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https://github.com/blakeblackshear/frigate.git
synced 2025-10-27 10:52:11 +01:00
rework object detection to watch the motion flag
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blakeblackshear
parent
5d894f006a
commit
53c9a7368d
@ -25,7 +25,7 @@ PATH_TO_LABELS = '/label_map.pbtext'
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# TODO: make dynamic?
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NUM_CLASSES = 90
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REGIONS = "300,0,0:300,300,0:300,600,0"
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REGIONS = "350,0,300:400,350,250:400,750,250"
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#REGIONS = os.getenv('REGIONS')
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DETECTED_OBJECTS = []
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@ -123,8 +123,11 @@ def main():
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shared_memory_objects.append({
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# create shared value for storing the time the frame was captured
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'frame_time': mp.Value('d', 0.0),
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# shared value for signaling to the capture process that we are ready for the next frame
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# (1 for ready 0 for not ready)
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'ready_for_frame': mp.Value('i', 1),
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# shared value for motion detection signal (1 for motion 0 for no motion)
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'motion_detected': mp.Value('i', 1),
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'motion_detected': mp.Value('i', 0),
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# create shared array for storing 10 detected objects
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# note: this must be a double even though the value you are storing
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# is a float. otherwise it stops updating the value in shared
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@ -164,66 +167,66 @@ def main():
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motion_processes.append(motion_process)
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object_parser = ObjectParser([obj['output_array'] for obj in shared_memory_objects])
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# object_parser.start()
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object_parser.start()
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capture_process.start()
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print("capture_process pid ", capture_process.pid)
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# for detection_process in detection_processes:
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# detection_process.start()
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# print("detection_process pid ", detection_process.pid)
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for detection_process in detection_processes:
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detection_process.start()
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print("detection_process pid ", detection_process.pid)
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for motion_process in motion_processes:
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motion_process.start()
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print("motion_process pid ", motion_process.pid)
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# app = Flask(__name__)
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app = Flask(__name__)
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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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# global DETECTED_OBJECTS
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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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# # make a copy of the current frame
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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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global DETECTED_OBJECTS
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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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# make a copy of the current frame
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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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# 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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# (255,255,255), 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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for region in regions:
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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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(255,255,255), 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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# app.run(host='0.0.0.0', debug=False)
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app.run(host='0.0.0.0', debug=False)
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capture_process.join()
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# for detection_process in detection_processes:
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# detection_process.join()
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for detection_process in detection_processes:
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detection_process.join()
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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_parser.join()
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# convert shared memory array into numpy array
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def tonumpyarray(mp_arr):
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@ -278,20 +281,22 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
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sess = tf.Session(graph=detection_graph)
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no_frames_available = -1
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frame_time = 0.0
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while True:
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now = datetime.datetime.now().timestamp()
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# if there is no motion detected
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if shared_motion.value == 0:
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time.sleep(0.01)
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continue
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# if there isnt a frame ready for processing
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if shared_frame_time.value == 0.0:
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# if there isnt a new frame ready for processing
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if shared_frame_time.value == frame_time:
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# save the first time there were no frames available
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if no_frames_available == -1:
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no_frames_available = datetime.datetime.now().timestamp()
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no_frames_available = now
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# if there havent been any frames available in 30 seconds,
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# sleep to avoid using so much cpu if the camera feed is down
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if no_frames_available > 0 and (datetime.datetime.now().timestamp() - no_frames_available) > 30:
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if no_frames_available > 0 and (now - no_frames_available) > 30:
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time.sleep(1)
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print("sleeping because no frames have been available in a while")
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else:
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@ -302,10 +307,8 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
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# we got a valid frame, so reset the timer
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no_frames_available = -1
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# if the frame is more than 0.5 second old, discard it
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if (datetime.datetime.now().timestamp() - shared_frame_time.value) > 0.5:
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# signal that we need a new frame
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shared_frame_time.value = 0.0
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# if the frame is more than 0.5 second old, ignore it
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if (now - shared_frame_time.value) > 0.5:
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# rest a little bit to avoid maxing out the CPU
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time.sleep(0.01)
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continue
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@ -313,8 +316,6 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
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# make a copy of the cropped frame
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cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy()
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frame_time = shared_frame_time.value
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# signal that the frame has been used so a new one will be ready
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shared_frame_time.value = 0.0
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# convert to RGB
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cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB)
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