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https://github.com/blakeblackshear/frigate.git
synced 2025-10-27 10:52:11 +01:00
support multiple coral devices (fixes #100)
This commit is contained in:
Blake Blackshear
parent
49fca1b839
commit
f946813ccb
53
benchmark.py
53
benchmark.py
@ -37,9 +37,7 @@ labels = load_labels('/labelmap.txt')
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# print(f"Processed for {duration:.2f} seconds.")
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# print(f"Average frame processing time: {mean(frame_times)*1000:.2f}ms")
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######
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# Separate process runner
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######
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def start(id, num_detections, detection_queue, event):
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object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue, event)
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start = datetime.datetime.now().timestamp()
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@ -51,38 +49,45 @@ def start(id, num_detections, detection_queue, event):
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frame_times.append(datetime.datetime.now().timestamp()-start_frame)
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duration = datetime.datetime.now().timestamp()-start
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object_detector.cleanup()
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print(f"{id} - Processed for {duration:.2f} seconds.")
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print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
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print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
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event = mp.Event()
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edgetpu_process = EdgeTPUProcess({'1': event})
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######
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# Separate process runner
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######
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# event = mp.Event()
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# detection_queue = mp.Queue()
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# edgetpu_process = EdgeTPUProcess(detection_queue, {'1': event}, 'usb:0')
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start(1, 1000, edgetpu_process.detection_queue, event)
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print(f"Average raw inference speed: {edgetpu_process.avg_inference_speed.value*1000:.2f}ms")
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# start(1, 1000, edgetpu_process.detection_queue, event)
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# print(f"Average raw inference speed: {edgetpu_process.avg_inference_speed.value*1000:.2f}ms")
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####
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# Multiple camera processes
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####
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# camera_processes = []
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camera_processes = []
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# pipes = {}
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# for x in range(0, 10):
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# pipes[x] = mp.Pipe(duplex=False)
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events = {}
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for x in range(0, 10):
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events[str(x)] = mp.Event()
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detection_queue = mp.Queue()
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edgetpu_process_1 = EdgeTPUProcess(detection_queue, events, 'usb:0')
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edgetpu_process_2 = EdgeTPUProcess(detection_queue, events, 'usb:1')
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# edgetpu_process = EdgeTPUProcess({str(key): value[1] for (key, value) in pipes.items()})
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for x in range(0, 10):
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camera_process = mp.Process(target=start, args=(x, 300, detection_queue, events[str(x)]))
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camera_process.daemon = True
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camera_processes.append(camera_process)
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# for x in range(0, 10):
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# camera_process = mp.Process(target=start, args=(x, 100, edgetpu_process.detection_queue, pipes[x][0]))
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# camera_process.daemon = True
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# camera_processes.append(camera_process)
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start_time = datetime.datetime.now().timestamp()
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# start = datetime.datetime.now().timestamp()
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for p in camera_processes:
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p.start()
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# for p in camera_processes:
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# p.start()
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for p in camera_processes:
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p.join()
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# for p in camera_processes:
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# p.join()
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# duration = datetime.datetime.now().timestamp()-start
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# print(f"Total - Processed for {duration:.2f} seconds.")
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duration = datetime.datetime.now().timestamp()-start_time
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print(f"Total - Processed for {duration:.2f} seconds.")
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@ -1,10 +1,13 @@
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web_port: 5000
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################
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## Tell frigate to look for a specific EdgeTPU device. Useful if you want to run multiple instances of frigate
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## on the same machine with multiple EdgeTPUs. https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
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## List of detectors.
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## Currently supported types: cpu, edgetpu
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## EdgeTPU requires device as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
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################
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tensorflow_device: usb
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detectors:
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- type: edgetpu
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device: usb
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mqtt:
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host: mqtt.server.com
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@ -61,15 +61,15 @@ FFMPEG_DEFAULT_CONFIG = {
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GLOBAL_OBJECT_CONFIG = CONFIG.get('objects', {})
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WEB_PORT = CONFIG.get('web_port', 5000)
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DEBUG = (CONFIG.get('debug', '0') == '1')
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TENSORFLOW_DEVICE = CONFIG.get('tensorflow_device')
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DETECTORS = CONFIG.get('detectors', [{'type': 'edgetpu', 'device': 'usb'}])
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class CameraWatchdog(threading.Thread):
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def __init__(self, camera_processes, config, tflite_process, tracked_objects_queue, stop_event):
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def __init__(self, camera_processes, config, detectors, detection_queue, tracked_objects_queue, stop_event):
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threading.Thread.__init__(self)
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self.camera_processes = camera_processes
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self.config = config
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self.tflite_process = tflite_process
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self.detectors = detectors
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self.detection_queue = detection_queue
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self.tracked_objects_queue = tracked_objects_queue
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self.stop_event = stop_event
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@ -85,15 +85,16 @@ class CameraWatchdog(threading.Thread):
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now = datetime.datetime.now().timestamp()
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# check the detection process
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detection_start = self.tflite_process.detection_start.value
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if (detection_start > 0.0 and
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now - detection_start > 10):
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print("Detection appears to be stuck. Restarting detection process")
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self.tflite_process.start_or_restart()
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elif not self.tflite_process.detect_process.is_alive():
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print("Detection appears to have stopped. Restarting detection process")
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self.tflite_process.start_or_restart()
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# check the detection processes
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for detector in self.detectors:
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detection_start = detector.detection_start.value
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if (detection_start > 0.0 and
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now - detection_start > 10):
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print("Detection appears to be stuck. Restarting detection process")
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detector.start_or_restart()
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elif not detector.detect_process.is_alive():
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print("Detection appears to have stopped. Restarting detection process")
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detector.start_or_restart()
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# check the camera processes
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for name, camera_process in self.camera_processes.items():
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@ -104,9 +105,9 @@ class CameraWatchdog(threading.Thread):
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camera_process['detection_fps'].value = 0.0
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camera_process['read_start'].value = 0.0
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process = mp.Process(target=track_camera, args=(name, self.config[name], camera_process['frame_queue'],
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camera_process['frame_shape'], self.tflite_process.detection_queue, self.tracked_objects_queue,
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camera_process['frame_shape'], self.detection_queue, self.tracked_objects_queue,
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camera_process['process_fps'], camera_process['detection_fps'],
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camera_process['read_start'], camera_process['detection_frame'], self.stop_event))
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camera_process['read_start'], self.stop_event))
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process.daemon = True
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camera_process['process'] = process
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process.start()
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@ -117,7 +118,7 @@ class CameraWatchdog(threading.Thread):
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frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
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ffmpeg_process = start_or_restart_ffmpeg(camera_process['ffmpeg_cmd'], frame_size)
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camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, camera_process['frame_queue'],
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camera_process['take_frame'], camera_process['camera_fps'], camera_process['detection_frame'], self.stop_event)
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camera_process['take_frame'], camera_process['camera_fps'], self.stop_event)
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camera_capture.start()
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camera_process['ffmpeg_process'] = ffmpeg_process
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camera_process['capture_thread'] = camera_capture
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@ -177,9 +178,15 @@ def main():
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out_events = {}
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for name in CONFIG['cameras'].keys():
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out_events[name] = mp.Event()
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# Start the shared tflite process
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tflite_process = EdgeTPUProcess(out_events=out_events, tf_device=TENSORFLOW_DEVICE)
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detection_queue = mp.Queue()
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detectors = []
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for detector in DETECTORS:
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if detector['type'] == 'cpu':
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detectors.append(EdgeTPUProcess(detection_queue, out_events=out_events, tf_device='cpu'))
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if detector['type'] == 'edgetpu':
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detectors.append(EdgeTPUProcess(detection_queue, out_events=out_events, tf_device=detector['device']))
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# create the camera processes
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camera_processes = {}
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@ -233,10 +240,10 @@ def main():
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detection_frame = mp.Value('d', 0.0)
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ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, frame_size)
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frame_queue = mp.Queue()
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frame_queue = mp.Queue(maxsize=2)
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camera_fps = EventsPerSecond()
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camera_fps.start()
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camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, frame_queue, take_frame, camera_fps, detection_frame, stop_event)
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camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, frame_queue, take_frame, camera_fps, stop_event)
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camera_capture.start()
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camera_processes[name] = {
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@ -265,7 +272,7 @@ def main():
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}
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camera_process = mp.Process(target=track_camera, args=(name, config, frame_queue, frame_shape,
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tflite_process.detection_queue, out_events[name], tracked_objects_queue, camera_processes[name]['process_fps'],
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detection_queue, out_events[name], tracked_objects_queue, camera_processes[name]['process_fps'],
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camera_processes[name]['detection_fps'],
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camera_processes[name]['read_start'], camera_processes[name]['detection_frame'], stop_event))
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camera_process.daemon = True
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@ -282,7 +289,7 @@ def main():
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object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue, stop_event)
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object_processor.start()
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camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, stop_event)
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camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], detectors, detection_queue, tracked_objects_queue, stop_event)
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camera_watchdog.start()
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def receiveSignal(signalNumber, frame):
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@ -293,7 +300,8 @@ def main():
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camera_watchdog.join()
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for camera_process in camera_processes.values():
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camera_process['capture_thread'].join()
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tflite_process.stop()
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for detector in detectors:
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detector.stop()
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sys.exit()
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signal.signal(signal.SIGTERM, receiveSignal)
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@ -350,12 +358,14 @@ def main():
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}
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}
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stats['coral'] = {
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'fps': round(total_detection_fps, 2),
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'inference_speed': round(tflite_process.avg_inference_speed.value*1000, 2),
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'detection_start': tflite_process.detection_start.value,
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'pid': tflite_process.detect_process.pid
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}
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stats['detectors'] = []
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for detector in detectors:
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stats['detectors'].append({
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'inference_speed': round(detector.avg_inference_speed.value*1000, 2),
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'detection_start': detector.detection_start.value,
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'pid': detector.detect_process.pid
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})
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stats['detection_fps'] = round(total_detection_fps, 2)
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return jsonify(stats)
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@ -48,15 +48,12 @@ class LocalObjectDetector(ObjectDetector):
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device_config = {"device": tf_device}
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edge_tpu_delegate = None
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try:
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print(f"Attempting to load TPU as {device_config['device']}")
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edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', device_config)
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print("TPU found")
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except ValueError:
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if tf_device != 'cpu':
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try:
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print(f"Attempting to load TPU as pci:0")
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edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', {"device": "pci:0"})
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print("PCIe TPU found")
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print(f"Attempting to load TPU as {device_config['device']}")
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edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', device_config)
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print("TPU found")
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except ValueError:
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print("No EdgeTPU detected. Falling back to CPU.")
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@ -135,9 +132,9 @@ def run_detector(detection_queue, out_events: Dict[str, mp.Event], avg_speed, st
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avg_speed.value = (avg_speed.value*9 + duration)/10
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class EdgeTPUProcess():
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def __init__(self, out_events, tf_device=None):
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def __init__(self, detection_queue, out_events, tf_device=None):
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self.out_events = out_events
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self.detection_queue = mp.Queue()
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self.detection_queue = detection_queue
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self.avg_inference_speed = mp.Value('d', 0.01)
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self.detection_start = mp.Value('d', 0.0)
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self.detect_process = None
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@ -192,3 +189,7 @@ class RemoteObjectDetector():
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))
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self.fps.update()
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return detections
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def cleanup(self):
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self.shm.unlink()
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self.out_shm.unlink()
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@ -117,10 +117,9 @@ def start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process=None):
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def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: FrameManager,
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frame_queue, take_frame: int, fps:EventsPerSecond, skipped_fps: EventsPerSecond,
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stop_event: mp.Event, detection_frame: mp.Value, current_frame: mp.Value):
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stop_event: mp.Event, current_frame: mp.Value):
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frame_num = 0
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last_frame = 0
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frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
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skipped_fps.start()
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while True:
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@ -147,8 +146,8 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
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skipped_fps.update()
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continue
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# if the detection process is more than 1 second behind, skip this frame
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if detection_frame.value > 0.0 and (last_frame - detection_frame.value) > 1:
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# if the queue is full, skip this frame
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if frame_queue.full():
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skipped_fps.update()
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continue
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@ -159,10 +158,9 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
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# add to the queue
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frame_queue.put(current_frame.value)
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last_frame = current_frame.value
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class CameraCapture(threading.Thread):
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def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, take_frame, fps, detection_frame, stop_event):
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def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, take_frame, fps, stop_event):
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threading.Thread.__init__(self)
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self.name = name
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self.frame_shape = frame_shape
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@ -175,13 +173,12 @@ class CameraCapture(threading.Thread):
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self.ffmpeg_process = ffmpeg_process
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self.current_frame = mp.Value('d', 0.0)
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self.last_frame = 0
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self.detection_frame = detection_frame
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self.stop_event = stop_event
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def run(self):
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self.skipped_fps.start()
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capture_frames(self.ffmpeg_process, self.name, self.frame_shape, self.frame_manager, self.frame_queue, self.take_frame,
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self.fps, self.skipped_fps, self.stop_event, self.detection_frame, self.current_frame)
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self.fps, self.skipped_fps, self.stop_event, self.current_frame)
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def track_camera(name, config, frame_queue, frame_shape, detection_queue, result_connection, detected_objects_queue, fps, detection_fps, read_start, detection_frame, stop_event):
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print(f"Starting process for {name}: {os.getpid()}")
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