2026-05-01 09:20:25 +01:00
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# Generated CSVs (regenerable from the tracking DBs + the merged TSV)
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2026-03-05 16:08:36 +00:00
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data/processed/*.csv
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2026-05-01 09:20:25 +01:00
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# Tracking DBs and target JSONs live outside the repo at /mnt/data/projects/cupido/
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Add offline tracking pipeline for video backlog
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>
2026-04-27 17:25:26 +01:00
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data/metadata/video_inventory.csv
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2026-05-01 09:30:22 +01:00
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# Per-user writable copy of the metadata TSV (see notebook setup cells).
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# The shared master is at /mnt/data/projects/cupido/all_video_info_merged.tsv.
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data/metadata/all_video_info_merged.tsv
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Add offline tracking pipeline for video backlog
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>
2026-04-27 17:25:26 +01:00
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data/logs/*.log
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2026-03-05 16:08:36 +00:00
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# Generated figures (reproducible from scripts)
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figures/*.png
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.venv/
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venv/
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env/
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*.egg-info/
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dist/
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build/
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# Jupyter
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.ipynb_checkpoints/
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# IDE
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.vscode/
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.idea/
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# OS
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.DS_Store
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Thumbs.db
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# Claude Code
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.claude/
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