将原始图片转换成TFRecord文件

# -*-coding = utf-8 -*-from __future__ import absolute_import,division,print_functionimport numpy as npimport tensorflow as tfimport timefrom os import walkfrom os.path import joinfrom scipy.misc import imread,imresizeDATA_DIR='data/'IMG_hight=227IMG_width=227IMG_channels=3NUM_train=7000NUM_validation=1144def read_images(path):    filenames=next(walk(path))[2]  #遍历目录    num_files=len(filenames)    images=np.zeros((num_files,IMG_hight,IMG_width,IMG_channels),dtype=np.uint8)  #遍历所有的图片，将图片热死则到[227,227,3]    labels=np.zeros((num_files,),dtype=np.uint8)    f=open('label.txt')    lines=f.readlines()    for i,filename in enumerate(filenames):        img=imread(join(path,filename))        img=imresize(img,(IMG_hight,IMG_width))        images[i]=img        labels[i]=int(lines[i])    f.close()    return images,labelsdef _int64_feature(value):  #生成整数型的属性    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))def _bytes_feature(value):  #生成字符串型的属性    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))def convert(images,labels,name):    num=images.shape[0]        #获取转换成TFRecord的数目    filename=name+'.tfrecords'    print('Writting',filename)    writer =tf.python_io.TFRecordWriter(filename)    for i in range(num):        img_raw=images[i].tostring()  #将图像矩阵转化为一个字符串        example=tf.train.Example(features=tf.train.Feature(features={     #将一个样例转化为Example Protocol Buffer，并将所有需要的信息写入数据结构            'label':_int64_feature(int(labels[i])),            'image_raw=':_bytes_feature(img_raw)        }))        writer.write(example.SerializeToString())        writer.close()        print('Writting end')def main(argv):    print('reading images begin')    start_time=time.time()    train_images,train_labels=read_images(DATA_DIR)    duration=time.time()-start_time    print("reading images end , cost %d sec"%duration)    validation_images=train_images[:NUM_validation,:,:,:]    validation_labels=train_labels[:NUM_validation]    train_images=train_images[NUM_validation:,:,:,:]    train_labels=train_labels[NUM_validation:]    print('convert to tfrecords begin')    start_time=time.time()    convert(train_images,train_labels,'train')    convert((validation_images,validation_labels,'validation'))    duration=time.time()-start_time    print('convert to tfrecords end, cost %d sec'%duration)if __name__=="__main__":    tf.app.run()

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