图像处理DOG 算法，python结合cv2实现

来源：互联网发布：欧卡2a卡优化编辑：程序博客网时间：2024/06/03 07:09

DoG (Difference of Gaussian)是灰度图像增强和角点检测的方法

#coding=utf-8import cv2import numpy as npdef  getExtrema(A, B, C, thresh):    height,width= A.shape    resu = np.ones((height, width), A.dtype) * 100    for row in range(1, height-1):        for col in range(1, width-1):            center = B[row, col]            if center < thresh:                continue            B[row, col] = B[row, col - 1]            minValue = np.vstack([A[row-1:row+2, col-1:col+2], B[row-1:row+2, col-1:col+2],C[row-1:row+2, col-1:col+2]]).min()            maxValue = np.vstack([A[row - 1:row + 2, col - 1:col + 2], B[row - 1:row + 2, col - 1:col + 2],                                  C[row - 1:row + 2, col - 1:col + 2]]).max()            if center < minValue:                resu[row, col] = 0            if center > maxValue:                resu[row, col] = 255            B[row, col] = center    return resudef addPoint(image, image_point):    height, width, dvim = image.shape    for row in range(0, height):        for col in range(0, width):            if image_point[row, col] == 255:                cv2.circle(image, (row, col), 5, thickness=1, color=[0,0,255])            elif image_point[row, col] == 0:                cv2.circle(image, (row, col), 5, thickness=1, color=[0,255,0])if  __name__ == "__main__":    image = cv2.imread('lena.jpg')    r,g,b = cv2.split(image)    image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)    image_gray_blur1 = cv2.GaussianBlur(image_gray, (3, 3), 0.3)    image_gray_blur2 = cv2.GaussianBlur(image_gray, (3, 3), 0.4)    image_gray_blur3 = cv2.GaussianBlur(image_gray, (3, 3), 0.5)    image_gray_blur4 = cv2.GaussianBlur(image_gray, (3, 3), 0.6)    image_gray_blur5 = cv2.GaussianBlur(image_gray, (3, 3), 0.7)    image_gray_blur6 = cv2.GaussianBlur(image_gray, (3, 3), 0.8)    image_gray_dog1 = image_gray_blur2 - image_gray_blur1    image_gray_dog2 = image_gray_blur4 - image_gray_blur3    image_gray_dog3 = image_gray_blur6 - image_gray_blur5    image_point = getExtrema(image_gray_dog1, image_gray_dog2, image_gray_dog3, 2)    #反过来的gbr    cv2.namedWindow("image_DOG", flags= cv2.WINDOW_NORMAL)    cv2.moveWindow("image_DOG", 300, 200)    addPoint(image, image_point)    cv2.imshow("image", cv2.imread("./lena.jpg"))    cv2.imshow("image_gray", image_gray)    cv2.imshow("image_gray_blur1", image_gray_blur1)    cv2.imshow("image_gray_blur2", image_gray_blur2)    cv2.imshow("image_gray_blur3", image_gray_blur3)    cv2.imshow("image_gray_blur4", image_gray_blur4)    cv2.imshow("image_gray_blur5", image_gray_blur5)    cv2.imshow("image_gray_blur6", image_gray_blur6)    cv2.imshow("image_gray_dog1", image_gray_dog1)    cv2.imshow("image_gray_dog2", image_gray_dog2)    cv2.imshow("image_gray_dog3", image_gray_dog3)    cv2.imshow("image_DOG", image)    cv2.imwrite("image_gray.jpg", image_gray,[int(cv2.IMWRITE_JPEG_QUALITY), 100])    cv2.imwrite("image_gray_blur1.jpg", image_gray_blur1,[int(cv2.IMWRITE_JPEG_QUALITY), 100])    cv2.imwrite("image_gray_blur2.jpg", image_gray_blur2,[int(cv2.IMWRITE_JPEG_QUALITY), 100])    cv2.imwrite("image_gray_blur3.jpg", image_gray_blur3,[int(cv2.IMWRITE_JPEG_QUALITY), 100])    cv2.imwrite("image_gray_blur4.jpg", image_gray_blur4,[int(cv2.IMWRITE_JPEG_QUALITY), 100])    cv2.imwrite("image_gray_blur5.jpg", image_gray_blur5,[int(cv2.IMWRITE_JPEG_QUALITY), 100])    cv2.imwrite("image_gray_blur6.jpg", image_gray_blur6,[int(cv2.IMWRITE_JPEG_QUALITY), 100])    cv2.imwrite("image_gray_dog1.jpg", image_gray_dog1,[int(cv2.IMWRITE_JPEG_QUALITY), 100])    cv2.imwrite("image_gray_dog2.jpg", image_gray_dog2,[int(cv2.IMWRITE_JPEG_QUALITY), 100])    cv2.imwrite("image_gray_dog3.jpg", image_gray_dog3,[int(cv2.IMWRITE_JPEG_QUALITY), 100])    cv2.imwrite("image_DOG.jpg", image,[int(cv2.IMWRITE_JPEG_QUALITY), 100] )    cv2.waitKey(0)    cv2.destroyAllWindows()