feat: update tracking working
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ziesorx
parent
9e4c23c75c
commit
255be78d43
2 changed files with 62 additions and 97 deletions
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@ -146,6 +146,17 @@ class TrackingPipelineIntegration:
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persist=True
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persist=True
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)
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)
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# Debug: Log raw detection results
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if tracking_results and hasattr(tracking_results, 'detections'):
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raw_detections = len(tracking_results.detections)
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if raw_detections > 0:
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class_names = [detection.class_name for detection in tracking_results.detections]
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logger.info(f"[DEBUG] Raw detections: {raw_detections}, classes: {class_names}")
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else:
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logger.debug(f"[DEBUG] No raw detections found")
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else:
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logger.debug(f"[DEBUG] No tracking results or detections attribute")
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# Process tracking results
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# Process tracking results
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tracked_vehicles = self.tracker.process_detections(
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tracked_vehicles = self.tracker.process_detections(
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tracking_results,
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tracking_results,
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@ -130,111 +130,65 @@ class VehicleTracker:
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logger.debug(f"Removing expired track {track_id}")
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logger.debug(f"Removing expired track {track_id}")
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del self.tracked_vehicles[track_id]
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del self.tracked_vehicles[track_id]
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# Process new detections
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# Process new detections from InferenceResult
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if hasattr(results, 'boxes') and results.boxes is not None:
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if hasattr(results, 'detections') and results.detections:
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boxes = results.boxes
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# Process detections from InferenceResult
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for detection in results.detections:
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# Skip if confidence is too low
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if detection.confidence < self.min_confidence:
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continue
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# Check if tracking is available
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# Check if class is in trigger classes
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if hasattr(boxes, 'id') and boxes.id is not None:
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if detection.class_name not in self.trigger_classes:
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# Process tracked objects
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continue
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for i, box in enumerate(boxes):
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# Get tracking ID
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track_id = int(boxes.id[i].item()) if boxes.id[i] is not None else None
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if track_id is None:
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continue
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# Get class and confidence
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# Use track_id if available, otherwise generate one
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cls_id = int(box.cls.item())
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track_id = detection.track_id if detection.track_id is not None else self.next_track_id
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confidence = float(box.conf.item())
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if detection.track_id is None:
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self.next_track_id += 1
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# Check if class is in trigger classes
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# Get bounding box from Detection object
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class_name = results.names[cls_id] if hasattr(results, 'names') else str(cls_id)
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x1, y1, x2, y2 = detection.bbox
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if class_name not in self.trigger_classes and confidence < self.min_confidence:
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bbox = (int(x1), int(y1), int(x2), int(y2))
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continue
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# Get bounding box
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# Update or create tracked vehicle
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x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)
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confidence = detection.confidence
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bbox = (x1, y1, x2, y2)
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if track_id in self.tracked_vehicles:
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# Update existing track
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vehicle = self.tracked_vehicles[track_id]
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vehicle.update_position(bbox, confidence)
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vehicle.display_id = display_id
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# Update or create tracked vehicle
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# Check stability
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if track_id in self.tracked_vehicles:
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stability = vehicle.calculate_stability()
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# Update existing track
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if stability > self.stability_threshold:
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vehicle = self.tracked_vehicles[track_id]
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vehicle.stable_frames += 1
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vehicle.update_position(bbox, confidence)
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if vehicle.stable_frames >= self.min_stable_frames:
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vehicle.display_id = display_id
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vehicle.is_stable = True
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# Check stability
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stability = vehicle.calculate_stability()
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if stability > self.stability_threshold:
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vehicle.stable_frames += 1
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if vehicle.stable_frames >= self.min_stable_frames:
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vehicle.is_stable = True
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else:
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vehicle.stable_frames = max(0, vehicle.stable_frames - 1)
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if vehicle.stable_frames < self.min_stable_frames:
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vehicle.is_stable = False
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logger.debug(f"Updated track {track_id}: conf={confidence:.2f}, "
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f"stable={vehicle.is_stable}, stability={stability:.2f}")
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else:
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else:
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# Create new track
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vehicle.stable_frames = max(0, vehicle.stable_frames - 1)
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vehicle = TrackedVehicle(
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if vehicle.stable_frames < self.min_stable_frames:
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track_id=track_id,
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vehicle.is_stable = False
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first_seen=current_time,
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last_seen=current_time,
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display_id=display_id,
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confidence=confidence,
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bbox=bbox,
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center=((x1 + x2) / 2, (y1 + y2) / 2),
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total_frames=1
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)
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vehicle.last_position_history.append(vehicle.center)
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self.tracked_vehicles[track_id] = vehicle
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logger.info(f"New vehicle tracked: ID={track_id}, display={display_id}")
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active_tracks.append(self.tracked_vehicles[track_id])
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logger.debug(f"Updated track {track_id}: conf={confidence:.2f}, "
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else:
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f"stable={vehicle.is_stable}, stability={stability:.2f}")
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# No tracking available, process as detections only
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else:
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logger.debug("No tracking IDs available, processing as detections only")
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# Create new track
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for i, box in enumerate(boxes):
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vehicle = TrackedVehicle(
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cls_id = int(box.cls.item())
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track_id=track_id,
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confidence = float(box.conf.item())
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first_seen=current_time,
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last_seen=current_time,
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display_id=display_id,
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confidence=confidence,
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bbox=bbox,
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center=((x1 + x2) / 2, (y1 + y2) / 2),
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total_frames=1
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)
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vehicle.last_position_history.append(vehicle.center)
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self.tracked_vehicles[track_id] = vehicle
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logger.info(f"New vehicle tracked: ID={track_id}, display={display_id}")
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# Check confidence threshold
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active_tracks.append(self.tracked_vehicles[track_id])
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if confidence < self.min_confidence:
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continue
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# Get bounding box
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x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)
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bbox = (x1, y1, x2, y2)
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center = ((x1 + x2) / 2, (y1 + y2) / 2)
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# Try to match with existing tracks by position
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matched_track = self._find_closest_track(center)
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if matched_track:
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matched_track.update_position(bbox, confidence)
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matched_track.display_id = display_id
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active_tracks.append(matched_track)
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else:
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# Create new track with generated ID
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track_id = self.next_track_id
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self.next_track_id += 1
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vehicle = TrackedVehicle(
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track_id=track_id,
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first_seen=current_time,
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last_seen=current_time,
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display_id=display_id,
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confidence=confidence,
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bbox=bbox,
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center=center,
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total_frames=1
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)
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vehicle.last_position_history.append(center)
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self.tracked_vehicles[track_id] = vehicle
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active_tracks.append(vehicle)
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logger.info(f"New vehicle detected (no tracking): ID={track_id}")
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return active_tracks
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return active_tracks
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