本文以火災視頻為例，得到前后兩幀變化部分區域，進而可以進行運動物體檢測。

import cv2 as cv import scipy.ndimagedef medium_filter(im, x, y, step):sum_s = []for k in range(-int(step / 2), int(step / 2) + 1):for m in range(-int(step / 2), int(step / 2) + 1):sum_s.append(im[x + k][y + m])sum_s.sort()return sum_s[(int(step * step / 2) + 1)]def frame_diff(img_1, img_2, Thre):gray1 = cv.cvtColor(img_1, cv.COLOR_BGR2GRAY) # 灰度化gray1 = cv.GaussianBlur(gray1, (3, 3), 0) # 高斯濾波gray2 = cv.cvtColor(img_2, cv.COLOR_BGR2GRAY)gray2 = cv.GaussianBlur(gray2, (3, 3), 0)d_frame = cv.absdiff(gray1, gray2)ret, d_frame = cv.threshold(d_frame, Thre, 255, cv.THRESH_BINARY)result = scipy.ndimage.median_filter(d_frame, (7, 7)) # 對結果進行中值濾波return resultif __name__ == '__main__':capture = cv.VideoCapture("house5.mp4")Thre = 8 #Thre表示像素閾值ret1, frame1 = capture.read()frame2_copy = frame1while(True):frame1 = frame2_copyret2, frame2 = capture.read()if not ret2:print("... end of video file reached")breakd_frame = frame_diff(frame1, frame2, Thre)cv.namedWindow('Result of original img', 0)cv.imshow('Result of original img', frame2)cv.waitKey(2)cv.namedWindow('Result of frame diff', 0)cv.imshow('Result of frame diff', d_frame)cv.waitKey(2)frame2_copy = frame2.copy()

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