图像的均值和方差python_计算图像数据集RGB各通道的均值和方差
第一种写法，先读进来，再计算。比较耗内存。
import cv2
import numpy as np
import torch
startt = 700
CNum = 100 # 挑选多少图片进行计算
imgs=[]
for i in range(startt, startt+CNum):
img_path = os.path.join(root_path, filename[i])
img = cv2.imread(img_path)
img = img[:, :, :, np.newaxis]
imgs.append(torch.Tensor(img))
torch_imgs = torch.cat(imgs, dim=3)
means, stdevs = [], []
for i in range(3):
pixels = torch_imgs[:, :, i, :] # 拉成一行
means.append(torch.mean(pixels))
stdevs.append(torch.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))
第二种写法，读一张算一张，比较耗时：先过一遍计算出均值，再过一遍计算出方差。
import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread
startt = 4000
CNum = 1000 # 挑选多少图片进行计算
num = 1000 * 3200 * 1800 # 这里(3200,1800)是每幅图片的大小，所有图片尺寸都一样
imgs=[]
R_channel = 0
G_channel = 0
B_channel = 0
for i in range(startt, startt+CNum):
img = imread(os.path.join(root_path, filename[i]))
R_channel = R_channel + np.sum(img[:, :, 0])
G_channel = G_channel + np.sum(img[:, :, 1])
B_channel = B_channel + np.sum(img[:, :, 2])
R_mean = R_channel / num
G_mean = G_channel / num
B_mean = B_channel / num
R_channel = 0
G_channel = 0
B_channel = 0
for i in range(startt, startt+CNum):
img = imread(os.path.join(root_path, filename[i]))
R_channel = R_channel + np.sum(np.power(img[:, :, 0]-R_mean, 2) )
G_channel = G_channel + np.sum(np.power(img[:, :, 1]-G_mean, 2) )
B_channel = B_channel + np.sum(np.power(img[:, :, 2]-B_mean, 2) )
R_std = np.sqrt(R_channel/num)
G_std = np.sqrt(G_channel/num)
B_std = np.sqrt(B_channel/num)
# R:65.045966 G:70.3931815 B:78.0636285
print("R_mean is %f, G_mean is %f, B_mean is %f" % (R_mean, G_mean, B_mean))
print("R_std is %f, G_std is %f, B_std is %f" % (R_std, G_std, B_std))
第三种写法，只需要遍历一次：在一轮循环中计算出x，x^2； 然后x'=sum(x)/N ，又有sum(x^2)，根据下式：
S^2
= sum((x-x')^2 )/N = sum(x^2+x'^2-2xx')/N
= {sum(x^2) + sum(x'^2) - 2x'*sum(x) }/N
= {sum(x^2) + N*(x'^2) - 2x'*(N*x') }/N
= {sum(x^2) - N*(x'^2) }/N
= sum(x^2)/N - x'^2
S = sqrt( sum(x^2)/N - (sum(x)/N )^2 )
可以知道，只需要经过一次遍历，就可以计算出数据集的均值和方差。
import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread
startt = 5000
CNum = 1000 # 挑选多少图片进行计算
R_channel = 0
G_channel = 0
B_channel = 0
R_channel_square = 0
G_channel_square = 0
B_channel_square = 0
pixels_num = 0
imgs = []
for i in range(startt, startt+CNum):
img = imread(os.path.join(root_path, filename[i]))
h, w, _ = img.shape
pixels_num += h*w # 统计单个通道的像素数量
R_temp = img[:, :, 0]
R_channel += np.sum(R_temp)
R_channel_square += np.sum(np.power(R_temp, 2.0))
G_temp = img[:, :, 1]
G_channel += np.sum(G_temp)
G_channel_square += np.sum(np.power(G_temp, 2.0))
B_temp = img[:, :, 2]
B_channel = B_channel + np.sum(B_temp)
B_channel_square += np.sum(np.power(B_temp, 2.0))
R_mean = R_channel / pixels_num
G_mean = G_channel / pixels_num
B_mean = B_channel / pixels_num
"""
S^2
= sum((x-x')^2 )/N = sum(x^2+x'^2-2xx')/N
= {sum(x^2) + sum(x'^2) - 2x'*sum(x) }/N
= {sum(x^2) + N*(x'^2) - 2x'*(N*x') }/N
= {sum(x^2) - N*(x'^2) }/N
= sum(x^2)/N - x'^2
"""
R_std = np.sqrt(R_channel_square/pixels_num - R_mean*R_mean)
G_std = np.sqrt(G_channel_square/pixels_num - G_mean*G_mean)
B_std = np.sqrt(B_channel_square/pixels_num - B_mean*B_mean)
print("R_mean is %f, G_mean is %f, B_mean is %f" % (R_mean, G_mean, B_mean))
print("R_std is %f, G_std is %f, B_std is %f" % (R_std, G_std, B_std))