Refactor annotation handling and inference processing: Update 'time' attribute in Annotation class to private, streamline video time formatting in main processing function, and adjust annotations dictionary access in detection conversion. Ensure consistency in handling input frames during inference.

main.py

@@ -109,10 +109,9 @@ class AIConfigDto(BaseModel):
 
 def detection_to_dto(det) -> DetectionDto:
     import constants_inf
-    ann = constants_inf.get_annotations_dict()
     label = ""
-    if det.cls in ann:
-        label = ann[det.cls].name
+    if det.cls in constants_inf.annotations_dict:
+        label = constants_inf.annotations_dict[det.cls].name
     return DetectionDto(
         centerX=det.x,
         centerY=det.y,
@@ -178,14 +177,10 @@ def _post_annotation_to_service(token_mgr: TokenManager, media_id: str,
     try:
         token = token_mgr.get_valid_token()
         image_b64 = base64.b64encode(annotation.image).decode() if annotation.image else None
-        total_seconds = annotation.time // 1000 if annotation.time else 0
-        hours = total_seconds // 3600
-        minutes = (total_seconds % 3600) // 60
-        seconds = total_seconds % 60
         payload = {
             "mediaId": media_id,
             "source": 0,
-            "videoTime": f"{hours:02d}:{minutes:02d}:{seconds:02d}",
+            "videoTime": f"00:00:{annotation.time // 1000:02d}" if annotation.time else "00:00:00",
             "detections": [d.model_dump() for d in dtos],
         }
         if image_b64: