blakeblackshear.frigate/frigate/motion.py

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2019-02-26 03:27:02 +01:00
import datetime
import numpy as np
import cv2
import imutils
from . util import tonumpyarray
# do the actual motion detection
def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion_detected, motion_changed,
frame_shape, region_size, region_x_offset, region_y_offset, min_motion_area, mask, debug):
# shape shared input array into frame for processing
arr = tonumpyarray(shared_arr).reshape(frame_shape)
avg_frame = None
avg_delta = None
frame_time = 0.0
motion_frames = 0
while True:
now = datetime.datetime.now().timestamp()
with frame_ready:
# if there isnt a frame ready for processing or it is old, wait for a signal
if shared_frame_time.value == frame_time or (now - shared_frame_time.value) > 0.5:
frame_ready.wait()
# lock and make a copy of the cropped frame
with frame_lock:
cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy().astype('uint8')
frame_time = shared_frame_time.value
# convert to grayscale
gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
# apply image mask to remove areas from motion detection
gray[mask] = [255]
# apply gaussian blur
gray = cv2.GaussianBlur(gray, (21, 21), 0)
if avg_frame is None:
avg_frame = gray.copy().astype("float")
continue
# look at the delta from the avg_frame
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg_frame))
if avg_delta is None:
avg_delta = frameDelta.copy().astype("float")
# compute the average delta over the past few frames
# the alpha value can be modified to configure how sensitive the motion detection is.
# higher values mean the current frame impacts the delta a lot, and a single raindrop may
# register as motion, too low and a fast moving person wont be detected as motion
# this also assumes that a person is in the same location across more than a single frame
cv2.accumulateWeighted(frameDelta, avg_delta, 0.2)
# compute the threshold image for the current frame
current_thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
# black out everything in the avg_delta where there isnt motion in the current frame
avg_delta_image = cv2.convertScaleAbs(avg_delta)
avg_delta_image[np.where(current_thresh==[0])] = [0]
# then look for deltas above the threshold, but only in areas where there is a delta
# in the current frame. this prevents deltas from previous frames from being included
thresh = cv2.threshold(avg_delta_image, 25, 255, cv2.THRESH_BINARY)[1]
# dilate the thresholded image to fill in holes, then find contours
# on thresholded image
thresh = cv2.dilate(thresh, None, iterations=2)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
motion_found = False
# loop over the contours
for c in cnts:
# if the contour is big enough, count it as motion
contour_area = cv2.contourArea(c)
if contour_area > min_motion_area:
motion_found = True
if debug:
cv2.drawContours(cropped_frame, [c], -1, (0, 255, 0), 2)
x, y, w, h = cv2.boundingRect(c)
cv2.putText(cropped_frame, str(contour_area), (x, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 100, 0), 2)
else:
break
if motion_found:
motion_frames += 1
# if there have been enough consecutive motion frames, report motion
if motion_frames >= 3:
# only average in the current frame if the difference persists for at least 3 frames
cv2.accumulateWeighted(gray, avg_frame, 0.01)
motion_detected.set()
with motion_changed:
motion_changed.notify_all()
else:
# when no motion, just keep averaging the frames together
cv2.accumulateWeighted(gray, avg_frame, 0.01)
motion_frames = 0
if motion_detected.is_set():
motion_detected.clear()
with motion_changed:
motion_changed.notify_all()
if debug and motion_frames == 3:
cv2.imwrite("/lab/debug/motion-{}-{}-{}.jpg".format(region_x_offset, region_y_offset, datetime.datetime.now().timestamp()), cropped_frame)
cv2.imwrite("/lab/debug/avg_delta-{}-{}-{}.jpg".format(region_x_offset, region_y_offset, datetime.datetime.now().timestamp()), avg_delta_image)