#分道計(jì)算每個(gè)通道的直方圖img0 = cv2.imread('12.jpg')hist_b = cv2.calcHist([img0],[0],None,[256],[0,256])hist_g = cv2.calcHist([img0],[1],None,[256],[0,256])hist_r = cv2.calcHist([img0],[2],None,[256],[0,256])def gamma_trans(img,gamma): #具體做法先歸一化到1，然后gamma作為指數(shù)值求出新的像素值再還原 gamma_table = [np.power(x/255.0,gamma)*255.0 for x in range(256)] gamma_table = np.round(np.array(gamma_table)).astype(np.uint8) #實(shí)現(xiàn)映射用的是Opencv的查表函數(shù) return cv2.LUT(img0,gamma_table)img0_corrted = gamma_trans(img0, 0.5)cv2.imshow('img0',img0)cv2.imshow('gamma_image',img0_corrted)cv2.imwrite('gamma_image.png',img0_corrted)#分通道計(jì)算Gamma校正后的直方圖hist_b_c =cv2.calcHist([img0_corrted],[0],None,[256],[0,256])hist_g_c =cv2.calcHist([img0_corrted],[1],None,[256],[0,256])hist_r_c =cv2.calcHist([img0_corrted],[2],None,[256],[0,256])fig = plt.figure('gamma')pix_hists = [[hist_b, hist_g, hist_r],    [hist_b_c, hist_g_c, hist_r_c]]pix_vals = range(256)for sub_plt, pix_hist in zip([121, 122], pix_hists): ax = fig.add_subplot(sub_plt, projection='3d') for c, z, channel_hist in zip(['b', 'g', 'r'], [20, 10, 0], pix_hist):  cs = [c] * 256  ax.bar(pix_vals, channel_hist, zs=z, zdir='y', color=cs, alpha=0.618, edgecolor='none', lw=0) ax.set_xlabel('Pixel Values') ax.set_xlim([0, 256]) ax.set_ylabel('Count') ax.set_zlabel('Channels')plt.show()cv2.waitKey()