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#! /usr/bin/python

# -*- coding: utf-8 -*-

"""You will learn.

1. How to save data into TFRecord format file.

2. How to read data from TFRecord format file by using Queue and Thread.

Reference:

-----------

English : https://indico.io/blog/tensorflow-data-inputs-part1-placeholders-protobufs-queues/

https://www.tensorflow.org/versions/master/how_tos/reading_data/index.html

https://www.tensorflow.org/versions/master/api_docs/python/io_ops.html#readers

Chinese : http://blog.csdn.net/u012759136/article/details/52232266

https://github.com/ycszen/tf_lab/blob/master/reading_data/TensorFlow高效加载数据的方法.md

More

------

1. tutorial_tfrecord2.py

2. tutorial_cifar10_tfrecord.py

"""

import os

import numpy as np

import tensorflow as tf

from PIL import Image

import tensorlayer as tl

## Save data ==================================================================

classes = ['/data/cat', '/data/dog'] # cat is 0, dog is 1

cwd = os.getcwd()

writer = tf.python_io.TFRecordWriter("train.tfrecords")

for index, name in enumerate(classes):

class_path = cwd + name + "/"

for img_name in os.listdir(class_path):

img_path = class_path + img_name

img = Image.open(img_path)

img = img.resize((224, 224))

## Visualize the image as follow:

# tl.visualize.frame(I=img, second=5, saveable=False, name='frame', fig_idx=12836)

## Converts a image to bytes

img_raw = img.tobytes()

## Convert the bytes back to image as follow:

# image = Image.frombytes('RGB', (224,224), img_raw)

# tl.visualize.frame(I=image, second=1, saveable=False, name='frame', fig_idx=1236)

## Write the data into TF format

# image : Feature + BytesList

# label : Feature + Int64List or FloatList

# sentence : FeatureList + Int64List , see Google's im2txt example

example = tf.train.Example(features=tf.train.Features(feature={ # SequenceExample for seuqnce example

"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),

'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),

}))

writer.write(example.SerializeToString()) # Serialize To String

writer.close()

## Load Data Method 1: Simple read ============================================

# read data one by one in order

for serialized_example in tf.python_io.tf_record_iterator("train.tfrecords"):

example = tf.train.Example() # SequenceExample for seuqnce example

example.ParseFromString(serialized_example)

img_raw = example.features.feature['img_raw'].bytes_list.value

label = example.features.feature['label'].int64_list.value

## converts a image from bytes

image = Image.frombytes('RGB', (224, 224), img_raw[0])

tl.visualize.frame(np.asarray(image), second=0.5, saveable=False, name='frame', fig_idx=1283)

print(label)

## Read Data Method 2: Queue and Thread =======================================

# use sess.run to get a batch of data

def read_and_decode(filename):

# generate a queue with a given file name

filename_queue = tf.train.string_input_producer([filename])

reader = tf.TFRecordReader()

_, serialized_example = reader.read(filename_queue) # return the file and the name of file

features = tf.parse_single_example(

serialized_example, # see parse_single_sequence_example for sequence example

features={

'label': tf.FixedLenFeature([], tf.int64),

'img_raw': tf.FixedLenFeature([], tf.string),

}

)

# You can do more image distortion here for training data

img = tf.decode_raw(features['img_raw'], tf.uint8)

img = tf.reshape(img, [224, 224, 3])

# img = tf.cast(img, tf.float32) * (1. / 255) - 0.5

label = tf.cast(features['label'], tf.int32)

return img, label

img, label = read_and_decode("train.tfrecords")

## Use shuffle_batch or batch

# see https://www.tensorflow.org/versions/master/api_docs/python/io_ops.html#shuffle_batch

img_batch, label_batch = tf.train.shuffle_batch(

[img, label], batch_size=4, capacity=2000, min_after_dequeue=1000, num_threads=16

)

print("img_batch : %s" % img_batch._shape)

print("label_batch : %s" % label_batch._shape)

# init = tf.initialize_all_variables()

with tf.Session() as sess:

# sess.run(init)

tl.layers.initialize_global_variables(sess)

coord = tf.train.Coordinator()

threads = tf.train.start_queue_runners(sess=sess, coord=coord)

for i in range(3): # number of mini-batch (step)

print("Step %d" % i)

val, l = sess.run([img_batch, label_batch])

print(val.shape, l)

tl.visualize.images2d(val, second=1, saveable=False, name='batch', dtype=None, fig_idx=2020121)

coord.request_stop()

coord.join(threads)

sess.close()

#