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make motion detection less sensitive to rain

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reduces the significance of fast moving objects and prioritizes objects that overlap in location across. multiple frames
This commit is contained in:
blakeblackshear
2019-02-20 06:20:52 -06:00
parent f54fa2e56c
commit 496b96b4f7
2 changed files with 34 additions and 15 deletions
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45
detect_objects.py
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@@ -434,17 +434,11 @@ def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion
arr = tonumpyarray(shared_arr).reshape(frame_shape)
avg_frame = None
last_motion = -1
avg_delta = None
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) > 2:
last_motion = -1
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
@@ -459,7 +453,7 @@ def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion
# convert to grayscale
gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
# apply image mask
# apply image mask to remove areas from motion detection
gray[mask] = [255]
# apply gaussian blur
@@ -470,15 +464,33 @@ def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion
continue
# look at the delta from the avg_frame
cv2.accumulateWeighted(gray, avg_frame, 0.01)
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg_frame))
thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
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
# put it over the edge, 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 = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
# if there are no contours, there is no motion
@@ -506,15 +518,22 @@ def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion
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
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)
if __name__ == '__main__':
mp.freeze_support()