编程实现直方图规定化的处理程序；给定图像Fig7A和图像Fig7B，把Fig7A图像直方图规范化为接近Fig7B图像直方图的分布。
#直方图规定化 import cv2 import numpy as np from matplotlib import pyplot as plt img1 = cv2.imread('Fig6A.jpg') img2 = cv2.imread('Fig6B.jpg') img_hsv1 = cv2.cvtColor(img1, cv2.COLOR_BGR2HSV) # bgr转hsv img_hsv2 = cv2.cvtColor(img2, cv2.COLOR_BGR2HSV) color = ('h', 's', 'v') for i, col in enumerate(color): # histr = cv2.calcHist([img_hsv1], [i], None, [256], [0, 256]) hist1, bins = np.histogram(img_hsv1[:, :, i].ravel(), 256, [0, 256]) hist2, bins = np.histogram(img_hsv2[:, :, i].ravel(), 256, [0, 256]) cdf1 = hist1.cumsum() # 灰度值0-255的累计值数组 cdf2 = hist2.cumsum() cdf1_hist = hist1.cumsum() / cdf1.max() # 灰度值的累计值的比率 cdf2_hist = hist2.cumsum() / cdf2.max() diff_cdf = [[0 for j in range(256)] for k in range(256)] # diff_cdf 里是每2个灰度值比率间的差值 for j in range(256): for k in range(256): diff_cdf[j][k] = abs(cdf1_hist[j] - cdf2_hist[k]) lut = [0 for j in range(256)] # 映射表 for j in range(256): min = diff_cdf[j][0] index = 0 for k in range(256): # 直方图规定化的映射原理 if min > diff_cdf[j][k]: min = diff_cdf[j][k] index = k lut[j] = ([j, index]) h = int(img_hsv1.shape[0]) w = int(img_hsv1.shape[1]) for j in range(h): # 对原图像进行灰度值的映射 for k in range(w): img_hsv1[j, k, i] = lut[img_hsv1[j, k, i]][1] hsv_img1 = cv2.cvtColor(img_hsv1, cv2.COLOR_HSV2BGR) # hsv转bgr hsv_img2 = cv2.cvtColor(img_hsv2, cv2.COLOR_HSV2BGR) cv2.namedWindow('firstpic', 0) cv2.resizeWindow('firstpic', 670, 900) cv2.namedWindow('targetpic', 0) cv2.resizeWindow('targetpic', 670, 900) cv2.namedWindow('defpic', 0) cv2.resizeWindow('defpic', 670, 900) cv2.imshow('firstpic', img1) cv2.imshow('targetpic',img2) # cv2.imshow('img1', img_hsv1) cv2.imshow('defpic', hsv_img1) cv2.waitKey(0) cv2.destroyAllWindows()结果如下
