initial implementation of zones

config/config.example.yml

@@ -68,6 +68,41 @@ objects:
  max_area: 100000
  threshold: 0.5
 
+zones:
+ #################
+ # Name of the zone
+ ################
+ front_steps:
+ cameras:
+ front_door:
+ ####################
+ # For each camera, a list of x,y coordinates to define the polygon of the zone.
+ # Can also be a comma separated string of all x,y coordinates combined.
+ # The same zone can exist across multiple cameras if they have overlapping FOVs.
+ # An object is determined to be in the zone based on whether or not the bottom center
+ # of it's bounding box is within the polygon. The polygon must have at least 3 points.
+ # Coordinates can be generated at https://www.image-map.net/
+ ####################
+ coordinates:
+ - 545,1077
+ - 747,939
+ - 788,805
+ ################
+ # Zone level object filters. These are applied in addition to the global and camera filters
+ # and should be more restrictive than the global and camera filters. The global and camera
+ # filters are applied upstream.
+ ################
+ filters:
+ person:
+ min_area: 5000
+ max_area: 100000
+ threshold: 0.5
+ driveway:
+ cameras:
+ front_door:
+ coordinates: 545,1077,747,939,788,805
+ yard:
+
 cameras:
  back:
  ffmpeg:
@@ -137,6 +172,7 @@ cameras:
  ################
  snapshots:
  show_timestamp: True
+ draw_zones: False
 
  ################
  # Camera level object config. This config is merged with the global config above.

detect_objects.py

@@ -171,7 +171,8 @@ def on_connect(client, userdata, flags, rc):
  ##
  for name, config in CONFIG['cameras'].items():
  config['snapshots'] = {
- 'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True)
+ 'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True),
+ 'draw_zones': config.get('snapshots', {}).get('draw_zones', False)
  }
 
  # Queue for cameras to push tracked objects to
@@ -264,8 +265,8 @@ def on_connect(client, userdata, flags, rc):
 
  event_processor = EventProcessor(CONFIG['cameras'], camera_processes, '/cache', '/clips', event_queue)
  event_processor.start()
-
- object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue)
+ 
+ object_processor = TrackedObjectProcessor(CONFIG['cameras'], CONFIG.get('zones', {}), client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue)
  object_processor.start()
 
  camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, plasma_process)

frigate/object_processing.py

@@ -27,10 +27,34 @@ def filter_false_positives(event):
  return True
  return False
 
+def zone_filtered(obj, object_config):
+ object_name = obj['label']
+ object_filters = object_config.get('filters', {})
+
+ if object_name in object_filters:
+ obj_settings = object_filters[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['score']:
+ return True
+
+ return False
+
 class TrackedObjectProcessor(threading.Thread):
- def __init__(self, config, client, topic_prefix, tracked_objects_queue, event_queue):
+ def __init__(self, camera_config, zone_config, client, topic_prefix, tracked_objects_queue, event_queue):
  threading.Thread.__init__(self)
- self.config = config
+ self.camera_config = camera_config
+ self.zone_config = zone_config
  self.client = client
  self.topic_prefix = topic_prefix
  self.tracked_objects_queue = tracked_objects_queue
@@ -43,6 +67,28 @@ def __init__(self, config, client, topic_prefix, tracked_objects_queue, event_qu
  'current_frame_time': 0.0,
  'object_id': None
  })
+ self.zone_data = defaultdict(lambda: {
+ 'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
+ 'contours': {}
+ })
+
+ # create zone contours
+ for name, config in zone_config.items():
+ for camera, camera_zone_config in config.items():
+ coordinates = camera_zone_config['coordinates']
+ if isinstance(coordinates, list):
+ self.zone_data[name]['contours'][camera] = np.array([[int(p.split(',')[0]), int(p.split(',')[1])] for p in coordinates])
+ elif isinstance(coordinates, str):
+ points = coordinates.split(',')
+ self.zone_data[name]['contours'][camera] = 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 {name} - {camera}")
+
+ # set colors for zones
+ colors = plt.cm.get_cmap('tab10', len(self.zone_data.keys()))
+ for i, zone in enumerate(self.zone_data.values()):
+ zone['color'] = tuple(int(round(255 * c)) for c in colors(i)[:3])
+
  self.plasma_client = PlasmaManager()
 
  def get_best(self, camera, label):
@@ -58,7 +104,7 @@ def run(self):
  while True:
  camera, frame_time, current_tracked_objects = self.tracked_objects_queue.get()
 
- config = self.config[camera]
+ camera_config = self.camera_config[camera]
  best_objects = self.camera_data[camera]['best_objects']
  current_object_status = self.camera_data[camera]['object_status']
  tracked_objects = self.camera_data[camera]['tracked_objects']
@@ -89,6 +135,17 @@ def run(self):
 
  self.camera_data[camera]['current_frame_time'] = frame_time
 
+ # build a dict of objects in each zone for current camera
+ current_objects_in_zones = defaultdict(lambda: [])
+ for obj in tracked_objects.values():
+ bottom_center = (obj['centroid'][0], obj['box'][3])
+ # check each zone
+ for name, zone in self.zone_data.items():
+ # check each camera with a contour for the zone
+ for camera, contour in zone['contours'].items():
+ if cv2.pointPolygonTest(contour, bottom_center, False) >= 0 and not zone_filtered(obj, self.zone_config[name][camera].get('filters', {})):
+ current_objects_in_zones[name].append(obj['label'])
+
  ###
  # Draw tracked objects on the frame
  ###
@@ -111,10 +168,16 @@ def run(self):
  region = obj['region']
  cv2.rectangle(current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
 
- if config['snapshots']['show_timestamp']:
+ if camera_config['snapshots']['show_timestamp']:
  time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
  cv2.putText(current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
 
+ if camera_config['snapshots']['draw_zones']:
+ for name, zone in self.zone_data.items():
+ thickness = 2 if len(current_objects_in_zones[name]) == 0 else 8
+ if camera in zone['contours']:
+ cv2.drawContours(current_frame, [zone['contours'][camera]], -1, zone['color'], thickness)
+
  ###
  # Set the current frame
  ###
@@ -152,7 +215,26 @@ def run(self):
  ###
  # Report over MQTT
  ###
- # count objects by type
+
+ # get the zones that are relevant for this camera
+ relevant_zones = [zone for zone, config in self.zone_config.items() if camera in config]
+ # for each zone
+ for zone in relevant_zones:
+ # create the set of labels in the current frame and previously reported
+ labels_for_zone = set(current_objects_in_zones[zone] + list(self.zone_data[zone]['object_status'][camera].keys()))
+ # for each label
+ for label in labels_for_zone:
+ # compute the current 'ON' vs 'OFF' status by checking if any camera sees the object in the zone
+ previous_state = any([camera[label] == 'ON' for camera in self.zone_data[zone]['object_status'].values()])
+ self.zone_data[zone]['object_status'][camera][label] = 'ON' if label in current_objects_in_zones[zone] else 'OFF'
+ new_state = any([camera[label] == 'ON' for camera in self.zone_data[zone]['object_status'].values()])
+ # 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)
+
+ # count by type
  obj_counter = Counter()
  for obj in tracked_objects.values():
  obj_counter[obj['label']] += 1