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