opencv python: image gradient

And a first derivative operator Soble
second derivative of the Laplacian

Edge:

Soble operator:

Second Derivative:

Laplace operator:

import cv2 as cv
import numpy as np


# 图像梯度（由x,y方向上的偏导数和偏移构成），有一阶导数（sobel算子）和二阶导数（Laplace算子）
# 用于求解图像边缘，一阶的极大值，二阶的零点
# 一阶偏导在图像中为一阶差分，再变成算子（即权值）与图像像素值乘积相加，二阶同理
def sobel_demo(image):
    grad_x = cv.Sobel(image, cv.CV_32F, 1, 0)  # 采用Scharr边缘更突出
    grad_y = cv.Sobel(image, cv.CV_32F, 0, 1)

    gradx = cv.convertScaleAbs(grad_x)  # 由于算完的图像有正有负，所以对其取绝对值
    grady = cv.convertScaleAbs(grad_y)

    # 计算两个图像的权值和，dst = src1*alpha + src2*beta + gamma
    gradxy = cv.addWeighted(gradx, 0.5, grady, 0.5, 0)

    cv.imshow("gradx", gradx)
    cv.imshow("grady", grady)
    cv.imshow("gradient", gradxy)


def laplace_demo(image):  # 二阶导数，边缘更细
    dst = cv.Laplacian(image,cv.CV_32F)
    lpls = cv.convertScaleAbs(dst)
    cv.imshow("laplace_demo", lpls)


def custom_laplace(image):
    # 以下算子与上面的Laplace_demo()是一样的，增强采用np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
    kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
    dst = cv.filter2D(image, cv.CV_32F, kernel=kernel)
    lpls = cv.convertScaleAbs(dst)
    cv.imshow("custom_laplace", lpls)


def main():
    src = cv.imread("../images/lena.jpg")
    cv.imshow("lena",src)
    # sobel_demo(src)
    laplace_demo(src)
    custom_laplace(src)
    cv.waitKey(0)  # 等有键输入或者1000ms后自动将窗口消除，0表示只用键输入结束窗口
    cv.destroyAllWindows()  # 关闭所有窗口


if __name__ == '__main__':
    main()