Giorgio
e4da7691d5
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>
118 lines
5.6 KiB
Markdown
118 lines
5.6 KiB
Markdown
# Task List
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## Completed Work
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- [x] Extract ROI data from SQLite databases grouped by trained/untrained
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- [x] Calculate inter-fly distances at each time point
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- [x] Align data to barrier opening time (t=0)
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- [x] Plot average distance over time (entire experiment + 300s window)
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- [x] Track fly identities across frames (Hungarian algorithm)
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- [x] Calculate max velocity over 10-second moving windows
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- [x] Statistical tests (t-tests, Cohen's d) comparing groups
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- [x] ML classification attempt (Logistic Regression, Random Forest)
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- [x] Clustering analysis (K-means)
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- [x] Organize project structure for student handoff
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## Priority: Bimodal Hypothesis Analysis
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See `docs/bimodal_hypothesis.md` for detailed methodology.
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### Phase 1: Per-ROI Feature Extraction
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- [ ] Compute per-ROI summary statistics from aligned distance data
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- Mean distance post-opening (0-300s)
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- Median distance post-opening
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- Fraction of time at distance < 50px ("close proximity")
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- Mean max velocity post-opening
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- [ ] Create a summary DataFrame with N=18 trained + N=18 untrained rows
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- [ ] **Note**: Only 30 ROIs have data (Machine 139 missing = 6 ROIs lost)
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### Phase 2: Distribution Visualization
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- [ ] Plot histograms/KDE of per-ROI metrics for each group
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- [ ] Look for bimodality in trained group vs unimodality in untrained
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### Phase 3: Formal Bimodality Testing
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- [ ] Hartigan's dip test on trained per-ROI distributions
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- [ ] Fit Gaussian Mixture Models (1 vs 2 components) to trained data
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- [ ] Compare BIC scores to determine optimal number of components
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### Phase 4: Subgroup Identification
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- [ ] If bimodal: classify trained ROIs as "learner" vs "non-learner" using GMM posteriors
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- [ ] Compare learner subgroup vs untrained group (expect larger effect size)
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### Phase 5: Effect Size Re-estimation
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- [ ] Mann-Whitney U test (appropriate for small N)
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- [ ] Bootstrap confidence intervals for effect sizes
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- [ ] Account for session as random effect
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## Maintenance Items
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- [ ] Investigate missing Machine 139 data (has metadata but no tracking DB)
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- [ ] Add `diptest` to requirements.txt when starting bimodal analysis
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- [ ] Consider converting pixel distances to physical units (need calibration)
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- [ ] The second notebook (`flies_analysis.ipynb`) re-runs from DB extraction - consider deprecating
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## Phase: Offline Tracking of 2024 Video Backlog (added 2026-04-27)
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### Recap
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Tracked so far (5 sessions, all from 2025-07-15, machines 076/145/268). The DBs in
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`data/raw/` use tracker `ConstrainedMultiFlyTracker` and template
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`HD_Mating_Arena_6_ROIS.json` (2 flies × 6 ROIs per video).
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The metadata file `../all_video_info_merged.xlsx` indexes a different set of
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experiments: 7 dates from 2024-09-17 → 2024-10-21, 16 ethoscope machines,
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63 unique (date, machine) sessions = 484 ROI-rows. **None of the already-tracked
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sessions are in this xlsx — these are fresh recordings to track.**
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Inventory: see `data/metadata/video_inventory.csv` (built by
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`scripts/build_video_inventory.py`).
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- 1163 video sessions on disk under `/mnt/ethoscope_data/videos/`
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- 63/63 xlsx (date, machine) sessions have video on disk
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- 129 video instances need tracking (some (date, machine) have 2-4 recordings/day)
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### Plan
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The HD-mating-arena videos have no auto-detectable targets — the user must
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manually click 3 reference points (L-shape: top, corner, left) per video. Once
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all targets are picked, tracking can run in the background.
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- [x] **Step 1 — Inventory**: `scripts/build_video_inventory.py` →
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`data/metadata/video_inventory.csv`. 63 (date,machine) sessions match
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the xlsx, all videos found, 129 video instances need tracking.
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- [x] **Step 2 — Manual target picker**: `scripts/pick_targets.py`. Loops over
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videos with `in_xlsx & ~already_tracked & no JSON yet`; per video, shows
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a representative frame, captures 3 clicks (top, corner, left), saves
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`data/targets/<video_basename>.json`. Skips videos already done.
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- [x] **Step 3 — Background tracker**: `scripts/track_videos.py`. Reads target
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JSONs, builds 6 ROIs from the HD-mating-arena geometry, runs
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`MovieVirtualCamera` + `MultiFlyTracker` + `SQLiteResultWriter`, writes
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`data/tracked/<basename>_tracking.db`. Idempotent. Smoke-tested
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end-to-end: 90s of video → ~3000 rows/ROI, areas in 800-2000 band.
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- [x] **Step 4 — Tracking deps**: `requirements-tracking.txt`.
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### Still TODO
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- [ ] User to run `pick_targets.py` (interactive — needs DISPLAY) on the 129
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pending videos.
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- [ ] Run `track_videos.py --jobs 4` against the resulting JSONs.
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- [ ] (Optional) `auto_detect_targets.py` exists as a fallback for videos that
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DO have visible targets (saves clicks). Confirmed not useful on the
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2025-07-15 batch — these arenas don't have black target dots — but worth
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trying on 2024 batches before falling back to manual.
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- [ ] Decide what to do with the 4 (date, machine) sessions that have 3-4
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recordings/day instead of 2 (e.g. ETHOSCOPE_086 on 2024-09-17 has 4).
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One of them is at lower resolution (1280x960) — likely an aborted take.
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### Open questions / risks
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- Some (date, machine) combos have 3-4 recordings (e.g. ETHOSCOPE_086 on
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2024-09-17). Need to figure out which is the real "test" video vs aborted
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takes — possibly use video duration or filename pattern.
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- One mismatched-resolution file: `1280x960@25fps-20q` instead of
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`1920x1088@25fps-28q` — flag for inspection.
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- The original `ConstrainedMultiFlyTracker` is no longer in the ethoscope repo;
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`MultiFlyTracker` is its likely successor. Validate output schema matches
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what the existing analysis pipeline expects (`load_roi_data.py`, etc.).
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## Discovered During Work
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(Add new items here as they come up during analysis)
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