chore: initial commit for Infrared Equipment Detection System

- Add YOLO-based infrared thermal image detection system
- Add dataset configuration (dataset.yaml) with 12 equipment classes
- Add training, validation and detection scripts
- Add temperature feature extraction and fault diagnosis modules
- Update README and CLAUDE.md with correct dataset paths
- Optimize .gitignore to track dataset.yaml while ignoring large data files

src/diagnose.py

@@ -0,0 +1,203 @@
+"""
+故障诊断模块
+基于检测结果和温度特征进行故障诊断
+"""
+
+import json
+from typing import Dict, List, Optional, Union
+from pathlib import Path
+import numpy as np
+
+from .extract_temp import TempFeatureExtractor
+from .detect import InfraredDetector
+
+
+class FaultDiagnoser:
+    """故障诊断器"""
+
+    def __init__(self,
+                 model_path: Optional[str] = None,
+                 temp_threshold: Optional[Dict] = None):
+        """
+        初始化诊断器
+
+        Args:
+            model_path: YOLO模型路径
+            temp_threshold: 温度阈值配置
+        """
+        self.detector = InfraredDetector(model_path) if model_path else None
+        self.extractor = TempFeatureExtractor(model_path)
+        self.temp_threshold = temp_threshold or {}
+
+    def diagnose(self, image: Union[str, np.ndarray]) -> Dict:
+        """
+        对图像进行故障诊断
+
+        Args:
+            image: 图像路径或数组
+
+        Returns:
+            诊断结果
+        """
+        result = {
+            'image': str(image) if isinstance(image, str) else 'array',
+            'detections': [],
+            'faults': [],
+            'summary': {}
+        }
+
+        if self.detector and self.detector.model:
+            detections = self.detector.detect(image)
+            result['detections'] = detections
+
+            for det in detections:
+                bbox = det['bbox']
+                x1, y1, x2, y2 = map(int, bbox)
+
+                if isinstance(image, str):
+                    import cv2
+                    img = cv2.imread(image, cv2.IMREAD_GRAYSCALE)
+                else:
+                    img = image
+
+                roi = img[y1:y2, x1:x2]
+                temp_features = self.extractor.extract_from_roi(roi)
+
+                fault_status = self._check_fault(temp_features)
+                temp_features['fault_status'] = fault_status
+                temp_features['fault_type'] = self._classify_fault(temp_features)
+
+                result['faults'].append({
+                    'bbox': bbox,
+                    'confidence': det['confidence'],
+                    **temp_features
+                })
+        else:
+            raise ValueError("Detector not initialized with model")
+
+        result['summary'] = self._generate_summary(result['faults'])
+        return result
+
+    def _check_fault(self, temp_features: Dict) -> str:
+        """
+        根据温度特征判断是否有故障
+
+        Args:
+            temp_features: 温度特征
+
+        Returns:
+            'normal' 或 'abnormal'
+        """
+        max_temp = temp_features.get('temp_max', 0)
+        mean_temp = temp_features.get('temp_mean', 0)
+        std_temp = temp_features.get('temp_std', 0)
+
+        max_threshold = self.temp_threshold.get('max_temp', 200)
+        std_threshold = self.temp_threshold.get('std_temp', 50)
+
+        if max_temp > max_threshold or std_temp > std_threshold:
+            return 'abnormal'
+        return 'normal'
+
+    def _classify_fault(self, temp_features: Dict) -> str:
+        """
+        故障分类
+
+        Args:
+            temp_features: 温度特征
+
+        Returns:
+            故障类型
+        """
+        max_temp = temp_features.get('temp_max', 0)
+        mean_temp = temp_features.get('temp_mean', 0)
+        std_temp = temp_features.get('temp_std', 0)
+
+        temp_range = max_temp - mean_temp
+
+        if std_temp > 30:
+            return 'hotspot'
+        elif temp_range > 50:
+            return 'local_overheating'
+        elif max_temp > 180:
+            return 'overheat'
+        else:
+            return 'suspect'
+
+    def _generate_summary(self, faults: List[Dict]) -> Dict:
+        """
+        生成诊断摘要
+
+        Args:
+            faults: 故障列表
+
+        Returns:
+            摘要信息
+        """
+        if not faults:
+            return {'status': 'no_detection', 'fault_count': 0}
+
+        abnormal_count = sum(1 for f in faults if f.get('fault_status') == 'abnormal')
+        fault_types = [f.get('fault_type', 'unknown') for f in faults]
+
+        return {
+            'status': 'fault_detected' if abnormal_count > 0 else 'normal',
+            'fault_count': len(faults),
+            'abnormal_count': abnormal_count,
+            'fault_types': list(set(fault_types))
+        }
+
+    def batch_diagnose(self, image_dir: Union[str, Path],
+                       output_path: Union[str, Path]) -> None:
+        """
+        批量诊断
+
+        Args:
+            image_dir: 图像目录
+            output_path: 输出JSON路径
+        """
+        image_dir = Path(image_dir)
+        results = []
+
+        for img_path in sorted(image_dir.glob('*')):
+            if img_path.suffix.lower() not in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
+                continue
+
+            try:
+                result = self.diagnose(str(img_path))
+                result['image'] = img_path.name
+                results.append(result)
+            except Exception as e:
+                print(f"Error processing {img_path.name}: {e}")
+                continue
+
+        with open(output_path, 'w', encoding='utf-8') as f:
+            json.dump(results, f, indent=2, ensure_ascii=False)
+
+        print(f"Batch diagnosis complete. {len(results)} images processed.")
+        print(f"Results saved to: {output_path}")
+
+
+def load_threshold_config(config_path: str) -> Dict:
+    """加载温度阈值配置"""
+    with open(config_path, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser(description='Fault Diagnosis')
+    parser.add_argument('--model', type=str, required=True, help='YOLO model path')
+    parser.add_argument('--image-dir', type=str, required=True, help='Image directory')
+    parser.add_argument('--output', type=str, required=True, help='Output JSON path')
+    parser.add_argument('--threshold', type=str, help='Threshold config JSON')
+
+    args = parser.parse_args()
+
+    threshold = None
+    if args.threshold:
+        threshold = load_threshold_config(args.threshold)
+
+    diagnoser = FaultDiagnoser(args.model, threshold)
+    diagnoser.batch_diagnose(args.image_dir, args.output)