147 lines
4.4 KiB
Python
147 lines
4.4 KiB
Python
#!/usr/bin/env python3
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"""
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Seed a scenario from a video chunk.
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Creates a Timeline (frames in MinIO) + Branch + Checkpoint marked
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as a scenario. No pipeline, no Redis, no SSE.
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Prerequisites:
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- Postgres reachable (Kind NodePort or local)
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- MinIO reachable (Kind NodePort or local)
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Usage:
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python tests/detect/manual/seed_scenario.py
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python tests/detect/manual/seed_scenario.py --video media/mpr/out/chunks/.../chunk_0001.mp4
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Then open:
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http://mpr.local.ar/detection/?job=<TIMELINE_ID>#/editor/detect_edges
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"""
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from __future__ import annotations
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import argparse
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import logging
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import os
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import sys
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import uuid
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parser = argparse.ArgumentParser(description="Seed a scenario")
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parser.add_argument("--video",
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default="media/mpr/out/chunks/95043d50-4df6-4ac8-bbd5-2ba873117c6e/chunk_0000.mp4")
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parser.add_argument("--label", default="chelsea_edges_default",
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help="Scenario label for bookmarking")
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parser.add_argument("--fps", type=float, default=2.0, help="Frames per second to extract")
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parser.add_argument("--max-frames", type=int, default=20, help="Max frames to extract")
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parser.add_argument("--db-url",
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default=os.environ.get("DATABASE_URL", "postgresql://mpr:mpr@localhost:5432/mpr"))
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parser.add_argument("--s3-url",
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default=os.environ.get("S3_ENDPOINT_URL", "http://localhost:9000"))
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args = parser.parse_args()
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os.environ["DATABASE_URL"] = args.db_url
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os.environ["S3_ENDPOINT_URL"] = args.s3_url
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os.environ.setdefault("AWS_ACCESS_KEY_ID", "minioadmin")
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os.environ.setdefault("AWS_SECRET_ACCESS_KEY", "minioadmin")
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sys.path.insert(0, ".")
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logging.basicConfig(level=logging.INFO, format="%(levelname)-7s %(name)s — %(message)s")
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logger = logging.getLogger(__name__)
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def extract_frames_ffmpeg(video_path: str, fps: float, max_frames: int):
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"""Extract frames using ffmpeg — no pipeline dependencies."""
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import subprocess
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import tempfile
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from pathlib import Path
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import numpy as np
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from PIL import Image
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from core.detect.models import Frame
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tmpdir = tempfile.mkdtemp(prefix="scenario_")
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pattern = os.path.join(tmpdir, "frame_%04d.jpg")
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cmd = [
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"ffmpeg", "-i", video_path,
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"-vf", f"fps={fps}",
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"-frames:v", str(max_frames),
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"-q:v", "2",
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pattern,
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"-y", "-loglevel", "error",
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]
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subprocess.run(cmd, check=True)
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frames = []
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for jpg in sorted(Path(tmpdir).glob("frame_*.jpg")):
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seq = int(jpg.stem.split("_")[1]) - 1
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img = Image.open(jpg).convert("RGB")
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image_array = np.array(img)
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frame = Frame(
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sequence=seq,
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chunk_id=0,
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timestamp=seq / fps,
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image=image_array,
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)
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frames.append(frame)
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jpg.unlink()
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Path(tmpdir).rmdir()
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return frames
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def main():
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video_path = args.video
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if not os.path.exists(video_path):
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logger.error("Video not found: %s", video_path)
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sys.exit(1)
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logger.info("Video: %s", video_path)
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logger.info("Label: %s", args.label)
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# Ensure DB tables exist
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from core.db.connection import create_tables
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create_tables()
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# Extract frames
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logger.info("Extracting frames (fps=%.1f, max=%d)...", args.fps, args.max_frames)
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frames = extract_frames_ffmpeg(video_path, args.fps, args.max_frames)
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logger.info("Extracted %d frames", len(frames))
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# Create timeline + branch + checkpoint
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from core.detect.checkpoint.storage import create_timeline, save_stage_output
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timeline_id, branch_id = create_timeline(
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source_video=video_path,
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profile_name="soccer_broadcast",
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frames=frames,
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fps=args.fps,
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)
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# Mark as scenario
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from core.db import get_latest_checkpoint
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from core.db.connection import get_session
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with get_session() as session:
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checkpoint = get_latest_checkpoint(session, branch_id)
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if checkpoint:
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checkpoint.is_scenario = True
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checkpoint.scenario_label = args.label
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session.commit()
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logger.info("")
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logger.info("Scenario created:")
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logger.info(" Timeline: %s", timeline_id)
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logger.info(" Branch: %s", branch_id)
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logger.info(" Label: %s", args.label)
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logger.info(" Frames: %d", len(frames))
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logger.info("")
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logger.info("Open in editor:")
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logger.info(" http://mpr.local.ar/detection/?job=%s#/editor/detect_edges", timeline_id)
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if __name__ == "__main__":
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main()
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