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https://github.com/blakeblackshear/frigate.git
synced 2024-10-15 20:07:23 +02:00
use a lock and condition to signal to motion detection processes
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blakeblackshear
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284a96d5c7
commit
b67f323215
@ -200,11 +200,15 @@ def main():
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shared_arr = mp.Array(ctypes.c_uint16, 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 while writing
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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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# 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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capture_process = mp.Process(target=fetch_frames, args=(shared_arr,
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shared_frame_time, [region['ready_for_frame'] for region in regions], frame_shape))
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shared_frame_time, frame_lock, frame_ready, frame_shape))
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capture_process.daemon = True
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detection_processes = []
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@ -222,7 +226,7 @@ def main():
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for index, region in enumerate(regions):
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motion_process = mp.Process(target=detect_motion, args=(shared_arr,
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shared_frame_time,
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region['ready_for_frame'],
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frame_lock, frame_ready,
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region['motion_detected'],
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frame_shape,
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region['size'], region['x_offset'], region['y_offset'],
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@ -311,7 +315,7 @@ def tonumpyarray(mp_arr):
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# fetch the frames as fast a possible, only decoding the frames when the
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# detection_process has consumed the current frame
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def fetch_frames(shared_arr, shared_frame_time, ready_for_frame_flags, frame_shape):
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def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_shape):
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# convert shared memory array into numpy and shape into image array
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arr = tonumpyarray(shared_arr).reshape(frame_shape)
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@ -326,20 +330,17 @@ def fetch_frames(shared_arr, shared_frame_time, ready_for_frame_flags, frame_sha
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# snapshot the time the frame was grabbed
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frame_time = datetime.datetime.now()
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if ret:
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# if the anyone is ready for the next frame decode it
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# otherwise skip this frame and move onto the next one
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if any(flag.value == 1 for flag in ready_for_frame_flags):
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# go ahead and decode the current frame
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ret, frame = video.retrieve()
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if ret:
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arr[:] = frame
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shared_frame_time.value = frame_time.timestamp()
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# signal to the detection_processes by setting the shared_frame_time
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for flag in ready_for_frame_flags:
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flag.value = 0
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else:
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# sleep a little to reduce CPU usage
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time.sleep(0.1)
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# go ahead and decode the current frame
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ret, frame = video.retrieve()
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if ret:
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# Lock access and update frame
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frame_lock.acquire()
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arr[:] = frame
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shared_frame_time.value = frame_time.timestamp()
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frame_lock.release()
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# Notify with the condition that a new frame is ready
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with frame_ready:
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frame_ready.notify_all()
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video.release()
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@ -404,7 +405,7 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
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shared_output_arr[:] = objects + [0.0] * (60-len(objects))
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# do the actual motion detection
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def detect_motion(shared_arr, shared_frame_time, ready_for_frame, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset, min_motion_area, debug):
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def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset, min_motion_area, debug):
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# shape shared input array into frame for processing
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arr = tonumpyarray(shared_arr).reshape(frame_shape)
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@ -419,39 +420,17 @@ def detect_motion(shared_arr, shared_frame_time, ready_for_frame, shared_motion,
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if last_motion > 0 and (now - last_motion) > 2:
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last_motion = -1
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shared_motion.value = 0
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# if there isnt a 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 = 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 (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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# rest a little bit to avoid maxing out the CPU
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time.sleep(0.1)
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if ready_for_frame.value == 0:
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ready_for_frame.value = 1
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continue
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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 (now - shared_frame_time.value) > 0.5:
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# signal that we need a new frame
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ready_for_frame.value = 1
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# rest a little bit to avoid maxing out the CPU
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time.sleep(0.1)
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continue
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with frame_ready:
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# if there isnt a frame ready for processing or it is old, wait for a signal
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if shared_frame_time.value == frame_time or (now - shared_frame_time.value) > 0.5:
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frame_ready.wait()
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# make a copy of the cropped frame
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# lock and make a copy of the cropped frame
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frame_lock.acquire()
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cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy().astype('uint8')
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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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ready_for_frame.value = 1
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frame_lock.release()
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# convert to grayscale
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gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
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