#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()