python list mean python list mean std

首先先把数据集的图片路径保存在一个txt文件夹里面

import os


def generate(dir, label):
    listText = open('list.txt', 'a')
    for file in dir:
        fileType = os.path.split(file)
        if fileType[1] == '.txt':
            continue
        name = file + ' ' + str(int(label)) + '\n'
        listText.write(name)
    listText.close()

outer_path = 'E:/lly/data/'  # 这里是你的图片的目录

if __name__ == '__main__':
    i = 1
    num = 0
    personlist = os.listdir(outer_path)  # 列举文件夹
    personlist.sort()
    for person in personlist:
        personPath = outer_path+person + "/"

        fingerlist = os.listdir(personPath)
        fingerlist.sort()

        for finger in fingerlist:

            finallPATH=os.path.join(outer_path, person,finger)
            finallPATH=finallPATH.replace('\\', '/')

            listText = open('image_list.txt', 'a')
            fileType = os.path.split(finallPATH)

            name = finallPATH+ '\n'

            listText.write(name)

        listText.close()
        i += 1

计算自己数据集的均值和方差：

# -*- coding: utf-8 -*-**
import numpy as np
import cv2
import random
import os

# calculate means and std  注意换行\n符号**
# train.txt中每一行是图像的位置信息**
path = 'C:/Users/lenovo/PycharmProjects/my/image_list.txt'
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)

    for line in lines:
        print(line)
        print('{}/{}'.format(index, len(lines)))
        index += 1
        a = os.path.join(line)

       # print(a[:-1])
        num_imgs += 1
        img = cv2.imread(a[:-1])
        print(img, 22)
        img = np.asarray(img)

        img = img.astype(np.float32) / 255.
        for i in range(3):
            means[i] += img[:, :, i].mean()
            stdevs[i] += img[:, :, i].std()
print(num_imgs)
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))