vgg16代码实现风格迁移
时间: 2023-11-29 15:43:59 浏览: 40
利用VGG16实现风格迁移的步骤如下:
1. 利用VGG16构建三个神经网络,分别对应内容图像输入、风格图像输入和白噪声图像。
```
cModel = VGG16(include_top=False, weights='imagenet', input_tensor=cImArr)
sModel = VGG16(include_top=False, weights='imagenet', input_tensor=sImArr)
gModel = VGG16(include_top=False, weights='imagenet', input_tensor=gImPlaceholder)
```
2. 利用VGG16的部分层输出获取风格特征和内容特征。
3. 定义损失函数,包括内容损失和风格损失。
4. 定义优化器,使用梯度下降法最小化损失函数。
5. 迭代更新白噪声图像,直到达到预设的迭代次数或损失函数收敛。
相关问题
如何利用vgg19实现风格迁移
利用vgg19实现风格迁移的方法通常包括以下步骤:
1.准备数据:需要准备一张内容图片和一张风格图片。
2.加载模型:使用预训练的VGG19模型,可以在Keras或PyTorch中加载。
3.选择层:选择需要进行风格迁移的VGG19模型中的一些层,这些层将用于提取内容和风格特征。
4.计算内容损失:通过比较内容图像和生成的图像之间的特征差异,计算内容损失。
5.计算风格损失:通过比较风格图像和生成的图像之间的特征差异,计算风格损失。
6.计算总损失:将内容损失和风格损失加权组合成总损失,用于优化模型。
7.优化模型:通过反向传播算法,优化总损失,生成新的图像。
可以使用以下代码实现vgg19的风格迁移:
``` python
import tensorflow as tf
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.applications.vgg19 import preprocess_input
import numpy as np
def load_and_process_image(image_path):
img = load_img(image_path)
img = img_to_array(img)
img = preprocess_input(img)
img = np.expand_dims(img, axis=0)
return img
def deprocess_image(x):
x[:, :, 0] += 103.939
x[:, :, 1] += 116.779
x[:, :, 2] += 123.68
x = x[:, :, ::-1]
x = np.clip(x, 0, 255).astype('uint8')
return x
def get_content_loss(base_content, target):
return tf.reduce_mean(tf.square(base_content - target))
def gram_matrix(input_tensor):
channels = int(input_tensor.shape[-1])
a = tf.reshape(input_tensor, [-1, channels])
n = tf.shape(a)[0]
gram = tf.matmul(a, a, transpose_a=True)
return gram / tf.cast(n, tf.float32)
def get_style_loss(base_style, gram_target):
gram_style = gram_matrix(base_style)
return tf.reduce_mean(tf.square(gram_style - gram_target))
def get_feature_representations(model, content_path, style_path):
content_image = load_and_process_image(content_path)
style_image = load_and_process_image(style_path)
content_outputs = model(content_image)
style_outputs = model(style_image)
content_features = [content_layer[0] for content_layer in content_outputs[:4]]
style_features = [style_layer[0] for style_layer in style_outputs[:4]]
return content_features, style_features
def compute_loss(model, loss_weights, init_image, gram_style_features, content_features):
style_weight, content_weight = loss_weights
model_outputs = model(init_image)
style_output_features = model_outputs[:4]
content_output_features = model_outputs[4:]
style_score = 0
content_score = 0
weight_per_style_layer = 1.0 / float(len(style_output_features))
for target_style, comb_style in zip(gram_style_features, style_output_features):
style_score += weight_per_style_layer * get_style_loss(comb_style[0], target_style)
weight_per_content_layer = 1.0 / float(len(content_output_features))
for target_content, comb_content in zip(content_features, content_output_features):
content_score += weight_per_content_layer * get_content_loss(comb_content[0], target_content)
style_score *= style_weight
content_score *= content_weight
loss = style_score + content_score
return loss, style_score, content_score
def compute_grads(cfg):
with tf.GradientTape() as tape:
all_loss = compute_loss(**cfg)
total_loss = all_loss[0]
return tape.gradient(total_loss, cfg['init_image']), all_loss
def run_style_transfer(content_path, style_path, num_iterations=1000, content_weight=1e3, style_weight=1e-2):
model = VGG19(include_top=False, weights='imagenet')
for layer in model.layers:
layer.trainable = False
content_features, style_features = get_feature_representations(model, content_path, style_path)
gram_style_features = [gram_matrix(style_feature) for style_feature in style_features]
init_image = load_and_process_image(content_path)
init_image = tf.Variable(init_image, dtype=tf.float32)
opt = tf.optimizers.Adam(learning_rate=5, beta_1=0.99, epsilon=1e-1)
loss_weights = (style_weight, content_weight)
cfg = {
'model': model,
'loss_weights': loss_weights,
'init_image': init_image,
'gram_style_features': gram_style_features,
'content_features': content_features
}
best_loss, best_img = float('inf'), None
for i in range(num_iterations):
grads, all_loss = compute_grads(cfg)
loss, style_score, content_score = all_loss
opt.apply_gradients([(grads, init_image)])
clipped = tf.clip_by_value(init_image, clip_value_min=0.0, clip_value_max=255.0)
init_image.assign(clipped)
if loss < best_loss:
best_loss = loss
best_img = deprocess_image(init_image.numpy())
print("Iteration: {}".format(i))
return best_img
```
这个函数可以根据您提供的内容图像和风格图像生成新的图像,可以调整参数来改变生成图像的外观。
vgg19图像风格迁移代码
VGG19图像风格迁移是一种通过深度学习实现图像风格转移的方法,其基于深度神经网络VGG19。其代码实现可以分为以下几步:
1. 导入必要的库:包括numpy、tensorflow等。
2. 加载预训练的VGG19模型:使用tensorflow的tf.keras.applications中的VGG19模型进行预训练。
3. 提取内容图片和风格图片的特征:使用预训练的VGG19模型提取内容图片和风格图片的特征。
4. 定义损失函数:将内容损失和风格损失加权求和作为总损失函数。
5. 使用优化器进行训练:通过梯度下降优化器来更新生成图片。
以下是简单的代码实现:
```
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications import VGG19
from tensorflow.keras.preprocessing.image import load_img, img_to_array
# 加载VGG19模型,去掉最后一层输出
vgg = VGG19(include_top=False, weights='imagenet')
# 定义内容图片和风格图片的路径
content_path = 'content.jpg'
style_path = 'style.jpg'
# 加载图片并进行预处理
def load_and_process_img(path):
img = load_img(path)
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
img = tf.keras.applications.vgg19.preprocess_input(img)
return img
# 提取内容图片和风格图片的特征
def get_feature_representations(model, content_path, style_path):
content_image = load_and_process_img(content_path)
style_image = load_and_process_img(style_path)
content_feature_maps = model.predict(content_image)
style_feature_maps = model.predict(style_image)
return content_feature_maps, style_feature_maps
# 计算Gram矩阵
def gram_matrix(input_tensor):
channels = int(input_tensor.shape[-1])
a = tf.reshape(input_tensor, [-1, channels])
n = tf.shape(a)
gram = tf.matmul(a, a, transpose_a=True)
return gram / tf.cast(n, tf.float32)
# 定义内容损失
def get_content_loss(base_content, target):
return tf.reduce_mean(tf.square(base_content - target))
# 定义风格损失
def get_style_loss(base_style, gram_target):
return tf.reduce_mean(tf.square(gram_matrix(base_style) - gram_target))
# 定义总损失函数
def compute_loss(model, loss_weights, init_image, gram_style_features, content_features):
style_weight, content_weight = loss_weights
model_outputs = model(init_image)
style_output_features = model_outputs[:len(gram_style_features)]
content_output_features = model_outputs[len(gram_style_features):]
style_score = 0
content_score = 0
weight_per_style_layer = 1.0 / float(len(style_output_features))
for target_style, comb_style in zip(gram_style_features, style_output_features):
style_score += weight_per_style_layer * get_style_loss(comb_style, target_style)
weight_per_content_layer = 1.0 / float(len(content_output_features))
for target_content, comb_content in zip(content_features, content_output_features):
content_score += weight_per_content_layer* get_content_loss(comb_content, target_content)
style_score *= style_weight
content_score *= content_weight
loss = style_score + content_score
return loss
# 定义优化器
def run_style_transfer(content_path, style_path,
num_iterations=1000,
content_weight=1e3,
style_weight=1e-2):
# 提取内容图片和风格图片的特征
content_features, style_features = get_feature_representations(vgg, content_path, style_path)
gram_style_features = [gram_matrix(feature) for feature in style_features]
init_image = load_and_process_img(content_path)
init_image = tf.Variable(init_image, dtype=tf.float32)
opt = tf.optimizers.Adam(learning_rate=5, beta_1=0.99, epsilon=1e-1)
# 定义损失权重
loss_weights = (style_weight, content_weight)
# 进行训练
for i in range(num_iterations):
with tf.GradientTape() as tape:
loss = compute_loss(vgg, loss_weights, init_image, gram_style_features, content_features)
grad = tape.gradient(loss, init_image)
opt.apply_gradients([(grad, init_image)])
clipped = tf.clip_by_value(init_image, clip_value_min=0.0, clip_value_max=255.0)
init_image.assign(clipped)
return init_image.numpy()
```
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