mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-12-23 19:11:14 +01:00
117 lines
5.0 KiB
Python
117 lines
5.0 KiB
Python
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
|
|
last_motion = -1
|
|
frame_time = 0.0
|
|
motion_frames = 0
|
|
while True:
|
|
now = datetime.datetime.now().timestamp()
|
|
|
|
# if it has been long enough since the last motion, clear the flag
|
|
if last_motion > 0 and (now - last_motion) > 5:
|
|
last_motion = -1
|
|
if motion_detected.is_set():
|
|
motion_detected.clear()
|
|
with motion_changed:
|
|
motion_changed.notify_all()
|
|
|
|
|
|
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()
|
|
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()
|
|
last_motion = now
|
|
else:
|
|
# when no motion, just keep averaging the frames together
|
|
cv2.accumulateWeighted(gray, avg_frame, 0.01)
|
|
motion_frames = 0
|
|
|
|
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)
|