Replace pick_barrier.py with thumbnail-grid UX
Old version showed inter-fly distance plots and asked the analyst to
click a timeline. The new version reads frames directly from the .mp4
and shows a 10×6 grid of timestamped thumbnails — the analyst just
clicks the frame where the barrier opens.
Two-stage refinement:
- Coarse grid: 60 thumbs spanning the 5-min search window at ~5 s
spacing. Pick the rough moment.
- Fine grid: 60 thumbs at 0.2 s spacing centred on the coarse pick.
Pick the exact frame.
Auto-detector still feeds the starting position. Sequential video
decode (one cv2 pass through the relevant range) instead of seek-per-
frame, so each grid loads in a few seconds.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
parent
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commit
e8c7f23d4d
1 changed files with 241 additions and 161 deletions
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@ -1,47 +1,48 @@
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"""Interactive picker for barrier-opening time per tracked video.
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"""Interactive picker for barrier-opening time, frame-by-frame thumbnail style.
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Loops through tracked DBs that don't yet have a barrier-opening
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annotation. For each, plots the windowed mean inter-fly distance for
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all 6 ROIs over the first 5 minutes and lets the analyst click the
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moment the barrier opens (when most flies start coming close together).
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The auto-detector's best-effort guess is shown as a starting position.
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For each video that doesn't yet have a barrier-opening annotation, show a
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10x6 grid of timestamped thumbnails extracted directly from the .mp4.
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The analyst clicks the thumbnail at (or just after) the moment the
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barrier opens; the picker then refines with a second tighter grid for
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sub-second precision.
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Two-stage flow per video:
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1. Coarse grid: 60 thumbs spanning the 5-min search window (5 s spacing).
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Click → pick that 5 s slot.
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2. Fine grid: 60 thumbs spanning ±6 s of the coarse pick (0.2 s spacing).
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Click → final answer with 0.2 s precision.
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Output: data/metadata/barrier_opening.csv with columns
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machine_name, session_date, session_time, opening_s, trim_first_s, notes
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`opening_s` is the moment of barrier opening, measured from the start
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of the recording (NOT the start of any trimmed copy). `trim_first_s`
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is an optional annotation for videos with a misframed start that
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should be ignored by analysis (defaults to 0).
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Window keys:
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click place the opening cursor at that time
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ENTER save and advance
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[, ] shift cursor by 1 s left / right
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{, } shift cursor by 5 s left / right
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n skip this video for THIS run (no row written)
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u mark this video unusable (writes opening_s = NaN, notes = "unusable")
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r reset cursor to the auto-detected position
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click select thumbnail at that timestamp
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n skip this video for THIS run
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u mark unusable (opening_s = NaN)
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b back to coarse grid (after seeing fine grid)
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q / ESC save+quit
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Usage:
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python pick_barrier.py
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python pick_barrier.py --redo # re-pick videos that already have a row
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python pick_barrier.py --redo
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python pick_barrier.py --limit 10
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python pick_barrier.py --db /path/to/specific_tracking.db
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"""
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from __future__ import annotations
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import argparse
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import re
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import sqlite3
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import sys
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from pathlib import Path
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from config import DATA_METADATA, VIDEO_INFO_TSV
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from config import DATA_METADATA, INVENTORY_CSV, VIDEO_INFO_TSV
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from detect_barrier_opening import (
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SEARCH_END_S, STEP_S, WINDOW_S,
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per_frame_distance, sliding_mean,
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@ -51,43 +52,30 @@ OUT_CSV = DATA_METADATA / "barrier_opening.csv"
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OUT_COLS = ["machine_name", "session_date", "session_time",
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"opening_s", "trim_first_s", "notes"]
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DB_NAME_RE = re.compile(
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r"^(\d{4}-\d{2}-\d{2})_(\d{2}-\d{2}-\d{2})_([0-9a-f]{32})__"
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)
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def parse_db_filename(db_path: Path) -> tuple[str, str, str] | None:
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"""Pull (date, time, machine_uuid) out of a tracking DB filename."""
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import re
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m = re.match(
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r"^(\d{4}-\d{2}-\d{2})_(\d{2}-\d{2}-\d{2})_([0-9a-f]{32})__",
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db_path.name,
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)
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if not m:
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GRID_ROWS, GRID_COLS = 6, 10
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N_THUMBS = GRID_ROWS * GRID_COLS # 60
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COARSE_SPAN_S = SEARCH_END_S # 0..300s, ~5s spacing
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FINE_SPAN_S = 12.0 # ±6s around coarse pick → ~0.2s spacing
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def auto_suggest(db_path: Path) -> float | None:
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"""Median of per-ROI biggest-drop times. None if too noisy."""
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try:
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conn = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True)
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except sqlite3.Error:
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return None
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return m.group(1), m.group(2), m.group(3)
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def load_distance_traces(db_path: Path) -> dict[int, pd.DataFrame]:
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"""For each ROI 1..6, return windowed-mean DF; empty if ROI missing."""
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out: dict[int, pd.DataFrame] = {}
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with sqlite3.connect(f"file:{db_path}?mode=ro", uri=True) as conn:
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candidates = []
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for roi in range(1, 7):
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try:
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df = pd.read_sql_query(
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f"SELECT t, x, y, id FROM ROI_{roi}", conn
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)
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df = pd.read_sql_query(f"SELECT t, x, y, id FROM ROI_{roi}", conn)
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except Exception:
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out[roi] = pd.DataFrame()
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continue
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dist = per_frame_distance(df)
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out[roi] = sliding_mean(dist, WINDOW_S, STEP_S, SEARCH_END_S)
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return out
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def auto_suggest(traces: dict[int, pd.DataFrame]) -> float | None:
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"""Median of per-ROI biggest-drop times. Returns None if too noisy."""
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candidates = []
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for roi, smean in traces.items():
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if len(smean) < 30:
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continue
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# Find the time of the largest decrease in median(pre)–median(post).
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smean = sliding_mean(dist, WINDOW_S, STEP_S, SEARCH_END_S)
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pad = max(1, int(WINDOW_S / STEP_S))
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if len(smean) < 2 * pad + 1:
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continue
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@ -102,103 +90,200 @@ def auto_suggest(traces: dict[int, pd.DataFrame]) -> float | None:
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best_t = float(smean["mid_t"].iloc[i])
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if best_drop > 30 and best_t is not None:
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candidates.append(best_t)
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conn.close()
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if not candidates:
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return None
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return float(np.median(candidates))
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def show_picker(
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db_path: Path,
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machine_name: str,
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session_date: str,
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session_time: str,
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auto_t: float | None,
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initial_t: float,
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) -> dict | None:
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"""Open the picker window. Returns a dict ready for OUT_CSV, or None to skip."""
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traces = load_distance_traces(db_path)
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if all(s.empty for s in traces.values()):
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print(f" ! no usable ROI traces in {db_path.name}; skipping")
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return None
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def grab_thumbnails(video_path: Path, target_times_s: np.ndarray,
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thumb_w: int = 320) -> list[np.ndarray | None]:
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"""Read thumbnails at the requested timestamps via a single sequential pass.
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fig, axes = plt.subplots(6, 1, figsize=(13, 12), sharex=True)
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fig.suptitle(
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f"{machine_name} {session_date} {session_time}\n"
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f"click ↦ set opening · ENTER save · "
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f"[/] ±1s · {{/}} ±5s · n skip · u unusable · r reset · q quit",
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fontsize=10,
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Linear-decode is much faster than seeking per-frame on H.264. We read
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frames sequentially from the earliest target onward, keeping only the
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ones at requested target frames.
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"""
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cap = cv2.VideoCapture(str(video_path))
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fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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src_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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src_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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if total_frames <= 0:
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cap.release()
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return [None] * len(target_times_s)
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target_frames = np.clip(
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(target_times_s * fps).round().astype(int), 0, total_frames - 1
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)
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sort_idx = np.argsort(target_frames)
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sorted_targets = target_frames[sort_idx]
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state: dict = {"t": float(initial_t), "auto": auto_t, "result": None}
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out: list[np.ndarray | None] = [None] * len(target_times_s)
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if sorted_targets.size == 0:
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cap.release()
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return out
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def redraw():
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for ax, (roi, smean) in zip(axes, sorted(traces.items())):
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ax.cla()
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if smean.empty:
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ax.text(0.5, 0.5, f"ROI {roi}: no data",
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transform=ax.transAxes, ha="center", va="center", color="grey")
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cap.set(cv2.CAP_PROP_POS_FRAMES, int(sorted_targets[0]))
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cur_frame = int(sorted_targets[0])
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last_frame_data: np.ndarray | None = None
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scale = thumb_w / src_w if src_w > 0 else 1.0
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thumb_h = max(1, int(round(src_h * scale)))
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for ord_i, target in zip(sort_idx, sorted_targets):
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while cur_frame <= target:
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ret, frame = cap.read()
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if not ret:
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last_frame_data = None
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break
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last_frame_data = frame
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cur_frame += 1
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if last_frame_data is not None:
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small = cv2.resize(last_frame_data, (thumb_w, thumb_h),
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interpolation=cv2.INTER_AREA)
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out[ord_i] = cv2.cvtColor(small, cv2.COLOR_BGR2RGB)
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cap.release()
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return out
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def show_thumbnail_grid(
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video_path: Path,
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center_t: float,
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span_s: float,
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title: str,
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) -> tuple[float | None, str]:
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"""Show a 10×6 thumbnail grid; return (clicked_time, action).
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`action` is one of: 'pick', 'skip', 'unusable', 'back', 'quit'.
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`clicked_time` is None unless action == 'pick'.
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"""
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half = span_s / 2.0
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times = np.linspace(max(0.0, center_t - half), center_t + half, N_THUMBS)
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print(f" loading {N_THUMBS} thumbnails ({times[0]:.1f}–{times[-1]:.1f}s)...", flush=True)
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thumbs = grab_thumbnails(video_path, times)
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fig, axes = plt.subplots(GRID_ROWS, GRID_COLS, figsize=(20, 11))
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fig.suptitle(
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f"{title}\nclick a thumbnail · n=skip · u=unusable · b=back · q=quit",
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fontsize=11,
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)
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state = {"time": None, "action": None}
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for ax, t, thumb in zip(axes.flat, times, thumbs):
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if thumb is not None:
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ax.imshow(thumb)
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else:
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ax.plot(smean["mid_t"], smean["mean_dist"], color="steelblue", lw=1.0)
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ax.set_ylabel(f"ROI {roi}")
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if state["auto"] is not None:
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ax.axvline(state["auto"], color="orange", ls=":", lw=0.8, alpha=0.8)
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ax.axvline(state["t"], color="red", lw=1.5)
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ax.set_xlim(0, SEARCH_END_S)
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ax.grid(True, alpha=0.3)
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axes[-1].set_xlabel("time (s)")
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axes[0].set_title(f"orange dotted = auto-suggested · red = current pick: {state['t']:.1f} s",
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fontsize=9)
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fig.canvas.draw_idle()
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ax.set_facecolor("black")
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ax.text(0.5, 0.5, "no frame",
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transform=ax.transAxes, ha="center", va="center", color="white")
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# Format time as M:SS.s for readability
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m, s = divmod(t, 60)
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ax.set_title(f"{int(m):d}:{s:05.2f}", fontsize=8, pad=1)
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ax.set_xticks([]); ax.set_yticks([])
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fig.subplots_adjust(left=0.01, right=0.99, top=0.93, bottom=0.01,
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wspace=0.03, hspace=0.18)
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def on_click(event):
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if event.inaxes in axes and event.xdata is not None:
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state["t"] = max(0.0, min(SEARCH_END_S, float(event.xdata)))
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redraw()
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if event.inaxes is None:
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return
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for i, ax in enumerate(axes.flat):
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if ax is event.inaxes:
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state["time"] = float(times[i])
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state["action"] = "pick"
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plt.close(fig)
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return
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def on_key(event):
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k = event.key
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if k == "enter":
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state["result"] = {
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"machine_name": machine_name,
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"session_date": session_date,
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"session_time": session_time,
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"opening_s": round(state["t"], 1),
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"trim_first_s": 0,
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"notes": "",
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}
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plt.close(fig)
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elif k == "n":
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state["result"] = "skip"
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plt.close(fig)
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if k == "n":
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state["action"] = "skip"; plt.close(fig)
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elif k == "u":
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state["result"] = {
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"machine_name": machine_name,
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"session_date": session_date,
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"session_time": session_time,
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"opening_s": np.nan,
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"trim_first_s": 0,
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"notes": "unusable",
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}
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plt.close(fig)
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elif k == "r" and state["auto"] is not None:
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state["t"] = state["auto"]
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redraw()
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elif k in ("[", "]"):
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state["t"] = max(0.0, min(SEARCH_END_S, state["t"] + (-1 if k == "[" else 1)))
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redraw()
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elif k in ("{", "}"):
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state["t"] = max(0.0, min(SEARCH_END_S, state["t"] + (-5 if k == "{" else 5)))
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redraw()
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state["action"] = "unusable"; plt.close(fig)
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elif k == "b":
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state["action"] = "back"; plt.close(fig)
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elif k in ("q", "escape"):
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state["result"] = "quit"
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plt.close(fig)
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state["action"] = "quit"; plt.close(fig)
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fig.canvas.mpl_connect("button_press_event", on_click)
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fig.canvas.mpl_connect("key_press_event", on_key)
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redraw()
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plt.show()
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return state["time"], state["action"] or "skip"
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return state["result"]
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def pick_for_video(
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video_path: Path,
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db_path: Path | None,
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machine_name: str,
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session_date: str,
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session_time: str,
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) -> dict | str | None:
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"""Run the two-stage thumbnail picker. Return dict, 'skip', or 'quit'."""
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auto_t = auto_suggest(db_path) if db_path else None
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print(f" auto-suggest: {f'{auto_t:.1f}s' if auto_t else '(none)'}")
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# Stage 1: coarse grid centred on auto-suggest (or 150 s default).
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coarse_center = auto_t if auto_t is not None else COARSE_SPAN_S / 2
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title_coarse = f"COARSE {machine_name} {session_date} {session_time} · spanning 5 min"
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while True:
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coarse_t, action = show_thumbnail_grid(
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video_path, coarse_center, COARSE_SPAN_S, title_coarse
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)
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if action == "skip":
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return "skip"
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if action == "unusable":
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return {
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"machine_name": machine_name, "session_date": session_date,
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"session_time": session_time, "opening_s": np.nan,
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"trim_first_s": 0, "notes": "unusable",
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}
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if action == "quit":
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return "quit"
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if action == "back":
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continue # already at the top stage; redraw
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if action == "pick" and coarse_t is not None:
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break
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# Stage 2: fine grid around the coarse pick.
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title_fine = (f"FINE {machine_name} {session_date} {session_time} "
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f"· ±{FINE_SPAN_S/2:.0f} s around {coarse_t:.1f} s")
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while True:
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fine_t, action = show_thumbnail_grid(
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video_path, coarse_t, FINE_SPAN_S, title_fine
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)
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if action == "back":
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return pick_for_video(video_path, db_path, machine_name,
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session_date, session_time)
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if action == "skip":
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return "skip"
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if action == "unusable":
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return {
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"machine_name": machine_name, "session_date": session_date,
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"session_time": session_time, "opening_s": np.nan,
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"trim_first_s": 0, "notes": "unusable",
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}
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if action == "quit":
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return "quit"
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if action == "pick" and fine_t is not None:
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return {
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"machine_name": machine_name, "session_date": session_date,
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"session_time": session_time, "opening_s": round(fine_t, 1),
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"trim_first_s": 0, "notes": "",
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}
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def lookup_video_path(machine_name: str, session_date: str,
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session_time: str, inv: pd.DataFrame) -> Path | None:
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"""Find the mp4 path for (machine, date, time) in the inventory."""
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match = inv[
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(inv["machine_name"] == machine_name)
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& (inv["session_date"] == session_date)
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& (inv["session_time"] == session_time)
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]
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if match.empty:
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return None
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return Path(match.iloc[0]["mp4_path"])
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def main() -> None:
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|
|
@ -208,7 +293,7 @@ def main() -> None:
|
|||
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")
|
||||
help="annotate this specific tracking DB only")
|
||||
args = parser.parse_args()
|
||||
|
||||
OUT_CSV.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
|
@ -218,10 +303,15 @@ def main() -> None:
|
|||
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.
|
||||
if not INVENTORY_CSV.exists():
|
||||
sys.exit(f"Inventory not found at {INVENTORY_CSV}. Run build_video_inventory.py first.")
|
||||
inv = pd.read_csv(INVENTORY_CSV)
|
||||
|
||||
# Build the queue: every (machine, date, time) referenced by the merged
|
||||
# TSV that has a tracking DB on disk and isn't yet annotated.
|
||||
tsv = pd.read_csv(VIDEO_INFO_TSV, sep="\t")
|
||||
queue = []
|
||||
queue: list[tuple[Path, Path, str, str, str]] = []
|
||||
seen: set[tuple[str, str, str]] = set()
|
||||
for col in ("training_db_path", "testing_db_path"):
|
||||
for _, row in tsv.iterrows():
|
||||
db = row[col]
|
||||
|
|
@ -230,28 +320,25 @@ def main() -> None:
|
|||
db_path = Path(db)
|
||||
if not db_path.exists():
|
||||
continue
|
||||
parsed = parse_db_filename(db_path)
|
||||
if parsed is None:
|
||||
m = DB_NAME_RE.match(db_path.name)
|
||||
if not m:
|
||||
continue
|
||||
session_date, session_time, _ = parsed
|
||||
session_date, session_time = m.group(1), m.group(2)
|
||||
key = (row["machine_name"], session_date, session_time)
|
||||
if key in seen:
|
||||
continue
|
||||
seen.add(key)
|
||||
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
|
||||
video = lookup_video_path(*key, inv)
|
||||
if video is None or not video.exists():
|
||||
print(f" ! no video for {key}; skipping")
|
||||
continue
|
||||
queue.append((db_path, video, *key))
|
||||
|
||||
if args.db:
|
||||
target = Path(args.db).resolve()
|
||||
queue = [q for q in queue if Path(q[0]).resolve() == target]
|
||||
queue = [q for q in queue if q[0].resolve() == target]
|
||||
if not queue:
|
||||
sys.exit(f"DB not found in queue: {args.db}")
|
||||
|
||||
|
|
@ -259,34 +346,26 @@ def main() -> None:
|
|||
queue = queue[: args.limit]
|
||||
|
||||
if not queue:
|
||||
print("Nothing to pick. All eligible DBs already have a barrier_opening row.")
|
||||
print("Nothing to pick. All eligible videos 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")
|
||||
print("Window keys: click=pick · n=skip · u=unusable · b=back · q=quit")
|
||||
|
||||
saved = skipped = unusable = 0
|
||||
for i, (db, machine_name, session_date, session_time) in enumerate(queue, 1):
|
||||
for i, (db, video, 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)
|
||||
result = pick_for_video(video, db, machine_name, session_date, session_time)
|
||||
|
||||
if result is None or result == "skip":
|
||||
skipped += 1
|
||||
continue
|
||||
if result == "quit":
|
||||
print(" quit requested — saving what we have and exiting")
|
||||
print(" quit requested — saving 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"]) &
|
||||
|
|
@ -297,6 +376,7 @@ def main() -> None:
|
|||
out[OUT_COLS].to_csv(OUT_CSV, index=False)
|
||||
if pd.isna(result["opening_s"]):
|
||||
unusable += 1
|
||||
print(" saved as unusable")
|
||||
else:
|
||||
saved += 1
|
||||
print(f" saved opening_s = {result['opening_s']} s")
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue