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https://github.com/blakeblackshear/frigate.git
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switch to a queue for detected objects and expire objects after 1 second
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blakeblackshear
parent
122a1666ca
commit
b4e5c812ce
@ -81,51 +81,63 @@ def detect_objects(cropped_frame, sess, detection_graph, region_size, region_x_o
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score = scores[0, index]
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if score > 0.5:
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box = boxes[0, index].tolist()
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box[0] = (box[0] * region_size) + region_y_offset
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box[1] = (box[1] * region_size) + region_x_offset
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box[2] = (box[2] * region_size) + region_y_offset
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box[3] = (box[3] * region_size) + region_x_offset
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objects += [value, scores[0, index]] + box
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# only get the first 10 objects
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if len(objects) == 60:
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break
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objects.append({
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'name': str(category_index.get(value).get('name')),
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'score': float(score),
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'ymin': int((box[0] * region_size) + region_y_offset),
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'xmin': int((box[1] * region_size) + region_x_offset),
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'ymax': int((box[2] * region_size) + region_y_offset),
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'xmax': int((box[3] * region_size) + region_x_offset)
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})
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return objects
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class ObjectParser(threading.Thread):
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def __init__(self, objects_changed, objects_parsed, object_arrays):
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def __init__(self, object_queue, objects_parsed):
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threading.Thread.__init__(self)
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self._objects_changed = objects_changed
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self._object_queue = object_queue
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self._objects_parsed = objects_parsed
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self._object_arrays = object_arrays
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def run(self):
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global DETECTED_OBJECTS
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while True:
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detected_objects = []
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# wait until object detection has run
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# TODO: what if something else changed while I was processing???
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with self._objects_changed:
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self._objects_changed.wait()
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obj = self._object_queue.get()
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print(obj)
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DETECTED_OBJECTS.append(obj)
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for object_array in self._object_arrays:
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object_index = 0
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while(object_index < 60 and object_array[object_index] > 0):
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object_class = object_array[object_index]
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detected_objects.append({
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'name': str(category_index.get(object_class).get('name')),
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'score': object_array[object_index+1],
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'ymin': int(object_array[object_index+2]),
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'xmin': int(object_array[object_index+3]),
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'ymax': int(object_array[object_index+4]),
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'xmax': int(object_array[object_index+5])
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})
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object_index += 6
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DETECTED_OBJECTS = detected_objects
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# notify that objects were parsed
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with self._objects_parsed:
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self._objects_parsed.notify_all()
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class ObjectCleaner(threading.Thread):
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def __init__(self, objects_parsed):
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threading.Thread.__init__(self)
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self._objects_parsed = objects_parsed
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def run(self):
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global DETECTED_OBJECTS
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while True:
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# expire the objects that are more than 1 second old
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now = datetime.datetime.now().timestamp()
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# look for the first object found within the last second
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# (newest objects are appended to the end)
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detected_objects = DETECTED_OBJECTS.copy()
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num_to_delete = 0
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for obj in detected_objects:
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if now-obj['frame_time']<1:
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break
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num_to_delete += 1
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if num_to_delete > 0:
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del DETECTED_OBJECTS[:num_to_delete]
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# notify that parsed objects were changed
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with self._objects_parsed:
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self._objects_parsed.notify_all()
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# wait a bit before checking for more expired frames
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time.sleep(0.2)
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class MqttMotionPublisher(threading.Thread):
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def __init__(self, client, topic_prefix, motion_changed, motion_flags):
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threading.Thread.__init__(self)
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@ -165,20 +177,17 @@ class MqttObjectPublisher(threading.Thread):
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# initialize the payload
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payload = {}
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for obj in self.object_classes:
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payload[obj] = []
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# wait until objects have been parsed
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with self.objects_parsed:
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self.objects_parsed.wait()
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# loop over detected objects and populate
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# the payload
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# add all the person scores in detected objects and
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# average over past 1 seconds (5fps)
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detected_objects = DETECTED_OBJECTS.copy()
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for obj in detected_objects:
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if obj['name'] in self.object_classes:
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payload[obj['name']].append(obj)
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avg_person_score = sum([obj['score'] for obj in detected_objects if obj['name'] == 'person'])/5
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payload['person'] = int(avg_person_score*100)
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# send message for objects if different
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new_payload = json.dumps(payload, sort_keys=True)
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if new_payload != last_sent_payload:
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@ -229,10 +238,10 @@ def main():
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frame_ready = mp.Condition()
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# Condition for notifying that motion status changed globally
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motion_changed = mp.Condition()
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# Condition for notifying that object detection ran
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objects_changed = mp.Condition()
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# Condition for notifying that objects were parsed
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objects_parsed = mp.Condition()
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# Queue for detected objects
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object_queue = mp.Queue()
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# shape current frame so it can be treated as an image
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frame_arr = tonumpyarray(shared_arr).reshape(frame_shape)
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@ -244,11 +253,10 @@ def main():
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motion_processes = []
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for region in regions:
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detection_process = mp.Process(target=process_frames, args=(shared_arr,
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region['output_array'],
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object_queue,
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shared_frame_time,
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frame_lock, frame_ready,
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region['motion_detected'],
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objects_changed,
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frame_shape,
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region['size'], region['x_offset'], region['y_offset'],
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False))
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@ -267,8 +275,10 @@ def main():
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motion_process.daemon = True
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motion_processes.append(motion_process)
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object_parser = ObjectParser(objects_changed, objects_parsed, [region['output_array'] for region in regions])
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object_parser = ObjectParser(object_queue, objects_parsed)
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object_parser.start()
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object_cleaner = ObjectCleaner(objects_parsed)
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object_cleaner.start()
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client = mqtt.Client()
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client.will_set(MQTT_TOPIC_PREFIX+'/available', payload='offline', qos=1, retain=True)
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@ -347,6 +357,7 @@ def main():
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for motion_process in motion_processes:
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motion_process.join()
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object_parser.join()
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object_cleaner.join()
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mqtt_publisher.join()
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# convert shared memory array into numpy array
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@ -391,8 +402,8 @@ def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_s
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video.release()
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# do the actual object detection
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def process_frames(shared_arr, shared_output_arr, shared_frame_time, frame_lock, frame_ready,
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motion_detected, objects_changed, frame_shape, region_size, region_x_offset, region_y_offset,
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def process_frames(shared_arr, object_queue, shared_frame_time, frame_lock, frame_ready,
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motion_detected, frame_shape, region_size, region_x_offset, region_y_offset,
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debug):
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debug = True
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# shape shared input array into frame for processing
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@ -429,10 +440,10 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, frame_lock,
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cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB)
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# do the object detection
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objects = detect_objects(cropped_frame_rgb, sess, detection_graph, region_size, region_x_offset, region_y_offset, debug)
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# copy the detected objects to the output array, filling the array when needed
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shared_output_arr[:] = objects + [0.0] * (60-len(objects))
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with objects_changed:
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objects_changed.notify_all()
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for obj in objects:
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obj['frame_time'] = frame_time
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object_queue.put(obj)
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# do the actual motion detection
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def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion_detected, motion_changed,
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