使用 VGG16 实现图像识别分类，使用 VGG 19 实现艺术风格转移

使用 VGG16 实现图像识别分类及VGG 19 实现艺术风格转移

一、VGG 简介

1.1 网络架构

• 训练输入： 固定尺寸224*224的RGB图像。
• 预处理：每个像素值减去训练集上的RGB均值。
• 卷积核：一系列3*3卷积核堆叠， 步长为1， 采用padding保持卷积后图像空
  间分辨率不变。
• 空间池化：紧随卷积“堆”的最大池化，为2*2滑动窗口， 步长为2。
• 全连接层： 特征提取完成后，接三个全连接层， 前两个为4096通道， 第三个
  为1000通道， 最后是一个soft-max层， 输出概率。
• 所有隐藏层都用非线性修正ReLu
• 下图是论文中提到的一些网络的架构
  
  
  

• 关于，本文实验代码，以及训练好的 VGG 参数均可以在此处下载：

链接:https://pan.baidu.com/s/1KfVDmiNwMPAIAfTLlLIXTw 密码:oh5f

1.2 VGG 代码上使用到的一些函数讲解

• 在 vgg 的喂入的 x 的 shape 是 [BATCH_SIZE,IMAGE_PIXELS]，例如[ 50,28,28,3] 则说明一次喂入50张，像素为 28 x 28 的三通道的图像。
• np.load np.save:将数组以二进制格式保存到磁盘，扩展名为.npy 。
• 对于字典，我们可以采用字典 class 的 item()函数方法进行遍历(键值对)
• tf.shape(a)和 a.get_shape() 比较

相同点：都可以得到 tensor a 的尺寸
不同点： tf.shape()中 a 的数据类型可以是 tensor, list, array；而
a.get_shape()中 a 的数据类型只能是 tensor,且返回的是一个元组（tuple）

import tensorflow as tf
import numpy as np

x=tf.constant([[1,2,3],[4,5,6]]
y = [[1,2,3],[4,5,6]]            
z=np.arange(24).reshape([2,3,4]))
sess=tf.Session()
# tf.shape()
x_shape=tf.shape(x) # x_shape 是一个 tensor
y_shape=tf.shape(y) # <tf.Tensor 'Shape_2:0' shape=(2,) dtype=int32>
z_shape=tf.shape(z) # <tf.Tensor 'Shape_5:0' shape=(3,) dtype=int32>
print sess.run(x_shape) # 结果:[2 3]
print sess.run(y_shape) # 结果:[2 3]
print sess.run(z_shape) # 结果:[2 3 4]
#a.get_shape()
x_shape=x.get_shape() # 返回的是 TensorShape([Dimension(2),Dimension(3)]),
# 不能使用 sess.run()， 因为返回的不是 tensor 或 string,而是元组
x_shape=x.get_shape().as_list() # 可以使用 as_list()得到具体的尺寸， x_shape=[2 3]
y_shape=y.get_shape() # AttributeError: 'list' object has no attribute 'get_shape'
z_shape=z.get_shape() # AttributeError: 'numpy.ndarray' object has no attribute 'get_shape'

• np.save：写数组到文件（未压缩二进制形式），文件默认的扩展名是.npy。
  np.save(“名.npy”，某数组):将某数组写入“名.npy”文件。
  某变量 = np.load(“名.npy”， encoding = ” “).item()：将“名.npy”文件读出给某变量。 encoding = ” ” 可以不写‘latin1’ 、 ‘ASCII’ 、 ‘bytes’ ，默认为’ASCII’

>>> import numpy as np
A = np.arange(15).reshape(3,5)
>>> A
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
>>> np.save("A.npy",A) #如果文件路径末尾没有扩展名.npy，该扩展名会被
自动加上。
>>> B=np.load("A.npy")
>>> B
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])

• tf.split(dimension, num_split, input)：
  dimension： 输入张量的哪一个维度，如果是 0 就表示对第 0 维度进行切割。
  num_split： 切割的数量，如果是 2 就表示输入张量被切成 2 份，每一份是一个
  列表。
• tf.concat(concat_dim, values)
• 还原 VGG 网络结果，实现特定应用的示意图，如下:
  

二、基于 VGG16 实现图像识别和分类TensorFlow 实现

# This is the VGG.py 最为主要的文件，实现了 VGG 的对象化管理，通过类成员函数的方法实现了前向传播，以及网络的构建

# coding: utf-8 
import os 
import time
import inspect  
import numpy as np 
import matplotlib.pyplot as plt 
import tensorflow as tf 

# 样本 BGR 的平均值 
VGG_MEAN = [103.939, 116.779, 123.68] 

# 将 VGG 按照对象的方式进行管理
class Vgg16():
    def __init__(self, vgg16_path=None):
        # 如果 vgg16_path 为 None 则说明使用默认的路径，当前路径拼接 vgg16.npy
        if vgg16_path is None:
            vgg16_path = os.path.join(os.getcwd(),'vgg16.npy')
            # 然后，遍历模型参数 npy 文件,使用字典的 item()函数访问字典的每个元素，并且导入参数到 data_dict 
            self.data_dict = np.load(vgg16_path,encoding='latin1').item()

        # 在初始化函数里面，答应 data_dict 里面的参数，以便观察其形状
        for x in self.data_dict:
            print x 

    # 定义前向传播 
    def forward(self,images):
        print "Build model started"
        start_time = time.time()        # 获取前向传播的开始时间
        rgb_scaled = images * 255.0     # 逐像素乘以 255.0（根据原论文所述的初始化步骤）

        # 从 GRB 转换色彩通道到 BGR，也可使用 cv 中的 GRBtoBGR,并且为了减去均值操作而拆分
        red, green, blue = tf.split(rgb_scaled,3,3) 

        # 以下 assert 都是加入断言，用来判断每个操作后的维度变化是否和预期一致，image 的第 0 个维度是 batch 数，后面的维度才是图像的描述
        assert red.get_shape().as_list()[1:] == [224, 224, 1] 
        assert green.get_shape().as_list()[1:] == [224, 224, 1] 
        assert blue.get_shape().as_list()[1:] == [224, 224, 1] 

        # 逐样本减去每个通道的像素平均值，这种操作可以移除图像的平均亮度值，该方法常用在灰度图像上
        bgr = tf.concat([
            blue - VGG_MEAN[0],
            green - VGG_MEAN[1],
            red - VGG_MEAN[2]],3) 

        assert bgr.get_shape().as_list()[1:] == [224,224,3] 

        # 接下来构建 VGG 的 16 层网络（包含 5 段卷积， 3 层全连接），并逐层根据命名空间读取网络参数
        # 第一段卷积，含有两个卷积层，后面接最大池化层，用来缩小图片尺寸
        self.conv1_1 = self.conv_layer(bgr,'conv1_1') 
        self.conv1_2 = self.conv_layer(self.conv1_1,'conv1_2')  
        self.pool1 = self.max_pool_2x2(self.conv1_2,'pool1')

        # 第二段卷积 
        self.conv2_1 = self.conv_layer(self.pool1,'conv2_1')
        self.conv2_2 = self.conv_layer(self.conv2_1,'conv2_2')
        self.pool2 = self.max_pool_2x2(self.conv2_2,'pool2')

        # 第三段卷积，包含三个卷积层，一个最大池化层
        self.conv3_1 = self.conv_layer(self.pool2,'conv3_1')
        self.conv3_2 = self.conv_layer(self.conv3_1,'conv3_2')
        self.conv3_3 = self.conv_layer(self.conv3_2,'conv3_3')
        self.pool3 = self.max_pool_2x2(self.conv3_3,'pool3')

        # 第四段卷积，包含三个卷积层，一个最大池化层
        self.conv4_1 = self.conv_layer(self.pool3,'conv4_1')
        self.conv4_2 = self.conv_layer(self.conv4_1,'conv4_2')
        self.conv4_3 = self.conv_layer(self.conv4_2,'conv4_3')
        self.pool4 = self.max_pool_2x2(self.conv4_3,'pool4')

        # 第五段卷积，包含三个卷积层，一个最大池化层
        self.conv5_1 = self.conv_layer(self.pool4,'conv5_1')
        self.conv5_2 = self.conv_layer(self.conv5_1,'conv5_2')
        self.conv5_3 = self.conv_layer(self.conv5_2,'conv5_3')
        self.pool5 = self.max_pool_2x2(self.conv5_3,'pool5')

        # 第六层全连接
        self.fc6 = self.fc_layer(self.pool5,'fc6')
        self.relu6 = tf.nn.relu(self.fc6)

        # 第七层全连接，和上一层同理
        self.fc7 = self.fc_layer(self.relu6,'fc7')
        self.relu7 = tf.nn.relu(self.fc7)

        # 第八层全连接
        self.fc8 = self.fc_layer(self.relu7,'fc8')

        # 经过最后一层的全连接后，再做 softmax 分类，得到属于各类别的概率
        self.prob = tf.nn.softmax(self.fc8,name='prob')

        end_time = time.time()
        print 'time consuming: %f'% (end_time - start_time)

        self.data_dict = None       # 清空本次读取到的模型参数字典


    def conv_layer(self, x, name):
        with tf.variable_scope(name):         # 根据命名空间找到对应卷积层的网络参数
            w = self.get_conv_filter(name)    # 得到该层的卷积核 
            conv = tf.nn.conv2d(x, w, [1, 1, 1, 1],padding='SAME')      # 卷积计算
            conv_biases = self.get_bias(name)       # 读取偏置项
            result = tf.nn.relu(tf.nn.bias_add(conv,conv_biases)) 
            return result

    # 定义获取卷积核的函数
    def get_conv_filter(self,name):
        return tf.constant(self.data_dict[name][0],name='filter') 

    # 定义获取偏置项的函数
    def get_bias(self,name):
        return tf.constant(self.data_dict[name][1],name='biases')

    # 定义最大池化操作
    def max_pool_2x2(self,x,name):
        return tf.nn.max_pool(x,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='SAME',name=name)

    # 定义全连接层的前向传播计算
    def fc_layer(self, x, name):
        with tf.variable_scope(name):     # 根据命名空间 name 做全连接层的计算
            shape = x.get_shape().as_list()         # 获取该层的维度信息列表
            dim = 1     # 记录维度乘积 
            for i in shape[1:]:
                dim *= i                            # 将每层的维度相乘

            # 改变特征图的形状，也就是将得到的多维特征做拉伸操作，只在进入第六层全连接层做该操作
            x = tf.reshape(x,[-1,dim])
            w = self.get_fc_weight(name)            # 读到权重值
            b = self.get_bias(name) 

            result = tf.nn.bias_add(tf.matmul(x,w),b)       # 对该层输入做权值求和，再加上偏置
            return result 

    def get_fc_weight(self,name):       # 根据命名空间 name 从参数字典中取到对应的权重
        return tf.constant(self.data_dict[name][0],name='weights')  


# THis is the utils 主要是一些使用到的工具函数定义实现，辅助函数，包括图像的预处理等等 
# coding: utf-8 
from skimage import io, transform
import numpy as np 
import matplotlib.pyplot as plt 
import tensorflow as tf 
from pylab import mpl 

mpl.rcParams['font.sans-serif']=['SimHei'] # 正常显示中文标签
mpl.rcParams['axes.unicode_minus']=False # 正常显示正负号

def load_image(path):
    fig = plt.figure('Centre and Resize')
    img = io.imread(path)           # 根据传入的路径读入图片 
    img = img / 255.0               # 将像素归一化到 [0,1]

    # 将该画布分为一行三列
    ax0 = fig.add_subplot(131)      # 把下面的图像放在该画布的第一个位置
    ax0.set_xlabel(u'Original Picture')     # 添加子标签
    ax0.imshow(img)                         # 添加展示该图像 

    # 找到该图像的最短边
    short_edge = min(img.shape[:2])

    # 把图像的 w 和 h 分别减去最短边，并求平均
    y = (img.shape[0] - short_edge) / 2 
    x = (img.shape[1] - short_edge) / 2 
    crop_img = img[y:y+short_edge,x:x+short_edge]      #  取出切分出的中心图像 

    # 把下面的图像放在该画布的第二个位置
    ax1 = fig.add_subplot(132) 
    ax1.set_xlabel(u'Centre Picture')                  # 把下面的图像放在该画布的第二个位置
    ax1.imshow(crop_img)

    re_img = transform.resize(crop_img,(224,224))      # resize 成固定的 imag_szie

    ax2 = fig.add_subplot(133)                          # 把下面的图像放在该画布的第三个位置
    ax2.set_xlabel(u'Resize Picture')
    ax2.imshow(re_img)

    img_ready = re_img.reshape((1, 224, 224, 3))

    return img_ready

# 定义百分比转换函数
def percent(value):
    return '%.2f%%' %(value * 100) 

# This is the app.py 这是实现特定应用的程序，我们使用落地的程序 
# coding: utf-8 
import numpy as np 
import matplotlib.pyplot as plt 
import tensorflow as tf 
import time
# 导入自定义模块
import vgg16 
import utils
from Nclasses import labels


image_path = raw_input('Input the path and image name:') 
# 调用 load_image()函数，对待测试的图像做一些预处理操作
img_ready = utils.load_image(image_path)

#定义一个 figure 画图窗口，并指定窗口的名称，也可以设置窗口修的大小
fig = plt.figure(u'Top-5 预测结果') 

with tf.Session() as sess: 
    # 定义一个维度为[1,224,224,3],类型为 float32 的 tensor 占位符
    images = tf.placeholder(tf.float32,[1, 224, 224, 3])
    vgg = vgg16.Vgg16() 

    # 调用类的成员方法 forward()，并传入待测试图像，这也就是网络前向传播的过程
    vgg.forward(images)

    start_time = time.time()
    # 将一个 batch 的数据喂入网络，得到网络的预测输出
    probability = sess.run(vgg.prob,feed_dict={images:img_ready})
    end_time = time.time()
    print 'Cumputer times: %f'% (end_time - start_time)

    # np.argsort 函数返回预测值（ probability 的数据结构[[各预测类别的概率值]]）由小到大的索引值，
    # 并取出预测概率最大的五个索引值
    top5 = np.argsort(probability[0])[-1:-6:-1]
    print 'Top5: ', top5 

    # 定义两个 list---对应的概率值和实际标签（ zebra）
    values = [] 
    bar_label = [] 
    for n, i in enumerate(top5):
        # 枚举上面取出的五个索引值 
        print 'n: ', n
        print 'i: ', i
        values.append(probability[0][i])         # 将索引值对应的预测概率值取出并放入 values 
        bar_label.append(labels[i])              # 根据索引值取出对应的实际标签并放入 bar_label
    print i, ":", labels[i], "----", utils.percent(probability[0][i])       # 打印属于某个类别的概率 

    ax = fig.add_subplot(111)                      # 将画布划分为一行一列，并把下图放入其中 

    # bar()函数绘制柱状图，参数 range(len(values)是柱子下标， values 表示柱高的列表（也就是五个预测概率值，
    # tick_label 是每个柱子上显示的标签（实际对应的标签）， width 是柱子的宽度， fc 是柱子的颜色）
    ax.bar(range(len(values)),values,tick_label=bar_label,width=0.5,fc='g')
    ax.set_ylabel(u'probabilityit')         # 设置纵轴标签
    ax.set_title(u'Top-5')                  # 添加标题
    for a, b in zip(range(len(values)),values):
        # 在每个柱子的顶端添加对应的预测概率值， a， b 表示坐标， b+0.0005 表示要把文本信息放置在高于每个柱子顶端0.0005 的位置，
        # center 是表示文本位于柱子顶端水平方向上的的中间位置， bottom 是将文本水平放置在柱子顶端垂直方向上的底端位置， fontsize 是字号
        ax.text(a,b+0.0005,utils.percent(b),ha='center',va='bottom',fontsize=7)
    plt.show()

三、基于 VGG 19 实现艺术风格转移

• 这里，我们会基于 Leon Gatys 的论文《艺术风格的神经网络算法》，我们不会从新训练系数矩阵，因为我们只是想获得图片在VGGNet19模型中的某些隐藏层上的特征矩阵。所以我们这里使用已经训练好的VGGNet19模型的参数，故通过使用官方已经训练好了的系数矩阵，通过 Scipy 的 io.loadmat 进行加载，以实现特定应用，故这里先给出已经训练好了的 VGG19 的模型参数的下载地址，如下：

下载地址：http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat

• 根据命令行参数提示，你可以在 shell 上执行如下命令行即可，开始训练，这里需要注意，我们需要两个图片作为输入，一个是希望艺术化的原始图片，第二个是具有 style 风格的的艺术图片本身。 –content 配置原图片参数，–style 配置想要转换成的艺术风格的图片参数，

• content 图片

• style 风格图片

• 此外，我们这里考虑到时间因素，想快速得到结果，故只选择了迭代 100，当然如果可以迭代 1000 次，按理说图片转移风格会更加真实。这里，读者可以自行实验。

python neural_style.py --content content.jpg --styles style.jpg --checkpoint-iterations=10 --iterations=100 --checkpoint-output=out%s.jpg --output=outfinal.jpg

• 下面是执行的过程图示
  

• 100 次迭代后，如果读者想得到更好的效果，可自行增加迭代次数：
  

• 源代码如下：

• vgg.py

# Copyright (c) 2015-2017 Anish Athalye. Released under GPLv3.

import tensorflow as tf
import numpy as np
import scipy.io

VGG19_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'
)

def load_net(data_path):
    data = scipy.io.loadmat(data_path)
    mean = data['normalization'][0][0][0]
    mean_pixel = np.mean(mean, axis=(0, 1))
    weights = data['layers'][0]
    return weights, mean_pixel

def net_preloaded(weights, input_image, pooling):
    net = {}
    current = input_image
    for i, name in enumerate(VGG19_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 = np.transpose(kernels, (1, 0, 2, 3))
            bias = bias.reshape(-1)
            current = _conv_layer(current, kernels, bias)
        elif kind == 'relu':
            current = tf.nn.relu(current)
        elif kind == 'pool':
            current = _pool_layer(current, pooling)
        net[name] = current

    assert len(net) == len(VGG19_LAYERS)
    return net

def _conv_layer(input, weights, bias):
    conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1),
            padding='SAME')
    return tf.nn.bias_add(conv, bias)


def _pool_layer(input, pooling):
    if pooling == 'avg':
        return tf.nn.avg_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),
                padding='SAME')
    else:
        return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),
                padding='SAME')

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


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

• stylize.py

# Copyright (c) 2015-2017 Anish Athalye. Released under GPLv3.

import vgg

import tensorflow as tf
import numpy as np

from sys import stderr

from PIL import Image

CONTENT_LAYERS = ('relu4_2', 'relu5_2')
STYLE_LAYERS = ('relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1')

try:
    reduce
except NameError:
    from functools import reduce


def stylize(network, initial, initial_noiseblend, content, styles, preserve_colors, iterations,
        content_weight, content_weight_blend, style_weight, style_layer_weight_exp, style_blend_weights, tv_weight,
        learning_rate, beta1, beta2, epsilon, pooling,
        print_iterations=None, checkpoint_iterations=None):
    """
    Stylize images.

    This function yields tuples (iteration, image); `iteration` is None
    if this is the final image (the last iteration).  Other tuples are yielded
    every `checkpoint_iterations` iterations.

    :rtype: iterator[tuple[int|None,image]]
    """
    shape = (1,) + content.shape
    style_shapes = [(1,) + style.shape for style in styles]
    content_features = {}
    style_features = [{} for _ in styles]

    vgg_weights, vgg_mean_pixel = vgg.load_net(network)

    layer_weight = 1.0
    style_layers_weights = {}
    for style_layer in STYLE_LAYERS:
        style_layers_weights[style_layer] = layer_weight
        layer_weight *= style_layer_weight_exp

    # normalize style layer weights
    layer_weights_sum = 0
    for style_layer in STYLE_LAYERS:
        layer_weights_sum += style_layers_weights[style_layer]
    for style_layer in STYLE_LAYERS:
        style_layers_weights[style_layer] /= layer_weights_sum

    # compute content features in feedforward mode
    g = tf.Graph()
    with g.as_default(), g.device('/cpu:0'), tf.Session() as sess:
        image = tf.placeholder('float', shape=shape)
        net = vgg.net_preloaded(vgg_weights, image, pooling)
        content_pre = np.array([vgg.preprocess(content, vgg_mean_pixel)])
        for layer in CONTENT_LAYERS:
            content_features[layer] = net[layer].eval(feed_dict={image: content_pre})

    # compute style features in feedforward mode
    for i in range(len(styles)):
        g = tf.Graph()
        with g.as_default(), g.device('/cpu:0'), tf.Session() as sess:
            image = tf.placeholder('float', shape=style_shapes[i])
            net = vgg.net_preloaded(vgg_weights, image, pooling)
            style_pre = np.array([vgg.preprocess(styles[i], vgg_mean_pixel)])
            for layer in STYLE_LAYERS:
                features = net[layer].eval(feed_dict={image: style_pre})
                features = np.reshape(features, (-1, features.shape[3]))
                gram = np.matmul(features.T, features) / features.size
                style_features[i][layer] = gram

    initial_content_noise_coeff = 1.0 - initial_noiseblend

    # make stylized image using backpropogation
    with tf.Graph().as_default():
        if initial is None:
            noise = np.random.normal(size=shape, scale=np.std(content) * 0.1)
            initial = tf.random_normal(shape) * 0.256
        else:
            initial = np.array([vgg.preprocess(initial, vgg_mean_pixel)])
            initial = initial.astype('float32')
            noise = np.random.normal(size=shape, scale=np.std(content) * 0.1)
            initial = (initial) * initial_content_noise_coeff + (tf.random_normal(shape) * 0.256) * (1.0 - initial_content_noise_coeff)
        image = tf.Variable(initial)
        net = vgg.net_preloaded(vgg_weights, image, pooling)

        # content loss
        content_layers_weights = {}
        content_layers_weights['relu4_2'] = content_weight_blend
        content_layers_weights['relu5_2'] = 1.0 - content_weight_blend

        content_loss = 0
        content_losses = []
        for content_layer in CONTENT_LAYERS:
            content_losses.append(content_layers_weights[content_layer] * content_weight * (2 * tf.nn.l2_loss(
                    net[content_layer] - content_features[content_layer]) /
                    content_features[content_layer].size))
        content_loss += reduce(tf.add, content_losses)

        # style loss
        style_loss = 0
        for i in range(len(styles)):
            style_losses = []
            for style_layer in STYLE_LAYERS:
                layer = net[style_layer]
                _, height, width, number = map(lambda i: i.value, layer.get_shape())
                size = height * width * number
                feats = tf.reshape(layer, (-1, number))
                gram = tf.matmul(tf.transpose(feats), feats) / size
                style_gram = style_features[i][style_layer]
                style_losses.append(style_layers_weights[style_layer] * 2 * tf.nn.l2_loss(gram - style_gram) / style_gram.size)
            style_loss += style_weight * style_blend_weights[i] * reduce(tf.add, style_losses)

        # total variation denoising
        tv_y_size = _tensor_size(image[:,1:,:,:])
        tv_x_size = _tensor_size(image[:,:,1:,:])
        tv_loss = tv_weight * 2 * (
                (tf.nn.l2_loss(image[:,1:,:,:] - image[:,:shape[1]-1,:,:]) /
                    tv_y_size) +
                (tf.nn.l2_loss(image[:,:,1:,:] - image[:,:,:shape[2]-1,:]) /
                    tv_x_size))
        # overall loss
        loss = content_loss + style_loss + tv_loss

        # optimizer setup
        train_step = tf.train.AdamOptimizer(learning_rate, beta1, beta2, epsilon).minimize(loss)

        def print_progress():
            stderr.write('  content loss: %g\n' % content_loss.eval())
            stderr.write('    style loss: %g\n' % style_loss.eval())
            stderr.write('       tv loss: %g\n' % tv_loss.eval())
            stderr.write('    total loss: %g\n' % loss.eval())

        # optimization
        best_loss = float('inf')
        best = None
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            stderr.write('Optimization started...\n')
            if (print_iterations and print_iterations != 0):
                print_progress()
            for i in range(iterations):
                stderr.write('Iteration %4d/%4d\n' % (i + 1, iterations))
                train_step.run()

                last_step = (i == iterations - 1)
                if last_step or (print_iterations and i % print_iterations == 0):
                    print_progress()

                if (checkpoint_iterations and i % checkpoint_iterations == 0) or last_step:
                    this_loss = loss.eval()
                    if this_loss < best_loss:
                        best_loss = this_loss
                        best = image.eval()

                    img_out = vgg.unprocess(best.reshape(shape[1:]), vgg_mean_pixel)

                    if preserve_colors and preserve_colors == True:
                        original_image = np.clip(content, 0, 255)
                        styled_image = np.clip(img_out, 0, 255)

                        # Luminosity transfer steps:
                        # 1. Convert stylized RGB->grayscale accoriding to Rec.601 luma (0.299, 0.587, 0.114)
                        # 2. Convert stylized grayscale into YUV (YCbCr)
                        # 3. Convert original image into YUV (YCbCr)
                        # 4. Recombine (stylizedYUV.Y, originalYUV.U, originalYUV.V)
                        # 5. Convert recombined image from YUV back to RGB

                        # 1
                        styled_grayscale = rgb2gray(styled_image)
                        styled_grayscale_rgb = gray2rgb(styled_grayscale)

                        # 2
                        styled_grayscale_yuv = np.array(Image.fromarray(styled_grayscale_rgb.astype(np.uint8)).convert('YCbCr'))

                        # 3
                        original_yuv = np.array(Image.fromarray(original_image.astype(np.uint8)).convert('YCbCr'))

                        # 4
                        w, h, _ = original_image.shape
                        combined_yuv = np.empty((w, h, 3), dtype=np.uint8)
                        combined_yuv[..., 0] = styled_grayscale_yuv[..., 0]
                        combined_yuv[..., 1] = original_yuv[..., 1]
                        combined_yuv[..., 2] = original_yuv[..., 2]

                        # 5
                        img_out = np.array(Image.fromarray(combined_yuv, 'YCbCr').convert('RGB'))


                    yield (
                        (None if last_step else i),
                        img_out
                    )


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

def rgb2gray(rgb):
    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])

def gray2rgb(gray):
    w, h = gray.shape
    rgb = np.empty((w, h, 3), dtype=np.float32)
    rgb[:, :, 2] = rgb[:, :, 1] = rgb[:, :, 0] = gray
    return rgb

• neural_style.py

# Copyright (c) 2015-2017 Anish Athalye. Released under GPLv3.

import os

import numpy as np
import scipy.misc

from stylize import stylize

import math
from argparse import ArgumentParser

from PIL import Image

# default arguments
CONTENT_WEIGHT = 5e0
CONTENT_WEIGHT_BLEND = 1
STYLE_WEIGHT = 5e2
TV_WEIGHT = 1e2
STYLE_LAYER_WEIGHT_EXP = 1
LEARNING_RATE = 1e1
BETA1 = 0.9
BETA2 = 0.999
EPSILON = 1e-08
STYLE_SCALE = 1.0
ITERATIONS = 1000
VGG_PATH = 'imagenet-vgg-verydeep-19.mat'
POOLING = 'max'

def build_parser():
    parser = ArgumentParser()
    parser.add_argument('--content',
            dest='content', help='content image',
            metavar='CONTENT', required=True)
    parser.add_argument('--styles',
            dest='styles',
            nargs='+', help='one or more style images',
            metavar='STYLE', required=True)
    parser.add_argument('--output',
            dest='output', help='output path',
            metavar='OUTPUT', required=True)
    parser.add_argument('--iterations', type=int,
            dest='iterations', help='iterations (default %(default)s)',
            metavar='ITERATIONS', default=ITERATIONS)
    parser.add_argument('--print-iterations', type=int,
            dest='print_iterations', help='statistics printing frequency',
            metavar='PRINT_ITERATIONS')
    parser.add_argument('--checkpoint-output',
            dest='checkpoint_output', help='checkpoint output format, e.g. output%%s.jpg',
            metavar='OUTPUT')
    parser.add_argument('--checkpoint-iterations', type=int,
            dest='checkpoint_iterations', help='checkpoint frequency',
            metavar='CHECKPOINT_ITERATIONS')
    parser.add_argument('--width', type=int,
            dest='width', help='output width',
            metavar='WIDTH')
    parser.add_argument('--style-scales', type=float,
            dest='style_scales',
            nargs='+', help='one or more style scales',
            metavar='STYLE_SCALE')
    parser.add_argument('--network',
            dest='network', help='path to network parameters (default %(default)s)',
            metavar='VGG_PATH', default=VGG_PATH)
    parser.add_argument('--content-weight-blend', type=float,
            dest='content_weight_blend', help='content weight blend, conv4_2 * blend + conv5_2 * (1-blend) (default %(default)s)',
            metavar='CONTENT_WEIGHT_BLEND', default=CONTENT_WEIGHT_BLEND)
    parser.add_argument('--content-weight', type=float,
            dest='content_weight', help='content weight (default %(default)s)',
            metavar='CONTENT_WEIGHT', default=CONTENT_WEIGHT)
    parser.add_argument('--style-weight', type=float,
            dest='style_weight', help='style weight (default %(default)s)',
            metavar='STYLE_WEIGHT', default=STYLE_WEIGHT)
    parser.add_argument('--style-layer-weight-exp', type=float,
            dest='style_layer_weight_exp', help='style layer weight exponentional increase - weight(layer<n+1>) = weight_exp*weight(layer<n>) (default %(default)s)',
            metavar='STYLE_LAYER_WEIGHT_EXP', default=STYLE_LAYER_WEIGHT_EXP)
    parser.add_argument('--style-blend-weights', type=float,
            dest='style_blend_weights', help='style blending weights',
            nargs='+', metavar='STYLE_BLEND_WEIGHT')
    parser.add_argument('--tv-weight', type=float,
            dest='tv_weight', help='total variation regularization weight (default %(default)s)',
            metavar='TV_WEIGHT', default=TV_WEIGHT)
    parser.add_argument('--learning-rate', type=float,
            dest='learning_rate', help='learning rate (default %(default)s)',
            metavar='LEARNING_RATE', default=LEARNING_RATE)
    parser.add_argument('--beta1', type=float,
            dest='beta1', help='Adam: beta1 parameter (default %(default)s)',
            metavar='BETA1', default=BETA1)
    parser.add_argument('--beta2', type=float,
            dest='beta2', help='Adam: beta2 parameter (default %(default)s)',
            metavar='BETA2', default=BETA2)
    parser.add_argument('--eps', type=float,
            dest='epsilon', help='Adam: epsilon parameter (default %(default)s)',
            metavar='EPSILON', default=EPSILON)
    parser.add_argument('--initial',
            dest='initial', help='initial image',
            metavar='INITIAL')
    parser.add_argument('--initial-noiseblend', type=float,
            dest='initial_noiseblend', help='ratio of blending initial image with normalized noise (if no initial image specified, content image is used) (default %(default)s)',
            metavar='INITIAL_NOISEBLEND')
    parser.add_argument('--preserve-colors', action='store_true',
            dest='preserve_colors', help='style-only transfer (preserving colors) - if color transfer is not needed')
    parser.add_argument('--pooling',
            dest='pooling', help='pooling layer configuration: max or avg (default %(default)s)',
            metavar='POOLING', default=POOLING)
    return parser


def main():
    parser = build_parser()
    options = parser.parse_args()

    if not os.path.isfile(options.network):
        parser.error("Network %s does not exist. (Did you forget to download it?)" % options.network)

    content_image = imread(options.content)
    style_images = [imread(style) for style in options.styles]

    width = options.width
    if width is not None:
        new_shape = (int(math.floor(float(content_image.shape[0]) /
                content_image.shape[1] * width)), width)
        content_image = scipy.misc.imresize(content_image, new_shape)
    target_shape = content_image.shape
    for i in range(len(style_images)):
        style_scale = STYLE_SCALE
        if options.style_scales is not None:
            style_scale = options.style_scales[i]
        style_images[i] = scipy.misc.imresize(style_images[i], style_scale *
                target_shape[1] / style_images[i].shape[1])

    style_blend_weights = options.style_blend_weights
    if style_blend_weights is None:
        # default is equal weights
        style_blend_weights = [1.0/len(style_images) for _ in style_images]
    else:
        total_blend_weight = sum(style_blend_weights)
        style_blend_weights = [weight/total_blend_weight
                               for weight in style_blend_weights]

    initial = options.initial
    if initial is not None:
        initial = scipy.misc.imresize(imread(initial), content_image.shape[:2])
        # Initial guess is specified, but not noiseblend - no noise should be blended
        if options.initial_noiseblend is None:
            options.initial_noiseblend = 0.0
    else:
        # Neither inital, nor noiseblend is provided, falling back to random generated initial guess
        if options.initial_noiseblend is None:
            options.initial_noiseblend = 1.0
        if options.initial_noiseblend < 1.0:
            initial = content_image

    if options.checkpoint_output and "%s" not in options.checkpoint_output:
        parser.error("To save intermediate images, the checkpoint output "
                     "parameter must contain `%s` (e.g. `foo%s.jpg`)")

    for iteration, image in stylize(
        network=options.network,
        initial=initial,
        initial_noiseblend=options.initial_noiseblend,
        content=content_image,
        styles=style_images,
        preserve_colors=options.preserve_colors,
        iterations=options.iterations,
        content_weight=options.content_weight,
        content_weight_blend=options.content_weight_blend,
        style_weight=options.style_weight,
        style_layer_weight_exp=options.style_layer_weight_exp,
        style_blend_weights=style_blend_weights,
        tv_weight=options.tv_weight,
        learning_rate=options.learning_rate,
        beta1=options.beta1,
        beta2=options.beta2,
        epsilon=options.epsilon,
        pooling=options.pooling,
        print_iterations=options.print_iterations,
        checkpoint_iterations=options.checkpoint_iterations
    ):
        output_file = None
        combined_rgb = image
        if iteration is not None:
            if options.checkpoint_output:
                output_file = options.checkpoint_output % iteration
        else:
            output_file = options.output
        if output_file:
            imsave(output_file, combined_rgb)


def imread(path):
    img = scipy.misc.imread(path).astype(np.float)
    if len(img.shape) == 2:
        # grayscale
        img = np.dstack((img,img,img))
    elif img.shape[2] == 4:
        # PNG with alpha channel
        img = img[:,:,:3]
    return img


def imsave(path, img):
    img = np.clip(img, 0, 255).astype(np.uint8)
    Image.fromarray(img).save(path, quality=95)

if __name__ == '__main__':
    main()