From af1c5cd019fa3d7a26c61c1fbbf3891aae5dfec8 Mon Sep 17 00:00:00 2001
From: p82b7rtam <1761133400@qq.com>
Date: Mon, 29 Dec 2025 16:59:22 +0800
Subject: [PATCH] Delete 'feature_extractor.py'

---
 feature_extractor.py | 103 -------------------------------------------
 1 file changed, 103 deletions(-)
 delete mode 100644 feature_extractor.py

diff --git a/feature_extractor.py b/feature_extractor.py
deleted file mode 100644
index 32d85b6..0000000
--- a/feature_extractor.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""
-风格特征提取器模块 - 基础版本和传统方法。
-"""
-
-import numpy as np
-from typing import Tuple, Dict, Any
-from abc import ABC, abstractmethod
-
-
-class BaseFeatureExtractor(ABC):
-    """特征提取器基类"""
-
-    @abstractmethod
-    def extract(self, image: np.ndarray) -> Dict[str, Any]:
-        """提取特征抽象方法"""
-        pass
-
-    @abstractmethod
-    def get_features(self) -> Dict[str, Any]:
-        """获取特征抽象方法"""
-        pass
-
-
-class StyleFeatureExtractor(BaseFeatureExtractor):
-    """
-    传统风格特征提取器。
-    通过计算图像的直方图和局部纹理特征来定义"风格"。
-    """
-
-    def __init__(self, config: Dict[str, Any]):
-        """
-        初始化提取器。
-
-        Args:
-            config: 配置字典，包含特征提取参数。
-        """
-        self.config = config
-        self.style_features: Dict[str, Any] = {}
-
-    def extract(self, image: np.ndarray) -> Dict[str, Any]:
-        """
-        从图像中提取风格特征。
-
-        Args:
-            image: 输入图像，形状为 (H, W, C)，值域 [0, 255]。
-
-        Returns:
-            一个包含各种风格特征的字典。
-        """
-        # 确保图像在[0, 1]范围
-        image_normalized = image.astype(np.float32) / 255.0
-
-        features = {}
-
-        # 1. 颜色直方图 (RGB通道)
-        hist_r, _ = np.histogram(image_normalized[:, :, 0].ravel(), bins=32, range=(0, 1))
-        hist_g, _ = np.histogram(image_normalized[:, :, 1].ravel(), bins=32, range=(0, 1))
-        hist_b, _ = np.histogram(image_normalized[:, :, 2].ravel(), bins=32, range=(0, 1))
-        features['color_histogram'] = np.concatenate([hist_r, hist_g, hist_b])
-
-        # 2. 局部纹理特征
-        diff_h = np.abs(np.diff(image_normalized, axis=1)).mean()
-        diff_v = np.abs(np.diff(image_normalized, axis=0)).mean()
-        features['texture_strength'] = np.array([diff_h, diff_v])
-
-        # 3. 平均颜色
-        features['mean_color'] = image_normalized.mean(axis=(0, 1))
-
-        # 4. 颜色标准差
-        features['color_std'] = image_normalized.std(axis=(0, 1))
-
-        self.style_features = features
-        return features
-
-    def get_features(self) -> Dict[str, Any]:
-        """
-        获取最近一次提取的风格特征。
-
-        Returns:
-            风格特征字典。
-        """
-        return self.style_features.copy()
-
-
-# 工厂函数，便于创建不同类型的提取器
-def create_feature_extractor(extractor_type: str, config: Dict[str, Any]) -> BaseFeatureExtractor:
-    """
-    创建特征提取器工厂函数。
-
-    Args:
-        extractor_type: 提取器类型 ('traditional' 或 'neural')
-        config: 配置字典
-
-    Returns:
-        特征提取器实例
-    """
-    if extractor_type == 'traditional':
-        return StyleFeatureExtractor(config)
-    elif extractor_type == 'neural':
-        from .neural_feature_extractor import NeuralStyleFeatureExtractor
-        return NeuralStyleFeatureExtractor(config)
-    else:
-        raise ValueError(f"不支持的提取器类型: {extractor_type}")
\ No newline at end of file