计算图像数据集RGB各通道的均值和方差

最新推荐文章于 2023-12-06 14:03:01 发布

转载最新推荐文章于 2023-12-06 14:03:01 发布

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原文链接：http://www.cnblogs.com/liugl7/p/10874958.html

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#python

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’(Nx’) }/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))

转载于:https://www.cnblogs.com/liugl7/p/10874958.html