12、OpenCV Python 图像梯度

__author__ = "WSX"
import cv2 as cv
import numpy as np

def lapalian_demo(image):  #拉普拉斯算子
    #dst = cv.Laplacian(image, cv.CV_32F) #内置函数来实现
    #lpls = cv.convertScaleAbs(dst)
    kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])  #自定义来实现
    dst = cv.filter2D(image, cv.CV_32F, kernel=kernel)
    lpls = cv.convertScaleAbs(dst)
    cv.imshow("lapalian_demo", lpls)


def sobel_demo(image): #sobel算子
    grad_x = cv.Scharr(image, cv.CV_32F, 1, 0) #x的一阶导数
    grad_y = cv.Scharr(image, cv.CV_32F, 0, 1)
    gradx = cv.convertScaleAbs(grad_x)  #  先绝对值  再转到8位图像上
    grady = cv.convertScaleAbs(grad_y)
    cv.imshow("gradient-x", gradx)  #左右有差异的表现
    cv.imshow("gradient-y", grady)  #上下有差异的表现
    gradxy = cv.addWeighted(gradx, 0.5, grady, 0.5, 0)  #一起表现
    cv.imshow("gradient", gradxy)

def main():
    img = cv.imread("1.JPG")
    cv.namedWindow("Show", cv.WINDOW_AUTOSIZE)
    cv.imshow("Show", img)
    sobel_demo(img)

    cv.waitKey(0)
    cv.destroyAllWindows()

main()