2色彩转化，高斯模糊

Posted 2021-03-16 wml2018

1#色彩空间转化

def color_space_demo(image):
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
cv.imshow("gray", gray)
hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV)
cv.imshow("hsv", hsv)
yuv = cv.cvtColor(image, cv.COLOR_BGR2YUV)
cv.imshow("yuv", yuv)
Ycrcb = cv.cvtColor(image, cv.COLOR_BGR2YCrCb)
cv.imshow("ycrcb", Ycrcb)

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2几种常见模糊

#3模糊
import cv2 as cv
import numpy as np


#均值模糊
def blur_demo(image):
    dst = cv.blur(image, (5, 5))#参数1为水平方向模糊数值，参数2为竖直方向
    cv.imshow("blur_demo", dst)


#中值模糊--可以用于去除椒盐去噪
def median_blur_demo(image):
    dst = cv.medianBlur(image, 5)
    cv.imshow("median_blur_demo", dst)


#自由定义
def custom_blur_demo(image):
    #kernel = np.ones([5, 5], np.float32)/25
    kernel = np.array([[0, -1, 0],[-1, 5, -1],[0, -1, 0]], np.float32)
    dst = cv.filter2D(image, -1, kernel=kernel)
    cv.imshow("custom_blur_demo", dst)


print("--------- Hello Python ---------")
src = cv.imread("D:/vcprojects/images/demo.png")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)
custom_blur_demo(src)
cv.waitKey(0)

cv.destroyAllWindows()

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3高斯模糊

#高斯模糊--重要，有效，滤镜效果，语法：st = cv.GaussianBlur(src, (0, 0), 15)
src = cv.imread("D:/vcprojects/images/example.png")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)

t1 = cv.getTickCount()
#gaussian_noise(src)
t2 = cv.getTickCount()
time = (t2 - t1)/cv.getTickFrequency()
print("time consume : %s"%(time*1000))
dst = cv.GaussianBlur(src, (0, 0), 15)
cv.imshow("Gaussian Blur", dst)

#高斯双边--美颜磨皮效果
#均值迁移--羽化效果

import cv2 as cv
import numpy as np


def bi_demo(image):#高斯双边
    dst = cv.bilateralFilter(image, 0, 100, 15)
    cv.imshow("bi_demo", dst)


def shift_demo(image):#均值迁移
    dst = cv.pyrMeanShiftFiltering(image, 10, 50)
    cv.imshow("shift_demo", dst)


print("--------- Hello Python ---------")
src = cv.imread("C:/Users/wml/Desktop/wml/ym.jpg")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)
# bi_demo(src)
shift_demo(src)
cv.waitKey(0)
cv.destroyAllWindows()