<reponame>Coralma/tensorflow-sample<filename>cnn/input_data.py
# By @<NAME>
# <EMAIL>

# 11.08 2017 更新
# 最近入驻了网易云课堂讲师，我的第一门课《使用亚马逊云计算训练深度学习模型》。
# 有兴趣的同学可以学习交流。
# * 我的网易云课堂主页： http://study.163.com/provider/400000000275062/index.htm

# 深度学习QQ群, 1群满): 153032765
# 2群：462661267
# The aim of this project is to use TensorFlow to process our own data.
#    - input_data.py:  read in data and generate batches
#    - model: build the model architecture
#    - training: train

# I used Ubuntu with Python 3.5, TensorFlow 1.0*, other OS should also be good.
# With current settings, 10000 traing steps needed 50 minutes on my laptop.


# data: cats vs. dogs from Kaggle
# Download link: https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data
# data size: ~540M

# How to run?
# 1. run the training.py once
# 2. call the run_training() in the console to train the model.

# Note:
# it is suggested to restart your kenel to train the model multiple times
# (in order to clear all the variables in the memory)
# Otherwise errors may occur: conv1/weights/biases already exist......


# %%

import tensorflow as tf
import numpy as np
import os

# %%

# you need to change this to your data directory
train_dir = 'D:/projects-python/ai-training-data/data/train/'


def get_files(file_dir):
    '''
    Args:
        file_dir: file directory
    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)

    image_list = list(temp[:, 0])
    label_list = list(temp[:, 1])
    label_list = [int(i) for i in label_list]

    return image_list, label_list


# %%

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)

    ######################################
    # data argumentation should go to here
    ######################################

    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.
    # 如果想看到正常的图片，请注释掉111行（标准化）和 126行（image_batch = tf.cast(image_batch, tf.float32)）
    # 训练时不要注释掉！
    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/kevin/tensorflow/cats_vs_dogs/data/train/'
#
# image_list, label_list = get_files(train_dir)
# image_batch, label_batch = get_batch(image_list, label_list, 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([image_batch, 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)


# %%





