环境配置
安装必要的Python库，包括TensorFlow或PyTorch作为深度学习框架，以及OpenCV或Pillow用于图像处理。推荐使用conda或pip创建虚拟环境以避免依赖冲突。
pip install tensorflow opencv-python numpy选择预训练模型
下载VGG19或ResNet等预训练模型作为风格迁移的基础网络。这些模型已在大规模数据集上训练，能够有效提取图像特征。Hugging Face或TensorFlow Hub提供可直接调用的模型。
import tensorflow_hub as hub hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')图像预处理
将内容图像和风格图像调整为相同尺寸，通常为512x512像素。使用OpenCV读取图像并归一化像素值到[0,1]范围。
import cv2 content_img = cv2.resize(cv2.imread('content.jpg'), (512, 512)) / 255.0 style_img = cv2.resize(cv2.imread('style.jpg'), (512, 512)) / 255.0风格迁移执行
将预处理后的图像输入模型。TensorFlow Hub的模型可直接输出结果图像，无需手动定义损失函数和优化步骤。
outputs = hub_model(tf.constant(content_img), tf.constant(style_img)) stylized_img = outputs[0]结果后处理
将模型输出的张量转换为可显示的图像格式。可能需要调整亮度和对比度，或使用OpenCV的cvtColor函数转换颜色空间。
import numpy as np result = np.clip(stylized_img[0].numpy() * 255, 0, 255).astype('uint8') cv2.imwrite('result.jpg', cv2.cvtColor(result, cv2.COLOR_RGB2BGR))https://www.zhihu.com/zvideo/1994209323853817439/
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参数调优
尝试调整风格权重参数控制风格化强度。不同模型可能提供content_weight和style_weight等参数，需通过实验确定最佳值。
# 部分模型支持权重调整 outputs = hub_model(content_img, style_img, content_weight=1e4, style_weight=1e-2)高级技巧
对于视频风格迁移，可逐帧处理并利用光流算法保持时序一致性。实时应用需考虑模型轻量化，如使用MobileNetV3等轻量架构替换VGG19。
