major refactor

core/schema/serializers/pipeline.py

@@ -1,8 +1,6 @@
 """
 Serializers for detection pipeline runtime models.
 
-Mirrors core/schema/models/detect_pipeline.py.
-
 Special handling:
   - Frame.image (np.ndarray → S3, excluded from JSON)
   - TextCandidate.frame (object ref → frame_sequence integer)
@@ -13,7 +11,7 @@ from __future__ import annotations
 
 import dataclasses
 
-from core.schema.models.detect_pipeline import (
+from core.schema.models.pipeline import (
     BoundingBox,
     BrandDetection,
     BrandStats,
@@ -59,13 +57,12 @@ def deserialize_frames_with_download(meta: list[dict], manifest: dict, job_id: s
 
 def serialize_text_candidate(tc: TextCandidate) -> dict:
     bbox_dict = dataclasses.asdict(tc.bbox)
-    result = {
+    return {
         "frame_sequence": tc.frame.sequence,
         "bbox": bbox_dict,
         "text": tc.text,
         "ocr_confidence": tc.ocr_confidence,
     }
-    return result
 
 
 def serialize_text_candidates(candidates: list[TextCandidate]) -> list[dict]:
@@ -75,13 +72,12 @@ def serialize_text_candidates(candidates: list[TextCandidate]) -> list[dict]:
 def deserialize_text_candidate(data: dict, frame_map: dict[int, Frame]) -> TextCandidate:
     frame = frame_map[data["frame_sequence"]]
     bbox = safe_construct(BoundingBox, data["bbox"])
-    candidate = TextCandidate(
+    return TextCandidate(
         frame=frame,
         bbox=bbox,
         text=data["text"],
         ocr_confidence=data["ocr_confidence"],
     )
-    return candidate
 
 
 def deserialize_text_candidates(data: list[dict], frame_map: dict[int, Frame]) -> list[TextCandidate]: