如何在 tensorflow 中将 jpeg 图像目录转换为 TFRecords 文件? [英] How do I convert a directory of jpeg images to TFRecords file in tensorflow?

问题描述

我有训练数据，它是一个 jpeg 图像目录和一个包含文件名和相关类别标签的相应文本文件.我正在尝试将此训练数据转换为 tfrecords 文件，如 tensorflow 文档中所述.我花了相当多的时间试图让它发挥作用，但 tensorflow 中没有示例演示如何使用任何读取器读取 jpeg 文件并使用 tfrecordwriter 将它们添加到 tfrecord

I have training data that is a directory of jpeg images and a corresponding text file containing the file name and the associated category label. I am trying to convert this training data into a tfrecords file as described in the tensorflow documentation. I have spent quite some time trying to get this to work but there are no examples in tensorflow that demonstrate how to use any of the readers to read in jpeg files and add them to a tfrecord using tfrecordwriter

推荐答案

我希望这会有所帮助:

filename_queue = tf.train.string_input_producer(['/Users/HANEL/Desktop/tf.png']) #  list of files to read

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

my_img = tf.image.decode_png(value) # use decode_png or decode_jpeg decoder based on your files.

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)

for i in range(1): #length of your filename list
  image = my_img.eval() #here is your image Tensor :) 

print(image.shape)
Image.show(Image.fromarray(np.asarray(image)))

coord.request_stop()
coord.join(threads)

要将所有图像作为张量数组获取，请使用以下代码示例.

For getting all images as an array of tensors use the following code example.

ImageFlow 的 Github 存储库

更新:

在上一个答案中，我只是告诉了如何以 TF 格式读取图像，但没有将其保存在 TFRecords 中.为此，您应该使用:

In the previous answer I just told how to read an image in TF format, but not saving it in TFRecords. For that you should use:

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]))

# images and labels array as input
def convert_to(images, labels, name):
  num_examples = labels.shape[0]
  if images.shape[0] != num_examples:
    raise ValueError("Images size %d does not match label size %d." %
                     (images.shape[0], num_examples))
  rows = images.shape[1]
  cols = images.shape[2]
  depth = images.shape[3]

  filename = os.path.join(FLAGS.directory, name + '.tfrecords')
  print('Writing', filename)
  writer = tf.python_io.TFRecordWriter(filename)
  for index in range(num_examples):
    image_raw = images[index].tostring()
    example = tf.train.Example(features=tf.train.Features(feature={
        'height': _int64_feature(rows),
        'width': _int64_feature(cols),
        'depth': _int64_feature(depth),
        'label': _int64_feature(int(labels[index])),
        'image_raw': _bytes_feature(image_raw)}))
    writer.write(example.SerializeToString())

更多信息这里

然后你像这样读取数据:

And you read the data like this:

# Remember to generate a file name queue of you 'train.TFRecord' file path
def read_and_decode(filename_queue):
  reader = tf.TFRecordReader()
  _, serialized_example = reader.read(filename_queue)
  features = tf.parse_single_example(
    serialized_example,
    dense_keys=['image_raw', 'label'],
    # Defaults are not specified since both keys are required.
    dense_types=[tf.string, tf.int64])

  # Convert from a scalar string tensor (whose single string has
  image = tf.decode_raw(features['image_raw'], tf.uint8)

  image = tf.reshape(image, [my_cifar.n_input])
  image.set_shape([my_cifar.n_input])

  # OPTIONAL: Could reshape into a 28x28 image and apply distortions
  # here.  Since we are not applying any distortions in this
  # example, and the next step expects the image to be flattened
  # into a vector, we don't bother.

  # Convert from [0, 255] -> [-0.5, 0.5] floats.
  image = tf.cast(image, tf.float32)
  image = tf.cast(image, tf.float32) * (1. / 255) - 0.5

  # Convert label from a scalar uint8 tensor to an int32 scalar.
  label = tf.cast(features['label'], tf.int32)

  return image, label

这篇关于如何在 tensorflow 中将 jpeg 图像目录转换为 TFRecords 文件?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！