Tensorflow read images with labels

Solution 1:

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)

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

Solution 2:

There are three main steps to solving this problem:

1. Populate the tf.train.string_input_producer() with a list of strings containing the original, space-delimited string containing the filename and the label.

2. Use tf.read_file(filename) rather than tf.WholeFileReader() to read your image files. tf.read_file() is a stateless op that consumes a single filename and produces a single string containing the contents of the file. It has the advantage that it's a pure function, so it's easy to associate data with the input and the output. For example, your read_my_file_format function would become:

   def read_my_file_format(filename_and_label_tensor):
     """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.
     """
     filename, label = tf.decode_csv(filename_and_label_tensor, [[""], [""]], " ")
     file_contents = tf.read_file(filename)
     example = tf.image.decode_png(file_contents)
     return example, label
   

3. Invoke the new version of read_my_file_format by passing a single dequeued element from the input_queue:

   image, label = read_my_file_format(input_queue.dequeue())         
   

You can then use the image and label tensors in the remainder of your model.

Solution 3:

In addition to the answers provided there are few other things you can do:

Encode your label into the filename. If you have N different categories you can rename your files to something like: 0_file001, 5_file002, N_file003. Afterwards when you read the data from a reader key, value = reader.read(filename_queue) your key/value are:

The output of Read will be a filename (key) and the contents of that file (value)

Then parse your filename, extract the label and convert it to int. This will require a little bit of preprocessing of the data.

Use TFRecords which will allow you to store the data and labels at the same file.