第一种写法,先读进来,再计算。比较耗内存。
import cv2 import numpy as np import torch startt = 700 CNum = 100 # 挑选多少图片进行计算 imgs=[] for i in range(startt, startt+CNum): img_path = os.path.join(root_path, filename[i]) img = cv2.imread(img_path) img = img[:, :, :, np.newaxis] imgs.append(torch.Tensor(img)) torch_imgs = torch.cat(imgs, dim=3) means, stdevs = [], [] for i in range(3): pixels = torch_imgs[:, :, i, :] # 拉成一行 means.append(torch.mean(pixels)) stdevs.append(torch.std(pixels)) # cv2 读取的图像格式为BGR,PIL/Skimage读取到的都是RGB不用转 means.reverse() # BGR --> RGB stdevs.reverse() print("normMean = {}".format(means)) print("normStd = {}".format(stdevs))
第二种写法,读一张算一张,比较耗时:先过一遍计算出均值,再过一遍计算出方差。
import os from PIL import Image import matplotlib.pyplot as plt import numpy as np from scipy.misc import imread startt = 4000 CNum = 1000 # 挑选多少图片进行计算 num = 1000 * 3200 * 1800 # 这里(3200,1800)是每幅图片的大小,所有图片尺寸都一样 imgs=[] R_channel = 0 G_channel = 0 B_channel = 0 for i in range(startt, startt+CNum): img = imread(os.path.join(root_path, filename[i])) R_channel = R_channel + np.sum(img[:, :, 0]) G_channel = G_channel + np.sum(img[:, :, 1]) B_channel = B_channel + np.sum(img[:, :, 2]) R_mean = R_channel / num G_mean = G_channel / num B_mean = B_channel / num R_channel = 0 G_channel = 0 B_channel = 0 for i in range(startt, startt+CNum): img = imread(os.path.join(root_path, filename[i])) R_channel = R_channel + np.sum(np.power(img[:, :, 0]-R_mean, 2) ) G_channel = G_channel + np.sum(np.power(img[:, :, 1]-G_mean, 2) ) B_channel = B_channel + np.sum(np.power(img[:, :, 2]-B_mean, 2) ) R_std = np.sqrt(R_channel/num) G_std = np.sqrt(G_channel/num) B_std = np.sqrt(B_channel/num) # R:65.045966 G:70.3931815 B:78.0636285 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))