formatting cleanup

frigate/__main__.py

@@ -1,4 +1,6 @@
-import faulthandler; faulthandler.enable()
+import faulthandler
+
+faulthandler.enable()
 import sys
 import threading
 
@@ -6,10 +8,10 @@ threading.current_thread().name = "frigate"
 
 from frigate.app import FrigateApp
 
-cli = sys.modules['flask.cli']
+cli = sys.modules["flask.cli"]
 cli.show_server_banner = lambda *x: None
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     frigate_app = FrigateApp()
 
     frigate_app.start()

frigate/app.py

@@ -31,7 +31,8 @@ from frigate.zeroconf import broadcast_zeroconf
 
 logger = logging.getLogger(__name__)
 
-class FrigateApp():
+
+class FrigateApp:
     def __init__(self):
         self.stop_event = mp.Event()
         self.config: FrigateConfig = None
@@ -56,60 +57,78 @@ class FrigateApp():
 
         tmpfs_size = self.config.clips.tmpfs_cache_size
         if tmpfs_size:
-             logger.info(f"Creating tmpfs of size {tmpfs_size}")
-             rc = os.system(f"mount -t tmpfs -o size={tmpfs_size} tmpfs {CACHE_DIR}")
-             if rc != 0:
-                 logger.error(f"Failed to create tmpfs, error code: {rc}")
-    
+            logger.info(f"Creating tmpfs of size {tmpfs_size}")
+            rc = os.system(f"mount -t tmpfs -o size={tmpfs_size} tmpfs {CACHE_DIR}")
+            if rc != 0:
+                logger.error(f"Failed to create tmpfs, error code: {rc}")
+
     def init_logger(self):
-        self.log_process = mp.Process(target=log_process, args=(self.log_queue,), name='log_process')
+        self.log_process = mp.Process(
+            target=log_process, args=(self.log_queue,), name="log_process"
+        )
         self.log_process.daemon = True
         self.log_process.start()
         root_configurer(self.log_queue)
-    
+
     def init_config(self):
-        config_file = os.environ.get('CONFIG_FILE', '/config/config.yml')
+        config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
         self.config = FrigateConfig(config_file=config_file)
 
         for camera_name in self.config.cameras.keys():
             # create camera_metrics
             self.camera_metrics[camera_name] = {
-                'camera_fps': mp.Value('d', 0.0),
-                'skipped_fps': mp.Value('d', 0.0),
-                'process_fps': mp.Value('d', 0.0),
-                'detection_enabled': mp.Value('i', self.config.cameras[camera_name].detect.enabled),
-                'detection_fps': mp.Value('d', 0.0),
-                'detection_frame': mp.Value('d', 0.0),
-                'read_start': mp.Value('d', 0.0),
-                'ffmpeg_pid': mp.Value('i', 0),
-                'frame_queue': mp.Queue(maxsize=2),
+                "camera_fps": mp.Value("d", 0.0),
+                "skipped_fps": mp.Value("d", 0.0),
+                "process_fps": mp.Value("d", 0.0),
+                "detection_enabled": mp.Value(
+                    "i", self.config.cameras[camera_name].detect.enabled
+                ),
+                "detection_fps": mp.Value("d", 0.0),
+                "detection_frame": mp.Value("d", 0.0),
+                "read_start": mp.Value("d", 0.0),
+                "ffmpeg_pid": mp.Value("i", 0),
+                "frame_queue": mp.Queue(maxsize=2),
             }
-        
+
     def check_config(self):
         for name, camera in self.config.cameras.items():
-            assigned_roles = list(set([r for i in camera.ffmpeg.inputs for r in i.roles]))
-            if not camera.clips.enabled and 'clips' in assigned_roles:
-                logger.warning(f"Camera {name} has clips assigned to an input, but clips is not enabled.")
-            elif camera.clips.enabled and not 'clips' in assigned_roles:
-                logger.warning(f"Camera {name} has clips enabled, but clips is not assigned to an input.")
-
-            if not camera.record.enabled and 'record' in assigned_roles:
-                logger.warning(f"Camera {name} has record assigned to an input, but record is not enabled.")
-            elif camera.record.enabled and not 'record' in assigned_roles:
-                logger.warning(f"Camera {name} has record enabled, but record is not assigned to an input.")
-
-            if not camera.rtmp.enabled and 'rtmp' in assigned_roles:
-                logger.warning(f"Camera {name} has rtmp assigned to an input, but rtmp is not enabled.")
-            elif camera.rtmp.enabled and not 'rtmp' in assigned_roles:
-                logger.warning(f"Camera {name} has rtmp enabled, but rtmp is not assigned to an input.")
-    
+            assigned_roles = list(
+                set([r for i in camera.ffmpeg.inputs for r in i.roles])
+            )
+            if not camera.clips.enabled and "clips" in assigned_roles:
+                logger.warning(
+                    f"Camera {name} has clips assigned to an input, but clips is not enabled."
+                )
+            elif camera.clips.enabled and not "clips" in assigned_roles:
+                logger.warning(
+                    f"Camera {name} has clips enabled, but clips is not assigned to an input."
+                )
+
+            if not camera.record.enabled and "record" in assigned_roles:
+                logger.warning(
+                    f"Camera {name} has record assigned to an input, but record is not enabled."
+                )
+            elif camera.record.enabled and not "record" in assigned_roles:
+                logger.warning(
+                    f"Camera {name} has record enabled, but record is not assigned to an input."
+                )
+
+            if not camera.rtmp.enabled and "rtmp" in assigned_roles:
+                logger.warning(
+                    f"Camera {name} has rtmp assigned to an input, but rtmp is not enabled."
+                )
+            elif camera.rtmp.enabled and not "rtmp" in assigned_roles:
+                logger.warning(
+                    f"Camera {name} has rtmp enabled, but rtmp is not assigned to an input."
+                )
+
     def set_log_levels(self):
         logging.getLogger().setLevel(self.config.logger.default)
         for log, level in self.config.logger.logs.items():
             logging.getLogger(log).setLevel(level)
-        
-        if not 'geventwebsocket.handler' in self.config.logger.logs:
-            logging.getLogger('geventwebsocket.handler').setLevel('ERROR')
+
+        if not "geventwebsocket.handler" in self.config.logger.logs:
+            logging.getLogger("geventwebsocket.handler").setLevel("ERROR")
 
     def init_queues(self):
         # Queues for clip processing
@@ -117,13 +136,15 @@ class FrigateApp():
         self.event_processed_queue = mp.Queue()
 
         # Queue for cameras to push tracked objects to
-        self.detected_frames_queue = mp.Queue(maxsize=len(self.config.cameras.keys())*2)
+        self.detected_frames_queue = mp.Queue(
+            maxsize=len(self.config.cameras.keys()) * 2
+        )
 
     def init_database(self):
         migrate_db = SqliteExtDatabase(self.config.database.path)
 
         # Run migrations
-        del(logging.getLogger('peewee_migrate').handlers[:])
+        del logging.getLogger("peewee_migrate").handlers[:]
         router = Router(migrate_db)
         router.run()
 
@@ -137,7 +158,13 @@ class FrigateApp():
         self.stats_tracking = stats_init(self.camera_metrics, self.detectors)
 
     def init_web_server(self):
-        self.flask_app = create_app(self.config, self.db, self.stats_tracking, self.detected_frames_processor, self.mqtt_client)
+        self.flask_app = create_app(
+            self.config,
+            self.db,
+            self.stats_tracking,
+            self.detected_frames_processor,
+            self.mqtt_client,
+        )
 
     def init_mqtt(self):
         self.mqtt_client = create_mqtt_client(self.config, self.camera_metrics)
@@ -146,56 +173,108 @@ class FrigateApp():
         model_shape = (self.config.model.height, self.config.model.width)
         for name in self.config.cameras.keys():
             self.detection_out_events[name] = mp.Event()
-            shm_in = mp.shared_memory.SharedMemory(name=name, create=True, size=self.config.model.height*self.config.model.width*3)
-            shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}", create=True, size=20*6*4)
+            shm_in = mp.shared_memory.SharedMemory(
+                name=name,
+                create=True,
+                size=self.config.model.height * self.config.model.width * 3,
+            )
+            shm_out = mp.shared_memory.SharedMemory(
+                name=f"out-{name}", create=True, size=20 * 6 * 4
+            )
             self.detection_shms.append(shm_in)
             self.detection_shms.append(shm_out)
 
         for name, detector in self.config.detectors.items():
-            if detector.type == 'cpu':
-                self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, 'cpu', detector.num_threads)
-            if detector.type == 'edgetpu':
-                self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, detector.device, detector.num_threads)
+            if detector.type == "cpu":
+                self.detectors[name] = EdgeTPUProcess(
+                    name,
+                    self.detection_queue,
+                    self.detection_out_events,
+                    model_shape,
+                    "cpu",
+                    detector.num_threads,
+                )
+            if detector.type == "edgetpu":
+                self.detectors[name] = EdgeTPUProcess(
+                    name,
+                    self.detection_queue,
+                    self.detection_out_events,
+                    model_shape,
+                    detector.device,
+                    detector.num_threads,
+                )
 
     def start_detected_frames_processor(self):
-        self.detected_frames_processor = TrackedObjectProcessor(self.config, self.mqtt_client, self.config.mqtt.topic_prefix, 
-            self.detected_frames_queue, self.event_queue, self.event_processed_queue, self.stop_event)
+        self.detected_frames_processor = TrackedObjectProcessor(
+            self.config,
+            self.mqtt_client,
+            self.config.mqtt.topic_prefix,
+            self.detected_frames_queue,
+            self.event_queue,
+            self.event_processed_queue,
+            self.stop_event,
+        )
         self.detected_frames_processor.start()
 
     def start_camera_processors(self):
         model_shape = (self.config.model.height, self.config.model.width)
         for name, config in self.config.cameras.items():
-            camera_process = mp.Process(target=track_camera, name=f"camera_processor:{name}", args=(name, config, model_shape,
-                self.detection_queue, self.detection_out_events[name], self.detected_frames_queue, 
-                self.camera_metrics[name]))
+            camera_process = mp.Process(
+                target=track_camera,
+                name=f"camera_processor:{name}",
+                args=(
+                    name,
+                    config,
+                    model_shape,
+                    self.detection_queue,
+                    self.detection_out_events[name],
+                    self.detected_frames_queue,
+                    self.camera_metrics[name],
+                ),
+            )
             camera_process.daemon = True
-            self.camera_metrics[name]['process'] = camera_process
+            self.camera_metrics[name]["process"] = camera_process
             camera_process.start()
             logger.info(f"Camera processor started for {name}: {camera_process.pid}")
 
     def start_camera_capture_processes(self):
         for name, config in self.config.cameras.items():
-            capture_process = mp.Process(target=capture_camera, name=f"camera_capture:{name}", args=(name, config,
-                self.camera_metrics[name]))
+            capture_process = mp.Process(
+                target=capture_camera,
+                name=f"camera_capture:{name}",
+                args=(name, config, self.camera_metrics[name]),
+            )
             capture_process.daemon = True
-            self.camera_metrics[name]['capture_process'] = capture_process
+            self.camera_metrics[name]["capture_process"] = capture_process
             capture_process.start()
             logger.info(f"Capture process started for {name}: {capture_process.pid}")
-    
+
     def start_event_processor(self):
-        self.event_processor = EventProcessor(self.config, self.camera_metrics, self.event_queue, self.event_processed_queue, self.stop_event)
+        self.event_processor = EventProcessor(
+            self.config,
+            self.camera_metrics,
+            self.event_queue,
+            self.event_processed_queue,
+            self.stop_event,
+        )
         self.event_processor.start()
-    
+
     def start_event_cleanup(self):
         self.event_cleanup = EventCleanup(self.config, self.stop_event)
         self.event_cleanup.start()
-    
+
     def start_recording_maintainer(self):
         self.recording_maintainer = RecordingMaintainer(self.config, self.stop_event)
         self.recording_maintainer.start()
 
     def start_stats_emitter(self):
-        self.stats_emitter = StatsEmitter(self.config, self.stats_tracking, self.mqtt_client, self.config.mqtt.topic_prefix, self.stop_event)
+        self.stats_emitter = StatsEmitter(
+            self.config,
+            self.stats_tracking,
+            self.mqtt_client,
+            self.config.mqtt.topic_prefix,
+            self.stop_event,
+        )
         self.stats_emitter.start()
 
     def start_watchdog(self):
@@ -238,14 +317,16 @@ class FrigateApp():
         def receiveSignal(signalNumber, frame):
             self.stop()
             sys.exit()
-        
+
         signal.signal(signal.SIGTERM, receiveSignal)
 
-        server = pywsgi.WSGIServer(('127.0.0.1', 5001), self.flask_app, handler_class=WebSocketHandler)
+        server = pywsgi.WSGIServer(
+            ("127.0.0.1", 5001), self.flask_app, handler_class=WebSocketHandler
+        )
         server.serve_forever()
 
         self.stop()
-    
+
     def stop(self):
         logger.info(f"Stopping...")
         self.stop_event.set()

frigate/const.py

@@ -1,3 +1,3 @@
-CLIPS_DIR = '/media/frigate/clips'
-RECORD_DIR = '/media/frigate/recordings'
-CACHE_DIR = '/tmp/cache'
+CLIPS_DIR = "/media/frigate/clips"
+RECORD_DIR = "/media/frigate/recordings"
+CACHE_DIR = "/tmp/cache"

frigate/edgetpu.py

@@ -1,48 +1,49 @@
 import datetime
-import hashlib
 import logging
 import multiprocessing as mp
 import os
 import queue
-import threading
 import signal
+import threading
 from abc import ABC, abstractmethod
-from multiprocessing.connection import Connection
-from setproctitle import setproctitle
 from typing import Dict
 
 import numpy as np
 import tflite_runtime.interpreter as tflite
+from setproctitle import setproctitle
 from tflite_runtime.interpreter import load_delegate
 
 from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen
 
 logger = logging.getLogger(__name__)
 
-def load_labels(path, encoding='utf-8'):
-  """Loads labels from file (with or without index numbers).
-  Args:
-    path: path to label file.
-    encoding: label file encoding.
-  Returns:
-    Dictionary mapping indices to labels.
-  """
-  with open(path, 'r', encoding=encoding) as f:
-    lines = f.readlines()
-    if not lines:
-        return {}
-
-    if lines[0].split(' ', maxsplit=1)[0].isdigit():
-        pairs = [line.split(' ', maxsplit=1) for line in lines]
-        return {int(index): label.strip() for index, label in pairs}
-    else:
-        return {index: line.strip() for index, line in enumerate(lines)}
+
+def load_labels(path, encoding="utf-8"):
+    """Loads labels from file (with or without index numbers).
+    Args:
+      path: path to label file.
+      encoding: label file encoding.
+    Returns:
+      Dictionary mapping indices to labels.
+    """
+    with open(path, "r", encoding=encoding) as f:
+        lines = f.readlines()
+        if not lines:
+            return {}
+
+        if lines[0].split(" ", maxsplit=1)[0].isdigit():
+            pairs = [line.split(" ", maxsplit=1) for line in lines]
+            return {int(index): label.strip() for index, label in pairs}
+        else:
+            return {index: line.strip() for index, line in enumerate(lines)}
+
 
 class ObjectDetector(ABC):
     @abstractmethod
-    def detect(self, tensor_input, threshold = .4):
+    def detect(self, tensor_input, threshold=0.4):
         pass
 
+
 class LocalObjectDetector(ObjectDetector):
     def __init__(self, tf_device=None, num_threads=3, labels=None):
         self.fps = EventsPerSecond()
@@ -57,27 +58,29 @@ class LocalObjectDetector(ObjectDetector):
 
         edge_tpu_delegate = None
 
-        if tf_device != 'cpu':
+        if tf_device != "cpu":
             try:
                 logger.info(f"Attempting to load TPU as {device_config['device']}")
-                edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', device_config)
+                edge_tpu_delegate = load_delegate("libedgetpu.so.1.0", device_config)
                 logger.info("TPU found")
                 self.interpreter = tflite.Interpreter(
-                    model_path='/edgetpu_model.tflite',
-                    experimental_delegates=[edge_tpu_delegate])
+                    model_path="/edgetpu_model.tflite",
+                    experimental_delegates=[edge_tpu_delegate],
+                )
             except ValueError:
                 logger.info("No EdgeTPU detected.")
                 raise
         else:
             self.interpreter = tflite.Interpreter(
-                model_path='/cpu_model.tflite', num_threads=num_threads)
-        
+                model_path="/cpu_model.tflite", num_threads=num_threads
+            )
+
         self.interpreter.allocate_tensors()
 
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
-    
-    def detect(self, tensor_input, threshold=.4):
+
+    def detect(self, tensor_input, threshold=0.4):
         detections = []
 
         raw_detections = self.detect_raw(tensor_input)
@@ -85,28 +88,49 @@ class LocalObjectDetector(ObjectDetector):
         for d in raw_detections:
             if d[1] < threshold:
                 break
-            detections.append((
-                self.labels[int(d[0])],
-                float(d[1]),
-                (d[2], d[3], d[4], d[5])
-            ))
+            detections.append(
+                (self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
+            )
         self.fps.update()
         return detections
 
     def detect_raw(self, tensor_input):
-        self.interpreter.set_tensor(self.tensor_input_details[0]['index'], tensor_input)
+        self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
         self.interpreter.invoke()
-        boxes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[0]['index']))
-        label_codes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[1]['index']))
-        scores = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[2]['index']))
+        boxes = np.squeeze(
+            self.interpreter.get_tensor(self.tensor_output_details[0]["index"])
+        )
+        label_codes = np.squeeze(
+            self.interpreter.get_tensor(self.tensor_output_details[1]["index"])
+        )
+        scores = np.squeeze(
+            self.interpreter.get_tensor(self.tensor_output_details[2]["index"])
+        )
 
-        detections = np.zeros((20,6), np.float32)
+        detections = np.zeros((20, 6), np.float32)
         for i, score in enumerate(scores):
-            detections[i] = [label_codes[i], score, boxes[i][0], boxes[i][1], boxes[i][2], boxes[i][3]]
-        
+            detections[i] = [
+                label_codes[i],
+                score,
+                boxes[i][0],
+                boxes[i][1],
+                boxes[i][2],
+                boxes[i][3],
+            ]
+
         return detections
 
-def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.Event], avg_speed, start, model_shape, tf_device, num_threads):
+
+def run_detector(
+    name: str,
+    detection_queue: mp.Queue,
+    out_events: Dict[str, mp.Event],
+    avg_speed,
+    start,
+    model_shape,
+    tf_device,
+    num_threads,
+):
     threading.current_thread().name = f"detector:{name}"
     logger = logging.getLogger(f"detector.{name}")
     logger.info(f"Starting detection process: {os.getpid()}")
@@ -114,9 +138,10 @@ def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.
     listen()
 
     stop_event = mp.Event()
+
     def receiveSignal(signalNumber, frame):
         stop_event.set()
-    
+
     signal.signal(signal.SIGTERM, receiveSignal)
     signal.signal(signal.SIGINT, receiveSignal)
 
@@ -126,12 +151,9 @@ def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.
     outputs = {}
     for name in out_events.keys():
         out_shm = mp.shared_memory.SharedMemory(name=f"out-{name}", create=False)
-        out_np = np.ndarray((20,6), dtype=np.float32, buffer=out_shm.buf)
-        outputs[name] = {
-            'shm': out_shm,
-            'np': out_np
-        }
-    
+        out_np = np.ndarray((20, 6), dtype=np.float32, buffer=out_shm.buf)
+        outputs[name] = {"shm": out_shm, "np": out_np}
+
     while True:
         if stop_event.is_set():
             break
@@ -140,7 +162,9 @@ def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.
             connection_id = detection_queue.get(timeout=5)
         except queue.Empty:
             continue
-        input_frame = frame_manager.get(connection_id, (1,model_shape[0],model_shape[1],3))
+        input_frame = frame_manager.get(
+            connection_id, (1, model_shape[0], model_shape[1], 3)
+        )
 
         if input_frame is None:
             continue
@@ -148,26 +172,35 @@ def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.
         # detect and send the output
         start.value = datetime.datetime.now().timestamp()
         detections = object_detector.detect_raw(input_frame)
-        duration = datetime.datetime.now().timestamp()-start.value
-        outputs[connection_id]['np'][:] = detections[:]
+        duration = datetime.datetime.now().timestamp() - start.value
+        outputs[connection_id]["np"][:] = detections[:]
         out_events[connection_id].set()
         start.value = 0.0
 
-        avg_speed.value = (avg_speed.value*9 + duration)/10
-        
-class EdgeTPUProcess():
-    def __init__(self, name, detection_queue, out_events, model_shape, tf_device=None, num_threads=3):
+        avg_speed.value = (avg_speed.value * 9 + duration) / 10
+
+
+class EdgeTPUProcess:
+    def __init__(
+        self,
+        name,
+        detection_queue,
+        out_events,
+        model_shape,
+        tf_device=None,
+        num_threads=3,
+    ):
         self.name = name
         self.out_events = out_events
         self.detection_queue = detection_queue
-        self.avg_inference_speed = mp.Value('d', 0.01)
-        self.detection_start = mp.Value('d', 0.0)
+        self.avg_inference_speed = mp.Value("d", 0.01)
+        self.detection_start = mp.Value("d", 0.0)
         self.detect_process = None
         self.model_shape = model_shape
         self.tf_device = tf_device
         self.num_threads = num_threads
         self.start_or_restart()
-    
+
     def stop(self):
         self.detect_process.terminate()
         logging.info("Waiting for detection process to exit gracefully...")
@@ -181,11 +214,25 @@ class EdgeTPUProcess():
         self.detection_start.value = 0.0
         if (not self.detect_process is None) and self.detect_process.is_alive():
             self.stop()
-        self.detect_process = mp.Process(target=run_detector, name=f"detector:{self.name}", args=(self.name, self.detection_queue, self.out_events, self.avg_inference_speed, self.detection_start, self.model_shape, self.tf_device, self.num_threads))
+        self.detect_process = mp.Process(
+            target=run_detector,
+            name=f"detector:{self.name}",
+            args=(
+                self.name,
+                self.detection_queue,
+                self.out_events,
+                self.avg_inference_speed,
+                self.detection_start,
+                self.model_shape,
+                self.tf_device,
+                self.num_threads,
+            ),
+        )
         self.detect_process.daemon = True
         self.detect_process.start()
 
-class RemoteObjectDetector():
+
+class RemoteObjectDetector:
     def __init__(self, name, labels, detection_queue, event, model_shape):
         self.labels = load_labels(labels)
         self.name = name
@@ -193,11 +240,15 @@ class RemoteObjectDetector():
         self.detection_queue = detection_queue
         self.event = event
         self.shm = mp.shared_memory.SharedMemory(name=self.name, create=False)
-        self.np_shm = np.ndarray((1,model_shape[0],model_shape[1],3), dtype=np.uint8, buffer=self.shm.buf)
-        self.out_shm = mp.shared_memory.SharedMemory(name=f"out-{self.name}", create=False)
-        self.out_np_shm = np.ndarray((20,6), dtype=np.float32, buffer=self.out_shm.buf)
-    
-    def detect(self, tensor_input, threshold=.4):
+        self.np_shm = np.ndarray(
+            (1, model_shape[0], model_shape[1], 3), dtype=np.uint8, buffer=self.shm.buf
+        )
+        self.out_shm = mp.shared_memory.SharedMemory(
+            name=f"out-{self.name}", create=False
+        )
+        self.out_np_shm = np.ndarray((20, 6), dtype=np.float32, buffer=self.out_shm.buf)
+
+    def detect(self, tensor_input, threshold=0.4):
         detections = []
 
         # copy input to shared memory
@@ -213,14 +264,12 @@ class RemoteObjectDetector():
         for d in self.out_np_shm:
             if d[1] < threshold:
                 break
-            detections.append((
-                self.labels[int(d[0])],
-                float(d[1]),
-                (d[2], d[3], d[4], d[5])
-            ))
+            detections.append(
+                (self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
+            )
         self.fps.update()
         return detections
-    
+
     def cleanup(self):
         self.shm.unlink()
         self.out_shm.unlink()

frigate/events.py

@@ -20,10 +20,13 @@ from peewee import fn
 
 logger = logging.getLogger(__name__)
 
+
 class EventProcessor(threading.Thread):
-    def __init__(self, config, camera_processes, event_queue, event_processed_queue, stop_event):
+    def __init__(
+        self, config, camera_processes, event_queue, event_processed_queue, stop_event
+    ):
         threading.Thread.__init__(self)
-        self.name = 'event_processor'
+        self.name = "event_processor"
         self.config = config
         self.camera_processes = camera_processes
         self.cached_clips = {}
@@ -33,31 +36,35 @@ class EventProcessor(threading.Thread):
         self.stop_event = stop_event
 
     def should_create_clip(self, camera, event_data):
-        if event_data['false_positive']:
+        if event_data["false_positive"]:
             return False
-        
+
         # if there are required zones and there is no overlap
         required_zones = self.config.cameras[camera].clips.required_zones
-        if len(required_zones) > 0 and not set(event_data['entered_zones']) & set(required_zones):
-            logger.debug(f"Not creating clip for {event_data['id']} because it did not enter required zones")
+        if len(required_zones) > 0 and not set(event_data["entered_zones"]) & set(
+            required_zones
+        ):
+            logger.debug(
+                f"Not creating clip for {event_data['id']} because it did not enter required zones"
+            )
             return False
 
         return True
-    
+
     def refresh_cache(self):
         cached_files = os.listdir(CACHE_DIR)
 
         files_in_use = []
         for process in psutil.process_iter():
             try:
-                if process.name() != 'ffmpeg':
+                if process.name() != "ffmpeg":
                     continue
 
                 flist = process.open_files()
                 if flist:
                     for nt in flist:
                         if nt.path.startswith(CACHE_DIR):
-                            files_in_use.append(nt.path.split('/')[-1])
+                            files_in_use.append(nt.path.split("/")[-1])
             except:
                 continue
 
@@ -65,119 +72,154 @@ class EventProcessor(threading.Thread):
             if f in files_in_use or f in self.cached_clips:
                 continue
 
-            camera = '-'.join(f.split('-')[:-1])
-            start_time = datetime.datetime.strptime(f.split('-')[-1].split('.')[0], '%Y%m%d%H%M%S')
-        
-            ffprobe_cmd = " ".join([
-                'ffprobe',
-                '-v',
-                'error',
-                '-show_entries',
-                'format=duration',
-                '-of',
-                'default=noprint_wrappers=1:nokey=1',
-                f"{os.path.join(CACHE_DIR,f)}"
-            ])
+            camera = "-".join(f.split("-")[:-1])
+            start_time = datetime.datetime.strptime(
+                f.split("-")[-1].split(".")[0], "%Y%m%d%H%M%S"
+            )
+
+            ffprobe_cmd = " ".join(
+                [
+                    "ffprobe",
+                    "-v",
+                    "error",
+                    "-show_entries",
+                    "format=duration",
+                    "-of",
+                    "default=noprint_wrappers=1:nokey=1",
+                    f"{os.path.join(CACHE_DIR,f)}",
+                ]
+            )
             p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
             (output, err) = p.communicate()
             p_status = p.wait()
             if p_status == 0:
-                duration = float(output.decode('utf-8').strip())
+                duration = float(output.decode("utf-8").strip())
             else:
                 logger.info(f"bad file: {f}")
-                os.remove(os.path.join(CACHE_DIR,f))
+                os.remove(os.path.join(CACHE_DIR, f))
                 continue
 
             self.cached_clips[f] = {
-                'path': f,
-                'camera': camera,
-                'start_time': start_time.timestamp(),
-                'duration': duration
+                "path": f,
+                "camera": camera,
+                "start_time": start_time.timestamp(),
+                "duration": duration,
             }
 
         if len(self.events_in_process) > 0:
-            earliest_event = min(self.events_in_process.values(), key=lambda x:x['start_time'])['start_time']
+            earliest_event = min(
+                self.events_in_process.values(), key=lambda x: x["start_time"]
+            )["start_time"]
         else:
             earliest_event = datetime.datetime.now().timestamp()
 
         # if the earliest event exceeds the max seconds, cap it
         max_seconds = self.config.clips.max_seconds
-        if datetime.datetime.now().timestamp()-earliest_event > max_seconds:
-            earliest_event = datetime.datetime.now().timestamp()-max_seconds
-        
+        if datetime.datetime.now().timestamp() - earliest_event > max_seconds:
+            earliest_event = datetime.datetime.now().timestamp() - max_seconds
+
         for f, data in list(self.cached_clips.items()):
-            if earliest_event-90 > data['start_time']+data['duration']:
+            if earliest_event - 90 > data["start_time"] + data["duration"]:
                 del self.cached_clips[f]
                 logger.debug(f"Cleaning up cached file {f}")
-                os.remove(os.path.join(CACHE_DIR,f))
-        
+                os.remove(os.path.join(CACHE_DIR, f))
+
         # if we are still using more than 90% of the cache, proactively cleanup
         cache_usage = shutil.disk_usage("/tmp/cache")
-        if cache_usage.used/cache_usage.total > .9 and cache_usage.free < 200000000 and len(self.cached_clips) > 0:
+        if (
+            cache_usage.used / cache_usage.total > 0.9
+            and cache_usage.free < 200000000
+            and len(self.cached_clips) > 0
+        ):
             logger.warning("More than 90% of the cache is used.")
-            logger.warning("Consider increasing space available at /tmp/cache or reducing max_seconds in your clips config.")
+            logger.warning(
+                "Consider increasing space available at /tmp/cache or reducing max_seconds in your clips config."
+            )
             logger.warning("Proactively cleaning up the cache...")
-            while cache_usage.used/cache_usage.total > .9:
-                oldest_clip = min(self.cached_clips.values(), key=lambda x:x['start_time'])
-                del self.cached_clips[oldest_clip['path']]
-                os.remove(os.path.join(CACHE_DIR,oldest_clip['path']))
+            while cache_usage.used / cache_usage.total > 0.9:
+                oldest_clip = min(
+                    self.cached_clips.values(), key=lambda x: x["start_time"]
+                )
+                del self.cached_clips[oldest_clip["path"]]
+                os.remove(os.path.join(CACHE_DIR, oldest_clip["path"]))
                 cache_usage = shutil.disk_usage("/tmp/cache")
 
     def create_clip(self, camera, event_data, pre_capture, post_capture):
         # get all clips from the camera with the event sorted
-        sorted_clips = sorted([c for c in self.cached_clips.values() if c['camera'] == camera], key = lambda i: i['start_time'])
+        sorted_clips = sorted(
+            [c for c in self.cached_clips.values() if c["camera"] == camera],
+            key=lambda i: i["start_time"],
+        )
 
         # if there are no clips in the cache or we are still waiting on a needed file check every 5 seconds
         wait_count = 0
-        while len(sorted_clips) == 0 or sorted_clips[-1]['start_time'] + sorted_clips[-1]['duration'] < event_data['end_time']+post_capture:
+        while (
+            len(sorted_clips) == 0
+            or sorted_clips[-1]["start_time"] + sorted_clips[-1]["duration"]
+            < event_data["end_time"] + post_capture
+        ):
             if wait_count > 4:
-                logger.warning(f"Unable to create clip for {camera} and event {event_data['id']}. There were no cache files for this event.")
+                logger.warning(
+                    f"Unable to create clip for {camera} and event {event_data['id']}. There were no cache files for this event."
+                )
                 return False
             logger.debug(f"No cache clips for {camera}. Waiting...")
             time.sleep(5)
             self.refresh_cache()
             # get all clips from the camera with the event sorted
-            sorted_clips = sorted([c for c in self.cached_clips.values() if c['camera'] == camera], key = lambda i: i['start_time'])
+            sorted_clips = sorted(
+                [c for c in self.cached_clips.values() if c["camera"] == camera],
+                key=lambda i: i["start_time"],
+            )
             wait_count += 1
-        
-        playlist_start = event_data['start_time']-pre_capture
-        playlist_end = event_data['end_time']+post_capture
+
+        playlist_start = event_data["start_time"] - pre_capture
+        playlist_end = event_data["end_time"] + post_capture
         playlist_lines = []
         for clip in sorted_clips:
             # clip ends before playlist start time, skip
-            if clip['start_time']+clip['duration'] < playlist_start:
+            if clip["start_time"] + clip["duration"] < playlist_start:
                 continue
             # clip starts after playlist ends, finish
-            if clip['start_time'] > playlist_end:
+            if clip["start_time"] > playlist_end:
                 break
             playlist_lines.append(f"file '{os.path.join(CACHE_DIR,clip['path'])}'")
             # if this is the starting clip, add an inpoint
-            if clip['start_time'] < playlist_start:
-                playlist_lines.append(f"inpoint {int(playlist_start-clip['start_time'])}")
+            if clip["start_time"] < playlist_start:
+                playlist_lines.append(
+                    f"inpoint {int(playlist_start-clip['start_time'])}"
+                )
             # if this is the ending clip, add an outpoint
-            if clip['start_time']+clip['duration'] > playlist_end:
-                playlist_lines.append(f"outpoint {int(playlist_end-clip['start_time'])}")
+            if clip["start_time"] + clip["duration"] > playlist_end:
+                playlist_lines.append(
+                    f"outpoint {int(playlist_end-clip['start_time'])}"
+                )
 
         clip_name = f"{camera}-{event_data['id']}"
         ffmpeg_cmd = [
-            'ffmpeg',
-            '-y',
-            '-protocol_whitelist',
-            'pipe,file',
-            '-f',
-            'concat',
-            '-safe',
-            '0',
-            '-i',
-            '-',
-            '-c',
-            'copy',
-            '-movflags',
-            '+faststart',
-            f"{os.path.join(CLIPS_DIR, clip_name)}.mp4"
+            "ffmpeg",
+            "-y",
+            "-protocol_whitelist",
+            "pipe,file",
+            "-f",
+            "concat",
+            "-safe",
+            "0",
+            "-i",
+            "-",
+            "-c",
+            "copy",
+            "-movflags",
+            "+faststart",
+            f"{os.path.join(CLIPS_DIR, clip_name)}.mp4",
         ]
 
-        p = sp.run(ffmpeg_cmd, input="\n".join(playlist_lines), encoding='ascii', capture_output=True)
+        p = sp.run(
+            ffmpeg_cmd,
+            input="\n".join(playlist_lines),
+            encoding="ascii",
+            capture_output=True,
+        )
         if p.returncode != 0:
             logger.error(p.stderr)
             return False
@@ -199,68 +241,80 @@ class EventProcessor(threading.Thread):
             logger.debug(f"Event received: {event_type} {camera} {event_data['id']}")
             self.refresh_cache()
 
-            if event_type == 'start':
-                self.events_in_process[event_data['id']] = event_data
+            if event_type == "start":
+                self.events_in_process[event_data["id"]] = event_data
 
-            if event_type == 'end':
+            if event_type == "end":
                 clips_config = self.config.cameras[camera].clips
 
                 clip_created = False
                 if self.should_create_clip(camera, event_data):
-                    if clips_config.enabled and (clips_config.objects is None or event_data['label'] in clips_config.objects):
-                        clip_created = self.create_clip(camera, event_data, clips_config.pre_capture, clips_config.post_capture)
-                
-                if clip_created or event_data['has_snapshot']:
+                    if clips_config.enabled and (
+                        clips_config.objects is None
+                        or event_data["label"] in clips_config.objects
+                    ):
+                        clip_created = self.create_clip(
+                            camera,
+                            event_data,
+                            clips_config.pre_capture,
+                            clips_config.post_capture,
+                        )
+
+                if clip_created or event_data["has_snapshot"]:
                     Event.create(
-                        id=event_data['id'],
-                        label=event_data['label'],
+                        id=event_data["id"],
+                        label=event_data["label"],
                         camera=camera,
-                        start_time=event_data['start_time'],
-                        end_time=event_data['end_time'],
-                        top_score=event_data['top_score'],
-                        false_positive=event_data['false_positive'],
-                        zones=list(event_data['entered_zones']),
-                        thumbnail=event_data['thumbnail'],
+                        start_time=event_data["start_time"],
+                        end_time=event_data["end_time"],
+                        top_score=event_data["top_score"],
+                        false_positive=event_data["false_positive"],
+                        zones=list(event_data["entered_zones"]),
+                        thumbnail=event_data["thumbnail"],
                         has_clip=clip_created,
-                        has_snapshot=event_data['has_snapshot'],
+                        has_snapshot=event_data["has_snapshot"],
                     )
-                del self.events_in_process[event_data['id']]
-                self.event_processed_queue.put((event_data['id'], camera))
+                del self.events_in_process[event_data["id"]]
+                self.event_processed_queue.put((event_data["id"], camera))
+
 
 class EventCleanup(threading.Thread):
     def __init__(self, config: FrigateConfig, stop_event):
         threading.Thread.__init__(self)
-        self.name = 'event_cleanup'
+        self.name = "event_cleanup"
         self.config = config
         self.stop_event = stop_event
         self.camera_keys = list(self.config.cameras.keys())
 
     def expire(self, media):
         ## Expire events from unlisted cameras based on the global config
-        if media == 'clips':
+        if media == "clips":
             retain_config = self.config.clips.retain
-            file_extension = 'mp4'
-            update_params = {'has_clip': False}
+            file_extension = "mp4"
+            update_params = {"has_clip": False}
         else:
             retain_config = self.config.snapshots.retain
-            file_extension = 'jpg'
-            update_params = {'has_snapshot': False}
-        
-        distinct_labels = (Event.select(Event.label)
-                    .where(Event.camera.not_in(self.camera_keys))
-                    .distinct())
-        
+            file_extension = "jpg"
+            update_params = {"has_snapshot": False}
+
+        distinct_labels = (
+            Event.select(Event.label)
+            .where(Event.camera.not_in(self.camera_keys))
+            .distinct()
+        )
+
         # loop over object types in db
         for l in distinct_labels:
             # get expiration time for this label
             expire_days = retain_config.objects.get(l.label, retain_config.default)
-            expire_after = (datetime.datetime.now() - datetime.timedelta(days=expire_days)).timestamp()
+            expire_after = (
+                datetime.datetime.now() - datetime.timedelta(days=expire_days)
+            ).timestamp()
             # grab all events after specific time
-            expired_events = (
-                Event.select()
-                    .where(Event.camera.not_in(self.camera_keys), 
-                        Event.start_time < expire_after, 
-                        Event.label == l.label)
+            expired_events = Event.select().where(
+                Event.camera.not_in(self.camera_keys),
+                Event.start_time < expire_after,
+                Event.label == l.label,
             )
             # delete the media from disk
             for event in expired_events:
@@ -268,56 +322,57 @@ class EventCleanup(threading.Thread):
                 media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}")
                 media.unlink(missing_ok=True)
             # update the clips attribute for the db entry
-            update_query = (
-                Event.update(update_params)
-                    .where(Event.camera.not_in(self.camera_keys), 
-                        Event.start_time < expire_after, 
-                        Event.label == l.label)
+            update_query = Event.update(update_params).where(
+                Event.camera.not_in(self.camera_keys),
+                Event.start_time < expire_after,
+                Event.label == l.label,
             )
             update_query.execute()
 
         ## Expire events from cameras based on the camera config
         for name, camera in self.config.cameras.items():
-            if media == 'clips':
+            if media == "clips":
                 retain_config = camera.clips.retain
             else:
                 retain_config = camera.snapshots.retain
             # get distinct objects in database for this camera
-            distinct_labels = (Event.select(Event.label)
-                    .where(Event.camera == name)
-                    .distinct())
+            distinct_labels = (
+                Event.select(Event.label).where(Event.camera == name).distinct()
+            )
 
             # loop over object types in db
             for l in distinct_labels:
                 # get expiration time for this label
                 expire_days = retain_config.objects.get(l.label, retain_config.default)
-                expire_after = (datetime.datetime.now() - datetime.timedelta(days=expire_days)).timestamp()
+                expire_after = (
+                    datetime.datetime.now() - datetime.timedelta(days=expire_days)
+                ).timestamp()
                 # grab all events after specific time
-                expired_events = (
-                    Event.select()
-                        .where(Event.camera == name, 
-                            Event.start_time < expire_after, 
-                            Event.label == l.label)
+                expired_events = Event.select().where(
+                    Event.camera == name,
+                    Event.start_time < expire_after,
+                    Event.label == l.label,
                 )
                 # delete the grabbed clips from disk
                 for event in expired_events:
                     media_name = f"{event.camera}-{event.id}"
-                    media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}")
+                    media = Path(
+                        f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}"
+                    )
                     media.unlink(missing_ok=True)
                 # update the clips attribute for the db entry
-                update_query = (
-                    Event.update(update_params)
-                        .where( Event.camera == name, 
-                            Event.start_time < expire_after, 
-                            Event.label == l.label)
+                update_query = Event.update(update_params).where(
+                    Event.camera == name,
+                    Event.start_time < expire_after,
+                    Event.label == l.label,
                 )
                 update_query.execute()
 
     def purge_duplicates(self):
         duplicate_query = """with grouped_events as (
           select id,
-            label, 
-            camera, 
+            label,
+            camera,
           	has_snapshot,
           	has_clip,
           	row_number() over (
@@ -327,7 +382,7 @@ class EventCleanup(threading.Thread):
           from event
         )
 
-        select distinct id, camera, has_snapshot, has_clip from grouped_events 
+        select distinct id, camera, has_snapshot, has_clip from grouped_events
         where copy_number > 1;"""
 
         duplicate_events = Event.raw(duplicate_query)
@@ -341,13 +396,15 @@ class EventCleanup(threading.Thread):
                 media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
                 media.unlink(missing_ok=True)
 
-        (Event.delete()
-            .where( Event.id << [event.id for event in duplicate_events] )
-            .execute())
-    
+        (
+            Event.delete()
+            .where(Event.id << [event.id for event in duplicate_events])
+            .execute()
+        )
+
     def run(self):
         counter = 0
-        while(True):
+        while True:
             if self.stop_event.is_set():
                 logger.info(f"Exiting event cleanup...")
                 break
@@ -359,14 +416,12 @@ class EventCleanup(threading.Thread):
                 continue
             counter = 0
 
-            self.expire('clips')
-            self.expire('snapshots')
+            self.expire("clips")
+            self.expire("snapshots")
             self.purge_duplicates()
 
             # drop events from db where has_clip and has_snapshot are false
-            delete_query = (
-                Event.delete()
-                    .where( Event.has_clip == False, 
-                        Event.has_snapshot == False)
+            delete_query = Event.delete().where(
+                Event.has_clip == False, Event.has_snapshot == False
             )
             delete_query.execute()

frigate/http.py

@@ -9,8 +9,15 @@ from functools import reduce
 import cv2
 import gevent
 import numpy as np
-from flask import (Blueprint, Flask, Response, current_app, jsonify,
-                   make_response, request)
+from flask import (
+    Blueprint,
+    Flask,
+    Response,
+    current_app,
+    jsonify,
+    make_response,
+    request,
+)
 from flask_sockets import Sockets
 from peewee import SqliteDatabase, operator, fn, DoesNotExist
 from playhouse.shortcuts import model_to_dict
@@ -23,10 +30,11 @@ from frigate.version import VERSION
 
 logger = logging.getLogger(__name__)
 
-bp = Blueprint('frigate', __name__)
-ws = Blueprint('ws', __name__)
+bp = Blueprint("frigate", __name__)
+ws = Blueprint("ws", __name__)
 
-class MqttBackend():
+
+class MqttBackend:
     """Interface for registering and updating WebSocket clients."""
 
     def __init__(self, mqtt_client, topic_prefix):
@@ -42,36 +50,48 @@ class MqttBackend():
         try:
             json_message = json.loads(message)
             json_message = {
-                'topic': f"{self.topic_prefix}/{json_message['topic']}",
-                'payload': json_message['payload'],
-                'retain': json_message.get('retain', False)
+                "topic": f"{self.topic_prefix}/{json_message['topic']}",
+                "payload": json_message.get["payload"],
+                "retain": json_message.get("retain", False),
             }
         except:
             logger.warning("Unable to parse websocket message as valid json.")
             return
 
-        logger.debug(f"Publishing mqtt message from websockets at {json_message['topic']}.")
-        self.mqtt_client.publish(json_message['topic'], json_message['payload'], retain=json_message['retain'])
+        logger.debug(
+            f"Publishing mqtt message from websockets at {json_message['topic']}."
+        )
+        self.mqtt_client.publish(
+            json_message["topic"],
+            json_message["payload"],
+            retain=json_message["retain"],
+        )
 
     def run(self):
         def send(client, userdata, message):
             """Sends mqtt messages to clients."""
             try:
                 logger.debug(f"Received mqtt message on {message.topic}.")
-                ws_message = json.dumps({
-                    'topic': message.topic.replace(f"{self.topic_prefix}/",""),
-                    'payload': message.payload.decode()
-                })
+                ws_message = json.dumps(
+                    {
+                        "topic": message.topic.replace(f"{self.topic_prefix}/", ""),
+                        "payload": message.payload.decode(),
+                    }
+                )
             except:
                 # if the payload can't be decoded don't relay to clients
-                logger.debug(f"MQTT payload for {message.topic} wasn't text. Skipping...")
+                logger.debug(
+                    f"MQTT payload for {message.topic} wasn't text. Skipping..."
+                )
                 return
 
             for client in self.clients:
                 try:
                     client.send(ws_message)
                 except:
-                    logger.debug("Removing websocket client due to a closed connection.")
+                    logger.debug(
+                        "Removing websocket client due to a closed connection."
+                    )
                     self.clients.remove(client)
 
         self.mqtt_client.message_callback_add(f"{self.topic_prefix}/#", send)
@@ -80,7 +100,14 @@ class MqttBackend():
         """Maintains mqtt subscription in the background."""
         gevent.spawn(self.run)
 
-def create_app(frigate_config, database: SqliteDatabase, stats_tracking, detected_frames_processor, mqtt_client):
+
+def create_app(
+    frigate_config,
+    database: SqliteDatabase,
+    stats_tracking,
+    detected_frames_processor,
+    mqtt_client,
+):
     app = Flask(__name__)
     sockets = Sockets(app)
 
@@ -105,14 +132,16 @@ def create_app(frigate_config, database: SqliteDatabase, stats_tracking, detecte
 
     return app
 
-@bp.route('/')
+
+@bp.route("/")
 def is_healthy():
     return "Frigate is running. Alive and healthy!"
 
-@bp.route('/events/summary')
+
+@bp.route("/events/summary")
 def events_summary():
-    has_clip = request.args.get('has_clip', type=int)
-    has_snapshot = request.args.get('has_snapshot', type=int)
+    has_clip = request.args.get("has_clip", type=int)
+    has_snapshot = request.args.get("has_snapshot", type=int)
 
     clauses = []
 
@@ -126,35 +155,40 @@ def events_summary():
         clauses.append((1 == 1))
 
     groups = (
-        Event
-            .select(
-                Event.camera,
-                Event.label,
-                fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')).alias('day'),
-                Event.zones,
-                fn.COUNT(Event.id).alias('count')
-            )
-            .where(reduce(operator.and_, clauses))
-            .group_by(
-                Event.camera,
-                Event.label,
-                fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')),
-                Event.zones
-            )
+        Event.select(
+            Event.camera,
+            Event.label,
+            fn.strftime(
+                "%Y-%m-%d", fn.datetime(Event.start_time, "unixepoch", "localtime")
+            ).alias("day"),
+            Event.zones,
+            fn.COUNT(Event.id).alias("count"),
+        )
+        .where(reduce(operator.and_, clauses))
+        .group_by(
+            Event.camera,
+            Event.label,
+            fn.strftime(
+                "%Y-%m-%d", fn.datetime(Event.start_time, "unixepoch", "localtime")
+            ),
+            Event.zones,
         )
+    )
 
     return jsonify([e for e in groups.dicts()])
 
-@bp.route('/events/<id>')
+
+@bp.route("/events/<id>")
 def event(id):
     try:
         return model_to_dict(Event.get(Event.id == id))
     except DoesNotExist:
         return "Event not found", 404
 
-@bp.route('/events/<id>/thumbnail.jpg')
+
+@bp.route("/events/<id>/thumbnail.jpg")
 def event_thumbnail(id):
-    format = request.args.get('format', 'ios')
+    format = request.args.get("format", "ios")
     thumbnail_bytes = None
     try:
         event = Event.get(Event.id == id)
@@ -162,7 +196,9 @@ def event_thumbnail(id):
     except DoesNotExist:
         # see if the object is currently being tracked
         try:
-            for camera_state in current_app.detected_frames_processor.camera_states.values():
+            for (
+                camera_state
+            ) in current_app.detected_frames_processor.camera_states.values():
                 if id in camera_state.tracked_objects:
                     tracked_obj = camera_state.tracked_objects.get(id)
                     if not tracked_obj is None:
@@ -174,18 +210,27 @@ def event_thumbnail(id):
         return "Event not found", 404
 
     # android notifications prefer a 2:1 ratio
-    if format == 'android':
+    if format == "android":
         jpg_as_np = np.frombuffer(thumbnail_bytes, dtype=np.uint8)
         img = cv2.imdecode(jpg_as_np, flags=1)
-        thumbnail = cv2.copyMakeBorder(img, 0, 0, int(img.shape[1]*0.5), int(img.shape[1]*0.5), cv2.BORDER_CONSTANT, (0,0,0))
-        ret, jpg = cv2.imencode('.jpg', thumbnail, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
+        thumbnail = cv2.copyMakeBorder(
+            img,
+            0,
+            0,
+            int(img.shape[1] * 0.5),
+            int(img.shape[1] * 0.5),
+            cv2.BORDER_CONSTANT,
+            (0, 0, 0),
+        )
+        ret, jpg = cv2.imencode(".jpg", thumbnail, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
         thumbnail_bytes = jpg.tobytes()
 
     response = make_response(thumbnail_bytes)
-    response.headers['Content-Type'] = 'image/jpg'
+    response.headers["Content-Type"] = "image/jpg"
     return response
 
-@bp.route('/events/<id>/snapshot.jpg')
+
+@bp.route("/events/<id>/snapshot.jpg")
 def event_snapshot(id):
     jpg_bytes = None
     try:
@@ -193,20 +238,24 @@ def event_snapshot(id):
         if not event.has_snapshot:
             return "Snapshot not available", 404
         # read snapshot from disk
-        with open(os.path.join(CLIPS_DIR, f"{event.camera}-{id}.jpg"), 'rb') as image_file:
+        with open(
+            os.path.join(CLIPS_DIR, f"{event.camera}-{id}.jpg"), "rb"
+        ) as image_file:
             jpg_bytes = image_file.read()
     except DoesNotExist:
         # see if the object is currently being tracked
         try:
-            for camera_state in current_app.detected_frames_processor.camera_states.values():
+            for (
+                camera_state
+            ) in current_app.detected_frames_processor.camera_states.values():
                 if id in camera_state.tracked_objects:
                     tracked_obj = camera_state.tracked_objects.get(id)
                     if not tracked_obj is None:
                         jpg_bytes = tracked_obj.get_jpg_bytes(
-                            timestamp=request.args.get('timestamp', type=int),
-                            bounding_box=request.args.get('bbox', type=int),
-                            crop=request.args.get('crop', type=int),
-                            height=request.args.get('h', type=int)
+                            timestamp=request.args.get("timestamp", type=int),
+                            bounding_box=request.args.get("bbox", type=int),
+                            crop=request.args.get("crop", type=int),
+                            height=request.args.get("h", type=int),
                         )
         except:
             return "Event not found", 404
@@ -214,20 +263,21 @@ def event_snapshot(id):
         return "Event not found", 404
 
     response = make_response(jpg_bytes)
-    response.headers['Content-Type'] = 'image/jpg'
+    response.headers["Content-Type"] = "image/jpg"
     return response
 
-@bp.route('/events')
+
+@bp.route("/events")
 def events():
-    limit = request.args.get('limit', 100)
-    camera = request.args.get('camera')
-    label = request.args.get('label')
-    zone = request.args.get('zone')
-    after = request.args.get('after', type=float)
-    before = request.args.get('before', type=float)
-    has_clip = request.args.get('has_clip', type=int)
-    has_snapshot = request.args.get('has_snapshot', type=int)
-    include_thumbnails = request.args.get('include_thumbnails', default=1, type=int)
+    limit = request.args.get("limit", 100)
+    camera = request.args.get("camera")
+    label = request.args.get("label")
+    zone = request.args.get("zone")
+    after = request.args.get("after", type=float)
+    before = request.args.get("before", type=float)
+    has_clip = request.args.get("has_clip", type=int)
+    has_snapshot = request.args.get("has_snapshot", type=int)
+    include_thumbnails = request.args.get("include_thumbnails", default=1, type=int)
 
     clauses = []
     excluded_fields = []
@@ -239,7 +289,7 @@ def events():
         clauses.append((Event.label == label))
 
     if zone:
-        clauses.append((Event.zones.cast('text') % f"*\"{zone}\"*"))
+        clauses.append((Event.zones.cast("text") % f'*"{zone}"*'))
 
     if after:
         clauses.append((Event.start_time >= after))
@@ -259,116 +309,142 @@ def events():
     if len(clauses) == 0:
         clauses.append((1 == 1))
 
-    events =    (Event.select()
-                .where(reduce(operator.and_, clauses))
-                .order_by(Event.start_time.desc())
-                .limit(limit))
+    events = (
+        Event.select()
+        .where(reduce(operator.and_, clauses))
+        .order_by(Event.start_time.desc())
+        .limit(limit)
+    )
 
     return jsonify([model_to_dict(e, exclude=excluded_fields) for e in events])
 
-@bp.route('/config')
+
+@bp.route("/config")
 def config():
     return jsonify(current_app.frigate_config.to_dict())
 
-@bp.route('/version')
+
+@bp.route("/version")
 def version():
     return VERSION
 
-@bp.route('/stats')
+
+@bp.route("/stats")
 def stats():
     stats = stats_snapshot(current_app.stats_tracking)
     return jsonify(stats)
 
-@bp.route('/<camera_name>/<label>/best.jpg')
+
+@bp.route("/<camera_name>/<label>/best.jpg")
 def best(camera_name, label):
     if camera_name in current_app.frigate_config.cameras:
         best_object = current_app.detected_frames_processor.get_best(camera_name, label)
-        best_frame = best_object.get('frame')
+        best_frame = best_object.get("frame")
         if best_frame is None:
-            best_frame = np.zeros((720,1280,3), np.uint8)
+            best_frame = np.zeros((720, 1280, 3), np.uint8)
         else:
             best_frame = cv2.cvtColor(best_frame, cv2.COLOR_YUV2BGR_I420)
 
-        crop = bool(request.args.get('crop', 0, type=int))
+        crop = bool(request.args.get("crop", 0, type=int))
         if crop:
-            box = best_object.get('box', (0,0,300,300))
-            region = calculate_region(best_frame.shape, box[0], box[1], box[2], box[3], 1.1)
-            best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
+            box = best_object.get("box", (0, 0, 300, 300))
+            region = calculate_region(
+                best_frame.shape, box[0], box[1], box[2], box[3], 1.1
+            )
+            best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
 
-        height = int(request.args.get('h', str(best_frame.shape[0])))
-        width = int(height*best_frame.shape[1]/best_frame.shape[0])
+        height = int(request.args.get("h", str(best_frame.shape[0])))
+        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, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
+        best_frame = cv2.resize(
+            best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA
+        )
+        ret, jpg = cv2.imencode(".jpg", best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
         response = make_response(jpg.tobytes())
-        response.headers['Content-Type'] = 'image/jpg'
+        response.headers["Content-Type"] = "image/jpg"
         return response
     else:
         return "Camera named {} not found".format(camera_name), 404
 
-@bp.route('/<camera_name>')
+
+@bp.route("/<camera_name>")
 def mjpeg_feed(camera_name):
-    fps = int(request.args.get('fps', '3'))
-    height = int(request.args.get('h', '360'))
+    fps = int(request.args.get("fps", "3"))
+    height = int(request.args.get("h", "360"))
     draw_options = {
-        'bounding_boxes': request.args.get('bbox', type=int),
-        'timestamp': request.args.get('timestamp', type=int),
-        'zones': request.args.get('zones', type=int),
-        'mask': request.args.get('mask', type=int),
-        'motion_boxes': request.args.get('motion', type=int),
-        'regions': request.args.get('regions', type=int),
+        "bounding_boxes": request.args.get("bbox", type=int),
+        "timestamp": request.args.get("timestamp", type=int),
+        "zones": request.args.get("zones", type=int),
+        "mask": request.args.get("mask", type=int),
+        "motion_boxes": request.args.get("motion", type=int),
+        "regions": request.args.get("regions", type=int),
     }
     if camera_name in current_app.frigate_config.cameras:
         # return a multipart response
-        return Response(imagestream(current_app.detected_frames_processor, camera_name, fps, height, draw_options),
-                        mimetype='multipart/x-mixed-replace; boundary=frame')
+        return Response(
+            imagestream(
+                current_app.detected_frames_processor,
+                camera_name,
+                fps,
+                height,
+                draw_options,
+            ),
+            mimetype="multipart/x-mixed-replace; boundary=frame",
+        )
     else:
         return "Camera named {} not found".format(camera_name), 404
 
-@bp.route('/<camera_name>/latest.jpg')
+
+@bp.route("/<camera_name>/latest.jpg")
 def latest_frame(camera_name):
     draw_options = {
-        'bounding_boxes': request.args.get('bbox', type=int),
-        'timestamp': request.args.get('timestamp', type=int),
-        'zones': request.args.get('zones', type=int),
-        'mask': request.args.get('mask', type=int),
-        'motion_boxes': request.args.get('motion', type=int),
-        'regions': request.args.get('regions', type=int),
+        "bounding_boxes": request.args.get("bbox", type=int),
+        "timestamp": request.args.get("timestamp", type=int),
+        "zones": request.args.get("zones", type=int),
+        "mask": request.args.get("mask", type=int),
+        "motion_boxes": request.args.get("motion", type=int),
+        "regions": request.args.get("regions", type=int),
     }
     if camera_name in current_app.frigate_config.cameras:
         # max out at specified FPS
-        frame = current_app.detected_frames_processor.get_current_frame(camera_name, draw_options)
+        frame = current_app.detected_frames_processor.get_current_frame(
+            camera_name, draw_options
+        )
         if frame is None:
-            frame = np.zeros((720,1280,3), np.uint8)
+            frame = np.zeros((720, 1280, 3), np.uint8)
 
-        height = int(request.args.get('h', str(frame.shape[0])))
-        width = int(height*frame.shape[1]/frame.shape[0])
+        height = int(request.args.get("h", str(frame.shape[0])))
+        width = int(height * frame.shape[1] / frame.shape[0])
 
         frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
 
-        ret, jpg = cv2.imencode('.jpg', frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
+        ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
         response = make_response(jpg.tobytes())
-        response.headers['Content-Type'] = 'image/jpg'
+        response.headers["Content-Type"] = "image/jpg"
         return response
     else:
         return "Camera named {} not found".format(camera_name), 404
 
+
 def imagestream(detected_frames_processor, camera_name, fps, height, draw_options):
     while True:
         # max out at specified FPS
         gevent.sleep(1/fps)
         frame = detected_frames_processor.get_current_frame(camera_name, draw_options)
         if frame is None:
-            frame = np.zeros((height,int(height*16/9),3), np.uint8)
+            frame = np.zeros((height, int(height * 16 / 9), 3), np.uint8)
 
-        width = int(height*frame.shape[1]/frame.shape[0])
+        width = int(height * frame.shape[1] / frame.shape[0])
         frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_LINEAR)
 
-        ret, jpg = cv2.imencode('.jpg', frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
-        yield (b'--frame\r\n'
-            b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
+        ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
+        yield (
+            b"--frame\r\n"
+            b"Content-Type: image/jpeg\r\n\r\n" + jpg.tobytes() + b"\r\n\r\n"
+        )
+
 
-@ws.route('/ws')
+@ws.route("/ws")
 def echo_socket(socket):
     current_app.mqtt_backend.register(socket)
 

frigate/log.py

@@ -13,22 +13,25 @@ from collections import deque
 def listener_configurer():
     root = logging.getLogger()
     console_handler = logging.StreamHandler()
-    formatter = logging.Formatter('%(name)-30s %(levelname)-8s: %(message)s')
+    formatter = logging.Formatter("%(name)-30s %(levelname)-8s: %(message)s")
     console_handler.setFormatter(formatter)
     root.addHandler(console_handler)
     root.setLevel(logging.INFO)
 
+
 def root_configurer(queue):
     h = handlers.QueueHandler(queue)
     root = logging.getLogger()
     root.addHandler(h)
     root.setLevel(logging.INFO)
 
+
 def log_process(log_queue):
     stop_event = mp.Event()
+
     def receiveSignal(signalNumber, frame):
         stop_event.set()
-    
+
     signal.signal(signal.SIGTERM, receiveSignal)
     signal.signal(signal.SIGINT, receiveSignal)
 
@@ -45,6 +48,7 @@ def log_process(log_queue):
         logger = logging.getLogger(record.name)
         logger.handle(record)
 
+
 # based on https://codereview.stackexchange.com/a/17959
 class LogPipe(threading.Thread):
     def __init__(self, log_name, level):
@@ -61,23 +65,20 @@ class LogPipe(threading.Thread):
         self.start()
 
     def fileno(self):
-        """Return the write file descriptor of the pipe
-        """
+        """Return the write file descriptor of the pipe"""
         return self.fdWrite
 
     def run(self):
-        """Run the thread, logging everything.
-        """
-        for line in iter(self.pipeReader.readline, ''):
-            self.deque.append(line.strip('\n'))
+        """Run the thread, logging everything."""
+        for line in iter(self.pipeReader.readline, ""):
+            self.deque.append(line.strip("\n"))
 
         self.pipeReader.close()
-    
+
     def dump(self):
         while len(self.deque) > 0:
             self.logger.log(self.level, self.deque.popleft())
 
     def close(self):
-        """Close the write end of the pipe.
-        """
+        """Close the write end of the pipe."""
         os.close(self.fdWrite)

frigate/motion.py

@@ -4,26 +4,37 @@ import numpy as np
 from frigate.config import MotionConfig
 
 
-class MotionDetector():
+class MotionDetector:
     def __init__(self, frame_shape, config: MotionConfig):
         self.config = config
         self.frame_shape = frame_shape
-        self.resize_factor = frame_shape[0]/config.frame_height
-        self.motion_frame_size = (config.frame_height, config.frame_height*frame_shape[1]//frame_shape[0])
+        self.resize_factor = frame_shape[0] / config.frame_height
+        self.motion_frame_size = (
+            config.frame_height,
+            config.frame_height * frame_shape[1] // frame_shape[0],
+        )
         self.avg_frame = np.zeros(self.motion_frame_size, np.float)
         self.avg_delta = np.zeros(self.motion_frame_size, np.float)
         self.motion_frame_count = 0
         self.frame_counter = 0
-        resized_mask = cv2.resize(config.mask, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
-        self.mask = np.where(resized_mask==[0])
+        resized_mask = cv2.resize(
+            config.mask,
+            dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
+            interpolation=cv2.INTER_LINEAR,
+        )
+        self.mask = np.where(resized_mask == [0])
 
     def detect(self, frame):
         motion_boxes = []
 
-        gray = frame[0:self.frame_shape[0], 0:self.frame_shape[1]]
+        gray = frame[0 : self.frame_shape[0], 0 : self.frame_shape[1]]
 
         # resize frame
-        resized_frame = cv2.resize(gray, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
+        resized_frame = cv2.resize(
+            gray,
+            dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
+            interpolation=cv2.INTER_LINEAR,
+        )
 
         # TODO: can I improve the contrast of the grayscale image here?
 
@@ -48,7 +59,9 @@ class MotionDetector():
 
             # compute the threshold image for the current frame
             # TODO: threshold
-            current_thresh = cv2.threshold(frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY)[1]
+            current_thresh = cv2.threshold(
+                frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY
+            )[1]
 
             # black out everything in the avg_delta where there isnt motion in the current frame
             avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
@@ -56,7 +69,9 @@ class MotionDetector():
 
             # then look for deltas above the threshold, but only in areas where there is a delta
             # in the current frame. this prevents deltas from previous frames from being included
-            thresh = cv2.threshold(avg_delta_image, self.config.threshold, 255, cv2.THRESH_BINARY)[1]
+            thresh = cv2.threshold(
+                avg_delta_image, self.config.threshold, 255, cv2.THRESH_BINARY
+            )[1]
 
             # dilate the thresholded image to fill in holes, then find contours
             # on thresholded image
@@ -70,16 +85,27 @@ class MotionDetector():
                 contour_area = cv2.contourArea(c)
                 if contour_area > self.config.contour_area:
                     x, y, w, h = cv2.boundingRect(c)
-                    motion_boxes.append((int(x*self.resize_factor), int(y*self.resize_factor), int((x+w)*self.resize_factor), int((y+h)*self.resize_factor)))
-        
+                    motion_boxes.append(
+                        (
+                            int(x * self.resize_factor),
+                            int(y * self.resize_factor),
+                            int((x + w) * self.resize_factor),
+                            int((y + h) * self.resize_factor),
+                        )
+                    )
+
         if len(motion_boxes) > 0:
             self.motion_frame_count += 1
             if self.motion_frame_count >= 10:
                 # only average in the current frame if the difference persists for a bit
-                cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
+                cv2.accumulateWeighted(
+                    resized_frame, self.avg_frame, self.config.frame_alpha
+                )
         else:
             # when no motion, just keep averaging the frames together
-            cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
+            cv2.accumulateWeighted(
+                resized_frame, self.avg_frame, self.config.frame_alpha
+            )
             self.motion_frame_count = 0
 
         return motion_boxes

frigate/mqtt.py

@@ -7,6 +7,7 @@ from frigate.config import FrigateConfig
 
 logger = logging.getLogger(__name__)
 
+
 def create_mqtt_client(config: FrigateConfig, camera_metrics):
     mqtt_config = config.mqtt
 
@@ -14,15 +15,15 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
         payload = message.payload.decode()
         logger.debug(f"on_clips_toggle: {message.topic} {payload}")
 
-        camera_name = message.topic.split('/')[-3]
+        camera_name = message.topic.split("/")[-3]
 
         clips_settings = config.cameras[camera_name].clips
 
-        if payload == 'ON':
+        if payload == "ON":
             if not clips_settings.enabled:
                 logger.info(f"Turning on clips for {camera_name} via mqtt")
                 clips_settings._enabled = True
-        elif payload == 'OFF':
+        elif payload == "OFF":
             if clips_settings.enabled:
                 logger.info(f"Turning off clips for {camera_name} via mqtt")
                 clips_settings._enabled = False
@@ -36,15 +37,15 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
         payload = message.payload.decode()
         logger.debug(f"on_snapshots_toggle: {message.topic} {payload}")
 
-        camera_name = message.topic.split('/')[-3]
+        camera_name = message.topic.split("/")[-3]
 
         snapshots_settings = config.cameras[camera_name].snapshots
 
-        if payload == 'ON':
+        if payload == "ON":
             if not snapshots_settings.enabled:
                 logger.info(f"Turning on snapshots for {camera_name} via mqtt")
                 snapshots_settings._enabled = True
-        elif payload == 'OFF':
+        elif payload == "OFF":
             if snapshots_settings.enabled:
                 logger.info(f"Turning off snapshots for {camera_name} via mqtt")
                 snapshots_settings._enabled = False
@@ -53,21 +54,21 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
 
         state_topic = f"{message.topic[:-4]}/state"
         client.publish(state_topic, payload, retain=True)
-    
+
     def on_detect_command(client, userdata, message):
         payload = message.payload.decode()
         logger.debug(f"on_detect_toggle: {message.topic} {payload}")
 
-        camera_name = message.topic.split('/')[-3]
+        camera_name = message.topic.split("/")[-3]
 
         detect_settings = config.cameras[camera_name].detect
 
-        if payload == 'ON':
+        if payload == "ON":
             if not camera_metrics[camera_name]["detection_enabled"].value:
                 logger.info(f"Turning on detection for {camera_name} via mqtt")
                 camera_metrics[camera_name]["detection_enabled"].value = True
                 detect_settings._enabled = True
-        elif payload == 'OFF':
+        elif payload == "OFF":
             if camera_metrics[camera_name]["detection_enabled"].value:
                 logger.info(f"Turning off detection for {camera_name} via mqtt")
                 camera_metrics[camera_name]["detection_enabled"].value = False
@@ -88,21 +89,32 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
             elif rc == 5:
                 logger.error("MQTT Not authorized")
             else:
-                logger.error("Unable to connect to MQTT: Connection refused. Error code: " + str(rc))
-            
+                logger.error(
+                    "Unable to connect to MQTT: Connection refused. Error code: "
+                    + str(rc)
+                )
+
         logger.info("MQTT connected")
         client.subscribe(f"{mqtt_config.topic_prefix}/#")
-        client.publish(mqtt_config.topic_prefix+'/available', 'online', retain=True)   
+        client.publish(mqtt_config.topic_prefix + "/available", "online", retain=True)
 
-    client = mqtt.Client(client_id=mqtt_config.client_id)    
+    client = mqtt.Client(client_id=mqtt_config.client_id)
     client.on_connect = on_connect
-    client.will_set(mqtt_config.topic_prefix+'/available', payload='offline', qos=1, retain=True)
-    
+    client.will_set(
+        mqtt_config.topic_prefix + "/available", payload="offline", qos=1, retain=True
+    )
+
     # register callbacks
     for name in config.cameras.keys():
-        client.message_callback_add(f"{mqtt_config.topic_prefix}/{name}/clips/set", on_clips_command)
-        client.message_callback_add(f"{mqtt_config.topic_prefix}/{name}/snapshots/set", on_snapshots_command)
-        client.message_callback_add(f"{mqtt_config.topic_prefix}/{name}/detect/set", on_detect_command)
+        client.message_callback_add(
+            f"{mqtt_config.topic_prefix}/{name}/clips/set", on_clips_command
+        )
+        client.message_callback_add(
+            f"{mqtt_config.topic_prefix}/{name}/snapshots/set", on_snapshots_command
+        )
+        client.message_callback_add(
+            f"{mqtt_config.topic_prefix}/{name}/detect/set", on_detect_command
+        )
 
     if not mqtt_config.user is None:
         client.username_pw_set(mqtt_config.user, password=mqtt_config.password)
@@ -115,10 +127,20 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
     client.loop_start()
 
     for name in config.cameras.keys():
-        client.publish(f"{mqtt_config.topic_prefix}/{name}/clips/state", 'ON' if config.cameras[name].clips.enabled else 'OFF', retain=True)
-        client.publish(f"{mqtt_config.topic_prefix}/{name}/snapshots/state", 'ON' if config.cameras[name].snapshots.enabled else 'OFF', retain=True)
-        client.publish(f"{mqtt_config.topic_prefix}/{name}/detect/state", 'ON' if config.cameras[name].detect.enabled else 'OFF', retain=True)
-
-    client.subscribe(f"{mqtt_config.topic_prefix}/#")
+        client.publish(
+            f"{mqtt_config.topic_prefix}/{name}/clips/state",
+            "ON" if config.cameras[name].clips.enabled else "OFF",
+            retain=True,
+        )
+        client.publish(
+            f"{mqtt_config.topic_prefix}/{name}/snapshots/state",
+            "ON" if config.cameras[name].snapshots.enabled else "OFF",
+            retain=True,
+        )
+        client.publish(
+            f"{mqtt_config.topic_prefix}/{name}/detect/state",
+            "ON" if config.cameras[name].detect.enabled else "OFF",
+            retain=True,
+        )
 
     return client

frigate/object_processing.py

@@ -24,44 +24,49 @@ from frigate.util import SharedMemoryFrameManager, draw_box_with_label, calculat
 
 logger = logging.getLogger(__name__)
 
-PATH_TO_LABELS = '/labelmap.txt'
+PATH_TO_LABELS = "/labelmap.txt"
 
 LABELS = load_labels(PATH_TO_LABELS)
-cmap = plt.cm.get_cmap('tab10', len(LABELS.keys()))
+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 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
+        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):
+    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']+.05:
+    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:
+    if new_obj["area"] > current_thumb["area"] * 1.1:
         return True
 
     return False
 
-class TrackedObject():
+
+class TrackedObject:
     def __init__(self, camera, camera_config: CameraConfig, frame_cache, obj_data):
         self.obj_data = obj_data
         self.camera = camera
@@ -78,14 +83,14 @@ class TrackedObject():
         self.previous = self.to_dict()
 
         # start the score history
-        self.score_history = [self.obj_data['score']]
+        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
+        threshold = self.camera_config.objects.filters[self.obj_data["label"]].threshold
         if self.computed_score < threshold:
             return True
         return False
@@ -94,17 +99,17 @@ class TrackedObject():
         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))
+            scores += [0.0] * (3 - len(scores))
         return median(scores)
 
     def update(self, current_frame_time, obj_data):
         significant_update = False
         self.obj_data.update(obj_data)
         # if the object is not in the current frame, add a 0.0 to the score history
-        if self.obj_data['frame_time'] != current_frame_time:
+        if self.obj_data["frame_time"] != current_frame_time:
             self.score_history.append(0.0)
         else:
-            self.score_history.append(self.obj_data['score'])
+            self.score_history.append(self.obj_data["score"])
         # only keep the last 10 scores
         if len(self.score_history) > 10:
             self.score_history = self.score_history[-10:]
@@ -117,27 +122,26 @@ class TrackedObject():
 
         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, self.obj_data, self.camera_config.frame_shape)
+            if self.thumbnail_data is None or is_better_thumbnail(
+                self.thumbnail_data, self.obj_data, self.camera_config.frame_shape
             ):
                 self.thumbnail_data = {
-                    'frame_time': self.obj_data['frame_time'],
-                    'box': self.obj_data['box'],
-                    'area': self.obj_data['area'],
-                    'region': self.obj_data['region'],
-                    'score': self.obj_data['score']
+                    "frame_time": self.obj_data["frame_time"],
+                    "box": self.obj_data["box"],
+                    "area": self.obj_data["area"],
+                    "region": self.obj_data["region"],
+                    "score": self.obj_data["score"],
                 }
                 significant_update = True
 
         # check zones
         current_zones = []
-        bottom_center = (self.obj_data['centroid'][0], self.obj_data['box'][3])
+        bottom_center = (self.obj_data["centroid"][0], self.obj_data["box"][3])
         # check each zone
         for name, zone in self.camera_config.zones.items():
             contour = zone.contour
             # check if the object is in the zone
-            if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0):
+            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)
@@ -152,91 +156,131 @@ class TrackedObject():
 
     def to_dict(self, include_thumbnail: bool = False):
         return {
-            'id': self.obj_data['id'],
-            'camera': self.camera,
-            'frame_time': self.obj_data['frame_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'],
-            'region': self.obj_data['region'],
-            'current_zones': self.current_zones.copy(),
-            'entered_zones': list(self.entered_zones).copy(),
-            'thumbnail': base64.b64encode(self.get_thumbnail()).decode('utf-8') if include_thumbnail else None
+            "id": self.obj_data["id"],
+            "camera": self.camera,
+            "frame_time": self.obj_data["frame_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"],
+            "region": self.obj_data["region"],
+            "current_zones": self.current_zones.copy(),
+            "entered_zones": list(self.entered_zones).copy(),
+            "thumbnail": base64.b64encode(self.get_thumbnail()).decode("utf-8")
+            if include_thumbnail
+            else None,
         }
 
     def get_thumbnail(self):
-        if self.thumbnail_data is None or not self.thumbnail_data['frame_time'] in self.frame_cache:
-            ret, jpg = cv2.imencode('.jpg', np.zeros((175,175,3), np.uint8))
+        if (
+            self.thumbnail_data is None
+            or not self.thumbnail_data["frame_time"] 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)
+        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))
+            ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8))
             return jpg.tobytes()
-    
-    def get_jpg_bytes(self, timestamp=False, bounding_box=False, crop=False, height=None):
+
+    def get_jpg_bytes(
+        self, timestamp=False, bounding_box=False, crop=False, height=None
+    ):
         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)
+            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")
+            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 = COLOR_MAP[self.obj_data['label']]
+            color = COLOR_MAP[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)
+            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']
-            region = calculate_region(best_frame.shape, box[0], box[1], box[2], box[3], 1.1)
-            best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
+            box = self.thumbnail_data["box"]
+            region = calculate_region(
+                best_frame.shape, box[0], box[1], box[2], box[3], 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)
+            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:
-            time_to_show = datetime.datetime.fromtimestamp(self.thumbnail_data['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
-            size = cv2.getTextSize(time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2)
+            time_to_show = datetime.datetime.fromtimestamp(
+                self.thumbnail_data["frame_time"]
+            ).strftime("%m/%d/%Y %H:%M:%S")
+            size = cv2.getTextSize(
+                time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2
+            )
             text_width = size[0][0]
-            desired_size = max(150, 0.33*best_frame.shape[1])
-            font_scale = desired_size/text_width
-            cv2.putText(best_frame, time_to_show, (5, best_frame.shape[0]-7), cv2.FONT_HERSHEY_SIMPLEX,
-                fontScale=font_scale, color=(255, 255, 255), thickness=2)
-
-        ret, jpg = cv2.imencode('.jpg', best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
+            desired_size = max(150, 0.33 * best_frame.shape[1])
+            font_scale = desired_size / text_width
+            cv2.putText(
+                best_frame,
+                time_to_show,
+                (5, best_frame.shape[0] - 7),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                fontScale=font_scale,
+                color=(255, 255, 255),
+                thickness=2,
+            )
+
+        ret, jpg = cv2.imencode(".jpg", best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
         if ret:
             return jpg.tobytes()
         else:
             return None
 
+
 def zone_filtered(obj: TrackedObject, object_config):
-    object_name = obj.obj_data['label']
+    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']:
+        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']:
+        if obj_settings.max_area < obj.obj_data["area"]:
             return True
 
         # if the score is lower than the threshold, skip
@@ -245,8 +289,9 @@ def zone_filtered(obj: TrackedObject, object_config):
 
     return False
 
+
 # Maintains the state of a camera
-class CameraState():
+class CameraState:
     def __init__(self, name, config, frame_manager):
         self.name = name
         self.config = config
@@ -269,46 +314,87 @@ class CameraState():
         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()}
+            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'):
+        if draw_options.get("bounding_boxes"):
             # draw the bounding boxes on the frame
             for obj in tracked_objects.values():
                 thickness = 2
-                color = COLOR_MAP[obj['label']]
+                color = COLOR_MAP[obj["label"]]
 
-                if obj['frame_time'] != frame_time:
+                if obj["frame_time"] != frame_time:
                     thickness = 1
-                    color = (255,0,0)
+                    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"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
+                box = obj["box"]
+                draw_box_with_label(
+                    frame_copy,
+                    box[0],
+                    box[1],
+                    box[2],
+                    box[3],
+                    obj["label"],
+                    f"{int(obj['score']*100)}% {int(obj['area'])}",
+                    thickness=thickness,
+                    color=color,
+                )
 
-        if draw_options.get('regions'):
+        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)
+                cv2.rectangle(
+                    frame_copy,
+                    (region[0], region[1]),
+                    (region[2], region[3]),
+                    (0, 255, 0),
+                    2,
+                )
 
-        if draw_options.get('zones'):
+        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
+                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("mask"):
+            mask_overlay = np.where(self.camera_config.motion.mask == [0])
+            frame_copy[mask_overlay] = [0, 0, 0]
 
-        if draw_options.get('motion_boxes'):
+        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)
+                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'):
-            time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
-            cv2.putText(frame_copy, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
+        if draw_options.get("timestamp"):
+            time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime(
+                "%m/%d/%Y %H:%M:%S"
+            )
+            cv2.putText(
+                frame_copy,
+                time_to_show,
+                (10, 30),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                fontScale=0.8,
+                color=(255, 255, 255),
+                thickness=2,
+            )
 
         return frame_copy
 
@@ -324,7 +410,9 @@ class CameraState():
         self.regions = 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)
+        current_frame = self.frame_manager.get(
+            frame_id, self.camera_config.frame_shape_yuv
+        )
 
         current_ids = current_detections.keys()
         previous_ids = self.tracked_objects.keys()
@@ -333,10 +421,12 @@ class CameraState():
         updated_ids = list(set(current_ids).intersection(previous_ids))
 
         for id in new_ids:
-            new_obj = self.tracked_objects[id] = TrackedObject(self.name, self.camera_config, self.frame_cache, current_detections[id])
+            new_obj = self.tracked_objects[id] = TrackedObject(
+                self.name, self.camera_config, self.frame_cache, current_detections[id]
+            )
 
             # call event handlers
-            for c in self.callbacks['start']:
+            for c in self.callbacks["start"]:
                 c(self.name, new_obj, frame_time)
 
         for id in updated_ids:
@@ -345,75 +435,107 @@ class CameraState():
 
             if significant_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:
+                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 publish
             # and the last update is greater than the last publish
-            if frame_time - updated_obj.last_published > 5 and updated_obj.last_updated > updated_obj.last_published:
+            if (
+                frame_time - updated_obj.last_published > 5
+                and updated_obj.last_updated > updated_obj.last_published
+            ):
                 # call event handlers
-                for c in self.callbacks['update']:
+                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 = self.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']:
+            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 self.tracked_objects.values():
-            object_type = obj.obj_data['label']
+            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'] != self.current_frame_time:
+            if (
+                obj.false_positive
+                or obj.thumbnail_data["frame_time"] != self.current_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):
+                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']:
+                    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']:
+                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()
         for obj in self.tracked_objects.values():
             if not obj.false_positive:
-                obj_counter[obj.obj_data['label']] += 1
+                obj_counter[obj.obj_data["label"]] += 1
 
         # report on detected objects
         for obj_name, count in obj_counter.items():
             if count != self.object_counts[obj_name]:
                 self.object_counts[obj_name] = count
-                for c in self.callbacks['object_status']:
+                for c in self.callbacks["object_status"]:
                     c(self.name, obj_name, 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 not obj_name in obj_counter]
+        expired_objects = [
+            obj_name
+            for obj_name, count in self.object_counts.items()
+            if count > 0 and not obj_name in obj_counter
+        ]
         for obj_name in expired_objects:
             self.object_counts[obj_name] = 0
-            for c in self.callbacks['object_status']:
+            for c in self.callbacks["object_status"]:
                 c(self.name, obj_name, 0)
-            for c in self.callbacks['snapshot']:
+            for c in self.callbacks["snapshot"]:
                 c(self.name, self.best_objects[obj_name], frame_time)
 
         # cleanup thumbnail frame cache
-        current_thumb_frames = set([obj.thumbnail_data['frame_time'] for obj in self.tracked_objects.values() if not obj.false_positive])
-        current_best_frames = set([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 not t in current_thumb_frames and not t in current_best_frames]
+        current_thumb_frames = set(
+            [
+                obj.thumbnail_data["frame_time"]
+                for obj in self.tracked_objects.values()
+                if not obj.false_positive
+            ]
+        )
+        current_best_frames = set(
+            [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 not t in current_thumb_frames and not t in current_best_frames
+        ]
         for t in thumb_frames_to_delete:
             del self.frame_cache[t]
 
@@ -423,8 +545,18 @@ class CameraState():
                 self.frame_manager.delete(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, stop_event):
+    def __init__(
+        self,
+        config: FrigateConfig,
+        client,
+        topic_prefix,
+        tracked_objects_queue,
+        event_queue,
+        event_processed_queue,
+        stop_event,
+    ):
         threading.Thread.__init__(self)
         self.name = "detected_frames_processor"
         self.config = config
@@ -438,37 +570,56 @@ class TrackedObjectProcessor(threading.Thread):
         self.frame_manager = SharedMemoryFrameManager()
 
         def start(camera, obj: TrackedObject, current_frame_time):
-            self.event_queue.put(('start', camera, obj.to_dict()))
+            self.event_queue.put(("start", camera, obj.to_dict()))
 
         def update(camera, obj: TrackedObject, current_frame_time):
             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)
+            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
 
         def end(camera, obj: TrackedObject, current_frame_time):
             snapshot_config = self.config.cameras[camera].snapshots
             event_data = obj.to_dict(include_thumbnail=True)
-            event_data['has_snapshot'] = False
+            event_data["has_snapshot"] = False
             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)
+                message = {
+                    "before": obj.previous,
+                    "after": obj.to_dict(),
+                    "type": "end",
+                }
+                self.client.publish(
+                    f"{self.topic_prefix}/events", json.dumps(message), retain=False
+                )
                 # write snapshot to disk if enabled
                 if snapshot_config.enabled and self.should_save_snapshot(camera, obj):
                     jpg_bytes = obj.get_jpg_bytes(
                         timestamp=snapshot_config.timestamp,
                         bounding_box=snapshot_config.bounding_box,
                         crop=snapshot_config.crop,
-                        height=snapshot_config.height
+                        height=snapshot_config.height,
                     )
                     if jpg_bytes is None:
-                        logger.warning(f"Unable to save snapshot for {obj.obj_data['id']}.")
+                        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:
+                        with open(
+                            os.path.join(
+                                CLIPS_DIR, f"{camera}-{obj.obj_data['id']}.jpg"
+                            ),
+                            "wb",
+                        ) as j:
                             j.write(jpg_bytes)
-                        event_data['has_snapshot'] = True
-            self.event_queue.put(('end', camera, event_data))
-        
+                        event_data["has_snapshot"] = True
+            self.event_queue.put(("end", camera, event_data))
+
         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):
@@ -476,24 +627,32 @@ class TrackedObjectProcessor(threading.Thread):
                     timestamp=mqtt_config.timestamp,
                     bounding_box=mqtt_config.bounding_box,
                     crop=mqtt_config.crop,
-                    height=mqtt_config.height
+                    height=mqtt_config.height,
                 )
 
                 if jpg_bytes is None:
-                    logger.warning(f"Unable to send mqtt snapshot for {obj.obj_data['id']}.")
+                    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)
-        
+                    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)
+            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)
+            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
 
         # {
@@ -510,7 +669,9 @@ class TrackedObjectProcessor(threading.Thread):
         # if there are required zones and there is no overlap
         required_zones = self.config.cameras[camera].snapshots.required_zones
         if len(required_zones) > 0 and not obj.entered_zones & set(required_zones):
-            logger.debug(f"Not creating snapshot for {obj.obj_data['id']} because it did not enter required zones")
+            logger.debug(
+                f"Not creating snapshot for {obj.obj_data['id']} because it did not enter required zones"
+            )
             return False
 
         return True
@@ -519,7 +680,9 @@ class TrackedObjectProcessor(threading.Thread):
         # 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 obj.entered_zones & set(required_zones):
-            logger.debug(f"Not sending mqtt for {obj.obj_data['id']} because it did not enter required zones")
+            logger.debug(
+                f"Not sending mqtt for {obj.obj_data['id']} because it did not enter required zones"
+            )
             return False
 
         return True
@@ -530,7 +693,9 @@ class TrackedObjectProcessor(threading.Thread):
         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'])
+            best["frame"] = camera_state.frame_cache.get(
+                best_obj.thumbnail_data["frame_time"]
+            )
             return best
         else:
             return {}
@@ -545,13 +710,21 @@ class TrackedObjectProcessor(threading.Thread):
                 break
 
             try:
-                camera, frame_time, current_tracked_objects, motion_boxes, regions = self.tracked_objects_queue.get(True, 10)
+                (
+                    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)
+            camera_state.update(
+                frame_time, current_tracked_objects, motion_boxes, regions
+            )
 
             # update zone counts for each label
             # for each zone in the current camera
@@ -560,23 +733,35 @@ class TrackedObjectProcessor(threading.Thread):
                 obj_counter = Counter()
                 for obj in camera_state.tracked_objects.values():
                     if zone in obj.current_zones and not obj.false_positive:
-                        obj_counter[obj.obj_data['label']] += 1
+                        obj_counter[obj.obj_data["label"]] += 1
 
                 # update counts and publish status
-                for label in set(list(self.zone_data[zone].keys()) + list(obj_counter.keys())):
+                for label in set(
+                    list(self.zone_data[zone].keys()) + list(obj_counter.keys())
+                ):
                     # 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
+                        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)
+                            self.client.publish(
+                                f"{self.topic_prefix}/{zone}/{label}",
+                                new_count,
+                                retain=False,
+                            )
                     # 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)
+                            self.client.publish(
+                                f"{self.topic_prefix}/{zone}/{label}",
+                                obj_counter[label],
+                                retain=False,
+                            )
 
             # cleanup event finished queue
             while not self.event_processed_queue.empty():

frigate/objects.py

@@ -16,24 +16,24 @@ from frigate.config import DetectConfig
 from frigate.util import draw_box_with_label
 
 
-class ObjectTracker():
+class ObjectTracker:
     def __init__(self, config: DetectConfig):
         self.tracked_objects = {}
         self.disappeared = {}
         self.max_disappeared = config.max_disappeared
 
     def register(self, index, obj):
-        rand_id = ''.join(random.choices(string.ascii_lowercase + string.digits, k=6))
+        rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
         id = f"{obj['frame_time']}-{rand_id}"
-        obj['id'] = id
-        obj['start_time'] = obj['frame_time']
+        obj["id"] = id
+        obj["start_time"] = obj["frame_time"]
         self.tracked_objects[id] = obj
         self.disappeared[id] = 0
 
     def deregister(self, id):
         del self.tracked_objects[id]
         del self.disappeared[id]
-    
+
     def update(self, id, new_obj):
         self.disappeared[id] = 0
         self.tracked_objects[id].update(new_obj)
@@ -42,45 +42,49 @@ class ObjectTracker():
         # group by name
         new_object_groups = defaultdict(lambda: [])
         for obj in new_objects:
-            new_object_groups[obj[0]].append({
-                'label': obj[0],
-                'score': obj[1],
-                'box': obj[2],
-                'area': obj[3],
-                'region': obj[4],
-                'frame_time': frame_time
-            })
-        
+            new_object_groups[obj[0]].append(
+                {
+                    "label": obj[0],
+                    "score": obj[1],
+                    "box": obj[2],
+                    "area": obj[3],
+                    "region": obj[4],
+                    "frame_time": frame_time,
+                }
+            )
+
         # update any tracked objects with labels that are not
         # seen in the current objects and deregister if needed
         for obj in list(self.tracked_objects.values()):
-            if not obj['label'] in new_object_groups:
-                if self.disappeared[obj['id']] >= self.max_disappeared:
-                    self.deregister(obj['id'])
+            if not obj["label"] in new_object_groups:
+                if self.disappeared[obj["id"]] >= self.max_disappeared:
+                    self.deregister(obj["id"])
                 else:
-                    self.disappeared[obj['id']] += 1
-        
+                    self.disappeared[obj["id"]] += 1
+
         if len(new_objects) == 0:
             return
-        
+
         # track objects for each label type
         for label, group in new_object_groups.items():
-            current_objects = [o for o in self.tracked_objects.values() if o['label'] == label]
-            current_ids = [o['id'] for o in current_objects]
-            current_centroids = np.array([o['centroid'] for o in current_objects])
+            current_objects = [
+                o for o in self.tracked_objects.values() if o["label"] == label
+            ]
+            current_ids = [o["id"] for o in current_objects]
+            current_centroids = np.array([o["centroid"] for o in current_objects])
 
             # compute centroids of new objects
             for obj in group:
-                centroid_x = int((obj['box'][0]+obj['box'][2]) / 2.0)
-                centroid_y = int((obj['box'][1]+obj['box'][3]) / 2.0)
-                obj['centroid'] = (centroid_x, centroid_y)
+                centroid_x = int((obj["box"][0] + obj["box"][2]) / 2.0)
+                centroid_y = int((obj["box"][1] + obj["box"][3]) / 2.0)
+                obj["centroid"] = (centroid_x, centroid_y)
 
             if len(current_objects) == 0:
                 for index, obj in enumerate(group):
                     self.register(index, obj)
                 return
-            
-            new_centroids = np.array([o['centroid'] for o in group])
+
+            new_centroids = np.array([o["centroid"] for o in group])
 
             # compute the distance between each pair of tracked
             # centroids and new centroids, respectively -- our
@@ -130,9 +134,9 @@ class ObjectTracker():
             unusedCols = set(range(0, D.shape[1])).difference(usedCols)
 
             # in the event that the number of object centroids is
-			# equal or greater than the number of input centroids
-			# we need to check and see if some of these objects have
-			# potentially disappeared
+            # equal or greater than the number of input centroids
+            # we need to check and see if some of these objects have
+            # potentially disappeared
             if D.shape[0] >= D.shape[1]:
                 for row in unusedRows:
                     id = current_ids[row]

frigate/process_clip.py

@@ -16,37 +16,43 @@ from frigate.edgetpu import LocalObjectDetector
 from frigate.motion import MotionDetector
 from frigate.object_processing import COLOR_MAP, CameraState
 from frigate.objects import ObjectTracker
-from frigate.util import (DictFrameManager, EventsPerSecond,
-                          SharedMemoryFrameManager, draw_box_with_label)
-from frigate.video import (capture_frames, process_frames,
-                           start_or_restart_ffmpeg)
+from frigate.util import (
+    DictFrameManager,
+    EventsPerSecond,
+    SharedMemoryFrameManager,
+    draw_box_with_label,
+)
+from frigate.video import capture_frames, process_frames, start_or_restart_ffmpeg
 
 logging.basicConfig()
 logging.root.setLevel(logging.DEBUG)
 
 logger = logging.getLogger(__name__)
 
+
 def get_frame_shape(source):
-    ffprobe_cmd = " ".join([
-        'ffprobe',
-        '-v',
-        'panic',
-        '-show_error',
-        '-show_streams',
-        '-of',
-        'json',
-        '"'+source+'"'
-    ])
+    ffprobe_cmd = " ".join(
+        [
+            "ffprobe",
+            "-v",
+            "panic",
+            "-show_error",
+            "-show_streams",
+            "-of",
+            "json",
+            '"' + source + '"',
+        ]
+    )
     p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
     (output, err) = p.communicate()
     p_status = p.wait()
     info = json.loads(output)
 
-    video_info = [s for s in info['streams'] if s['codec_type'] == 'video'][0]
+    video_info = [s for s in info["streams"] if s["codec_type"] == "video"][0]
+
+    if video_info["height"] != 0 and video_info["width"] != 0:
+        return (video_info["height"], video_info["width"], 3)
 
-    if video_info['height'] != 0 and video_info['width'] != 0:
-        return (video_info['height'], video_info['width'], 3)
-    
     # fallback to using opencv if ffprobe didnt succeed
     video = cv2.VideoCapture(source)
     ret, frame = video.read()
@@ -54,14 +60,17 @@ def get_frame_shape(source):
     video.release()
     return frame_shape
 
-class ProcessClip():
+
+class ProcessClip:
     def __init__(self, clip_path, frame_shape, config: FrigateConfig):
         self.clip_path = clip_path
-        self.camera_name = 'camera'
+        self.camera_name = "camera"
         self.config = config
-        self.camera_config = self.config.cameras['camera']
+        self.camera_config = self.config.cameras["camera"]
         self.frame_shape = self.camera_config.frame_shape
-        self.ffmpeg_cmd = [c['cmd'] for c in self.camera_config.ffmpeg_cmds if 'detect' in c['roles']][0]
+        self.ffmpeg_cmd = [
+            c["cmd"] for c in self.camera_config.ffmpeg_cmds if "detect" in c["roles"]
+        ][0]
         self.frame_manager = SharedMemoryFrameManager()
         self.frame_queue = mp.Queue()
         self.detected_objects_queue = mp.Queue()
@@ -70,37 +79,66 @@ class ProcessClip():
     def load_frames(self):
         fps = EventsPerSecond()
         skipped_fps = EventsPerSecond()
-        current_frame = mp.Value('d', 0.0)
-        frame_size = self.camera_config.frame_shape_yuv[0] * self.camera_config.frame_shape_yuv[1]
-        ffmpeg_process = start_or_restart_ffmpeg(self.ffmpeg_cmd, logger, sp.DEVNULL, frame_size)
-        capture_frames(ffmpeg_process, self.camera_name, self.camera_config.frame_shape_yuv, self.frame_manager, 
-            self.frame_queue, fps, skipped_fps, current_frame)
+        current_frame = mp.Value("d", 0.0)
+        frame_size = (
+            self.camera_config.frame_shape_yuv[0]
+            * self.camera_config.frame_shape_yuv[1]
+        )
+        ffmpeg_process = start_or_restart_ffmpeg(
+            self.ffmpeg_cmd, logger, sp.DEVNULL, frame_size
+        )
+        capture_frames(
+            ffmpeg_process,
+            self.camera_name,
+            self.camera_config.frame_shape_yuv,
+            self.frame_manager,
+            self.frame_queue,
+            fps,
+            skipped_fps,
+            current_frame,
+        )
         ffmpeg_process.wait()
         ffmpeg_process.communicate()
-    
-    def process_frames(self, objects_to_track=['person'], object_filters={}):
+
+    def process_frames(self, objects_to_track=["person"], object_filters={}):
         mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
         mask[:] = 255
-        motion_detector = MotionDetector(self.frame_shape, mask, self.camera_config.motion)
+        motion_detector = MotionDetector(
+            self.frame_shape, mask, self.camera_config.motion
+        )
 
-        object_detector = LocalObjectDetector(labels='/labelmap.txt')
+        object_detector = LocalObjectDetector(labels="/labelmap.txt")
         object_tracker = ObjectTracker(self.camera_config.detect)
         process_info = {
-            'process_fps': mp.Value('d', 0.0),
-            'detection_fps': mp.Value('d', 0.0),
-            'detection_frame': mp.Value('d', 0.0)
+            "process_fps": mp.Value("d", 0.0),
+            "detection_fps": mp.Value("d", 0.0),
+            "detection_frame": mp.Value("d", 0.0),
         }
         stop_event = mp.Event()
         model_shape = (self.config.model.height, self.config.model.width)
 
-        process_frames(self.camera_name, self.frame_queue, self.frame_shape, model_shape, 
-            self.frame_manager, motion_detector, object_detector, object_tracker, 
-            self.detected_objects_queue, process_info, 
-            objects_to_track, object_filters, mask, stop_event, exit_on_empty=True)
-    
+        process_frames(
+            self.camera_name,
+            self.frame_queue,
+            self.frame_shape,
+            model_shape,
+            self.frame_manager,
+            motion_detector,
+            object_detector,
+            object_tracker,
+            self.detected_objects_queue,
+            process_info,
+            objects_to_track,
+            object_filters,
+            mask,
+            stop_event,
+            exit_on_empty=True,
+        )
+
     def top_object(self, debug_path=None):
         obj_detected = False
         top_computed_score = 0.0
+
         def handle_event(name, obj, frame_time):
             nonlocal obj_detected
             nonlocal top_computed_score
@@ -108,48 +146,85 @@ class ProcessClip():
                 top_computed_score = obj.computed_score
             if not obj.false_positive:
                 obj_detected = True
-        self.camera_state.on('new', handle_event)
-        self.camera_state.on('update', handle_event)
 
-        while(not self.detected_objects_queue.empty()):
-            camera_name, frame_time, current_tracked_objects, motion_boxes, regions = self.detected_objects_queue.get()
+        self.camera_state.on("new", handle_event)
+        self.camera_state.on("update", handle_event)
+
+        while not self.detected_objects_queue.empty():
+            (
+                camera_name,
+                frame_time,
+                current_tracked_objects,
+                motion_boxes,
+                regions,
+            ) = self.detected_objects_queue.get()
             if not debug_path is None:
-                self.save_debug_frame(debug_path, frame_time, current_tracked_objects.values())
+                self.save_debug_frame(
+                    debug_path, frame_time, current_tracked_objects.values()
+                )
+
+            self.camera_state.update(
+                frame_time, current_tracked_objects, motion_boxes, regions
+            )
 
-            self.camera_state.update(frame_time, current_tracked_objects, motion_boxes, regions)
-        
         self.frame_manager.delete(self.camera_state.previous_frame_id)
-        
-        return {
-            'object_detected': obj_detected,
-            'top_score': top_computed_score
-        }
-    
+
+        return {"object_detected": obj_detected, "top_score": top_computed_score}
+
     def save_debug_frame(self, debug_path, frame_time, tracked_objects):
-        current_frame = cv2.cvtColor(self.frame_manager.get(f"{self.camera_name}{frame_time}", self.camera_config.frame_shape_yuv), cv2.COLOR_YUV2BGR_I420)
+        current_frame = cv2.cvtColor(
+            self.frame_manager.get(
+                f"{self.camera_name}{frame_time}", self.camera_config.frame_shape_yuv
+            ),
+            cv2.COLOR_YUV2BGR_I420,
+        )
         # draw the bounding boxes on the frame
         for obj in tracked_objects:
             thickness = 2
-            color = (0,0,175)
+            color = (0, 0, 175)
 
-            if obj['frame_time'] != frame_time:
+            if obj["frame_time"] != frame_time:
                 thickness = 1
-                color = (255,0,0)
+                color = (255, 0, 0)
             else:
-                color = (255,255,0)
+                color = (255, 255, 0)
 
             # draw the bounding boxes on the frame
-            box = obj['box']
-            draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['id'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
+            box = obj["box"]
+            draw_box_with_label(
+                current_frame,
+                box[0],
+                box[1],
+                box[2],
+                box[3],
+                obj["id"],
+                f"{int(obj['score']*100)}% {int(obj['area'])}",
+                thickness=thickness,
+                color=color,
+            )
             # draw the regions on the frame
-            region = obj['region']
-            draw_box_with_label(current_frame, region[0], region[1], region[2], region[3], 'region', "", thickness=1, color=(0,255,0))
-        
-        cv2.imwrite(f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg", current_frame)
+            region = obj["region"]
+            draw_box_with_label(
+                current_frame,
+                region[0],
+                region[1],
+                region[2],
+                region[3],
+                "region",
+                "",
+                thickness=1,
+                color=(0, 255, 0),
+            )
+
+        cv2.imwrite(
+            f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg",
+            current_frame,
+        )
+
 
 @click.command()
 @click.option("-p", "--path", required=True, help="Path to clip or directory to test.")
-@click.option("-l", "--label", default='person', help="Label name to detect.")
+@click.option("-l", "--label", default="person", help="Label name to detect.")
 @click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.")
 @click.option("-s", "--scores", default=None, help="File to save csv of top scores")
 @click.option("--debug-path", default=None, help="Path to output frames for debugging.")
@@ -159,34 +234,37 @@ def process(path, label, threshold, scores, debug_path):
         files = os.listdir(path)
         files.sort()
         clips = [os.path.join(path, file) for file in files]
-    elif os.path.isfile(path):  
+    elif os.path.isfile(path):
         clips.append(path)
 
     json_config = {
-        'mqtt': {
-            'host': 'mqtt'
-        },
-        'cameras': {
-            'camera': {
-                'ffmpeg': {
-                    'inputs': [
-                        { 'path': 'path.mp4', 'global_args': '', 'input_args': '', 'roles': ['detect'] }
+        "mqtt": {"host": "mqtt"},
+        "cameras": {
+            "camera": {
+                "ffmpeg": {
+                    "inputs": [
+                        {
+                            "path": "path.mp4",
+                            "global_args": "",
+                            "input_args": "",
+                            "roles": ["detect"],
+                        }
                     ]
                 },
-                'height': 1920,
-                'width': 1080
+                "height": 1920,
+                "width": 1080,
             }
-        }
+        },
     }
 
     results = []
     for c in clips:
         logger.info(c)
         frame_shape = get_frame_shape(c)
-        
-        json_config['cameras']['camera']['height'] = frame_shape[0]
-        json_config['cameras']['camera']['width'] = frame_shape[1]
-        json_config['cameras']['camera']['ffmpeg']['inputs'][0]['path'] = c
+
+        json_config["cameras"]["camera"]["height"] = frame_shape[0]
+        json_config["cameras"]["camera"]["width"] = frame_shape[1]
+        json_config["cameras"]["camera"]["ffmpeg"]["inputs"][0]["path"] = c
 
         config = FrigateConfig(config=FRIGATE_CONFIG_SCHEMA(json_config))
 
@@ -197,12 +275,15 @@ def process(path, label, threshold, scores, debug_path):
         results.append((c, process_clip.top_object(debug_path)))
 
     if not scores is None:
-        with open(scores, 'w') as writer:
+        with open(scores, "w") as writer:
             for result in results:
                 writer.write(f"{result[0]},{result[1]['top_score']}\n")
-    
-    positive_count = sum(1 for result in results if result[1]['object_detected'])
-    print(f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s).")
 
-if __name__ == '__main__':
+    positive_count = sum(1 for result in results if result[1]["object_detected"])
+    print(
+        f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s)."
+    )
+
+
+if __name__ == "__main__":
     process()

frigate/record.py

@@ -18,41 +18,47 @@ logger = logging.getLogger(__name__)
 
 SECONDS_IN_DAY = 60 * 60 * 24
 
+
 def remove_empty_directories(directory):
-  # list all directories recursively and sort them by path,
-  # longest first
-  paths = sorted(
-      [x[0] for x in os.walk(RECORD_DIR)],
-      key=lambda p: len(str(p)),
-      reverse=True,
-  )
-  for path in paths:
-      # don't delete the parent
-      if path == RECORD_DIR:
-          continue
-      if len(os.listdir(path)) == 0:
-          os.rmdir(path)
+    # list all directories recursively and sort them by path,
+    # longest first
+    paths = sorted(
+        [x[0] for x in os.walk(RECORD_DIR)],
+        key=lambda p: len(str(p)),
+        reverse=True,
+    )
+    for path in paths:
+        # don't delete the parent
+        if path == RECORD_DIR:
+            continue
+        if len(os.listdir(path)) == 0:
+            os.rmdir(path)
+
 
 class RecordingMaintainer(threading.Thread):
     def __init__(self, config: FrigateConfig, stop_event):
         threading.Thread.__init__(self)
-        self.name = 'recording_maint'
+        self.name = "recording_maint"
         self.config = config
         self.stop_event = stop_event
 
     def move_files(self):
-        recordings = [d for d in os.listdir(RECORD_DIR) if os.path.isfile(os.path.join(RECORD_DIR, d)) and d.endswith(".mp4")]
+        recordings = [
+            d
+            for d in os.listdir(RECORD_DIR)
+            if os.path.isfile(os.path.join(RECORD_DIR, d)) and d.endswith(".mp4")
+        ]
 
         files_in_use = []
         for process in psutil.process_iter():
             try:
-                if process.name() != 'ffmpeg':
+                if process.name() != "ffmpeg":
                     continue
                 flist = process.open_files()
                 if flist:
                     for nt in flist:
                         if nt.path.startswith(RECORD_DIR):
-                            files_in_use.append(nt.path.split('/')[-1])
+                            files_in_use.append(nt.path.split("/")[-1])
             except:
                 continue
 
@@ -60,44 +66,53 @@ class RecordingMaintainer(threading.Thread):
             if f in files_in_use:
                 continue
 
-            camera = '-'.join(f.split('-')[:-1])
-            start_time = datetime.datetime.strptime(f.split('-')[-1].split('.')[0], '%Y%m%d%H%M%S')
-        
-            ffprobe_cmd = " ".join([
-                'ffprobe',
-                '-v',
-                'error',
-                '-show_entries',
-                'format=duration',
-                '-of',
-                'default=noprint_wrappers=1:nokey=1',
-                f"{os.path.join(RECORD_DIR,f)}"
-            ])
+            camera = "-".join(f.split("-")[:-1])
+            start_time = datetime.datetime.strptime(
+                f.split("-")[-1].split(".")[0], "%Y%m%d%H%M%S"
+            )
+
+            ffprobe_cmd = " ".join(
+                [
+                    "ffprobe",
+                    "-v",
+                    "error",
+                    "-show_entries",
+                    "format=duration",
+                    "-of",
+                    "default=noprint_wrappers=1:nokey=1",
+                    f"{os.path.join(RECORD_DIR,f)}",
+                ]
+            )
             p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
             (output, err) = p.communicate()
             p_status = p.wait()
             if p_status == 0:
-                duration = float(output.decode('utf-8').strip())
+                duration = float(output.decode("utf-8").strip())
             else:
                 logger.info(f"bad file: {f}")
-                os.remove(os.path.join(RECORD_DIR,f))
+                os.remove(os.path.join(RECORD_DIR, f))
                 continue
 
-            directory = os.path.join(RECORD_DIR, start_time.strftime('%Y-%m/%d/%H'), camera)
+            directory = os.path.join(
+                RECORD_DIR, start_time.strftime("%Y-%m/%d/%H"), camera
+            )
 
             if not os.path.exists(directory):
                 os.makedirs(directory)
 
             file_name = f"{start_time.strftime('%M.%S.mp4')}"
 
-            os.rename(os.path.join(RECORD_DIR,f), os.path.join(directory,file_name))
+            os.rename(os.path.join(RECORD_DIR, f), os.path.join(directory, file_name))
 
     def expire_files(self):
         delete_before = {}
         for name, camera in self.config.cameras.items():
-            delete_before[name] = datetime.datetime.now().timestamp() - SECONDS_IN_DAY*camera.record.retain_days
+            delete_before[name] = (
+                datetime.datetime.now().timestamp()
+                - SECONDS_IN_DAY * camera.record.retain_days
+            )
 
-        for p in Path('/media/frigate/recordings').rglob("*.mp4"):
+        for p in Path("/media/frigate/recordings").rglob("*.mp4"):
             if not p.parent.name in delete_before:
                 continue
             if p.stat().st_mtime < delete_before[p.parent.name]:
@@ -106,7 +121,7 @@ class RecordingMaintainer(threading.Thread):
     def run(self):
         counter = 0
         self.expire_files()
-        while(True):
+        while True:
             if self.stop_event.is_set():
                 logger.info(f"Exiting recording maintenance...")
                 break
@@ -120,6 +135,3 @@ class RecordingMaintainer(threading.Thread):
                 counter = 0
 
             self.move_files()
-
-
-   

frigate/stats.py

@@ -11,14 +11,16 @@ from frigate.version import VERSION
 
 logger = logging.getLogger(__name__)
 
+
 def stats_init(camera_metrics, detectors):
     stats_tracking = {
-        'camera_metrics': camera_metrics,
-        'detectors': detectors,
-        'started': int(time.time())
+        "camera_metrics": camera_metrics,
+        "detectors": detectors,
+        "started": int(time.time()),
     }
     return stats_tracking
 
+
 def get_fs_type(path):
     bestMatch = ""
     fsType = ""
@@ -28,53 +30,62 @@ def get_fs_type(path):
             bestMatch = part.mountpoint
     return fsType
 
+
 def stats_snapshot(stats_tracking):
-    camera_metrics = stats_tracking['camera_metrics']
+    camera_metrics = stats_tracking["camera_metrics"]
     stats = {}
 
     total_detection_fps = 0
 
     for name, camera_stats in camera_metrics.items():
-        total_detection_fps += camera_stats['detection_fps'].value
+        total_detection_fps += camera_stats["detection_fps"].value
         stats[name] = {
-            'camera_fps': round(camera_stats['camera_fps'].value, 2),
-            'process_fps': round(camera_stats['process_fps'].value, 2),
-            'skipped_fps': round(camera_stats['skipped_fps'].value, 2),
-            'detection_fps': round(camera_stats['detection_fps'].value, 2),
-            'pid': camera_stats['process'].pid,
-            'capture_pid': camera_stats['capture_process'].pid
+            "camera_fps": round(camera_stats["camera_fps"].value, 2),
+            "process_fps": round(camera_stats["process_fps"].value, 2),
+            "skipped_fps": round(camera_stats["skipped_fps"].value, 2),
+            "detection_fps": round(camera_stats["detection_fps"].value, 2),
+            "pid": camera_stats["process"].pid,
+            "capture_pid": camera_stats["capture_process"].pid,
         }
 
-    stats['detectors'] = {}
+    stats["detectors"] = {}
     for name, detector in stats_tracking["detectors"].items():
-        stats['detectors'][name] = {
-            'inference_speed': round(detector.avg_inference_speed.value * 1000, 2),
-            'detection_start': detector.detection_start.value,
-            'pid': detector.detect_process.pid
+        stats["detectors"][name] = {
+            "inference_speed": round(detector.avg_inference_speed.value * 1000, 2),
+            "detection_start": detector.detection_start.value,
+            "pid": detector.detect_process.pid,
         }
-    stats['detection_fps'] = round(total_detection_fps, 2)
+    stats["detection_fps"] = round(total_detection_fps, 2)
 
-    stats['service'] = {
-        'uptime': (int(time.time()) - stats_tracking['started']),
-        'version': VERSION,
-        'storage': {}
+    stats["service"] = {
+        "uptime": (int(time.time()) - stats_tracking["started"]),
+        "version": VERSION,
+        "storage": {},
     }
 
     for path in [RECORD_DIR, CLIPS_DIR, CACHE_DIR, "/dev/shm"]:
         storage_stats = shutil.disk_usage(path)
-        stats['service']['storage'][path] = {
-            'total': round(storage_stats.total/1000000, 1),
-            'used': round(storage_stats.used/1000000, 1),
-            'free': round(storage_stats.free/1000000, 1),
-            'mount_type': get_fs_type(path)
+        stats["service"]["storage"][path] = {
+            "total": round(storage_stats.total / 1000000, 1),
+            "used": round(storage_stats.used / 1000000, 1),
+            "free": round(storage_stats.free / 1000000, 1),
+            "mount_type": get_fs_type(path),
         }
 
     return stats
 
+
 class StatsEmitter(threading.Thread):
-    def __init__(self, config: FrigateConfig, stats_tracking, mqtt_client, topic_prefix, stop_event):
+    def __init__(
+        self,
+        config: FrigateConfig,
+        stats_tracking,
+        mqtt_client,
+        topic_prefix,
+        stop_event,
+    ):
         threading.Thread.__init__(self)
-        self.name = 'frigate_stats_emitter'
+        self.name = "frigate_stats_emitter"
         self.config = config
         self.stats_tracking = stats_tracking
         self.mqtt_client = mqtt_client
@@ -88,5 +99,7 @@ class StatsEmitter(threading.Thread):
                 logger.info(f"Exiting watchdog...")
                 break
             stats = stats_snapshot(self.stats_tracking)
-            self.mqtt_client.publish(f"{self.topic_prefix}/stats", json.dumps(stats), retain=False)
+            self.mqtt_client.publish(
+                f"{self.topic_prefix}/stats", json.dumps(stats), retain=False
+            )
             time.sleep(self.config.mqtt.stats_interval)

frigate/test/test_config.py

@@ -3,431 +3,339 @@ from unittest import TestCase, main
 import voluptuous as vol
 from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig
 
+
 class TestConfig(TestCase):
     def setUp(self):
         self.minimal = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "mqtt": {"host": "mqtt"},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920
+                    "height": 1080,
+                    "width": 1920,
                 }
-            }
+            },
         }
+
     def test_empty(self):
         FRIGATE_CONFIG_SCHEMA({})
 
     def test_minimal(self):
         FRIGATE_CONFIG_SCHEMA(self.minimal)
-    
+
     def test_config_class(self):
         FrigateConfig(config=self.minimal)
-    
+
     def test_inherit_tracked_objects(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'objects': {
-                'track': ['person', 'dog']
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "mqtt": {"host": "mqtt"},
+            "objects": {"track": ["person", "dog"]},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920
+                    "height": 1080,
+                    "width": 1920,
                 }
-            }
+            },
         }
         frigate_config = FrigateConfig(config=config)
-        assert('dog' in frigate_config.cameras['back'].objects.track)
-    
+        assert "dog" in frigate_config.cameras["back"].objects.track
+
     def test_override_tracked_objects(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'objects': {
-                'track': ['person', 'dog']
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "mqtt": {"host": "mqtt"},
+            "objects": {"track": ["person", "dog"]},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920,
-                    'objects': {
-                        'track': ['cat']
-                    }
+                    "height": 1080,
+                    "width": 1920,
+                    "objects": {"track": ["cat"]},
                 }
-            }
+            },
         }
         frigate_config = FrigateConfig(config=config)
-        assert('cat' in frigate_config.cameras['back'].objects.track)
-    
+        assert "cat" in frigate_config.cameras["back"].objects.track
+
     def test_default_object_filters(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'objects': {
-                'track': ['person', 'dog']
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "mqtt": {"host": "mqtt"},
+            "objects": {"track": ["person", "dog"]},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920
+                    "height": 1080,
+                    "width": 1920,
                 }
-            }
+            },
         }
         frigate_config = FrigateConfig(config=config)
-        assert('dog' in frigate_config.cameras['back'].objects.filters)
-    
+        assert "dog" in frigate_config.cameras["back"].objects.filters
+
     def test_inherit_object_filters(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
+            "mqtt": {"host": "mqtt"},
+            "objects": {
+                "track": ["person", "dog"],
+                "filters": {"dog": {"threshold": 0.7}},
             },
-            'objects': {
-                'track': ['person', 'dog'],
-                'filters': {
-                    'dog': {
-                        'threshold': 0.7
-                    }
-                }
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920
+                    "height": 1080,
+                    "width": 1920,
                 }
-            }
+            },
         }
         frigate_config = FrigateConfig(config=config)
-        assert('dog' in frigate_config.cameras['back'].objects.filters)
-        assert(frigate_config.cameras['back'].objects.filters['dog'].threshold == 0.7)
-    
+        assert "dog" in frigate_config.cameras["back"].objects.filters
+        assert frigate_config.cameras["back"].objects.filters["dog"].threshold == 0.7
+
     def test_override_object_filters(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "mqtt": {"host": "mqtt"},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920,
-                    'objects': {
-                        'track': ['person', 'dog'],
-                        'filters': {
-                            'dog': {
-                                'threshold': 0.7
-                            }
-                        }
-                    }
+                    "height": 1080,
+                    "width": 1920,
+                    "objects": {
+                        "track": ["person", "dog"],
+                        "filters": {"dog": {"threshold": 0.7}},
+                    },
                 }
-            }
+            },
         }
         frigate_config = FrigateConfig(config=config)
-        assert('dog' in frigate_config.cameras['back'].objects.filters)
-        assert(frigate_config.cameras['back'].objects.filters['dog'].threshold == 0.7)
-    
+        assert "dog" in frigate_config.cameras["back"].objects.filters
+        assert frigate_config.cameras["back"].objects.filters["dog"].threshold == 0.7
+
     def test_global_object_mask(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'objects': {
-                'track': ['person', 'dog']
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "mqtt": {"host": "mqtt"},
+            "objects": {"track": ["person", "dog"]},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920,
-                    'objects': {
-                        'mask': '0,0,1,1,0,1',
-                        'filters': {
-                            'dog': {
-                                'mask': '1,1,1,1,1,1'
-                            }
-                        }
-                    }
+                    "height": 1080,
+                    "width": 1920,
+                    "objects": {
+                        "mask": "0,0,1,1,0,1",
+                        "filters": {"dog": {"mask": "1,1,1,1,1,1"}},
+                    },
                 }
-            }
+            },
         }
         frigate_config = FrigateConfig(config=config)
-        assert('dog' in frigate_config.cameras['back'].objects.filters)
-        assert(len(frigate_config.cameras['back'].objects.filters['dog']._raw_mask) == 2)
-        assert(len(frigate_config.cameras['back'].objects.filters['person']._raw_mask) == 1)
-    
+        assert "dog" in frigate_config.cameras["back"].objects.filters
+        assert len(frigate_config.cameras["back"].objects.filters["dog"]._raw_mask) == 2
+        assert (
+            len(frigate_config.cameras["back"].objects.filters["person"]._raw_mask) == 1
+        )
+
     def test_ffmpeg_params_global(self):
         config = {
-            'ffmpeg': {
-                'input_args': ['-re']
-            },
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "ffmpeg": {"input_args": ["-re"]},
+            "mqtt": {"host": "mqtt"},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920,
-                    'objects': {
-                        'track': ['person', 'dog'],
-                        'filters': {
-                            'dog': {
-                                'threshold': 0.7
-                            }
-                        }
-                    }
+                    "height": 1080,
+                    "width": 1920,
+                    "objects": {
+                        "track": ["person", "dog"],
+                        "filters": {"dog": {"threshold": 0.7}},
+                    },
                 }
-            }
+            },
         }
         frigate_config = FrigateConfig(config=config)
-        assert('-re' in frigate_config.cameras['back'].ffmpeg_cmds[0]['cmd'])
+        assert "-re" in frigate_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
 
     def test_ffmpeg_params_camera(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "mqtt": {"host": "mqtt"},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ],
-                        'input_args': ['-re']
+                        "input_args": ["-re"],
+                    },
+                    "height": 1080,
+                    "width": 1920,
+                    "objects": {
+                        "track": ["person", "dog"],
+                        "filters": {"dog": {"threshold": 0.7}},
                     },
-                    'height': 1080,
-                    'width': 1920,
-                    'objects': {
-                        'track': ['person', 'dog'],
-                        'filters': {
-                            'dog': {
-                                'threshold': 0.7
-                            }
-                        }
-                    }
                 }
-            }
+            },
         }
         frigate_config = FrigateConfig(config=config)
-        assert('-re' in frigate_config.cameras['back'].ffmpeg_cmds[0]['cmd'])
+        assert "-re" in frigate_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
 
     def test_ffmpeg_params_input(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'], 'input_args': ['-re'] }
+            "mqtt": {"host": "mqtt"},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                                "input_args": ["-re"],
+                            }
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920,
-                    'objects': {
-                        'track': ['person', 'dog'],
-                        'filters': {
-                            'dog': {
-                                'threshold': 0.7
-                            }
-                        }
-                    }
+                    "height": 1080,
+                    "width": 1920,
+                    "objects": {
+                        "track": ["person", "dog"],
+                        "filters": {"dog": {"threshold": 0.7}},
+                    },
                 }
-            }
+            },
         }
         frigate_config = FrigateConfig(config=config)
-        assert('-re' in frigate_config.cameras['back'].ffmpeg_cmds[0]['cmd'])
-    
-    
+        assert "-re" in frigate_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
+
     def test_inherit_clips_retention(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'clips': {
-                'retain': {
-                    'default': 20,
-                    'objects': {
-                        'person': 30
-                    }
-                }
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "mqtt": {"host": "mqtt"},
+            "clips": {"retain": {"default": 20, "objects": {"person": 30}}},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920
+                    "height": 1080,
+                    "width": 1920,
                 }
-            }
+            },
         }
         frigate_config = FrigateConfig(config=config)
-        assert(frigate_config.cameras['back'].clips.retain.objects['person'] == 30)
-    
+        assert frigate_config.cameras["back"].clips.retain.objects["person"] == 30
+
     def test_roles_listed_twice_throws_error(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'clips': {
-                'retain': {
-                    'default': 20,
-                    'objects': {
-                        'person': 30
-                    }
-                }
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] },
-                            { 'path': 'rtsp://10.0.0.1:554/video2', 'roles': ['detect'] }
+            "mqtt": {"host": "mqtt"},
+            "clips": {"retain": {"default": 20, "objects": {"person": 30}}},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]},
+                            {"path": "rtsp://10.0.0.1:554/video2", "roles": ["detect"]},
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920
+                    "height": 1080,
+                    "width": 1920,
                 }
-            }
+            },
         }
         self.assertRaises(vol.MultipleInvalid, lambda: FrigateConfig(config=config))
-    
+
     def test_zone_matching_camera_name_throws_error(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'clips': {
-                'retain': {
-                    'default': 20,
-                    'objects': {
-                        'person': 30
-                    }
-                }
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "mqtt": {"host": "mqtt"},
+            "clips": {"retain": {"default": 20, "objects": {"person": 30}}},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920,
-                    'zones': {
-                        'back': {
-                            'coordinates': '1,1,1,1,1,1'
-                        }
-                    }
+                    "height": 1080,
+                    "width": 1920,
+                    "zones": {"back": {"coordinates": "1,1,1,1,1,1"}},
                 }
-            }
+            },
         }
         self.assertRaises(vol.MultipleInvalid, lambda: FrigateConfig(config=config))
-    
+
     def test_clips_should_default_to_global_objects(self):
         config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'clips': {
-                'retain': {
-                    'default': 20,
-                    'objects': {
-                        'person': 30
-                    }
-                }
-            },
-            'objects': {
-                'track': ['person', 'dog']
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+            "mqtt": {"host": "mqtt"},
+            "clips": {"retain": {"default": 20, "objects": {"person": 30}}},
+            "objects": {"track": ["person", "dog"]},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920,
-                    'clips': {
-                        'enabled': True
-                    }
+                    "height": 1080,
+                    "width": 1920,
+                    "clips": {"enabled": True},
                 }
-            }
+            },
         }
         config = FrigateConfig(config=config)
-        assert(config.cameras['back'].clips.objects is None)
-    
+        assert config.cameras["back"].clips.objects is None
+
     def test_role_assigned_but_not_enabled(self):
         json_config = {
-            'mqtt': {
-                'host': 'mqtt'
-            },
-            'cameras': {
-                'back': {
-                    'ffmpeg': {
-                        'inputs': [
-                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect', 'rtmp'] },
-                            { 'path': 'rtsp://10.0.0.1:554/record', 'roles': ['record'] }
+            "mqtt": {"host": "mqtt"},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect", "rtmp"],
+                            },
+                            {"path": "rtsp://10.0.0.1:554/record", "roles": ["record"]},
                         ]
                     },
-                    'height': 1080,
-                    'width': 1920
+                    "height": 1080,
+                    "width": 1920,
                 }
-            }
+            },
         }
 
         config = FrigateConfig(config=json_config)
-        ffmpeg_cmds = config.cameras['back'].ffmpeg_cmds
-        assert(len(ffmpeg_cmds) == 1)
-        assert(not 'clips' in ffmpeg_cmds[0]['roles'])
+        ffmpeg_cmds = config.cameras["back"].ffmpeg_cmds
+        assert len(ffmpeg_cmds) == 1
+        assert not "clips" in ffmpeg_cmds[0]["roles"]
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main(verbosity=2)

frigate/test/test_yuv_region_2_rgb.py

@@ -3,37 +3,39 @@ import numpy as np
 from unittest import TestCase, main
 from frigate.util import yuv_region_2_rgb
 
+
 class TestYuvRegion2RGB(TestCase):
     def setUp(self):
         self.bgr_frame = np.zeros((100, 200, 3), np.uint8)
         self.bgr_frame[:] = (0, 0, 255)
-        self.bgr_frame[5:55, 5:55] = (255,0,0)
+        self.bgr_frame[5:55, 5:55] = (255, 0, 0)
         # cv2.imwrite(f"bgr_frame.jpg", self.bgr_frame)
         self.yuv_frame = cv2.cvtColor(self.bgr_frame, cv2.COLOR_BGR2YUV_I420)
 
     def test_crop_yuv(self):
-        cropped = yuv_region_2_rgb(self.yuv_frame, (10,10,50,50))
+        cropped = yuv_region_2_rgb(self.yuv_frame, (10, 10, 50, 50))
         # ensure the upper left pixel is blue
-        assert(np.all(cropped[0, 0] == [0, 0, 255]))
+        assert np.all(cropped[0, 0] == [0, 0, 255])
 
     def test_crop_yuv_out_of_bounds(self):
-        cropped = yuv_region_2_rgb(self.yuv_frame, (0,0,200,200))
+        cropped = yuv_region_2_rgb(self.yuv_frame, (0, 0, 200, 200))
         # cv2.imwrite(f"cropped.jpg", cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))
         # ensure the upper left pixel is red
         # the yuv conversion has some noise
-        assert(np.all(cropped[0, 0] == [255, 1, 0]))
+        assert np.all(cropped[0, 0] == [255, 1, 0])
         # ensure the bottom right is black
-        assert(np.all(cropped[199, 199] == [0, 0, 0]))
+        assert np.all(cropped[199, 199] == [0, 0, 0])
 
     def test_crop_yuv_portrait(self):
         bgr_frame = np.zeros((1920, 1080, 3), np.uint8)
         bgr_frame[:] = (0, 0, 255)
-        bgr_frame[5:55, 5:55] = (255,0,0)
+        bgr_frame[5:55, 5:55] = (255, 0, 0)
         # cv2.imwrite(f"bgr_frame.jpg", self.bgr_frame)
         yuv_frame = cv2.cvtColor(bgr_frame, cv2.COLOR_BGR2YUV_I420)
 
         cropped = yuv_region_2_rgb(yuv_frame, (0, 852, 648, 1500))
         # cv2.imwrite(f"cropped.jpg", cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main(verbosity=2)

frigate/util.py

@@ -19,9 +19,20 @@ import numpy as np
 logger = logging.getLogger(__name__)
 
 
-def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thickness=2, color=None, position='ul'):
+def draw_box_with_label(
+    frame,
+    x_min,
+    y_min,
+    x_max,
+    y_max,
+    label,
+    info,
+    thickness=2,
+    color=None,
+    position="ul",
+):
     if color is None:
-        color = (0,0,255)
+        color = (0, 0, 255)
     display_text = "{}: {}".format(label, info)
     cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, thickness)
     font_scale = 0.5
@@ -32,113 +43,122 @@ def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thicknes
     text_height = size[0][1]
     line_height = text_height + size[1]
     # set the text start position
-    if position == 'ul':
+    if position == "ul":
         text_offset_x = x_min
-        text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
-    elif position == 'ur':
-        text_offset_x = x_max - (text_width+8)
-        text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
-    elif position == 'bl':
+        text_offset_y = 0 if y_min < line_height else y_min - (line_height + 8)
+    elif position == "ur":
+        text_offset_x = x_max - (text_width + 8)
+        text_offset_y = 0 if y_min < line_height else y_min - (line_height + 8)
+    elif position == "bl":
         text_offset_x = x_min
         text_offset_y = y_max
-    elif position == 'br':
-        text_offset_x = x_max - (text_width+8)
+    elif position == "br":
+        text_offset_x = x_max - (text_width + 8)
         text_offset_y = y_max
     # make the coords of the box with a small padding of two pixels
-    textbox_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y + line_height))
+    textbox_coords = (
+        (text_offset_x, text_offset_y),
+        (text_offset_x + text_width + 2, text_offset_y + line_height),
+    )
     cv2.rectangle(frame, textbox_coords[0], textbox_coords[1], color, cv2.FILLED)
-    cv2.putText(frame, display_text, (text_offset_x, text_offset_y + line_height - 3), font, fontScale=font_scale, color=(0, 0, 0), thickness=2)
+    cv2.putText(
+        frame,
+        display_text,
+        (text_offset_x, text_offset_y + line_height - 3),
+        font,
+        fontScale=font_scale,
+        color=(0, 0, 0),
+        thickness=2,
+    )
 
-def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):    
+
+def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):
     # size is the longest edge and divisible by 4
-    size = int(max(xmax-xmin, ymax-ymin)//4*4*multiplier)
+    size = int(max(xmax - xmin, ymax - ymin) // 4 * 4 * multiplier)
     # dont go any smaller than 300
     if size < 300:
         size = 300
 
     # x_offset is midpoint of bounding box minus half the size
-    x_offset = int((xmax-xmin)/2.0+xmin-size/2.0)
+    x_offset = int((xmax - xmin) / 2.0 + xmin - size / 2.0)
     # if outside the image
     if x_offset < 0:
         x_offset = 0
-    elif x_offset > (frame_shape[1]-size):
-        x_offset = max(0, (frame_shape[1]-size))
+    elif x_offset > (frame_shape[1] - size):
+        x_offset = max(0, (frame_shape[1] - size))
 
     # y_offset is midpoint of bounding box minus half the size
-    y_offset = int((ymax-ymin)/2.0+ymin-size/2.0)
+    y_offset = int((ymax - ymin) / 2.0 + ymin - size / 2.0)
     # # if outside the image
     if y_offset < 0:
         y_offset = 0
-    elif y_offset > (frame_shape[0]-size):
-        y_offset = max(0, (frame_shape[0]-size))
+    elif y_offset > (frame_shape[0] - size):
+        y_offset = max(0, (frame_shape[0] - size))
+
+    return (x_offset, y_offset, x_offset + size, y_offset + size)
 
-    return (x_offset, y_offset, x_offset+size, y_offset+size)
 
 def get_yuv_crop(frame_shape, crop):
     # crop should be (x1,y1,x2,y2)
-    frame_height = frame_shape[0]//3*2
+    frame_height = frame_shape[0] // 3 * 2
     frame_width = frame_shape[1]
 
     # compute the width/height of the uv channels
-    uv_width = frame_width//2 # width of the uv channels
-    uv_height = frame_height//4 # height of the uv channels
+    uv_width = frame_width // 2  # width of the uv channels
+    uv_height = frame_height // 4  # height of the uv channels
 
     # compute the offset for upper left corner of the uv channels
-    uv_x_offset = crop[0]//2 # x offset of the uv channels
-    uv_y_offset = crop[1]//4 # y offset of the uv channels
+    uv_x_offset = crop[0] // 2  # x offset of the uv channels
+    uv_y_offset = crop[1] // 4  # y offset of the uv channels
 
     # compute the width/height of the uv crops
-    uv_crop_width  = (crop[2] - crop[0])//2 # width of the cropped uv channels
-    uv_crop_height = (crop[3] - crop[1])//4 # height of the cropped uv channels
+    uv_crop_width = (crop[2] - crop[0]) // 2  # width of the cropped uv channels
+    uv_crop_height = (crop[3] - crop[1]) // 4  # height of the cropped uv channels
 
     # ensure crop dimensions are multiples of 2 and 4
-    y = (
-        crop[0],
-        crop[1],
-        crop[0]      + uv_crop_width*2,
-        crop[1]      + uv_crop_height*4
-    )
+    y = (crop[0], crop[1], crop[0] + uv_crop_width * 2, crop[1] + uv_crop_height * 4)
 
     u1 = (
-        0            + uv_x_offset,
+        0 + uv_x_offset,
         frame_height + uv_y_offset,
-        0            + uv_x_offset  +  uv_crop_width,
-        frame_height + uv_y_offset  +  uv_crop_height
+        0 + uv_x_offset + uv_crop_width,
+        frame_height + uv_y_offset + uv_crop_height,
     )
 
     u2 = (
-        uv_width     + uv_x_offset,
+        uv_width + uv_x_offset,
         frame_height + uv_y_offset,
-        uv_width     + uv_x_offset  +  uv_crop_width,
-        frame_height + uv_y_offset  +  uv_crop_height
+        uv_width + uv_x_offset + uv_crop_width,
+        frame_height + uv_y_offset + uv_crop_height,
     )
 
     v1 = (
-        0            + uv_x_offset,
-        frame_height + uv_height    +  uv_y_offset,
-        0            + uv_x_offset  +  uv_crop_width,
-        frame_height + uv_height    +  uv_y_offset  +  uv_crop_height
+        0 + uv_x_offset,
+        frame_height + uv_height + uv_y_offset,
+        0 + uv_x_offset + uv_crop_width,
+        frame_height + uv_height + uv_y_offset + uv_crop_height,
     )
 
     v2 = (
-        uv_width     + uv_x_offset,
-        frame_height + uv_height    +  uv_y_offset,
-        uv_width     + uv_x_offset  +  uv_crop_width,
-        frame_height + uv_height    +  uv_y_offset + uv_crop_height
+        uv_width + uv_x_offset,
+        frame_height + uv_height + uv_y_offset,
+        uv_width + uv_x_offset + uv_crop_width,
+        frame_height + uv_height + uv_y_offset + uv_crop_height,
     )
 
     return y, u1, u2, v1, v2
 
+
 def yuv_region_2_rgb(frame, region):
     try:
-        height = frame.shape[0]//3*2
+        height = frame.shape[0] // 3 * 2
         width = frame.shape[1]
 
         # get the crop box if the region extends beyond the frame
         crop_x1 = max(0, region[0])
         crop_y1 = max(0, region[1])
         # ensure these are a multiple of 4
-        crop_x2 = min(width,  region[2])
+        crop_x2 = min(width, region[2])
         crop_y2 = min(height, region[3])
         crop_box = (crop_x1, crop_y1, crop_x2, crop_y2)
 
@@ -148,64 +168,65 @@ def yuv_region_2_rgb(frame, region):
         y_channel_x_offset = abs(min(0, region[0]))
         y_channel_y_offset = abs(min(0, region[1]))
 
-        uv_channel_x_offset = y_channel_x_offset//2
-        uv_channel_y_offset = y_channel_y_offset//4
+        uv_channel_x_offset = y_channel_x_offset // 2
+        uv_channel_y_offset = y_channel_y_offset // 4
 
         # create the yuv region frame
         # make sure the size is a multiple of 4
-        size = (region[3] - region[1])//4*4
-        yuv_cropped_frame = np.zeros((size+size//2, size), np.uint8)
+        size = (region[3] - region[1]) // 4 * 4
+        yuv_cropped_frame = np.zeros((size + size // 2, size), np.uint8)
         # fill in black
         yuv_cropped_frame[:] = 128
-        yuv_cropped_frame[0:size,0:size] = 16
+        yuv_cropped_frame[0:size, 0:size] = 16
 
         # copy the y channel
         yuv_cropped_frame[
-                y_channel_y_offset:y_channel_y_offset + y[3] - y[1],
-                y_channel_x_offset:y_channel_x_offset + y[2] - y[0]
-            ] = frame[
-                y[1]:y[3], 
-                y[0]:y[2]
-            ]
+            y_channel_y_offset : y_channel_y_offset + y[3] - y[1],
+            y_channel_x_offset : y_channel_x_offset + y[2] - y[0],
+        ] = frame[y[1] : y[3], y[0] : y[2]]
 
         uv_crop_width = u1[2] - u1[0]
         uv_crop_height = u1[3] - u1[1]
 
         # copy u1
         yuv_cropped_frame[
-                size + uv_channel_y_offset:size + uv_channel_y_offset + uv_crop_height,
-                0    + uv_channel_x_offset:0    + uv_channel_x_offset + uv_crop_width
-            ] = frame[
-                u1[1]:u1[3], 
-                u1[0]:u1[2]
-            ]
+            size + uv_channel_y_offset : size + uv_channel_y_offset + uv_crop_height,
+            0 + uv_channel_x_offset : 0 + uv_channel_x_offset + uv_crop_width,
+        ] = frame[u1[1] : u1[3], u1[0] : u1[2]]
 
         # copy u2
         yuv_cropped_frame[
-                size    + uv_channel_y_offset:size    + uv_channel_y_offset + uv_crop_height,
-                size//2 + uv_channel_x_offset:size//2 + uv_channel_x_offset + uv_crop_width
-            ] = frame[
-                u2[1]:u2[3], 
-                u2[0]:u2[2]
-            ]
+            size + uv_channel_y_offset : size + uv_channel_y_offset + uv_crop_height,
+            size // 2
+            + uv_channel_x_offset : size // 2
+            + uv_channel_x_offset
+            + uv_crop_width,
+        ] = frame[u2[1] : u2[3], u2[0] : u2[2]]
 
         # copy v1
         yuv_cropped_frame[
-                size+size//4 + uv_channel_y_offset:size+size//4 + uv_channel_y_offset + uv_crop_height,
-                0            + uv_channel_x_offset:0            + uv_channel_x_offset + uv_crop_width
-            ] = frame[
-                v1[1]:v1[3], 
-                v1[0]:v1[2]
-            ]
+            size
+            + size // 4
+            + uv_channel_y_offset : size
+            + size // 4
+            + uv_channel_y_offset
+            + uv_crop_height,
+            0 + uv_channel_x_offset : 0 + uv_channel_x_offset + uv_crop_width,
+        ] = frame[v1[1] : v1[3], v1[0] : v1[2]]
 
         # copy v2
         yuv_cropped_frame[
-                size+size//4 + uv_channel_y_offset:size+size//4 + uv_channel_y_offset + uv_crop_height,
-                size//2      + uv_channel_x_offset:size//2      + uv_channel_x_offset + uv_crop_width
-            ] = frame[
-                v2[1]:v2[3], 
-                v2[0]:v2[2]
-            ]
+            size
+            + size // 4
+            + uv_channel_y_offset : size
+            + size // 4
+            + uv_channel_y_offset
+            + uv_crop_height,
+            size // 2
+            + uv_channel_x_offset : size // 2
+            + uv_channel_x_offset
+            + uv_crop_width,
+        ] = frame[v2[1] : v2[3], v2[0] : v2[2]]
 
         return cv2.cvtColor(yuv_cropped_frame, cv2.COLOR_YUV2RGB_I420)
     except:
@@ -213,27 +234,32 @@ def yuv_region_2_rgb(frame, region):
         print(f"region: {region}")
         raise
 
+
 def intersection(box_a, box_b):
     return (
         max(box_a[0], box_b[0]),
         max(box_a[1], box_b[1]),
         min(box_a[2], box_b[2]),
-        min(box_a[3], box_b[3])
+        min(box_a[3], box_b[3]),
     )
 
+
 def area(box):
-    return (box[2]-box[0] + 1)*(box[3]-box[1] + 1)
-    
+    return (box[2] - box[0] + 1) * (box[3] - box[1] + 1)
+
+
 def intersection_over_union(box_a, box_b):
     # determine the (x, y)-coordinates of the intersection rectangle
     intersect = intersection(box_a, box_b)
 
     # compute the area of intersection rectangle
-    inter_area = max(0, intersect[2] - intersect[0] + 1) * max(0, intersect[3] - intersect[1] + 1)
+    inter_area = max(0, intersect[2] - intersect[0] + 1) * max(
+        0, intersect[3] - intersect[1] + 1
+    )
 
     if inter_area == 0:
         return 0.0
-    
+
     # compute the area of both the prediction and ground-truth
     # rectangles
     box_a_area = (box_a[2] - box_a[0] + 1) * (box_a[3] - box_a[1] + 1)
@@ -247,25 +273,29 @@ def intersection_over_union(box_a, box_b):
     # return the intersection over union value
     return iou
 
+
 def clipped(obj, frame_shape):
     # if the object is within 5 pixels of the region border, and the region is not on the edge
     # consider the object to be clipped
     box = obj[2]
     region = obj[4]
-    if ((region[0] > 5 and box[0]-region[0] <= 5) or 
-        (region[1] > 5 and box[1]-region[1] <= 5) or
-        (frame_shape[1]-region[2] > 5 and region[2]-box[2] <= 5) or
-        (frame_shape[0]-region[3] > 5 and region[3]-box[3] <= 5)):
+    if (
+        (region[0] > 5 and box[0] - region[0] <= 5)
+        or (region[1] > 5 and box[1] - region[1] <= 5)
+        or (frame_shape[1] - region[2] > 5 and region[2] - box[2] <= 5)
+        or (frame_shape[0] - region[3] > 5 and region[3] - box[3] <= 5)
+    ):
         return True
     else:
         return False
 
+
 class EventsPerSecond:
     def __init__(self, max_events=1000):
         self._start = None
         self._max_events = max_events
         self._timestamps = []
-    
+
     def start(self):
         self._start = datetime.datetime.now().timestamp()
 
@@ -274,23 +304,28 @@ class EventsPerSecond:
             self.start()
         self._timestamps.append(datetime.datetime.now().timestamp())
         # truncate the list when it goes 100 over the max_size
-        if len(self._timestamps) > self._max_events+100:
-            self._timestamps = self._timestamps[(1-self._max_events):]
+        if len(self._timestamps) > self._max_events + 100:
+            self._timestamps = self._timestamps[(1 - self._max_events) :]
 
     def eps(self, last_n_seconds=10):
         if self._start is None:
             self.start()
-		# compute the (approximate) events in the last n seconds
+        # compute the (approximate) events in the last n seconds
         now = datetime.datetime.now().timestamp()
-        seconds = min(now-self._start, last_n_seconds)
-        return len([t for t in self._timestamps if t > (now-last_n_seconds)]) / seconds
+        seconds = min(now - self._start, last_n_seconds)
+        return (
+            len([t for t in self._timestamps if t > (now - last_n_seconds)]) / seconds
+        )
+
 
 def print_stack(sig, frame):
     traceback.print_stack(frame)
 
+
 def listen():
     signal.signal(signal.SIGUSR1, print_stack)
 
+
 def create_mask(frame_shape, mask):
     mask_img = np.zeros(frame_shape, np.uint8)
     mask_img[:] = 255
@@ -304,11 +339,15 @@ def create_mask(frame_shape, mask):
 
     return mask_img
 
+
 def add_mask(mask, mask_img):
-    points = mask.split(',')
-    contour =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
+    points = mask.split(",")
+    contour = np.array(
+        [[int(points[i]), int(points[i + 1])] for i in range(0, len(points), 2)]
+    )
     cv2.fillPoly(mask_img, pts=[contour], color=(0))
 
+
 class FrameManager(ABC):
     @abstractmethod
     def create(self, name, size) -> AnyStr:
@@ -326,29 +365,31 @@ class FrameManager(ABC):
     def delete(self, name):
         pass
 
+
 class DictFrameManager(FrameManager):
     def __init__(self):
         self.frames = {}
-    
+
     def create(self, name, size) -> AnyStr:
         mem = bytearray(size)
         self.frames[name] = mem
         return mem
-    
+
     def get(self, name, shape):
         mem = self.frames[name]
         return np.ndarray(shape, dtype=np.uint8, buffer=mem)
-    
+
     def close(self, name):
         pass
-    
+
     def delete(self, name):
         del self.frames[name]
 
+
 class SharedMemoryFrameManager(FrameManager):
     def __init__(self):
         self.shm_store = {}
-    
+
     def create(self, name, size) -> AnyStr:
         shm = shared_memory.SharedMemory(name=name, create=True, size=size)
         self.shm_store[name] = shm

frigate/video.py

@@ -1,12 +1,7 @@
-import base64
-import copy
-import ctypes
 import datetime
 import itertools
-import json
 import logging
 import multiprocessing as mp
-import os
 import queue
 import subprocess as sp
 import signal
@@ -16,7 +11,7 @@ from collections import defaultdict
 from setproctitle import setproctitle
 from typing import Dict, List
 
-import cv2
+from cv2 import cv2
 import numpy as np
 
 from frigate.config import CameraConfig
@@ -24,19 +19,25 @@ from frigate.edgetpu import RemoteObjectDetector
 from frigate.log import LogPipe
 from frigate.motion import MotionDetector
 from frigate.objects import ObjectTracker
-from frigate.util import (EventsPerSecond, FrameManager,
-                          SharedMemoryFrameManager, area, calculate_region,
-                          clipped, draw_box_with_label, intersection,
-                          intersection_over_union, listen, yuv_region_2_rgb)
+from frigate.util import (
+    EventsPerSecond,
+    FrameManager,
+    SharedMemoryFrameManager,
+    calculate_region,
+    clipped,
+    listen,
+    yuv_region_2_rgb,
+)
 
 logger = logging.getLogger(__name__)
 
+
 def filtered(obj, objects_to_track, object_filters):
     object_name = obj[0]
 
     if not object_name in objects_to_track:
         return True
-    
+
     if object_name in object_filters:
         obj_settings = object_filters[object_name]
 
@@ -44,7 +45,7 @@ def filtered(obj, objects_to_track, object_filters):
         # detected object, don't add it to detected objects
         if obj_settings.min_area > obj[3]:
             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[3]:
@@ -53,29 +54,36 @@ def filtered(obj, objects_to_track, object_filters):
         # if the score is lower than the min_score, skip
         if obj_settings.min_score > obj[1]:
             return True
-    
+
         if not obj_settings.mask is None:
             # compute the coordinates of the object and make sure
             # the location isnt outside the bounds of the image (can happen from rounding)
-            y_location = min(int(obj[2][3]), len(obj_settings.mask)-1)
-            x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(obj_settings.mask[0])-1)
+            y_location = min(int(obj[2][3]), len(obj_settings.mask) - 1)
+            x_location = min(
+                int((obj[2][2] - obj[2][0]) / 2.0) + obj[2][0],
+                len(obj_settings.mask[0]) - 1,
+            )
 
             # if the object is in a masked location, don't add it to detected objects
             if obj_settings.mask[y_location][x_location] == 0:
                 return True
-        
+
     return False
 
+
 def create_tensor_input(frame, model_shape, region):
     cropped_frame = yuv_region_2_rgb(frame, region)
 
     # Resize to 300x300 if needed
     if cropped_frame.shape != (model_shape[0], model_shape[1], 3):
-        cropped_frame = cv2.resize(cropped_frame, dsize=model_shape, interpolation=cv2.INTER_LINEAR)
-    
+        cropped_frame = cv2.resize(
+            cropped_frame, dsize=model_shape, interpolation=cv2.INTER_LINEAR
+        )
+
     # Expand dimensions since the model expects images to have shape: [1, height, width, 3]
     return np.expand_dims(cropped_frame, axis=0)
 
+
 def stop_ffmpeg(ffmpeg_process, logger):
     logger.info("Terminating the existing ffmpeg process...")
     ffmpeg_process.terminate()
@@ -88,18 +96,43 @@ def stop_ffmpeg(ffmpeg_process, logger):
         ffmpeg_process.communicate()
     ffmpeg_process = None
 
-def start_or_restart_ffmpeg(ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None):
+
+def start_or_restart_ffmpeg(
+    ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None
+):
     if not ffmpeg_process is None:
         stop_ffmpeg(ffmpeg_process, logger)
 
     if frame_size is None:
-        process = sp.Popen(ffmpeg_cmd, stdout = sp.DEVNULL, stderr=logpipe, stdin = sp.DEVNULL, start_new_session=True)
+        process = sp.Popen(
+            ffmpeg_cmd,
+            stdout=sp.DEVNULL,
+            stderr=logpipe,
+            stdin=sp.DEVNULL,
+            start_new_session=True,
+        )
     else:
-        process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, stderr=logpipe, stdin = sp.DEVNULL, bufsize=frame_size*10, start_new_session=True)
+        process = sp.Popen(
+            ffmpeg_cmd,
+            stdout=sp.PIPE,
+            stderr=logpipe,
+            stdin=sp.DEVNULL,
+            bufsize=frame_size * 10,
+            start_new_session=True,
+        )
     return process
 
-def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: FrameManager, 
-    frame_queue, fps:mp.Value, skipped_fps: mp.Value, current_frame: mp.Value):
+
+def capture_frames(
+    ffmpeg_process,
+    camera_name,
+    frame_shape,
+    frame_manager: FrameManager,
+    frame_queue,
+    fps: mp.Value,
+    skipped_fps: mp.Value,
+    current_frame: mp.Value,
+):
 
     frame_size = frame_shape[0] * frame_shape[1]
     frame_rate = EventsPerSecond()
@@ -119,7 +152,9 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
             logger.info(f"{camera_name}: ffmpeg sent a broken frame. {e}")
 
             if ffmpeg_process.poll() != None:
-                logger.info(f"{camera_name}: ffmpeg process is not running. exiting capture thread...")
+                logger.info(
+                    f"{camera_name}: ffmpeg process is not running. exiting capture thread..."
+                )
                 frame_manager.delete(frame_name)
                 break
             continue
@@ -138,8 +173,11 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
         # add to the queue
         frame_queue.put(current_frame.value)
 
+
 class CameraWatchdog(threading.Thread):
-    def __init__(self, camera_name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event):
+    def __init__(
+        self, camera_name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event
+    ):
         threading.Thread.__init__(self)
         self.logger = logging.getLogger(f"watchdog.{camera_name}")
         self.camera_name = camera_name
@@ -159,22 +197,27 @@ class CameraWatchdog(threading.Thread):
         self.start_ffmpeg_detect()
 
         for c in self.config.ffmpeg_cmds:
-            if 'detect' in c['roles']:
+            if "detect" in c["roles"]:
                 continue
-            logpipe = LogPipe(f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}", logging.ERROR)
-            self.ffmpeg_other_processes.append({
-                'cmd': c['cmd'],
-                'logpipe': logpipe,
-                'process': start_or_restart_ffmpeg(c['cmd'], self.logger, logpipe)
-            })
-        
+            logpipe = LogPipe(
+                f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}",
+                logging.ERROR,
+            )
+            self.ffmpeg_other_processes.append(
+                {
+                    "cmd": c["cmd"],
+                    "logpipe": logpipe,
+                    "process": start_or_restart_ffmpeg(c["cmd"], self.logger, logpipe),
+                }
+            )
+
         time.sleep(10)
         while True:
             if self.stop_event.is_set():
                 stop_ffmpeg(self.ffmpeg_detect_process, self.logger)
                 for p in self.ffmpeg_other_processes:
-                    stop_ffmpeg(p['process'], self.logger)
-                    p['logpipe'].close()
+                    stop_ffmpeg(p["process"], self.logger)
+                    p["logpipe"].close()
                 self.logpipe.close()
                 break
 
@@ -184,7 +227,9 @@ class CameraWatchdog(threading.Thread):
                 self.logpipe.dump()
                 self.start_ffmpeg_detect()
             elif now - self.capture_thread.current_frame.value > 20:
-                self.logger.info(f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg...")
+                self.logger.info(
+                    f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg..."
+                )
                 self.ffmpeg_detect_process.terminate()
                 try:
                     self.logger.info("Waiting for ffmpeg to exit gracefully...")
@@ -193,25 +238,37 @@ class CameraWatchdog(threading.Thread):
                     self.logger.info("FFmpeg didnt exit. Force killing...")
                     self.ffmpeg_detect_process.kill()
                     self.ffmpeg_detect_process.communicate()
-            
+
             for p in self.ffmpeg_other_processes:
-                poll = p['process'].poll()
+                poll = p["process"].poll()
                 if poll == None:
                     continue
-                p['logpipe'].dump()
-                p['process'] = start_or_restart_ffmpeg(p['cmd'], self.logger, p['logpipe'], ffmpeg_process=p['process'])
-            
+                p["logpipe"].dump()
+                p["process"] = start_or_restart_ffmpeg(
+                    p["cmd"], self.logger, p["logpipe"], ffmpeg_process=p["process"]
+                )
+
             # wait a bit before checking again
             time.sleep(10)
-    
+
     def start_ffmpeg_detect(self):
-        ffmpeg_cmd = [c['cmd'] for c in self.config.ffmpeg_cmds if 'detect' in c['roles']][0]
-        self.ffmpeg_detect_process = start_or_restart_ffmpeg(ffmpeg_cmd, self.logger, self.logpipe, self.frame_size)
+        ffmpeg_cmd = [
+            c["cmd"] for c in self.config.ffmpeg_cmds if "detect" in c["roles"]
+        ][0]
+        self.ffmpeg_detect_process = start_or_restart_ffmpeg(
+            ffmpeg_cmd, self.logger, self.logpipe, self.frame_size
+        )
         self.ffmpeg_pid.value = self.ffmpeg_detect_process.pid
-        self.capture_thread = CameraCapture(self.camera_name, self.ffmpeg_detect_process, self.frame_shape, self.frame_queue, 
-            self.camera_fps)
+        self.capture_thread = CameraCapture(
+            self.camera_name,
+            self.ffmpeg_detect_process,
+            self.frame_shape,
+            self.frame_queue,
+            self.camera_fps,
+        )
         self.capture_thread.start()
 
+
 class CameraCapture(threading.Thread):
     def __init__(self, camera_name, ffmpeg_process, frame_shape, frame_queue, fps):
         threading.Thread.__init__(self)
@@ -223,32 +280,59 @@ class CameraCapture(threading.Thread):
         self.skipped_fps = EventsPerSecond()
         self.frame_manager = SharedMemoryFrameManager()
         self.ffmpeg_process = ffmpeg_process
-        self.current_frame = mp.Value('d', 0.0)
+        self.current_frame = mp.Value("d", 0.0)
         self.last_frame = 0
 
     def run(self):
         self.skipped_fps.start()
-        capture_frames(self.ffmpeg_process, self.camera_name, self.frame_shape, self.frame_manager, self.frame_queue,
-            self.fps, self.skipped_fps, self.current_frame)
+        capture_frames(
+            self.ffmpeg_process,
+            self.camera_name,
+            self.frame_shape,
+            self.frame_manager,
+            self.frame_queue,
+            self.fps,
+            self.skipped_fps,
+            self.current_frame,
+        )
+
 
 def capture_camera(name, config: CameraConfig, process_info):
     stop_event = mp.Event()
+
     def receiveSignal(signalNumber, frame):
         stop_event.set()
-    
+
     signal.signal(signal.SIGTERM, receiveSignal)
     signal.signal(signal.SIGINT, receiveSignal)
 
-    frame_queue = process_info['frame_queue']
-    camera_watchdog = CameraWatchdog(name, config, frame_queue, process_info['camera_fps'], process_info['ffmpeg_pid'], stop_event)
+    frame_queue = process_info["frame_queue"]
+    camera_watchdog = CameraWatchdog(
+        name,
+        config,
+        frame_queue,
+        process_info["camera_fps"],
+        process_info["ffmpeg_pid"],
+        stop_event,
+    )
     camera_watchdog.start()
     camera_watchdog.join()
 
-def track_camera(name, config: CameraConfig, model_shape, detection_queue, result_connection, detected_objects_queue, process_info):
+
+def track_camera(
+    name,
+    config: CameraConfig,
+    model_shape,
+    detection_queue,
+    result_connection,
+    detected_objects_queue,
+    process_info,
+):
     stop_event = mp.Event()
+
     def receiveSignal(signalNumber, frame):
         stop_event.set()
-    
+
     signal.signal(signal.SIGTERM, receiveSignal)
     signal.signal(signal.SIGINT, receiveSignal)
 
@@ -256,71 +340,113 @@ def track_camera(name, config: CameraConfig, model_shape, detection_queue, resul
     setproctitle(f"frigate.process:{name}")
     listen()
 
-    frame_queue = process_info['frame_queue']
-    detection_enabled = process_info['detection_enabled']
+    frame_queue = process_info["frame_queue"]
+    detection_enabled = process_info["detection_enabled"]
 
     frame_shape = config.frame_shape
     objects_to_track = config.objects.track
     object_filters = config.objects.filters
 
     motion_detector = MotionDetector(frame_shape, config.motion)
-    object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection, model_shape)
+    object_detector = RemoteObjectDetector(
+        name, "/labelmap.txt", detection_queue, result_connection, model_shape
+    )
 
     object_tracker = ObjectTracker(config.detect)
 
     frame_manager = SharedMemoryFrameManager()
 
-    process_frames(name, frame_queue, frame_shape, model_shape, frame_manager, motion_detector, object_detector,
-        object_tracker, detected_objects_queue, process_info, objects_to_track, object_filters, detection_enabled, stop_event)
+    process_frames(
+        name,
+        frame_queue,
+        frame_shape,
+        model_shape,
+        frame_manager,
+        motion_detector,
+        object_detector,
+        object_tracker,
+        detected_objects_queue,
+        process_info,
+        objects_to_track,
+        object_filters,
+        detection_enabled,
+        stop_event,
+    )
 
     logger.info(f"{name}: exiting subprocess")
 
+
 def reduce_boxes(boxes):
     if len(boxes) == 0:
         return []
-    reduced_boxes = cv2.groupRectangles([list(b) for b in itertools.chain(boxes, boxes)], 1, 0.2)[0]
+    reduced_boxes = cv2.groupRectangles(
+        [list(b) for b in itertools.chain(boxes, boxes)], 1, 0.2
+    )[0]
     return [tuple(b) for b in reduced_boxes]
 
+
 # modified from https://stackoverflow.com/a/40795835
 def intersects_any(box_a, boxes):
     for box in boxes:
-        if box_a[2] < box[0] or box_a[0] > box[2] or box_a[1] > box[3] or box_a[3] < box[1]:
+        if (
+            box_a[2] < box[0]
+            or box_a[0] > box[2]
+            or box_a[1] > box[3]
+            or box_a[3] < box[1]
+        ):
             continue
         return True
 
-def detect(object_detector, frame, model_shape, region, objects_to_track, object_filters):
+
+def detect(
+    object_detector, frame, model_shape, region, objects_to_track, object_filters
+):
     tensor_input = create_tensor_input(frame, model_shape, region)
 
     detections = []
     region_detections = object_detector.detect(tensor_input)
     for d in region_detections:
         box = d[2]
-        size = region[2]-region[0]
+        size = region[2] - region[0]
         x_min = int((box[1] * size) + region[0])
         y_min = int((box[0] * size) + region[1])
         x_max = int((box[3] * size) + region[0])
         y_max = int((box[2] * size) + region[1])
-        det = (d[0],
+        det = (
+            d[0],
             d[1],
             (x_min, y_min, x_max, y_max),
-            (x_max-x_min)*(y_max-y_min),
-            region)
+            (x_max - x_min) * (y_max - y_min),
+            region,
+        )
         # apply object filters
         if filtered(det, objects_to_track, object_filters):
             continue
         detections.append(det)
     return detections
 
-def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_shape,
-    frame_manager: FrameManager, motion_detector: MotionDetector, 
-    object_detector: RemoteObjectDetector, object_tracker: ObjectTracker,
-    detected_objects_queue: mp.Queue, process_info: Dict,
-    objects_to_track: List[str], object_filters, detection_enabled: mp.Value, stop_event,
-    exit_on_empty: bool = False):
-    
-    fps = process_info['process_fps']
-    detection_fps = process_info['detection_fps']
-    current_frame_time = process_info['detection_frame']
+
+def process_frames(
+    camera_name: str,
+    frame_queue: mp.Queue,
+    frame_shape,
+    model_shape,
+    frame_manager: FrameManager,
+    motion_detector: MotionDetector,
+    object_detector: RemoteObjectDetector,
+    object_tracker: ObjectTracker,
+    detected_objects_queue: mp.Queue,
+    process_info: Dict,
+    objects_to_track: List[str],
+    object_filters,
+    detection_enabled: mp.Value,
+    stop_event,
+    exit_on_empty: bool = False,
+):
+
+    fps = process_info["process_fps"]
+    detection_fps = process_info["detection_fps"]
+    current_frame_time = process_info["detection_frame"]
 
     fps_tracker = EventsPerSecond()
     fps_tracker.start()
@@ -340,7 +466,9 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
 
         current_frame_time.value = frame_time
 
-        frame = frame_manager.get(f"{camera_name}{frame_time}", (frame_shape[0]*3//2, frame_shape[1]))
+        frame = frame_manager.get(
+            f"{camera_name}{frame_time}", (frame_shape[0] * 3 // 2, frame_shape[1])
+        )
 
         if frame is None:
             logger.info(f"{camera_name}: frame {frame_time} is not in memory store.")
@@ -349,7 +477,9 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
         if not detection_enabled.value:
             fps.value = fps_tracker.eps()
             object_tracker.match_and_update(frame_time, [])
-            detected_objects_queue.put((camera_name, frame_time, object_tracker.tracked_objects, [], []))
+            detected_objects_queue.put(
+                (camera_name, frame_time, object_tracker.tracked_objects, [], [])
+            )
             detection_fps.value = object_detector.fps.eps()
             frame_manager.close(f"{camera_name}{frame_time}")
             continue
@@ -358,27 +488,44 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
         motion_boxes = motion_detector.detect(frame)
 
         # only get the tracked object boxes that intersect with motion
-        tracked_object_boxes = [obj['box'] for obj in object_tracker.tracked_objects.values() if intersects_any(obj['box'], motion_boxes)]
+        tracked_object_boxes = [
+            obj["box"]
+            for obj in object_tracker.tracked_objects.values()
+            if intersects_any(obj["box"], motion_boxes)
+        ]
 
         # combine motion boxes with known locations of existing objects
         combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
 
         # compute regions
-        regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
-            for a in combined_boxes]
+        regions = [
+            calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
+            for a in combined_boxes
+        ]
 
         # combine overlapping regions
         combined_regions = reduce_boxes(regions)
 
         # re-compute regions
-        regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
-            for a in combined_regions]
+        regions = [
+            calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
+            for a in combined_regions
+        ]
 
         # resize regions and detect
         detections = []
         for region in regions:
-            detections.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters))
-        
+            detections.extend(
+                detect(
+                    object_detector,
+                    frame,
+                    model_shape,
+                    region,
+                    objects_to_track,
+                    object_filters,
+                )
+            )
+
         #########
         # merge objects, check for clipped objects and look again up to 4 times
         #########
@@ -396,8 +543,10 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
             for group in detected_object_groups.values():
 
                 # apply non-maxima suppression to suppress weak, overlapping bounding boxes
-                boxes = [(o[2][0], o[2][1], o[2][2]-o[2][0], o[2][3]-o[2][1])
-                    for o in group]
+                boxes = [
+                    (o[2][0], o[2][1], o[2][2] - o[2][0], o[2][3] - o[2][1])
+                    for o in group
+                ]
                 confidences = [o[1] for o in group]
                 idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
 
@@ -406,17 +555,26 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
                     if clipped(obj, frame_shape):
                         box = obj[2]
                         # calculate a new region that will hopefully get the entire object
-                        region = calculate_region(frame_shape, 
-                            box[0], box[1],
-                            box[2], box[3])
+                        region = calculate_region(
+                            frame_shape, box[0], box[1], box[2], box[3]
+                        )
 
                         regions.append(region)
-                        
-                        selected_objects.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters))
+
+                        selected_objects.extend(
+                            detect(
+                                object_detector,
+                                frame,
+                                model_shape,
+                                region,
+                                objects_to_track,
+                                object_filters,
+                            )
+                        )
 
                         refining = True
                     else:
-                        selected_objects.append(obj)            
+                        selected_objects.append(obj)
             # set the detections list to only include top, complete objects
             # and new detections
             detections = selected_objects
@@ -426,18 +584,28 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
 
         # Limit to the detections overlapping with motion areas
         # to avoid picking up stationary background objects
-        detections_with_motion = [d for d in detections if intersects_any(d[2], motion_boxes)]
+        detections_with_motion = [
+            d for d in detections if intersects_any(d[2], motion_boxes)
+        ]
 
         # now that we have refined our detections, we need to track objects
         object_tracker.match_and_update(frame_time, detections_with_motion)
 
         # add to the queue if not full
-        if(detected_objects_queue.full()):
+        if detected_objects_queue.full():
             frame_manager.delete(f"{camera_name}{frame_time}")
             continue
         else:
             fps_tracker.update()
             fps.value = fps_tracker.eps()
-            detected_objects_queue.put((camera_name, frame_time, object_tracker.tracked_objects, motion_boxes, regions))
+            detected_objects_queue.put(
+                (
+                    camera_name,
+                    frame_time,
+                    object_tracker.tracked_objects,
+                    motion_boxes,
+                    regions,
+                )
+            )
             detection_fps.value = object_detector.fps.eps()
             frame_manager.close(f"{camera_name}{frame_time}")

frigate/watchdog.py

@@ -7,10 +7,11 @@ import signal
 
 logger = logging.getLogger(__name__)
 
+
 class FrigateWatchdog(threading.Thread):
     def __init__(self, detectors, stop_event):
         threading.Thread.__init__(self)
-        self.name = 'frigate_watchdog'
+        self.name = "frigate_watchdog"
         self.detectors = detectors
         self.stop_event = stop_event
 
@@ -29,9 +30,10 @@ class FrigateWatchdog(threading.Thread):
             # check the detection processes
             for detector in self.detectors.values():
                 detection_start = detector.detection_start.value
-                if (detection_start > 0.0 and 
-                    now - detection_start > 10):
-                    logger.info("Detection appears to be stuck. Restarting detection process...")
+                if detection_start > 0.0 and now - detection_start > 10:
+                    logger.info(
+                        "Detection appears to be stuck. Restarting detection process..."
+                    )
                     detector.start_or_restart()
                 elif not detector.detect_process.is_alive():
                     logger.info("Detection appears to have stopped. Exiting frigate...")

frigate/zeroconf.py

@@ -31,6 +31,7 @@ def get_local_ip() -> str:
     finally:
         sock.close()
 
+
 def broadcast_zeroconf(frigate_id):
     zeroconf = Zeroconf(interfaces=InterfaceChoice.Default, ip_version=IPVersion.V4Only)
 

migrations/001_create_events_table.py

@@ -32,10 +32,14 @@ except ImportError:
 
 SQL = pw.SQL
 
+
 def migrate(migrator, database, fake=False, **kwargs):
-    migrator.sql('CREATE TABLE IF NOT EXISTS "event" ("id" VARCHAR(30) NOT NULL PRIMARY KEY, "label" VARCHAR(20) NOT NULL, "camera" VARCHAR(20) NOT NULL, "start_time" DATETIME NOT NULL, "end_time" DATETIME NOT NULL, "top_score" REAL NOT NULL, "false_positive" INTEGER NOT NULL, "zones" JSON NOT NULL, "thumbnail" TEXT NOT NULL)')
+    migrator.sql(
+        'CREATE TABLE IF NOT EXISTS "event" ("id" VARCHAR(30) NOT NULL PRIMARY KEY, "label" VARCHAR(20) NOT NULL, "camera" VARCHAR(20) NOT NULL, "start_time" DATETIME NOT NULL, "end_time" DATETIME NOT NULL, "top_score" REAL NOT NULL, "false_positive" INTEGER NOT NULL, "zones" JSON NOT NULL, "thumbnail" TEXT NOT NULL)'
+    )
     migrator.sql('CREATE INDEX IF NOT EXISTS "event_label" ON "event" ("label")')
     migrator.sql('CREATE INDEX IF NOT EXISTS "event_camera" ON "event" ("camera")')
 
+
 def rollback(migrator, database, fake=False, **kwargs):
     pass

migrations/002_add_clip_snapshot.py

@@ -35,7 +35,12 @@ SQL = pw.SQL
 
 
 def migrate(migrator, database, fake=False, **kwargs):
-    migrator.add_fields(Event, has_clip=pw.BooleanField(default=True), has_snapshot=pw.BooleanField(default=True))
+    migrator.add_fields(
+        Event,
+        has_clip=pw.BooleanField(default=True),
+        has_snapshot=pw.BooleanField(default=True),
+    )
+
 
 def rollback(migrator, database, fake=False, **kwargs):
-    migrator.remove_fields(Event, ['has_clip', 'has_snapshot'])
+    migrator.remove_fields(Event, ["has_clip", "has_snapshot"])