refactor stage 1

tests/detect/manual/seed_scenario.py

@@ -1,26 +1,20 @@
 #!/usr/bin/env python3
 """
-Seed a scenario checkpoint from a video chunk.
+Seed a scenario from a video chunk.
 
-Extracts frames via ffmpeg, uploads to MinIO, creates a StageCheckpoint
-in Postgres marked as a scenario. No pipeline, no Redis, no SSE.
+Creates a Timeline (frames in MinIO) + Branch + Checkpoint marked
+as a scenario. No pipeline, no Redis, no SSE.
 
 Prerequisites:
-  - Postgres reachable (port-forward or local)
-  - MinIO reachable (port-forward or local)
+  - Postgres reachable (Kind NodePort or local)
+  - MinIO reachable (Kind NodePort or local)
 
 Usage:
-    # With K8s port-forwards:
-    kubectl port-forward svc/postgres 5432:5432 &
-    kubectl port-forward svc/minio 9000:9000 &
-
     python tests/detect/manual/seed_scenario.py
-
-    # Custom video:
     python tests/detect/manual/seed_scenario.py --video media/mpr/out/chunks/.../chunk_0001.mp4
 
 Then open:
-    http://mpr.local.ar/detection/?job=<JOB_ID>#/editor/detect_edges
+    http://mpr.local.ar/detection/?job=<TIMELINE_ID>#/editor/detect_edges
 """
 
 from __future__ import annotations
@@ -31,7 +25,7 @@ import os
 import sys
 import uuid
 
-parser = argparse.ArgumentParser(description="Seed a scenario checkpoint")
+parser = argparse.ArgumentParser(description="Seed a scenario")
 parser.add_argument("--video",
                     default="media/mpr/out/chunks/95043d50-4df6-4ac8-bbd5-2ba873117c6e/chunk_0000.mp4")
 parser.add_argument("--label", default="chelsea_edges_default",
@@ -44,7 +38,6 @@ parser.add_argument("--s3-url",
                     default=os.environ.get("S3_ENDPOINT_URL", "http://localhost:9000"))
 args = parser.parse_args()
 
-# Set env before imports
 os.environ["DATABASE_URL"] = args.db_url
 os.environ["S3_ENDPOINT_URL"] = args.s3_url
 os.environ.setdefault("AWS_ACCESS_KEY_ID", "minioadmin")
@@ -57,7 +50,7 @@ logger = logging.getLogger(__name__)
 
 
 def extract_frames_ffmpeg(video_path: str, fps: float, max_frames: int):
-    """Extract frames using ffmpeg subprocess — no pipeline dependencies."""
+    """Extract frames using ffmpeg — no pipeline dependencies."""
     import subprocess
     import tempfile
     from pathlib import Path
@@ -82,7 +75,7 @@ def extract_frames_ffmpeg(video_path: str, fps: float, max_frames: int):
 
     frames = []
     for jpg in sorted(Path(tmpdir).glob("frame_*.jpg")):
-        seq = int(jpg.stem.split("_")[1]) - 1  # 0-indexed
+        seq = int(jpg.stem.split("_")[1]) - 1
         img = Image.open(jpg).convert("RGB")
         image_array = np.array(img)
         frame = Frame(
@@ -99,7 +92,6 @@ def extract_frames_ffmpeg(video_path: str, fps: float, max_frames: int):
 
 
 def main():
-    job_id = str(uuid.uuid4())
     video_path = args.video
 
     if not os.path.exists(video_path):
@@ -107,7 +99,6 @@ def main():
         sys.exit(1)
 
     logger.info("Video: %s", video_path)
-    logger.info("Job ID: %s", job_id)
     logger.info("Label: %s", args.label)
 
     # Ensure DB tables exist
@@ -119,57 +110,37 @@ def main():
     frames = extract_frames_ffmpeg(video_path, args.fps, args.max_frames)
     logger.info("Extracted %d frames", len(frames))
 
-    # Upload frames to MinIO
-    from detect.checkpoint.frames import save_frames
-    logger.info("Uploading frames to MinIO...")
-    manifest = save_frames(job_id, frames)
-    logger.info("Uploaded %d frames", len(manifest))
+    # Create timeline + branch + checkpoint
+    from detect.checkpoint.storage import create_timeline, save_stage_output
 
-    # Build frame metadata
-    frames_meta = [
-        {
-            "sequence": f.sequence,
-            "chunk_id": f.chunk_id,
-            "timestamp": f.timestamp,
-            "perceptual_hash": "",
-        }
-        for f in frames
-    ]
-
-    # All frames are "filtered" (no scene filter ran)
-    filtered_sequences = [f.sequence for f in frames]
-
-    # Save checkpoint as scenario
-    from core.db.detect import save_stage_checkpoint
-    from detect.checkpoint.frames import CHECKPOINT_PREFIX
-
-    checkpoint = save_stage_checkpoint(
-        job_id=job_id,
-        stage="filter_scenes",
-        stage_index=1,
-        frames_prefix=f"{CHECKPOINT_PREFIX}/{job_id}/frames/",
-        frames_manifest={str(k): v for k, v in manifest.items()},
-        frames_meta=frames_meta,
-        filtered_frame_sequences=filtered_sequences,
-        stage_output_key="",
-        stats={"frames_extracted": len(frames), "frames_after_scene_filter": len(frames)},
-        config_snapshot={},
-        config_overrides={},
-        video_path=video_path,
+    timeline_id, branch_id = create_timeline(
+        source_video=video_path,
         profile_name="soccer_broadcast",
-        is_scenario=True,
-        scenario_label=args.label,
+        frames=frames,
+        fps=args.fps,
     )
 
+    # Mark as scenario
+    from core.db.detect import get_latest_checkpoint
+    from core.db.connection import get_session
+
+    checkpoint = get_latest_checkpoint(branch_id)
+    if checkpoint:
+        checkpoint.is_scenario = True
+        checkpoint.scenario_label = args.label
+        with get_session() as session:
+            session.add(checkpoint)
+            session.commit()
+
     logger.info("")
     logger.info("Scenario created:")
-    logger.info("  ID: %s", checkpoint.id)
-    logger.info("  Job: %s", job_id)
+    logger.info("  Timeline: %s", timeline_id)
+    logger.info("  Branch: %s", branch_id)
     logger.info("  Label: %s", args.label)
     logger.info("  Frames: %d", len(frames))
     logger.info("")
     logger.info("Open in editor:")
-    logger.info("  http://mpr.local.ar/detection/?job=%s#/editor/detect_edges", job_id)
+    logger.info("  http://mpr.local.ar/detection/?job=%s#/editor/detect_edges", timeline_id)
 
 
 if __name__ == "__main__":