Tensorflow image reading & display

Question

I've got a bunch of images in a format similar to Cifar10 (binary file, size = 96*96*3 bytes per image), one image after another (STL-10 dataset). The file I'm opening has 138MB.

I tried to read & check the contents of the Tensors containing the images to be sure that the reading is done right, however I have two questions -

1. Does the FixedLengthRecordReader load the whole file, however just provide inputs one at a time? Since reading the first size bytes should be relatively fast. However, the code takes about two minutes to run.
2. How to get the actual image contents in a displayable format, or display them internally to validate that the images are read well? I did sess.run(uint8image), however the result is empty.

The code is below:

import tensorflow as tf def read_stl10(filename_queue):   class STL10Record(object):     pass   result = STL10Record()    result.height = 96   result.width = 96   result.depth = 3   image_bytes = result.height * result.width * result.depth   record_bytes = image_bytes    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)   result.key, value = reader.read(filename_queue)   print value   record_bytes = tf.decode_raw(value, tf.uint8)    depth_major = tf.reshape(tf.slice(record_bytes, [0], [image_bytes]),                        [result.depth, result.height, result.width])   result.uint8image = tf.transpose(depth_major, [1, 2, 0])   return result # probably a hack since I should've provided a string tensor  filename_queue = tf.train.string_input_producer(['./data/train_X']) image = read_stl10(filename_queue)  print image.uint8image with tf.Session() as sess:   result = sess.run(image.uint8image)   print result, type(result) 

Output:

Tensor("ReaderRead:1", shape=TensorShape([]), dtype=string) Tensor("transpose:0", shape=TensorShape([Dimension(96), Dimension(96), Dimension(3)]), dtype=uint8) I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op parallelism threads: 4 I tensorflow/core/common_runtime/local_session.cc:45] Local session inter op parallelism threads: 4 [empty line for last print] Process finished with exit code 137 

I'm running this on my CPU, if that adds anything.

EDIT: I found the pure TensorFlow solution thanks to Rosa. Apparently, when using the string_input_producer, in order to see the results, you need to initialize the queue runners. The only required thing to add to the code above is the second line from below:

... with tf.Session() as sess:     tf.train.start_queue_runners(sess=sess) ... 

Afterwards, the image in the result can be displayed with matplotlib.pyplot.imshow(result). I hope this helps someone. If you have any further questions, feel free to ask me or check the link in Rosa's answer.

Answer 1

Just to give a complete answer:

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 png or jpg decoder based on your files.  init_op = tf.global_variables_initializer() 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.fromarray(np.asarray(image)).show()    coord.request_stop()   coord.join(threads) 

Or if you have a directory of images you can add them all via this Github source file

@mttk and @salvador-dali: I hope it is what you need

Answer 2

According to the documentation you can decode JPEG/PNG images.

It should be something like this:

import tensorflow as tf  filenames = ['/image_dir/img.jpg'] filename_queue = tf.train.string_input_producer(filenames)  reader = tf.WholeFileReader() key, value = reader.read(filename_queue)  images = tf.image.decode_jpeg(value, channels=3) 

You can find a bit more info here