improve detection processing and restart when stuck

detect_objects.py

@@ -68,6 +68,11 @@ class CameraWatchdog(threading.Thread):
             # wait a bit before checking
             time.sleep(30)
 
+            if (self.tflite_process.detection_start.value > 0.0 and 
+                datetime.datetime.now().timestamp() - self.tflite_process.detection_start.value > 10):
+                print("Detection appears to be stuck. Restarting detection process")
+                time.sleep(30)
+
             for name, camera_process in self.camera_processes.items():
                 process = camera_process['process']
                 if not process.is_alive():
@@ -75,9 +80,8 @@ class CameraWatchdog(threading.Thread):
                     camera_process['fps'].value = float(self.config[name]['fps'])
                     camera_process['skipped_fps'].value = 0.0
                     camera_process['detection_fps'].value = 0.0
-                    self.object_processor.camera_data[name]['current_frame_time'] = None
                     process = mp.Process(target=track_camera, args=(name, self.config[name], FFMPEG_DEFAULT_CONFIG, GLOBAL_OBJECT_CONFIG, 
-                        self.tflite_process.detect_lock, self.tflite_process.detect_ready, self.tflite_process.frame_ready, self.tracked_objects_queue, 
+                        self.tflite_process.detection_queue, self.tracked_objects_queue, 
                         camera_process['fps'], camera_process['skipped_fps'], camera_process['detection_fps']))
                     process.daemon = True
                     camera_process['process'] = process
@@ -139,7 +143,7 @@ def main():
             'detection_fps': mp.Value('d', 0.0)
         }
         camera_process = mp.Process(target=track_camera, args=(name, config, FFMPEG_DEFAULT_CONFIG, GLOBAL_OBJECT_CONFIG, 
-            tflite_process.detect_lock, tflite_process.detect_ready, tflite_process.frame_ready, tracked_objects_queue, 
+            tflite_process.detection_queue, tracked_objects_queue, 
             camera_processes[name]['fps'], camera_processes[name]['skipped_fps'], camera_processes[name]['detection_fps']))
         camera_process.daemon = True
         camera_processes[name]['process'] = camera_process
@@ -173,14 +177,16 @@ def main():
         for name, camera_stats in camera_processes.items():
             total_detection_fps += camera_stats['detection_fps'].value
             stats[name] = {
-                'fps': camera_stats['fps'].value,
+                'fps': round(camera_stats['fps'].value, 2),
-                'skipped_fps': camera_stats['skipped_fps'].value,
+                'skipped_fps': round(camera_stats['skipped_fps'].value, 2),
-                'detection_fps': camera_stats['detection_fps'].value
+                'detection_fps': round(camera_stats['detection_fps'].value, 2)
             }
         
         stats['coral'] = {
-            'fps': total_detection_fps,
+            'fps': round(total_detection_fps, 2),
-            'inference_speed': round(tflite_process.avg_inference_speed.value*1000, 2)
+            'inference_speed': round(tflite_process.avg_inference_speed.value*1000, 2),
+            'detection_queue': tflite_process.detection_queue.qsize(),
+            'detection_start': tflite_process.detection_start.value
         }
 
         rc = plasma_process.poll()

frigate/edgetpu.py

@@ -1,8 +1,10 @@
 import os
 import datetime
+import hashlib
 import multiprocessing as mp
 import numpy as np
 import SharedArray as sa
+import pyarrow.plasma as plasma
 import tflite_runtime.interpreter as tflite
 from tflite_runtime.interpreter import load_delegate
 from frigate.util import EventsPerSecond
@@ -60,71 +62,68 @@ class ObjectDetector():
         
         return detections
 
-class EdgeTPUProcess():
+def run_detector(detection_queue, avg_speed, start):
-    def __init__(self):
-        # TODO: see if we can use the plasma store with a queue and maintain the same speeds
-        try:
-            sa.delete("frame")
-        except:
-            pass
-        try:
-            sa.delete("detections")
-        except:
-            pass
-
-        self.input_frame = sa.create("frame", shape=(1,300,300,3), dtype=np.uint8)
-        self.detections = sa.create("detections", shape=(20,6), dtype=np.float32)
-
-        self.detect_lock = mp.Lock()
-        self.detect_ready = mp.Event()
-        self.frame_ready = mp.Event()
-        self.avg_inference_speed = mp.Value('d', 0.01)
-
-        def run_detector(detect_ready, frame_ready, avg_speed):
     print(f"Starting detection process: {os.getpid()}")
+    plasma_client = plasma.connect("/tmp/plasma")
     object_detector = ObjectDetector()
-            input_frame = sa.attach("frame")
-            detections = sa.attach("detections")
 
     while True:
-                # wait until a frame is ready
+        object_id_str = detection_queue.get()
-                frame_ready.wait()
+        object_id_hash = hashlib.sha1(str.encode(object_id_str))
-                start = datetime.datetime.now().timestamp()
+        object_id = plasma.ObjectID(object_id_hash.digest())
-                # signal that the process is busy
+        input_frame = plasma_client.get(object_id, timeout_ms=0)
-                frame_ready.clear()
+
-                detections[:] = object_detector.detect_raw(input_frame)
+        start.value = datetime.datetime.now().timestamp()
-                # signal that the process is ready to detect
+
-                detect_ready.set()
+        # detect and put the output in the plasma store
-                duration = datetime.datetime.now().timestamp()-start
+        object_id_out = hashlib.sha1(str.encode(f"out-{object_id_str}")).digest()
+        plasma_client.put(object_detector.detect_raw(input_frame), plasma.ObjectID(object_id_out))
+
+        duration = datetime.datetime.now().timestamp()-start.value
+        start.value = 0.0
         avg_speed.value = (avg_speed.value*9 + duration)/10
         
-        self.detect_process = mp.Process(target=run_detector, args=(self.detect_ready, self.frame_ready, self.avg_inference_speed))
+class EdgeTPUProcess():
+    def __init__(self):
+        self.detection_queue = mp.Queue()
+        self.avg_inference_speed = mp.Value('d', 0.01)
+        self.detection_start = mp.Value('d', 0.0)
+        self.detect_process = None
+        self.start_or_restart()
+
+    def start_or_restart(self):
+        self.detection_start.value = 0.0
+        if (not self.detect_process is None) and self.detect_process.is_alive():
+            self.detect_process.terminate()
+            print("Waiting for detection process to exit gracefully...")
+            self.detect_process.join(timeout=30)
+            if self.detect_process.exitcode is None:
+                print("Detection process didnt exit. Force killing...")
+                self.detect_process.kill()
+                self.detect_process.join()
+        self.detect_process = mp.Process(target=run_detector, args=(self.detection_queue, self.avg_inference_speed, self.detection_start))
         self.detect_process.daemon = True
         self.detect_process.start()
 
 class RemoteObjectDetector():
-    def __init__(self, labels, detect_lock, detect_ready, frame_ready):
+    def __init__(self, name, labels, detection_queue):
         self.labels = load_labels(labels)
-
+        self.name = name
-        self.input_frame = sa.attach("frame")
-        self.detections = sa.attach("detections")
-
         self.fps = EventsPerSecond()
-
+        self.plasma_client = plasma.connect("/tmp/plasma")
-        self.detect_lock = detect_lock
+        self.detection_queue = detection_queue
-        self.detect_ready = detect_ready
-        self.frame_ready = frame_ready
     
     def detect(self, tensor_input, threshold=.4):
         detections = []
-        with self.detect_lock:
+
-            self.input_frame[:] = tensor_input
+        now = f"{self.name}-{str(datetime.datetime.now().timestamp())}"
-            # unset detections and signal that a frame is ready
+        object_id_frame = plasma.ObjectID(hashlib.sha1(str.encode(now)).digest())
-            self.detect_ready.clear()
+        object_id_detections = plasma.ObjectID(hashlib.sha1(str.encode(f"out-{now}")).digest())
-            self.frame_ready.set()
+        self.plasma_client.put(tensor_input, object_id_frame)
-            # wait until the detection process is finished,
+        self.detection_queue.put(now)
-            self.detect_ready.wait()
+        raw_detections = self.plasma_client.get(object_id_detections)
-            for d in self.detections:
+
+        for d in raw_detections:
             if d[1] < threshold:
                 break
             detections.append((
@@ -132,5 +131,6 @@ class RemoteObjectDetector():
                 float(d[1]),
                 (d[2], d[3], d[4], d[5])
             ))
+        self.plasma_client.delete([object_id_frame, object_id_detections])
         self.fps.update()
         return detections

frigate/object_processing.py

@@ -34,7 +34,6 @@ class TrackedObjectProcessor(threading.Thread):
             'best_objects': {},
             'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
             'tracked_objects': {},
-            'current_frame_time': None,
             'current_frame': np.zeros((720,1280,3), np.uint8),
             'object_id': None
         })
@@ -48,9 +47,6 @@ class TrackedObjectProcessor(threading.Thread):
     def get_current_frame(self, camera):
         return self.camera_data[camera]['current_frame']
 
-    def get_current_frame_time(self, camera):
-        return self.camera_data[camera]['current_frame_time']
-
     def run(self):
         while True:
             camera, frame_time, tracked_objects = self.tracked_objects_queue.get()
@@ -93,7 +89,6 @@ class TrackedObjectProcessor(threading.Thread):
                 # Set the current frame as ready
                 ###
                 self.camera_data[camera]['current_frame'] = current_frame
-                self.camera_data[camera]['current_frame_time'] = frame_time
 
                 # store the object id, so you can delete it at the next loop
                 previous_object_id = self.camera_data[camera]['object_id']

frigate/video.py

@@ -114,7 +114,7 @@ def start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process=None):
     print(" ".join(ffmpeg_cmd))
     return sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, bufsize=frame_size*10)
 
-def track_camera(name, config, ffmpeg_global_config, global_objects_config, detect_lock, detect_ready, frame_ready, detected_objects_queue, fps, skipped_fps, detection_fps):
+def track_camera(name, config, ffmpeg_global_config, global_objects_config, detection_queue, detected_objects_queue, fps, skipped_fps, detection_fps):
     print(f"Starting process for {name}: {os.getpid()}")
 
     # Merge the ffmpeg config with the global config
@@ -172,7 +172,7 @@ def track_camera(name, config, ffmpeg_global_config, global_objects_config, dete
         mask[:] = 255
 
     motion_detector = MotionDetector(frame_shape, mask, resize_factor=6)
-    object_detector = RemoteObjectDetector('/labelmap.txt', detect_lock, detect_ready, frame_ready)
+    object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue)
 
     object_tracker = ObjectTracker(10)
     
@@ -196,8 +196,8 @@ def track_camera(name, config, ffmpeg_global_config, global_objects_config, dete
             rc = ffmpeg_process.poll()
             if rc is not None:
                 print(f"{name}: ffmpeg_process exited unexpectedly with {rc}")
-                time.sleep(10)
                 ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process)
+                time.sleep(10)
             else:
                 print(f"{name}: ffmpeg_process is still running but didnt return any bytes")
             continue