mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-12-29 00:06:19 +01:00
86 lines
3.9 KiB
Python
86 lines
3.9 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)
|
|
|
|
# TODO: can I improve the contrast of the grayscale image here?
|
|
|
|
# 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
|