TensorFlow实现FCN

FCN的网络结构：

    FCN全名叫做全卷机神经网络，它在经典的VGGNet的基础上，把VGG网络最后的全连接层全部去掉，换为卷积层。为了能对图像进行分割，FCN对卷积后的结果进行了反卷积，生成和原图一样的尺寸输出，然后经过softmax就能对每个像素进行分类。具体的网络结果如下：

论文参考《Fully Convolutional Networks for Semantic Segmentation》，代码实现参考：https://github.com/shekkizh/FCN.tensorflow

代码详解：

    代码的实现有四个python文件，分别是FCN.py、BatchDatasetReader.py、TensorFlowUtils.py、read_MITSceneParsingData.py。将这四个文件放在一个当前目录 . 下，然后去这里下载VGG网络的权重参数，下载好后的文件路径为./Model_zoo/imagenet-vgg-verydeep-19.mat，然后去这里下载训练会用到的数据集，并解压到路径: ./Data_zoo/MIT_SceneParsing/ADEChallengeData2016。训练时把FCN.py中的全局变量mode该为“train”，运行该文件。测试时改为“visualize”运行即可。

FCN.py为主文件，代码如下：

from __future__ import print_function
import tensorflow as tf
import numpy as np

import TensorflowUtils as utils
import read_MITSceneParsingData as scene_parsing
import datetime
import BatchDatsetReader as dataset
from six.moves import xrange

batch_size=2                             # batch 大小
logs_dir="logs/"
data_dir= "Data_zoo/MIT_SceneParsing/"   # 存放数据集的路径，需要提前下载
data_name="ADEChallengeData2016"
learning_rate=1e-4                                           # 学习率
model_path="Model_zoo/imagenet-vgg-verydeep-19.mat"          # VGG网络参数文件，需要提前下载
debug= False
mode='train'                             # 训练模式train | visualize

MODEL_URL = 'http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat' #训练好的VGGNet参数

MAX_ITERATION = int(1e5 + 1)   # 最大迭代次数
NUM_OF_CLASSESS = 151          # 类的个数
IMAGE_SIZE = 224               # 图像尺寸

# 根据载入的权重建立原始的 VGGNet 的网络
def vgg_net(weights, image):
    layers = (
        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
        'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3','relu3_3', 'conv3_4', 'relu3_4', 'pool3',
        'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3','relu4_3', 'conv4_4', 'relu4_4', 'pool4',
        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3','relu5_3', 'conv5_4', 'relu5_4'
    )

    net = {}
    current = image
    for i, name in enumerate(layers):
        kind = name[:4]
        if kind == 'conv':
            kernels, bias = weights[i][0][0][0][0]
            # matconvnet: weights are [width, height, in_channels, out_channels]
            # tensorflow: weights are [height, width, in_channels, out_channels]
            kernels = utils.get_variable(np.transpose(kernels, (1, 0, 2, 3)), name=name + "_w")
            bias = utils.get_variable(bias.reshape(-1), name=name + "_b")
            current = utils.conv2d_basic(current, kernels, bias)
            print ("当前形状：",np.shape(current))
        elif kind == 'relu':
            current = tf.nn.relu(current, name=name)
            if debug:
                utils.add_activation_summary(current)
        elif kind == 'pool':
            current = utils.avg_pool_2x2(current)
            print ("当前形状：",np.shape(current))
        net[name] = current

    return net


# FCN的网络结构定义，网络中用到的参数是迁移VGG训练好的参数
def inference(image, keep_prob):
    """
    Semantic segmentation network definition
    :param image: input image. Should have values in range 0-255
    :param keep_prob:
    :return:
    """
    # 加载模型数据    
    print ("原始图像：",np.shape(image))
    model_data = utils.get_model_data(model_path)

    mean = model_data['normalization'][0][0][0]
    mean_pixel = np.mean(mean, axis=(0, 1))

    weights = np.squeeze(model_data['layers'])

    # 图像预处理
    processed_image = utils.process_image(image, mean_pixel)
    print ("预处理后的图像:",np.shape(processed_image))

    with tf.variable_scope("inference"):
        # 建立原始的VGGNet-19网络

        print ("开始建立VGG网络：")
        image_net = vgg_net(weights, processed_image)

        # 在VGGNet-19之后添加 一个池化层和三个卷积层
        conv_final_layer = image_net["conv5_3"]
        print ("VGG处理后的图像：",np.shape(conv_final_layer))

        pool5 = utils.max_pool_2x2(conv_final_layer)

        print ("pool5：",np.shape(pool5))

        W6 = utils.weight_variable([7, 7, 512, 4096], name="W6")
        b6 = utils.bias_variable([4096], name="b6")
        conv6 = utils.conv2d_basic(pool5, W6, b6)
        relu6 = tf.nn.relu(conv6, name="relu6")
        if debug:
            utils.add_activation_summary(relu6)
        relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob)

        print ("conv6:",np.shape(relu_dropout6))

        W7 = utils.weight_variable([1, 1, 4096, 4096], name="W7")
        b7 = utils.bias_variable([4096], name="b7")
        conv7 = utils.conv2d_basic(relu_dropout6, W7, b7)
        relu7 = tf.nn.relu(conv7, name="relu7")
        if debug:
            utils.add_activation_summary(relu7)
        relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob)

        print ("conv7:",np.shape(relu_dropout7))

        W8 = utils.weight_variable([1, 1, 4096, NUM_OF_CLASSESS], name="W8")
        b8 = utils.bias_variable([NUM_OF_CLASSESS], name="b8")
        conv8 = utils.conv2d_basic(relu_dropout7, W8, b8)

        print ("conv8:",np.shape(conv8))
        # annotation_pred1 = tf.argmax(conv8, dimension=3, name="prediction1")

        # 对卷积后的结果进行反卷积操作
        deconv_shape1 = image_net["pool4"].get_shape()
        W_t1 = utils.weight_variable([4, 4, deconv_shape1[3].value, NUM_OF_CLASSESS], name="W_t1")
        b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1")
        conv_t1 = utils.conv2d_transpose_strided(conv8, W_t1, b_t1, output_shape=tf.shape(image_net["pool4"]))
        fuse_1 = tf.add(conv_t1, image_net["pool4"], name="fuse_1")

        print ("pool4 and de_conv8 ==> fuse1:",np.shape(fuse_1)) # (14, 14, 512)

        deconv_shape2 = image_net["pool3"].get_shape()
        W_t2 = utils.weight_variable([4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name="W_t2")
        b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
        conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape(image_net["pool3"]))
        fuse_2 = tf.add(conv_t2, image_net["pool3"], name="fuse_2") 

        print ("pool3 and deconv_fuse1 ==> fuse2:",np.shape(fuse_2)) # (28, 28, 256)

        shape = tf.shape(image)
        deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], NUM_OF_CLASSESS])
        W_t3 = utils.weight_variable([16, 16, NUM_OF_CLASSESS, deconv_shape2[3].value], name="W_t3")
        b_t3 = utils.bias_variable([NUM_OF_CLASSESS], name="b_t3")
        conv_t3 = utils.conv2d_transpose_strided(fuse_2, W_t3, b_t3, output_shape=deconv_shape3, stride=8)

        print ("conv_t3:",[np.shape(image)[1],np.shape(image)[2],NUM_OF_CLASSESS]) # (224,224,151)

        annotation_pred = tf.argmax(conv_t3, dimension=3, name="prediction")  # (224,224,1)
        

    return tf.expand_dims(annotation_pred, dim=3), conv_t3


# 返回优化器
def train(loss_val, var_list):
    optimizer = tf.train.AdamOptimizer(learning_rate)
    grads = optimizer.compute_gradients(loss_val, var_list=var_list)
    if debug:
        # print(len(var_list))
        for grad, var in grads:
            utils.add_gradient_summary(grad, var)
    return optimizer.apply_gradients(grads)

# 主函数,返回优化器的操作步骤
def main(argv=None):
   
    keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
    image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="input_image")
    annotation = tf.placeholder(tf.int32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 1], name="annotation")

    print("setting up vgg initialized conv layers ...")

    # 定义好FCN的网络模型
    pred_annotation, logits = inference(image, keep_probability)
    
    # 定义损失函数，这里使用交叉熵的平均值作为损失函数
    loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
                                                                          labels=tf.squeeze(annotation, squeeze_dims=[3]),
                                                                         name="entropy")))
    # 定义优化器 
    trainable_var = tf.trainable_variables()

    if debug:
        for var in trainable_var:
            utils.add_to_regularization_and_summary(var)
    train_op = train(loss, trainable_var)

    # 加载数据集
    print("Setting up image reader...")

    train_records, valid_records = scene_parsing.read_dataset(data_dir,data_name)

    print("训练集的大小:",len(train_records))
    print("验证集的大小:",len(valid_records))

    print("Setting up dataset reader")

    image_options = {'resize': True, 'resize_size': IMAGE_SIZE}
    if mode == 'train':
        train_dataset_reader = dataset.BatchDatset(train_records, image_options)
    validation_dataset_reader = dataset.BatchDatset(valid_records, image_options)

    # 开始训练模型
    sess = tf.Session()
    print("Setting up Saver...")
    saver = tf.train.Saver()

    sess.run(tf.global_variables_initializer())
    ckpt = tf.train.get_checkpoint_state(logs_dir)
    if ckpt and ckpt.model_checkpoint_path:
        saver.restore(sess, ckpt.model_checkpoint_path)
        print("Model restored...")

    if mode == "train":
        for itr in xrange(MAX_ITERATION):
            train_images, train_annotations = train_dataset_reader.next_batch(batch_size)
            print (np.shape(train_images),np.shape(train_annotations))
            feed_dict = {image: train_images, annotation: train_annotations, keep_probability: 0.85}

            sess.run(train_op, feed_dict=feed_dict)
            print ("step:",itr)

            if itr % 10 == 0:
                train_loss = sess.run(loss, feed_dict=feed_dict)
                print("Step: %d, Train_loss:%g" % (itr, train_loss))      

            if itr % 500 == 0:
                valid_images, valid_annotations = validation_dataset_reader.next_batch(batch_size)
                valid_loss= sess.run(loss, feed_dict={image: valid_images, annotation: valid_annotations,
                                                       keep_probability: 1.0})
                print("%s ---> Validation_loss: %g" % (datetime.datetime.now(), valid_loss))

                saver.save(sess, logs_dir + "model.ckpt", itr)

    elif mode == "visualize":
        valid_images, valid_annotations = validation_dataset_reader.get_random_batch(batch_size)
        pred = sess.run(pred_annotation, feed_dict={image: valid_images, annotation: valid_annotations,
                                                    keep_probability: 1.0})
        valid_annotations = np.squeeze(valid_annotations, axis=3)
        pred = np.squeeze(pred, axis=3)

        for itr in range(batch_size):
            utils.save_image(valid_images[itr].astype(np.uint8), logs_dir, name="inp_" + str(5+itr))
            utils.save_image(valid_annotations[itr].astype(np.uint8), logs_dir, name="gt_" + str(5+itr))
            utils.save_image(pred[itr].astype(np.uint8), logs_dir, name="pred_" + str(5+itr))
            print("Saved image: %d" % itr)


if __name__ == "__main__":
    tf.app.run()

BatchDatasetReader.py主要用于制作数据集batch块，代码如下：

#coding=utf-8
import numpy as np
import scipy.misc as misc

# 批量读取数据集的类
class BatchDatset:
    files = []
    images = []
    annotations = []
    image_options = {}
    batch_offset = 0
    epochs_completed = 0

    def __init__(self, records_list, image_options={}):
        """
          Intialize a generic file reader with batching for list of files
        :param records_list: list of file records to read -
          sample record:
           {'image': f, 'annotation': annotation_file, 'filename': filename}
        :param image_options: A dictionary of options for modifying the output image
          Available options:
            resize = True/ False
            resize_size = #size of output image - does bilinear resize
            color=True/False
        """
        print("Initializing Batch Dataset Reader...")
        print(image_options)
        self.files = records_list
        self.image_options = image_options
        self._read_images()

    def _read_images(self):
        self.__channels = True

        # 读取训练集图像
        self.images = np.array([self._transform(filename['image']) for filename in self.files])
        self.__channels = False
        
        # 读取label的图像，由于label图像是二维的，这里需要扩展为三维
        self.annotations = np.array(
            [np.expand_dims(self._transform(filename['annotation']), axis=3) for filename in self.files])
        print (self.images.shape)
        print (self.annotations.shape)

    # 把图像转为 numpy数组
    def _transform(self, filename):
        image = misc.imread(filename)
        if self.__channels and len(image.shape) < 3:  # make sure images are of shape(h,w,3)
            image = np.array([image for i in range(3)])

        if self.image_options.get("resize", False) and self.image_options["resize"]:
            resize_size = int(self.image_options["resize_size"])
            resize_image = misc.imresize(image,[resize_size, resize_size], interp='nearest')
        else:
            resize_image = image

        return np.array(resize_image)

    def get_records(self):
        return self.images, self.annotations

    def reset_batch_offset(self, offset=0):
        self.batch_offset = offset

    def next_batch(self, batch_size):
        start = self.batch_offset
        self.batch_offset += batch_size
        if self.batch_offset > self.images.shape[0]:
            # Finished epoch
            self.epochs_completed += 1
            print("****************** Epochs completed: " + str(self.epochs_completed) + "******************")
            # Shuffle the data
            perm = np.arange(self.images.shape[0])
            np.random.shuffle(perm)
            self.images = self.images[perm]
            self.annotations = self.annotations[perm] 
            # Start next epoch
            start = 0
            self.batch_offset = batch_size

        end = self.batch_offset
        return self.images[start:end], self.annotations[start:end]

    def get_random_batch(self, batch_size):
        indexes = np.random.randint(0, self.images.shape[0], size=[batch_size]).tolist()
        return self.images[indexes], self.annotations[indexes]

TensorFlowUtils.py主要定义了一些工具函数，如变量初始化、卷积反卷积操作、池化操作、批量归一化、图像预处理等，代码如下：

#coding=utf-8
# Utils used with tensorflow implemetation
import tensorflow as tf
import numpy as np
import scipy.misc as misc
import os, sys
from six.moves import urllib
import tarfile
import zipfile
import scipy.io

# 下载VGG模型的数据
def get_model_data(file_path):
    if not os.path.exists(file_path):
        raise IOError("VGG Model not found!")
    data = scipy.io.loadmat(file_path)
    return data

def save_image(image, save_dir, name, mean=None):
    """
    Save image by unprocessing if mean given else just save
    :param mean:
    :param image:
    :param save_dir:
    :param name:
    :return:
    """
    if mean:
        image = unprocess_image(image, mean)
    misc.imsave(os.path.join(save_dir, name + ".png"), image)

def get_variable(weights, name):
    init = tf.constant_initializer(weights, dtype=tf.float32)
    var = tf.get_variable(name=name, initializer=init,  shape=weights.shape)
    return var

def weight_variable(shape, stddev=0.02, name=None):
    # print(shape)
    initial = tf.truncated_normal(shape, stddev=stddev)
    if name is None:
        return tf.Variable(initial)
    else:
        return tf.get_variable(name, initializer=initial)

def bias_variable(shape, name=None):
    initial = tf.constant(0.0, shape=shape)
    if name is None:
        return tf.Variable(initial)
    else:
        return tf.get_variable(name, initializer=initial)

def get_tensor_size(tensor):
    from operator import mul
    return reduce(mul, (d.value for d in tensor.get_shape()), 1)

def conv2d_basic(x, W, bias):
    conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
    return tf.nn.bias_add(conv, bias)

def conv2d_strided(x, W, b):
    conv = tf.nn.conv2d(x, W, strides=[1, 2, 2, 1], padding="SAME")
    return tf.nn.bias_add(conv, b)

def conv2d_transpose_strided(x, W, b, output_shape=None, stride = 2):
    # print x.get_shape()
    # print W.get_shape()
    if output_shape is None:
        output_shape = x.get_shape().as_list()
        output_shape[1] *= 2
        output_shape[2] *= 2
        output_shape[3] = W.get_shape().as_list()[2]
    # print output_shape
    conv = tf.nn.conv2d_transpose(x, W, output_shape, strides=[1, stride, stride, 1], padding="SAME")
    return tf.nn.bias_add(conv, b)

def leaky_relu(x, alpha=0.0, name=""):
    return tf.maximum(alpha * x, x, name)

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")

def avg_pool_2x2(x):
    return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")

def local_response_norm(x):
    return tf.nn.lrn(x, depth_radius=5, bias=2, alpha=1e-4, beta=0.75)

def batch_norm(x, n_out, phase_train, scope='bn', decay=0.9, eps=1e-5):
    """
    Code taken from http://stackoverflow.com/a/34634291/2267819
    """
    with tf.variable_scope(scope):
        beta = tf.get_variable(name='beta', shape=[n_out], initializer=tf.constant_initializer(0.0)
                               , trainable=True)
        gamma = tf.get_variable(name='gamma', shape=[n_out], initializer=tf.random_normal_initializer(1.0, 0.02),
                                trainable=True)
        batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments')
        ema = tf.train.ExponentialMovingAverage(decay=decay)

        def mean_var_with_update():
            ema_apply_op = ema.apply([batch_mean, batch_var])
            with tf.control_dependencies([ema_apply_op]):
                return tf.identity(batch_mean), tf.identity(batch_var)

        mean, var = tf.cond(phase_train,
                            mean_var_with_update,
                            lambda: (ema.average(batch_mean), ema.average(batch_var)))
        normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, eps)
    return normed

def process_image(image, mean_pixel):
    return image - mean_pixel

def unprocess_image(image, mean_pixel):
    return image + mean_pixel

def bottleneck_unit(x, out_chan1, out_chan2, down_stride=False, up_stride=False, name=None):
    """
    Modified implementation from github ry?!
    """

    def conv_transpose(tensor, out_channel, shape, strides, name=None):
        out_shape = tensor.get_shape().as_list()
        in_channel = out_shape[-1]
        kernel = weight_variable([shape, shape, out_channel, in_channel], name=name)
        shape[-1] = out_channel
        return tf.nn.conv2d_transpose(x, kernel, output_shape=out_shape, strides=[1, strides, strides, 1],
                                      padding='SAME', name='conv_transpose')

    def conv(tensor, out_chans, shape, strides, name=None):
        in_channel = tensor.get_shape().as_list()[-1]
        kernel = weight_variable([shape, shape, in_channel, out_chans], name=name)
        return tf.nn.conv2d(x, kernel, strides=[1, strides, strides, 1], padding='SAME', name='conv')

    def bn(tensor, name=None):
        """
        :param tensor: 4D tensor input
        :param name: name of the operation
        :return: local response normalized tensor - not using batch normalization :(
        """
        return tf.nn.lrn(tensor, depth_radius=5, bias=2, alpha=1e-4, beta=0.75, name=name)

    in_chans = x.get_shape().as_list()[3]

    if down_stride or up_stride:
        first_stride = 2
    else:
        first_stride = 1

    with tf.variable_scope('res%s' % name):
        if in_chans == out_chan2:
            b1 = x
        else:
            with tf.variable_scope('branch1'):
                if up_stride:
                    b1 = conv_transpose(x, out_chans=out_chan2, shape=1, strides=first_stride,
                                        name='res%s_branch1' % name)
                else:
                    b1 = conv(x, out_chans=out_chan2, shape=1, strides=first_stride, name='res%s_branch1' % name)
                b1 = bn(b1, 'bn%s_branch1' % name, 'scale%s_branch1' % name)

        with tf.variable_scope('branch2a'):
            if up_stride:
                b2 = conv_transpose(x, out_chans=out_chan1, shape=1, strides=first_stride, name='res%s_branch2a' % name)
            else:
                b2 = conv(x, out_chans=out_chan1, shape=1, strides=first_stride, name='res%s_branch2a' % name)
            b2 = bn(b2, 'bn%s_branch2a' % name, 'scale%s_branch2a' % name)
            b2 = tf.nn.relu(b2, name='relu')

        with tf.variable_scope('branch2b'):
            b2 = conv(b2, out_chans=out_chan1, shape=3, strides=1, name='res%s_branch2b' % name)
            b2 = bn(b2, 'bn%s_branch2b' % name, 'scale%s_branch2b' % name)
            b2 = tf.nn.relu(b2, name='relu')

        with tf.variable_scope('branch2c'):
            b2 = conv(b2, out_chans=out_chan2, shape=1, strides=1, name='res%s_branch2c' % name)
            b2 = bn(b2, 'bn%s_branch2c' % name, 'scale%s_branch2c' % name)

        x = b1 + b2
        return tf.nn.relu(x, name='relu')

read_MITSceneParsingData.py主要是用于读取数据集的数据，代码如下：

#coding=utf-8
import numpy as np
import os
import random
from six.moves import cPickle as pickle
from tensorflow.python.platform import gfile
import glob

import TensorflowUtils as utils

# DATA_URL = 'http://sceneparsing.csail.mit.edu/data/ADEChallengeData2016.zip'
DATA_URL = 'http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip'


def read_dataset(data_dir,data_name):
    pickle_filename = "MITSceneParsing.pickle"    
    pickle_filepath = os.path.join(data_dir, pickle_filename)

    if not os.path.exists(pickle_filepath): 
          
        result = create_image_lists(os.path.join(data_dir, data_name))

        print ("Pickling ...")
        with open(pickle_filepath, 'wb') as f:
            pickle.dump(result, f, pickle.HIGHEST_PROTOCOL)
    else:
        print ("Found pickle file!")

    with open(pickle_filepath, 'rb') as f:
        result = pickle.load(f)
        training_records = result['training']
        validation_records = result['validation']
        del result

    return training_records, validation_records

'''
  返回一个字典:
  image_list{ 
           "training":[{'image': image_full_name, 'annotation': annotation_file, 'image_filename': },......],
           "validation":[{'image': image_full_name, 'annotation': annotation_file, 'filename': filename},......]
           }
'''
def create_image_lists(image_dir):
    if not gfile.Exists(image_dir):
        print("Image directory '" + image_dir + "' not found.")
        return None
    directories = ['training', 'validation']
    image_list = {}

    for directory in directories:
        file_list = []
        image_list[directory] = []

        # 获取images目录下所有的图片名
        file_glob = os.path.join(image_dir, "images", directory, '*.' + 'jpg')
        file_list.extend(glob.glob(file_glob))             

        if not file_list:
            print('No files found')
        else:
            for f in file_list:
                # 注意注意，下面的分割符号，在window上为：\\,在Linux撒花姑娘为 : /
                filename = os.path.splitext(f.split("\\")[-1])[0]  # 图片名前缀
                annotation_file = os.path.join(image_dir, "annotations", directory, filename + '.png')
                if os.path.exists(annotation_file):
                    record = {'image': f, 'annotation': annotation_file, 'filename': filename}
                    image_list[directory].append(record)
                else:
                    print("Annotation file not found for %s - Skipping" % filename)

        random.shuffle(image_list[directory])
        no_of_images = len(image_list[directory])
        print ('No. of %s files: %d' % (directory, no_of_images))

    return image_list