""" Zone-based Entry/Exit Tracker Tracks people entering and exiting based on zone detection with cooldown mechanism. """ import time import cv2 from collections import defaultdict class ZoneTracker: def __init__(self, frame_width, entry_zone_percent=0.4, exit_zone_percent=0.4, cooldown_seconds=2.0, center_buffer_percent=0.1): """ Initialize the zone tracker. Args: frame_width: Width of the video frame in pixels entry_zone_percent: Percentage of frame width for entry zone (left side) exit_zone_percent: Percentage of frame width for exit zone (right side) cooldown_seconds: Time in seconds before same person can be counted again center_buffer_percent: Percentage of center to ignore (prevents false counts) """ self.frame_width = frame_width self.entry_zone_percent = entry_zone_percent self.exit_zone_percent = exit_zone_percent self.cooldown_seconds = cooldown_seconds self.center_buffer_percent = center_buffer_percent # Calculate zone boundaries self.entry_zone_end = int(frame_width * entry_zone_percent) buffer_width = int(frame_width * center_buffer_percent) self.center_start = int(frame_width / 2 - buffer_width / 2) self.center_end = int(frame_width / 2 + buffer_width / 2) self.exit_zone_start = int(frame_width * (1 - exit_zone_percent)) # Counters self.total_entered = 0 self.total_exited = 0 # Track faces with timestamps to prevent double-counting # Key: face_id (centroid hash), Value: (zone, timestamp) self.tracked_faces = {} self.face_cooldowns = defaultdict(float) # Track last seen zone for each face (to detect zone transitions) self.last_zone = {} def get_zone(self, face_x, face_w): """ Determine which zone a face is in based on its position. Args: face_x: X coordinate of face (left edge) face_w: Width of face bounding box face_center: Center X of the face Returns: 'entry' if in entry zone, 'exit' if in exit zone, 'center' if in buffer, None otherwise """ face_center = face_x + face_w // 2 # Check if in center buffer zone (ignore) if self.center_start <= face_center <= self.center_end: return 'center' # Check entry zone (left side) if face_center < self.entry_zone_end: return 'entry' # Check exit zone (right side) if face_center > self.exit_zone_start: return 'exit' # In the middle zone (between entry/exit and center buffer) return None def _get_face_id(self, face_x, face_y, face_w, face_h): """ Generate a simple ID for a face based on its position and size. This is a basic approach - in production, use proper tracking algorithms. Args: face_x, face_y: Top-left coordinates face_w, face_h: Width and height Returns: A simple hash-like ID for tracking """ # Use approximate position and size to create a simple ID # This helps group similar detections as the same person grid_x = face_x // 50 grid_y = face_y // 50 size_category = (face_w + face_h) // 50 return f"{grid_x}_{grid_y}_{size_category}" def process_faces(self, faces): """ Process detected faces and update entry/exit counts. Args: faces: List of tuples (x, y, w, h, confidence) from face detector Returns: Dictionary with updated counts and zone info """ current_time = time.time() current_zones = {} # Process each detected face for face in faces: face_x, face_y, face_w, face_h, confidence = face face_id = self._get_face_id(face_x, face_y, face_w, face_h) zone = self.get_zone(face_x, face_w) if zone is None or zone == 'center': continue current_zones[face_id] = zone # Check if this face is in cooldown if face_id in self.face_cooldowns: if current_time - self.face_cooldowns[face_id] < self.cooldown_seconds: continue # Still in cooldown, skip # Check for zone transitions or first detection if face_id not in self.last_zone: # First time seeing this face - mark the zone self.last_zone[face_id] = zone self.tracked_faces[face_id] = (zone, current_time) else: # Face has been seen before - check for valid transition last_zone = self.last_zone[face_id] # Only count if we have a clear zone assignment # Entry: person appears in entry zone # Exit: person appears in exit zone if zone == 'entry' and last_zone != 'entry': # Person entered self.total_entered += 1 self.face_cooldowns[face_id] = current_time self.last_zone[face_id] = zone elif zone == 'exit' and last_zone != 'exit': # Person exited self.total_exited += 1 self.face_cooldowns[face_id] = current_time self.last_zone[face_id] = zone # Clean up old tracking data for faces no longer detected faces_to_remove = [] for face_id in self.last_zone: if face_id not in current_zones: # Face no longer detected, but keep in memory for a bit if face_id in self.tracked_faces: last_seen = self.tracked_faces[face_id][1] if current_time - last_seen > 5.0: # Remove after 5 seconds faces_to_remove.append(face_id) for face_id in faces_to_remove: if face_id in self.last_zone: del self.last_zone[face_id] if face_id in self.tracked_faces: del self.tracked_faces[face_id] if face_id in self.face_cooldowns: del self.face_cooldowns[face_id] return { 'total_entered': self.total_entered, 'total_exited': self.total_exited, 'current_occupancy': self.total_entered - self.total_exited, 'zones': current_zones } def get_counts(self): """Get current count statistics.""" return { 'total_entered': self.total_entered, 'total_exited': self.total_exited, 'current_occupancy': self.total_entered - self.total_exited } def reset_counts(self): """Reset all counters and tracking data.""" self.total_entered = 0 self.total_exited = 0 self.tracked_faces.clear() self.face_cooldowns.clear() self.last_zone.clear() def draw_zones(self, frame): """ Draw zone boundaries on the frame for visualization. Args: frame: Frame to draw on Returns: Frame with zone boundaries drawn """ result_frame = frame.copy() h = frame.shape[0] # Draw entry zone (left, green) cv2.rectangle(result_frame, (0, 0), (self.entry_zone_end, h), (0, 255, 0), 2) cv2.putText(result_frame, "ENTRY", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) # Draw exit zone (right, red) cv2.rectangle(result_frame, (self.exit_zone_start, 0), (self.frame_width, h), (0, 0, 255), 2) cv2.putText(result_frame, "EXIT", (self.exit_zone_start + 10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) # Draw center buffer (yellow, semi-transparent) overlay = result_frame.copy() cv2.rectangle(overlay, (self.center_start, 0), (self.center_end, h), (0, 255, 255), -1) cv2.addWeighted(overlay, 0.2, result_frame, 0.8, 0, result_frame) return result_frame