opencv+python -- 图像直方图应用

直方图均衡化

cv2.equalizeHist函数原型：equalizeHist(src[, dst]) 。函数equalizeHist的作用：直方图均衡化，提高图像质量。
createCLAHE函数原型：createCLAHE([, clipLimit[, tileGridSize]]) -> retval
clipLimit参数表示对比度的大小。
tileGridSize参数表示每次处理块的大小 。

import cv2 as cv
import numpy


def equalHist_demo(image):
    # 全局直方图均衡化
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)  # opencv的直方图均衡化要基于单通道灰度图像
    dst = cv.equalizeHist(gray)   # 自动调整图像对比度，把图像变得更清晰
    cv.imshow("equalHist_demo", dst)


def clahe_demo(image):
    # 局部直方图均衡化
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    clahe = cv.createCLAHE(clipLimit=10.0, tileGridSize=(8, 8))
    dst = clahe.apply(gray)
    cv.imshow("clahe_demo", dst)


def create_rgb_demo(image):
    height, width, channel = image.shape
    rgbHist = numpy.zeros([16*16*16, 1], numpy.float32)
    bsize = 256 / 16
    for row in range(height):
        for col in range(width):
            b = image[row, col, 0]
            g = image[row, col, 1]
            r = image[row, col, 2]
            # 下降成16个bin，由256*256*256降维4096
            # 不是很懂
            index = numpy.int(b/bsize)*16*16 + numpy.int(g/bsize)*16 + numpy.int(r/bsize)
            rgbHist[numpy.int(index), 0] = rgbHist[numpy.int(index), 0] + 1
    return rgbHist


def hist_compare(image1, image2):
    hist1 = create_rgb_demo(image1)
    hist2 = create_rgb_demo(image2)
    match1 = cv.compareHist(hist1, hist2, cv.HISTCMP_BHATTACHARYYA)  # 越小越相似
    match2 = cv.compareHist(hist1, hist2, cv.HISTCMP_CORREL)       # 越大越相似
    match3 = cv.compareHist(hist1, hist2, cv.HISTCMP_CHISQR)       # 越小越相似
    print("巴氏距离：%s 相关性：%s 卡方：%s" % (match1, match2, match3))


src = cv.imread("./data/lena.jpg", cv.IMREAD_COLOR)
cv.namedWindow("lena", cv.WINDOW_AUTOSIZE)
cv.imshow("lena", src)
equalHist_demo(src)
clahe_demo(src)

# image1 = cv.imread("./data/lena.jpg")
# image2 = cv.imread("girl.jpg")
# cv.imshow("image1", image1)
# cv.imshow("image2", image2)
# hist_compare(image1, image2)
cv.waitKey(0)
cv.destroyAllWindows()

直方图均衡化.png

直方图比较结果
巴氏距离：0.9759536695421235 相关性：-0.006443074743513555 卡方：121843214.63453875