from PySide6.QtCore import QObject, Signal, QThread, Qt, QMutex, QWaitCondition, QTimer 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 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.source = None self.source_type = None self.source_fps = 0 self.performance_metrics = {} self.mutex = QMutex() # 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 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() 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): """ Get properties of video source Returns: bool: True if source was successfully opened, False otherwise """ try: print(f"🔍 Opening video source for properties check: {self.source}") cap = cv2.VideoCapture(self.source) # Verify capture opened successfully if not cap.isOpened(): print(f"❌ Failed to open video source: {self.source}") return False # Read properties self.source_fps = cap.get(cv2.CAP_PROP_FPS) if self.source_fps <= 0: print("⚠ī¸ Source FPS not available, using default 30 FPS") self.source_fps = 30.0 # Default if undetectable 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)) # Try reading a test frame to confirm source is truly working ret, test_frame = cap.read() if not ret or test_frame is None: print("⚠ī¸ Could not read test frame from source") # For camera sources, try one more time with delay if self.source_type == "camera": print("🔄 Retrying camera initialization...") time.sleep(1.0) # Wait a moment for camera to initialize 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 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 while self._running and cap.isOpened(): 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 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() # 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: # Check for crossing over the last N frames (configurable window) 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") # 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 (always define, even if no detections) 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") print(f"[STATS] Moving: {vehicles_moving}, Violating: {vehicles_violating}") # 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 individual violation signals for each violation if violations: for violation in violations: print(f"🚨 Emitting RED LIGHT VIOLATION: Track ID {violation['track_id']}") # Add additional data to the violation 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 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 } # 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) # --- Ensure analytics update every frame --- 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 [] violations = [] # Violations are disabled 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() from PySide6.QtCore import QObject, Signal, QThread, Qt, QMutex, QWaitCondition, QTimer 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 import traceback # Add this at the top for exception printing # Add parent directory to path for imports sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) 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 ) from utils.enhanced_annotation_utils import ( enhanced_draw_detections, draw_performance_overlay, enhanced_cv_to_qimage, enhanced_cv_to_pixmap ) 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' # Vehicle classes 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' # Add more as needed 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 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 (for video progress bar) def __init__(self, model_manager=None): print("[DEBUG] VideoController __init__ called") """ Initialize video controller. Args: model_manager: Model manager instance for detection and violation """ super().__init__() self._running = False self.source = None self.source_type = None self.source_fps = 0 self.performance_metrics = {} self.mutex = QMutex() # 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 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 } # 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() # Playback control variables self.playback_position = 0 # Current position in the video (in milliseconds) self.detection_enabled = True # Detection enabled/disabled flag 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): """ Get properties of video source Returns: bool: True if source was successfully opened, False otherwise """ try: print(f"🔍 Opening video source for properties check: {self.source}") cap = cv2.VideoCapture(self.source) # Verify capture opened successfully if not cap.isOpened(): print(f"❌ Failed to open video source: {self.source}") return False # Read properties self.source_fps = cap.get(cv2.CAP_PROP_FPS) if self.source_fps <= 0: print("⚠ī¸ Source FPS not available, using default 30 FPS") self.source_fps = 30.0 # Default if undetectable 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)) # Try reading a test frame to confirm source is truly working ret, test_frame = cap.read() if not ret or test_frame is None: print("⚠ī¸ Could not read test frame from source") # For camera sources, try one more time with delay if self.source_type == "camera": print("🔄 Retrying camera initialization...") time.sleep(1.0) # Wait a moment for camera to initialize 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()}") def stop(self): """Stop video processing""" if self._running: print("DEBUG: Stopping video processing") self._running = False # Properly terminate the thread 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 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(f"DEBUG: Opening video source: {self.source} (type: {type(self.source)})") cap = None max_retries = 3 retry_delay = 1.0 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(): ret, test_frame = capture.read() if ret and test_frame is not None: print(f"✅ Source opened successfully: {src}") 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}") 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 if isinstance(self.source, str) and os.path.exists(self.source): 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()): camera_idx = int(self.source) if isinstance(self.source, str) else self.source print(f"📹 Opening camera with index: {camera_idx}") cap = try_open_source(camera_idx) if cap is None and os.name == 'nt': print("🔄 Trying camera with DirectShow backend...") cap = try_open_source(camera_idx + cv2.CAP_DSHOW) else: print(f"🌐 Opening source as string: {self.source}") cap = try_open_source(str(self.source)) if cap is None: print(f"❌ Failed to open video source after all attempts: {self.source}") 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 if not cap or not cap.isOpened(): print(f"ERROR: Could not open video source {self.source}") 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 frame_time = 1.0 / self.source_fps if self.source_fps > 0 else 0.033 prev_time = time.time() print(f"SUCCESS: Video source opened: {self.source}") print(f"Source info - FPS: {self.source_fps}, Size: {self.frame_width}x{self.frame_height}") frame_error_count = 0 max_consecutive_errors = 10 while self._running and cap.isOpened(): try: ret, frame = cap.read() 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 time.sleep(0.1) continue frame_error_count = 0 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 process_start = time.time() # --- Detection, tracking, annotation, violation logic (single-pass) --- detection_start = time.time() detections = [] if self.model_manager: detections = self.model_manager.detect(frame) 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) 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 detection_time = (time.time() - detection_start) * 1000 violation_start = time.time() violations = [] violation_time = (time.time() - violation_start) * 1000 if self.model_manager: detections = self.model_manager.update_tracking(detections, frame) if detections and isinstance(detections[0], tuple): detections = [ {'id': d[0], 'bbox': d[1], 'confidence': d[2], 'class_id': d[3]} for d in detections ] process_time = (time.time() - process_start) * 1000 self.processing_times.append(process_time) 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 self.performance_metrics = { 'FPS': f"{fps_smoothed:.1f}", 'Detection (ms)': f"{detection_time:.1f}", 'Total (ms)': f"{process_time:.1f}" } self.mutex.lock() self.current_frame = frame.copy() self.current_detections = detections self.mutex.unlock() annotated_frame = frame.copy() # --- CRITICAL: Always initialize annotated_frame as a copy of frame --- # Detection and violation processing process_start = time.time() # Process detections detection_start = time.time() detections = [] if self.model_manager: # Always use confidence threshold 0.3 detections = self.model_manager.detect(frame) # Normalize class names and assign unique IDs next_vehicle_id = 1 used_ids = set() for i, det in enumerate(detections): # Normalize class name if 'class_name' in det: det['class_name'] = normalize_class_name(det['class_name']) # Assign unique ID for vehicles if det.get('class_name') in ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle']: if 'id' not in det or det['id'] in used_ids or det['id'] is None: det['id'] = next_vehicle_id det['track_id'] = next_vehicle_id next_vehicle_id += 1 else: det['track_id'] = det['id'] used_ids.add(det['id']) # Ensure confidence is at least 0.3 if 'confidence' not in det or det['confidence'] < 0.3: det['confidence'] = 0.3 # Traffic light color detection if unknown if det.get('class_name') == 'traffic light': if 'traffic_light_color' not in det or det['traffic_light_color'] == 'unknown' or (isinstance(det['traffic_light_color'], dict) and det['traffic_light_color'].get('color', 'unknown') == 'unknown'): det['traffic_light_color'] = detect_traffic_light_color(frame, det['bbox']) 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): 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() # --- DEBUG: Print all detection class_ids and class_names --- print("[DEBUG] All detections (class_id, class_name):") for det in detections: print(f" class_id={det.get('class_id')}, class_name={det.get('class_name')}, conf={det.get('confidence')}, bbox={det.get('bbox')}") # --- END DEBUG --- # --- 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: # Accept both class_id and class_name for traffic light is_tl = False if 'class_name' in det: is_tl = is_traffic_light(det.get('class_name')) elif 'class_id' in det: # Map class_id to class_name if possible class_id = det.get('class_id') # You may need to adjust this mapping based on your model if class_id == 0: det['class_name'] = 'traffic light' is_tl = True if is_tl: 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 # --- DETAILED CROSSWALK DETECTION LOGIC --- 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 crosswalk_utils2.py's function to detect crosswalk and violation line annotated_frame, crosswalk_bbox, violation_line_y, debug_info = self.detect_crosswalk_and_violation_line( annotated_frame, traffic_light_position ) print(f"[CROSSWALK] Detection result: crosswalk_bbox={{crosswalk_bbox is not None}}, violation_line_y={{violation_line_y}}") # Optionally, draw debug overlays or use debug_info for analytics 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: # Filter vehicle detections vehicle_classes = ['car', 'truck', 'bus', 'motorcycle', 'van', 'bicycle'] vehicle_dets = [] h, w = frame.shape[:2] print(f"[TRACK DEBUG] All detections:") for det in detections: print(f" Det: class={det.get('class_name')}, conf={det.get('confidence')}, bbox={det.get('bbox')}") 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 0.0005 <= area_ratio <= 0.25: # Loosened lower bound 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 [0.0005, 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) print(f"[TRACK DEBUG] Tracker returned {len(tracks)} tracks") 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: # Only use dict access for tracker output if not isinstance(track, dict) or 'bbox' not in track or track['bbox'] is None: print(f"Warning: Track has no bbox, skipping: {track}") continue print(f"[TRACK DEBUG] Tracker output: {track}") track_id = track.get('id') bbox = track.get('bbox') if bbox is None: print(f"Warning: Track has no bbox, skipping: {track}") continue x1, y1, x2, y2 = map(float, bbox) # Use y2 (bottom of bbox) for robust line crossing bottom_y = y2 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'] 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(bottom_y) # Use bottom_y instead of center_y self.vehicle_statuses[track_id]['last_position'] = bottom_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, 'bottom_y': bottom_y, 'is_moving': is_moving, 'is_violation': is_violation }) # 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'] bottom_y = tracked['bottom_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 (bottom_y) curr_y = position_history[-i] # Later position (bottom_y) 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 is_red_light = self.latest_traffic_light and self.latest_traffic_light.get('color') == 'red' actively_crossing = (line_crossed_in_window and is_moving and is_red_light) if 'crossed_during_red' not in self.vehicle_statuses[track_id]: self.vehicle_statuses[track_id]['crossed_during_red'] = False if actively_crossing: suspicious_jumps = self.vehicle_statuses[track_id].get('suspicious_jumps', 0) if suspicious_jumps <= 1: 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") 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 is_violation = (self.vehicle_statuses[track_id]['crossed_during_red'] and is_red_light) 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) tracked['is_violation'] = is_violation if actively_crossing and self.vehicle_statuses[track_id].get('suspicious_jumps', 0) <= 1: violating_vehicle_ids.add(track_id) timestamp = datetime.now() 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', 'timestamp': timestamp, 'line_position': violation_line_y, 'movement': crossing_details if crossing_details else {'prev_y': bottom_y, 'current_y': bottom_y}, 'crossing_window': self.crossing_check_window, 'position_history': list(position_history[-10:]) }) 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") # --- DRAWING/ANNOTATION LOGIC (add overlays before emitting frame) --- # 1. Draw vehicle bounding boxes and IDs for tracked in tracked_vehicles: bbox = tracked['bbox'] track_id = tracked['id'] is_violation = tracked.get('is_violation', False) color = (0, 0, 255) if is_violation else (0, 255, 0) cv2.rectangle(annotated_frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), color, 2) cv2.putText(annotated_frame, f'ID:{track_id}', (int(bbox[0]), int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) # 2. Draw traffic light color box if has_traffic_lights and len(traffic_lights) > 0: for tl in traffic_lights: bbox = tl.get('bbox') color_name = tl.get('color', 'unknown') color_map = {'red': (0,0,255), 'yellow': (0,255,255), 'green': (0,255,0)} box_color = color_map.get(color_name, (255,255,255)) if bbox is not None: cv2.rectangle(annotated_frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), box_color, 2) cv2.putText(annotated_frame, color_name, (int(bbox[0]), int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, box_color, 2) # 3. Draw violation line if violation_line_y is not None: cv2.line(annotated_frame, (0, int(violation_line_y)), (annotated_frame.shape[1], int(violation_line_y)), (0,0,255), 3) # --- Frame emission logic (robust, single-pass) --- # Emit raw_frame_ready (original frame, detections, fps) self.raw_frame_ready.emit(frame.copy(), list(detections), self.actual_fps) # Emit frame_np_ready (annotated frame for display) self.frame_np_ready.emit(annotated_frame) # Emit frame_ready (QPixmap, detections, metrics) try: pixmap = convert_cv_to_pixmap(annotated_frame) except Exception as e: print(f"[ERROR] convert_cv_to_pixmap failed: {e}") pixmap = None self.frame_ready.emit(pixmap, list(detections), dict(self.performance_metrics)) # Emit stats_ready (metrics) stats = dict(self.performance_metrics) if hasattr(self, 'latest_traffic_light'): stats['traffic_light_color'] = self.latest_traffic_light self.stats_ready.emit(stats) except Exception as e: print(f"Video processing error: {e}") import traceback traceback.print_exc() finally: self._running = False 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") def play(self): """Start or resume video playback (for file sources)""" print("[VideoController] play() called") self.start() def pause(self): """Pause video playback (for file sources)""" print("[VideoController] pause() called") # No render_timer def seek(self, value): """Seek to a specific frame (for file sources)""" print(f"[VideoController] seek() called with value: {value}") if self.source_type == "file" and hasattr(self, 'cap') and self.cap is not None: try: self.cap.set(cv2.CAP_PROP_POS_FRAMES, value) print(f"[VideoController] Seeked to frame {value}") except Exception as e: print(f"[VideoController] Seek failed: {e}") else: print("[VideoController] Seek not supported for this source type.") def set_detection_enabled(self, enabled): """Enable or disable detection during playback""" print(f"[VideoController] set_detection_enabled({enabled}) called") self.detection_enabled = enabled # In your _process_frame or detection logic, wrap detection with: # if self.detection_enabled: # ... run detection ... # else: # ... skip detection ... from PySide6.QtCore import QObject, Signal, QThread, Qt, QMutex, QWaitCondition, QTimer 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 auto_select_model_device = Signal() device_info_ready = Signal(dict) # Signal emitted when OpenVINO device info is ready 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.source = None self.source_type = None self.source_fps = 0 self.performance_metrics = {} self.mutex = QMutex() # 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 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() # Query OpenVINO devices at startup and emit info self.query_openvino_devices() def query_openvino_devices(self): """ Query available OpenVINO devices and their properties, emit device_info_ready signal. """ try: from openvino.runtime import Core core = Core() devices = core.available_devices device_info = {} for device in devices: try: properties = core.get_property(device, {}) except Exception: properties = {} device_info[device] = properties print(f"[OpenVINO] Available devices: {device_info}") self.device_info_ready.emit(device_info) except Exception as e: print(f"[OpenVINO] Could not query devices: {e}") self.device_info_ready.emit({'error': str(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): """ Get properties of video source Returns: bool: True if source was successfully opened, False otherwise """ try: print(f"🔍 Opening video source for properties check: {self.source}") cap = cv2.VideoCapture(self.source) # Verify capture opened successfully if not cap.isOpened(): print(f"❌ Failed to open video source: {self.source}") return False # Read properties self.source_fps = cap.get(cv2.CAP_PROP_FPS) if self.source_fps <= 0: print("⚠ī¸ Source FPS not available, using default 30 FPS") self.source_fps = 30.0 # Default if undetectable 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)) # Try reading a test frame to confirm source is truly working ret, test_frame = cap.read() if not ret or test_frame is None: print("⚠ī¸ Could not read test frame from source") # For camera sources, try one more time with delay if self.source_type == "camera": print("🔄 Retrying camera initialization...") time.sleep(1.0) # Wait a moment for camera to initialize 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 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 play(self): """Start or resume video processing.""" if not self._running: self._running = True if not self.thread.isRunning(): self.thread.start() if hasattr(self, 'render_timer') and not self.render_timer.isActive(): self.render_timer.start(30) def pause(self): """Pause video processing (stop timer, keep thread alive).""" if hasattr(self, 'render_timer') and self.render_timer.isActive(): self.render_timer.stop() self._running = False 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 while self._running and cap.isOpened(): 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 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) print("[DEBUG] Raw detections:") for det in detections: print(f" class_name: {det.get('class_name')}, class_id: {det.get('class_id')}, confidence: {det.get('confidence')}") # 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() # 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: # Check for crossing over the last N frames (configurable window) 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") # 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 (always define, even if no detections) 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") print(f"[STATS] Moving: {vehicles_moving}, Violating: {vehicles_violating}") # 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 individual violation signals for each violation if violations: for violation in violations: print(f"🚨 Emitting RED LIGHT VIOLATION: Track ID {violation['track_id']}") # Add additional data to the violation 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"✅ 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 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, 'cars': sum(1 for d in detections if d.get('class_name', '').lower() == 'car'), 'trucks': sum(1 for d in detections if d.get('class_name', '').lower() == 'truck'), 'peds': sum(1 for d in detections if d.get('class_name', '').lower() in ['person', 'pedestrian', 'human']), 'model': getattr(self.inference_model, 'name', '-') if hasattr(self, 'inference_model') else '-', 'device': getattr(self.inference_model, 'device', '-') if hasattr(self, 'inference_model') else '-' } # 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) # --- Ensure analytics update every frame --- 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: now = time.time() if now - getattr(self, '_last_no_frame_log', 0) > 2: print("⚠ī¸ No frame available to process") self._last_no_frame_log = now 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//2-140, 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() if self.current_detections is not None: detections = self.current_detections.copy() else: detections = [] violations = [] # Violations are disabled metrics = self.performance_metrics.copy() except Exception as e: print(f"Error copying frame data: {e}") self.mutex.unlock() return self.mutex.unlock() # --- Frame processing logic (drawing, annotations, etc) --- # Draw FPS on frame if 'FPS' in metrics: cv2.putText(frame, f"FPS: {metrics['FPS']}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) # Draw detections for det in 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) # Draw bounding box cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) # Put label text cv2.putText(frame, f"{label} ({confidence:.2f})", (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) # --- END OF FRAME PROCESSING LOGIC --- # Emit the processed frame for display self.frame_np_ready.emit(frame) except Exception as e: print(f"Error in _process_frame: {e}") finally: self.mutex.unlock()