jupyter 风格迁移

Jupyter 风格迁移是一种将一张图片的风格应用到另一张图片上的技术。这个技术基于深度学习模型，可以将一张图片的风格转移到另一张图片上，从而创造出新的风格独特的图片。在 Jupyter 中，可以使用 Python 的深度学习框架如 TensorFlow 或 PyTorch 来实现风格迁移。

以下是一个简单的 Jupyter 风格迁移的代码示例：

import tensorflow as tf
import numpy as np
import PIL.Image
import matplotlib.pyplot as plt

# 加载图片
def load_image(img_path, max_size=512):
    img = PIL.Image.open(img_path)
    if max(img.size) > max_size:
        size = max_size
    else:
        size = max(img.size)
    img = img.resize((size, int(size * (img.size[1] / img.size[0]))))
    img = np.array(img)
    return img

# 显示图片
def show_image(image):
    plt.imshow(image.astype('uint8'))
    plt.show()

# 加载模型
model = tf.keras.applications.VGG19(include_top=False, weights='imagenet')

# 提取特征
def get_feature_representations(model, content_path, style_path):
    content_image = load_image(content_path)
    style_image = load_image(style_path)
    content_image = tf.keras.applications.vgg19.preprocess_input(content_image)
    style_image = tf.keras.applications.vgg19.preprocess_input(style_image)
    content_features = model(content_image)
    style_features = model(style_image)
    return content_features, style_features

# 计算 gram 矩阵
def gram_matrix(input_tensor):
    result = tf.linalg.einsum('bijc,bijd->bcd', input_tensor, input_tensor)
    shape = tf.shape(input_tensor)
    num_locations = tf.cast(shape[1] * shape[2], tf.float32)
    return result / (num_locations)

# 计算风格损失
def calculate_style_loss(style_features, generated_features):
    style_grams = [gram_matrix(style_feature) for style_feature in style_features]
    generated_grams = [gram_matrix(generated_feature) for generated_feature in generated_features]
    style_loss = tf.add_n([tf.reduce_mean((style_gram - generated_gram) ** 2) for style_gram, generated_gram in zip(style_grams, generated_grams)])
    return style_loss / len(style_features)

# 计算内容损失
def calculate_content_loss(content_features, generated_features):
    content_loss = tf.reduce_mean((content_features - generated_features) ** 2)
    return content_loss

# 计算总损失
def calculate_total_loss(model, loss_weights, generated_image, content_features, style_features):
    content_weight, style_weight = loss_weights
    model_outputs = model(generated_image)
    content_output = model_outputs[3]
    style_outputs = model_outputs[:-1]
    content_loss = calculate_content_loss(content_features, content_output)
    style_loss = calculate_style_loss(style_features, style_outputs)
    total_loss = content_weight * content_loss + style_weight * style_loss
    return total_loss

# 计算梯度
def calculate_gradients(model, loss_weights, generated_image, content_features, style_features):
    with tf.GradientTape() as tape:
        total_loss = calculate_total_loss(model, loss_weights, generated_image, content_features, style_features)
    gradients = tape.gradient(total_loss, generated_image)
    return gradients

# 迭代更新图片
def run_style_transfer(content_path, style_path, epochs=10, steps_per_epoch=100, content_weight=1e3, style_weight=1e-2):
    content_features, style_features = get_feature_representations(model, content_path, style_path)
    loss_weights = (content_weight, style_weight)
    generated_image = tf.Variable(load_image(content_path))
    opt = tf.optimizers.Adam(learning_rate=5, beta_1=0.99, epsilon=1e-1)
    for epoch in range(epochs):
        for step in range(steps_per_epoch):
            gradients = calculate_gradients(model, loss_weights, generated_image, content_features, style_features)
            opt.apply_gradients([(gradients, generated_image)])
            generated_image.assign(tf.clip_by_value(generated_image, 0, 255))
        show_image(generated_image.numpy())
    return generated_image

# 运行风格迁移
content_path = 'path/to/content/image'
style_path = 'path/to/style/image'
generated_image = run_style_transfer(content_path, style_path, epochs=10, steps_per_epoch=100, content_weight=1e3, style_weight=1e-2)

这段代码会将 `content_path` 中的图片的内容与 `style_path` 中的图片的风格进行结合，并输出一张新的图片。可以通过调整 `epochs`、`steps_per_epoch`、`content_weight` 和 `style_weight` 等参数来调整生成图片的效果。