Deleted the 5 stale pre-pipeline tracking DBs and the data/raw/ directory.
Dropped DATA_RAW from config.py; build_video_inventory now scans
TRACKING_OUTPUT_DIR for already-tracked sessions. Notebooks no longer
import DATA_RAW. README, PLANNING and todo updated to reflect that the
repo holds only code + small curated metadata, never bulky DBs.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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>
Consolidates everything bulky (tracking DBs, targets, metadata
spreadsheet) under a single DATA_VOLUME root outside the ownCloud-synced
repo. Notebooks now use a visible DATA_DIR = Path(...) idiom rather than
walking up the filesystem with PROJECT_ROOT.parent — easier for students
with no Python background to follow.
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>
- 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>
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>
Reorganized flat 41-file directory into structured layout with:
- scripts/ for Python analysis code with shared config.py
- notebooks/ for Jupyter analysis notebooks
- data/ split into raw/, metadata/, processed/
- docs/ with analysis summary, experimental design, and bimodal hypothesis tutorial
- tasks/ with todo checklist and lessons learned
- Comprehensive README, PLANNING.md, and .gitignore
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
