data.py

""" Rude Carnie: Age and Gender Deep Learning with Tensorflow found at
https://github.com/dpressel/rude-carnie
"""
# ==============================================================================
# MIT License
#
# Modifications copyright (c) 2018 Image & Vision Computing Lab, Institute of Information Science, Academia Sinica
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# ==============================================================================
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.ops import array_ops
from datetime import datetime
import os
import numpy as np
import tensorflow as tf

from distutils.version import LooseVersion
from pdb import set_trace as bp

VERSION_GTE_0_12_0 = LooseVersion(tf.__version__) >= LooseVersion('0.12.0')

# Name change in TF v 0.12.0
if VERSION_GTE_0_12_0:
	standardize_image = tf.image.per_image_standardization
else:
	standardize_image = tf.image.per_image_whitening

def data_files(data_dir, subset):
	"""Returns a python list of all (sharded) data subset files.
	Returns:
		python list of all (sharded) data set files.
	Raises:
		ValueError: if there are not data_files matching the subset.
	"""
	if subset not in ['train','validation','test']:
		print('Invalid subset!')
		exit(-1)

	tf_record_pattern = os.path.join(data_dir, '%s-*' % subset)
	data_files = tf.gfile.Glob(tf_record_pattern)
	print(data_files)
	if not data_files:
		print('No files found for data dir %s at %s' % (subset, data_dir))
		exit(-1)

	return data_files

def decode_jpeg(image_buffer, scope=None):
	"""Decode a JPEG string into one 3-D float image Tensor.
	Args:
	image_buffer: scalar string Tensor.
	scope: Optional scope for op_scope.
	Returns:
	3-D float Tensor with values ranging from [0, 1).
	"""

	with tf.op_scope([image_buffer], scope, 'decode_jpeg'):
		# Decode the string as an RGB JPEG.
		# Note that the resulting image contains an unknown height and width
		# that is set dynamically by decode_jpeg. In other words, the height
		# and width of image is unknown at compile-time.
		image = tf.image.decode_jpeg(image_buffer, channels=3)

		# After this point, all image pixels reside in [0,1)
		# until the very end, when they're rescaled to (-1, 1).  The various
		# adjust_* ops all require this range for dtype float.
		image = tf.image.convert_image_dtype(image, dtype=tf.float32)

		return image

def distort_image(image, height, width):
	# Image processing for training the network. Note the many random
	# distortions applied to the image.
	distorted_image = tf.random_crop(image, [height, width, 3])
	# Randomly flip the image horizontally.
	distorted_image = tf.image.random_flip_left_right(distorted_image)
	# Because these operations are not commutative, consider randomizing
	# the order their operation.
	distorted_image = tf.image.random_brightness(distorted_image,max_delta=63)
	distorted_image = tf.image.random_contrast(distorted_image,lower=0.2, upper=1.8)

	return distorted_image

def _is_tensor(x):
	return isinstance(x, (tf.Tensor, tf.Variable))

def eval_image(image, height, width):
	return tf.image.resize_images(image, [height, width])

def data_normalization(image):

	image = standardize_image(image)

	return image

def image_preprocessing(image_buffer, image_size, train, thread_id=0):
	"""Decode and preprocess one image for evaluation or training.
	Args:
	image_buffer: JPEG encoded string Tensor
	train: boolean
	thread_id: integer indicating preprocessing thread
	Returns:
	3-D float Tensor containing an appropriately scaled image
	Raises:
	ValueError: if user does not provide bounding box
	"""

	image = decode_jpeg(image_buffer)

	if train:
		image = distort_image(image, image_size, image_size)
	else:
		image = eval_image(image, image_size, image_size)

	image = data_normalization(image)
	return image

def multiparse_example_proto(example_serialized):
	# Dense features in Example proto.
	feature_map = {
		'image/encoded': tf.FixedLenFeature([], dtype=tf.string,default_value=''),
		'image/filename': tf.FixedLenFeature([], dtype=tf.string,default_value=''),
		'image/ageclass/label': tf.FixedLenFeature([1], dtype=tf.int64,default_value=-1),
		'image/genderclass/label': tf.FixedLenFeature([1], dtype=tf.int64,default_value=-1),
		'image/class/text': tf.FixedLenFeature([], dtype=tf.string,default_value=''),
		'image/height': tf.FixedLenFeature([1], dtype=tf.int64,default_value=-1),
		'image/width': tf.FixedLenFeature([1], dtype=tf.int64,default_value=-1),
	}

	features = tf.parse_single_example(example_serialized, feature_map)
	agelabel = tf.cast(features['image/ageclass/label'], dtype=tf.int32)
	genderlabel = tf.cast(features['image/genderclass/label'], dtype=tf.int32)

	return features['image/encoded'], agelabel, genderlabel, features['image/filename']

def multibatch_inputs(data_dir, batch_size, image_size, train, datatype, num_preprocess_threads=4,
	num_readers=1, input_queue_memory_factor=16):

	with tf.name_scope('batch_processing'):
		if train:
			files = data_files(data_dir, datatype)
			filename_queue = tf.train.string_input_producer(files,shuffle=True,capacity=16)
		else:
			files = data_files(data_dir, datatype)
			filename_queue = tf.train.string_input_producer(files,shuffle=False,capacity=1)

		if num_preprocess_threads % 4:
			raise ValueError('Please make num_preprocess_threads a multiple '
				'of 4 (%d % 4 != 0).', num_preprocess_threads)

		if num_readers < 1:
			raise ValueError('Please make num_readers at least 1')

		# Approximate number of examples per shard.
		examples_per_shard = 1024
		# Size the random shuffle queue to balance between good global
		# mixing (more examples) and memory use (fewer examples).
		# 1 image uses 299*299*3*4 bytes = 1MB
		# The default input_queue_memory_factor is 16 implying a shuffling queue
		# size: examples_per_shard * 16 * 1MB = 17.6GB
		min_queue_examples = examples_per_shard * input_queue_memory_factor
		if train:
			examples_queue = tf.RandomShuffleQueue(
				capacity=min_queue_examples + 3 * batch_size,
				min_after_dequeue=min_queue_examples,
				dtypes=[tf.string])
		else:
			examples_queue = tf.FIFOQueue(
				capacity=examples_per_shard + 3 * batch_size,
				dtypes=[tf.string])

		# Create multiple readers to populate the queue of examples.
		if num_readers > 1:
			enqueue_ops = []
			for _ in range(num_readers):
				reader = tf.TFRecordReader()
				_, value = reader.read(filename_queue)
				enqueue_ops.append(examples_queue.enqueue([value]))

			tf.train.queue_runner.add_queue_runner(
				tf.train.queue_runner.QueueRunner(examples_queue, enqueue_ops))
			example_serialized = examples_queue.dequeue()
		else:
			reader = tf.TFRecordReader()
			_, example_serialized = reader.read(filename_queue)

		images_agegenderlabels_fnames = []
		for thread_id in range(num_preprocess_threads):
			# Parse a serialized Example proto to extract the image and metadata.
			image_buffer, agelabel_index, genderlabel_index, fname = multiparse_example_proto(example_serialized)
			image = image_preprocessing(image_buffer, image_size, train, thread_id)
			images_agegenderlabels_fnames.append([image, agelabel_index, genderlabel_index, fname])

		images, agelabel_index_batch, genderlabel_index_batch, fnames = tf.train.batch_join(
			images_agegenderlabels_fnames,batch_size=batch_size,capacity=2*num_preprocess_threads*batch_size)

		images = tf.cast(images, tf.float32)
		images = tf.reshape(images, shape=[batch_size, image_size, image_size, 3])

		# Display the training images in the visualizer.
		tf.summary.image('images', images, 20)

		return images, tf.reshape(agelabel_index_batch, [batch_size]), tf.reshape(genderlabel_index_batch, [batch_size]) ,fnames


def multiinputs(data_dir, batch_size=128, image_size=227, train=True, num_preprocess_threads=4, datatype=''):
	
	with tf.device('/cpu:0'):
		images, agelabels, genderlabels, filenames = multibatch_inputs(
			data_dir, batch_size, image_size, train,
			datatype, num_preprocess_threads=num_preprocess_threads,
			num_readers=1)
	return images, agelabels, genderlabels, filenames

def batch_inputs_mod(data_dir, batch_size, image_size, train, datatype, classtype, 
	num_preprocess_threads=4,num_readers=1, input_queue_memory_factor=16):

	with tf.name_scope('batch_processing'):

		if train:
			files = data_files(data_dir, datatype)
			filename_queue = tf.train.string_input_producer(files, shuffle=True, capacity=16)
		else:
			files = data_files(data_dir, datatype)
			filename_queue = tf.train.string_input_producer(files, shuffle=False, capacity=1)

		if num_preprocess_threads % 4:
			raise ValueError('Please make num_preprocess_threads a multiple '
				'of 4 (%d % 4 != 0).', num_preprocess_threads)

		if num_readers < 1:
			raise ValueError('Please make num_readers at least 1')

		examples_per_shard = 1024

		min_queue_examples = examples_per_shard*input_queue_memory_factor
		if train:
			examples_queue = tf.RandomShuffleQueue(
				capacity=min_queue_examples + 3 * batch_size,
				min_after_dequeue=min_queue_examples,
				dtypes=[tf.string])
		else:
			examples_queue = tf.FIFOQueue(
				capacity=examples_per_shard + 3 * batch_size,
				dtypes=[tf.string])

		if num_readers > 1:
			enqueue_ops = []
			for _ in range(num_readers):
				reader = tf.TFRecordReader()
				_, value = reader.read(filename_queue)
				enqueue_ops.append(examples_queue.enqueue([value]))

			tf.train.queue_runner.add_queue_runner(
				tf.train.queue_runner.QueueRunner(examples_queue, enqueue_ops))
			example_serialized = examples_queue.dequeue()
		else:
			reader = tf.TFRecordReader()
			_, example_serialized = reader.read(filename_queue)

		if classtype == 'Age':
			images_agelabels_fnames = []
			for thread_id in range(num_preprocess_threads):
				image_buffer, agelabel_index, genderlabel_index, fname = multiparse_example_proto(example_serialized)
				image = image_preprocessing(image_buffer, image_size, train, thread_id)
				images_agelabels_fnames.append([image, agelabel_index, fname])

			images, agelabel_index_batch, fnames = tf.train.batch_join(
				images_agelabels_fnames,batch_size=batch_size,capacity=2 * num_preprocess_threads * batch_size)

			images = tf.cast(images, tf.float32)
			images = tf.reshape(images, shape=[batch_size, image_size, image_size, 3])

			# Display the training images in the visualizer.
			tf.summary.image('images', images, 20)

			return images, tf.reshape(agelabel_index_batch, [batch_size]),fnames

		elif classtype == 'Gender':
			images_genderlabels_fnames = []
			for thread_id in range(num_preprocess_threads):
				image_buffer, agelabel_index, genderlabel_index, fname = multiparse_example_proto(example_serialized)
				image = image_preprocessing(image_buffer, image_size, train, thread_id)
				images_genderlabels_fnames.append([image, genderlabel_index, fname])

			images, genderlabel_index_batch, fnames = tf.train.batch_join(
				images_genderlabels_fnames,
				batch_size=batch_size,
				capacity=2 * num_preprocess_threads * batch_size)

			images = tf.cast(images, tf.float32)
			images = tf.reshape(images, shape=[batch_size, image_size, image_size, 3])

			# Display the training images in the visualizer.
			tf.summary.image('images', images, 20)

			return images, tf.reshape(genderlabel_index_batch, [batch_size]),fnames		

def inputs_mod(data_dir, batch_size=128, image_size=227, train=True, num_preprocess_threads=4, classtype='', datatype=''):

	with tf.device('/cpu:0'):
		images, labels, filenames = batch_inputs_mod(
			data_dir, batch_size, image_size, train,
			datatype ,classtype=classtype, num_preprocess_threads=num_preprocess_threads,
			num_readers=1)
	return images, labels, filenames