神经网络训练中计算输入图像的均值和方差

计算pytorch中图像输入的均值和方差

• 均值和标准差是验证图像质量的常用指标。其中：
  均值反映了图像的亮度，均值越大说明图像亮度越大，反之越小；
  标准差反映了图像像素值与均值的离散程度，标准差越大说明图像的质量越好；

# -*- coding: utf-8 -*-
import numpy as np
import cv2
import random
import os
# calculate means and std  注意换行\n符号
path = '/home/dell/Desktop/train.txt'    #图像列表文件
means = [0, 0, 0]
stdevs = [0, 0, 0]
 
index = 1
num_imgs = 0
with open(path, 'r') as f:
    lines = f.readlines()
    #random.shuffle(lines)
    print(lines) 
    for line in lines:
        print(line)
        print('{}/{}'.format(index, len(lines)))
        index += 1
        a=os.path.join('/home/dell/Desktop/2019BaiduXJTU/data/train',line)  #更改图像所在位置
        #print(a[:-1])
        num_imgs += 1
        img = cv2.imread(a[:-1])
        img = np.asarray(img)
        print(img)
        img = img.astype(np.float32) / 255.
        for i in range(3):
            means[i] += img[:, :, i].mean()
            stdevs[i] += img[:, :, i].std()

means.reverse()
stdevs.reverse()
 
means = np.asarray(means) / num_imgs
stdevs = np.asarray(stdevs) / num_imgs
 
print("normMean = {}".format(means))
print("normStd = {}".format(stdevs))
print('transforms.Normalize(normMean = {}, normStd = {})'.format(means, stdevs))

实例运行结果

normMean = [0.48215192 0.43029259 0.30252379]
normStd = [0.13762468 0.14890029 0.15740861]
transforms.Normalize(normMean = [0.48215192 0.43029259 0.30252379], normStd = [0.13762468 0.14890029 0.15740861])