Python計算圖片數據集的均值方差示例詳解
前言
在做圖像處理的時候,有時候需要得到整個數據集的均值方差數值,以下代碼可以解決你的煩惱:
(做這個之前一定保證所有的圖片都是統一尺寸,不然算出來不對,我的代碼裡設計的是512*512,可以自己調整,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)) # 設置需要轉換的圖片大小 # 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之後的文件夾路徑 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' # 數據集目錄 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 # 這裡(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))
可能有點慢,慢慢等著就行。。。。。。。
最後得到的結果是介個
參考
計算數據集均值和方差
import os from PIL import Image import matplotlib.pyplot as plt import numpy as np from scipy.misc import imread filepath = ‘/home/JPEGImages‘ # 數據集目錄 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 # 這裡(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))
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