import cv2 as cv import numpy as np def clamp(pv): if pv > 255: return 255 if pv < 0: return 0 else: return pv def gaussian_noise(image): h, w, c = image.shape for row in range(h): for col in range(w): s = np.random.normal(0, 20, 3) ##高斯概率分布u,σ,n b = image[row, col, 0] # blue g = image[row, col, 1] # green r = image[row, col, 2] # red image[row, col, 0] = clamp(b + s[0]) image[row, col, 1] = clamp(g + s[1]) image[row, col, 2] = clamp(r + s[2]) cv.imshow("noise image", image) print("--------- Python OpenCV Tutorial ---------") src = cv.imread("C:/Users/weiqiangwen/Desktop/sest/contours.png") cv.namedWindow("input contours",cv.WINDOW_AUTOSIZE) cv.imshow("contours", src) t1 = cv.getTickCount() #gaussian_noise(src) dst = cv.GaussianBlur(src, (0, 0), 15) #高斯模糊 cv.imshow("Gaussian Blur", dst) t2 = cv.getTickCount() time = (t2 - t1)/cv.getTickFrequency() print("time consume : %s"%(time*1000)) cv.waitKey(0) cv.destroyAllWindows()
第五天opecv高斯模糊
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转载自blog.csdn.net/qq_32340685/article/details/83347691
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