How do I convert a directory of jpeg images to TFRecords file in tensorflow?

Question

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

Answer 1

I hope this helps:

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) 

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

Github repo of ImageFlow

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Update:

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()) 

More info here

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