TensorFlow：有沒有辦法找到編碼到TFRecord文件中的圖像的文件名？

Question

我想知道在創建tfrecord文件時編碼圖像的文件名信息是否可以編碼到TFRecord文件中，如果是這樣，那麼如何解碼這些信息呢？解碼時，文件名是張量對象嗎？

Answer 1

就像fabrizioM說的那樣，如果你想使用它們，你必須將源文件存儲在tfrecords文件中。 Here is an example：

#!/usr/bin/env python 

"""Example for reading and writing tfrecords.""" 

import tensorflow as tf 
from PIL import Image 
import numpy as np 
import scipy.misc 


def _int64_feature(value): 
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) 


def _bytes_feature(value): 
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) 


def write_images(filenames=['Aurelia-aurita-3.jpg'], 
       labels=[0], 
       tf_records_filename="example.tfrecords"): 
    """ 
    Write images to tfrecords file. 

    Parameters 
    ---------- 
    filenames : list of strings 
     List containing the paths to image files. 
    labels : list of integers 
    tf_records_filename : string 
     Where the file gets stored 
    """ 
    filename_queue = tf.train.string_input_producer(filenames) 

    reader = tf.WholeFileReader() 
    key, value = reader.read(filename_queue) 

    my_img = tf.image.decode_jpeg(value) 

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

     # Start populating the filename queue. 
     coord = tf.train.Coordinator() 
     threads = tf.train.start_queue_runners(coord=coord) 

     writer = tf.python_io.TFRecordWriter(tf_records_filename) 
     for i in range(len(filenames)): 
      image = my_img.eval() # image is an image tensor 

      image_raw = image.tostring() 
      rows = image.shape[0] 
      cols = image.shape[1] 

      if np.ndim(image) == 3: 
       depth = image.shape[2] 
      else: 
       depth = 1 

      example = tf.train.Example(features=tf.train.Features(feature={ 
       'height': _int64_feature(rows), 
       'width': _int64_feature(cols), 
       'depth': _int64_feature(depth), 
       'label': _int64_feature(labels[i]), 
       'image_raw': _bytes_feature(image_raw), 
       'src': _bytes_feature(filenames[i])})) 
      writer.write(example.SerializeToString()) 
     coord.request_stop() 
     coord.join(threads) 


def read_and_decode(filename_queue): 
    """Read and decode them from filename_queue.""" 
    reader = tf.TFRecordReader() 
    _, serialized_example = reader.read(filename_queue) 
    features = tf.parse_single_example(
     serialized_example, 
     # Defaults are not specified since both keys are required. 
     features={ 
      'image_raw': tf.FixedLenFeature([], tf.string), 
      'label': tf.FixedLenFeature([], tf.int64), 
      'height': tf.FixedLenFeature([], tf.int64), 
      'width': tf.FixedLenFeature([], tf.int64), 
      'depth': tf.FixedLenFeature([], tf.int64), 
      'src': tf.FixedLenFeature([], tf.string) 
     }) 
    image = tf.decode_raw(features['image_raw'], tf.uint8) 
    label = tf.cast(features['label'], tf.int32) 
    height = tf.cast(features['height'], tf.int32) 
    width = tf.cast(features['width'], tf.int32) 
    depth = tf.cast(features['depth'], tf.int32) 
    # fn = tf.cast(features['filename'], tf.str) 
    return image, label, height, width, depth, features['src'] 


def get_all_records(record_filename): 
    """Get all records from record_filename.""" 
    records = [] 
    with tf.Session() as sess: 
     fn_queue = tf.train.string_input_producer([record_filename]) 
     image, label, height, width, depth, src = read_and_decode(fn_queue) 
     image = tf.reshape(image, tf.stack([height, width, 3])) 
     init_op = tf.global_variables_initializer() 
     sess.run(init_op) 
     coord = tf.train.Coordinator() 
     threads = tf.train.start_queue_runners(coord=coord) 
     nr_of_images = 1 
     for i in range(nr_of_images): 
      example, label, src = sess.run([image, label, src]) 
      img = Image.fromarray(example, 'RGB') 
      records.append({'image': img, 'label': label, 
          'src': src}) 
     coord.request_stop() 
     coord.join(threads) 
    return records 

write_images() 
records = get_all_records('example.tfrecords') 
print(records[0]['src']) 
scipy.misc.imshow(records[0]['image'])