import cv2 as cv

import numpy as np

from matplotlib import pyplot as plt

‘’’

def plot_demo(image): #直方图

plt.hist(image.ravel(), 256, [0, 256])

plt.show()

def image_hist(image): #每一个通道的直方图

color = (‘blue’, ‘green’, ‘red’)

for i, color in enumerate(color):

hist = cv.calcHist([image], [i], None, [256], [0, 56])

plt.plot(hist, color=color)

plt.xlim([0, 256])

plt.show()

def equalHist_demo(image): #直方图均衡化，只能对灰度图进行

gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)

dst = cv.equalizeHist(gray)

cv.imshow(“equalHist_demo”, dst)

‘’’

def calhe_demo(image): #局部均衡化

gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)

clahe = cv.createCLAHE(clipLimit=5.0, tileGridSize=(8,8))

dst = clahe.apply(gray)

cv.imshow(“calhe_demo”, dst)

def create_rbg_hist(image):

h, w, c = image.shape

rgbHist = np.zeros([16

16

16, 1], np.float32)

bsize = 256 / 16

for row in range(h):

for col in range(w):

b = image[row, col, 0]

g = image[row, col, 1]

r = image[row, col, 2]

index = np.int(b/bsize)

16

16 + np.int(g/bsize)*16 + np.int(r/bsize)

rgbHist[np.int(index), 0] = rgbHist[np.int(index), 0] + 1

return rgbHist

def hist_compare(image1, image2):

hist1 = create_rbg_hist(image1)

hist2 = create_rbg_hist(image2)

match1 = cv.compareHist(hist1, hist2, cv.HISTCMP_BHATTACHARYYA) #巴氏距离

match2 = cv.compareHist(hist1, hist2, cv.HISTCMP_CORREL) #相关系数

match3 = cv.compareHist(hist1, hist2, cv.HISTCMP_CHISQR) #卡方

print(“巴氏距离 : %s, 相关性 : %s, 卡方 : %s”%(match1, match2, match3))

print("-----------helli python--------------")

src = cv.imread(“E:/image/lena1.tiff”)

cv.namedWindow(“input image”, cv.WINDOW_AUTOSIZE)

#cv.imshow(“input image”, src)

#equalHist_demo(src)

#calhe_demo(src)

image1 = cv.imread(“E:/image/lena1.jpg”)

image2 = cv.imread(“E:/image/pepper1.tiff”)

cv.imshow(“image1”, image1)

cv.imshow(“image2”, image2)

create_rbg_hist(image1)

hist_compare(image1, image2)

cv.waitKey(0)

cv.destroyAllWindows()