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# eg9-train-val-txt.py

#_*_ coding:utf8 _*_

##################################################################### 定义一个类，这个类将用于批量处理图像，包括若干功能：## (1)统一图像的格式为jpg。## (2)将多个图片文件夹构造成一个用于图像分类的数据集，产生txt文件，每一行的格式为：图片路径 标签## (3)txt文件按照7:3的比例分为训练集和测试集，并随机打乱顺序###################################################################

importosimportsysimportshutilimportcv2importrandom#类的定义

classGeneDataset():def __init__(self, rootdir):

self.rootdir=rootdir

self.subdirs=[]

self.subdirimages=[]

self.numclasses=0

self.lines=[]deflooksubdir(self):

list_dirs=os.walk(self.rootdir)for root, dirs, files inlist_dirs:for d indirs:

self.subdirs.append(os.path.join(root,d))print( "subdir=",os.path.join(root,d) ) #print "subdir=",os.path.join(root,d)

self.numclasses = self.numclasses + 1

defreformat(self):

label=0for subdir inself.subdirs:

list_dirs=os.walk(subdir)for root, dirs, files inlist_dirs:for f infiles:

srcname=os.path.join(root,f)print( "srcname",srcname ) #print "srcname",srcname

srcformat = srcname.split('.')[-1]if srcformat is not ".jpg":

img=cv2.imread(srcname)

newname= srcname.replace(srcformat,'jpg')print( "newname=",newname ) #print "newname=",newname

cv2.imwrite(newname,img)

self.lines.append(newname+' '+str(label)+'\n')

os.remove(srcname)

label= label + 1

defsplit_train_val(self,trainfile,testfile):iflen(self.lines):

random.shuffle(self.lines)

ftrainfile= open(trainfile,'w')

ftestfile= open(testfile,'w')

trainlength= int(0.7*len(self.lines))for i inrange(0,trainlength):

ftrainfile.write(self.lines[i])for i inrange(trainlength,len(self.lines)):

ftestfile.write(self.lines[i])#类的创建和使用#myclassdataset = GeneDataset(sys.argv[1])

myclassdataset = GeneDataset("/home/u/wp_data/learn-sh/新图像/")

myclassdataset.looksubdir()

myclassdataset.reformat()

myclassdataset.split_train_val('train.txt','test.txt')