input_data.py

import tensorflow as tf
import numpy as np
import os
import math

# you need to change this to your data directory
train_dir = '/kaggle/working/data/train/'

def get_files(file_dir, ratio):
    """
    Args:
        file_dir: file directory
        ratio:ratio of validation datasets
    Returns:
        list of images and labels
    """
    cats = []
    label_cats = []
    dogs = []
    label_dogs = []
    for file in os.listdir(file_dir):
        name = file.split(sep='.')
        if name[0]=='cat':
            cats.append(file_dir + file)
            label_cats.append(0)
        else:
            dogs.append(file_dir + file)
            label_dogs.append(1)
    print('There are %d cats\nThere are %d dogs' %(len(cats), len(dogs)))

    image_list = np.hstack((cats, dogs))
    label_list = np.hstack((label_cats, label_dogs))

    temp = np.array([image_list, label_list])
    temp = temp.transpose()
    np.random.shuffle(temp)

    all_image_list = temp[:, 0]
    all_label_list = temp[:, 1]

    n_sample = len(all_label_list)
    n_val = math.ceil(n_sample*ratio) # number of validation samples
    n_train = n_sample - n_val # number of trainning samples

    tra_images = all_image_list[0:n_train]
    tra_labels = all_label_list[0:n_train]
    tra_labels = [int(float(i)) for i in tra_labels]
    val_images = all_image_list[n_train:-1]
    val_labels = all_label_list[n_train:-1]
    val_labels = [int(float(i)) for i in val_labels]

    return tra_images,tra_labels,val_images,val_labels


def get_batch(image, label, image_W, image_H, batch_size, capacity):
    """
    Args:
        image: list type
        label: list type
        image_W: image width
        image_H: image height
        batch_size: batch size
        capacity: the maximum elements in queue
    Returns:
        image_batch: 4D tensor [batch_size, width, height, 3], dtype=tf.float32
        label_batch: 1D tensor [batch_size], dtype=tf.int32
    """

    image = tf.cast(image, tf.string)
    label = tf.cast(label, tf.int32)

    # make an input queue
    input_queue = tf.train.slice_input_producer([image, label])

    label = input_queue[1]
    image_contents = tf.read_file(input_queue[0])
    image = tf.image.decode_jpeg(image_contents, channels=3)

    image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H)

    # if you want to test the generated batches of images, you might want to comment the following line.
    image = tf.image.per_image_standardization(image)

    image_batch, label_batch = tf.train.batch([image, label],
                                                batch_size= batch_size,
                                                num_threads= 64,
                                                capacity = capacity)
    #you can also use shuffle_batch
#    image_batch, label_batch = tf.train.shuffle_batch([image,label],
#                                                      batch_size=BATCH_SIZE,
#                                                      num_threads=64,
#                                                      capacity=CAPACITY,
#                                                      min_after_dequeue=CAPACITY-1)

    label_batch = tf.reshape(label_batch, [batch_size])
    image_batch = tf.cast(image_batch, tf.float32)

    return image_batch, label_batch



# TEST
# To test the generated batches of images
# When training the model, Do comment the following codes


#import matplotlib.pyplot as plt
#
#BATCH_SIZE = 2
#CAPACITY = 256
#IMG_W = 208
#IMG_H = 208
#
#train_dir = '/home/acrobat/DataSets/cats_vs_dogs/train/'
#ratio = 0.2
#tra_images, tra_labels, val_images, val_labels = get_files(train_dir, ratio)
#tra_image_batch, tra_label_batch = get_batch(tra_images, tra_labels, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
#
#
#
#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:
#
#            img, label = sess.run([tra_image_batch, tra_label_batch])
#
#            # just test one batch
#            for j in np.arange(BATCH_SIZE):
#                print('label: %d' %label[j])
#                plt.imshow(img[j,:,:,:])
#                plt.show()
#            i+=1
#
#    except tf.errors.OutOfRangeError:
#        print('done!')
#    finally:
#        coord.request_stop()
#    coord.join(threads)