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- # -*- coding: utf-8 -*-
- # @Time : 2019/12/7 14:46
- # @Author : zhoujun
- import numpy as np
- import cv2
- import os
- import random
- from tqdm import tqdm
- # calculate means and std
- train_txt_path = "./train_val_list.txt"
- CNum = 10000 # 挑选多少图片进行计算
- img_h, img_w = 640, 640
- imgs = np.zeros([img_w, img_h, 3, 1])
- means, stdevs = [], []
- with open(train_txt_path, "r") as f:
- lines = f.readlines()
- random.shuffle(lines) # shuffle , 随机挑选图片
- for i in tqdm(range(CNum)):
- img_path = lines[i].split("\t")[0]
- img = cv2.imread(img_path)
- img = cv2.resize(img, (img_h, img_w))
- img = img[:, :, :, np.newaxis]
- imgs = np.concatenate((imgs, img), axis=3)
- # print(i)
- imgs = imgs.astype(np.float32) / 255.0
- for i in tqdm(range(3)):
- pixels = imgs[:, :, i, :].ravel() # 拉成一行
- means.append(np.mean(pixels))
- stdevs.append(np.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))
- print("transforms.Normalize(normMean = {}, normStd = {})".format(means, stdevs))
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