Giorgio Gilestro
231c7a437f
Notebooks now use Path.home() / "cupido" for the repo root (works for any user inside the JupyterLab container), and the offline-tracking scripts read the ethoscope source-tree location from the new ETHOSCOPE_SRC config constant — defaulting to ~/Code/ethoscope_project/... and overridable via the ETHOSCOPE_SRC environment variable. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
"""Try auto-detection of L-shape targets on each video and save JSON sidecars.
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Useful for:
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- videos that DO have visible black-circle targets (saves manual clicks);
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- as a smoke test of the whole pipeline before running the picker.
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Failure is silent — videos that fail auto-detection are simply not written
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to disk, leaving them for the manual `pick_targets.py` tool.
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Output JSON has the same shape as the manual picker's so `track_videos.py`
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can consume either.
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"""
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from __future__ import annotations
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import argparse
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import datetime as dt
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import json
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import logging
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import sys
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from pathlib import Path
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import cv2
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import numpy as np
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import pandas as pd
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from config import ETHOSCOPE_SRC, INVENTORY_CSV, TARGETS_DIR
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# ethoscope source tree
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sys.path.insert(0, str(ETHOSCOPE_SRC))
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from ethoscope.roi_builders.target_roi_builder import TargetGridROIBuilder # noqa: E402
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def detect_one(video_path: Path, frame_idx: int) -> tuple[list[list[int]], int] | None:
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"""Run ethoscope target detection on one frame; return (points, frame_idx) or None."""
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cap = cv2.VideoCapture(str(video_path))
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if not cap.isOpened():
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return None
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n = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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if n > 0 and frame_idx >= n:
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frame_idx = max(0, n - 1)
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cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
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ok, frame = cap.read()
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cap.release()
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if not ok or frame is None:
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return None
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# The detector expects a single-channel image (grey) like ethoscope cameras produce.
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if frame.ndim == 3:
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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else:
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gray = frame
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# We don't actually need a fully-configured grid here — _find_target_coordinates
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# alone gives us the 3 reference points.
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builder = TargetGridROIBuilder(n_rows=2, n_cols=3)
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try:
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ref = builder._find_target_coordinates(gray)
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except Exception as e:
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logging.debug(f"detection failed for {video_path.name}: {e}")
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return None
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if ref is None:
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return None
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return [[int(p[0]), int(p[1])] for p in ref], frame_idx
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def main() -> None:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--frame", type=int, default=125)
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parser.add_argument("--limit", type=int, default=None)
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parser.add_argument("--video", type=str, default=None,
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help="run on a single video path (skips inventory)")
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parser.add_argument("--overwrite", action="store_true",
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help="overwrite existing JSON sidecars")
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args = parser.parse_args()
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TARGETS_DIR.mkdir(parents=True, exist_ok=True)
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if args.video:
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videos = [Path(args.video)]
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else:
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if not INVENTORY_CSV.exists():
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sys.exit("Inventory missing — run build_video_inventory.py first.")
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inv = pd.read_csv(INVENTORY_CSV)
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todo = inv[inv["in_xlsx"] & ~inv["already_tracked"]]
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videos = [Path(p) for p in todo["mp4_path"].tolist()]
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if args.limit:
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videos = videos[: args.limit]
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n_ok = n_fail = n_skip = 0
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for v in videos:
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out = TARGETS_DIR / f"{v.stem}.json"
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if out.exists() and not args.overwrite:
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n_skip += 1
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continue
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result = detect_one(v, args.frame)
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if result is None:
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n_fail += 1
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print(f" fail: {v.name}")
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continue
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points, used_frame = result
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out.write_text(json.dumps({
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"video_path": str(v),
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"frame_index": int(used_frame),
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"reference_points": points,
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"order": ["top", "corner", "left"],
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"picked_at": dt.datetime.now().isoformat(timespec="seconds"),
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"method": "auto",
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}, indent=2))
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n_ok += 1
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print(f" ok: {v.name} → {points}")
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print(f"\nDone. ok={n_ok} fail={n_fail} skipped(existing)={n_skip}")
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if __name__ == "__main__":
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logging.basicConfig(level=logging.WARNING, format="%(levelname)s %(message)s")
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main()
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