eerwitt/load_jpeg_with_tensorflow.py

## load_jpeg_with_tensorflow.py
# Typical setup to include TensorFlow.
import tensorflow as tf

# Make a queue of file names including all the JPEG images files in the relative
# image directory.
filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("./images/*.jpg"))

# Read an entire image file which is required since they're JPEGs, if the images
# are too large they could be split in advance to smaller files or use the Fixed
# reader to split up the file.
image_reader = tf.WholeFileReader()

# Read a whole file from the queue, the first returned value in the tuple is the
# filename which we are ignoring.
_, image_file = image_reader.read(filename_queue)

# Decode the image as a JPEG file, this will turn it into a Tensor which we can
# then use in training.
image = tf.image.decode_jpeg(image_file)

# Start a new session to show example output.
with tf.Session() as sess:
    # Required to get the filename matching to run.
    tf.initialize_all_variables().run()

    # Coordinate the loading of image files.
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    # Get an image tensor and print its value.
    image_tensor = sess.run([image])
    print(image_tensor)

    # Finish off the filename queue coordinator.
    coord.request_stop()
    coord.join(threads)
	# Typical setup to include TensorFlow.
	import tensorflow as tf

	# Make a queue of file names including all the JPEG images files in the relative
	# image directory.
	filename_queue = tf.train.string_input_producer(
	tf.train.match_filenames_once("./images/*.jpg"))

	# Read an entire image file which is required since they're JPEGs, if the images
	# are too large they could be split in advance to smaller files or use the Fixed
	# reader to split up the file.
	image_reader = tf.WholeFileReader()

	# Read a whole file from the queue, the first returned value in the tuple is the
	# filename which we are ignoring.
	_, image_file = image_reader.read(filename_queue)

	# Decode the image as a JPEG file, this will turn it into a Tensor which we can
	# then use in training.
	image = tf.image.decode_jpeg(image_file)

	# Start a new session to show example output.
	with tf.Session() as sess:
	# Required to get the filename matching to run.
	tf.initialize_all_variables().run()

	# Coordinate the loading of image files.
	coord = tf.train.Coordinator()
	threads = tf.train.start_queue_runners(coord=coord)

	# Get an image tensor and print its value.
	image_tensor = sess.run([image])
	print(image_tensor)

	# Finish off the filename queue coordinator.
	coord.request_stop()
	coord.join(threads)