mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-10-15 20:07:23 +02:00
115 lines
5.0 KiB
Python
115 lines
5.0 KiB
Python
import datetime
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import numpy as np
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import cv2
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import imutils
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from . util import tonumpyarray
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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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frame_shape, region_size, region_x_offset, region_y_offset, min_motion_area, mask, 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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avg_frame = None
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avg_delta = None
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frame_time = 0.0
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motion_frames = 0
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while True:
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now = datetime.datetime.now().timestamp()
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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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# lock and make a copy of the cropped frame
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with frame_lock:
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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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# convert to grayscale
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gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
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# apply image mask to remove areas from motion detection
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gray[mask] = [255]
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# apply gaussian blur
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gray = cv2.GaussianBlur(gray, (21, 21), 0)
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if avg_frame is None:
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avg_frame = gray.copy().astype("float")
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continue
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# look at the delta from the avg_frame
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frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg_frame))
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if avg_delta is None:
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avg_delta = frameDelta.copy().astype("float")
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# compute the average delta over the past few frames
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# the alpha value can be modified to configure how sensitive the motion detection is.
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# higher values mean the current frame impacts the delta a lot, and a single raindrop may
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# register as motion, too low and a fast moving person wont be detected as motion
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# this also assumes that a person is in the same location across more than a single frame
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cv2.accumulateWeighted(frameDelta, avg_delta, 0.2)
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# compute the threshold image for the current frame
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current_thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
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# black out everything in the avg_delta where there isnt motion in the current frame
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avg_delta_image = cv2.convertScaleAbs(avg_delta)
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avg_delta_image[np.where(current_thresh==[0])] = [0]
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# then look for deltas above the threshold, but only in areas where there is a delta
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# in the current frame. this prevents deltas from previous frames from being included
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thresh = cv2.threshold(avg_delta_image, 25, 255, cv2.THRESH_BINARY)[1]
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# dilate the thresholded image to fill in holes, then find contours
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# on thresholded image
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thresh = cv2.dilate(thresh, None, iterations=2)
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cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cnts = imutils.grab_contours(cnts)
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# if there are no contours, there is no motion
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if len(cnts) < 1:
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motion_frames = 0
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continue
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motion_found = False
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# loop over the contours
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for c in cnts:
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# if the contour is big enough, count it as motion
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contour_area = cv2.contourArea(c)
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if contour_area > min_motion_area:
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motion_found = True
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if debug:
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cv2.drawContours(cropped_frame, [c], -1, (0, 255, 0), 2)
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x, y, w, h = cv2.boundingRect(c)
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cv2.putText(cropped_frame, str(contour_area), (x, y),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 100, 0), 2)
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else:
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break
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if motion_found:
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motion_frames += 1
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# if there have been enough consecutive motion frames, report motion
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if motion_frames >= 3:
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# only average in the current frame if the difference persists for at least 3 frames
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cv2.accumulateWeighted(gray, avg_frame, 0.01)
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motion_detected.set()
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with motion_changed:
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motion_changed.notify_all()
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else:
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# when no motion, just keep averaging the frames together
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cv2.accumulateWeighted(gray, avg_frame, 0.01)
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motion_frames = 0
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if motion_detected.is_set():
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motion_detected.clear()
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with motion_changed:
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motion_changed.notify_all()
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if debug and motion_frames == 3:
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cv2.imwrite("/lab/debug/motion-{}-{}-{}.jpg".format(region_x_offset, region_y_offset, datetime.datetime.now().timestamp()), cropped_frame)
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cv2.imwrite("/lab/debug/avg_delta-{}-{}-{}.jpg".format(region_x_offset, region_y_offset, datetime.datetime.now().timestamp()), avg_delta_image)
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