- merge_2025_07_15_into_xlsx.py: pivot the legacy 2025_07_15_metadata_fixed.csv
into the unified xlsx schema (one row per fly, training_date_time +
testing_date_time). Backs up the xlsx before writing. 24 new rows
across machines 076 / 139 / 145 / 268.
- pick_targets.py: --video flag to bypass the inventory's in_xlsx filter,
so a specific mp4 can be picked outside the normal flow.
- explore_barrier_signal.py: visualises raw y(t), per-frame inter-fly
distance, and sliding min/mean distance against a known
barrier-opening time. Used for prototyping the detector.
- detect_barrier_opening.py: per-ROI sliding-window mean-distance
change-point estimator (median across ROIs). Currently noisy on a
one-video calibration set; will be re-tuned once the 4 missing
2025-07-15 videos are re-tracked.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Targets relocated alongside the tracking DBs (out of ownCloud sync) so
the docker mount already covers them and ownCloud no longer churns on
JSON sidecars. Updated config, fixed a stale docstring in pick_targets,
and dropped the now-moot data/targets/*.json gitignore rule.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The 2024 video set in all_video_info_merged.xlsx covers 63 (date, machine)
sessions — 129 video instances — that have no auto-detectable targets, so
ROI placement requires manual reference-point selection. This commit adds
the three-stage pipeline that lets a user click for an hour, then walk
away while the tracker grinds overnight:
1. build_video_inventory.py — scan /mnt/ethoscope_data/videos/ and join
against the xlsx, producing data/metadata/video_inventory.csv
2. pick_targets.py — interactive matplotlib/Tk picker. User clicks
TOP/CORNER/LEFT (the L-shape ethoscope expects); after the third
click the 6 ROI rectangles are drawn on top of the frame so geometry
can be verified before saving. Also supports marking a video
'unusable' (FOV wrong) so it's permanently skipped, frame stepping
by ±1s/±5%/midpoint, point editing in --redo mode, and a crosshair
cursor that survives matplotlib's per-motion cursor reset.
3. track_videos.py — headless batch tracker. Reads the JSON sidecars,
builds 6 ROIs from the HD-mating-arena geometry, runs MultiFlyTracker
against the merged.mp4 via MovieVirtualCamera, writes SQLite DBs to
data/tracked/. Idempotent (skips done DBs), parallel via --jobs,
subclasses MovieVirtualCamera so frames stay BGR (MultiFlyTracker
calls cvtColor(BGR2GRAY) without checking channel count).
Plus auto_detect_targets.py (fallback that runs ethoscope's auto-detector
in case any videos do have visible target dots), monitor_tracking.py
(progress + ETA from data/tracked/ ground truth, --watch for live view),
and tracking_geometry.py (single source of truth for the affine math
shared by picker and tracker).
requirements-tracking.txt pins the extra deps (opencv-python, openpyxl,
gitpython, netifaces, mysql-connector-python) — these are only needed
for the tracking pipeline, not the existing analysis notebooks.
Verified end-to-end on one of the user-picked videos: ~4000 rows/ROI in
a 120s slice, fly bounding boxes in the expected 800-2000 px² band.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
