minor fixes for detection

Backend/agents/priority/agent.py

@@ -56,7 +56,7 @@ Priority Scale:
 1 = CRITICAL (Public safety, electrical hazards, major hazards)
 2 = HIGH (Potholes, road damage, fallen trees)
 3 = MEDIUM (Garbage, broken signs, minor structures)
-4 = LOW (Parking violations, minor vandalism)
+4 = LOW (Parking violations)
 
 Consider safety impact, infrastructure criticality, and community accessibility.
 

Backend/agents/vision/agent.py

@@ -56,15 +56,36 @@ class VisionAgent(BaseAgent):
     async def download_image(self, remote_path: str) -> bytes:
         return await download_from_supabase(remote_path)
     
-    async def save_annotated(self, results, original_path: str, subfolder: str) -> str:
-        im_array = results[0].plot()
-        
+    async def save_annotated(
+        self,
+        image_data: bytes,
+        detections: list[DetectionBox],
+        primary_class_id: Optional[int],
+        original_path: str,
+        subfolder: str,
+    ) -> str:
+        nparr = np.frombuffer(image_data, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        if img is None:
+            raise ValueError("Invalid image data")
+
+        if primary_class_id is not None:
+            draw_detections = [d for d in detections if d.class_id == primary_class_id]
+        else:
+            draw_detections = []
+
+        for d in draw_detections:
+            x1, y1, x2, y2 = map(int, d.bbox)
+            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
+            label = f"{d.class_name} {d.confidence:.2f}"
+            cv2.putText(img, label, (x1, max(y1 - 10, 0)), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3)
+
         original_name = Path(original_path).stem
         annotated_filename = f"annotated_{original_name}.jpg"
-        
-        _, buffer = cv2.imencode('.jpg', im_array, [cv2.IMWRITE_JPEG_QUALITY, 90])
+
+        _, buffer = cv2.imencode('.jpg', img, [cv2.IMWRITE_JPEG_QUALITY, 90])
         image_bytes = buffer.tobytes()
-        
+
         remote_path = await save_bytes(image_bytes, annotated_filename, subfolder=subfolder)
         return remote_path
     
@@ -162,8 +183,9 @@ class VisionAgent(BaseAgent):
     ) -> tuple[list[DetectionBox], str, Optional[IssueCategory], float, Optional[str]]:
         image_data = await self.download_image(image_path)
         results, inference_time = await self.run_inference(image_data)
-        annotated_path = await self.save_annotated(results, image_path, subfolder)
         detections = self.extract_detections(results)
+        primary_class_id = max(detections, key=lambda d: d.confidence).class_id if detections else None
+        annotated_path = await self.save_annotated(image_data, detections, primary_class_id, image_path, subfolder)
 
         gemini_category = None
         gemini_confidence = 0.0

Backend/core/schemas.py

@@ -33,7 +33,6 @@ class IssueCategory(StrEnum):
     BROKEN_SIGN = "Broken Road Sign Issues"
     FALLEN_TREE = "Fallen Trees"
     GARBAGE = "Littering/Garbage on Public Places"
-    VANDALISM = "Vandalism Issues"
     DEAD_ANIMAL = "Dead Animal Pollution"
     DAMAGED_CONCRETE = "Damaged Concrete Structures"
     DAMAGED_ELECTRIC = "Damaged Electric Wires and Poles"
@@ -46,7 +45,6 @@ CLASS_ID_TO_CATEGORY = {
     3: IssueCategory.BROKEN_SIGN,
     4: IssueCategory.FALLEN_TREE,
     5: IssueCategory.GARBAGE,
-    6: IssueCategory.VANDALISM,
     7: IssueCategory.DEAD_ANIMAL,
     8: IssueCategory.DAMAGED_CONCRETE,
     9: IssueCategory.DAMAGED_ELECTRIC,

Backend/utils/fuzzy_match.py

@@ -26,10 +26,6 @@ CATEGORY_KEYWORDS: dict[str, list[str]] = {
         "garbage", "trash", "litter", "waste", "rubbish", "dump", "dirty",
         "filth", "debris", "plastic", "pile", "mess", "junk", "disposal"
     ],
-    "Vandalism Issues": [
-        "vandal", "graffiti", "spray", "paint", "defaced",
-        "smashed", "destroyed", "torn"
-    ],
     "Dead Animal Pollution": [
         "dead", "animal", "carcass", "body", "corpse", "rotting", "smell",
         "stink", "dog", "cat", "bird", "cow", "roadkill"