彩色圖像均衡化

在HSI空間對亮度分量進行均衡化

def method_one():img = cv2.imread('../assets/Fig6.png')his = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)cv2.imshow('img', img)his_i = his[:, :, 1]equal_i = cv2.equalizeHist(hsl_i)his[:, :, 1] = equal_idst = cv2.cvtColor(his, cv2.COLOR_HLS2BGR)cv2.imshow('dst', dst)cv2.waitKey(0)cv2.destroyAllWindows()

對彩色圖像的R，G，B三通道進行均衡化

def method_two():img = cv2.imread('../assets/Fig6.png')(b, g, r) = cv2.split(img)equal_b = cv2.equalizeHist(b)equal_g = cv2.equalizeHist(g)equal_r = cv2.equalizeHist(r)dst = cv2.merge((equal_b, equal_g, equal_r))cv2.imshow('img', img)cv2.imshow('dst', dst)cv2.waitKey(0)cv2.destroyAllWindows()

對彩色圖像進行規定話

現在有兩幅圖，FigA 和 FigB，現在要對FigA進行規定化，使FigA 圖像的直方圖規范化最接近FigB的情況。

def method_three():img = cv2.imread('../assets/Fig6A.jpg')dst = cv2.imread('../assets/Fig6B.jpg')def_img = cv2.imread('../assets/Fig6A.jpg')color = ('b', 'g', 'r')for i, col in enumerate(color):hist1, bins = np.histogram(img[:, :, i].ravel(), 256, [0, 256])hist2, bins = np.histogram(dst[:, :, i].ravel(), 256, [0, 256])# 獲得累計直方圖cdf1 = hist1.cumsum()cdf2 = hist2.cumsum()# 歸一化處理cdf1_hist = hist1.cumsum() / cdf1.max()cdf2_hist = hist2.cumsum() / cdf2.max()# diff_cdf 里是每2個灰度值比率間的差值diff_cdf = [0]*256for j in range(256):for k in range(256):diff_cdf[j][k] = abs(cdf1_hist[j] - cdf2_hist[k])# FigA 中的灰度級與目標灰度級的對應表lut = np.zeros(256, dtype=np.int)for j in range(256):squ_min = diff_cdf[j][0]index = 0for k in range(256):if squ_min > diff_cdf[j][k]:squ_min = diff_cdf[j][k]index = klut[j] = ([j, index])h = int(img.shape[0])w = int(dst.shape[1])# 對原圖像進行灰度值的映射for j in range(h):for k in range(w):def_img[j, k, i] = lut[img[j, k, i]][1]cv2.namedWindow('Fig6A', 0)cv2.resizeWindow('Fig6A', 400, 520)cv2.namedWindow('Fig6B', 0)cv2.resizeWindow('Fig6B', 400, 520)cv2.namedWindow('def', 0)cv2.resizeWindow('def', 400, 520)cv2.imshow('Fig6A', img)cv2.imshow('Fig6B', dst)cv2.imshow('def', def_img)cv2.waitKey(0)cv2.destroyAllWindows()

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