feat: improve face tracking and matching logic in ZoneTracker
- Introduce unique face ID generation and enhance face matching based on proximity and size - Refactor face ID generation to use centroids and size for better accuracy - Update tracked face data structure to include centroid, zone, timestamp, and size - Improve comments for clarity on face tracking and matching processes
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@@ -39,12 +39,15 @@ class ZoneTracker:
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self.total_exited = 0
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# Track faces with timestamps to prevent double-counting
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# Key: face_id (centroid hash), Value: (zone, timestamp)
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# Key: face_id (unique ID), Value: {'centroid': (x, y), 'zone': zone, 'timestamp': time, 'size': (w, h)}
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self.tracked_faces = {}
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self.face_cooldowns = defaultdict(float)
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# Track last seen zone for each face (to detect zone transitions)
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self.last_zone = {}
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# Unique face ID counter
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self.next_face_id = 1
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def get_zone(self, face_x, face_w):
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"""
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@@ -75,24 +78,46 @@ class ZoneTracker:
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# In the middle zone (between entry/exit and center buffer)
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return None
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def _get_face_id(self, face_x, face_y, face_w, face_h):
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def _calculate_centroid(self, face_x, face_y, face_w, face_h):
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"""Calculate the centroid of a face bounding box."""
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return (face_x + face_w // 2, face_y + face_h // 2)
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def _calculate_distance(self, pt1, pt2):
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"""Calculate Euclidean distance between two points."""
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return ((pt1[0] - pt2[0])**2 + (pt1[1] - pt2[1])**2)**0.5
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def _match_face_to_tracked(self, centroid, size):
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"""
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Generate a simple ID for a face based on its position and size.
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This is a basic approach - in production, use proper tracking algorithms.
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Match a detected face to an existing tracked face based on proximity.
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Args:
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face_x, face_y: Top-left coordinates
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face_w, face_h: Width and height
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centroid: (x, y) centroid of the detected face
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size: (w, h) size of the detected face
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Returns:
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A simple hash-like ID for tracking
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face_id if matched, None if new face
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"""
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# Use approximate position and size to create a simple ID
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# This helps group similar detections as the same person
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grid_x = face_x // 50
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grid_y = face_y // 50
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size_category = (face_w + face_h) // 50
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return f"{grid_x}_{grid_y}_{size_category}"
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max_distance = 100 # Maximum pixel distance to consider it the same face
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max_size_diff = 50 # Maximum size difference to consider it the same face
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for face_id, face_data in self.tracked_faces.items():
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# Skip if face hasn't been seen recently (within last 2 seconds)
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time_since_seen = time.time() - face_data.get('timestamp', 0)
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if time_since_seen > 2.0:
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continue
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tracked_centroid = face_data.get('centroid')
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tracked_size = face_data.get('size', (0, 0))
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if tracked_centroid:
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distance = self._calculate_distance(centroid, tracked_centroid)
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size_diff = abs(size[0] + size[1] - tracked_size[0] - tracked_size[1])
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# Match if close enough in position and size
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if distance < max_distance and size_diff < max_size_diff:
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return face_id
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return None
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def process_faces(self, faces):
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"""
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@@ -110,24 +135,41 @@ class ZoneTracker:
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# Process each detected face
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for face in faces:
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face_x, face_y, face_w, face_h, confidence = face
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face_id = self._get_face_id(face_x, face_y, face_w, face_h)
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centroid = self._calculate_centroid(face_x, face_y, face_w, face_h)
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zone = self.get_zone(face_x, face_w)
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if zone is None or zone == 'center':
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continue
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# Try to match this face to an existing tracked face
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face_id = self._match_face_to_tracked(centroid, (face_w, face_h))
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if face_id is None:
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# New face - assign a new ID
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face_id = self.next_face_id
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self.next_face_id += 1
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current_zones[face_id] = zone
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# Update tracked face data
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self.tracked_faces[face_id] = {
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'centroid': centroid,
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'zone': zone,
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'timestamp': current_time,
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'size': (face_w, face_h)
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}
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# Check if this face is in cooldown
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if face_id in self.face_cooldowns:
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if current_time - self.face_cooldowns[face_id] < self.cooldown_seconds:
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continue # Still in cooldown, skip
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# Still in cooldown, update zone but don't count
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self.last_zone[face_id] = zone
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continue
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# Check for zone transitions or first detection
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if face_id not in self.last_zone:
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# First time seeing this face - count if in entry/exit zone
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self.last_zone[face_id] = zone
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self.tracked_faces[face_id] = (zone, current_time)
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# Count on first detection in entry/exit zones
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if zone == 'entry':
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@@ -155,14 +197,17 @@ class ZoneTracker:
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self.total_exited += 1
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self.face_cooldowns[face_id] = current_time
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self.last_zone[face_id] = zone
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else:
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# Same zone or transition we don't care about - just update
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self.last_zone[face_id] = zone
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# Clean up old tracking data for faces no longer detected
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faces_to_remove = []
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for face_id in self.last_zone:
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for face_id in list(self.last_zone.keys()):
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if face_id not in current_zones:
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# Face no longer detected, but keep in memory for a bit
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if face_id in self.tracked_faces:
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last_seen = self.tracked_faces[face_id][1]
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last_seen = self.tracked_faces[face_id].get('timestamp', 0)
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if current_time - last_seen > 5.0: # Remove after 5 seconds
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faces_to_remove.append(face_id)
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