|
|
# -*- 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))
|