Python计算图片数据集的均值方差

得到整个数据集的均值方差数值

import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
# from scipy.misc import imread 
## scipy_1.3.1 not allowed
from imageio import imread

filepath = '/home/deeplearning/NEW/tianchi_data/VOC2007-4/JPEGImages'  # 数据集目录

pathDir = os.listdir(filepath)
 
R_channel = 0
G_channel = 0
B_channel = 0
for idx in range(len(pathDir)):
    filename = pathDir[idx]
    img = imread(os.path.join(filepath, filename))
    R_channel = R_channel + np.sum(img[:, :, 0])
    G_channel = G_channel + np.sum(img[:, :, 1])
    B_channel = B_channel + np.sum(img[:, :, 2])
 
num = len(pathDir) * 1024 * 1024  # 这里（1024,1024）是每幅图片的大小，所有图片尺寸都一样
R_mean = R_channel / num  # or /255.0
G_mean = G_channel / num
B_mean = B_channel / num
 
R_channel = 0
G_channel = 0
B_channel = 0
for idx in range(len(pathDir)):
    filename = pathDir[idx]
    img = imread(os.path.join(filepath, filename))
    R_channel = R_channel + np.sum((img[:, :, 0] - R_mean) ** 2)
    G_channel = G_channel + np.sum((img[:, :, 1] - G_mean) ** 2)
    B_channel = B_channel + np.sum((img[:, :, 2] - B_mean) ** 2)
 
R_var = np.sqrt(R_channel / num)
G_var = np.sqrt(G_channel / num)
B_var = np.sqrt(B_channel / num)
print("R_mean is %f, G_mean is %f, B_mean is %f" % (R_mean, G_mean, B_mean))
print("R_var is %f, G_var is %f, B_var is %f" % (R_var, G_var, B_var))

# R_mean is 143.532975, G_mean is 145.831770, B_mean is 151.186388
# R_var is 48.226279, G_var is 45.276815, B_var is 40.371132

参考：https://blog.csdn.net/weixin_41765699/article/details/100118660