#!/usr/bin/env python3
"""Run scared_dataset_benchmark over many keyframes and print a Markdown table.

Usage:
    scripts/scared_overview.py [--bench PATH] [--disparities N] [--png-dir DIR]
                               [--json-dir DIR] [--depth-range MIN_M MAX_M]
                               KEYFRAME_DIR...

Each KEYFRAME_DIR must hold Left_Image.png, Right_Image.png and
endoscope_calibration.yaml. Accuracy columns are filled in only for keyframes
that also contain point_cloud.obj.
"""
import argparse
import json
import os
import subprocess
import sys


def run(bench, kf, disparities, png_dir, json_dir, depth_range):
    label = "/".join(kf.rstrip("/").split("/")[-2:])
    cmd = [bench, kf, str(disparities)]
    if png_dir:
        os.makedirs(png_dir, exist_ok=True)
        cmd.append(os.path.join(png_dir, label.replace("/", "_") + ".png"))
    else:
        cmd.append("-")
    if depth_range:
        cmd += [str(depth_range[0]), str(depth_range[1])]
    proc = subprocess.run(cmd, capture_output=True, text=True)
    if proc.returncode != 0:
        print(f"{label}: benchmark failed\n{proc.stderr}", file=sys.stderr)
        return label, None
    result = json.loads(proc.stdout)
    if json_dir:
        os.makedirs(json_dir, exist_ok=True)
        with open(os.path.join(json_dir, label.replace("/", "_") + ".json"), "w") as f:
            json.dump(result, f, indent=2)
    return label, result


def fmt_mm(v):
    return "" if v is None else f"{v * 1000:.2f}"


def fmt_pct(v):
    return "" if v is None else f"{v * 100:.1f}"


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--bench", default="build-opencv/src/cloud_point/scared_dataset_benchmark")
    ap.add_argument("--disparities", type=int, default=160)
    ap.add_argument("--png-dir")
    ap.add_argument("--json-dir")
    ap.add_argument("--depth-range", nargs=2, type=float, metavar=("MIN_M", "MAX_M"),
                    help="depth filter in metres (default: builder defaults 0.01..10)")
    ap.add_argument("keyframes", nargs="+")
    args = ap.parse_args()

    rows = [run(args.bench, kf, args.disparities, args.png_dir, args.json_dir,
                args.depth_range)
            for kf in args.keyframes]

    print("| keyframe | valid % | z p5 / median / p95 (mm) | match ms | GT | coverage % | MAE3D mm | RMSE3D mm | median mm | <1 mm % | <2 mm % | <5 mm % |")
    print("|---|---|---|---|---|---|---|---|---|---|---|---|")
    for label, r in rows:
        if r is None:
            print(f"| {label} | failed | | | | | | | | | | |")
            continue
        z = f"{r['z_p05_m']*1000:.0f} / {r['z_median_m']*1000:.0f} / {r['z_p95_m']*1000:.0f}"
        gt = r.get("has_ground_truth", False)
        print("| {} | {} | {} | {:.0f} | {} | {} | {} | {} | {} | {} | {} | {} |".format(
            label, fmt_pct(r["valid_fraction"]), z, r["matching_ms"],
            "yes" if gt else "no",
            fmt_pct(r.get("coverage")), fmt_mm(r.get("mae_3d_m")),
            fmt_mm(r.get("rmse_3d_m")), fmt_mm(r.get("median_3d_m")),
            fmt_pct(r.get("within_1mm")), fmt_pct(r.get("within_2mm")),
            fmt_pct(r.get("within_5mm"))))


if __name__ == "__main__":
    main()