major refactor

core/schema/serializers/pipeline.py

@@ -0,0 +1,104 @@
+"""
+Serializers for detection pipeline runtime models.
+
+Special handling:
+  - Frame.image (np.ndarray → S3, excluded from JSON)
+  - TextCandidate.frame (object ref → frame_sequence integer)
+Everything else uses dataclasses.asdict() via safe_construct.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+
+from core.schema.models.pipeline import (
+    BoundingBox,
+    BrandDetection,
+    BrandStats,
+    DetectionReport,
+    Frame,
+    PipelineStats,
+    TextCandidate,
+)
+from ._common import safe_construct, serialize_dataclass, serialize_dataclass_list
+
+
+# ---------------------------------------------------------------------------
+# Frame — image goes to S3 separately
+# ---------------------------------------------------------------------------
+
+def serialize_frame_meta(frame: Frame) -> dict:
+    """Serialize Frame metadata only (no image)."""
+    result = dataclasses.asdict(frame)
+    del result["image"]
+    return result
+
+
+def serialize_frames_with_upload(frames: list[Frame], job_id: str) -> tuple[list[dict], dict[int, str]]:
+    """Upload frame images to S3, return metadata + manifest."""
+    from detect.checkpoint.frames import save_frames
+
+    manifest = save_frames(job_id, frames)
+    meta = [serialize_frame_meta(f) for f in frames]
+    return meta, manifest
+
+
+def deserialize_frames_with_download(meta: list[dict], manifest: dict, job_id: str) -> list[Frame]:
+    """Load frames from S3 + metadata."""
+    from detect.checkpoint.frames import load_frames
+
+    int_manifest = {int(k): v for k, v in manifest.items()}
+    return load_frames(int_manifest, meta)
+
+
+# ---------------------------------------------------------------------------
+# TextCandidate — frame ref is an object, stored as sequence int
+# ---------------------------------------------------------------------------
+
+def serialize_text_candidate(tc: TextCandidate) -> dict:
+    bbox_dict = dataclasses.asdict(tc.bbox)
+    return {
+        "frame_sequence": tc.frame.sequence,
+        "bbox": bbox_dict,
+        "text": tc.text,
+        "ocr_confidence": tc.ocr_confidence,
+    }
+
+
+def serialize_text_candidates(candidates: list[TextCandidate]) -> list[dict]:
+    return [serialize_text_candidate(tc) for tc in candidates]
+
+
+def deserialize_text_candidate(data: dict, frame_map: dict[int, Frame]) -> TextCandidate:
+    frame = frame_map[data["frame_sequence"]]
+    bbox = safe_construct(BoundingBox, data["bbox"])
+    return TextCandidate(
+        frame=frame,
+        bbox=bbox,
+        text=data["text"],
+        ocr_confidence=data["ocr_confidence"],
+    )
+
+
+def deserialize_text_candidates(data: list[dict], frame_map: dict[int, Frame]) -> list[TextCandidate]:
+    return [deserialize_text_candidate(d, frame_map) for d in data]
+
+
+# ---------------------------------------------------------------------------
+# BoundingBox, BrandDetection, PipelineStats, etc — standard dataclasses
+# ---------------------------------------------------------------------------
+
+def deserialize_bounding_box(data: dict) -> BoundingBox:
+    return safe_construct(BoundingBox, data)
+
+
+def deserialize_brand_detection(data: dict) -> BrandDetection:
+    return safe_construct(BrandDetection, data)
+
+
+def deserialize_pipeline_stats(data: dict) -> PipelineStats:
+    return safe_construct(PipelineStats, data)
+
+
+def deserialize_detection_report(data: dict) -> DetectionReport:
+    return safe_construct(DetectionReport, data)