计算图像数据集RGB各通道的均值和方差（转载）

计算图像数据集RGB各通道的均值和方差

第一种写法，先读进来，再计算。比较耗内存。

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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。

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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))

　　

第三种写法，只需要遍历一次：在一轮循环中计算出x，x^2；  然后x'=sum(x)/N ，又有sum(x^2)，根据下式：

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

S = sqrt( sum(x^2)/N - (sum(x)/N )^2   )

可以知道，只需要经过一次遍历，就可以计算出数据集的均值和方差。

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import os from PIL import Image import matplotlib.pyplot as plt import numpy as np from scipy.misc import imread   startt = 5000 CNum = 1000   # 挑选多少图片进行计算 R_channel = 0 G_channel = 0 B_channel = 0 R_channel_square = 0 G_channel_square = 0 B_channel_square = 0 pixels_num = 0   imgs = [] for i in range(startt, startt+CNum):     img = imread(os.path.join(root_path, filename[i]))     h, w, _ = img.shape     pixels_num += h*w       # 统计单个通道的像素数量       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))   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))