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准备

官方教程： 任意风格的快速风格转换

模型下载地址： https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2

学习

加载要处理的内容图片和风格图片

# 用于将图像裁剪为方形def crop_center(image):# 图片原始形状shape = image.shape# 新形状new_shape = min(shape[1], shape[2])offset_y = max(shape[1]-shape[2], 0) // 2offset_x = max(shape[2]-shape[1], 0) // 2# 返回新图片image = tf.image.crop_to_bounding_box(image, offset_y, offset_x, new_shape, new_shape)return image# 加载并预处理图片def load_image(image_url, image_size=(256, 256), preserve_aspect_ratio=True):# 缓存图像文件image_path = tf.keras.utils.get_file(os.path.basename(image_url)[-128:], image_url)# 加载并转换为float32 numpy数组，添加批次维度，并规范化为范围[0，1]。img = tf.io.decode_image(tf.io.read_file(image_path),channels=3, dtype=tf.float32)[tf.newaxis, ...]img = crop_center(img)img = tf.image.resize(img, image_size, preserve_aspect_ratio=True)return img# 展示图片def show_n(images, titles=('',)):n = len(images)image_sizes = [image.shape[1] for image in images]w = (image_sizes[0] * 6) // 320plt.figure(figsize=(w * n, w))gs = gridspec.GridSpec(1, n, width_ratios=image_sizes)for i in range(n):plt.subplot(gs[i])plt.imshow(images[i][0], aspect='equal')plt.axis('off')plt.title(titles[i] if len(titles) > i else '')plt.show()content_image_url = 'https://scpic3.chinaz.net/files/default/imgs/2023-11-16/6e397d19e172be9f_s.jpg'
style_image_url = 'https://scpic3.chinaz.net/files/default/imgs/2023-11-05/d217bbaf821e3a73_s.jpg'
output_image_size = 384# 调整内容图像的大小
content_img_size = (output_image_size, output_image_size)
#  样式图片大小
style_img_size = (256, 256)
# 加载并展示图片
content_image = load_image(content_image_url, content_img_size)
style_image = load_image(style_image_url, style_img_size)
style_image = tf.nn.avg_pool(style_image, ksize=[3, 3], strides=[1, 1], padding='SAME')
show_n([content_image, style_image], ['Content image', 'Style image'])

加载模型进行风格迁移

# 加载模型
hub_module = hub.load('./magenta_arbitrary-image-stylization-v1-256_2')
# 风格迁移
outputs = hub_module(tf.constant(content_image), tf.constant(style_image))
stylized_image = outputs[0]
# 展示迁移后的图片
show_n([content_image, style_image, stylized_image], titles=['Original content image', 'Style image', 'Stylized image'])

加载本地图片
加载本地图片的话，只需要将加载网络图片的代码改成下面的

def load_image(image_url, image_size=(256, 256), preserve_aspect_ratio=True):# 缓存图像文件# image_path = tf.keras.utils.get_file(#     os.path.basename(image_url)[-128:], image_url)# 加载并转换为float32 numpy数组，添加批次维度，并规范化为范围[0，1]。img = tf.io.decode_image(tf.io.read_file(image_url),channels=3, dtype=tf.float32)[tf.newaxis, ...]img = crop_center(img)img = tf.image.resize(img, image_size, preserve_aspect_ratio=True)return img

下面的效果图是基于一只狗和梵高的星空生成的

完整代码

# import os
from matplotlib import gridspec
import matplotlib.pylab as plt
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub# 用于将图像裁剪为方形def crop_center(image):# 图片原始形状shape = image.shape# 新形状new_shape = min(shape[1], shape[2])offset_y = max(shape[1]-shape[2], 0) // 2offset_x = max(shape[2]-shape[1], 0) // 2# 返回新图片image = tf.image.crop_to_bounding_box(image, offset_y, offset_x, new_shape, new_shape)return image# 加载并预处理图片def load_image(image_url, image_size=(256, 256), preserve_aspect_ratio=True):# 缓存图像文件# image_path = tf.keras.utils.get_file(#     os.path.basename(image_url)[-128:], image_url)# 加载并转换为float32 numpy数组，添加批次维度，并规范化为范围[0，1]。img = tf.io.decode_image(tf.io.read_file(image_url),channels=3, dtype=tf.float32)[tf.newaxis, ...]img = crop_center(img)img = tf.image.resize(img, image_size, preserve_aspect_ratio=True)return img# 展示图片def show_n(images, titles=('',)):n = len(images)image_sizes = [image.shape[1] for image in images]w = (image_sizes[0] * 6) // 320plt.figure(figsize=(w * n, w))gs = gridspec.GridSpec(1, n, width_ratios=image_sizes)for i in range(n):plt.subplot(gs[i])plt.imshow(images[i][0], aspect='equal')plt.axis('off')plt.title(titles[i] if len(titles) > i else '')plt.show()content_image_url = 'image/dog.png'
style_image_url = 'image/fangao.png'
output_image_size = 384# 调整内容图像的大小
content_img_size = (output_image_size, output_image_size)
#  样式图片大小
style_img_size = (256, 256)
# 加载图片
content_image = load_image(content_image_url, content_img_size)
style_image = load_image(style_image_url, style_img_size)
style_image = tf.nn.avg_pool(style_image, ksize=[3, 3], strides=[1, 1], padding='SAME')
# 展示图片
# show_n([content_image, style_image], ['Content image', 'Style image'])# 加载模型
hub_module = hub.load('./magenta_arbitrary-image-stylization-v1-256_2')
# 风格迁移
outputs = hub_module(tf.constant(content_image), tf.constant(style_image))
stylized_image = outputs[0]
# 展示迁移后的图片
show_n([content_image, style_image, stylized_image], titles=['Original content image', 'Style image', 'Stylized image'])