```python import os import numpy as np import imageio if __name__ == "__main__": R_channel = 0. G_channel = 0. B_channel = 0. R_channel_square = 0. G_channel_square = 0. B_channel_square = 0. filename = [] root_path = "dataset\\train" for image in sorted(os.listdir(os.path.join(root_path, "imgs"))): filename.append(os.path.join(root_path, "imgs", image)) for label in sorted(os.listdir(os.path.join(root_path, "labels"))): filename.append(os.path.join(root_path, "labels", label)) imgs=[] length = len(filename) pixels_num = 768*1024*length for i in range(length): if i<10000: img = imageio.imread(filename[i]) else: img = np.array(imageio.imread(filename[i])).astype("uint8")[:, :, np.newaxis].repeat(3,2) img = img / 255 R_temp = img[:, :, 0] R_channel += np.sum(R_temp) R_channel_square += np.sum(np.power(R_temp, 2.0)) G_temp = img[:, :, 1] G_channel += np.sum(G_temp) G_channel_square += np.sum(np.power(G_temp, 2.0)) B_temp = img[:, :, 2] B_channel = B_channel + np.sum(B_temp) B_channel_square += np.sum(np.power(B_temp, 2.0)) print(i,"/",length) R_mean = R_channel / pixels_num G_mean = G_channel / pixels_num B_mean = B_channel / pixels_num """ S^2 = sum((x-x')^2 )/N = sum(x^2+x'^2-2xx')/N = {sum(x^2) + sum(x'^2) - 2x'*sum(x) }/N = {sum(x^2) + N*(x'^2) - 2x'*(N*x') }/N = {sum(x^2) - N*(x'^2) }/N = sum(x^2)/N - x'^2 """ R_std = np.sqrt(R_channel_square/pixels_num - R_mean*R_mean) G_std = np.sqrt(G_channel_square/pixels_num - G_mean*G_mean) B_std = np.sqrt(B_channel_square/pixels_num - B_mean*B_mean) print("R_mean is %f, G_mean is %f, B_mean is %f" % (R_mean, G_mean, B_mean)) print("R_std is %f, G_std is %f, B_std is %f" % (R_std, G_std, B_std)) ```