opencv学习笔记 python实现 图像梯度与图像边缘

import cv2 as cv


def sobel_image(img):
    grad_x = cv.Sobel(img, cv.CV_32F, 1, 0)#x方向一阶导数
    #图像  图像深度，支持src.depth()和ddepth的组合  x方向上差分阶数 y方向差分阶数
    grad_y = cv.Sobel(img, cv.CV_32F, 0, 1)#y方向一阶导数
    #print(grad_x)
    gradx = cv.convertScaleAbs(grad_x)#可通过print查看区别
    #线性输入数组转化为无符号（绝对值）8为整形
    #print(gradx)
    grady = cv.convertScaleAbs(grad_y)
    cv.imshow("x", gradx)
    cv.imshow("y", grady)
    gradxy = cv.addWeighted(grad_x, 0.5, grad_y, 0.5, 0)#将两幅图像整合
    #第一个数组  第一个数组的权重  第二个数组 第二个数组的权重 gamma
    #dst = src1[I]*alpha+ src2[I]*beta + gamma;输出图像式子
    cv.imshow("x&&y", gradxy)


img = cv.imread("d://work//1.jpg")
sobel_image(img)
cv.waitKey(0)
cv.destroyAllWindows()

def scharr_image(image):
    grad_x = cv.Scharr(image, cv.CV_32F, 1, 0)
    grad_y = cv.Scharr(image, cv.CV_32F, 0, 1)
    gradx = cv.convertScaleAbs(grad_x)
    grady = cv.convertScaleAbs(grad_y)
    cv.imshow("x", grad_x)
    cv.imshow("y", grad_y)
    gradxy = cv.addWeighted(gradx, 0.5, grady, 0.5, 0)
    cv.imshow("x&y_s", gradxy)

def lpls_image(image):
    dst = cv.Laplacian(image, cv.CV_32F)
    lpls = cv.convertScaleAbs(dst)
    cv.imshow("lpls", lpls)

def lpls_image(image):
    #dst = cv.Laplacian(image, cv.CV_32F)
    #lpls = cv.convertScaleAbs(dst)
    dst = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
    lpls = cv.filter2D(image, cv.CV_32F, dst)
    lpls = cv.convertScaleAbs(lpls)
    cv.imshow("lpls", lpls)

import cv2 as cv
import  numpy as np

#索贝尔算子
def sobel_image(img):
    grad_x = cv.Sobel(img, cv.CV_32F, 1, 0)#x方向一阶导数
    #图像  图像深度，支持src.depth()和ddepth的组合  x方向上差分阶数 y方向差分阶数
    grad_y = cv.Sobel(img, cv.CV_32F, 0, 1)#y方向一阶导数
    #print(grad_x)
    gradx = cv.convertScaleAbs(grad_x)#可通过print查看区别
    #线性输入数组转化为无符号（绝对值）8为整形
    #print(gradx)
    grady = cv.convertScaleAbs(grad_y)
    cv.imshow("x", gradx)
    cv.imshow("y", grady)
    gradxy = cv.addWeighted(grad_x, 0.5, grad_y, 0.5, 0)#将两幅图像整合
    #第一个数组  第一个数组的权重  第二个数组 第二个数组的权重 gamma
    #dst = src1[I]*alpha+ src2[I]*beta + gamma;输出图像式子
    cv.imshow("x&&y", gradxy)

def scharr_image(image):
    grad_x = cv.Scharr(image, cv.CV_32F, 1, 0)
    grad_y = cv.Scharr(image, cv.CV_32F, 0, 1)
    gradx = cv.convertScaleAbs(grad_x)
    grady = cv.convertScaleAbs(grad_y)
    cv.imshow("x", grad_x)
    cv.imshow("y", grad_y)
    gradxy = cv.addWeighted(gradx, 0.5, grady, 0.5, 0)
    cv.imshow("x&y_s", gradxy)

#拉普拉斯 内置4领域 和手动8领域写法
def lpls_image(image):
    dst = cv.Laplacian(image, cv.CV_32F)
    lpls = cv.convertScaleAbs(dst)
    #dst = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
    #lpls = cv.filter2D(image, cv.CV_32F, dst)
    #lpls = cv.convertScaleAbs(lpls)
    cv.imshow("lpls", lpls)


img = cv.imread("d://work//1.jpg")
lpls_image(img)
cv.waitKey(0)
cv.destroyAllWindows()