python + opencv图像处理（十二）——直方图应用

直方图均衡化
直方图比较
调整对比度：1、整体；2、局部
都是对灰色图像

from cv2 import cv2 as cv
import numpy as np 

# 直方图均衡化都是针对灰色图像
# 整体
def equalHist_demo(image):
    gray = cv.cvtColor(image,cv.COLOR_BGR2GRAY)    
    dst = cv.equalizeHist(gray) 
    cv.imshow('equalHist_demo',dst)

# 局部
def clahe_demo(image):
    gray = cv.cvtColor(image,cv.COLOR_BGR2GRAY)
    clahe = cv.createCLAHE(clipLimit=5.0,tileGridSize=(8,8))  # #clipLimit是对比度的大小，tileGridSize是每次处理块的大小
    dst = clahe.apply(gray)  
    cv.imshow('clahe_demo',dst)

def create_rgb_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

# 比较需要大小一样
def hist_compare(image1,image2):
    hist1 = create_rgb_hist(image1)
    hist2 = create_rgb_hist(image2)
    match1 = cv.compareHist(hist1,hist2,cv.HISTCMP_BHATTACHARYYA)
    match2 = cv.compareHist(hist1,hist2,cv.HISTCMP_CORREL)
    match3 = cv.compareHist(hist1,hist2,cv.HISTCMP_CHISQR)
    print('巴氏距离：%s, 相关性：%s, 卡方：%s'%(match1,match2,match3))



if __name__ == "__main__":
    filepath1 = "C:\\pictures\\6.jpg"
    img1 = cv.imread(filepath1)       # blue green red
    filepath2 = "C:\\pictures\\10.jpg"
    img2 = cv.imread(filepath2)
    # cv.namedWindow("input image",cv.WINDOW_AUTOSIZE)
    cv.imshow("input image1",img1)
    cv.imshow("input image2",img2)


    # equalHist_demo(img)
    # clahe_demo(img)
    hist_compare(img1,img2)

    cv.waitKey(0)
    cv.destroyAllWindows()

两个图的比较