notMNIST_input.py

#By @Kevin Xu
#kevin28520@gmail.com
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#The aim of this project is to use TensorFlow to transform our own data into TFRecord format.


# I used Windows with Python 3.5, TensorFlow 1.0*, other OS should also be good.
# I used the Spyder IDE.


# data: notMNIST
# http://yaroslavvb.blogspot.ca/2011/09/notmnist-dataset.html
# http://yaroslavvb.com/upload/notMNIST/




#%%

import tensorflow as tf
import numpy as np
import os
import matplotlib.pyplot as plt
import skimage.io as io


#%%

def get_file(file_dir):
    '''Get full image directory and corresponding labels
    Args:
        file_dir: file directory
    Returns:
        images: image directories, list, string
        labels: label, list, int
    '''

    images = []
    temp = []
    for root, sub_folders, files in os.walk(file_dir):
        # image directories
        for name in files:
            images.append(os.path.join(root, name))
        # get 10 sub-folder names
        for name in sub_folders:
            temp.append(os.path.join(root, name))
            
    # assign 10 labels based on the folder names
    labels = []        
    for one_folder in temp:        
        n_img = len(os.listdir(one_folder))
        letter = one_folder.split('/')[-1]
            
        if letter=='A':
            labels = np.append(labels, n_img*[1])
        elif letter=='B':
            labels = np.append(labels, n_img*[2])
        elif letter=='C':
            labels = np.append(labels, n_img*[3])
        elif letter=='D':
            labels = np.append(labels, n_img*[4])
        elif letter=='E':
            labels = np.append(labels, n_img*[5])
        elif letter=='F':
            labels = np.append(labels, n_img*[6])
        elif letter=='G':
            labels = np.append(labels, n_img*[7])
        elif letter=='H':
            labels = np.append(labels, n_img*[8])
        elif letter=='I':
            labels = np.append(labels, n_img*[9])
        else:
            labels = np.append(labels, n_img*[10])
    
    # shuffle
    temp = np.array([images, labels])
    temp = temp.transpose()
    np.random.shuffle(temp)
    
    image_list = list(temp[:, 0])
    label_list = list(temp[:, 1])
    label_list = [int(float(i)) for i in label_list]
             
    return image_list, label_list


#%%

def int64_feature(value):
  """Wrapper for inserting int64 features into Example proto."""
  if not isinstance(value, list):
    value = [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]))

#%%

def convert_to_tfrecord(images, labels, save_dir, name):
    '''convert all images and labels to one tfrecord file.
    Args:
        images: list of image directories, string type
        labels: list of labels, int type
        save_dir: the directory to save tfrecord file, e.g.: '/home/folder1/'
        name: the name of tfrecord file, string type, e.g.: 'train'
    Return:
        no return
    Note:
        converting needs some time, be patient...
    '''
    
    filename = os.path.join(save_dir, name + '.tfrecords')
    n_samples = len(labels)
    
    if np.shape(images)[0] != n_samples:
        raise ValueError('Images size %d does not match label size %d.' %(images.shape[0], n_samples))
    
    
    
    # wait some time here, transforming need some time based on the size of your data.
    writer = tf.python_io.TFRecordWriter(filename)
    print('\nTransform start......')
    for i in np.arange(0, n_samples):
        try:
            image = io.imread(images[i]) # type(image) must be array!
            image_raw = image.tostring()
            label = int(labels[i])
            example = tf.train.Example(features=tf.train.Features(feature={
                            'label':int64_feature(label),
                            'image_raw': bytes_feature(image_raw)}))
            writer.write(example.SerializeToString())
        except IOError as e:
            print('Could not read:', images[i])
            print('error: %s' %e)
            print('Skip it!\n')
    writer.close()
    print('Transform done!')
    

#%%

def read_and_decode(tfrecords_file, batch_size):
    '''read and decode tfrecord file, generate (image, label) batches
    Args:
        tfrecords_file: the directory of tfrecord file
        batch_size: number of images in each batch
    Returns:
        image: 4D tensor - [batch_size, width, height, channel]
        label: 1D tensor - [batch_size]
    '''
    # make an input queue from the tfrecord file
    filename_queue = tf.train.string_input_producer([tfrecords_file])
    
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    img_features = tf.parse_single_example(
                                        serialized_example,
                                        features={
                                               'label': tf.FixedLenFeature([], tf.int64),
                                               'image_raw': tf.FixedLenFeature([], tf.string),
                                               })
    image = tf.decode_raw(img_features['image_raw'], tf.uint8)
    
    ##########################################################
    # you can put data augmentation here, I didn't use it
    ##########################################################
    # all the images of notMNIST are 28*28, you need to change the image size if you use other dataset.
    
    image = tf.reshape(image, [28, 28])
    label = tf.cast(img_features['label'], tf.int32)    
    image_batch, label_batch = tf.train.batch([image, label],
                                                batch_size= batch_size,
                                                num_threads= 64, 
                                                capacity = 2000)
    return image_batch, tf.reshape(label_batch, [batch_size])

    

  
#%% Convert data to TFRecord

test_dir = 'C://Users//Windows7//Documents//Python Scripts//notMNIST//notMNIST_small//'
save_dir = 'C://Users//Windows7//Documents//Python Scripts//notMNIST//'
BATCH_SIZE = 25


#Convert test data: you just need to run it ONCE !
name_test = 'test'
images, labels = get_file(test_dir)
convert_to_tfrecord(images, labels, save_dir, name_test)


#%% TO test train.tfrecord file

def plot_images(images, labels):
    '''plot one batch size
    '''
    for i in np.arange(0, BATCH_SIZE):
        plt.subplot(5, 5, i + 1)
        plt.axis('off')
        plt.title(chr(ord('A') + labels[i] - 1), fontsize = 14)
        plt.subplots_adjust(top=1.5)
        plt.imshow(images[i])
    plt.show()


tfrecords_file = 'C://Users//Windows7//Documents//Python Scripts//notMNIST//test.tfrecords'
image_batch, label_batch = read_and_decode(tfrecords_file, batch_size=BATCH_SIZE)

with tf.Session()  as sess:
    
    i = 0
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    
    try:
        while not coord.should_stop() and i<1:
            # just plot one batch size            
            image, label = sess.run([image_batch, label_batch])
            plot_images(image, label)
            i+=1
            
    except tf.errors.OutOfRangeError:
        print('done!')
    finally:
        coord.request_stop()
    coord.join(threads)
    

#%%