Python怎么计算图片数据集的均值方差(python,开发技术)

Python批量reshape图片

#-*-coding:utf-8-*-"""CreatedonThuAug2316:06:352018@author:libo"""fromPILimportImageimportosdefimage_resize(image_path,new_path):#统一图片尺寸print('============>>修改图片尺寸')forimg_nameinos.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#processthe1channelimageimage.save(new_path+'/'+img_name)print("endtheprocessing!")if__name__=='__main__':print("readyfor::::::::")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)

importosfromPILimportImageimportmatplotlib.pyplotaspltimportnumpyasnpfromscipy.miscimportimreadfilepath=r'Z:\pycharm_projects\ssd\VOC2007\reshape'#数据集目录pathDir=os.listdir(filepath)R_channel=0G_channel=0B_channel=0foridxinrange(len(pathDir)):filename=pathDir[idx]img=imread(os.path.join(filepath,filename))/255.0R_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=0foridxinrange(len(pathDir)):filename=pathDir[idx]img=imread(os.path.join(filepath,filename))/255.0R_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_meanis%f,G_meanis%f,B_meanis%f"%(R_mean,G_mean,B_mean))print("R_varis%f,G_varis%f,B_varis%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

importosfromPILimportImageimportmatplotlib.pyplotaspltimportnumpyasnpfromscipy.miscimportimreadfilepath=‘/home/JPEGImages‘#数据集目录pathDir=os.listdir(filepath)R_channel=0G_channel=0B_channel=0foridxinxrange(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

R_channel=0G_channel=0B_channel=0

foridxinxrange(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_meanis%f,G_meanis%f,B_meanis%f"%(R_mean,G_mean,B_mean))print("R_varis%f,G_varis%f,B_varis%f"%(R_var,G_var,B_var))