计算图片均值和方差脚本
# coding: utf-8
import numpy as np
import cv2
import random
"""
随机挑选CNum张图片,进行按通道计算均值mean和标准差std
先将像素从0~255归一化至 0-1 再计算
"""
train_txt_path = '../../Data/train.txt'
CNum = 2000 # 挑选多少图片进行计算
img_h, img_w = 32, 32
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 range(CNum):
img_path = lines[i].rstrip().split()[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.
for i in range(3):
pixels = imgs[:,:,i,:].ravel() # 拉成一行
means.append(np.mean(pixels))
stdevs.append(np.std(pixels))
means.reverse() # BGR --> RGB
stdevs.reverse()
print("normMean = {}".format(means))
print("normStd = {}".format(stdevs))
print('transforms.Normalize(normMean = {}, normStd = {})'.format(means, stdevs))
打印输出:
normMean = [0.49680823, 0.48622987, 0.44980356]
normStd = [0.24765104, 0.24397221, 0.26272318]
transforms.Normalize(normMean = [0.49680823, 0.48622987, 0.44980356], normStd = [0.24765104, 0.24397221, 0.26272318])
所以:
print("normMean = {}".format(means))
print("normStd = {}".format(stdevs))
可以输出list
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