"""Interactive picker for barrier-opening time per tracked video. Loops through tracked DBs that don't yet have a barrier-opening annotation. For each, plots the windowed mean inter-fly distance for all 6 ROIs over the first 5 minutes and lets the analyst click the moment the barrier opens (when most flies start coming close together). The auto-detector's best-effort guess is shown as a starting position. Output: data/metadata/barrier_opening.csv with columns machine_name, session_date, session_time, opening_s, trim_first_s, notes `opening_s` is the moment of barrier opening, measured from the start of the recording (NOT the start of any trimmed copy). `trim_first_s` is an optional annotation for videos with a misframed start that should be ignored by analysis (defaults to 0). Window keys: click place the opening cursor at that time ENTER save and advance [, ] shift cursor by 1 s left / right {, } shift cursor by 5 s left / right n skip this video for THIS run (no row written) u mark this video unusable (writes opening_s = NaN, notes = "unusable") r reset cursor to the auto-detected position q / ESC save+quit Usage: python pick_barrier.py python pick_barrier.py --redo # re-pick videos that already have a row python pick_barrier.py --limit 10 """ from __future__ import annotations import argparse import sqlite3 import sys from pathlib import Path import matplotlib.pyplot as plt import numpy as np import pandas as pd from config import DATA_METADATA, VIDEO_INFO_TSV from detect_barrier_opening import ( SEARCH_END_S, STEP_S, WINDOW_S, per_frame_distance, sliding_mean, ) OUT_CSV = DATA_METADATA / "barrier_opening.csv" OUT_COLS = ["machine_name", "session_date", "session_time", "opening_s", "trim_first_s", "notes"] def parse_db_filename(db_path: Path) -> tuple[str, str, str] | None: """Pull (date, time, machine_uuid) out of a tracking DB filename.""" import re m = re.match( r"^(\d{4}-\d{2}-\d{2})_(\d{2}-\d{2}-\d{2})_([0-9a-f]{32})__", db_path.name, ) if not m: return None return m.group(1), m.group(2), m.group(3) def load_distance_traces(db_path: Path) -> dict[int, pd.DataFrame]: """For each ROI 1..6, return windowed-mean DF; empty if ROI missing.""" out: dict[int, pd.DataFrame] = {} with sqlite3.connect(f"file:{db_path}?mode=ro", uri=True) as conn: for roi in range(1, 7): try: df = pd.read_sql_query( f"SELECT t, x, y, id FROM ROI_{roi}", conn ) except Exception: out[roi] = pd.DataFrame() continue dist = per_frame_distance(df) out[roi] = sliding_mean(dist, WINDOW_S, STEP_S, SEARCH_END_S) return out def auto_suggest(traces: dict[int, pd.DataFrame]) -> float | None: """Median of per-ROI biggest-drop times. Returns None if too noisy.""" candidates = [] for roi, smean in traces.items(): if len(smean) < 30: continue # Find the time of the largest decrease in median(pre)–median(post). pad = max(1, int(WINDOW_S / STEP_S)) if len(smean) < 2 * pad + 1: continue best_drop = -np.inf best_t = None for i in range(pad, len(smean) - pad): pre = smean["mean_dist"].iloc[:i].median() post = smean["mean_dist"].iloc[i:].median() drop = pre - post if drop > best_drop: best_drop = drop best_t = float(smean["mid_t"].iloc[i]) if best_drop > 30 and best_t is not None: candidates.append(best_t) if not candidates: return None return float(np.median(candidates)) def show_picker( db_path: Path, machine_name: str, session_date: str, session_time: str, auto_t: float | None, initial_t: float, ) -> dict | None: """Open the picker window. Returns a dict ready for OUT_CSV, or None to skip.""" traces = load_distance_traces(db_path) if all(s.empty for s in traces.values()): print(f" ! no usable ROI traces in {db_path.name}; skipping") return None fig, axes = plt.subplots(6, 1, figsize=(13, 12), sharex=True) fig.suptitle( f"{machine_name} {session_date} {session_time}\n" f"click ↦ set opening · ENTER save · " f"[/] ±1s · {{/}} ±5s · n skip · u unusable · r reset · q quit", fontsize=10, ) state: dict = {"t": float(initial_t), "auto": auto_t, "result": None} def redraw(): for ax, (roi, smean) in zip(axes, sorted(traces.items())): ax.cla() if smean.empty: ax.text(0.5, 0.5, f"ROI {roi}: no data", transform=ax.transAxes, ha="center", va="center", color="grey") else: ax.plot(smean["mid_t"], smean["mean_dist"], color="steelblue", lw=1.0) ax.set_ylabel(f"ROI {roi}") if state["auto"] is not None: ax.axvline(state["auto"], color="orange", ls=":", lw=0.8, alpha=0.8) ax.axvline(state["t"], color="red", lw=1.5) ax.set_xlim(0, SEARCH_END_S) ax.grid(True, alpha=0.3) axes[-1].set_xlabel("time (s)") axes[0].set_title(f"orange dotted = auto-suggested · red = current pick: {state['t']:.1f} s", fontsize=9) fig.canvas.draw_idle() def on_click(event): if event.inaxes in axes and event.xdata is not None: state["t"] = max(0.0, min(SEARCH_END_S, float(event.xdata))) redraw() def on_key(event): k = event.key if k == "enter": state["result"] = { "machine_name": machine_name, "session_date": session_date, "session_time": session_time, "opening_s": round(state["t"], 1), "trim_first_s": 0, "notes": "", } plt.close(fig) elif k == "n": state["result"] = "skip" plt.close(fig) elif k == "u": state["result"] = { "machine_name": machine_name, "session_date": session_date, "session_time": session_time, "opening_s": np.nan, "trim_first_s": 0, "notes": "unusable", } plt.close(fig) elif k == "r" and state["auto"] is not None: state["t"] = state["auto"] redraw() elif k in ("[", "]"): state["t"] = max(0.0, min(SEARCH_END_S, state["t"] + (-1 if k == "[" else 1))) redraw() elif k in ("{", "}"): state["t"] = max(0.0, min(SEARCH_END_S, state["t"] + (-5 if k == "{" else 5))) redraw() elif k in ("q", "escape"): state["result"] = "quit" plt.close(fig) fig.canvas.mpl_connect("button_press_event", on_click) fig.canvas.mpl_connect("key_press_event", on_key) redraw() plt.show() return state["result"] def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--redo", action="store_true", help="re-pick videos that already have a row in the output CSV") parser.add_argument("--limit", type=int, default=None, help="only process the first N videos") parser.add_argument("--db", type=Path, default=None, help="annotate this specific DB only") args = parser.parse_args() OUT_CSV.parent.mkdir(parents=True, exist_ok=True) if OUT_CSV.exists(): out = pd.read_csv(OUT_CSV) else: out = pd.DataFrame(columns=OUT_COLS) done = set(zip(out["machine_name"], out["session_date"], out["session_time"])) # Build the queue: every tracked DB referenced by the merged TSV that # hasn't been picked yet. tsv = pd.read_csv(VIDEO_INFO_TSV, sep="\t") queue = [] for col in ("training_db_path", "testing_db_path"): for _, row in tsv.iterrows(): db = row[col] if not isinstance(db, str) or not db: continue db_path = Path(db) if not db_path.exists(): continue parsed = parse_db_filename(db_path) if parsed is None: continue session_date, session_time, _ = parsed key = (row["machine_name"], session_date, session_time) if key in done and not args.redo: continue queue.append((db_path, row["machine_name"], session_date, session_time)) # Dedup (a fly may reference the same DB for both training & testing). seen = set() deduped = [] for item in queue: k = (item[1], item[2], item[3]) if k not in seen: seen.add(k) deduped.append(item) queue = deduped if args.db: target = Path(args.db).resolve() queue = [q for q in queue if Path(q[0]).resolve() == target] if not queue: sys.exit(f"DB not found in queue: {args.db}") if args.limit: queue = queue[: args.limit] if not queue: print("Nothing to pick. All eligible DBs already have a barrier_opening row.") return print(f"Picking barrier-opening for {len(queue)} videos.") print("Window keys: click=set ENTER=save [/]=±1s {/}=±5s n=skip u=unusable r=reset q=quit") saved = skipped = unusable = 0 for i, (db, machine_name, session_date, session_time) in enumerate(queue, 1): prefix = f"[{i}/{len(queue)}] {machine_name} {session_date} {session_time}" print(f"\n{prefix}") traces = load_distance_traces(db) auto_t = auto_suggest(traces) initial = auto_t if auto_t is not None else 60.0 print(f" auto-suggest: " f"{f'{auto_t:.1f}s' if auto_t is not None else '(none)'}") result = show_picker(db, machine_name, session_date, session_time, auto_t, initial) if result is None or result == "skip": skipped += 1 continue if result == "quit": print(" quit requested — saving what we have and exiting") break # Append + dedup on key + persist after each save (crash-safe). new_row = pd.DataFrame([result]) out = pd.concat([ out[~((out.machine_name == result["machine_name"]) & (out.session_date == result["session_date"]) & (out.session_time == result["session_time"]))], new_row, ], ignore_index=True) out[OUT_COLS].to_csv(OUT_CSV, index=False) if pd.isna(result["opening_s"]): unusable += 1 else: saved += 1 print(f" saved opening_s = {result['opening_s']} s") print(f"\nDone: {saved} saved, {unusable} unusable, {skipped} skipped.") print(f" → {OUT_CSV}") if __name__ == "__main__": main()