- Tracked DBs now live at /mnt/data/projects/cupido/tracked/ (out of
ownCloud to avoid sync conflicts and bandwidth churn). config.py
TRACKING_OUTPUT_DIR points there; the docker-compose for ethoscope-lab
mounts it world-readable for JupyterHub users.
- New scripts/export_video_db_index.py joins all_video_info_merged.xlsx
with the video inventory and the on-disk DBs, producing a TSV that has
one row per fly/ROI plus training/testing video and DB paths. Handles
approximate xlsx times, cross-day training/testing, the 12 AM/PM
ambiguity, and date typos.
- scripts/load_roi_data.py rewritten as a TSV-driven loader returning a
single DataFrame with session and metadata columns. calculate_distances
and the two flies_analysis notebooks migrated to use it; downstream
trained/naive splits remain available via simple equality filters.
- Metadata vocabulary canonicalized: {naïve, niave, untrained, test} all
resolve to {trained, naive}. Normalization happens at the TSV-export
boundary (idempotent); the xlsx and the 2025-07-15 legacy CSV were
edited in place to remove the worst variants.
- scripts/monitor_tracking.py rate calculation fixed: with N parallel
workers, completions arrive in bursts; the old formula divided by burst
width and reported nonsense rates. Now uses a 6 h window denominator.
- scripts/track_videos.py: BGRMovieCamera retries cv2.read on transient
NFS hiccups and a post-tracking completeness gate (≥ 90 % of expected
duration via MAX(t) across all 6 ROIs) deletes silent partial DBs.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
283 lines
10 KiB
Python
283 lines
10 KiB
Python
"""Headless offline tracker.
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Reads target JSONs produced by `pick_targets.py`, builds the 6 ROIs of the
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HD mating arena from the L-shape reference points, runs ethoscope's
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`MultiFlyTracker` against the merged.mp4 file via `MovieVirtualCamera`, and
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writes a SQLite DB to `TRACKING_OUTPUT_DIR/<video_basename>_tracking.db`.
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Idempotent: skips videos whose tracking DB already exists (unless --redo).
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Usage:
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python track_videos.py # process all videos with target JSON
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python track_videos.py --redo # re-track even if DB exists
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python track_videos.py --jobs 4 # run up to 4 videos in parallel
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python track_videos.py --max-duration 1800 # cap each video at 30 min (sec)
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"""
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from __future__ import annotations
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import argparse
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import json
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import logging
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import os
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import sys
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import traceback
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from concurrent.futures import ProcessPoolExecutor, as_completed
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from pathlib import Path
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import numpy as np
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# Import ethoscope from the local source tree (no pip install).
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ETHOSCOPE_SRC = Path("/home/gg/Code/ethoscope_project/ethoscope/src/ethoscope")
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sys.path.insert(0, str(ETHOSCOPE_SRC))
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from config import TARGETS_DIR, TRACKING_OUTPUT_DIR # noqa: E402
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from tracking_geometry import HD_FG_DATA, compute_roi_polygons # noqa: E402
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def build_rois_from_targets(reference_points):
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"""Wrap the shared geometry into ethoscope `ROI` objects."""
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from ethoscope.core.roi import ROI
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polys = compute_roi_polygons(reference_points)
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return [ROI(poly.reshape((1, 4, 2)), idx=i + 1) for i, poly in enumerate(polys)]
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def track_one(json_path: Path, output_dir: Path, max_duration: float | None,
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redo: bool) -> tuple[str, str]:
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"""Track a single video. Returns (status, message). Run in subprocess.
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Statuses: "ok", "skip", "error".
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"""
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# Re-import inside subprocess so each worker has its own ethoscope state.
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import sys as _sys
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_sys.path.insert(0, str(ETHOSCOPE_SRC))
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import cv2
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from ethoscope.core.monitor import Monitor
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from ethoscope.hardware.input.cameras import MovieVirtualCamera
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from ethoscope.io.sqlite import SQLiteResultWriter
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from ethoscope.trackers.multi_fly_tracker import MultiFlyTracker
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import time as _time
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class BGRMovieCamera(MovieVirtualCamera):
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"""MovieVirtualCamera that keeps BGR frames AND retries on transient
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read failures.
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Two reasons for the override:
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1. MultiFlyTracker calls cv2.cvtColor(img, COLOR_BGR2GRAY) without
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checking whether img is already grayscale, so we must feed it
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3-channel input.
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2. cv2.VideoCapture.read() can return False on transient I/O hiccups
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(NFS contention when 8 workers pull big mp4s in parallel) without
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the file actually being at EOF. A naive "False -> StopIteration"
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handling makes the tracker silently exit mid-video and write a
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short, lying DB. We retry a few times and only treat persistent
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failures within the *interior* of the video as real EOF.
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"""
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_retry_count = 5
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_retry_backoff_s = 0.25
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_eof_safety_frames = 50 # near end-of-file, treat False as legitimate
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def _next_image(self):
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for attempt in range(self._retry_count):
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ret, frame = self.capture.read()
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if ret and frame is not None:
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return frame # BGR, untouched
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# If we're near the genuine end of the file, accept it.
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if (
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self._has_end_of_file
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and self._frame_idx >= self._total_n_frames - self._eof_safety_frames
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):
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return None
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# Otherwise, this is a suspected transient hiccup — back off
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# and try again. The capture is still open; cv2 will pick up
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# the next decoded frame.
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_time.sleep(self._retry_backoff_s)
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return None # truly persistent failure
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payload = json.loads(json_path.read_text())
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if payload.get("unusable"):
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reason = payload.get("reason") or "no reason given"
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return "skip", f"marked unusable: {reason}"
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video_path = Path(payload["video_path"])
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if not video_path.exists():
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return "error", f"video missing: {video_path}"
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out_db = output_dir / f"{video_path.stem}_tracking.db"
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if out_db.exists() and not redo:
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return "skip", f"DB exists: {out_db.name}"
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if out_db.exists():
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out_db.unlink()
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rois = build_rois_from_targets(payload["reference_points"])
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cam_kwargs = {"use_wall_clock": False}
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if max_duration is not None:
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cam_kwargs["max_duration"] = max_duration
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cam = BGRMovieCamera(str(video_path), **cam_kwargs)
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metadata = {
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"machine_id": payload.get("machine_uuid", "unknown"),
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"machine_name": payload.get("machine_name", "unknown"),
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"date_time": int(payload.get("session_epoch", 0)),
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"frame_width": cam.width,
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"frame_height": cam.height,
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"version": "offline-tracker-1",
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"experimental_info": "{}",
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"selected_options": json.dumps({
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"tracker": "MultiFlyTracker",
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"template": "HD_Mating_Arena_6_ROIS",
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"fg_data": HD_FG_DATA,
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"maxN": 2,
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}),
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"hardware_info": "{}",
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"reference_points": str([list(map(int, p)) for p in payload["reference_points"]]),
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"backup_filename": out_db.name,
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"result_writer_type": "SQLite3",
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"sqlite_source_path": str(out_db),
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}
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tracker_data = {
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"maxN": 2,
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"visualise": False,
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"fg_data": HD_FG_DATA,
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"adaptive_threshold": True,
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"min_fg_threshold": 10,
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"max_fg_threshold": 50,
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}
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db_credentials = {"name": str(out_db)}
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rw = SQLiteResultWriter(
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db_credentials, rois, metadata=metadata,
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make_dam_like_table=False, take_frame_shots=False, erase_old_db=True,
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)
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monit = Monitor(
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cam, MultiFlyTracker, rois,
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reference_points=payload["reference_points"],
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data=tracker_data,
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)
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try:
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with rw as result_writer:
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monit.run(result_writer=result_writer, drawer=None, verbose=False)
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except Exception:
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return "error", traceback.format_exc(limit=5)
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finally:
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try:
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cam._close()
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except Exception:
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pass
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if not out_db.exists():
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return "error", "tracking finished but DB was not created"
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# Post-tracking sanity check: did we cover most of the source video?
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# If not (cv2 retry exhausted, codec corruption, etc.), reject the DB so
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# it doesn't get cached as "done" — better an explicit failure than a
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# silent partial write.
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expected_ms = (cam._total_n_frames / 25.0) * 1000.0
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if max_duration is not None:
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expected_ms = min(expected_ms, max_duration * 1000.0)
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completeness_threshold = 0.90 # require ≥ 90 % of expected duration
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# Use MAX(t) across all ROIs — a single ROI can run dry early if its fly
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# stops moving, so the latest detection anywhere in the arena is the
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# better signal of how far the iterator actually got.
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import sqlite3 as _sqlite3
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try:
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_con = _sqlite3.connect(f"file:{out_db}?mode=ro", uri=True)
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t_max = 0
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for _i in range(1, 7):
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_v = _con.execute(f"SELECT MAX(t) FROM ROI_{_i}").fetchone()[0]
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if _v and _v > t_max:
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t_max = _v
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_con.close()
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except Exception:
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t_max = 0
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if expected_ms > 0 and t_max < expected_ms * completeness_threshold:
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out_db.unlink()
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for sidecar in (str(out_db) + "-wal", str(out_db) + "-shm"):
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Path(sidecar).unlink(missing_ok=True)
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ratio = t_max / expected_ms if expected_ms else 0
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return (
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"error",
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f"short output: t_max={t_max} ms vs expected {int(expected_ms)} ms "
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f"({ratio*100:.0f}%); DB removed",
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)
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return "ok", str(out_db)
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def main() -> None:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--redo", action="store_true", help="re-track even if DB exists")
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parser.add_argument("--jobs", type=int, default=1, help="parallel workers")
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parser.add_argument(
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"--max-duration", type=float, default=None,
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help="cap each video at this many seconds (default: full video)",
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)
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parser.add_argument("--limit", type=int, default=None, help="process only first N")
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parser.add_argument("--video", type=str, default=None,
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help="track a single video (mp4 path); requires its target JSON")
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args = parser.parse_args()
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TRACKING_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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if args.video:
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stem = Path(args.video).stem
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json_path = TARGETS_DIR / f"{stem}.json"
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if not json_path.exists():
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sys.exit(f"No target JSON for {args.video}: expected {json_path}")
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jsons = [json_path]
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else:
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jsons = sorted(TARGETS_DIR.glob("*.json"))
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if args.limit:
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jsons = jsons[: args.limit]
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if not jsons:
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print("No target JSONs found. Run pick_targets.py first.")
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return
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print(f"Tracking {len(jsons)} videos (jobs={args.jobs}, redo={args.redo}).")
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n_ok = n_skip = n_err = 0
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if args.jobs <= 1:
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for jp in jsons:
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print(f" → {jp.name}", flush=True)
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status, msg = track_one(jp, TRACKING_OUTPUT_DIR, args.max_duration, args.redo)
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print(f" {status}: {msg.splitlines()[-1] if msg else ''}", flush=True)
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n_ok += status == "ok"
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n_skip += status == "skip"
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n_err += status == "error"
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else:
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with ProcessPoolExecutor(max_workers=args.jobs) as ex:
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futs = {
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ex.submit(track_one, jp, TRACKING_OUTPUT_DIR, args.max_duration, args.redo): jp
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for jp in jsons
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}
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for fut in as_completed(futs):
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jp = futs[fut]
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try:
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status, msg = fut.result()
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except Exception as e:
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status, msg = "error", f"future raised: {e}"
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print(f" {jp.name}: {status} — {msg.splitlines()[-1] if msg else ''}",
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flush=True)
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n_ok += status == "ok"
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n_skip += status == "skip"
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n_err += status == "error"
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print(f"\nDone. ok={n_ok} skipped={n_skip} errors={n_err}")
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sys.exit(0 if n_err == 0 else 1)
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
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main()
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