Tensorflow 从tfrecord还原映像并保存在硬盘中

我有一系列tfrecord文件，这些文件是从图像文件夹中生成的。现在，我想反转这个过程，从tfrecord文件中提取图像文件，并将它们保存在存储器中。有没有办法做到这一点

以下是开始使用的示例代码

with tf.device('/cpu:0'):
    tf.reset_default_graph()
    # here a path to tfrecords file as list
    fq = tf.train.string_input_producer(tf.convert_to_tensor([/path/to/tfrecordsfiles]), num_epochs=1)
    reader = tf.TFRecordReader()
     _, v = reader.read(fq)
    fk = {
        'image/encoded': tf.FixedLenFeature((), tf.string, default_value='')}
    ex = tf.parse_single_example(v, fk)
    image = tf.image.decode_jpeg(
    ex['image/encoded'], dct_method='INTEGER_ACCURATE')

with tf.Session() as sess:
    coord = tf.train.Coordinator()
    tf.train.start_queue_runners(coord=coord, sess=sess)
    sess.run([tf.global_variables_initializer(),
          tf.local_variables_initializer()])

    # set the number of images in your tfrecords file
    num_images=100 
    for i in range(num_images):
        try:
            im_ = sess.run(image)
            # chnage the image save path here
            cv2.imwrite('/tmp/test' + str(i) + '.jpg', im_)
        except Exception as e:
            print(e)
        break

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这是适合我的代码。它最初取自Ishant Mrinal先前的回答。我只是添加了一些修改：

        #get the number of records in the tfrecord file
        c = 0

        for record in tf.python_io.tf_record_iterator(tfrecords_filename):
            c += 1

        totalFiles+=c

        logfile.write(" {} : {}".format(f, c))
        logfile.flush()
        print("going to restore {} files from {}".format(c,f))

        tf.reset_default_graph()

        # here a path to tfrecords file as list
        fq = tf.train.string_input_producer([tfrecords_filename], num_epochs=fileCount)
        reader = tf.TFRecordReader()
        _, v = reader.read(fq)
        fk = {
            'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
            'image/class/synset': tf.FixedLenFeature([], tf.string, default_value=''),
            'image/filename': tf.FixedLenFeature([], tf.string, default_value='')
            }

        ex = tf.parse_single_example(v, fk)
        image = tf.image.decode_jpeg(ex['image/encoded'], dct_method='INTEGER_ACCURATE')
        label = tf.cast(ex['image/class/synset'], tf.string)
        fileName = tf.cast(ex['image/filename'], tf.string)
        # The op for initializing the variables.
        init_op = tf.group(tf.global_variables_initializer(),
                           tf.local_variables_initializer())

        with tf.Session()  as sess:
            sess.run(init_op)

            coord = tf.train.Coordinator()
            threads = tf.train.start_queue_runners(coord=coord)

            # sess.run([tf.global_variables_initializer(),tf.local_variables_initializer()])

            # set the number of images in your tfrecords file
            num_images=c
            print("going to restore {} files from {}".format(num_images, f))
            for i in range(num_images):

                im_,lbl,fName = sess.run([image,label,fileName])

                lbl_=lbl.decode("utf-8")

                savePath=os.path.join(output_path,lbl_)
                if not os.path.exists(savePath):
                    os.makedirs(savePath)
                fName_=os.path.join(savePath, fName.decode("utf-8").split('_')[1])

                # chnage the image save path here
                cv2.imwrite(fName_ , im_)


            coord.request_stop()
            coord.join(threads)

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谢谢你的回答。它进行了一些修改。