"""Прогон геометрической части конвейера: сколько кандидатов и где.

Это опорный эксперимент: на `doubleT_obstacle` обязан стабильно находиться
реальный объект на ~55 м, на пустых бэгах — считается поток ложных кандидатов.

    python tools/probe_detect.py --bag data/for_hackathon/doubleT_obstacle --frames 40 --verbose
    python tools/probe_detect.py --all --frames 60
"""
from __future__ import annotations

import argparse
import time

import numpy as np

import _bootstrap as B  # noqa: F401
from flyguard import lamina
from flyguard.bag import Bag, find_bags
from flyguard.geometry import TrackFrame, fit_corridor, fit_rail_plane
from flyguard.lobula import find_candidates
from flyguard.retina import ScanLayout


def run(bag_path, n_frames: int, stride: int, fov: float, verbose: bool,
        **kw) -> dict:
    bag = Bag(bag_path)
    layout = ScanLayout.calibrate([pc for _, pc in bag.frames(start=2, stop=14)])
    cols = layout.column_slice(fov)
    lay = layout.sub(cols)

    plane = corridor = None
    per_frame, times = [], []
    hits = []
    for k, (_, pc) in enumerate(bag.frames(stop=n_frames * stride, stride=stride)):
        t0 = time.perf_counter()
        img = layout.project(pc).crop(cols)
        plane = fit_rail_plane(img, lay, prev=plane)
        tf = TrackFrame(img, lay, plane)
        corridor = fit_corridor(tf, prev=corridor)
        lam = lamina.process(tf.r, tf.valid)
        cands = find_candidates(tf, lam, corridor, **kw)
        times.append(time.perf_counter() - t0)

        per_frame.append(len(cands))
        hits.extend(cands)
        if verbose:
            head = "  ".join(
                f"[{c.d:6.1f}м u{c.u:+5.2f} h{c.h:4.2f} {c.width:4.2f}×{c.height:4.2f}м "
                f"n={c.n_rays:4d} gap={c.gap:6.1f} def={c.floor_deficit:6.1f}]"
                for c in cands[:4])
            print(f"  кадр {k*stride:4d}: {len(cands):3d} канд.  {head}")

    n = np.array(per_frame)
    ds = np.array([c.d for c in hits]) if hits else np.zeros(0)
    res = dict(name=bag.path.name, frames=len(n),
               mean=float(n.mean()) if n.size else 0.0,
               median=float(np.median(n)) if n.size else 0.0,
               p95=float(np.percentile(n, 95)) if n.size else 0.0,
               zero=float(np.mean(n == 0)) if n.size else 0.0,
               total=int(n.sum()),
               t_ms=float(np.median(times) * 1e3),
               far=int((ds > 60).sum()))
    print(f"{res['name']:40s} кадров {res['frames']:4d} | кандидатов/кадр "
          f"среднее {res['mean']:6.2f} медиана {res['median']:4.0f} p95 {res['p95']:5.0f} | "
          f"пустых кадров {res['zero']:5.1%} | дальше 60 м {res['far']:5d} | "
          f"{res['t_ms']:6.1f} мс/кадр")
    return res


def main() -> None:
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument("--bag")
    ap.add_argument("--all", action="store_true")
    ap.add_argument("--frames", type=int, default=50)
    ap.add_argument("--stride", type=int, default=2)
    ap.add_argument("--fov", type=float, default=25.0)
    ap.add_argument("--verbose", action="store_true")
    ap.add_argument("--h-lo", type=float, default=0.28)
    ap.add_argument("--min-rays", type=int, default=4)
    ap.add_argument("--half-width", type=float, default=1.7)
    args = ap.parse_args()

    kw = dict(h_lo=args.h_lo, min_rays=args.min_rays, half_width=args.half_width)
    bags = find_bags(B.DATA / "for_hackathon") if args.all else [args.bag]
    for b in bags:
        run(b, args.frames, args.stride, args.fov, args.verbose, **kw)


if __name__ == "__main__":
    main()