehicle_statuses[track_id]['suspicious_jumps'] = 0 # # Update position history and last position # self.vehicle_history[track_id].append(center_y) # self.vehicle_statuses[track_id]['last_position'] = center_y # # BALANCED movement detection - detect clear movement while avoiding false positives # is_moving = False # movement_detected = False # if len(self.vehicle_history[track_id]) >= 3: # Require at least 3 frames for movement detection # recent_positions = list(self.vehicle_history[track_id]) # # Check movement over 3 frames for quick response # if len(recent_positions) >= 3: # movement_3frames = abs(recent_positions[-1] - recent_positions[-3]) # if movement_3frames > self.movement_threshold: # More responsive threshold # movement_detected = True # print(f"[MOVEMENT] Vehicle ID={track_id} MOVING: 3-frame movement = {movement_3frames:.1f}") # # Confirm with longer movement for stability (if available) # if len(recent_positions) >= 5: # movement_5frames = abs(recent_positions[-1] - recent_positions[-5]) # if movement_5frames > self.movement_threshold * 1.5: # Moderate threshold for 5 frames # movement_detected = True # print(f"[MOVEMENT] Vehicle ID={track_id} MOVING: 5-frame movement = {movement_5frames:.1f}") # # Store historical movement for smoothing - require consistent movement # self.vehicle_statuses[track_id]['recent_movement'].append(movement_detected) # if len(self.vehicle_statuses[track_id]['recent_movement']) > 4: # Shorter history for quicker response # self.vehicle_statuses[track_id]['recent_movement'].pop(0) # # BALANCED: Require majority of recent frames to show movement (2 out of 4) # recent_movement_count = sum(self.vehicle_statuses[track_id]['recent_movement']) # total_recent_frames = len(self.vehicle_statuses[track_id]['recent_movement']) # if total_recent_frames >= 2 and recent_movement_count >= (total_recent_frames * 0.5): # 50% of frames must show movement # is_moving = True # print(f"[TRACK DEBUG] Vehicle ID={track_id} is_moving={is_moving} (threshold={self.movement_threshold})") # # Initialize as not violating # is_violation = False # tracked_vehicles.append({ # 'id': track_id, # 'bbox': bbox, # 'center_y': center_y, # 'is_moving': is_moving, # 'is_violation': is_violation # }) # print(f"[DEBUG] ByteTrack tracked {len(tracked_vehicles)} vehicles") # for i, tracked in enumerate(tracked_vehicles): # print(f" Vehicle {i}: ID={tracked['id']}, center_y={tracked['center_y']:.1f}, moving={tracked['is_moving']}, violating={tracked['is_violation']}") # # DEBUG: Print all tracked vehicle IDs and their bboxes for this frame # if tracked_vehicles: # print(f"[DEBUG] All tracked vehicles this frame:") # for v in tracked_vehicles: # print(f" ID={v['id']} bbox={v['bbox']} center_y={v.get('center_y', 'NA')}") # else: # print("[DEBUG] No tracked vehicles this frame!") # # Clean up old vehicle data # current_track_ids = [tracked['id'] for tracked in tracked_vehicles] # self._cleanup_old_vehicle_data(current_track_ids) # except Exception as e: # print(f"[ERROR] Vehicle tracking failed: {e}") # import traceback # traceback.print_exc() # else: # print("[WARN] ByteTrack vehicle tracker not available!") # # Process violations - CHECK VEHICLES THAT CROSS THE LINE OVER A WINDOW OF FRAMES # # IMPORTANT: Only process violations if BOTH traffic light is detected AND crosswalk is detected AND red light AND violation line exists # # Handle case when no traffic light is detected in video # if not has_traffic_lights: # print("[INFO] No traffic light detected in video - violation detection disabled") # # Emit status to UI (only once per session to avoid spam) # if not hasattr(self, '_no_traffic_light_notified'): # self.stats_ready.emit({ # 'status': 'monitoring_only', # 'message': 'No traffic light detected - monitoring vehicles only', # 'violation_detection_active': False, # 'timestamp': time.time() # }) # self._no_traffic_light_notified = True # else: # # Check if traffic light is red (only when traffic light exists) # is_red_light = self.latest_traffic_light and self.latest_traffic_light.get('color') == 'red' # # New condition: ALL of these must be true for violation line processing: # # 1. Traffic lights detected (has_traffic_lights) # # 2. Crosswalk detected (crosswalk_detected) # # 3. Red light is currently active (is_red_light) # # 4. Violation line exists (violation_line_y is not None) # # 5. Vehicles are being tracked (tracked_vehicles) # if (has_traffic_lights and crosswalk_detected and is_red_light and # violation_line_y is not None and tracked_vehicles): # print(f"[VIOLATION DEBUG] ALL CONDITIONS MET - Traffic light: {has_traffic_lights}, Crosswalk: {crosswalk_detected}, Red light: {is_red_light}, Line Y: {violation_line_y}, Vehicles: {len(tracked_vehicles)}") # # Check each tracked vehicle for violations # for tracked in tracked_vehicles: # track_id = tracked['id'] # center_y = tracked['center_y'] # is_moving = tracked['is_moving'] # # Get position history for this vehicle # position_history = list(self.vehicle_history[track_id]) # # Enhanced crossing detection: check over a window of frames # line_crossed_in_window = False # crossing_details = None # if len(position_history) >= 2: # window_size = min(self.crossing_check_window, len(position_history)) # for i in range(1, window_size): # prev_y = position_history[-(i+1)] # Earlier position # curr_y = position_history[-i] # Later position # # Check if vehicle crossed the line in this frame pair # if prev_y < violation_line_y and curr_y >= violation_line_y: # line_crossed_in_window = True # crossing_details = { # 'frames_ago': i, # 'prev_y': prev_y, # 'curr_y': curr_y, # 'window_checked': window_size # } # print(f"[VIOLATION DEBUG] Vehicle ID={track_id} crossed line {i} frames ago: {prev_y:.1f} -> {curr_y:.1f}") # break # print(f"[VIOLATION DEBUG] Vehicle ID={track_id}: latest_traffic_light={self.latest_traffic_light}") # print(f"[VIOLATION DEBUG] Vehicle ID={track_id}: position_history={[f'{p:.1f}' for p in position_history[-5:]]}"); # Show last 5 positions # print(f"[VIOLATION DEBUG] Vehicle ID={track_id}: line_crossed_in_window={line_crossed_in_window}, crossing_details={crossing_details}") # # Enhanced violation detection: vehicle crossed the line while moving (red light already verified above) # actively_crossing = (line_crossed_in_window and is_moving) # # Initialize violation status for new vehicles # if 'crossed_during_red' not in self.vehicle_statuses[track_id]: # self.vehicle_statuses[track_id]['crossed_during_red'] = False # # Mark vehicle as having crossed during red if it actively crosses # if actively_crossing: # # Additional validation: ensure it's not a false positive from ID switch # suspicious_jumps = self.vehicle_statuses[track_id].get('suspicious_jumps', 0) # if suspicious_jumps <= 1: # Allow crossing if not too many suspicious jumps # self.vehicle_statuses[track_id]['crossed_during_red'] = True # print(f"[VIOLATION ALERT] Vehicle ID={track_id} CROSSED line during red light!") # print(f" -> Crossing details: {crossing_details}") # else: # print(f"[VIOLATION IGNORED] Vehicle ID={track_id} crossing ignored due to {suspicious_jumps} suspicious jumps") # # IMPORTANT: Reset violation status when light turns green (regardless of position) # if not is_red_light: # if self.vehicle_statuses[track_id]['crossed_during_red']: # print(f"[VIOLATION RESET] Vehicle ID={track_id} violation status reset (light turned green)") # self.vehicle_statuses[track_id]['crossed_during_red'] = False # # Vehicle is violating ONLY if it crossed during red and light is still red # is_violation = (self.vehicle_statuses[track_id]['crossed_during_red'] and is_red_light) # # Track current violation state for analytics - only actual crossings # self.vehicle_statuses[track_id]['violation_history'].append(actively_crossing) # if len(self.vehicle_statuses[track_id]['violation_history']) > 5: # self.vehicle_statuses[track_id]['violation_history'].pop(0) # print(f"[VIOLATION DEBUG] Vehicle ID={track_id}: center_y={center_y:.1f}, line={violation_line_y}") # print(f" history_window={[f'{p:.1f}' for p in position_history[-self.crossing_check_window:]]}") # print(f" moving={is_moving}, red_light={is_red_light}") # print(f" actively_crossing={actively_crossing}, crossed_during_red={self.vehicle_statuses[track_id]['crossed_during_red']}") # print(f" suspicious_jumps={self.vehicle_statuses[track_id].get('suspicious_jumps', 0)}") # print(f" FINAL_VIOLATION={is_violation}") # # Update violation status # tracked['is_violation'] = is_violation # if actively_crossing and self.vehicle_statuses[track_id].get('suspicious_jumps', 0) <= 1: # Only add if not too many suspicious jumps # # Add to violating vehicles set # violating_vehicle_ids.add(track_id) # # Add to violations list # timestamp = datetime.now() # Keep as datetime object, not string # violations.append({ # 'track_id': track_id, # 'id': track_id, # 'bbox': [int(tracked['bbox'][0]), int(tracked['bbox'][1]), int(tracked['bbox'][2]), int(tracked['bbox'][3])], # 'violation': 'line_crossing', # 'violation_type': 'line_crossing', # Add this for analytics compatibility # 'timestamp': timestamp, # 'line_position': violation_line_y, # 'movement': crossing_details if crossing_details else {'prev_y': center_y, 'current_y': center_y}, # 'crossing_window': self.crossing_check_window, # 'position_history': list(position_history[-10:]) # Include recent history for debugging # }) # print(f"[DEBUG] 🚨 VIOLATION DETECTED: Vehicle ID={track_id} CROSSED VIOLATION LINE") # print(f" Enhanced detection: {crossing_details}") # print(f" Position history: {[f'{p:.1f}' for p in position_history[-10:]]}") # print(f" Detection window: {self.crossing_check_window} frames") # print(f" while RED LIGHT & MOVING") # else: # # Log why violation detection was skipped # reasons = [] # if not crosswalk_detected: # reasons.append("No crosswalk detected") # if not is_red_light: # reasons.append(f"Light not red (current: {self.latest_traffic_light.get('color') if self.latest_traffic_light else 'None'})") # if violation_line_y is None: # reasons.append("No violation line") # if not tracked_vehicles: # reasons.append("No vehicles tracked") # if reasons: # print(f"[INFO] Violation detection skipped: {', '.join(reasons)}") # # --- ENHANCED VIOLATION DETECTION: Add new real-world scenarios --- # # 1. Pedestrian right-of-way violation (blocking crosswalk during green) # # 2. Improper stopping over crosswalk at red # # 3. Accelerating through yellow/amber light # pedestrian_dets = [det for det in detections if det.get('class_name') == 'person' and 'bbox' in det] # pedestrian_tracks = [] # for ped in pedestrian_dets: # x1, y1, x2, y2 = ped['bbox'] # center = ((x1 + x2) // 2, (y1 + y2) // 2) # pedestrian_tracks.append({'bbox': ped['bbox'], 'center': center}) # # Prepare crosswalk polygon for point-in-polygon checks # crosswalk_poly = None # if crosswalk_bbox is not None: # x, y, w, h = crosswalk_bbox # crosswalk_poly = (x, y, w, h) # stopline_poly = crosswalk_poly # For simplicity, use crosswalk as stopline # # Track amber/yellow light start time # amber_start_time = getattr(self, 'amber_start_time', None) # latest_light_color = self.latest_traffic_light.get('color') if isinstance(self.latest_traffic_light, dict) else self.latest_traffic_light # if latest_light_color == 'yellow' and amber_start_time is None: # amber_start_time = time.time() # self.amber_start_time = amber_start_time # elif latest_light_color != 'yellow': # self.amber_start_time = None # # Vehicle position history for speed calculation # vehicle_position_history = {} # for track in tracked_vehicles: # track_id = track['id'] # bbox = track['bbox'] # x1, y1, x2, y2 = bbox # center = ((x1 + x2) // 2, (y1 + y2) // 2) # # Store (center, timestamp) # if track_id not in vehicle_position_history: # vehicle_position_history[track_id] = [] # vehicle_position_history[track_id].append((center, time.time())) # track['center'] = center # # --- 1. Pedestrian right-of-way violation --- # if crosswalk_poly and latest_light_color == 'green' and pedestrian_tracks: # for track in tracked_vehicles: # # Only consider moving vehicles - stopped vehicles aren't likely to be violating # if track.get('is_moving', False) and point_in_polygon(track['center'], crosswalk_poly): # # Check for pedestrians in the crosswalk # for ped in pedestrian_tracks: # if point_in_polygon(ped['center'], crosswalk_poly): # # Vehicle is blocking crosswalk during green with pedestrian present # violations.append({ # 'track_id': track['id'], # 'id': track['id'], # 'bbox': [int(track['bbox'][0]), int(track['bbox'][1]), int(track['bbox'][2]), int(track['bbox'][3])], # 'violation': 'pedestrian_right_of_way', # 'violation_type': 'pedestrian_right_of_way', # 'timestamp': datetime.now(), # 'details': { # 'pedestrian_bbox': ped['bbox'], # 'crosswalk_bbox': crosswalk_bbox, # 'is_moving': track.get('is_moving', False), # 'traffic_light': latest_light_color # } # }) # print(f"[VIOLATION] Pedestrian right-of-way violation: Vehicle ID={track['id']} blocking crosswalk during green light with pedestrian present") # # --- 2. Improper stopping over crosswalk at red --- # if crosswalk_poly and latest_light_color == 'red': # for track in tracked_vehicles: # # Check if vehicle is not moving (to confirm it's stopped) # is_stopped = not track.get('is_moving', True) # if is_stopped and point_in_polygon(track['center'], crosswalk_poly): # # Calculate overlap ratio between vehicle and crosswalk # vx1, vy1, vx2, vy2 = track['bbox'] # cx, cy, cw, ch = crosswalk_poly # overlap_x1 = max(vx1, cx) # overlap_y1 = max(vy1, cy) # overlap_x2 = min(vx2, cx + cw) # overlap_y2 = min(vy2, cy + ch) # overlap_area = max(0, overlap_x2 - overlap_x1) * max(0, overlap_y2 - overlap_y1) # vehicle_area = (vx2 - vx1) * (vy2 - vy1) # overlap_ratio = overlap_area / max(vehicle_area, 1) # # Double-verify that vehicle is stopped by checking explicit speed # speed = 0.0 # hist = vehicle_position_history.get(track['id'], []) # if len(hist) >= 2: # (c1, t1), (c2, t2) = hist[-2], hist[-1] # dist = ((c2[0]-c1[0])**2 + (c2[1]-c1[1])**2)**0.5 # dt = max(t2-t1, 1e-3) # speed = dist / dt # # Vehicle must have significant overlap with crosswalk (>25%) and be stopped # if overlap_ratio > 0.25 and speed < 0.8: # violations.append({ # 'track_id': track['id'], # 'id': track['id'], # 'bbox': [int(track['bbox'][0]), int(track['bbox'][1]), int(track['bbox'][2]), int(track['bbox'][3])], # 'violation': 'stop_on_crosswalk', # 'violation_type': 'stop_on_crosswalk', # 'timestamp': datetime.now(), # 'details': { # 'overlap_ratio': overlap_ratio, # 'speed': speed, # 'crosswalk_bbox': crosswalk_bbox, # 'traffic_light': latest_light_color, # 'is_moving_flag': track.get('is_moving', None) # } # }) # print(f"[VIOLATION] Improper stop on crosswalk: Vehicle ID={track['id']} stopped on crosswalk during red light (overlap={overlap_ratio:.2f}, speed={speed:.2f})") # # --- 3. Accelerating through yellow/amber light --- # if stopline_poly and latest_light_color == 'yellow' and amber_start_time: # # Calculate time since light turned yellow # current_time = time.time() # time_since_yellow = current_time - amber_start_time # # Speed threshold (in pixels per second) - can be adjusted based on testing # speed_limit_px_per_sec = 8.0 # # Check each vehicle approaching the intersection # for track in tracked_vehicles: # # Check if vehicle is near the stop line/intersection # if point_in_polygon(track['center'], stopline_poly) or ( # track['center'][1] < stopline_poly[1] + stopline_poly[3] + 50 and # track['center'][1] > stopline_poly[1] - 50 # ): # # If the vehicle is moving (confirmed via tracker) # if track.get('is_moving', False): # # Calculate acceleration by looking at recent speed changes # hist = vehicle_position_history.get(track['id'], []) # if len(hist) >= 3: # # Calculate speeds at different time points # (c1, t1), (c2, t2), (c3, t3) = hist[-3], hist[-2], hist[-1] # # Speed at earlier point # v1 = ((c2[0]-c1[0])**2 + (c2[1]-c1[1])**2)**0.5 / max(t2-t1, 1e-3) # # Speed at later point # v2 = ((c3[0]-c2[0])**2 + (c3[1]-c2[1])**2)**0.5 / max(t3-t2, 1e-3) # # Acceleration violation if: # # 1. Speed increases significantly (>20%) # # 2. Final speed exceeds threshold # # 3. Yellow light is less than 3 seconds old (typical acceleration window) # if v2 > v1 * 1.2 and v2 > speed_limit_px_per_sec and time_since_yellow < 3.0: # violations.append({ # 'track_id': track['id'], # 'id': track['id'], # 'bbox': [int(track['bbox'][0]), int(track['bbox'][1]), int(track['bbox'][2]), int(track['bbox'][3])], # 'violation': 'amber_acceleration', # 'violation_type': 'amber_acceleration', # 'timestamp': datetime.now(), # 'details': { # 'speed_before': v1, # 'speed_after': v2, # 'acceleration': (v2-v1)/max(t3-t2, 1e-3), # 'time_since_yellow': time_since_yellow, # 'traffic_light': latest_light_color # } # }) # print(f"[VIOLATION] Amber light acceleration: Vehicle ID={track['id']} accelerated from {v1:.2f} to {v2:.2f} px/sec {time_since_yellow:.1f}s after yellow light") # # Emit progress signal after processing each frame # if hasattr(self, 'progress_ready'): # self.progress_ready.emit(int(cap.get(cv2.CAP_PROP_POS_FRAMES)), int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), time.time()) # # Draw detections with bounding boxes - NOW with violation info # # Only show traffic light and vehicle classes # allowed_classes = ['traffic light', 'car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle'] # filtered_detections = [det for det in detections if det.get('class_name') in allowed_classes] # print(f"Drawing {len(filtered_detections)} detection boxes on frame (filtered)") # # Statistics for debugging # vehicles_with_ids = 0 # vehicles_without_ids = 0 # vehicles_moving = 0 # vehicles_violating = 0 # if detections and len(detections) > 0: # # Only show traffic light and vehicle classes # allowed_classes = ['traffic light', 'car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle'] # filtered_detections = [det for det in detections if det.get('class_name') in allowed_classes] # print(f"Drawing {len(filtered_detections)} detection boxes on frame (filtered)") # # Statistics for debugging # vehicles_with_ids = 0 # vehicles_without_ids = 0 # vehicles_moving = 0 # vehicles_violating = 0 # for det in filtered_detections: # if 'bbox' in det: # bbox = det['bbox'] # x1, y1, x2, y2 = map(int, bbox) # label = det.get('class_name', 'object') # confidence = det.get('confidence', 0.0) # # Robustness: ensure label and confidence are not None # if label is None: # label = 'object' # if confidence is None: # confidence = 0.0 # class_id = det.get('class_id', -1) # # Check if this detection corresponds to a violating or moving vehicle # det_center_x = (x1 + x2) / 2 # det_center_y = (y1 + y2) / 2 # is_violating_vehicle = False # is_moving_vehicle = False # vehicle_id = None # # Match detection with tracked vehicles - IMPROVED MATCHING # if label in ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle'] and len(tracked_vehicles) > 0: # print(f"[MATCH DEBUG] Attempting to match {label} detection at ({det_center_x:.1f}, {det_center_y:.1f}) with {len(tracked_vehicles)} tracked vehicles") # best_match = None # best_distance = float('inf') # best_iou = 0.0 # for i, tracked in enumerate(tracked_vehicles): # track_bbox = tracked['bbox'] # track_x1, track_y1, track_x2, track_y2 = map(float, track_bbox) # # Calculate center distance # track_center_x = (track_x1 + track_x2) / 2 # track_center_y = (track_y1 + track_y2) / 2 # center_distance = ((det_center_x - track_center_x)**2 + (det_center_y - track_center_y)**2)**0.5 # # Calculate IoU (Intersection over Union) # intersection_x1 = max(x1, track_x1) # intersection_y1 = max(y1, track_y1) # intersection_x2 = min(x2, track_x2) # intersection_y2 = min(y2, track_y2) # if intersection_x2 > intersection_x1 and intersection_y2 > intersection_y1: # intersection_area = (intersection_x2 - intersection_x1) * (intersection_y2 - intersection_y1) # det_area = (x2 - x1) * (y2 - y1) # track_area = (track_x2 - track_x1) * (track_y2 - track_y1) # union_area = det_area + track_area - intersection_area # iou = intersection_area / union_area if union_area > 0 else 0 # else: # iou = 0 # print(f"[MATCH DEBUG] Track {i}: ID={tracked['id']}, center=({track_center_x:.1f}, {track_center_y:.1f}), distance={center_distance:.1f}, IoU={iou:.3f}") # # Use stricter matching criteria - prioritize IoU over distance # # Good match if: high IoU OR close center distance with some overlap # is_good_match = (iou > 0.3) or (center_distance < 60 and iou > 0.1) # if is_good_match: # print(f"[MATCH DEBUG] Track {i} is a good match (IoU={iou:.3f}, distance={center_distance:.1f})") # # Prefer higher IoU, then lower distance # match_score = iou + (100 - min(center_distance, 100)) / 100 # Composite score # if iou > best_iou or (iou == best_iou and center_distance < best_distance): # best_distance = center_distance # best_iou = iou # best_match = tracked # else: # print(f"[MATCH DEBUG] Track {i} failed matching criteria (IoU={iou:.3f}, distance={center_distance:.1f})") # if best_match: # vehicle_id = best_match['id'] # is_moving_vehicle = best_match.get('is_moving', False) # is_violating_vehicle = best_match.get('is_violation', False) # print(f"[MATCH SUCCESS] Detection at ({det_center_x:.1f},{det_center_y:.1f}) matched with track ID={vehicle_id}") # print(f" -> STATUS: moving={is_moving_vehicle}, violating={is_violating_vehicle}, IoU={best_iou:.3f}, distance={best_distance:.1f}") # else: # print(f"[MATCH FAILED] No suitable match found for {label} detection at ({det_center_x:.1f}, {det_center_y:.1f})") # print(f" -> Will draw as untracked detection with default color") # else: # if label not in ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle']: # print(f"[MATCH DEBUG] Skipping matching for non-vehicle label: {label}") # elif len(tracked_vehicles) == 0: # print(f"[MATCH DEBUG] No tracked vehicles available for matching") # else: # try: # if len(tracked_vehicles) > 0: # distances = [((det_center_x - (t['bbox'][0] + t['bbox'][2])/2)**2 + (det_center_y - (t['bbox'][1] + t['bbox'][3])/2)**2)**0.5 for t in tracked_vehicles[:3]] # print(f"[DEBUG] No match found for detection at ({det_center_x:.1f},{det_center_y:.1f}) - distances: {distances}") # else: # print(f"[DEBUG] No tracked vehicles available to match detection at ({det_center_x:.1f},{det_center_y:.1f})") # except NameError: # print(f"[DEBUG] No match found for detection (coords unavailable)") # if len(tracked_vehicles) > 0: # print(f"[DEBUG] Had {len(tracked_vehicles)} tracked vehicles available") # # Choose box color based on vehicle status # # PRIORITY: 1. Violating (RED) - crossed during red light 2. Moving (ORANGE) 3. Stopped (GREEN) # if is_violating_vehicle and vehicle_id is not None: # box_color = (0, 0, 255) # RED for violating vehicles (crossed line during red) # label_text = f"{label}:ID{vehicle_id}⚠ī¸" # thickness = 4 # vehicles_violating += 1 # print(f"[COLOR DEBUG] Drawing RED box for VIOLATING vehicle ID={vehicle_id} (crossed during red)") # elif is_moving_vehicle and vehicle_id is not None and not is_violating_vehicle: # box_color = (0, 165, 255) # ORANGE for moving vehicles (not violating) # label_text = f"{label}:ID{vehicle_id}" # thickness = 3 # vehicles_moving += 1 # print(f"[COLOR DEBUG] Drawing ORANGE box for MOVING vehicle ID={vehicle_id} (not violating)") # elif label in ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle'] and vehicle_id is not None: # box_color = (0, 255, 0) # Green for stopped vehicles # label_text = f"{label}:ID{vehicle_id}" # thickness = 2 # print(f"[COLOR DEBUG] Drawing GREEN box for STOPPED vehicle ID={vehicle_id}") # elif is_traffic_light(label): # box_color = (0, 0, 255) # Red for traffic lights # label_text = f"{label}" # thickness = 2 # else: # box_color = (0, 255, 0) # Default green for other objects # label_text = f"{label}" # thickness = 2 # # Update statistics # if label in ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle']: # if vehicle_id is not None: # vehicles_with_ids += 1 # else: # vehicles_without_ids += 1 # # Draw rectangle and label # cv2.rectangle(annotated_frame, (x1, y1), (x2, y2), box_color, thickness) # cv2.putText(annotated_frame, label_text, (x1, y1-10), # cv2.FONT_HERSHEY_SIMPLEX, 0.5, box_color, 2) # # id_text = f"ID: {det['id']}" # # # Calculate text size for background # # (tw, th), baseline = cv2.getTextSize(id_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2) # # # Draw filled rectangle for background (top-left of bbox) # # cv2.rectangle(annotated_frame, (x1, y1 - th - 8), (x1 + tw + 4, y1), (0, 0, 0), -1) # # # Draw the ID text in bold yellow # # cv2.putText(annotated_frame, id_text, (x1 + 2, y1 - 4), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA) # # print(f"[DEBUG] Detection ID: {det['id']} BBOX: {bbox} CLASS: {label} CONF: {confidence:.2f}") # if class_id == 9 or is_traffic_light(label): # try: # light_info = detect_traffic_light_color(annotated_frame, [x1, y1, x2, y2]) # if light_info.get("color", "unknown") == "unknown": # light_info = ensure_traffic_light_color(annotated_frame, [x1, y1, x2, y2]) # det['traffic_light_color'] = light_info # # Draw enhanced traffic light status # annotated_frame = draw_traffic_light_status(annotated_frame, bbox, light_info) # # --- Update latest_traffic_light for UI/console --- # self.latest_traffic_light = light_info # # Add a prominent traffic light status at the top of the frame # color = light_info.get('color', 'unknown') # confidence = light_info.get('confidence', 0.0) # if color == 'red': # status_color = (0, 0, 255) # Red # status_text = f"Traffic Light: RED ({confidence:.2f})" # # Draw a prominent red banner across the top # banner_height = 40 # cv2.rectangle(annotated_frame, (0, 0), (annotated_frame.shape[1], banner_height), (0, 0, 150), -1) # # Add text # font = cv2.FONT_HERSHEY_DUPLEX # font_scale = 0.9 # font_thickness = 2 # cv2.putText(annotated_frame, status_text, (10, banner_height-12), font, # font_scale, (255, 255, 255), font_thickness) # except Exception as e: # print(f"[WARN] Could not detect/draw traffic light color: {e}") # # Print statistics summary # print(f"[STATS] Vehicles: {vehicles_with_ids} with IDs, {vehicles_without_ids} without IDs") # # Handle multiple traffic lights with consensus approach # for det in detections: # if is_traffic_light(det.get('class_name')): # has_traffic_lights = True # if 'traffic_light_color' in det: # light_info = det['traffic_light_color'] # traffic_lights.append({'bbox': det['bbox'], 'color': light_info.get('color', 'unknown'), 'confidence': light_info.get('confidence', 0.0)}) # # Determine the dominant traffic light color based on confidence # if traffic_lights: # # Filter to just red lights and sort by confidence # red_lights = [tl for tl in traffic_lights if tl.get('color') == 'red'] # if red_lights: # # Use the highest confidence red light for display # highest_conf_red = max(red_lights, key=lambda x: x.get('confidence', 0)) # # Update the global traffic light status for consistent UI display # self.latest_traffic_light = { # 'color': 'red', # 'confidence': highest_conf_red.get('confidence', 0.0) # } # # Emit all violations as a batch for UI (optional) # if violations: # if hasattr(self, 'violations_batch_ready'): # self.violations_batch_ready.emit(violations) # # Emit individual violation signals for each violation # for violation in violations: # print(f"🚨 Emitting RED LIGHT VIOLATION: Track ID {violation['track_id']}") # violation['frame'] = frame # violation['violation_line_y'] = violation_line_y # self.violation_detected.emit(violation) # print(f"[DEBUG] Emitted {len(violations)} violation signals") # # Add FPS display directly on frame # # cv2.putText(annotated_frame, f"FPS: {fps_smoothed:.1f}", (10, 30), # # cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) # # # --- Always draw detected traffic light color indicator at top --- # # color = self.latest_traffic_light.get('color', 'unknown') if isinstance(self.latest_traffic_light, dict) else str(self.latest_traffic_light) # # confidence = self.latest_traffic_light.get('confidence', 0.0) if isinstance(self.latest_traffic_light, dict) else 0.0 # # indicator_size = 30 # # margin = 10 # # status_colors = { # # "red": (0, 0, 255), # # "yellow": (0, 255, 255), # # "green": (0, 255, 0), # # "unknown": (200, 200, 200) # # } # # draw_color = status_colors.get(color, (200, 200, 200)) # # # Draw circle indicator # # cv2.circle( # # annotated_frame, # # (annotated_frame.shape[1] - margin - indicator_size, margin + indicator_size), # # indicator_size, # # draw_color, # # -1 # # ) # # # Add color text # # cv2.putText( # # annotated_frame, # # f"{color.upper()} ({confidence:.2f})", # # (annotated_frame.shape[1] - margin - indicator_size - 120, margin + indicator_size + 10), # # cv2.FONT_HERSHEY_SIMPLEX, # # 0.7, # # (0, 0, 0), # # 2 # # ) # # Signal for raw data subscribers (now without violations) # # Emit with correct number of arguments # try: # self.raw_frame_ready.emit(frame.copy(), detections, fps_smoothed) # print(f"✅ raw_frame_ready signal emitted with {len(detections)} detections, fps={fps_smoothed:.1f}") # except Exception as e: # print(f"❌ Error emitting raw_frame_ready: {e}") # import traceback # traceback.print_exc() # # Emit the NumPy frame signal for direct display - annotated version for visual feedback # print(f"🔴 Emitting frame_np_ready signal with annotated_frame shape: {annotated_frame.shape}") # try: # # Make sure the frame can be safely transmitted over Qt's signal system # # Create a contiguous copy of the array # frame_copy = np.ascontiguousarray(annotated_frame) # print(f"🔍 Debug - Before emission: frame_copy type={type(frame_copy)}, shape={frame_copy.shape}, is_contiguous={frame_copy.flags['C_CONTIGUOUS']}") # self.frame_np_ready.emit(frame_copy) # print("✅ frame_np_ready signal emitted successfully") # except Exception as e: # print(f"❌ Error emitting frame: {e}") # import traceback # traceback.print_exc() # # Emit QPixmap for video detection tab (frame_ready) # try: # from PySide6.QtGui import QImage, QPixmap # rgb_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB) # h, w, ch = rgb_frame.shape # bytes_per_line = ch * w # qimg = QImage(rgb_frame.data, w, h, bytes_per_line, QImage.Format_RGB888) # pixmap = QPixmap.fromImage(qimg) # metrics = { # 'FPS': fps_smoothed, # 'Detection (ms)': detection_time # } # self.frame_ready.emit(pixmap, detections, metrics) # print("✅ frame_ready signal emitted for video detection tab") # except Exception as e: # print(f"❌ Error emitting frame_ready: {e}") # import traceback # traceback.print_exc() # # Emit stats signal for performance monitoring # # Count traffic lights for UI (confidence >= 0.5) # traffic_light_count = 0 # for det in detections: # if is_traffic_light(det.get('class_name')): # tl_conf = 0.0 # if 'traffic_light_color' in det and isinstance(det['traffic_light_color'], dict): # tl_conf = det['traffic_light_color'].get('confidence', 0.0) # if tl_conf >= 0.5: # traffic_light_count += 1 # # Count cars for UI (confidence >= 0.5) # car_count = 0 # for det in detections: # if det.get('class_name') == 'car' and det.get('confidence', 0.0) >= 0.5: # car_count += 1 # # Get model information from model manager # model_info = {} # if self.model_manager and hasattr(self.model_manager, 'get_current_model_info'): # model_info = self.model_manager.get_current_model_info() # print(f"🔧 DEBUG: Model info from manager: {model_info}") # stats = { # 'fps': fps_smoothed, # 'detection_fps': fps_smoothed, # Numeric value for analytics # 'detection_time': detection_time, # 'detection_time_ms': detection_time, # Numeric value for analytics # 'traffic_light_color': self.latest_traffic_light, # 'tlights': traffic_light_count, # Only confident traffic lights # 'cars': car_count, # Only confident cars # 'model_path': model_info.get('model_path', ''), # Add model path for UI # 'model_name': model_info.get('model_name', 'Unknown') # Add model name for UI # } # print(f"🔧 DEBUG: Stats with model info: model_name={stats.get('model_name')}, model_path={stats.get('model_path')}") # # Print detailed stats for debugging # tl_color = "unknown" # if isinstance(self.latest_traffic_light, dict): # tl_color = self.latest_traffic_light.get('color', 'unknown') # elif isinstance(self.latest_traffic_light, str): # tl_color = self.latest_traffic_light # print(f"đŸŸĸ Stats Updated: FPS={fps_smoothed:.2f}, Inference={detection_time:.2f}ms, Traffic Light={tl_color}") # # Emit stats signal # self.stats_ready.emit(stats) # # Emit performance stats for performance graphs # perf_stats = { # 'frame_idx': self.frame_count, # 'fps': fps_smoothed, # 'inference_time': detection_time, # 'device': getattr(self, 'current_device', 'CPU'), # 'resolution': getattr(self, 'current_resolution', f'{frame.shape[1]}x{frame.shape[0]}' if frame is not None else '-'), # 'model_name': model_info.get('model_name', 'Unknown'), # Add model name for performance graphs # 'is_spike': False, # TODO: Add spike logic if needed # 'is_res_change': False, # TODO: Add res change logic if needed # 'cpu_spike': False, # TODO: Add cpu spike logic if needed # } # print(f"[PERF] Emitting performance_stats_ready: {perf_stats}") # self.performance_stats_ready.emit(perf_stats) # # --- Update last analysis data for VLM --- # self._last_analysis_data = { # 'detections': detections, # 'tracked_vehicles': tracked_vehicles if 'tracked_vehicles' in locals() else [], # 'traffic_light': self.latest_traffic_light, # 'crosswalk_bbox': crosswalk_bbox if 'crosswalk_bbox' in locals() else None, # 'violation_line_y': violation_line_y if 'violation_line_y' in locals() else None, # 'crosswalk_detected': crosswalk_bbox is not None if 'crosswalk_bbox' in locals() else False, # 'traffic_light_position': traffic_light_position if has_traffic_lights else None, # 'frame_shape': frame.shape if frame is not None else None, # 'timestamp': time.time() # } # # --- Ensure analytics update every frame --- # # Always add traffic_light_color to each detection dict for analytics # for det in detections: # if is_traffic_light(det.get('class_name')): # if 'traffic_light_color' not in det: # det['traffic_light_color'] = self.latest_traffic_light if hasattr(self, 'latest_traffic_light') else {'color': 'unknown', 'confidence': 0.0} # if hasattr(self, 'analytics_controller') and self.analytics_controller is not None: # try: # self.analytics_controller.process_frame_data(frame, detections, stats) # print("[DEBUG] Called analytics_controller.process_frame_data for analytics update") # except Exception as e: # print(f"[ERROR] Could not update analytics: {e}") # # Control processing rate for file sources # if isinstance(self.source, str) and self.source_fps > 0: # frame_duration = time.time() - process_start # if frame_duration < frame_time: # time.sleep(frame_time - frame_duration) # cap.release() # except Exception as e: # print(f"Video processing error: {e}") # import traceback # traceback.print_exc() # finally: # self._running = False # def _process_frame(self): # """Process current frame for display with improved error handling""" # try: # self.mutex.lock() # if self.current_frame is None: # print("⚠ī¸ No frame available to process") # self.mutex.unlock() # # Check if we're running - if not, this is expected behavior # if not self._running: # return # # If we are running but have no frame, create a blank frame with error message # h, w = 480, 640 # Default size # blank_frame = np.zeros((h, w, 3), dtype=np.uint8) # cv2.putText(blank_frame, "No video input", (w//2-100, h//2), # cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) # # Emit this blank frame # try: # self.frame_np_ready.emit(blank_frame) # except Exception as e: # print(f"Error emitting blank frame: {e}") # return # # Make a copy of the data we need # try: # frame = self.current_frame.copy() # detections = self.current_detections.copy() if self.current_detections else [] # metrics = self.performance_metrics.copy() # except Exception as e: # print(f"Error copying frame data: {e}") # self.mutex.unlock() # return # self.mutex.unlock() # except Exception as e: # print(f"Critical error in _process_frame initialization: {e}") # import traceback # traceback.print_exc() # try: # self.mutex.unlock() # except: # pass # return # try: # # --- Simplified frame processing for display --- # # The violation logic is now handled in the main _run thread # # This method just handles basic display overlays # annotated_frame = frame.copy() # # Add performance overlays and debug markers - COMMENTED OUT for clean video display # # annotated_frame = draw_performance_overlay(annotated_frame, metrics) # # cv2.circle(annotated_frame, (20, 20), 10, (255, 255, 0), -1) # # Convert BGR to RGB before display (for PyQt/PySide) # frame_rgb = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB) # # Display the RGB frame in the UI (replace with your display logic) # # Example: self.image_label.setPixmap(QPixmap.fromImage(QImage(frame_rgb.data, w, h, QImage.Format_RGB888))) # except Exception as e: # print(f"Error in _process_frame: {e}") # import traceback # traceback.print_exc() # def _cleanup_old_vehicle_data(self, current_track_ids): # """ # Clean up tracking data for vehicles that are no longer being tracked. # This prevents memory leaks and improves performance. # Args: # current_track_ids: Set of currently active track IDs # """ # # Find IDs that are no longer active # old_ids = set(self.vehicle_history.keys()) - set(current_track_ids) # if old_ids: # print(f"[CLEANUP] Removing tracking data for {len(old_ids)} old vehicle IDs: {sorted(old_ids)}") # for old_id in old_ids: # # Remove from history and status tracking # if old_id in self.vehicle_history: # del self.vehicle_history[old_id] # if old_id in self.vehicle_statuses: # del self.vehicle_statuses[old_id] # print(f"[CLEANUP] Now tracking {len(self.vehicle_history)} active vehicles") # # --- Removed unused internal violation line detection methods and RedLightViolationSystem usage --- # def play(self): # """Alias for start(), for UI compatibility.""" # self.start() # def pause(self): # """Pause video processing.""" # print("[VideoController] Pause requested") # self.mutex.lock() # self._paused = True # self.mutex.unlock() # # Emit the last captured frame for VLM analysis if available # if hasattr(self, '_last_frame') and self._last_frame is not None: # print("[VideoController] Emitting last frame for VLM analysis during pause") # try: # # Emit the last frame with empty detections for VLM # self.raw_frame_ready.emit(self._last_frame.copy(), [], 0.0) # print("✅ Last frame emitted for VLM analysis") # except Exception as e: # print(f"❌ Error emitting last frame: {e}") # else: # print("[VideoController] No last frame available for VLM analysis") # # Emit pause state signal # self.pause_state_changed.emit(True) # print("[VideoController] Pause state signal emitted: True") # def resume(self): # """Resume video processing from pause.""" # print("[VideoController] Resume requested") # self.mutex.lock() # self._paused = False # self.pause_condition.wakeAll() # Wake up any waiting threads # self.mutex.unlock() # # Emit pause state signal # self.pause_state_changed.emit(False) # print("[VideoController] Pause state signal emitted: False") # def get_current_analysis_data(self): # """Get current analysis data for VLM insights.""" # return self._last_analysis_data.copy() if self._last_analysis_data else {} # def _on_scene_object_detected(self, obj_data: dict): # """Handle scene object detection signal""" # try: # # Forward scene object detection to analytics # print(f"[SCENE] Object detected: {obj_data.get('category', 'unknown')} " # f"(confidence: {obj_data.get('confidence', 0):.2f})") # except Exception as e: # print(f"Error handling scene object detection: {e}") # def _on_scene_analytics_updated(self, analytics_data: dict): # """Handle scene analytics update signal""" # try: # # Forward scene analytics to performance stats # camera_id = analytics_data.get('camera_id', 'unknown') # fps = analytics_data.get('fps', 0) # processing_time = analytics_data.get('processing_time_ms', 0) # object_count = analytics_data.get('object_count', 0) # # Update performance metrics with scene analytics # self.performance_metrics['Scene_FPS'] = fps # self.performance_metrics['Scene_Objects'] = object_count # self.performance_metrics['Scene_Processing_ms'] = processing_time # print(f"[SCENE] Analytics updated - FPS: {fps:.1f}, Objects: {object_count}, " # f"Processing: {processing_time:.1f}ms") # except Exception as e: # print(f"Error handling scene analytics update: {e}") # def _on_roi_event_detected(self, event_data: dict): # """Handle ROI event detection signal""" # try: # event_type = event_data.get('type', 'unknown') # roi_id = event_data.get('roi_id', 'unknown') # object_category = event_data.get('object_category', 'unknown') # print(f"[SCENE] ROI Event: {event_type} in {roi_id} - {object_category}") # # Emit as violation if it's a safety-related event # if 'safety' in event_type.lower() or 'violation' in event_type.lower(): # violation_data = { # 'type': 'roi_violation', # 'roi_id': roi_id, # 'object_category': object_category, # 'timestamp': event_data.get('timestamp'), # 'confidence': event_data.get('confidence', 1.0), # 'source': 'scene_analytics' # } # self.violation_detected.emit(violation_data) # except Exception as e: # print(f"Error handling ROI event: {e}") # @Slot(str) # def on_model_switched(self, device): # """Handle device switch from config panel.""" # try: # print(f"🔄 Video Controller: Device switch requested to {device}") # # Update our device reference # self.current_device = device # print(f"✅ Video Controller: current_device updated to {device}") # # If we have a model manager, the device switch should already be done # # Just log the current state for verification # if self.model_manager and hasattr(self.model_manager, 'detector'): # if hasattr(self.model_manager.detector, 'device'): # current_device = self.model_manager.detector.device # print(f"✅ Video Controller: Model manager detector now using device: {current_device}") # else: # print(f"✅ Video Controller: Model manager detector updated to {device}") # print(f"✅ Video Controller: Device switch to {device} completed") # except Exception as e: # print(f"❌ Video Controller: Error during device switch: {e}") from PySide6.QtCore import QObject, Signal, QThread, Qt, QMutex, QWaitCondition, QTimer, Slot from PySide6.QtGui import QImage, QPixmap import cv2 import time import numpy as np from datetime import datetime from collections import deque from typing import Dict, List, Optional import os import sys import math # Add parent directory to path for imports sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) # Import utilities from utils.annotation_utils import ( draw_detections, draw_performance_metrics, resize_frame_for_display, convert_cv_to_qimage, convert_cv_to_pixmap, pipeline_with_violation_line ) # Import enhanced annotation utilities from utils.enhanced_annotation_utils import ( enhanced_draw_detections, draw_performance_overlay, enhanced_cv_to_qimage, enhanced_cv_to_pixmap ) # Import traffic light color detection utilities from red_light_violation_pipeline import RedLightViolationPipeline from utils.traffic_light_utils import detect_traffic_light_color, draw_traffic_light_status, ensure_traffic_light_color from utils.crosswalk_utils2 import detect_crosswalk_and_violation_line, draw_violation_line, get_violation_line_y from controllers.bytetrack_tracker import ByteTrackVehicleTracker TRAFFIC_LIGHT_CLASSES = ["traffic light", "trafficlight", "tl"] TRAFFIC_LIGHT_NAMES = ['trafficlight', 'traffic light', 'tl', 'signal'] def normalize_class_name(class_name): """Normalizes class names from different models/formats to a standard name""" if not class_name: return "" name_lower = class_name.lower() # Traffic light variants if name_lower in ['traffic light', 'trafficlight', 'traffic_light', 'tl', 'signal']: return 'traffic light' # Keep specific vehicle classes (car, truck, bus) separate # Just normalize naming variations within each class if name_lower in ['car', 'auto', 'automobile']: return 'car' elif name_lower in ['truck']: return 'truck' elif name_lower in ['bus']: return 'bus' elif name_lower in ['motorcycle', 'scooter', 'motorbike', 'bike']: return 'motorcycle' # Person variants if name_lower in ['person', 'pedestrian', 'human']: return 'person' # Other common classes can be added here return class_name def is_traffic_light(class_name): """Helper function to check if a class name is a traffic light with normalization""" if not class_name: return False normalized = normalize_class_name(class_name) return normalized == 'traffic light' class VideoController(QObject): frame_ready = Signal(object, object, dict) # QPixmap, detections, metrics raw_frame_ready = Signal(np.ndarray, list, float) # frame, detections, fps frame_np_ready = Signal(np.ndarray) # Direct NumPy frame signal for display stats_ready = Signal(dict) # Dictionary with stats (fps, detection_time, traffic_light) violation_detected = Signal(dict) # Signal emitted when a violation is detected progress_ready = Signal(int, int, float) # value, max_value, timestamp device_info_ready = Signal(dict) # Signal to emit device info to the UI auto_select_model_device = Signal() # Signal for UI to request auto model/device selection performance_stats_ready = Signal(dict) # NEW: Signal for performance tab (fps, inference, device, res) violations_batch_ready = Signal(list) # NEW: Signal to emit a batch of violations pause_state_changed = Signal(bool) # Signal emitted when pause state changes (True=paused, False=playing) def __init__(self, model_manager=None): """ Initialize video controller. Args: model_manager: Model manager instance for detection and violation """ super().__init__() print("Loaded advanced VideoController from video_controller_new.py") # DEBUG: Confirm correct controller self._running = False self._paused = False # Add pause state self._last_frame = None # Store last frame for VLM analysis during pause self._last_analysis_data = {} # Store last analysis data for VLM self.source = None self.source_type = None self.source_fps = 0 self.performance_metrics = {} self.mutex = QMutex() self.pause_condition = QWaitCondition() # Add wait condition for pause # Performance tracking self.processing_times = deque(maxlen=100) # Store last 100 processing times self.fps_history = deque(maxlen=100) # Store last 100 FPS values self.start_time = time.time() self.frame_count = 0 self.actual_fps = 0.0 self.model_manager = model_manager self.inference_model = None self.tracker = None # Initialize device tracking if self.model_manager and hasattr(self.model_manager, 'config'): self.current_device = self.model_manager.config.get("detection", {}).get("device", "CPU") else: self.current_device = "CPU" print(f"🔧 Video Controller: Initialized with device: {self.current_device}") self.current_frame = None self.current_detections = [] # Traffic light state tracking self.latest_traffic_light = {"color": "unknown", "confidence": 0.0} # Vehicle tracking settings self.vehicle_history = {} # Dictionary to store vehicle position history self.vehicle_statuses = {} # Track stable movement status self.movement_threshold = 1.5 # ADJUSTED: More balanced movement detection (was 0.8) self.min_confidence_threshold = 0.3 # FIXED: Lower threshold for better detection (was 0.5) # Enhanced violation detection settings self.position_history_size = 20 # Increased from 10 to track longer history self.crossing_check_window = 8 # Check for crossings over the last 8 frames instead of just 2 self.max_position_jump = 50 # Maximum allowed position jump between frames (detect ID switches) # Set up violation detection try: from controllers.red_light_violation_detector import RedLightViolationDetector self.violation_detector = RedLightViolationDetector() print("✅ Red light violation detector initialized") except Exception as e: self.violation_detector = None print(f"❌ Could not initialize violation detector: {e}") # Import crosswalk detection try: self.detect_crosswalk_and_violation_line = detect_crosswalk_and_violation_line # self.draw_violation_line = draw_violation_line print("✅ Crosswalk detection utilities imported") except Exception as e: print(f"❌ Could not import crosswalk detection: {e}") self.detect_crosswalk_and_violation_line = lambda frame, *args: (None, None, {}) # self.draw_violation_line = lambda frame, *args, **kwargs: frame # Configure thread self.thread = QThread() self.moveToThread(self.thread) self.thread.started.connect(self._run) # Performance measurement self.mutex = QMutex() self.condition = QWaitCondition() self.performance_metrics = { 'FPS': 0.0, 'Detection (ms)': 0.0, 'Total (ms)': 0.0 } # Setup render timer with more aggressive settings for UI updates self.render_timer = QTimer() self.render_timer.timeout.connect(self._process_frame) # Frame buffer self.current_frame = None self.current_detections = [] self.current_violations = [] # Debug counter for monitoring frame processing self.debug_counter = 0 self.violation_frame_counter = 0 # Add counter for violation processing # Initialize the traffic light color detection pipeline self.cv_violation_pipeline = RedLightViolationPipeline(debug=True) # Initialize vehicle tracker self.vehicle_tracker = ByteTrackVehicleTracker() # Add red light violation system # self.red_light_violation_system = RedLightViolationSystem() # Initialize scene analytics adapter try: from utils.scene_analytics import SceneAnalyticsAdapter self.scene_analytics = SceneAnalyticsAdapter(camera_id="desktop_main") self.scene_analytics.object_detected.connect(self._on_scene_object_detected) self.scene_analytics.scene_analytics_updated.connect(self._on_scene_analytics_updated) self.scene_analytics.roi_event_detected.connect(self._on_roi_event_detected) print("✅ Scene analytics adapter initialized") except Exception as e: self.scene_analytics = None print(f"❌ Could not initialize scene analytics: {e}") def set_source(self, source): """ Set video source (file path, camera index, or URL) Args: source: Video source - can be a camera index (int), file path (str), or URL (str). If None, defaults to camera 0. Returns: bool: True if source was set successfully, False otherwise """ print(f"đŸŽŦ VideoController.set_source called with: {source} (type: {type(source)})") # Store current state was_running = self._running # Stop current processing if running if self._running: print("⏚ī¸ Stopping current video processing") self.stop() try: # Handle source based on type with better error messages if source is None: print("⚠ī¸ Received None source, defaulting to camera 0") self.source = 0 self.source_type = "camera" elif isinstance(source, str) and source.strip(): if os.path.exists(source): # Valid file path self.source = source self.source_type = "file" print(f"📄 Source set to file: {self.source}") elif source.lower().startswith(("http://", "https://", "rtsp://", "rtmp://")): # URL stream self.source = source self.source_type = "url" print(f"🌐 Source set to URL stream: {self.source}") elif source.isdigit(): # String camera index (convert to int) self.source = int(source) self.source_type = "camera" print(f"📹 Source set to camera index: {self.source}") else: # Try as device path or special string self.source = source self.source_type = "device" print(f"📱 Source set to device path: {self.source}") elif isinstance(source, int): # Camera index self.source = source self.source_type = "camera" print(f"📹 Source set to camera index: {self.source}") else: # Unrecognized - default to camera 0 with warning print(f"⚠ī¸ Unrecognized source type: {type(source)}, defaulting to camera 0") self.source = 0 self.source_type = "camera" except Exception as e: print(f"❌ Error setting source: {e}") self.source = 0 self.source_type = "camera" return False # Get properties of the source (fps, dimensions, etc) print(f"🔍 Getting properties for source: {self.source}") success = self._get_source_properties() if success: print(f"✅ Successfully configured source: {self.source} ({self.source_type})") # Reset ByteTrack tracker for new source to ensure IDs start from 1 if hasattr(self, 'vehicle_tracker') and self.vehicle_tracker is not None: try: print("🔄 Resetting vehicle tracker for new source") self.vehicle_tracker.reset() except Exception as e: print(f"⚠ī¸ Could not reset vehicle tracker: {e}") # Emit successful source change self.stats_ready.emit({ 'source_changed': True, 'source_type': self.source_type, 'fps': self.source_fps if hasattr(self, 'source_fps') else 0, 'dimensions': f"{self.frame_width}x{self.frame_height}" if hasattr(self, 'frame_width') else "unknown" }) # Restart if previously running if was_running: print("â–ļī¸ Restarting video processing with new source") self.start() else: print(f"❌ Failed to configure source: {self.source}") # Notify UI about the error self.stats_ready.emit({ 'source_changed': False, 'error': f"Invalid video source: {self.source}", 'source_type': self.source_type, 'fps': 0, 'detection_time_ms': "0", 'traffic_light_color': {"color": "unknown", "confidence": 0.0} }) return False # Return success status return success def _get_source_properties(self): try: print(f"🔍 Opening video source for properties check: {self.source}") cap = cv2.VideoCapture(self.source) if not cap.isOpened(): print(f"❌ Failed to open video source: {self.source}") return False self.source_fps = cap.get(cv2.CAP_PROP_FPS) self.frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) self.frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) self.frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) ret, test_frame = cap.read() if not ret or test_frame is None: print("⚠ī¸ Could not read test frame from source") if self.source_type == "camera": print("🔄 Retrying camera initialization...") time.sleep(1.0) ret, test_frame = cap.read() if not ret or test_frame is None: print("❌ Camera initialization failed after retry") cap.release() return False else: print("❌ Could not read frames from video source") cap.release() return False # Release the capture cap.release() print(f"✅ Video source properties: {self.frame_width}x{self.frame_height}, {self.source_fps} FPS") return True except Exception as e: print(f"❌ Error getting source properties: {e}") return False return False def start(self): """Start video processing""" if not self._running: self._running = True self.start_time = time.time() self.frame_count = 0 self.debug_counter = 0 print("DEBUG: Starting video processing thread") # Reset ByteTrack tracker to ensure IDs start from 1 if hasattr(self, 'vehicle_tracker') and self.vehicle_tracker is not None: try: print("🔄 Resetting vehicle tracker for new session") self.vehicle_tracker.reset() except Exception as e: print(f"⚠ī¸ Could not reset vehicle tracker: {e}") # Start the processing thread - add more detailed debugging if not self.thread.isRunning(): print("🚀 Thread not running, starting now...") try: self.thread.start() print("✅ Thread started successfully") print(f"🔄 Thread state: running={self.thread.isRunning()}, finished={self.thread.isFinished()}") except Exception as e: print(f"❌ Failed to start thread: {e}") import traceback traceback.print_exc() else: print("⚠ī¸ Thread is already running!") print(f"🔄 Thread state: running={self.thread.isRunning()}, finished={self.thread.isFinished()}") # Start the render timer with a very aggressive interval (10ms = 100fps) # This ensures we can process frames as quickly as possible print("⏱ī¸ Starting render timer...") self.render_timer.start(10) print("✅ Render timer started at 100Hz") def stop(self): """Stop video processing""" if self._running: print("DEBUG: Stopping video processing") self._running = False # If paused, wake up the thread so it can exit self.mutex.lock() self._paused = False self.pause_condition.wakeAll() self.mutex.unlock() self.render_timer.stop() # Properly terminate the thread if self.thread.isRunning(): self.thread.quit() if not self.thread.wait(3000): # Wait 3 seconds max self.thread.terminate() print("WARNING: Thread termination forced") # Clear the current frame self.mutex.lock() self.current_frame = None self.mutex.unlock() print("DEBUG: Video processing stopped") def __del__(self): print("[VideoController] __del__ called. Cleaning up thread and timer.") self.stop() if self.thread.isRunning(): self.thread.quit() self.thread.wait(1000) self.render_timer.stop() def capture_snapshot(self) -> np.ndarray: """Capture current frame""" if self.current_frame is not None: return self.current_frame.copy() return None def _run(self): """Main processing loop (runs in thread)""" try: # Print the source we're trying to open print(f"DEBUG: Opening video source: {self.source} (type: {type(self.source)})") cap = None # Initialize capture variable # Try to open source with more robust error handling max_retries = 3 retry_delay = 1.0 # seconds # Function to attempt opening the source with multiple retries def try_open_source(src, retries=max_retries, delay=retry_delay): for attempt in range(1, retries + 1): print(f"đŸŽĨ Opening source (attempt {attempt}/{retries}): {src}") try: capture = cv2.VideoCapture(src) if capture.isOpened(): # Try to read a test frame to confirm it's working ret, test_frame = capture.read() if ret and test_frame is not None: print(f"✅ Source opened successfully: {src}") # Reset capture position for file sources if isinstance(src, str) and os.path.exists(src): capture.set(cv2.CAP_PROP_POS_FRAMES, 0) return capture else: print(f"⚠ī¸ Source opened but couldn't read frame: {src}") capture.release() else: print(f"⚠ī¸ Failed to open source: {src}") # Retry after delay if attempt < retries: print(f"Retrying in {delay:.1f} seconds...") time.sleep(delay) except Exception as e: print(f"❌ Error opening source {src}: {e}") if attempt < retries: print(f"Retrying in {delay:.1f} seconds...") time.sleep(delay) print(f"❌ Failed to open source after {retries} attempts: {src}") return None # Handle different source types if isinstance(self.source, str) and os.path.exists(self.source): # It's a valid file path print(f"📄 Opening video file: {self.source}") cap = try_open_source(self.source) elif isinstance(self.source, int) or (isinstance(self.source, str) and self.source.isdigit()): # It's a camera index camera_idx = int(self.source) if isinstance(self.source, str) else self.source print(f"📹 Opening camera with index: {camera_idx}") # For cameras, try with different backend options if it fails cap = try_open_source(camera_idx) # If failed, try with DirectShow backend on Windows if cap is None and os.name == 'nt': print("🔄 Trying camera with DirectShow backend...") cap = try_open_source(camera_idx + cv2.CAP_DSHOW) else: # Try as a string source (URL or device path) print(f"🌐 Opening source as string: {self.source}") cap = try_open_source(str(self.source)) # Check if we successfully opened the source if cap is None: print(f"❌ Failed to open video source after all attempts: {self.source}") # Notify UI about the error self.stats_ready.emit({ 'error': f"Could not open video source: {self.source}", 'fps': "0", 'detection_time_ms': "0", 'traffic_light_color': {"color": "unknown", "confidence": 0.0} }) return # Check again to ensure capture is valid if not cap or not cap.isOpened(): print(f"ERROR: Could not open video source {self.source}") # Emit a signal to notify UI about the error self.stats_ready.emit({ 'error': f"Failed to open video source: {self.source}", 'fps': "0", 'detection_time_ms': "0", 'traffic_light_color': {"color": "unknown", "confidence": 0.0} }) return # Configure frame timing based on source FPS frame_time = 1.0 / self.source_fps if self.source_fps > 0 else 0.033 prev_time = time.time() # Log successful opening print(f"SUCCESS: Video source opened: {self.source}") print(f"Source info - FPS: {self.source_fps}, Size: {self.frame_width}x{self.frame_height}") # Main processing loop frame_error_count = 0 max_consecutive_errors = 10 # --- Violation Rule Functions --- def point_in_polygon(point, polygon): # Simple point-in-rect for now; replace with polygon logic if needed x, y = point x1, y1, w, h = polygon return x1 <= x <= x1 + w and y1 <= y <= y1 + h def calculate_speed(track, history_dict): # Use last two positions for speed hist = history_dict.get(track['id'], []) if len(hist) < 2: return 0.0 (x1, y1), t1 = hist[-2] (x2, y2), t2 = hist[-1] dist = ((x2-x1)**2 + (y2-y1)**2)**0.5 dt = max(t2-t1, 1e-3) return dist / dt def check_vehicle_pedestrian_conflict(vehicle_track, pedestrian_tracks, crosswalk_poly, light_state): if light_state != 'green': return False if not point_in_polygon(vehicle_track['center'], crosswalk_poly): return False for ped in pedestrian_tracks: if point_in_polygon(ped['center'], crosswalk_poly): return True return False def check_stop_on_crosswalk(vehicle_track, crosswalk_poly, light_state, history_dict): if light_state != 'red': return False is_inside = point_in_polygon(vehicle_track['center'], crosswalk_poly) speed = calculate_speed(vehicle_track, history_dict) return is_inside and speed < 0.5 def check_amber_overspeed(vehicle_track, light_state, amber_start_time, stopline_poly, history_dict, speed_limit_px_per_sec): if light_state != 'amber': return False if not point_in_polygon(vehicle_track['center'], stopline_poly): return False current_time = time.time() speed = calculate_speed(vehicle_track, history_dict) if current_time > amber_start_time and speed > speed_limit_px_per_sec: return True return False # --- End Violation Rule Functions --- while self._running and cap.isOpened(): # Handle pause state self.mutex.lock() if self._paused: print("[VideoController] Video paused, waiting...") self.pause_condition.wait(self.mutex) print("[VideoController] Video resumed") self.mutex.unlock() # Exit if we're no longer running (could have stopped while paused) if not self._running: break try: ret, frame = cap.read() # Add critical frame debugging print(f"🟡 Frame read attempt: ret={ret}, frame={None if frame is None else frame.shape}") if not ret or frame is None: frame_error_count += 1 print(f"⚠ī¸ Frame read error ({frame_error_count}/{max_consecutive_errors})") if frame_error_count >= max_consecutive_errors: print("❌ Too many consecutive frame errors, stopping video thread") break # Skip this iteration and try again time.sleep(0.1) # Wait a bit before trying again continue # Reset the error counter if we successfully got a frame frame_error_count = 0 # Store the last frame for VLM analysis during pause self._last_frame = frame.copy() print(f"đŸŸĸ Last frame stored for VLM: {frame.shape}") except Exception as e: print(f"❌ Critical error reading frame: {e}") frame_error_count += 1 if frame_error_count >= max_consecutive_errors: print("❌ Too many errors, stopping video thread") break continue # Detection and violation processing process_start = time.time() # Process detections detection_start = time.time() detections = [] if self.model_manager: detections = self.model_manager.detect(frame) # Normalize class names for consistency and check for traffic lights traffic_light_indices = [] for i, det in enumerate(detections): if 'class_name' in det: original_name = det['class_name'] normalized_name = normalize_class_name(original_name) # Keep track of traffic light indices if normalized_name == 'traffic light' or original_name == 'traffic light': traffic_light_indices.append(i) if original_name != normalized_name: print(f"📊 Normalized class name: '{original_name}' -> '{normalized_name}'") det['class_name'] = normalized_name # Ensure we have at least one traffic light for debugging if not traffic_light_indices and self.source_type == 'video': print("⚠ī¸ No traffic lights detected, checking for objects that might be traffic lights...") # Try lowering the confidence threshold specifically for traffic lights # This is only for debugging purposes if self.model_manager and hasattr(self.model_manager, 'detect'): try: low_conf_detections = self.model_manager.detect(frame, conf_threshold=0.2) for det in low_conf_detections: if 'class_name' in det and det['class_name'] == 'traffic light': if det not in detections: print(f"đŸšĻ Found low confidence traffic light: {det['confidence']:.2f}") detections.append(det) except: pass detection_time = (time.time() - detection_start) * 1000 # Violation detection is disabled violation_start = time.time() violations = [] # if self.model_manager and detections: # violations = self.model_manager.detect_violations( # detections, frame, time.time() # ) violation_time = (time.time() - violation_start) * 1000 # Update tracking if available if self.model_manager: detections = self.model_manager.update_tracking(detections, frame) # If detections are returned as tuples, convert to dicts for downstream code if detections and isinstance(detections[0], tuple): # Convert (id, bbox, conf, class_id) to dict detections = [ {'id': d[0], 'bbox': d[1], 'confidence': d[2], 'class_id': d[3]} for d in detections ] # Calculate timing metrics process_time = (time.time() - process_start) * 1000 self.processing_times.append(process_time) # Update FPS now = time.time() self.frame_count += 1 elapsed = now - self.start_time if elapsed > 0: self.actual_fps = self.frame_count / elapsed fps_smoothed = 1.0 / (now - prev_time) if now > prev_time else 0 prev_time = now # Update metrics self.performance_metrics = { 'FPS': f"{fps_smoothed:.1f}", 'Detection (ms)': f"{detection_time:.1f}", 'Total (ms)': f"{process_time:.1f}" } # Store current frame data (thread-safe) self.mutex.lock() self.current_frame = frame.copy() self.current_detections = detections self.mutex.unlock() # --- SCENE ANALYTICS PROCESSING --- # Process detections through scene analytics if available if self.scene_analytics: try: scene_analytics_data = self.scene_analytics.process_frame(frame, detections) # Scene analytics automatically emit signals that we handle above except Exception as e: print(f"Error in scene analytics processing: {e}") # Process frame with annotations before sending to UI annotated_frame = frame.copy() # --- VIOLATION DETECTION LOGIC (Run BEFORE drawing boxes) --- # First get violation information so we can color boxes appropriately violating_vehicle_ids = set() # Track which vehicles are violating violations = [] # Initialize traffic light variables traffic_lights = [] has_traffic_lights = False # Handle multiple traffic lights with consensus approach traffic_light_count = 0 for det in detections: if is_traffic_light(det.get('class_name')): has_traffic_lights = True traffic_light_count += 1 if 'traffic_light_color' in det: light_info = det['traffic_light_color'] traffic_lights.append({'bbox': det['bbox'], 'color': light_info.get('color', 'unknown'), 'confidence': light_info.get('confidence', 0.0)}) print(f"[TRAFFIC LIGHT] Detected {traffic_light_count} traffic light(s), has_traffic_lights={has_traffic_lights}") if has_traffic_lights: print(f"[TRAFFIC LIGHT] Traffic light colors: {[tl.get('color', 'unknown') for tl in traffic_lights]}") # Get traffic light position for crosswalk detection traffic_light_position = None if has_traffic_lights: for det in detections: if is_traffic_light(det.get('class_name')) and 'bbox' in det: traffic_light_bbox = det['bbox'] # Extract center point from bbox for crosswalk utils x1, y1, x2, y2 = traffic_light_bbox traffic_light_position = ((x1 + x2) // 2, (y1 + y2) // 2) break # Run crosswalk detection ONLY if traffic light is detected crosswalk_bbox, violation_line_y, debug_info = None, None, {} if has_traffic_lights and traffic_light_position is not None: try: print(f"[CROSSWALK] Traffic light detected at {traffic_light_position}, running crosswalk detection") # Use new crosswalk_utils2 logic only when traffic light exists annotated_frame, crosswalk_bbox, violation_line_y, debug_info = detect_crosswalk_and_violation_line( annotated_frame, traffic_light_position=traffic_light_position ) print(f"[CROSSWALK] Detection result: crosswalk_bbox={crosswalk_bbox is not None}, violation_line_y={violation_line_y}") # --- Draw crosswalk region if detected and close to traffic light --- # (REMOVED: Do not draw crosswalk box or label) # if crosswalk_bbox is not None: # x, y, w, h = map(int, crosswalk_bbox) # tl_x, tl_y = traffic_light_position # crosswalk_center_y = y + h // 2 # distance = abs(crosswalk_center_y - tl_y) # print(f"[CROSSWALK DEBUG] Crosswalk bbox: {crosswalk_bbox}, Traffic light: {traffic_light_position}, vertical distance: {distance}") # if distance < 120: # cv2.rectangle(annotated_frame, (x, y), (x + w, y + h), (0, 255, 0), 3) # cv2.putText(annotated_frame, "Crosswalk", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2) # # Top and bottom edge of crosswalk # top_edge = y # bottom_edge = y + h # if abs(tl_y - top_edge) < abs(tl_y - bottom_edge): # crosswalk_edge_y = top_edge # else: # crosswalk_edge_y = bottom_edge if crosswalk_bbox is not None: x, y, w, h = map(int, crosswalk_bbox) tl_x, tl_y = traffic_light_position crosswalk_center_y = y + h // 2 distance = abs(crosswalk_center_y - tl_y) print(f"[CROSSWALK DEBUG] Crosswalk bbox: {crosswalk_bbox}, Traffic light: {traffic_light_position}, vertical distance: {distance}") # Top and bottom edge of crosswalk top_edge = y bottom_edge = y + h if abs(tl_y - top_edge) < abs(tl_y - bottom_edge): crosswalk_edge_y = top_edge else: crosswalk_edge_y = bottom_edge except Exception as e: print(f"[ERROR] Crosswalk detection failed: {e}") crosswalk_bbox, violation_line_y, debug_info = None, None, {} else: print(f"[CROSSWALK] No traffic light detected (has_traffic_lights={has_traffic_lights}), skipping crosswalk detection") # NO crosswalk detection without traffic light violation_line_y = None # Check if crosswalk is detected crosswalk_detected = crosswalk_bbox is not None stop_line_detected = debug_info.get('stop_line') is not None # ALWAYS process vehicle tracking (moved outside violation logic) tracked_vehicles = [] if hasattr(self, 'vehicle_tracker') and self.vehicle_tracker is not None: try: # Filter vehicle detections vehicle_classes = ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle'] vehicle_dets = [] h, w = frame.shape[:2] print(f"[TRACK DEBUG] Processing {len(detections)} total detections") for det in detections: if (det.get('class_name') in vehicle_classes and 'bbox' in det and det.get('confidence', 0) > self.min_confidence_threshold): # Check bbox dimensions bbox = det['bbox'] x1, y1, x2, y2 = bbox box_w, box_h = x2-x1, y2-y1 box_area = box_w * box_h area_ratio = box_area / (w * h) print(f"[TRACK DEBUG] Vehicle {det.get('class_name')} conf={det.get('confidence'):.2f}, area_ratio={area_ratio:.4f}") if 0.001 <= area_ratio <= 0.25: vehicle_dets.append(det) print(f"[TRACK DEBUG] Added vehicle: {det.get('class_name')} conf={det.get('confidence'):.2f}") else: print(f"[TRACK DEBUG] Rejected vehicle: area_ratio={area_ratio:.4f} not in range [0.001, 0.25]") print(f"[TRACK DEBUG] Filtered to {len(vehicle_dets)} vehicle detections") # Update tracker if len(vehicle_dets) > 0: print(f"[TRACK DEBUG] Updating tracker with {len(vehicle_dets)} vehicles...") tracks = self.vehicle_tracker.update(vehicle_dets, frame) # Filter out tracks without bbox to avoid warnings valid_tracks = [] for track in tracks: bbox = None if isinstance(track, dict): bbox = track.get('bbox', None) else: bbox = getattr(track, 'bbox', None) if bbox is not None: valid_tracks.append(track) else: print(f"Warning: Track has no bbox, skipping: {track}") tracks = valid_tracks print(f"[TRACK DEBUG] Tracker returned {len(tracks)} tracks (after bbox filter)") else: print(f"[TRACK DEBUG] No vehicles to track, skipping tracker update") tracks = [] # Process each tracked vehicle tracked_vehicles = [] track_ids_seen = [] for track in tracks: track_id = track['id'] bbox = track['bbox'] x1, y1, x2, y2 = map(float, bbox) center_y = (y1 + y2) / 2 # Check for duplicate IDs if track_id in track_ids_seen: print(f"[TRACK ERROR] Duplicate ID detected: {track_id}") track_ids_seen.append(track_id) print(f"[TRACK DEBUG] Processing track ID={track_id} bbox={bbox}") # Initialize or update vehicle history if track_id not in self.vehicle_history: from collections import deque self.vehicle_history[track_id] = deque(maxlen=self.position_history_size) # Initialize vehicle status if not exists if track_id not in self.vehicle_statuses: self.vehicle_statuses[track_id] = { 'recent_movement': [], 'violation_history': [], 'crossed_during_red': False, 'last_position': None, # Track last position for jump detection 'suspicious_jumps': 0 # Count suspicious position jumps } # Detect suspicious position jumps (potential ID switches) if self.vehicle_statuses[track_id]['last_position'] is not None: last_y = self.vehicle_statuses[track_id]['last_position'] center_y = (y1 + y2) / 2 position_jump = abs(center_y - last_y) if position_jump > self.max_position_jump: self.vehicle_statuses[track_id]['suspicious_jumps'] += 1 print(f"[TRACK WARNING] Vehicle ID={track_id} suspicious position jump: {last_y:.1f} -> {center_y:.1f} (jump={position_jump:.1f})") # If too many suspicious jumps, reset violation status to be safe if self.vehicle_statuses[track_id]['suspicious_jumps'] > 2: print(f"[TRACK RESET] Vehicle ID={track_id} has too many suspicious jumps, resetting violation status") self.vehicle_statuses[track_id]['crossed_during_red'] = False self.vehicle_statuses[track_id]['suspicious_jumps'] = 0 # Update position history and last position self.vehicle_history[track_id].append(center_y) self.vehicle_statuses[track_id]['last_position'] = center_y # BALANCED movement detection - detect clear movement while avoiding false positives is_moving = False movement_detected = False if len(self.vehicle_history[track_id]) >= 3: # Require at least 3 frames for movement detection recent_positions = list(self.vehicle_history[track_id]) # Check movement over 3 frames for quick response if len(recent_positions) >= 3: movement_3frames = abs(recent_positions[-1] - recent_positions[-3]) if movement_3frames > self.movement_threshold: # More responsive threshold movement_detected = True print(f"[MOVEMENT] Vehicle ID={track_id} MOVING: 3-frame movement = {movement_3frames:.1f}") # Confirm with longer movement for stability (if available) if len(recent_positions) >= 5: movement_5frames = abs(recent_positions[-1] - recent_positions[-5]) if movement_5frames > self.movement_threshold * 1.5: # Moderate threshold for 5 frames movement_detected = True print(f"[MOVEMENT] Vehicle ID={track_id} MOVING: 5-frame movement = {movement_5frames:.1f}") # Store historical movement for smoothing - require consistent movement self.vehicle_statuses[track_id]['recent_movement'].append(movement_detected) if len(self.vehicle_statuses[track_id]['recent_movement']) > 4: # Shorter history for quicker response self.vehicle_statuses[track_id]['recent_movement'].pop(0) # BALANCED: Require majority of recent frames to show movement (2 out of 4) recent_movement_count = sum(self.vehicle_statuses[track_id]['recent_movement']) total_recent_frames = len(self.vehicle_statuses[track_id]['recent_movement']) if total_recent_frames >= 2 and recent_movement_count >= (total_recent_frames * 0.5): # 50% of frames must show movement is_moving = True print(f"[TRACK DEBUG] Vehicle ID={track_id} is_moving={is_moving} (threshold={self.movement_threshold})") # Initialize as not violating is_violation = False tracked_vehicles.append({ 'id': track_id, 'bbox': bbox, 'center_y': center_y, 'is_moving': is_moving, 'is_violation': is_violation }) print(f"[DEBUG] ByteTrack tracked {len(tracked_vehicles)} vehicles") for i, tracked in enumerate(tracked_vehicles): print(f" Vehicle {i}: ID={tracked['id']}, center_y={tracked['center_y']:.1f}, moving={tracked['is_moving']}, violating={tracked['is_violation']}") # DEBUG: Print all tracked vehicle IDs and their bboxes for this frame if tracked_vehicles: print(f"[DEBUG] All tracked vehicles this frame:") for v in tracked_vehicles: print(f" ID={v['id']} bbox={v['bbox']} center_y={v.get('center_y', 'NA')}") else: print("[DEBUG] No tracked vehicles this frame!") # Clean up old vehicle data current_track_ids = [tracked['id'] for tracked in tracked_vehicles] self._cleanup_old_vehicle_data(current_track_ids) except Exception as e: print(f"[ERROR] Vehicle tracking failed: {e}") import traceback traceback.print_exc() else: print("[WARN] ByteTrack vehicle tracker not available!") # Process violations - CHECK VEHICLES THAT CROSS THE LINE OVER A WINDOW OF FRAMES # IMPORTANT: Only process violations if traffic light is detected AND violation line exists if has_traffic_lights and violation_line_y is not None and tracked_vehicles: print(f"[VIOLATION DEBUG] Traffic light present, checking {len(tracked_vehicles)} vehicles against violation line at y={violation_line_y}") # Check each tracked vehicle for violations for tracked in tracked_vehicles: track_id = tracked['id'] center_y = tracked['center_y'] is_moving = tracked['is_moving'] # Get position history for this vehicle position_history = list(self.vehicle_history[track_id]) # Enhanced crossing detection: check over a window of frames line_crossed_in_window = False crossing_details = None if len(position_history) >= 2: window_size = min(self.crossing_check_window, len(position_history)) for i in range(1, window_size): prev_y = position_history[-(i+1)] # Earlier position curr_y = position_history[-i] # Later position # Check if vehicle crossed the line in this frame pair if prev_y < violation_line_y and curr_y >= violation_line_y: line_crossed_in_window = True crossing_details = { 'frames_ago': i, 'prev_y': prev_y, 'curr_y': curr_y, 'window_checked': window_size } print(f"[VIOLATION DEBUG] Vehicle ID={track_id} crossed line {i} frames ago: {prev_y:.1f} -> {curr_y:.1f}") break # Check if traffic light is red is_red_light = self.latest_traffic_light and self.latest_traffic_light.get('color') == 'red' print(f"[VIOLATION DEBUG] Vehicle ID={track_id}: latest_traffic_light={self.latest_traffic_light}, is_red_light={is_red_light}") print(f"[VIOLATION DEBUG] Vehicle ID={track_id}: position_history={[f'{p:.1f}' for p in position_history[-5:]]}"); # Show last 5 positions print(f"[VIOLATION DEBUG] Vehicle ID={track_id}: line_crossed_in_window={line_crossed_in_window}, crossing_details={crossing_details}") # Enhanced violation detection: vehicle crossed the line while moving and light is red actively_crossing = (line_crossed_in_window and is_moving and is_red_light) # Initialize violation status for new vehicles if 'crossed_during_red' not in self.vehicle_statuses[track_id]: self.vehicle_statuses[track_id]['crossed_during_red'] = False # Mark vehicle as having crossed during red if it actively crosses if actively_crossing: # Additional validation: ensure it's not a false positive from ID switch suspicious_jumps = self.vehicle_statuses[track_id].get('suspicious_jumps', 0) if suspicious_jumps <= 1: # Allow crossing if not too many suspicious jumps self.vehicle_statuses[track_id]['crossed_during_red'] = True print(f"[VIOLATION ALERT] Vehicle ID={track_id} CROSSED line during red light!") print(f" -> Crossing details: {crossing_details}") else: print(f"[VIOLATION IGNORED] Vehicle ID={track_id} crossing ignored due to {suspicious_jumps} suspicious jumps") # IMPORTANT: Reset violation status when light turns green (regardless of position) if not is_red_light: if self.vehicle_statuses[track_id]['crossed_during_red']: print(f"[VIOLATION RESET] Vehicle ID={track_id} violation status reset (light turned green)") self.vehicle_statuses[track_id]['crossed_during_red'] = False # Vehicle is violating ONLY if it crossed during red and light is still red is_violation = (self.vehicle_statuses[track_id]['crossed_during_red'] and is_red_light) # Track current violation state for analytics - only actual crossings self.vehicle_statuses[track_id]['violation_history'].append(actively_crossing) if len(self.vehicle_statuses[track_id]['violation_history']) > 5: self.vehicle_statuses[track_id]['violation_history'].pop(0) print(f"[VIOLATION DEBUG] Vehicle ID={track_id}: center_y={center_y:.1f}, line={violation_line_y}") print(f" history_window={[f'{p:.1f}' for p in position_history[-self.crossing_check_window:]]}") print(f" moving={is_moving}, red_light={is_red_light}") print(f" actively_crossing={actively_crossing}, crossed_during_red={self.vehicle_statuses[track_id]['crossed_during_red']}") print(f" suspicious_jumps={self.vehicle_statuses[track_id].get('suspicious_jumps', 0)}") print(f" FINAL_VIOLATION={is_violation}") # Update violation status tracked['is_violation'] = is_violation if actively_crossing and self.vehicle_statuses[track_id].get('suspicious_jumps', 0) <= 1: # Only add if not too many suspicious jumps # Add to violating vehicles set violating_vehicle_ids.add(track_id) # Add to violations list timestamp = datetime.now() # Keep as datetime object, not string violations.append({ 'track_id': track_id, 'id': track_id, 'bbox': [int(tracked['bbox'][0]), int(tracked['bbox'][1]), int(tracked['bbox'][2]), int(tracked['bbox'][3])], 'violation': 'line_crossing', 'violation_type': 'line_crossing', # Add this for analytics compatibility 'timestamp': timestamp, 'line_position': violation_line_y, 'movement': crossing_details if crossing_details else {'prev_y': center_y, 'current_y': center_y}, 'crossing_window': self.crossing_check_window, 'position_history': list(position_history[-10:]) # Include recent history for debugging }) print(f"[DEBUG] 🚨 VIOLATION DETECTED: Vehicle ID={track_id} CROSSED VIOLATION LINE") print(f" Enhanced detection: {crossing_details}") print(f" Position history: {[f'{p:.1f}' for p in position_history[-10:]]}") print(f" Detection window: {self.crossing_check_window} frames") print(f" while RED LIGHT & MOVING") # --- ENHANCED VIOLATION DETECTION: Add new real-world scenarios --- # 1. Pedestrian right-of-way violation (blocking crosswalk during green) # 2. Improper stopping over crosswalk at red # 3. Accelerating through yellow/amber light pedestrian_dets = [det for det in detections if det.get('class_name') == 'person' and 'bbox' in det] pedestrian_tracks = [] for ped in pedestrian_dets: x1, y1, x2, y2 = ped['bbox'] center = ((x1 + x2) // 2, (y1 + y2) // 2) pedestrian_tracks.append({'bbox': ped['bbox'], 'center': center}) # Prepare crosswalk polygon for point-in-polygon checks crosswalk_poly = None if crosswalk_bbox is not None: x, y, w, h = crosswalk_bbox crosswalk_poly = (x, y, w, h) stopline_poly = crosswalk_poly # For simplicity, use crosswalk as stopline # Track amber/yellow light start time amber_start_time = getattr(self, 'amber_start_time', None) latest_light_color = self.latest_traffic_light.get('color') if isinstance(self.latest_traffic_light, dict) else self.latest_traffic_light if latest_light_color == 'yellow' and amber_start_time is None: amber_start_time = time.time() self.amber_start_time = amber_start_time elif latest_light_color != 'yellow': self.amber_start_time = None # Vehicle position history for speed calculation vehicle_position_history = {} for track in tracked_vehicles: track_id = track['id'] bbox = track['bbox'] x1, y1, x2, y2 = bbox center = ((x1 + x2) // 2, (y1 + y2) // 2) # Store (center, timestamp) if track_id not in vehicle_position_history: vehicle_position_history[track_id] = [] vehicle_position_history[track_id].append((center, time.time())) track['center'] = center # --- 1. Pedestrian right-of-way violation --- if crosswalk_poly and latest_light_color == 'green' and pedestrian_tracks: for track in tracked_vehicles: # Only consider moving vehicles - stopped vehicles aren't likely to be violating if track.get('is_moving', False) and point_in_polygon(track['center'], crosswalk_poly): # Check for pedestrians in the crosswalk for ped in pedestrian_tracks: if point_in_polygon(ped['center'], crosswalk_poly): # Vehicle is blocking crosswalk during green with pedestrian present violations.append({ 'track_id': track['id'], 'id': track['id'], 'bbox': [int(track['bbox'][0]), int(track['bbox'][1]), int(track['bbox'][2]), int(track['bbox'][3])], 'violation': 'pedestrian_right_of_way', 'violation_type': 'pedestrian_right_of_way', 'timestamp': datetime.now(), 'details': { 'pedestrian_bbox': ped['bbox'], 'crosswalk_bbox': crosswalk_bbox, 'is_moving': track.get('is_moving', False), 'traffic_light': latest_light_color } }) print(f"[VIOLATION] Pedestrian right-of-way violation: Vehicle ID={track['id']} blocking crosswalk during green light with pedestrian present") # --- 2. Improper stopping over crosswalk at red --- if crosswalk_poly and latest_light_color == 'red': for track in tracked_vehicles: # Check if vehicle is not moving (to confirm it's stopped) is_stopped = not track.get('is_moving', True) if is_stopped and point_in_polygon(track['center'], crosswalk_poly): # Calculate overlap ratio between vehicle and crosswalk vx1, vy1, vx2, vy2 = track['bbox'] cx, cy, cw, ch = crosswalk_poly overlap_x1 = max(vx1, cx) overlap_y1 = max(vy1, cy) overlap_x2 = min(vx2, cx + cw) overlap_y2 = min(vy2, cy + ch) overlap_area = max(0, overlap_x2 - overlap_x1) * max(0, overlap_y2 - overlap_y1) vehicle_area = (vx2 - vx1) * (vy2 - vy1) overlap_ratio = overlap_area / max(vehicle_area, 1) # Double-verify that vehicle is stopped by checking explicit speed speed = 0.0 hist = vehicle_position_history.get(track['id'], []) if len(hist) >= 2: (c1, t1), (c2, t2) = hist[-2], hist[-1] dist = ((c2[0]-c1[0])**2 + (c2[1]-c1[1])**2)**0.5 dt = max(t2-t1, 1e-3) speed = dist / dt # Vehicle must have significant overlap with crosswalk (>25%) and be stopped if overlap_ratio > 0.25 and speed < 0.8: violations.append({ 'track_id': track['id'], 'id': track['id'], 'bbox': [int(track['bbox'][0]), int(track['bbox'][1]), int(track['bbox'][2]), int(track['bbox'][3])], 'violation': 'stop_on_crosswalk', 'violation_type': 'stop_on_crosswalk', 'timestamp': datetime.now(), 'details': { 'overlap_ratio': overlap_ratio, 'speed': speed, 'crosswalk_bbox': crosswalk_bbox, 'traffic_light': latest_light_color, 'is_moving_flag': track.get('is_moving', None) } }) print(f"[VIOLATION] Improper stop on crosswalk: Vehicle ID={track['id']} stopped on crosswalk during red light (overlap={overlap_ratio:.2f}, speed={speed:.2f})") # --- 3. Accelerating through yellow/amber light --- if stopline_poly and latest_light_color == 'yellow' and amber_start_time: # Calculate time since light turned yellow current_time = time.time() time_since_yellow = current_time - amber_start_time # Speed threshold (in pixels per second) - can be adjusted based on testing speed_limit_px_per_sec = 8.0 # Check each vehicle approaching the intersection for track in tracked_vehicles: # Check if vehicle is near the stop line/intersection if point_in_polygon(track['center'], stopline_poly) or ( track['center'][1] < stopline_poly[1] + stopline_poly[3] + 50 and track['center'][1] > stopline_poly[1] - 50 ): # If the vehicle is moving (confirmed via tracker) if track.get('is_moving', False): # Calculate acceleration by looking at recent speed changes hist = vehicle_position_history.get(track['id'], []) if len(hist) >= 3: # Calculate speeds at different time points (c1, t1), (c2, t2), (c3, t3) = hist[-3], hist[-2], hist[-1] # Speed at earlier point v1 = ((c2[0]-c1[0])**2 + (c2[1]-c1[1])**2)**0.5 / max(t2-t1, 1e-3) # Speed at later point v2 = ((c3[0]-c2[0])**2 + (c3[1]-c2[1])**2)**0.5 / max(t3-t2, 1e-3) # Acceleration violation if: # 1. Speed increases significantly (>20%) # 2. Final speed exceeds threshold # 3. Yellow light is less than 3 seconds old (typical acceleration window) if v2 > v1 * 1.2 and v2 > speed_limit_px_per_sec and time_since_yellow < 3.0: violations.append({ 'track_id': track['id'], 'id': track['id'], 'bbox': [int(track['bbox'][0]), int(track['bbox'][1]), int(track['bbox'][2]), int(track['bbox'][3])], 'violation': 'amber_acceleration', 'violation_type': 'amber_acceleration', 'timestamp': datetime.now(), 'details': { 'speed_before': v1, 'speed_after': v2, 'acceleration': (v2-v1)/max(t3-t2, 1e-3), 'time_since_yellow': time_since_yellow, 'traffic_light': latest_light_color } }) print(f"[VIOLATION] Amber light acceleration: Vehicle ID={track['id']} accelerated from {v1:.2f} to {v2:.2f} px/sec {time_since_yellow:.1f}s after yellow light") # Emit progress signal after processing each frame if hasattr(self, 'progress_ready'): self.progress_ready.emit(int(cap.get(cv2.CAP_PROP_POS_FRAMES)), int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), time.time()) # Draw detections with bounding boxes - NOW with violation info # Only show traffic light and vehicle classes allowed_classes = ['traffic light', 'car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle'] filtered_detections = [det for det in detections if det.get('class_name') in allowed_classes] print(f"Drawing {len(filtered_detections)} detection boxes on frame (filtered)") # Statistics for debugging vehicles_with_ids = 0 vehicles_without_ids = 0 vehicles_moving = 0 vehicles_violating = 0 if detections and len(detections) > 0: # Only show traffic light and vehicle classes allowed_classes = ['traffic light', 'car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle'] filtered_detections = [det for det in detections if det.get('class_name') in allowed_classes] print(f"Drawing {len(filtered_detections)} detection boxes on frame (filtered)") # Statistics for debugging vehicles_with_ids = 0 vehicles_without_ids = 0 vehicles_moving = 0 vehicles_violating = 0 for det in filtered_detections: if 'bbox' in det: bbox = det['bbox'] x1, y1, x2, y2 = map(int, bbox) label = det.get('class_name', 'object') confidence = det.get('confidence', 0.0) # Robustness: ensure label and confidence are not None if label is None: label = 'object' if confidence is None: confidence = 0.0 class_id = det.get('class_id', -1) # Check if this detection corresponds to a violating or moving vehicle det_center_x = (x1 + x2) / 2 det_center_y = (y1 + y2) / 2 is_violating_vehicle = False is_moving_vehicle = False vehicle_id = None # Match detection with tracked vehicles - IMPROVED MATCHING if label in ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle'] and len(tracked_vehicles) > 0: print(f"[MATCH DEBUG] Attempting to match {label} detection at ({det_center_x:.1f}, {det_center_y:.1f}) with {len(tracked_vehicles)} tracked vehicles") best_match = None best_distance = float('inf') best_iou = 0.0 for i, tracked in enumerate(tracked_vehicles): track_bbox = tracked['bbox'] track_x1, track_y1, track_x2, track_y2 = map(float, track_bbox) # Calculate center distance track_center_x = (track_x1 + track_x2) / 2 track_center_y = (track_y1 + track_y2) / 2 center_distance = ((det_center_x - track_center_x)**2 + (det_center_y - track_center_y)**2)**0.5 # Calculate IoU (Intersection over Union) intersection_x1 = max(x1, track_x1) intersection_y1 = max(y1, track_y1) intersection_x2 = min(x2, track_x2) intersection_y2 = min(y2, track_y2) if intersection_x2 > intersection_x1 and intersection_y2 > intersection_y1: intersection_area = (intersection_x2 - intersection_x1) * (intersection_y2 - intersection_y1) det_area = (x2 - x1) * (y2 - y1) track_area = (track_x2 - track_x1) * (track_y2 - track_y1) union_area = det_area + track_area - intersection_area iou = intersection_area / union_area if union_area > 0 else 0 else: iou = 0 print(f"[MATCH DEBUG] Track {i}: ID={tracked['id']}, center=({track_center_x:.1f}, {track_center_y:.1f}), distance={center_distance:.1f}, IoU={iou:.3f}") # Use stricter matching criteria - prioritize IoU over distance # Good match if: high IoU OR close center distance with some overlap is_good_match = (iou > 0.3) or (center_distance < 60 and iou > 0.1) if is_good_match: print(f"[MATCH DEBUG] Track {i} is a good match (IoU={iou:.3f}, distance={center_distance:.1f})") # Prefer higher IoU, then lower distance match_score = iou + (100 - min(center_distance, 100)) / 100 # Composite score if iou > best_iou or (iou == best_iou and center_distance < best_distance): best_distance = center_distance best_iou = iou best_match = tracked else: print(f"[MATCH DEBUG] Track {i} failed matching criteria (IoU={iou:.3f}, distance={center_distance:.1f})") if best_match: vehicle_id = best_match['id'] is_moving_vehicle = best_match.get('is_moving', False) is_violating_vehicle = best_match.get('is_violation', False) print(f"[MATCH SUCCESS] Detection at ({det_center_x:.1f},{det_center_y:.1f}) matched with track ID={vehicle_id}") print(f" -> STATUS: moving={is_moving_vehicle}, violating={is_violating_vehicle}, IoU={best_iou:.3f}, distance={best_distance:.1f}") else: print(f"[MATCH FAILED] No suitable match found for {label} detection at ({det_center_x:.1f}, {det_center_y:.1f})") print(f" -> Will draw as untracked detection with default color") else: if label not in ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle']: print(f"[MATCH DEBUG] Skipping matching for non-vehicle label: {label}") elif len(tracked_vehicles) == 0: print(f"[MATCH DEBUG] No tracked vehicles available for matching") else: try: if len(tracked_vehicles) > 0: distances = [((det_center_x - (t['bbox'][0] + t['bbox'][2])/2)**2 + (det_center_y - (t['bbox'][1] + t['bbox'][3])/2)**2)**0.5 for t in tracked_vehicles[:3]] print(f"[DEBUG] No match found for detection at ({det_center_x:.1f},{det_center_y:.1f}) - distances: {distances}") else: print(f"[DEBUG] No tracked vehicles available to match detection at ({det_center_x:.1f},{det_center_y:.1f})") except NameError: print(f"[DEBUG] No match found for detection (coords unavailable)") if len(tracked_vehicles) > 0: print(f"[DEBUG] Had {len(tracked_vehicles)} tracked vehicles available") # Choose box color based on vehicle status # PRIORITY: 1. Violating (RED) - crossed during red light 2. Moving (ORANGE) 3. Stopped (GREEN) if is_violating_vehicle and vehicle_id is not None: box_color = (0, 0, 255) # RED for violating vehicles (crossed line during red) label_text = f"{label}:ID{vehicle_id}⚠ī¸" thickness = 4 vehicles_violating += 1 print(f"[COLOR DEBUG] Drawing RED box for VIOLATING vehicle ID={vehicle_id} (crossed during red)") elif is_moving_vehicle and vehicle_id is not None and not is_violating_vehicle: box_color = (0, 165, 255) # ORANGE for moving vehicles (not violating) label_text = f"{label}:ID{vehicle_id}" thickness = 3 vehicles_moving += 1 print(f"[COLOR DEBUG] Drawing ORANGE box for MOVING vehicle ID={vehicle_id} (not violating)") elif label in ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle'] and vehicle_id is not None: box_color = (0, 255, 0) # Green for stopped vehicles label_text = f"{label}:ID{vehicle_id}" thickness = 2 print(f"[COLOR DEBUG] Drawing GREEN box for STOPPED vehicle ID={vehicle_id}") elif is_traffic_light(label): box_color = (0, 0, 255) # Red for traffic lights label_text = f"{label}" thickness = 2 else: box_color = (0, 255, 0) # Default green for other objects label_text = f"{label}" thickness = 2 # Update statistics if label in ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle']: if vehicle_id is not None: vehicles_with_ids += 1 else: vehicles_without_ids += 1 # Draw rectangle and label cv2.rectangle(annotated_frame, (x1, y1), (x2, y2), box_color, thickness) cv2.putText(annotated_frame, label_text, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, box_color, 2) # id_text = f"ID: {det['id']}" # # Calculate text size for background # (tw, th), baseline = cv2.getTextSize(id_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2) # # Draw filled rectangle for background (top-left of bbox) # cv2.rectangle(annotated_frame, (x1, y1 - th - 8), (x1 + tw + 4, y1), (0, 0, 0), -1) # # Draw the ID text in bold yellow # cv2.putText(annotated_frame, id_text, (x1 + 2, y1 - 4), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA) # print(f"[DEBUG] Detection ID: {det['id']} BBOX: {bbox} CLASS: {label} CONF: {confidence:.2f}") if class_id == 9 or is_traffic_light(label): try: light_info = detect_traffic_light_color(annotated_frame, [x1, y1, x2, y2]) if light_info.get("color", "unknown") == "unknown": light_info = ensure_traffic_light_color(annotated_frame, [x1, y1, x2, y2]) det['traffic_light_color'] = light_info # Draw enhanced traffic light status annotated_frame = draw_traffic_light_status(annotated_frame, bbox, light_info) # --- Update latest_traffic_light for UI/console --- self.latest_traffic_light = light_info # Add a prominent traffic light status at the top of the frame color = light_info.get('color', 'unknown') confidence = light_info.get('confidence', 0.0) if color == 'red': status_color = (0, 0, 255) # Red status_text = f"Traffic Light: RED ({confidence:.2f})" # Draw a prominent red banner across the top banner_height = 40 cv2.rectangle(annotated_frame, (0, 0), (annotated_frame.shape[1], banner_height), (0, 0, 150), -1) # Add text font = cv2.FONT_HERSHEY_DUPLEX font_scale = 0.9 font_thickness = 2 cv2.putText(annotated_frame, status_text, (10, banner_height-12), font, font_scale, (255, 255, 255), font_thickness) except Exception as e: print(f"[WARN] Could not detect/draw traffic light color: {e}") # Print statistics summary print(f"[STATS] Vehicles: {vehicles_with_ids} with IDs, {vehicles_without_ids} without IDs") # Handle multiple traffic lights with consensus approach for det in detections: if is_traffic_light(det.get('class_name')): has_traffic_lights = True if 'traffic_light_color' in det: light_info = det['traffic_light_color'] traffic_lights.append({'bbox': det['bbox'], 'color': light_info.get('color', 'unknown'), 'confidence': light_info.get('confidence', 0.0)}) # Determine the dominant traffic light color based on confidence if traffic_lights: # Filter to just red lights and sort by confidence red_lights = [tl for tl in traffic_lights if tl.get('color') == 'red'] if red_lights: # Use the highest confidence red light for display highest_conf_red = max(red_lights, key=lambda x: x.get('confidence', 0)) # Update the global traffic light status for consistent UI display self.latest_traffic_light = { 'color': 'red', 'confidence': highest_conf_red.get('confidence', 0.0) } # Emit all violations as a batch for UI (optional) if violations: if hasattr(self, 'violations_batch_ready'): self.violations_batch_ready.emit(violations) # Emit individual violation signals for each violation for violation in violations: print(f"🚨 Emitting RED LIGHT VIOLATION: Track ID {violation['track_id']}") violation['frame'] = frame violation['violation_line_y'] = violation_line_y self.violation_detected.emit(violation) print(f"[DEBUG] Emitted {len(violations)} violation signals") # Add FPS display directly on frame # cv2.putText(annotated_frame, f"FPS: {fps_smoothed:.1f}", (10, 30), # cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) # # --- Always draw detected traffic light color indicator at top --- # color = self.latest_traffic_light.get('color', 'unknown') if isinstance(self.latest_traffic_light, dict) else str(self.latest_traffic_light) # confidence = self.latest_traffic_light.get('confidence', 0.0) if isinstance(self.latest_traffic_light, dict) else 0.0 # indicator_size = 30 # margin = 10 # status_colors = { # "red": (0, 0, 255), # "yellow": (0, 255, 255), # "green": (0, 255, 0), # "unknown": (200, 200, 200) # } # draw_color = status_colors.get(color, (200, 200, 200)) # # Draw circle indicator # cv2.circle( # annotated_frame, # (annotated_frame.shape[1] - margin - indicator_size, margin + indicator_size), # indicator_size, # draw_color, # -1 # ) # # Add color text # cv2.putText( # annotated_frame, # f"{color.upper()} ({confidence:.2f})", # (annotated_frame.shape[1] - margin - indicator_size - 120, margin + indicator_size + 10), # cv2.FONT_HERSHEY_SIMPLEX, # 0.7, # (0, 0, 0), # 2 # ) # Signal for raw data subscribers (now without violations) # Emit with correct number of arguments try: self.raw_frame_ready.emit(frame.copy(), detections, fps_smoothed) print(f"✅ raw_frame_ready signal emitted with {len(detections)} detections, fps={fps_smoothed:.1f}") except Exception as e: print(f"❌ Error emitting raw_frame_ready: {e}") import traceback traceback.print_exc() # Emit the NumPy frame signal for direct display - annotated version for visual feedback print(f"🔴 Emitting frame_np_ready signal with annotated_frame shape: {annotated_frame.shape}") try: # Make sure the frame can be safely transmitted over Qt's signal system # Create a contiguous copy of the array frame_copy = np.ascontiguousarray(annotated_frame) print(f"🔍 Debug - Before emission: frame_copy type={type(frame_copy)}, shape={frame_copy.shape}, is_contiguous={frame_copy.flags['C_CONTIGUOUS']}") self.frame_np_ready.emit(frame_copy) print("✅ frame_np_ready signal emitted successfully") except Exception as e: print(f"❌ Error emitting frame: {e}") import traceback traceback.print_exc() # Emit QPixmap for video detection tab (frame_ready) try: from PySide6.QtGui import QImage, QPixmap rgb_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB) h, w, ch = rgb_frame.shape bytes_per_line = ch * w qimg = QImage(rgb_frame.data, w, h, bytes_per_line, QImage.Format_RGB888) pixmap = QPixmap.fromImage(qimg) metrics = { 'FPS': fps_smoothed, 'Detection (ms)': detection_time } self.frame_ready.emit(pixmap, detections, metrics) print("✅ frame_ready signal emitted for video detection tab") except Exception as e: print(f"❌ Error emitting frame_ready: {e}") import traceback traceback.print_exc() # Emit stats signal for performance monitoring # Count traffic lights for UI (confidence >= 0.5) traffic_light_count = 0 for det in detections: if is_traffic_light(det.get('class_name')): tl_conf = 0.0 if 'traffic_light_color' in det and isinstance(det['traffic_light_color'], dict): tl_conf = det['traffic_light_color'].get('confidence', 0.0) if tl_conf >= 0.5: traffic_light_count += 1 # Count cars for UI (confidence >= 0.5) car_count = 0 for det in detections: if det.get('class_name') == 'car' and det.get('confidence', 0.0) >= 0.5: car_count += 1 stats = { 'fps': fps_smoothed, 'detection_fps': fps_smoothed, # Numeric value for analytics 'detection_time': detection_time, 'detection_time_ms': detection_time, # Numeric value for analytics 'traffic_light_color': self.latest_traffic_light, 'tlights': traffic_light_count, # Only confident traffic lights 'cars': car_count # Only confident cars } # Print detailed stats for debugging tl_color = "unknown" if isinstance(self.latest_traffic_light, dict): tl_color = self.latest_traffic_light.get('color', 'unknown') elif isinstance(self.latest_traffic_light, str): tl_color = self.latest_traffic_light print(f"đŸŸĸ Stats Updated: FPS={fps_smoothed:.2f}, Inference={detection_time:.2f}ms, Traffic Light={tl_color}") # Emit stats signal self.stats_ready.emit(stats) # Emit performance stats for performance graphs perf_stats = { 'frame_idx': self.frame_count, 'fps': fps_smoothed, 'inference_time': detection_time, 'device': getattr(self, 'current_device', 'CPU'), 'resolution': getattr(self, 'current_resolution', f'{frame.shape[1]}x{frame.shape[0]}' if frame is not None else '-'), 'is_spike': False, # TODO: Add spike logic if needed 'is_res_change': False, # TODO: Add res change logic if needed 'cpu_spike': False, # TODO: Add cpu spike logic if needed } print(f"[PERF] Emitting performance_stats_ready: {perf_stats}") self.performance_stats_ready.emit(perf_stats) # --- Update last analysis data for VLM --- self._last_analysis_data = { 'detections': detections, 'tracked_vehicles': tracked_vehicles if 'tracked_vehicles' in locals() else [], 'traffic_light': self.latest_traffic_light, 'crosswalk_bbox': crosswalk_bbox if 'crosswalk_bbox' in locals() else None, 'violation_line_y': violation_line_y if 'violation_line_y' in locals() else None, 'crosswalk_detected': crosswalk_bbox is not None if 'crosswalk_bbox' in locals() else False, 'traffic_light_position': traffic_light_position if has_traffic_lights else None, 'frame_shape': frame.shape if frame is not None else None, 'timestamp': time.time() } # --- Ensure analytics update every frame --- # Always add traffic_light_color to each detection dict for analytics for det in detections: if is_traffic_light(det.get('class_name')): if 'traffic_light_color' not in det: det['traffic_light_color'] = self.latest_traffic_light if hasattr(self, 'latest_traffic_light') else {'color': 'unknown', 'confidence': 0.0} if hasattr(self, 'analytics_controller') and self.analytics_controller is not None: try: self.analytics_controller.process_frame_data(frame, detections, stats) print("[DEBUG] Called analytics_controller.process_frame_data for analytics update") except Exception as e: print(f"[ERROR] Could not update analytics: {e}") # Control processing rate for file sources if isinstance(self.source, str) and self.source_fps > 0: frame_duration = time.time() - process_start if frame_duration < frame_time: time.sleep(frame_time - frame_duration) cap.release() except Exception as e: print(f"Video processing error: {e}") import traceback traceback.print_exc() finally: self._running = False def _process_frame(self): """Process current frame for display with improved error handling""" try: self.mutex.lock() if self.current_frame is None: print("⚠ī¸ No frame available to process") self.mutex.unlock() # Check if we're running - if not, this is expected behavior if not self._running: return # If we are running but have no frame, create a blank frame with error message h, w = 480, 640 # Default size blank_frame = np.zeros((h, w, 3), dtype=np.uint8) cv2.putText(blank_frame, "No video input", (w//2-100, h//2), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) # Emit this blank frame try: self.frame_np_ready.emit(blank_frame) except Exception as e: print(f"Error emitting blank frame: {e}") return # Make a copy of the data we need try: frame = self.current_frame.copy() detections = self.current_detections.copy() if self.current_detections else [] metrics = self.performance_metrics.copy() except Exception as e: print(f"Error copying frame data: {e}") self.mutex.unlock() return self.mutex.unlock() except Exception as e: print(f"Critical error in _process_frame initialization: {e}") import traceback traceback.print_exc() try: self.mutex.unlock() except: pass return try: # --- Simplified frame processing for display --- # The violation logic is now handled in the main _run thread # This method just handles basic display overlays annotated_frame = frame.copy() # Add performance overlays and debug markers - COMMENTED OUT for clean video display # annotated_frame = draw_performance_overlay(annotated_frame, metrics) # cv2.circle(annotated_frame, (20, 20), 10, (255, 255, 0), -1) # Convert BGR to RGB before display (for PyQt/PySide) frame_rgb = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB) # Display the RGB frame in the UI (replace with your display logic) # Example: self.image_label.setPixmap(QPixmap.fromImage(QImage(frame_rgb.data, w, h, QImage.Format_RGB888))) except Exception as e: print(f"Error in _process_frame: {e}") import traceback traceback.print_exc() def _cleanup_old_vehicle_data(self, current_track_ids): """ Clean up tracking data for vehicles that are no longer being tracked. This prevents memory leaks and improves performance. Args: current_track_ids: Set of currently active track IDs """ # Find IDs that are no longer active old_ids = set(self.vehicle_history.keys()) - set(current_track_ids) if old_ids: print(f"[CLEANUP] Removing tracking data for {len(old_ids)} old vehicle IDs: {sorted(old_ids)}") for old_id in old_ids: # Remove from history and status tracking if old_id in self.vehicle_history: del self.vehicle_history[old_id] if old_id in self.vehicle_statuses: del self.vehicle_statuses[old_id] print(f"[CLEANUP] Now tracking {len(self.vehicle_history)} active vehicles") # --- Removed unused internal violation line detection methods and RedLightViolationSystem usage --- def play(self): """Alias for start(), for UI compatibility.""" self.start() def pause(self): """Pause video processing.""" print("[VideoController] Pause requested") self.mutex.lock() self._paused = True self.mutex.unlock() # Emit the last captured frame for VLM analysis if available if hasattr(self, '_last_frame') and self._last_frame is not None: print("[VideoController] Emitting last frame for VLM analysis during pause") try: # Emit the last frame with empty detections for VLM self.raw_frame_ready.emit(self._last_frame.copy(), [], 0.0) print("✅ Last frame emitted for VLM analysis") except Exception as e: print(f"❌ Error emitting last frame: {e}") else: print("[VideoController] No last frame available for VLM analysis") # Emit pause state signal self.pause_state_changed.emit(True) print("[VideoController] Pause state signal emitted: True") def resume(self): """Resume video processing from pause.""" print("[VideoController] Resume requested") self.mutex.lock() self._paused = False self.pause_condition.wakeAll() # Wake up any waiting threads self.mutex.unlock() # Emit pause state signal self.pause_state_changed.emit(False) print("[VideoController] Pause state signal emitted: False") def get_current_analysis_data(self): """Get current analysis data for VLM insights.""" return self._last_analysis_data.copy() if self._last_analysis_data else {} def _on_scene_object_detected(self, obj_data: dict): """Handle scene object detection signal""" try: # Forward scene object detection to analytics print(f"[SCENE] Object detected: {obj_data.get('category', 'unknown')} " f"(confidence: {obj_data.get('confidence', 0):.2f})") except Exception as e: print(f"Error handling scene object detection: {e}") def _on_scene_analytics_updated(self, analytics_data: dict): """Handle scene analytics update signal""" try: # Forward scene analytics to performance stats camera_id = analytics_data.get('camera_id', 'unknown') fps = analytics_data.get('fps', 0) processing_time = analytics_data.get('processing_time_ms', 0) object_count = analytics_data.get('object_count', 0) # Update performance metrics with scene analytics self.performance_metrics['Scene_FPS'] = fps self.performance_metrics['Scene_Objects'] = object_count self.performance_metrics['Scene_Processing_ms'] = processing_time print(f"[SCENE] Analytics updated - FPS: {fps:.1f}, Objects: {object_count}, " f"Processing: {processing_time:.1f}ms") except Exception as e: print(f"Error handling scene analytics update: {e}") def _on_roi_event_detected(self, event_data: dict): """Handle ROI event detection signal""" try: event_type = event_data.get('type', 'unknown') roi_id = event_data.get('roi_id', 'unknown') object_category = event_data.get('object_category', 'unknown') print(f"[SCENE] ROI Event: {event_type} in {roi_id} - {object_category}") # Emit as violation if it's a safety-related event if 'safety' in event_type.lower() or 'violation' in event_type.lower(): violation_data = { 'type': 'roi_violation', 'roi_id': roi_id, 'object_category': object_category, 'timestamp': event_data.get('timestamp'), 'confidence': event_data.get('confidence', 1.0), 'source': 'scene_analytics' } self.violation_detected.emit(violation_data) except Exception as e: print(f"Error handling ROI event: {e}") @Slot(str) def on_model_switched(self, device): """Handle device switch from config panel.""" try: print(f"🔄 Video Controller: Device switch requested to {device}") # Update our device reference self.current_device = device print(f"✅ Video Controller: current_device updated to {device}") # If we have a model manager, the device switch should already be done # Just log the current state for verification if self.model_manager and hasattr(self.model_manager, 'detector'): if hasattr(self.model_manager.detector, 'device'): current_device = self.model_manager.detector.device print(f"✅ Video Controller: Model manager detector now using device: {current_device}") else: print(f"✅ Video Controller: Model manager detector updated to {device}") print(f"✅ Video Controller: Device switch to {device} completed") except Exception as e: print(f"❌ Video Controller: Error during device switch: {e}")