fix: optimize diagnosis output format and fix inconsistency between fault_status and fault_type

1. 输出格式优化:
   - faults中移除冗余的bbox和confidence字段
   - summary中fault_count改名为detection_count

2. fault_status与fault_type逻辑统一:
   - normal状态时fault_type为'none'，不再使用'suspect'
   - abnormal状态时才进行故障类型分类(hotspot/local_overheating/overheat)

3. 修复usage_example.py图像通道问题:
   - YOLO检测使用彩色图，温度提取时再转灰度图

PROGRESS.md

@@ -222,15 +222,13 @@
   "image": "FLIR0150.jpg",
   "detections": [...],
   "faults": [{
-    "bbox": [...],
-    "confidence": 0.995,
     "temp_max": 255.0,
     "fault_status": "abnormal",
     "fault_type": "hotspot"
   }],
   "summary": {
     "status": "fault_detected",
-    "fault_count": 1,
+    "detection_count": 1,
     "abnormal_count": 1
   }
 }

README.md

@@ -279,8 +279,6 @@ print(f"故障类型: {result['summary']['fault_types']}")
   ],
   "faults": [
     {
-      "bbox": [x1, y1, x2, y2],
-      "confidence": 0.995,
       "temp_max": 255.0,
       "temp_mean": 97.49,
       "temp_std": 45.2,
@@ -290,7 +288,7 @@ print(f"故障类型: {result['summary']['fault_types']}")
   ],
   "summary": {
     "status": "fault_detected",
-    "fault_count": 1,
+    "detection_count": 1,
     "abnormal_count": 1,
     "fault_types": ["hotspot"]
   }

scripts/usage_example.py

@@ -52,10 +52,13 @@ def example3_temperature_extraction():
     detector = InfraredDetector("weights/best.pt")
 
     # 读取图像
-    gray_img = cv2.imread(SAMPLE_IMAGE, cv2.IMREAD_GRAYSCALE)
+    color_img = cv2.imread(SAMPLE_IMAGE, cv2.IMREAD_COLOR)
 
-    # 检测目标（复用已加载的图像）
+    # 检测目标（彩色图）
-    detections = detector.detect(gray_img)
+    detections = detector.detect(color_img)
+
+    # 转为灰度图用于温度提取
+    gray_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)
 
     # 提取第一个检测框的温度特征
     if detections:

src/diagnose.py

@@ -67,13 +67,13 @@ class FaultDiagnoser:
 
                 fault_status = self._check_fault(temp_features)
                 temp_features['fault_status'] = fault_status
-                temp_features['fault_type'] = self._classify_fault(temp_features)
+                # abnormal 时才进行故障分类，normal 时类型为 none
+                if fault_status == 'abnormal':
+                    temp_features['fault_type'] = self._classify_fault(temp_features)
+                else:
+                    temp_features['fault_type'] = 'none'
 
-                result['faults'].append({
+                result['faults'].append(temp_features)
-                    'bbox': bbox,
-                    'confidence': det['confidence'],
-                    **temp_features
-                })
         else:
             raise ValueError("Detector not initialized with model")
 
@@ -137,14 +137,14 @@ class FaultDiagnoser:
             摘要信息
         """
         if not faults:
-            return {'status': 'no_detection', 'fault_count': 0}
+            return {'status': 'no_detection', 'detection_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),
+            'detection_count': len(faults),
             'abnormal_count': abnormal_count,
             'fault_types': list(set(fault_types))
         }