support multiple coral devices (fixes #100)

benchmark.py

@@ -37,9 +37,7 @@ labels = load_labels('/labelmap.txt')
 # print(f"Processed for {duration:.2f} seconds.")
 # print(f"Average frame processing time: {mean(frame_times)*1000:.2f}ms")
 
-######
-# Separate process runner
-######
+
 def start(id, num_detections, detection_queue, event):
   object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue, event)
   start = datetime.datetime.now().timestamp()
@@ -51,38 +49,45 @@ def start(id, num_detections, detection_queue, event):
     frame_times.append(datetime.datetime.now().timestamp()-start_frame)
 
   duration = datetime.datetime.now().timestamp()-start
+  object_detector.cleanup()
   print(f"{id} - Processed for {duration:.2f} seconds.")
+  print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
   print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
 
-event = mp.Event()
-edgetpu_process = EdgeTPUProcess({'1': event})
+######
+# Separate process runner
+######
+# event = mp.Event()
+# detection_queue = mp.Queue()
+# edgetpu_process = EdgeTPUProcess(detection_queue, {'1': event}, 'usb:0')
 
-start(1, 1000, edgetpu_process.detection_queue, event)
-print(f"Average raw inference speed: {edgetpu_process.avg_inference_speed.value*1000:.2f}ms")
+# start(1, 1000, edgetpu_process.detection_queue, event)
+# print(f"Average raw inference speed: {edgetpu_process.avg_inference_speed.value*1000:.2f}ms")
 
 ####
 # Multiple camera processes
 ####
-# camera_processes = []
+camera_processes = []
 
-# pipes = {}
-# for x in range(0, 10):
-#   pipes[x] = mp.Pipe(duplex=False)
+events = {}
+for x in range(0, 10):
+  events[str(x)] = mp.Event()
+detection_queue = mp.Queue()
+edgetpu_process_1 = EdgeTPUProcess(detection_queue, events, 'usb:0')
+edgetpu_process_2 = EdgeTPUProcess(detection_queue, events, 'usb:1')
 
-# edgetpu_process = EdgeTPUProcess({str(key): value[1] for (key, value) in pipes.items()})
+for x in range(0, 10):
+  camera_process = mp.Process(target=start, args=(x, 300, detection_queue, events[str(x)]))
+  camera_process.daemon = True
+  camera_processes.append(camera_process)
 
-# for x in range(0, 10):
-#   camera_process = mp.Process(target=start, args=(x, 100, edgetpu_process.detection_queue, pipes[x][0]))
-#   camera_process.daemon = True
-#   camera_processes.append(camera_process)
+start_time = datetime.datetime.now().timestamp()
 
-# start = datetime.datetime.now().timestamp()
+for p in camera_processes:
+  p.start()
 
-# for p in camera_processes:
-#   p.start()
+for p in camera_processes:
+  p.join()
 
-# for p in camera_processes:
-#   p.join()
-
-# duration = datetime.datetime.now().timestamp()-start
-# print(f"Total - Processed for {duration:.2f} seconds.")
+duration = datetime.datetime.now().timestamp()-start_time
+print(f"Total - Processed for {duration:.2f} seconds.")

config/config.example.yml

@@ -1,10 +1,13 @@
 web_port: 5000
 
 ################
-## Tell frigate to look for a specific EdgeTPU device. Useful if you want to run multiple instances of frigate
-## on the same machine with multiple EdgeTPUs. https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
+## List of detectors.
+## Currently supported types: cpu, edgetpu
+## EdgeTPU requires device as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
 ################
-tensorflow_device: usb
+detectors:
+  - type: edgetpu
+    device: usb
 
 mqtt:
   host: mqtt.server.com

detect_objects.py

@@ -61,15 +61,15 @@ FFMPEG_DEFAULT_CONFIG = {
 GLOBAL_OBJECT_CONFIG = CONFIG.get('objects', {})
 
 WEB_PORT = CONFIG.get('web_port', 5000)
-DEBUG = (CONFIG.get('debug', '0') == '1')
-TENSORFLOW_DEVICE = CONFIG.get('tensorflow_device')
+DETECTORS = CONFIG.get('detectors', [{'type': 'edgetpu', 'device': 'usb'}])
 
 class CameraWatchdog(threading.Thread):
-    def __init__(self, camera_processes, config, tflite_process, tracked_objects_queue, stop_event):
+    def __init__(self, camera_processes, config, detectors, detection_queue, tracked_objects_queue, stop_event):
         threading.Thread.__init__(self)
         self.camera_processes = camera_processes
         self.config = config
-        self.tflite_process = tflite_process
+        self.detectors = detectors
+        self.detection_queue = detection_queue
         self.tracked_objects_queue = tracked_objects_queue
         self.stop_event = stop_event
 
@@ -85,15 +85,16 @@ class CameraWatchdog(threading.Thread):
 
             now = datetime.datetime.now().timestamp()
 
-            # check the detection process
-            detection_start = self.tflite_process.detection_start.value
-            if (detection_start > 0.0 and 
-                now - detection_start > 10):
-                print("Detection appears to be stuck. Restarting detection process")
-                self.tflite_process.start_or_restart()
-            elif not self.tflite_process.detect_process.is_alive():
-                print("Detection appears to have stopped. Restarting detection process")
-                self.tflite_process.start_or_restart()
+            # check the detection processes
+            for detector in self.detectors:
+                detection_start = detector.detection_start.value
+                if (detection_start > 0.0 and 
+                    now - detection_start > 10):
+                    print("Detection appears to be stuck. Restarting detection process")
+                    detector.start_or_restart()
+                elif not detector.detect_process.is_alive():
+                    print("Detection appears to have stopped. Restarting detection process")
+                    detector.start_or_restart()
 
             # check the camera processes
             for name, camera_process in self.camera_processes.items():
@@ -104,9 +105,9 @@ class CameraWatchdog(threading.Thread):
                     camera_process['detection_fps'].value = 0.0
                     camera_process['read_start'].value = 0.0
                     process = mp.Process(target=track_camera, args=(name, self.config[name], camera_process['frame_queue'],
-                        camera_process['frame_shape'], self.tflite_process.detection_queue, self.tracked_objects_queue, 
+                        camera_process['frame_shape'], self.detection_queue, self.tracked_objects_queue, 
                         camera_process['process_fps'], camera_process['detection_fps'],
-                        camera_process['read_start'], camera_process['detection_frame'], self.stop_event))
+                        camera_process['read_start'], self.stop_event))
                     process.daemon = True
                     camera_process['process'] = process
                     process.start()
@@ -117,7 +118,7 @@ class CameraWatchdog(threading.Thread):
                     frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
                     ffmpeg_process = start_or_restart_ffmpeg(camera_process['ffmpeg_cmd'], frame_size)
                     camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, camera_process['frame_queue'], 
-                        camera_process['take_frame'], camera_process['camera_fps'], camera_process['detection_frame'], self.stop_event)
+                        camera_process['take_frame'], camera_process['camera_fps'], self.stop_event)
                     camera_capture.start()
                     camera_process['ffmpeg_process'] = ffmpeg_process
                     camera_process['capture_thread'] = camera_capture
@@ -177,9 +178,15 @@ def main():
     out_events = {}
     for name in CONFIG['cameras'].keys():
         out_events[name] = mp.Event()
-    
-    # Start the shared tflite process
-    tflite_process = EdgeTPUProcess(out_events=out_events, tf_device=TENSORFLOW_DEVICE)
+
+    detection_queue = mp.Queue()
+
+    detectors = []
+    for detector in DETECTORS:
+        if detector['type'] == 'cpu':
+            detectors.append(EdgeTPUProcess(detection_queue, out_events=out_events, tf_device='cpu'))
+        if detector['type'] == 'edgetpu':
+            detectors.append(EdgeTPUProcess(detection_queue, out_events=out_events, tf_device=detector['device']))
 
     # create the camera processes
     camera_processes = {}
@@ -233,10 +240,10 @@ def main():
         detection_frame = mp.Value('d', 0.0)
 
         ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, frame_size)
-        frame_queue = mp.Queue()
+        frame_queue = mp.Queue(maxsize=2)
         camera_fps = EventsPerSecond()
         camera_fps.start()
-        camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, frame_queue, take_frame, camera_fps, detection_frame, stop_event)
+        camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, frame_queue, take_frame, camera_fps, stop_event)
         camera_capture.start()
 
         camera_processes[name] = {
@@ -265,7 +272,7 @@ def main():
         }
 
         camera_process = mp.Process(target=track_camera, args=(name, config, frame_queue, frame_shape,
-            tflite_process.detection_queue, out_events[name], tracked_objects_queue, camera_processes[name]['process_fps'], 
+            detection_queue, out_events[name], tracked_objects_queue, camera_processes[name]['process_fps'], 
             camera_processes[name]['detection_fps'], 
             camera_processes[name]['read_start'], camera_processes[name]['detection_frame'], stop_event))
         camera_process.daemon = True
@@ -282,7 +289,7 @@ def main():
     object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue, stop_event)
     object_processor.start()
     
-    camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, stop_event)
+    camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], detectors, detection_queue, tracked_objects_queue, stop_event)
     camera_watchdog.start()
 
     def receiveSignal(signalNumber, frame):
@@ -293,7 +300,8 @@ def main():
         camera_watchdog.join()
         for camera_process in camera_processes.values():
             camera_process['capture_thread'].join()
-        tflite_process.stop()
+        for detector in detectors:
+            detector.stop()
         sys.exit()
     
     signal.signal(signal.SIGTERM, receiveSignal)
@@ -350,12 +358,14 @@ def main():
                 }
             }
         
-        stats['coral'] = {
-            'fps': round(total_detection_fps, 2),
-            'inference_speed': round(tflite_process.avg_inference_speed.value*1000, 2),
-            'detection_start': tflite_process.detection_start.value,
-            'pid': tflite_process.detect_process.pid
-        }
+        stats['detectors'] = []
+        for detector in detectors:
+            stats['detectors'].append({
+                '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)
 
         return jsonify(stats)
 

frigate/edgetpu.py

@@ -48,15 +48,12 @@ class LocalObjectDetector(ObjectDetector):
             device_config = {"device": tf_device}
 
         edge_tpu_delegate = None
-        try:
-            print(f"Attempting to load TPU as {device_config['device']}")
-            edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', device_config)
-            print("TPU found")
-        except ValueError:
+
+        if tf_device != 'cpu':
             try:
-                print(f"Attempting to load TPU as pci:0")
-                edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', {"device": "pci:0"})
-                print("PCIe TPU found")
+                print(f"Attempting to load TPU as {device_config['device']}")
+                edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', device_config)
+                print("TPU found")
             except ValueError:
                 print("No EdgeTPU detected. Falling back to CPU.")
         
@@ -135,9 +132,9 @@ def run_detector(detection_queue, out_events: Dict[str, mp.Event], avg_speed, st
         avg_speed.value = (avg_speed.value*9 + duration)/10
         
 class EdgeTPUProcess():
-    def __init__(self, out_events, tf_device=None):
+    def __init__(self, detection_queue, out_events, tf_device=None):
         self.out_events = out_events
-        self.detection_queue = mp.Queue()
+        self.detection_queue = detection_queue
         self.avg_inference_speed = mp.Value('d', 0.01)
         self.detection_start = mp.Value('d', 0.0)
         self.detect_process = None
@@ -192,3 +189,7 @@ class RemoteObjectDetector():
             ))
         self.fps.update()
         return detections
+    
+    def cleanup(self):
+        self.shm.unlink()
+        self.out_shm.unlink()

frigate/video.py

@@ -117,10 +117,9 @@ def start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process=None):
 
 def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: FrameManager, 
     frame_queue, take_frame: int, fps:EventsPerSecond, skipped_fps: EventsPerSecond, 
-    stop_event: mp.Event, detection_frame: mp.Value, current_frame: mp.Value):
+    stop_event: mp.Event, current_frame: mp.Value):
 
     frame_num = 0
-    last_frame = 0
     frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
     skipped_fps.start()
     while True:
@@ -147,8 +146,8 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
             skipped_fps.update()
             continue
 
-        # if the detection process is more than 1 second behind, skip this frame
-        if detection_frame.value > 0.0 and (last_frame - detection_frame.value) > 1:
+        # if the queue is full, skip this frame
+        if frame_queue.full():
             skipped_fps.update()
             continue
 
@@ -159,10 +158,9 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
 
         # add to the queue
         frame_queue.put(current_frame.value)
-        last_frame = current_frame.value
 
 class CameraCapture(threading.Thread):
-    def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, take_frame, fps, detection_frame, stop_event):
+    def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, take_frame, fps, stop_event):
         threading.Thread.__init__(self)
         self.name = name
         self.frame_shape = frame_shape
@@ -175,13 +173,12 @@ class CameraCapture(threading.Thread):
         self.ffmpeg_process = ffmpeg_process
         self.current_frame = mp.Value('d', 0.0)
         self.last_frame = 0
-        self.detection_frame = detection_frame
         self.stop_event = stop_event
 
     def run(self):
         self.skipped_fps.start()
         capture_frames(self.ffmpeg_process, self.name, self.frame_shape, self.frame_manager, self.frame_queue, self.take_frame,
-            self.fps, self.skipped_fps, self.stop_event, self.detection_frame, self.current_frame)
+            self.fps, self.skipped_fps, self.stop_event, self.current_frame)
 
 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):
     print(f"Starting process for {name}: {os.getpid()}")