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import os

from PIL import Image, ImageFile

import tensorflow as tf

import numpy as np





ImageFile.LOAD_TRUNCATED_IMAGES = True



CWD = '/home/qinlong/PycharmProjects/ProgNEU/NEU/cai_nl/classes'



def convert_to_tfrecord(cwd, output):

    classes = os.listdir(cwd)

    writer = tf.python_io.TFRecordWriter(output)

    for index, name in enumerate(classes):

        class_path = cwd + '/' + name + '/'

        for img_name in os.listdir(class_path):

            img_path = class_path + img_name 



            img = Image.open(img_path)

            img = img.resize((64, 64))

            img_raw = img.tobytes()     

            example = tf.train.Example(features=tf.train.Features(feature={

                'label': tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),

                'img_raw': tf.train.Feature(bytes_list = tf.train.BytesList(value=[img_raw]))

            }))    

            writer.write(example.SerializeToString())   

    writer.close()

    return output





def dataset_input_fn(batch_size, epoch, buffer_size=2048):

    filenames = ['/home/qinlong/PycharmProjects/ProgNEU/NEU/cai_nl/a_4.tfrecord']

    dataset = tf.data.TFRecordDataset(filenames)



    def parser(record):

        keys_to_features = {

            "image_data": tf.FixedLenFeature((), tf.string, default_value=""),

            "date_time": tf.FixedLenFeature((), tf.int64, default_value=0),

            "label": tf.FixedLenFeature((), tf.int64,

                                        default_value=tf.zeros(, dtype=tf.int64)),

        }

        parsed = tf.parse_single_example(record, keys_to_features)



        # Perform additional preprocessing on the parsed data.

        image = tf.image.decode_jpeg(parsed["image_data"])

        image = tf.reshape(image, [64, 64, 3])

        label = tf.cast(parsed["label"], tf.int32)



        return {"image_data": image, "date_time": parsed["date_time"]}, label



    dataset = dataset.map(parser)

    dataset = dataset.shuffle(buffer_size=10000)

    dataset = dataset.batch(batch_size=batch_size)

    dataset = dataset.repeat(epoch)



    iterator = dataset.make_one_shot_iterator()



    features, labels = iterator.get_next()

    return features, labels

InvalidArgumentError: Expected image (JPEG, PNG, or GIF), got empty
file [[{{node DecodeJpeg}} = DecodeJpegacceptable_fraction=1,
channels=0, dct_method="", fancy_upscaling=true, ratio=1,
try_recover_truncated=false]]
[[node IteratorGetNext (defined at
/home/qinlong/PycharmProjects/ProgNEU/NEU/cai_nl/main.py:23) =
IteratorGetNextoutput_shapes=[[?], [?,64,64,3], [?]],
output_types=[DT_INT64, DT_UINT8, DT_INT32],
_device="/job:localhost/replica:0/task:0/device:CPU:0"]]