blakeblackshear.frigate/frigate/motion.py
2022-02-18 21:18:26 -06:00

118 lines
4.6 KiB
Python

import cv2
import imutils
import numpy as np
from frigate.config import MotionConfig
class MotionDetector:
def __init__(self, frame_shape, config: MotionConfig):
self.config = config
self.frame_shape = frame_shape
self.resize_factor = frame_shape[0] / config.frame_height
self.motion_frame_size = (
config.frame_height,
config.frame_height * frame_shape[1] // frame_shape[0],
)
self.avg_frame = np.zeros(self.motion_frame_size, np.float)
self.avg_delta = np.zeros(self.motion_frame_size, np.float)
self.motion_frame_count = 0
self.frame_counter = 0
resized_mask = cv2.resize(
config.mask,
dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
interpolation=cv2.INTER_LINEAR,
)
self.mask = np.where(resized_mask == [0])
def detect(self, frame):
motion_boxes = []
gray = frame[0 : self.frame_shape[0], 0 : self.frame_shape[1]]
# resize frame
resized_frame = cv2.resize(
gray,
dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
interpolation=cv2.INTER_LINEAR,
)
# Improve contrast
minval = np.percentile(resized_frame, 5)
maxval = np.percentile(resized_frame, 95)
resized_frame = np.clip(resized_frame, minval, maxval)
resized_frame = (((resized_frame - minval) / (maxval - minval)) * 255).astype(
np.uint8
)
# convert to grayscale
# resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
# mask frame
resized_frame[self.mask] = [255]
# it takes ~30 frames to establish a baseline
# dont bother looking for motion
if self.frame_counter < 30:
self.frame_counter += 1
else:
# compare to average
frameDelta = cv2.absdiff(resized_frame, cv2.convertScaleAbs(self.avg_frame))
# compute the average delta over the past few frames
# 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
cv2.accumulateWeighted(frameDelta, self.avg_delta, self.config.delta_alpha)
# compute the threshold image for the current frame
# TODO: threshold
current_thresh = cv2.threshold(
frameDelta, self.config.threshold, 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(self.avg_delta)
avg_delta_image = cv2.bitwise_and(avg_delta_image, current_thresh)
# 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, self.config.threshold, 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, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
# 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 > self.config.contour_area:
x, y, w, h = cv2.boundingRect(c)
motion_boxes.append(
(
int(x * self.resize_factor),
int(y * self.resize_factor),
int((x + w) * self.resize_factor),
int((y + h) * self.resize_factor),
)
)
if len(motion_boxes) > 0:
self.motion_frame_count += 1
if self.motion_frame_count >= 10:
# only average in the current frame if the difference persists for a bit
cv2.accumulateWeighted(
resized_frame, self.avg_frame, self.config.frame_alpha
)
else:
# when no motion, just keep averaging the frames together
cv2.accumulateWeighted(
resized_frame, self.avg_frame, self.config.frame_alpha
)
self.motion_frame_count = 0
return motion_boxes