import json import hashlib import datetime import time import copy import cv2 import threading import queue import copy import numpy as np from collections import Counter, defaultdict import itertools import matplotlib.pyplot as plt from frigate.util import draw_box_with_label, SharedMemoryFrameManager from frigate.edgetpu import load_labels from typing import Callable, Dict from statistics import mean, median PATH_TO_LABELS = '/labelmap.txt' LABELS = load_labels(PATH_TO_LABELS) cmap = plt.cm.get_cmap('tab10', len(LABELS.keys())) COLOR_MAP = {} for key, val in LABELS.items(): COLOR_MAP[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3]) def zone_filtered(obj, object_config): object_name = obj['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.get('min_area',-1) > obj['area']: return True # if the detected object is larger than the # max area, don't add it to detected objects if obj_settings.get('max_area', 24000000) < obj['area']: return True # if the score is lower than the threshold, skip if obj_settings.get('threshold', 0) > obj['computed_score']: return True return False # Maintains the state of a camera class CameraState(): def __init__(self, name, config, frame_manager): self.name = name self.config = config self.frame_manager = frame_manager self.best_objects = {} self.object_status = defaultdict(lambda: 'OFF') self.tracked_objects = {} self.zone_objects = defaultdict(lambda: []) self._current_frame = np.zeros(self.config['frame_shape'], np.uint8) self.current_frame_lock = threading.Lock() self.current_frame_time = 0.0 self.previous_frame_id = None self.callbacks = defaultdict(lambda: []) def get_current_frame(self): with self.current_frame_lock: return np.copy(self._current_frame) def false_positive(self, obj): # once a true positive, always a true positive if not obj.get('false_positive', True): return False threshold = self.config['objects'].get('filters', {}).get(obj['label'], {}).get('threshold', 0.85) if obj['computed_score'] < threshold: return True return False def compute_score(self, obj): scores = obj['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 on(self, event_type: str, callback: Callable[[Dict], None]): self.callbacks[event_type].append(callback) def update(self, frame_time, tracked_objects): self.current_frame_time = frame_time # get the new frame and delete the old frame frame_id = f"{self.name}{frame_time}" with self.current_frame_lock: self._current_frame = self.frame_manager.get(frame_id, self.config['frame_shape']) if not self.previous_frame_id is None: self.frame_manager.delete(self.previous_frame_id) self.previous_frame_id = frame_id current_ids = tracked_objects.keys() previous_ids = self.tracked_objects.keys() removed_ids = list(set(previous_ids).difference(current_ids)) new_ids = list(set(current_ids).difference(previous_ids)) updated_ids = list(set(current_ids).intersection(previous_ids)) for id in new_ids: self.tracked_objects[id] = tracked_objects[id] self.tracked_objects[id]['zones'] = [] # start the score history self.tracked_objects[id]['score_history'] = [self.tracked_objects[id]['score']] # calculate if this is a false positive self.tracked_objects[id]['computed_score'] = self.compute_score(self.tracked_objects[id]) self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id]) # call event handlers for c in self.callbacks['start']: c(self.name, tracked_objects[id]) for id in updated_ids: self.tracked_objects[id].update(tracked_objects[id]) # if the object is not in the current frame, add a 0.0 to the score history if self.tracked_objects[id]['frame_time'] != self.current_frame_time: self.tracked_objects[id]['score_history'].append(0.0) else: self.tracked_objects[id]['score_history'].append(self.tracked_objects[id]['score']) # only keep the last 10 scores if len(self.tracked_objects[id]['score_history']) > 10: self.tracked_objects[id]['score_history'] = self.tracked_objects[id]['score_history'][-10:] # calculate if this is a false positive self.tracked_objects[id]['computed_score'] = self.compute_score(self.tracked_objects[id]) self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id]) # call event handlers for c in self.callbacks['update']: c(self.name, self.tracked_objects[id]) for id in removed_ids: # publish events to mqtt self.tracked_objects[id]['end_time'] = frame_time for c in self.callbacks['end']: c(self.name, self.tracked_objects[id]) del self.tracked_objects[id] # check to see if the objects are in any zones for obj in self.tracked_objects.values(): current_zones = [] bottom_center = (obj['centroid'][0], obj['box'][3]) # check each zone for name, zone in self.config['zones'].items(): contour = zone['contour'] # check if the object is in the zone and not filtered if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0 and not zone_filtered(obj, zone.get('filters', {}))): current_zones.append(name) obj['zones'] = current_zones # draw on the frame if not self._current_frame is None: # draw the bounding boxes on the frame for obj in self.tracked_objects.values(): thickness = 2 color = COLOR_MAP[obj['label']] if obj['frame_time'] != frame_time: thickness = 1 color = (255,0,0) # draw the bounding boxes on the frame box = obj['box'] draw_box_with_label(self._current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color) # draw the regions on the frame region = obj['region'] cv2.rectangle(self._current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1) if self.config['snapshots']['show_timestamp']: time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S") cv2.putText(self._current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2) if self.config['snapshots']['draw_zones']: for name, zone in self.config['zones'].items(): thickness = 8 if any([name in obj['zones'] for obj in self.tracked_objects.values()]) else 2 cv2.drawContours(self._current_frame, [zone['contour']], -1, zone['color'], thickness) # maintain best objects for obj in self.tracked_objects.values(): object_type = obj['label'] # if the object wasn't seen on the current frame, skip it if obj['frame_time'] != self.current_frame_time or obj['false_positive']: continue obj_copy = copy.deepcopy(obj) 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 obj_copy['score'] > current_best['score'] or (now - current_best['frame_time']) > self.config.get('best_image_timeout', 60): obj_copy['frame'] = np.copy(self._current_frame) self.best_objects[object_type] = obj_copy for c in self.callbacks['snapshot']: c(self.name, self.best_objects[object_type]) else: obj_copy['frame'] = np.copy(self._current_frame) self.best_objects[object_type] = obj_copy for c in self.callbacks['snapshot']: c(self.name, self.best_objects[object_type]) # update overall camera state for each object type obj_counter = Counter() for obj in self.tracked_objects.values(): if not obj['false_positive']: obj_counter[obj['label']] += 1 # report on detected objects for obj_name, count in obj_counter.items(): new_status = 'ON' if count > 0 else 'OFF' if new_status != self.object_status[obj_name]: self.object_status[obj_name] = new_status for c in self.callbacks['object_status']: c(self.name, obj_name, new_status) # expire any objects that are ON and no longer detected expired_objects = [obj_name for obj_name, status in self.object_status.items() if status == 'ON' and not obj_name in obj_counter] for obj_name in expired_objects: self.object_status[obj_name] = 'OFF' for c in self.callbacks['object_status']: c(self.name, obj_name, 'OFF') for c in self.callbacks['snapshot']: c(self.name, self.best_objects[obj_name]) class TrackedObjectProcessor(threading.Thread): def __init__(self, camera_config, client, topic_prefix, tracked_objects_queue, event_queue, stop_event): threading.Thread.__init__(self) self.camera_config = camera_config self.client = client self.topic_prefix = topic_prefix self.tracked_objects_queue = tracked_objects_queue self.event_queue = event_queue self.stop_event = stop_event self.camera_states: Dict[str, CameraState] = {} self.frame_manager = SharedMemoryFrameManager() def start(camera, obj): # publish events to mqtt self.client.publish(f"{self.topic_prefix}/{camera}/events/start", json.dumps(obj), retain=False) self.event_queue.put(('start', camera, obj)) def update(camera, obj): pass def end(camera, obj): self.client.publish(f"{self.topic_prefix}/{camera}/events/end", json.dumps(obj), retain=False) self.event_queue.put(('end', camera, obj)) def snapshot(camera, obj): if not 'frame' in obj: return best_frame = cv2.cvtColor(obj['frame'], cv2.COLOR_RGB2BGR) mqtt_config = self.camera_config[camera].get('mqtt', {'crop_to_region': False}) if mqtt_config.get('crop_to_region'): region = obj['region'] best_frame = best_frame[region[1]:region[3], region[0]:region[2]] if 'snapshot_height' in mqtt_config: height = int(mqtt_config['snapshot_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) ret, jpg = cv2.imencode('.jpg', best_frame) if ret: jpg_bytes = jpg.tobytes() self.client.publish(f"{self.topic_prefix}/{camera}/{obj['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.camera_config.keys(): camera_state = CameraState(camera, self.camera_config[camera], 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 self.camera_data = defaultdict(lambda: { 'best_objects': {}, 'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')), 'tracked_objects': {}, 'current_frame': np.zeros((720,1280,3), np.uint8), 'current_frame_time': 0.0, 'object_id': None }) # { # 'zone_name': { # 'person': ['camera_1', 'camera_2'] # } # } self.zone_data = defaultdict(lambda: defaultdict(lambda: set())) # set colors for zones all_zone_names = set([zone for config in self.camera_config.values() for zone in config['zones'].keys()]) zone_colors = {} colors = plt.cm.get_cmap('tab10', len(all_zone_names)) for i, zone in enumerate(all_zone_names): zone_colors[zone] = tuple(int(round(255 * c)) for c in colors(i)[:3]) # create zone contours for camera_config in self.camera_config.values(): for zone_name, zone_config in camera_config['zones'].items(): zone_config['color'] = zone_colors[zone_name] coordinates = zone_config['coordinates'] if isinstance(coordinates, list): zone_config['contour'] = np.array([[int(p.split(',')[0]), int(p.split(',')[1])] for p in coordinates]) elif isinstance(coordinates, str): points = coordinates.split(',') zone_config['contour'] = np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)]) else: print(f"Unable to parse zone coordinates for {zone_name} - {camera}") def get_best(self, camera, label): best_objects = self.camera_states[camera].best_objects if label in best_objects: return best_objects[label] else: return {} def get_current_frame(self, camera): return self.camera_states[camera].get_current_frame() def run(self): while True: if self.stop_event.is_set(): print(f"Exiting object processor...") break try: camera, frame_time, current_tracked_objects = 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) # update zone status for each label for zone in camera_state.config['zones'].keys(): # get labels for current camera and all labels in current zone labels_for_camera = set([obj['label'] for obj in camera_state.tracked_objects.values() if zone in obj['zones'] and not obj['false_positive']]) labels_to_check = labels_for_camera | set(self.zone_data[zone].keys()) # for each label in zone for label in labels_to_check: camera_list = self.zone_data[zone][label] # remove or add the camera to the list for the current label previous_state = len(camera_list) > 0 if label in labels_for_camera: camera_list.add(camera_state.name) elif camera_state.name in camera_list: camera_list.remove(camera_state.name) new_state = len(camera_list) > 0 # if the value is changing, send over MQTT if previous_state == False and new_state == True: self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'ON', retain=False) elif previous_state == True and new_state == False: self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'OFF', retain=False)