前言
在做图像处理的时候，有时候需要得到整个数据集的均值方差数值，以下代码可以解决你的烦恼：
（做这个之前一定保证所有的图片都是统一尺寸，不然算出来不对，我的代码里设计的是512*512，可以自己调整，同一尺寸的代码我也有：
Python批量reshape图片
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# -*- coding: utf-8 -*-"""Created on Thu Aug 23 16:06:35 2018@author: libo"""from PIL import Imageimport osdef image_resize(image_path, new_path): # 统一图片尺寸 print('============>>修改图片尺寸') for img_name in os.listdir(image_path): img_path = image_path + "/" + img_name # 获取该图片全称 image = Image.open(img_path) # 打开特定一张图片 image = image.resize((512, 512)) # 设置需要转换的图片大小 # 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" # 输入图片的文件夹路径 new_path = 'Z:/pycharm_projects/ssd/VOC2007/reshape' # resize之后的文件夹路径 image_resize(ori_path, new_path) |
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import osfrom PIL import Imageimport matplotlib.pyplot as pltimport numpy as npfrom scipy.misc import imreadfilepath = r'Z:\pycharm_projects\ssd\VOC2007\reshape' # 数据集目录pathDir = os.listdir(filepath)R_channel = 0G_channel = 0B_channel = 0for 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 # 这里（512,512）是每幅图片的大小，所有图片尺寸都一样R_mean = R_channel / numG_mean = G_channel / numB_mean = B_channel / numR_channel = 0G_channel = 0B_channel = 0for 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)) |
可能有点慢，慢慢等着就行。。。。。。。
最后得到的结果是介个
参考
计算数据集均值和方差
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import osfrom PIL import Image import matplotlib.pyplot as pltimport numpy as npfrom scipy.misc import imread filepath = ‘/home/JPEGImages‘ # 数据集目录pathDir = os.listdir(filepath)R_channel = 0G_channel = 0B_channel = 0for 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 # 这里（384,512）是每幅图片的大小，所有图片尺寸都一样R_mean = R_channel / numG_mean = G_channel / numB_mean = B_channel / num |
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R_channel = 0G_channel = 0B_channel = 0 |
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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 / numG_var = G_channel / numB_var = B_channel / numprint("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计算图片数据集的均值方差示例详解的详细内容，更多关于Python计算图片数据集均值方差的资料请关注服务器之家其它相关文章！
原文链接：https://blog.csdn.net/weixin_41765699/article/details/100118660
