How to convert a .png file to TFrecord tensorflow format?

Question

I have images in .png format and their labels in .csv format. I want to convert them in tfrecords format. I'm very new to tensorflow. If someone can point me towards all the things i need to know and how to do this. It'll be great.

I've scoured through the net. But some are outdated or some are very advanced.

Edit: My images are stored in a single directory.

Thanks

Answer 1

You have to convert your image into tf.train.Example in order to write it as tfrecord file. Here is a simple example of how you can do this.

Taking a look at csv file:

Code:

# The following functions can be used to convert a value to a type compatible
# with tf.train.Example.

def _bytes_feature(value):
    """Returns a bytes_list from a string / byte."""
    if isinstance(value, type(tf.constant(0))):
        value = value.numpy() # BytesList won't unpack a string from an EagerTensor.
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def _float_feature(value):
    """Returns a float_list from a float / double."""
    return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))

def _int64_feature(value):
    """Returns an int64_list from a bool / enum / int / uint."""
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))


def image_example(image_string, label):
    image_shape = tf.image.decode_png(image_string).shape
    feature = {
      'height': _int64_feature(image_shape[0]),
      'width': _int64_feature(image_shape[1]),
      'depth': _int64_feature(image_shape[2]),
      'label': _int64_feature(label),
      'image_raw': _bytes_feature(image_string),
    }
    return tf.train.Example(features=tf.train.Features(feature=feature))

The image_example functions return a tf.train.Example object of a single image.

You have to iterate over the data frame to create tf.train.Example object of every image and write the object using tf.io.TFRecordWriter.

Code:

record_file = 'images.tfrecords'
image_labels = {
    'cat': 0,
    'bridge': 1,
}
with tf.io.TFRecordWriter(record_file) as writer:
    for row in df.index:
        full_path = 'data/img/new/' + df['filename'][row]
        label = image_labels[df['label'][row]]
        image_string = tf.io.read_file(full_path)
        tf_example = image_example(image_string, label)
        writer.write(tf_example.SerializeToString())

For a complete tutorial on Reading/Writing TFRecord files see this.

If you have multiple labels you can create multiple features in your feature dictionary inside image_example. Code:

def image_example(image_string, label_color, label_type):
    image_shape = tf.image.decode_png(image_string).shape
    feature = {
      'height': _int64_feature(image_shape[0]),
      'width': _int64_feature(image_shape[1]),
      'depth': _int64_feature(image_shape[2]),
      'label_color': _int64_feature(label_color),
      'label_type': _int64_feature(label_type),
      'image_raw': _bytes_feature(image_string),
    }
    return tf.train.Example(features=tf.train.Features(feature=feature))