Tensorflow 读取带有标签的图像 [英] Tensorflow read images with labels

问题描述

我正在使用 Tensorflow 构建标准图像分类模型.为此，我有输入图像，每个图像都分配有一个标签({0,1} 中的数字).因此，可以使用以下格式将数据存储在列表中:

I am building a standard image classification model with Tensorflow. For this I have input images, each assigned with a label (number in {0,1}). The Data can hence be stored in a list using the following format:

/path/to/image_0 label_0
/path/to/image_1 label_1
/path/to/image_2 label_2
...

我想使用 TensorFlow 的排队系统来读取我的数据并将其提供给我的模型.忽略标签，可以通过使用 string_input_producer 和 wholeFileReader 轻松实现这一点.代码如下:

I want to use TensorFlow's queuing system to read my data and feed it to my model. Ignoring the labels, one can easily achieve this by using string_input_producer and wholeFileReader. Here the code:

def read_my_file_format(filename_queue):
  reader = tf.WholeFileReader()
  key, value = reader.read(filename_queue)
  example = tf.image.decode_png(value)
  return example

#removing label, obtaining list containing /path/to/image_x
image_list = [line[:-2] for line in image_label_list]

input_queue = tf.train.string_input_producer(image_list)                                                     
input_images = read_my_file_format(input_queue)

然而，标签在该过程中丢失了，因为图像数据作为输入管道的一部分被故意打乱.通过输入队列将标签与图像数据一起推送的最简单方法是什么?

However, the labels are lost in that process as the image data is purposely shuffled as part of the input pipeline. What is the easiest way of pushing the labels together with the image data through the input queues?

推荐答案

使用 slice_input_producer 提供了一个更简洁的解决方案.Slice Input Producer 允许我们创建一个包含任意多个可分离值的输入队列.这个问题的片段看起来像这样:

Using slice_input_producer provides a solution which is much cleaner. Slice Input Producer allows us to create an Input Queue containing arbitrarily many separable values. This snippet of the question would look like this:

def read_labeled_image_list(image_list_file):
    """Reads a .txt file containing pathes and labeles
    Args:
       image_list_file: a .txt file with one /path/to/image per line
       label: optionally, if set label will be pasted after each line
    Returns:
       List with all filenames in file image_list_file
    """
    f = open(image_list_file, 'r')
    filenames = []
    labels = []
    for line in f:
        filename, label = line[:-1].split(' ')
        filenames.append(filename)
        labels.append(int(label))
    return filenames, labels

def read_images_from_disk(input_queue):
    """Consumes a single filename and label as a ' '-delimited string.
    Args:
      filename_and_label_tensor: A scalar string tensor.
    Returns:
      Two tensors: the decoded image, and the string label.
    """
    label = input_queue[1]
    file_contents = tf.read_file(input_queue[0])
    example = tf.image.decode_png(file_contents, channels=3)
    return example, label

# Reads pfathes of images together with their labels
image_list, label_list = read_labeled_image_list(filename)

images = ops.convert_to_tensor(image_list, dtype=dtypes.string)
labels = ops.convert_to_tensor(label_list, dtype=dtypes.int32)

# Makes an input queue
input_queue = tf.train.slice_input_producer([images, labels],
                                            num_epochs=num_epochs,
                                            shuffle=True)

image, label = read_images_from_disk(input_queue)

# Optional Preprocessing or Data Augmentation
# tf.image implements most of the standard image augmentation
image = preprocess_image(image)
label = preprocess_label(label)

# Optional Image and Label Batching
image_batch, label_batch = tf.train.batch([image, label],
                                          batch_size=batch_size)

另请参阅 TensorVision 完整输入管道的示例.

See also the generic_input_producer from the TensorVision examples for full input-pipeline.

这篇关于Tensorflow 读取带有标签的图像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！