fix: restructure src layout to proper package directory

Move src/__init__.py, detect.py, diagnose.py, extract_temp.py, utils/
into src/infrared_data/ to fix wheel package structure. Previously the
whl placed files at root instead of infrared_data/ directory, causing
ModuleNotFoundError on install.

Also update pyproject.toml packages.find config to use standard
src-layout discovery.

src/infrared_data/utils/__init__.py

@@ -0,0 +1 @@
+# Utils Package

src/infrared_data/utils/temp_utils.py

@@ -0,0 +1,127 @@
+"""
+温度特征提取工具函数
+从红外图像ROI区域提取温度统计特征
+"""
+
+import numpy as np
+import cv2
+from typing import Dict, List, Tuple, Optional
+
+
+def extract_temp_stats(roi: np.ndarray) -> Dict[str, float]:
+    """
+    从ROI区域提取温度统计特征
+
+    Args:
+        roi: ROI区域图像数组
+
+    Returns:
+        温度统计特征字典
+    """
+    if roi.size == 0:
+        return {
+            'temp_max': 0.0,
+            'temp_min': 0.0,
+            'temp_mean': 0.0,
+            'temp_std': 0.0,
+            'temp_median': 0.0,
+        }
+
+    return {
+        'temp_max': float(np.max(roi)),
+        'temp_min': float(np.min(roi)),
+        'temp_mean': float(np.mean(roi)),
+        'temp_std': float(np.std(roi)),
+        'temp_median': float(np.median(roi)),
+    }
+
+
+def extract_temp_histogram(roi: np.ndarray, bins: int = 32) -> np.ndarray:
+    """
+    提取温度分布直方图特征
+
+    Args:
+        roi: ROI区域图像数组
+        bins: 直方图bin数量
+
+    Returns:
+        归一化直方图向量
+    """
+    if roi.size == 0:
+        return np.zeros(bins)
+
+    hist, _ = np.histogram(roi.flatten(), bins=bins, range=(0, 255))
+    hist = hist.astype(np.float32) / (hist.sum() + 1e-7)
+    return hist
+
+
+def calculate_temp_gradient(roi: np.ndarray) -> Dict[str, float]:
+    """
+    计算温度梯度特征（反映温度变化剧烈程度）
+
+    Args:
+        roi: ROI区域图像数组
+
+    Returns:
+        温度梯度统计
+    """
+    if roi.size == 0:
+        return {'gradient_mean': 0.0, 'gradient_std': 0.0}
+
+    grad_x = cv2.Sobel(roi, cv2.CV_32F, 1, 0, ksize=3)
+    grad_y = cv2.Sobel(roi, cv2.CV_32F, 0, 1, ksize=3)
+    gradient_magnitude = np.sqrt(grad_x**2 + grad_y**2)
+
+    return {
+        'gradient_mean': float(np.mean(gradient_magnitude)),
+        'gradient_std': float(np.std(gradient_magnitude)),
+    }
+
+
+def extract_temp_features(roi: np.ndarray, include_hist: bool = True,
+                          include_gradient: bool = True) -> Dict:
+    """
+    从ROI区域提取完整温度特征
+
+    Args:
+        roi: ROI区域图像数组
+        include_hist: 是否包含直方图特征
+        include_gradient: 是否包含梯度特征
+
+    Returns:
+        完整温度特征字典
+    """
+    features = extract_temp_stats(roi)
+
+    if include_hist:
+        features['temp_histogram'] = extract_temp_histogram(roi)
+
+    if include_gradient:
+        features.update(calculate_temp_gradient(roi))
+
+    return features
+
+
+def normalize_roi(roi: np.ndarray, method: str = 'minmax') -> np.ndarray:
+    """
+    对ROI进行归一化处理
+
+    Args:
+        roi: ROI区域图像数组
+        method: 归一化方法 ('minmax' 或 'zscore')
+
+    Returns:
+        归一化后的图像
+    """
+    if method == 'minmax':
+        min_val, max_val = roi.min(), roi.max()
+        if max_val - min_val < 1e-7:
+            return np.zeros_like(roi)
+        return (roi - min_val) / (max_val - min_val)
+    elif method == 'zscore':
+        mean_val, std_val = np.mean(roi), np.std(roi)
+        if std_val < 1e-7:
+            return np.zeros_like(roi)
+        return (roi - mean_val) / std_val
+    else:
+        raise ValueError(f"Unknown normalization method: {method}")