import base64 import datetime import json import logging import os import queue import threading from collections import Counter, defaultdict from statistics import median from typing import Callable import cv2 import numpy as np from frigate.config import CameraConfig, SnapshotsConfig, RecordConfig, FrigateConfig from frigate.const import CLIPS_DIR from frigate.util import ( SharedMemoryFrameManager, calculate_region, draw_box_with_label, draw_timestamp, ) logger = logging.getLogger(__name__) def on_edge(box, frame_shape): if ( box[0] == 0 or box[1] == 0 or box[2] == frame_shape[1] - 1 or box[3] == frame_shape[0] - 1 ): return True def is_better_thumbnail(current_thumb, new_obj, frame_shape) -> bool: # larger is better # cutoff images are less ideal, but they should also be smaller? # better scores are obviously better too # if the new_thumb is on an edge, and the current thumb is not if on_edge(new_obj["box"], frame_shape) and not on_edge( current_thumb["box"], frame_shape ): return False # if the score is better by more than 5% if new_obj["score"] > current_thumb["score"] + 0.05: return True # if the area is 10% larger if new_obj["area"] > current_thumb["area"] * 1.1: return True return False class TrackedObject: def __init__( self, camera, colormap, camera_config: CameraConfig, frame_cache, obj_data ): self.obj_data = obj_data self.camera = camera self.colormap = colormap self.camera_config = camera_config self.frame_cache = frame_cache self.current_zones = [] self.entered_zones = [] self.false_positive = True self.has_clip = False self.has_snapshot = False self.top_score = self.computed_score = 0.0 self.thumbnail_data = None self.last_updated = 0 self.last_published = 0 self.frame = None self.previous = self.to_dict() # start the score history self.score_history = [self.obj_data["score"]] def _is_false_positive(self): # once a true positive, always a true positive if not self.false_positive: return False threshold = self.camera_config.objects.filters[self.obj_data["label"]].threshold return self.computed_score < threshold def compute_score(self): scores = self.score_history[:] # pad with zeros if you dont have at least 3 scores if len(scores) < 3: scores += [0.0] * (3 - len(scores)) return median(scores) def update(self, current_frame_time, obj_data): thumb_update = False significant_change = False # if the object is not in the current frame, add a 0.0 to the score history if obj_data["frame_time"] != current_frame_time: self.score_history.append(0.0) else: self.score_history.append(obj_data["score"]) # only keep the last 10 scores if len(self.score_history) > 10: self.score_history = self.score_history[-10:] # calculate if this is a false positive self.computed_score = self.compute_score() if self.computed_score > self.top_score: self.top_score = self.computed_score self.false_positive = self._is_false_positive() if not self.false_positive: # determine if this frame is a better thumbnail if self.thumbnail_data is None or is_better_thumbnail( self.thumbnail_data, obj_data, self.camera_config.frame_shape ): self.thumbnail_data = { "frame_time": obj_data["frame_time"], "box": obj_data["box"], "area": obj_data["area"], "region": obj_data["region"], "score": obj_data["score"], } thumb_update = True # check zones current_zones = [] bottom_center = (obj_data["centroid"][0], obj_data["box"][3]) # check each zone for name, zone in self.camera_config.zones.items(): # if the zone is not for this object type, skip if len(zone.objects) > 0 and not obj_data["label"] in zone.objects: continue contour = zone.contour # check if the object is in the zone if cv2.pointPolygonTest(contour, bottom_center, False) >= 0: # if the object passed the filters once, dont apply again if name in self.current_zones or not zone_filtered(self, zone.filters): current_zones.append(name) if name not in self.entered_zones: self.entered_zones.append(name) if not self.false_positive: # if the zones changed, signal an update if set(self.current_zones) != set(current_zones): significant_change = True # if the position changed, signal an update if self.obj_data["position_changes"] != obj_data["position_changes"]: significant_change = True # if the motionless_count reaches the stationary threshold if ( self.obj_data["motionless_count"] == self.camera_config.detect.stationary.threshold ): significant_change = True # update at least once per minute if self.obj_data["frame_time"] - self.previous["frame_time"] > 60: significant_change = True self.obj_data.update(obj_data) self.current_zones = current_zones return (thumb_update, significant_change) def to_dict(self, include_thumbnail: bool = False): snapshot_time = ( self.thumbnail_data["frame_time"] if not self.thumbnail_data is None else 0.0 ) event = { "id": self.obj_data["id"], "camera": self.camera, "frame_time": self.obj_data["frame_time"], "snapshot_time": snapshot_time, "label": self.obj_data["label"], "top_score": self.top_score, "false_positive": self.false_positive, "start_time": self.obj_data["start_time"], "end_time": self.obj_data.get("end_time", None), "score": self.obj_data["score"], "box": self.obj_data["box"], "area": self.obj_data["area"], "ratio": self.obj_data["ratio"], "region": self.obj_data["region"], "stationary": self.obj_data["motionless_count"] > self.camera_config.detect.stationary.threshold, "motionless_count": self.obj_data["motionless_count"], "position_changes": self.obj_data["position_changes"], "current_zones": self.current_zones.copy(), "entered_zones": self.entered_zones.copy(), "has_clip": self.has_clip, "has_snapshot": self.has_snapshot, } if include_thumbnail: event["thumbnail"] = base64.b64encode(self.get_thumbnail()).decode("utf-8") return event def get_thumbnail(self): if ( self.thumbnail_data is None or self.thumbnail_data["frame_time"] not in self.frame_cache ): ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8)) jpg_bytes = self.get_jpg_bytes( timestamp=False, bounding_box=False, crop=True, height=175 ) if jpg_bytes: return jpg_bytes else: ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8)) return jpg.tobytes() def get_clean_png(self): if self.thumbnail_data is None: return None try: best_frame = cv2.cvtColor( self.frame_cache[self.thumbnail_data["frame_time"]], cv2.COLOR_YUV2BGR_I420, ) except KeyError: logger.warning( f"Unable to create clean png because frame {self.thumbnail_data['frame_time']} is not in the cache" ) return None ret, png = cv2.imencode(".png", best_frame) if ret: return png.tobytes() else: return None def get_jpg_bytes( self, timestamp=False, bounding_box=False, crop=False, height=None, quality=70 ): if self.thumbnail_data is None: return None try: best_frame = cv2.cvtColor( self.frame_cache[self.thumbnail_data["frame_time"]], cv2.COLOR_YUV2BGR_I420, ) except KeyError: logger.warning( f"Unable to create jpg because frame {self.thumbnail_data['frame_time']} is not in the cache" ) return None if bounding_box: thickness = 2 color = self.colormap[self.obj_data["label"]] # draw the bounding boxes on the frame box = self.thumbnail_data["box"] draw_box_with_label( best_frame, box[0], box[1], box[2], box[3], self.obj_data["label"], f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}", thickness=thickness, color=color, ) if crop: box = self.thumbnail_data["box"] box_size = 300 region = calculate_region( best_frame.shape, box[0], box[1], box[2], box[3], box_size, multiplier=1.1, ) best_frame = best_frame[region[1] : region[3], region[0] : region[2]] if height: width = int(height * best_frame.shape[1] / best_frame.shape[0]) best_frame = cv2.resize( best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA ) if timestamp: color = self.camera_config.timestamp_style.color draw_timestamp( best_frame, self.thumbnail_data["frame_time"], self.camera_config.timestamp_style.format, font_effect=self.camera_config.timestamp_style.effect, font_thickness=self.camera_config.timestamp_style.thickness, font_color=(color.blue, color.green, color.red), position=self.camera_config.timestamp_style.position, ) ret, jpg = cv2.imencode( ".jpg", best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), quality] ) if ret: return jpg.tobytes() else: return None def zone_filtered(obj: TrackedObject, object_config): object_name = obj.obj_data["label"] if object_name in object_config: obj_settings = object_config[object_name] # if the min area is larger than the # detected object, don't add it to detected objects if obj_settings.min_area > obj.obj_data["area"]: return True # if the detected object is larger than the # max area, don't add it to detected objects if obj_settings.max_area < obj.obj_data["area"]: return True # if the score is lower than the threshold, skip if obj_settings.threshold > obj.computed_score: return True # if the object is not proportionally wide enough if obj_settings.min_ratio > obj.obj_data["ratio"]: return True # if the object is proportionally too wide if obj_settings.max_ratio < obj.obj_data["ratio"]: return True return False # Maintains the state of a camera class CameraState: def __init__( self, name, config: FrigateConfig, frame_manager: SharedMemoryFrameManager ): self.name = name self.config = config self.camera_config = config.cameras[name] self.frame_manager = frame_manager self.best_objects: dict[str, TrackedObject] = {} self.object_counts = defaultdict(int) self.tracked_objects: dict[str, TrackedObject] = {} self.frame_cache = {} self.zone_objects = defaultdict(list) self._current_frame = np.zeros(self.camera_config.frame_shape_yuv, np.uint8) self.current_frame_lock = threading.Lock() self.current_frame_time = 0.0 self.motion_boxes = [] self.regions = [] self.previous_frame_id = None self.callbacks = defaultdict(list) def get_current_frame(self, draw_options={}): with self.current_frame_lock: frame_copy = np.copy(self._current_frame) frame_time = self.current_frame_time tracked_objects = {k: v.to_dict() for k, v in self.tracked_objects.items()} motion_boxes = self.motion_boxes.copy() regions = self.regions.copy() frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420) # draw on the frame if draw_options.get("bounding_boxes"): # draw the bounding boxes on the frame for obj in tracked_objects.values(): if obj["frame_time"] == frame_time: thickness = 2 color = self.config.model.colormap[obj["label"]] else: thickness = 1 color = (255, 0, 0) # draw the bounding boxes on the frame box = obj["box"] draw_box_with_label( frame_copy, box[0], box[1], box[2], box[3], obj["label"], f"{obj['score']:.0%} {int(obj['area'])}", thickness=thickness, color=color, ) if draw_options.get("regions"): for region in regions: cv2.rectangle( frame_copy, (region[0], region[1]), (region[2], region[3]), (0, 255, 0), 2, ) if draw_options.get("zones"): for name, zone in self.camera_config.zones.items(): thickness = ( 8 if any( name in obj["current_zones"] for obj in tracked_objects.values() ) else 2 ) cv2.drawContours(frame_copy, [zone.contour], -1, zone.color, thickness) if draw_options.get("mask"): mask_overlay = np.where(self.camera_config.motion.mask == [0]) frame_copy[mask_overlay] = [0, 0, 0] if draw_options.get("motion_boxes"): for m_box in motion_boxes: cv2.rectangle( frame_copy, (m_box[0], m_box[1]), (m_box[2], m_box[3]), (0, 0, 255), 2, ) if draw_options.get("timestamp"): color = self.camera_config.timestamp_style.color draw_timestamp( frame_copy, frame_time, self.camera_config.timestamp_style.format, font_effect=self.camera_config.timestamp_style.effect, font_thickness=self.camera_config.timestamp_style.thickness, font_color=(color.blue, color.green, color.red), position=self.camera_config.timestamp_style.position, ) return frame_copy def finished(self, obj_id): del self.tracked_objects[obj_id] def on(self, event_type: str, callback: Callable[[dict], None]): self.callbacks[event_type].append(callback) def update(self, frame_time, current_detections, motion_boxes, regions): # get the new frame frame_id = f"{self.name}{frame_time}" current_frame = self.frame_manager.get( frame_id, self.camera_config.frame_shape_yuv ) tracked_objects = self.tracked_objects.copy() current_ids = set(current_detections.keys()) previous_ids = set(tracked_objects.keys()) removed_ids = previous_ids.difference(current_ids) new_ids = current_ids.difference(previous_ids) updated_ids = current_ids.intersection(previous_ids) for id in new_ids: new_obj = tracked_objects[id] = TrackedObject( self.name, self.config.model.colormap, self.camera_config, self.frame_cache, current_detections[id], ) # call event handlers for c in self.callbacks["start"]: c(self.name, new_obj, frame_time) for id in updated_ids: updated_obj = tracked_objects[id] thumb_update, significant_update = updated_obj.update( frame_time, current_detections[id] ) if thumb_update: # ensure this frame is stored in the cache if ( updated_obj.thumbnail_data["frame_time"] == frame_time and frame_time not in self.frame_cache ): self.frame_cache[frame_time] = np.copy(current_frame) updated_obj.last_updated = frame_time # if it has been more than 5 seconds since the last thumb update # and the last update is greater than the last publish or # the object has changed significantly if ( frame_time - updated_obj.last_published > 5 and updated_obj.last_updated > updated_obj.last_published ) or significant_update: # call event handlers for c in self.callbacks["update"]: c(self.name, updated_obj, frame_time) updated_obj.last_published = frame_time for id in removed_ids: # publish events to mqtt removed_obj = tracked_objects[id] if not "end_time" in removed_obj.obj_data: removed_obj.obj_data["end_time"] = frame_time for c in self.callbacks["end"]: c(self.name, removed_obj, frame_time) # TODO: can i switch to looking this up and only changing when an event ends? # maintain best objects for obj in tracked_objects.values(): object_type = obj.obj_data["label"] # if the object's thumbnail is not from the current frame if obj.false_positive or obj.thumbnail_data["frame_time"] != frame_time: continue if object_type in self.best_objects: current_best = self.best_objects[object_type] now = datetime.datetime.now().timestamp() # if the object is a higher score than the current best score # or the current object is older than desired, use the new object if ( is_better_thumbnail( current_best.thumbnail_data, obj.thumbnail_data, self.camera_config.frame_shape, ) or (now - current_best.thumbnail_data["frame_time"]) > self.camera_config.best_image_timeout ): self.best_objects[object_type] = obj for c in self.callbacks["snapshot"]: c(self.name, self.best_objects[object_type], frame_time) else: self.best_objects[object_type] = obj for c in self.callbacks["snapshot"]: c(self.name, self.best_objects[object_type], frame_time) # update overall camera state for each object type obj_counter = Counter( obj.obj_data["label"] for obj in tracked_objects.values() if not obj.false_positive ) # keep track of all labels detected for this camera total_label_count = 0 # report on detected objects for obj_name, count in obj_counter.items(): total_label_count += count if count != self.object_counts[obj_name]: self.object_counts[obj_name] = count for c in self.callbacks["object_status"]: c(self.name, obj_name, count) # publish for all labels detected for this camera if total_label_count != self.object_counts.get("all"): self.object_counts["all"] = total_label_count for c in self.callbacks["object_status"]: c(self.name, "all", total_label_count) # expire any objects that are >0 and no longer detected expired_objects = [ obj_name for obj_name, count in self.object_counts.items() if count > 0 and obj_name not in obj_counter ] for obj_name in expired_objects: # Ignore the artificial all label if obj_name == "all": continue self.object_counts[obj_name] = 0 for c in self.callbacks["object_status"]: c(self.name, obj_name, 0) for c in self.callbacks["snapshot"]: c(self.name, self.best_objects[obj_name], frame_time) # cleanup thumbnail frame cache current_thumb_frames = { obj.thumbnail_data["frame_time"] for obj in tracked_objects.values() if not obj.false_positive } current_best_frames = { obj.thumbnail_data["frame_time"] for obj in self.best_objects.values() } thumb_frames_to_delete = [ t for t in self.frame_cache.keys() if t not in current_thumb_frames and t not in current_best_frames ] for t in thumb_frames_to_delete: del self.frame_cache[t] with self.current_frame_lock: self.tracked_objects = tracked_objects self.current_frame_time = frame_time self.motion_boxes = motion_boxes self.regions = regions self._current_frame = current_frame if self.previous_frame_id is not None: self.frame_manager.close(self.previous_frame_id) self.previous_frame_id = frame_id class TrackedObjectProcessor(threading.Thread): def __init__( self, config: FrigateConfig, client, topic_prefix, tracked_objects_queue, event_queue, event_processed_queue, video_output_queue, recordings_info_queue, stop_event, ): threading.Thread.__init__(self) self.name = "detected_frames_processor" self.config = config self.client = client self.topic_prefix = topic_prefix self.tracked_objects_queue = tracked_objects_queue self.event_queue = event_queue self.event_processed_queue = event_processed_queue self.video_output_queue = video_output_queue self.recordings_info_queue = recordings_info_queue self.stop_event = stop_event self.camera_states: dict[str, CameraState] = {} self.frame_manager = SharedMemoryFrameManager() self.last_motion_detected: dict[str, float] = {} def start(camera, obj: TrackedObject, current_frame_time): self.event_queue.put(("start", camera, obj.to_dict())) def update(camera, obj: TrackedObject, current_frame_time): obj.has_snapshot = self.should_save_snapshot(camera, obj) obj.has_clip = self.should_retain_recording(camera, obj) after = obj.to_dict() message = { "before": obj.previous, "after": after, "type": "new" if obj.previous["false_positive"] else "update", } self.client.publish( f"{self.topic_prefix}/events", json.dumps(message), retain=False ) obj.previous = after self.event_queue.put( ("update", camera, obj.to_dict(include_thumbnail=True)) ) def end(camera, obj: TrackedObject, current_frame_time): # populate has_snapshot obj.has_snapshot = self.should_save_snapshot(camera, obj) obj.has_clip = self.should_retain_recording(camera, obj) # write the snapshot to disk if obj.has_snapshot: snapshot_config: SnapshotsConfig = self.config.cameras[camera].snapshots jpg_bytes = obj.get_jpg_bytes( timestamp=snapshot_config.timestamp, bounding_box=snapshot_config.bounding_box, crop=snapshot_config.crop, height=snapshot_config.height, quality=snapshot_config.quality, ) if jpg_bytes is None: logger.warning(f"Unable to save snapshot for {obj.obj_data['id']}.") else: with open( os.path.join(CLIPS_DIR, f"{camera}-{obj.obj_data['id']}.jpg"), "wb", ) as j: j.write(jpg_bytes) # write clean snapshot if enabled if snapshot_config.clean_copy: png_bytes = obj.get_clean_png() if png_bytes is None: logger.warning( f"Unable to save clean snapshot for {obj.obj_data['id']}." ) else: with open( os.path.join( CLIPS_DIR, f"{camera}-{obj.obj_data['id']}-clean.png", ), "wb", ) as p: p.write(png_bytes) if not obj.false_positive: message = { "before": obj.previous, "after": obj.to_dict(), "type": "end", } self.client.publish( f"{self.topic_prefix}/events", json.dumps(message), retain=False ) self.event_queue.put(("end", camera, obj.to_dict(include_thumbnail=True))) def snapshot(camera, obj: TrackedObject, current_frame_time): mqtt_config = self.config.cameras[camera].mqtt if mqtt_config.enabled and self.should_mqtt_snapshot(camera, obj): jpg_bytes = obj.get_jpg_bytes( timestamp=mqtt_config.timestamp, bounding_box=mqtt_config.bounding_box, crop=mqtt_config.crop, height=mqtt_config.height, quality=mqtt_config.quality, ) if jpg_bytes is None: logger.warning( f"Unable to send mqtt snapshot for {obj.obj_data['id']}." ) else: self.client.publish( f"{self.topic_prefix}/{camera}/{obj.obj_data['label']}/snapshot", jpg_bytes, retain=True, ) def object_status(camera, object_name, status): self.client.publish( f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False ) for camera in self.config.cameras.keys(): camera_state = CameraState(camera, self.config, self.frame_manager) camera_state.on("start", start) camera_state.on("update", update) camera_state.on("end", end) camera_state.on("snapshot", snapshot) camera_state.on("object_status", object_status) self.camera_states[camera] = camera_state # { # 'zone_name': { # 'person': { # 'camera_1': 2, # 'camera_2': 1 # } # } # } self.zone_data = defaultdict(lambda: defaultdict(dict)) def should_save_snapshot(self, camera, obj: TrackedObject): if obj.false_positive: return False snapshot_config: SnapshotsConfig = self.config.cameras[camera].snapshots if not snapshot_config.enabled: return False # object never changed position if obj.obj_data["position_changes"] == 0: return False # if there are required zones and there is no overlap required_zones = snapshot_config.required_zones if len(required_zones) > 0 and not set(obj.entered_zones) & set(required_zones): logger.debug( f"Not creating snapshot for {obj.obj_data['id']} because it did not enter required zones" ) return False return True def should_retain_recording(self, camera, obj: TrackedObject): if obj.false_positive: return False record_config: RecordConfig = self.config.cameras[camera].record # Recording is disabled if not record_config.enabled: return False # object never changed position if obj.obj_data["position_changes"] == 0: return False # If there are required zones and there is no overlap required_zones = record_config.events.required_zones if len(required_zones) > 0 and not set(obj.entered_zones) & set(required_zones): logger.debug( f"Not creating clip for {obj.obj_data['id']} because it did not enter required zones" ) return False # If the required objects are not present if ( record_config.events.objects is not None and obj.obj_data["label"] not in record_config.events.objects ): logger.debug( f"Not creating clip for {obj.obj_data['id']} because it did not contain required objects" ) return False return True def should_mqtt_snapshot(self, camera, obj: TrackedObject): # object never changed position if obj.obj_data["position_changes"] == 0: return False # if there are required zones and there is no overlap required_zones = self.config.cameras[camera].mqtt.required_zones if len(required_zones) > 0 and not set(obj.entered_zones) & set(required_zones): logger.debug( f"Not sending mqtt for {obj.obj_data['id']} because it did not enter required zones" ) return False return True def update_mqtt_motion(self, camera, frame_time, motion_boxes): # publish if motion is currently being detected if motion_boxes: # only send ON if motion isn't already active if self.last_motion_detected.get(camera, 0) == 0: self.client.publish( f"{self.topic_prefix}/{camera}/motion", "ON", retain=False, ) # always updated latest motion self.last_motion_detected[camera] = frame_time elif self.last_motion_detected.get(camera, 0) > 0: mqtt_delay = self.config.cameras[camera].motion.mqtt_off_delay # If no motion, make sure the off_delay has passed if frame_time - self.last_motion_detected.get(camera, 0) >= mqtt_delay: self.client.publish( f"{self.topic_prefix}/{camera}/motion", "OFF", retain=False, ) # reset the last_motion so redundant `off` commands aren't sent self.last_motion_detected[camera] = 0 def get_best(self, camera, label): # TODO: need a lock here camera_state = self.camera_states[camera] if label in camera_state.best_objects: best_obj = camera_state.best_objects[label] best = best_obj.thumbnail_data.copy() best["frame"] = camera_state.frame_cache.get( best_obj.thumbnail_data["frame_time"] ) return best else: return {} def get_current_frame(self, camera, draw_options={}): return self.camera_states[camera].get_current_frame(draw_options) def run(self): while not self.stop_event.is_set(): try: ( camera, frame_time, current_tracked_objects, motion_boxes, regions, ) = self.tracked_objects_queue.get(True, 10) except queue.Empty: continue camera_state = self.camera_states[camera] camera_state.update( frame_time, current_tracked_objects, motion_boxes, regions ) self.update_mqtt_motion(camera, frame_time, motion_boxes) tracked_objects = [ o.to_dict() for o in camera_state.tracked_objects.values() ] self.video_output_queue.put( ( camera, frame_time, tracked_objects, motion_boxes, regions, ) ) # send info on this frame to the recordings maintainer self.recordings_info_queue.put( ( camera, frame_time, tracked_objects, motion_boxes, regions, ) ) # update zone counts for each label # for each zone in the current camera for zone in self.config.cameras[camera].zones.keys(): # count labels for the camera in the zone obj_counter = Counter( obj.obj_data["label"] for obj in camera_state.tracked_objects.values() if zone in obj.current_zones and not obj.false_positive ) total_label_count = 0 # update counts and publish status for label in set(self.zone_data[zone].keys()) | set(obj_counter.keys()): # Ignore the artificial all label if label == "all": continue # if we have previously published a count for this zone/label zone_label = self.zone_data[zone][label] if camera in zone_label: current_count = sum(zone_label.values()) zone_label[camera] = ( obj_counter[label] if label in obj_counter else 0 ) new_count = sum(zone_label.values()) if new_count != current_count: self.client.publish( f"{self.topic_prefix}/{zone}/{label}", new_count, retain=False, ) # Set the count for the /zone/all topic. total_label_count += new_count # if this is a new zone/label combo for this camera else: if label in obj_counter: zone_label[camera] = obj_counter[label] self.client.publish( f"{self.topic_prefix}/{zone}/{label}", obj_counter[label], retain=False, ) # Set the count for the /zone/all topic. total_label_count += obj_counter[label] # if we have previously published a count for this zone all labels zone_label = self.zone_data[zone]["all"] if camera in zone_label: current_count = sum(zone_label.values()) zone_label[camera] = total_label_count new_count = sum(zone_label.values()) if new_count != current_count: self.client.publish( f"{self.topic_prefix}/{zone}/all", new_count, retain=False, ) # if this is a new zone all label for this camera else: zone_label[camera] = total_label_count self.client.publish( f"{self.topic_prefix}/{zone}/all", total_label_count, retain=False, ) # cleanup event finished queue while not self.event_processed_queue.empty(): event_id, camera = self.event_processed_queue.get() self.camera_states[camera].finished(event_id) logger.info(f"Exiting object processor...")