人工智能-项目实践-迁移学习-使用VGG19迁移学习实现图像风格迁移.zip

# -*- coding: utf-8 -*- # @Time : 18-3-23 下午12:20 # @Author : AaronJny # @Email : [email protected] import tensorflow as tf import numpy as np import settings import scipy.io import scipy.misc class Model(object): def __init__(self, content_path, style_path): self.content = self.loadimg(content_path) # 加载内容图片 self.style = self.loadimg(style_path) # 加载风格图片 self.random_img = self.get_random_img() # 生成噪音内容图片 self.net = self.vggnet() # 建立vgg网络 def vggnet(self): # 读取预训练的vgg模型 vgg = scipy.io.loadmat(settings.VGG_MODEL_PATH) vgg_layers = vgg['layers'][0] net = {} # 使用预训练的模型参数构建vgg网络的卷积层和池化层 # 全连接层不需要 # 注意，除了input之外，这里参数都为constant，即常量 # 和平时不同，我们并不训练vgg的参数，它们保持不变 # 需要进行训练的是input，它即是我们最终生成的图像 net['input'] = tf.Variable(np.zeros([1, settings.IMAGE_HEIGHT, settings.IMAGE_WIDTH, 3]), dtype=tf.float32) # 参数对应的层数可以参考vgg模型图 net['conv1_1'] = self.conv_relu(net['input'], self.get_wb(vgg_layers, 0)) net['conv1_2'] = self.conv_relu(net['conv1_1'], self.get_wb(vgg_layers, 2)) net['pool1'] = self.pool(net['conv1_2']) net['conv2_1'] = self.conv_relu(net['pool1'], self.get_wb(vgg_layers, 5)) net['conv2_2'] = self.conv_relu(net['conv2_1'], self.get_wb(vgg_layers, 7)) net['pool2'] = self.pool(net['conv2_2']) net['conv3_1'] = self.conv_relu(net['pool2'], self.get_wb(vgg_layers, 10)) net['conv3_2'] = self.conv_relu(net['conv3_1'], self.get_wb(vgg_layers, 12)) net['conv3_3'] = self.conv_relu(net['conv3_2'], self.get_wb(vgg_layers, 14)) net['conv3_4'] = self.conv_relu(net['conv3_3'], self.get_wb(vgg_layers, 16)) net['pool3'] = self.pool(net['conv3_4']) net['conv4_1'] = self.conv_relu(net['pool3'], self.get_wb(vgg_layers, 19)) net['conv4_2'] = self.conv_relu(net['conv4_1'], self.get_wb(vgg_layers, 21)) net['conv4_3'] = self.conv_relu(net['conv4_2'], self.get_wb(vgg_layers, 23)) net['conv4_4'] = self.conv_relu(net['conv4_3'], self.get_wb(vgg_layers, 25)) net['pool4'] = self.pool(net['conv4_4']) net['conv5_1'] = self.conv_relu(net['pool4'], self.get_wb(vgg_layers, 28)) net['conv5_2'] = self.conv_relu(net['conv5_1'], self.get_wb(vgg_layers, 30)) net['conv5_3'] = self.conv_relu(net['conv5_2'], self.get_wb(vgg_layers, 32)) net['conv5_4'] = self.conv_relu(net['conv5_3'], self.get_wb(vgg_layers, 34)) net['pool5'] = self.pool(net['conv5_4']) return net def conv_relu(self, input, wb): """ 进行先卷积、后relu的运算 :param input: 输入层 :param wb: wb[0],wb[1] == w,b :return: relu后的结果 """ conv = tf.nn.conv2d(input, wb[0], strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(conv + wb[1]) return relu def pool(self, input): """ 进行max_pool操作 :param input: 输入层 :return: 池化后的结果 """ return tf.nn.max_pool(input, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') def get_wb(self, layers, i): """ 从预训练好的vgg模型中读取参数 :param layers: 训练好的vgg模型 :param i: vgg指定层数 :return: 该层的w,b """ w = tf.constant(layers[i][0][0][0][0][0]) bias = layers[i][0][0][0][0][1] b = tf.constant(np.reshape(bias, (bias.size))) return w, b def get_random_img(self): """ 根据噪音和内容图片，生成一张随机图片 :return: """ noise_image = np.random.uniform(-20, 20, [1, settings.IMAGE_HEIGHT, settings.IMAGE_WIDTH, 3]) random_img = noise_image * settings.NOISE + self.content * (1 - settings.NOISE) return random_img def loadimg(self, path): """ 加载一张图片，将其转化为符合要求的格式 :param path: :return: """ # 读取图片 image = scipy.misc.imread(path) # 重新设定图片大小 image = scipy.misc.imresize(image, [settings.IMAGE_HEIGHT, settings.IMAGE_WIDTH]) # 改变数组形状，其实就是把它变成一个batch_size=1的batch image = np.reshape(image, (1, settings.IMAGE_HEIGHT, settings.IMAGE_WIDTH, 3)) # 减去均值，使其数据分布接近0 image = image - settings.IMAGE_MEAN_VALUE return image if __name__ == '__main__': Model(settings.CONTENT_IMAGE, settings.STYLE_IMAGE)