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https://github.com/blakeblackshear/frigate.git
synced 2025-09-27 03:46:15 +08:00
WIP: convert to camera class
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blakeblackshear
@@ -20,23 +20,16 @@ from frigate.util import tonumpyarray
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from frigate.mqtt import MqttMotionPublisher, MqttObjectPublisher
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from frigate.objects import ObjectParser, ObjectCleaner, BestPersonFrame
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from frigate.motion import detect_motion
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from frigate.video import fetch_frames, FrameTracker
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from frigate.video import fetch_frames, FrameTracker, Camera
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from frigate.object_detection import FramePrepper, PreppedQueueProcessor
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with open('/config/config.yml') as f:
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# use safe_load instead load
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CONFIG = yaml.safe_load(f)
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rtsp_camera = CONFIG['cameras']['back']['rtsp']
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if (rtsp_camera['password'].startswith('$')):
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rtsp_camera['password'] = os.getenv(rtsp_camera['password'][1:])
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RTSP_URL = 'rtsp://{}:{}@{}:{}{}'.format(rtsp_camera['user'],
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rtsp_camera['password'], rtsp_camera['host'], rtsp_camera['port'],
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rtsp_camera['path'])
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MQTT_HOST = CONFIG['mqtt']['host']
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MQTT_PORT = CONFIG.get('mqtt', {}).get('port', 1883)
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MQTT_TOPIC_PREFIX = CONFIG['mqtt']['topic_prefix'] + '/back'
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MQTT_TOPIC_PREFIX = CONFIG.get('mqtt', {}).get('topic_prefix', 'frigate')
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MQTT_USER = CONFIG.get('mqtt', {}).get('user')
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MQTT_PASS = CONFIG.get('mqtt', {}).get('password')
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@@ -44,80 +37,6 @@ WEB_PORT = CONFIG.get('web_port', 5000)
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DEBUG = (CONFIG.get('debug', '0') == '1')
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def main():
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DETECTED_OBJECTS = []
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recent_frames = {}
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# Parse selected regions
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regions = CONFIG['cameras']['back']['regions']
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# capture a single frame and check the frame shape so the correct array
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# size can be allocated in memory
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video = cv2.VideoCapture(RTSP_URL)
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ret, frame = video.read()
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if ret:
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frame_shape = frame.shape
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else:
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print("Unable to capture video stream")
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exit(1)
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video.release()
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# compute the flattened array length from the array shape
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flat_array_length = frame_shape[0] * frame_shape[1] * frame_shape[2]
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# create shared array for storing the full frame image data
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shared_arr = mp.Array(ctypes.c_uint8, flat_array_length)
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# create shared value for storing the frame_time
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shared_frame_time = mp.Value('d', 0.0)
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# Lock to control access to the frame
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frame_lock = mp.Lock()
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# Condition for notifying that a new frame is ready
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frame_ready = 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 = queue.Queue()
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# Queue for prepped frames
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prepped_frame_queue = queue.Queue(len(regions)*2)
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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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# start the process to capture frames from the RTSP stream and store in a shared array
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capture_process = mp.Process(target=fetch_frames, args=(shared_arr,
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shared_frame_time, frame_lock, frame_ready, frame_shape, RTSP_URL))
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capture_process.daemon = True
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# for each region, start a separate thread to resize the region and prep for detection
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detection_prep_threads = []
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for region in regions:
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detection_prep_threads.append(FramePrepper(
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frame_arr,
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shared_frame_time,
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frame_ready,
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frame_lock,
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region['size'], region['x_offset'], region['y_offset'],
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prepped_frame_queue
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))
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prepped_queue_processor = PreppedQueueProcessor(
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prepped_frame_queue,
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object_queue
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)
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prepped_queue_processor.start()
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# start a thread to store recent motion frames for processing
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frame_tracker = FrameTracker(frame_arr, shared_frame_time, frame_ready, frame_lock,
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recent_frames)
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frame_tracker.start()
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# start a thread to store the highest scoring recent person frame
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best_person_frame = BestPersonFrame(objects_parsed, recent_frames, DETECTED_OBJECTS)
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best_person_frame.start()
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# start a thread to parse objects from the queue
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object_parser = ObjectParser(object_queue, objects_parsed, DETECTED_OBJECTS, regions)
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object_parser.start()
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# start a thread to expire objects from the detected objects list
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object_cleaner = ObjectCleaner(objects_parsed, DETECTED_OBJECTS)
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object_cleaner.start()
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# connect to mqtt and setup last will
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def on_connect(client, userdata, flags, rc):
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print("On connect called")
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@@ -128,84 +47,82 @@ def main():
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client.will_set(MQTT_TOPIC_PREFIX+'/available', payload='offline', qos=1, retain=True)
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if not MQTT_USER is None:
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client.username_pw_set(MQTT_USER, password=MQTT_PASS)
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client.connect(MQTT_HOST, MQTT_PORT, 60)
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client.loop_start()
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# Queue for prepped frames
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# TODO: set length to 1.5x the number of total regions
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prepped_frame_queue = queue.Queue(6)
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# start a thread to publish object scores (currently only person)
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mqtt_publisher = MqttObjectPublisher(client, MQTT_TOPIC_PREFIX, objects_parsed, DETECTED_OBJECTS)
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mqtt_publisher.start()
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# start the process of capturing frames
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capture_process.start()
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print("capture_process pid ", capture_process.pid)
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camera = Camera('back', CONFIG['cameras']['back'], prepped_frame_queue, client, MQTT_TOPIC_PREFIX)
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# start the object detection prep threads
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for detection_prep_thread in detection_prep_threads:
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detection_prep_thread.start()
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cameras = {
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'back': camera
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}
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prepped_queue_processor = PreppedQueueProcessor(
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cameras,
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prepped_frame_queue
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)
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prepped_queue_processor.start()
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camera.start()
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camera.join()
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# create a flask app that encodes frames a mjpeg on demand
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app = Flask(__name__)
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# app = Flask(__name__)
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@app.route('/best_person.jpg')
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def best_person():
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frame = np.zeros(frame_shape, np.uint8) if best_person_frame.best_frame is None else best_person_frame.best_frame
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ret, jpg = cv2.imencode('.jpg', frame)
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response = make_response(jpg.tobytes())
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response.headers['Content-Type'] = 'image/jpg'
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return response
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# @app.route('/best_person.jpg')
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# def best_person():
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# frame = np.zeros(frame_shape, np.uint8) if camera.get_best_person() is None else camera.get_best_person()
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# ret, jpg = cv2.imencode('.jpg', frame)
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# response = make_response(jpg.tobytes())
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# response.headers['Content-Type'] = 'image/jpg'
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# return response
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@app.route('/')
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def index():
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# return a multipart response
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return Response(imagestream(),
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mimetype='multipart/x-mixed-replace; boundary=frame')
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def imagestream():
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while True:
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# max out at 5 FPS
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time.sleep(0.2)
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# make a copy of the current detected objects
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detected_objects = DETECTED_OBJECTS.copy()
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# lock and make a copy of the current frame
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with frame_lock:
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frame = frame_arr.copy()
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# convert to RGB for drawing
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# draw the bounding boxes on the screen
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for obj in detected_objects:
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vis_util.draw_bounding_box_on_image_array(frame,
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obj['ymin'],
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obj['xmin'],
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obj['ymax'],
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obj['xmax'],
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color='red',
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thickness=2,
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display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
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use_normalized_coordinates=False)
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# @app.route('/')
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# def index():
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# # return a multipart response
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# return Response(imagestream(),
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# mimetype='multipart/x-mixed-replace; boundary=frame')
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# def imagestream():
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# while True:
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# # max out at 5 FPS
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# time.sleep(0.2)
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# # make a copy of the current detected objects
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# detected_objects = DETECTED_OBJECTS.copy()
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# # lock and make a copy of the current frame
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# with frame_lock:
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# frame = frame_arr.copy()
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# # convert to RGB for drawing
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# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# # draw the bounding boxes on the screen
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# for obj in detected_objects:
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# vis_util.draw_bounding_box_on_image_array(frame,
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# obj['ymin'],
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# obj['xmin'],
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# obj['ymax'],
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# obj['xmax'],
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# color='red',
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# thickness=2,
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# display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
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# use_normalized_coordinates=False)
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for region in regions:
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color = (255,255,255)
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cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
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(region['x_offset']+region['size'], region['y_offset']+region['size']),
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color, 2)
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# for region in regions:
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# color = (255,255,255)
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# cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
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# (region['x_offset']+region['size'], region['y_offset']+region['size']),
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# color, 2)
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# convert back to BGR
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frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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# encode the image into a jpg
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ret, jpg = cv2.imencode('.jpg', frame)
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yield (b'--frame\r\n'
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b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
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# # convert back to BGR
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# frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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# # encode the image into a jpg
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# ret, jpg = cv2.imencode('.jpg', frame)
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# yield (b'--frame\r\n'
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# b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
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app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
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capture_process.join()
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for detection_prep_thread in detection_prep_threads:
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detection_prep_thread.join()
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frame_tracker.join()
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best_person_frame.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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# app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
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if __name__ == '__main__':
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main()
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