Python計(jì)算圖片數(shù)據(jù)集的均值方差示例詳解

更新時(shí)間：2022年05月19日 14:31:06 作者：螢-火

這篇文章主要為大家介紹了Python計(jì)算圖片數(shù)據(jù)集的均值方差，有需要的朋友可以借鑒參考下，希望能夠有所幫助，祝大家多多進(jìn)步，早日升職加薪

前言

在做圖像處理的時(shí)候，有時(shí)候需要得到整個(gè)數(shù)據(jù)集的均值方差數(shù)值，以下代碼可以解決你的煩惱：

（做這個(gè)之前一定保證所有的圖片都是統(tǒng)一尺寸，不然算出來不對(duì)，我的代碼里設(shè)計(jì)的是512*512，可以自己調(diào)整，同一尺寸的代碼我也有：

Python批量reshape圖片

# -*- coding: utf-8 -*-
"""
Created on Thu Aug 23 16:06:35 2018
@author: libo
"""
from PIL import Image
import os
def image_resize(image_path, new_path):           # 統(tǒng)一圖片尺寸
    print('============>>修改圖片尺寸')
    for img_name in os.listdir(image_path):
        img_path = image_path + "/" + img_name    # 獲取該圖片全稱
        image = Image.open(img_path)              # 打開特定一張圖片
        image = image.resize((512, 512))          # 設(shè)置需要轉(zhuǎn)換的圖片大小
        # process the 1 channel image
        image.save(new_path + '/'+ img_name)
    print("end the processing!")
if __name__ == '__main__':
    print("ready for ::::::::  ")
    ori_path = r"Z:\pycharm_projects\ssd\VOC2007\JPEGImages"                # 輸入圖片的文件夾路徑
    new_path = 'Z:/pycharm_projects/ssd/VOC2007/reshape'                   # resize之后的文件夾路徑
    image_resize(ori_path, new_path)

import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread
filepath = r'Z:\pycharm_projects\ssd\VOC2007\reshape'  # 數(shù)據(jù)集目錄
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)) / 255.0
    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) * 512 * 512  # 這里（512,512）是每幅圖片的大小，所有圖片尺寸都一樣
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 idx in range(len(pathDir)):
    filename = pathDir[idx]
    img = imread(os.path.join(filepath, filename)) / 255.0
    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))

可能有點(diǎn)慢，慢慢等著就行。。。。。。。

最后得到的結(jié)果是介個(gè)

參考

計(jì)算數(shù)據(jù)集均值和方差

import os
from PIL import Image  
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread 
filepath = ‘/home/JPEGImages‘ # 數(shù)據(jù)集目錄
pathDir = os.listdir(filepath)
R_channel = 0
G_channel = 0
B_channel = 0
for idx in xrange(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) * 384 * 512 # 這里（384,512）是每幅圖片的大小，所有圖片尺寸都一樣
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 idx in xrange(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 = R_channel / num
G_var = G_channel / num
B_var = 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))

以上就是Python計(jì)算圖片數(shù)據(jù)集的均值方差示例詳解的詳細(xì)內(nèi)容，更多關(guān)于Python計(jì)算圖片數(shù)據(jù)集均值方差的資料請(qǐng)關(guān)注腳本之家其它相關(guān)文章！

您可能感興趣的文章: