AI模拟清明上河图

1、AI模拟清明上河图引言：我们知道清明上河图是我国国画的代表作之一，是中国十大传世名画之一。 为北宋风俗画，北宋画家张择端仅见的存世精品，属国宝级文物，现藏于北京故宫博物 院。清明上河图宽24.8厘米、长528.7厘米，绢本设色。作品以长卷形式，采用散点透视构图法，生动记录了中国十二世纪北宋都城东京（又称汴京，今河南开封）的城市 面貌和当时社会各阶层人民的生活状况，是北宋时期都城汴京当年繁荣的见证，也是北 宋城市经济情况的写照。这在中国乃至世界绘画史上都是独一无二的。在五米多长的画卷里，共绘了数量 庞大的各色人物，牛、骡、驴等牲畜，车、轿、大小船只，房屋、桥梁、城楼等各有特 色，体现了宋代建筑的

2、特征。具有很高的历史价值和艺术价值。清明上河图虽然 场面热闹，但表现的并非繁荣市景，而是一幅带有忧患意识的“盛世危图”，官兵懒散税 务重。而我们今天的项目就是通过对算法的改造，实现属于自己的清明上河图。下面我们将利用vggl9模型训练画作，详细步骤如下，并且我在每个代码上面都注 释了方便查看：首先我们导入先关的库：import tensorflow as tfimport numpy as npimport scipy.ioimport scipy.miscimport osimport time接着定义一些变量方便调用：CONTENT_IMG = 'l.pngfSTYLEJMG =

3、"sty.jpg'OUTPUT_DIR = 'neuraLstylejTansfejtensorflow/'再创建一个目录用来保存图片：if not os.path.exists(OUTPUT_DIR):os.mkdir(OUTPUT_DIR)定义生成图像的长宽通道等信息IMAGE.W = 400IMAGE.H = 300COLOR-C = 3NOISE.RATIO = 0.7BETA = 5ALPHA = 100再接着定义模型路径VGG_MODEL = ,imagenet-vgg-verydeep-19.mat,生成一个参数矩阵，作为图像的处理过程之一，对像

4、素值运算：MEAN.VALUES = np.array(123.68,116.779,103.939).reshape(l, 1,1, 3)再接着定义读取模型函数，下面我都有所注解：def load_vgg_model(path)： fitDetails of the VGG19 model:- 0 is convl_1 (3, 3, 3, 64)- 1 is relu- 2 is convl_2 (3, 3, 64, 64)- 3 is relu- 4 is maxpool- 5 is conv2_l (3, 3, 64,128)- 6 is relu- 7 is conv2_2 (3, 3

5、,128,128)- 8 is relu- 9 is maxpool- 10 is conv3_l (3, 3,128, 256)- 11 is relu- 12 is conv3_2 (3, 3, 256, 256)- 13 is relu- 14 is conv3_3 (3, 3, 256, 256)- 15 is relu- 16 is conv3_4 (3, 3, 256, 256)- 17 is relu- 18 is maxpool- 19 is conv4_l (3, 3, 256, 512)- 20 is relu- 21 is conv4_2 (3, 3, 512, 512)

6、- 22 is relu- 23 is conv4_3 (3, 3, 512, 512)- 24 is relu- 25 is conv4_4 (3, 3, 512, 512)- 26 is relu- 27 is maxpool- 28 is convS.l (3, 3, 512, 512)- 29 is relu- 30 is conv5_2 (3, 3, 512, 512)- 31 is relu- 32 is conv5_3 (3, 3, 512, 512)- 33 is relu- 34 is conv5_4 (3, 3, 512, 512)- 35 is relu- 36 is m

7、axpool- 37 is fullyconnected (7, 7, 512, 4096)- 38 is relu- 39 is fullyconnected (1,1, 4096, 4096)- 40 is relu- 41 is fullyconnected (1,1, 4096,1000)- 42 is softmaxvgg = scipy.io.loadmat(path) vggjayers = vgg'layers'#加载vgg模型获取模型各层参数和名称def-weights(layei; expectedJayer_name):W = vgg_layers0 la

8、yer 0 0 2 0 0 b = vgg_layersO layer 0 0 2 0 1 layer,name = vgg_layersO layer 0 0 0 0 assert layer_name = expected_layer_iiame return W b#将加载的变量初始化成tf可运算的张量类型，函数返回值为激活函数的输出def_conv2d_relu(prev_layei; layer, layer_name)：W； b = .weights(layei; layerjiame)W = tf.constant(W)b = tf.constant(np.reshape(b,

9、(b.size)return tf.nn.relu(tf.iin.conv2d(prevjayer, filter=W, strides=l, 1,1,1, pa(iding='SAME,) + b)#定义池化层函数def-avgpoolfprevjayer)：return tf.nn.avg_pool(prev_layei; ksize=l, 2, 2,1, strides=l, 2, 2,1, padding='SAME,)#将各层输出值都放到列表中方便加载，形成字典 graph = graph 'input1 = tf.Variablefnp.zerosCfl,

10、IMAGE.H, IMAGE.W, COLOR/), dtype=,float32')#定义'convlJL为vgg模型的第0层，输入层为上一层的'input graph'convl_l' = _convZd.relufgraphf'input', 0, 'convl.11) graphconvl_2' = _conv2d_relu(graph'convl_l,/ 2, onvl-Z') graphCavgpooll1 = _avgpool(graph'c<mvl_2')graph&#

11、39;conv2_l* = .convZd.relufgraphf'avgpooll', 5, onvZ.l') graphconv2_2' = _conv2d_relu(graph,conv2_l, 7, "convZ-Z') graph *avgpool2' = _avgpool(graph'coiiv2_2')graph 'conv3_l'graph'conv3_2'graph'conv3_3'graph ,conv3_4,= -conv2d_relu(graph&#

12、39;avgpool2, 10, 'conv3_l')= -conv2d_relu(graph,conv3_l,12, 'conv3_2') = -conv2d_relu(graph'conv3_2, 14/conv3_3')=conv2d.relu(graph*conv3_3, 16, ,conv3_4,)graph'avgpoo!3' = _avgpool(graph'conv3_4,)graph 'convl.l'graph'conv4_2'graph 'conv4_3'

13、;graph ,conv4_4,= -conv2d_relu(graph'avgpool3', 19, 'conv4_l*)= -conv2d_relu(graph'conv4_l, 21, ,conv4_2,) = -conv2d_relu(graph'conv4_2,J, 23, 'conv4_31)= -conv2d_relu(graph'conv4_3, 25, ,conv4_4,)graph 'conv5_l'graph 'conv5_2'graphCconvS.B'graph '

14、conv5_4'graph 'avgpool4' = _avgpool(graph'conv4_4') = -conv2d_relu(graph,avgpool4'> 28, 'convS-l1)= -conv2d_relu(graph,conv5_l, 30, 'conv5_2')=.convZd_relu(graph'conv5_2', 32,'conv5_3')=.convZd.relu(graph'conv5_3*, 34, ,conv5_4,)graph'av

15、gpoo!5' = _avgpool(graph'conv5_4')return graph为了实现自己的项目效果，设定损失函数：#定义内容损失函数，变量为tf计算图和vgg模型参数，返回值为损失值def contentJoss_func(sess, model)：#p 就是 model'conv4_2,)#St, x 是 model,conv4_2,)def .content Joss(p, x):#p 的值为 Tensor(HRelu_9:0,； shape=(l, 75,100, 512), dtype=float32),故 N 为512, M为75*10

16、0,分别为卷积核个数，卷积核大小的宽*100N = p.shape3M = p.shapel * p.shape2return (1 / (4 * N * M) * tf.reduce_sum(tf.pow(x - p, 2)return _contentjoss(sess.run(model,conv4.2,)f modelfconv4_2')STYLE.LAYERS = (fconvl_l; 0.5), fconv2_l; 1.0), CconyS.11, l.S), (fconv4_l; 3.0), (,conv5_l 4.0)#返回值为_styleoss的值*0.5,1,1.5

17、,4的加和def stylejoss_func(sess/ model):def _gram_matrix(F, N, M):Ft = tf.reshape(F, (M, N)return tf.inatmul(tf.transpose(Ft)f Ft)#a,x 都为，convl_l'j convZirconvBr/convdl', tonv5_l'中的参数遍历 def_style_loss(a, x)：#同内容损失函数N = a.shape3M = a.shapel * a.shape2A = .gram.matrixfa, N, M)G = _gram_matrix

18、(x, N, M)return (1 / (4 * N * 2 * M * 2) * tf.reduce_sum(tf.pow(G - A, 2)return sum(_stylejoss(sess.run(modellayer_name), modellayer_name) * w for , w in STYLE-LAYERS)再定义生成图片，读取图片，保存图片函数：#产生噪声图片def generate_noise_image(content_image, noise_ratio=NOISE_RATIO):#随机产生矩阵图片，矩阵元素内容符合标准正太分布noisejm

19、age = np.random.unifo011(-20, 20, (1, IMAGE.H, IMAGE,W,COLOR_C).astype('float32')#将产生的矩阵内各元素与神经网络加和inputjmage = noisejmage * noise_ratio + contentjmage * (1 - noise_ratio) return inputjmage#读取图片，改变尺寸，变成1行多列矩阵，将矩阵与初始值相减返回def loadjmage(path)：image = scipymisc.imread(path)image = scipy.misc.imr

20、esize(imagel (IMAGE.H, IMAGE.W)#image.shape 为800,600,3则(1, ) + image.shape)为1,800,600,3image = np.reshape(imagel (1,) + image.shape)#MEAN_VALUES =】ip.airay(123.68,116.779,103.939).reshape(l, 1,1, 3)#其中image为三通道矩阵，MEAN.VALUES为三维矩阵可以相减image = image - MEAN_VALUESreturn image#保存图片def save_image(path, im

21、age):image = image + MEAN_ VALUES并参见上面图像加载时多加了 1维，故形成时要减少维度，image = image 0#截取所有数值在5255之间的，因为像素值必须是这个范围。而参数运算后可能 会超过这个值image = np.clip(image, 0, 255).astype('uint8')#保存scipy.misc.imsavetpath, image)下面是训练加载：#启动计算图with tf.Session() as sess：#读取图片，返回值为减去MEAN_VALUES的矩阵，矩阵形状为1,800,600,3 contentjma

22、ge = load_image(CONTENT_IMG)style Jmage = loadJmage(STYLEJMG)# 加载vggl9模型，返回值为一个字典，里面为各网络层参数，输入和输出model = load_vgg_model(VGG_MODEL)# 产生噪声图片，返回值为随机矩阵加上网络层参数的新矩阵inputjmage = generate_noise_image(contentjmage)# 变量初始化sess.run(tf.globaLvariables_initializerQ)# 从网络层input层开始运算内容图片矩阵sess.run(model'input,.assigii(content Jmage)contentjoss = contentjoss.funcfsess, model)# 从网络层input层开始运算内容图片矩阵sess.run(modelvinput9.assign(stylejmage)style Joss = style_loss_fiinc(sess/ model)# 总损失为内容损失加上风格损失totaljoss = BETA * contentjoss + ALPHA * stylejoss# 建立优化器以调整参