搜索出一个完整的人脸神经风格迁移python算法

以下是一个完整的人脸神经风格迁移python算法：

import tensorflow as tf
import numpy as np
import PIL.Image
import time
import functools
import cv2

# 定义VGG19网络，用于提取风格和内容特征
def vgg_layers(layer_names):
    vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')
    vgg.trainable = False
    outputs = [vgg.get_layer(name).output for name in layer_names]
    model = tf.keras.Model([vgg.input], outputs)
    return model

# 定义图像预处理函数
def load_and_process_img(img_path):
    img = tf.keras.preprocessing.image.load_img(img_path)
    img = tf.keras.preprocessing.image.img_to_array(img)
    img = tf.image.resize(img, [512, 512])
    img = tf.keras.applications.vgg19.preprocess_input(img)
    return img

# 定义图像后处理函数
def deprocess_img(processed_img):
    x = processed_img.copy()
    if len(x.shape) == 4:
        x = np.squeeze(x, 0)
    assert len(x.shape) == 3, ("Input to deprocess image must be an image of "
                               "dimension [1, height, width, channel] or [height, width, channel]")
    if len(x.shape) != 3:
        raise ValueError("Invalid input to deprocessing image")
    # perform the inverse of the preprocessing step
    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

# 定义内容损失函数
def content_loss(base_content, target):
    return tf.reduce_mean(tf.square(base_content - target))

# 定义风格损失函数
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 style_loss(base_style, gram_target):
    gram_style = gram_matrix(base_style)
    return tf.reduce_mean(tf.square(gram_style - gram_target))

# 定义总变差损失函数
def total_variation_loss(image):
    x_deltas, y_deltas = tf.image.image_gradients(image)
    return tf.reduce_mean(tf.abs(x_deltas)) + tf.reduce_mean(tf.abs(y_deltas))

# 定义训练函数
def compute_loss(model, loss_weights, init_image, gram_style_features, content_features):
    style_weight, content_weight, tv_weight = loss_weights
    model_outputs = model(init_image)
    content_output = model_outputs['block5_conv2']
    tv_loss = total_variation_loss(init_image)
    content_loss_val = content_loss(content_features['block5_conv2'], content_output)
    style_loss_val = 0
    for name in gram_style_features.keys():
        style_output = model_outputs[name]
        style_loss_val += style_loss(style_output, gram_style_features[name])
    style_loss_val *= style_weight / len(gram_style_features.keys())
    content_loss_val *= content_weight / len(content_features.keys())
    tv_loss *= tv_weight
    total_loss = style_loss_val + content_loss_val + tv_loss
    return total_loss, style_loss_val, content_loss_val, tv_loss

@tf.function()
def train_step(model, loss_weights, init_image, gram_style_features, content_features, optimizer):
    with tf.GradientTape() as tape:
        total_loss, style_loss_val, content_loss_val, tv_loss = compute_loss(model, loss_weights, init_image, gram_style_features, content_features)
    grads = tape.gradient(total_loss, init_image)
    optimizer.apply_gradients([(grads, init_image)])
    init_image.assign(tf.clip_by_value(init_image, clip_value_min=0.0, clip_value_max=255.0))

# 定义主函数
def style_transfer(content_path, style_path, num_iterations=1000, content_weight=1e3, style_weight=1e-2, tv_weight=30):
    # 加载图像
    content_image = load_and_process_img(content_path)
    style_image = load_and_process_img(style_path)
    # 提取内容特征和风格特征
    content_features = vgg_layers(['block5_conv2'])(content_image)
    style_features = vgg_layers(['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1'])(style_image)
    gram_style_features = {name: gram_matrix(style_features[name]) for name in style_features.keys()}
    # 初始化目标图像
    init_image = tf.Variable(content_image, dtype=tf.float32)
    # 定义优化器
    optimizer = 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, tv_weight)
    # 训练
    best_loss, best_img = float('inf'), None
    for i in range(num_iterations):
        train_step(vgg_layers(['block5_conv2']), loss_weights, init_image, gram_style_features, content_features, optimizer)
        loss, style_loss_val, content_loss_val, tv_loss = compute_loss(vgg_layers(['block5_conv2']), loss_weights, init_image, gram_style_features, content_features)
        if loss < best_loss:
            best_loss = loss
            best_img = deprocess_img(init_image.numpy())
        if i % 100 == 0:
            print("Iteration: {}".format(i))
            print("Total loss: {:.4e}, "
                  "style loss: {:.4e}, "
                  "content loss: {:.4e}, "
                  "tv loss: {:.4e}".format(loss, style_loss_val, content_loss_val, tv_loss))
    return best_img

# 测试
content_path = 'content.jpg'
style_path = 'style.jpg'
result = style_transfer(content_path, style_path, num_iterations=1000, content_weight=1e3, style_weight=1e-2, tv_weight=30)
cv2.imshow("result", result)
cv2.waitKey(0)