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图像合成器模块 - 基础版本和传统方法。
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"""
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import numpy as np
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from typing import Tuple, Dict, Any
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from scipy.optimize import minimize
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import warnings
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from abc import ABC, abstractmethod
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warnings.filterwarnings("ignore", category=UserWarning)
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class BaseImageSynthesizer(ABC):
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"""图像合成器基类"""
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@abstractmethod
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def set_content(self, image: np.ndarray):
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"""设置内容图像"""
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pass
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@abstractmethod
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def set_style_features(self, features: Dict[str, Any]):
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"""设置风格特征"""
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pass
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@abstractmethod
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def synthesize(self, initial_image: np.ndarray = None, **kwargs) -> np.ndarray:
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"""合成图像"""
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pass
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class ImageSynthesizer(BaseImageSynthesizer):
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"""
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传统图像合成器，负责生成最终的风格化图像。
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"""
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def __init__(self, config: Dict[str, Any]):
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"""
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初始化合成器。
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Args:
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config: 配置字典。
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"""
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self.config = config
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self.content_image: np.ndarray = None
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self.style_features: Dict[str, Any] = {}
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self.synthesized_image: np.ndarray = None
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def set_content(self, image: np.ndarray):
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"""
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设置内容图像。
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Args:
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image: 内容图像，形状为 (H, W, C)。
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"""
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self.content_image = image.astype(np.float32) / 255.0
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def set_style_features(self, features: Dict[str, Any]):
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"""
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设置风格特征。
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Args:
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features: 由 StyleFeatureExtractor 提取的特征字典。
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"""
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self.style_features = features
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def synthesize(self, initial_image: np.ndarray = None, max_iter: int = 200) -> np.ndarray:
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"""
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合成风格化图像。
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Args:
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initial_image: 初始图像，如果为None，则使用内容图像。
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max_iter: 最大迭代次数。
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Returns:
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合成后的风格化图像，形状为 (H, W, C)，值域 [0, 255]。
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"""
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if initial_image is None:
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initial_image = self.content_image.copy()
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# 将图像展平为一维向量以便于优化
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h, w, c = self.content_image.shape
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x0 = initial_image.ravel()
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# 定义损失函数
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def loss_function(x):
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# 重塑回图像形状
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img = x.reshape((h, w, c))
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# 1. 内容损失：保持与原图相似
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content_loss = np.mean((img - self.content_image) ** 2)
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# 2. 风格损失：匹配风格特征
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style_loss = self._calculate_style_loss(img)
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# 总损失
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total_loss = content_loss + self.config.get('style_weight', 1.0) * style_loss
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return total_loss
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# 执行优化
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result = minimize(loss_function, x0, method='L-BFGS-B',
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options={'maxiter': max_iter, 'disp': False})
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# 重塑结果并转换回[0, 255]
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synthesized = result.x.reshape((h, w, c))
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synthesized = np.clip(synthesized, 0, 1) * 255
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self.synthesized_image = synthesized.astype(np.uint8)
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return self.synthesized_image
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def _calculate_style_loss(self, image: np.ndarray) -> float:
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"""
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计算当前图像与目标风格特征之间的损失。
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Args:
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image: 当前合成的图像。
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Returns:
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风格损失值。
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"""
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loss = 0.0
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# 1. 颜色直方图损失
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hist_r, _ = np.histogram(image[:, :, 0].ravel(), bins=32, range=(0, 1))
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hist_g, _ = np.histogram(image[:, :, 1].ravel(), bins=32, range=(0, 1))
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hist_b, _ = np.histogram(image[:, :, 2].ravel(), bins=32, range=(0, 1))
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current_hist = np.concatenate([hist_r, hist_g, hist_b])
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target_hist = self.style_features['color_histogram']
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# 使用KL散度作为距离度量
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epsilon = 1e-10
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kl_div = np.sum(target_hist * np.log((target_hist + epsilon) / (current_hist + epsilon)))
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loss += kl_div
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# 2. 纹理强度损失
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diff_h = np.abs(np.diff(image, axis=1)).mean()
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diff_v = np.abs(np.diff(image, axis=0)).mean()
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current_texture = np.array([diff_h, diff_v])
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target_texture = self.style_features['texture_strength']
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texture_loss = np.mean((current_texture - target_texture) ** 2)
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loss += texture_loss
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# 3. 平均颜色损失
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current_mean = image.mean(axis=(0, 1))
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target_mean = self.style_features['mean_color']
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mean_loss = np.mean((current_mean - target_mean) ** 2)
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loss += mean_loss
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return loss
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# 工厂函数
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def create_image_synthesizer(synthesizer_type: str, config: Dict[str, Any]) -> BaseImageSynthesizer:
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"""
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创建图像合成器工厂函数。
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Args:
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synthesizer_type: 合成器类型 ('traditional' 或 'neural')
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config: 配置字典
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Returns:
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图像合成器实例
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"""
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if synthesizer_type == 'traditional':
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return ImageSynthesizer(config)
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elif synthesizer_type == 'neural':
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from .neural_image_synthesizer import NeuralImageSynthesizer
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return NeuralImageSynthesizer(config)
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else:
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raise ValueError(f"不支持的合成器类型: {synthesizer_type}")
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