Add video duration_s to inventory and propagate to merged TSV

build_video_inventory.py now opens each mp4 with cv2 to record
duration_s. Cached: a video already in the previous inventory keeps
its computed duration, so re-runs only pay the cv2 cost for new
recordings.

export_video_db_index.py looks up the matched video's duration and
writes it as training_video_duration_s / testing_video_duration_s
alongside the existing path columns. Useful for spotting unusually
short or long sessions and for sanity checks on the tracker output.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

scripts/export_video_db_index.py

@@ -57,6 +57,7 @@ def parse_xlsx_time(value: str) -> tuple[str, int] | None:
 def build_session_index(inventory: pd.DataFrame) -> dict[tuple[str, str], list[dict]]:
     """Index inventory rows by (date, machine_name) → list of session dicts."""
     idx: dict[tuple[str, str], list[dict]] = {}
+    has_duration = "duration_s" in inventory.columns
     for row in inventory.itertuples(index=False):
         h, m, _s = (int(p) for p in str(row.session_time).split("-"))
         key = (row.session_date, row.machine_name)
@@ -64,6 +65,7 @@ def build_session_index(inventory: pd.DataFrame) -> dict[tuple[str, str], list[d
             "mp4_path": row.mp4_path,
             "session_datetime": row.session_datetime,
             "minutes": h * 60 + m,
+            "duration_s": float(row.duration_s) if has_duration and pd.notna(row.duration_s) else None,
         })
     return idx
 
@@ -83,7 +85,7 @@ def resolve_session(
     when: str,
     fallback_date: str | None,
     index: dict[tuple[str, str], list[dict]],
-) -> tuple[str, str]:
+) -> tuple[str, str, float | None]:
     """Look up the video + db whose start time is closest to `when`.
 
     Match strategy:
@@ -95,16 +97,18 @@ def resolve_session(
 
     Among candidates, pick the video whose start minute is closest to the
     xlsx-claimed time, within ±_TIME_TOLERANCE_MIN.
+
+    Returns (mp4_path, db_path, duration_s) — empty strings / None if no match.
     """
     parsed = parse_xlsx_time(when)
     if parsed is None:
-        return "", ""
+        return "", "", None
     date, target_min = parsed
     candidates = index.get((date, machine_name), [])
     if not candidates and fallback_date:
         candidates = index.get((fallback_date, machine_name), [])
     if not candidates:
-        return "", ""
+        return "", "", None
 
     def _gap(target: int, c: dict) -> int:
         # Reason: xlsx times like '1230AM' are ambiguous (12 AM vs 12 PM).
@@ -114,9 +118,9 @@ def resolve_session(
 
     best = min(candidates, key=lambda c: _gap(target_min, c))
     if _gap(target_min, best) > _TIME_TOLERANCE_MIN:
-        return "", ""
+        return "", "", None
     db = db_path_for_video(best["mp4_path"])
-    return best["mp4_path"], (str(db) if db else "")
+    return best["mp4_path"], (str(db) if db else ""), best.get("duration_s")
 
 
 # Variants of "naive" the xlsx has accumulated: 'naïve', 'niave', plus
@@ -151,19 +155,20 @@ def main() -> None:
     _normalize_metadata(df)
     date_iso = pd.to_datetime(df["date"]).dt.strftime("%Y-%m-%d")
 
-    train_videos, train_dbs, test_videos, test_dbs = [], [], [], []
+    train_videos, train_dbs, train_durs = [], [], []
+    test_videos, test_dbs, test_durs = [], [], []
     for fallback, row in zip(date_iso, df.itertuples(index=False)):
-        tv, td = resolve_session(row.machine_name, row.training_date_time, fallback, index)
-        sv, sd = resolve_session(row.machine_name, row.testing_date_time, fallback, index)
-        train_videos.append(tv)
-        train_dbs.append(td)
-        test_videos.append(sv)
-        test_dbs.append(sd)
+        tv, td, tdur = resolve_session(row.machine_name, row.training_date_time, fallback, index)
+        sv, sd, sdur = resolve_session(row.machine_name, row.testing_date_time, fallback, index)
+        train_videos.append(tv); train_dbs.append(td); train_durs.append(tdur)
+        test_videos.append(sv);  test_dbs.append(sd);  test_durs.append(sdur)
 
     df["training_video_path"] = train_videos
     df["training_db_path"] = train_dbs
+    df["training_video_duration_s"] = train_durs
     df["testing_video_path"] = test_videos
     df["testing_db_path"] = test_dbs
+    df["testing_video_duration_s"] = test_durs
 
     # Reason: an analyst flag for excluding individual fly/session rows that
     # turn out to be too noisy or otherwise unusable. Default True; flip to