python opencv——直方图应用

def equaliHist_demo(image):
    gray = cv.cvtColor(image,cv.COLOR_BGR2GRAY)
    dst = cv.equalizeHist(gray)         #直方图均衡化，用于提高图像的质量，该函数处理的图片一定是灰度图才可以
    cv.imshow('equaliHist_demo',dst)

def clahe_demo(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    clahe = cv.createCLAHE(clipLimit=2.0,tileGridSize=(8,8))　　#createCLAHE自适应均衡化图像
    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


def hist_compare(image1,image2):
    hist1 = create_rgb_hist(image1)
    hist2 = create_rgb_hist(image2)
    match1 = cv.compareHist(hist1,hist2,cv.HISTCMP_BHATTACHARYYA)   #巴氏距离   越接近0越相似
    match2 = cv.compareHist(hist1,hist2,cv.HISTCMP_CORREL)          #相关性     越接近1越相似
    match3 = cv.compareHist(hist1,hist2,cv.HISTCMP_CHISQR)          #卡方       越小越相似
    print('巴氏距离:%s，相关性:%s，卡方:%s'%(match1,match2,match3))