opencv_day6

import cv2
import matplotlib.pylab as plt
import numpy as np
img1 = cv2.imread('D:\\gudugudu\\Pictures\\weixin\\xiaotidaya.jpg', cv2.IMREAD_ANYCOLOR)
img2 = cv2.imread('D:\\gudugudu\\Pictures\\weixin\\guoqing.jpg', cv2.IMREAD_GRAYSCALE)
img3 = cv2.imread('D:\\gudugudu\\Pictures\\weixin\\zhaoyang.jpg', cv2.IMREAD_GRAYSCALE)
img_x = cv2.resize(img1, (500, 500))
img_y = cv2.resize(img2, (500, 500))
img_z = cv2.resize(img3, (500, 500))
vc = cv2.VideoCapture("D:\\gudugudu\\Pictures\\weixin\\WeChat_20240706150425.mp4")


#梯度计算

#Soble算子 det = cv2.Soble(src, drpth, dx, dy, ksize);上下，左右的差异
#计算方法： 右-左*系数， 上-下*系数；


def soble(img):
    soblex = cv2.Soble(img, cv2.CV_64F, 1, 0, ksize = 3)
    soblex = cv2.convertScaleAbs(soblex)
    cv2.imshow('name', soblex)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


#分别计算xy再求和；


def Soblexy(img):
    soblex = cv2.Soble(img, cv2.CV_64F, 1, 0, ksize = 3)
    sobley = cv2.Soble(img, cv2.CV_64F, 0, 1, ksize = 3)
    soblexy = cv2.addWeighted(soblex, 0.5, sobley, 0.5, 0)
    cv2.imshow('name', soblexy)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


#scharr算子： 数值大一些，结果的差异更加敏感
#laplacian算子： 对于差异更加敏感， 但是对于噪点的判断有误


def scharr(img):
    scharrx = cv2.Scharr(img, cv2.CV_64F, 1, 0)
    scharry = cv2.Scharr(img, cv2.CV_64F, 0, 1)
    scharrx = cv2.convertScaleAbs(scharrx)
    scharry = cv2.convertScaleAbs(scharry)
    scharrxy = cv2.addWeighted(scharrx, 0.5, scharry, 0.5, 0)
    cv2.imshow('name', scharrxy)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


def LapLacion(img):
    laplacion = cv2.Laplacian(img, cv2.CV_64F)
    laplacion = cv2.convertScaleAbs(laplacion)
    cv2.imshow('name', laplacion)
    cv2.waitKey(0)
    cv2.destroyAllWindows()