#!/usr/bin/env python
"""Measure the video<->SMPL-X frame offset EMPIRICALLY. Never assume it.

Why this exists
    The DEGAS captures were first tracked assuming `GT frame = video frame + 1`. That
    offset had been measured on a near-static frame, where every candidate looks alike,
    and it was wrong. A one-frame error is nearly invisible in a still overlay but
    poisons every downstream avatar. So the offset is now MEASURED, on motion, before a
    capture is published.

Method (needs no SMPL-X model, only what the dataset ships)
    The discriminating signal is the FAST-MOVING HAND against the ALPHA MATTE.
    A forearm is only ~40-80 px wide, and during a gesture the wrist moves tens of px
    per frame, so a projected wrist lands inside the silhouette at the correct offset and
    outside it one frame away. Slow signals (body centroid) cannot resolve +-1 frame and
    will happily report nonsense with a margin of ~0.01; that weak-signal trap is exactly
    what produced the original wrong +1, so this script refuses to answer when the margin
    is small.

    1. Rank fit frames by projected 2D WRIST SPEED; keep the fastest ones.
    2. Decode a contiguous low-res window of the ALPHA half (right 2048) of each camera.
    3. For each candidate offset d, measure the fraction of hand/wrist joints that land
       on foreground at video frame (fit_frame + d).
    4. Report argmax, plus the margin. A margin below --min-margin is INCONCLUSIVE and
       exits non-zero rather than guessing.

Usage
    python verify_alignment.py data/P1C1 --cams 6 12 --start 1300 --count 250
    python verify_alignment.py data/P1C1 --expect 0        # non-zero exit if it differs
"""
from __future__ import annotations

import argparse
import subprocess
import sys
from pathlib import Path

import numpy as np

sys.path.insert(0, str(Path(__file__).resolve().parent))
from load_capture import Capture  # noqa: E402


# SMPL-X joint indices that move fastest and sit on THIN geometry, which is what makes
# a one-frame error visible: wrists + both hands' finger joints.
HAND_JOINTS = np.r_[20, 21, np.arange(25, 55)]


def decode_alpha_window(video: Path, v_start: int, count: int, scale: float = 0.25
                        ) -> tuple[np.ndarray, float]:
    """(count, h, w) uint8 stack of the ALPHA half, downscaled by `scale`."""
    vf = (f"select='between(n\\,{v_start}\\,{v_start + count - 1})',"
          f"crop=iw/2:ih:iw/2:0,scale=iw*{scale}:ih*{scale},format=gray")
    proc = subprocess.run(
        ["ffmpeg", "-v", "error", "-threads", "1", "-i", str(video),
         "-vf", vf, "-vsync", "0", "-f", "rawvideo", "-pix_fmt", "gray", "-"],
        capture_output=True, check=True)
    buf = np.frombuffer(proc.stdout, np.uint8)
    if len(buf) % count:
        raise RuntimeError(f"{video.name}: raw size {len(buf)} not divisible by {count}")
    hw = len(buf) // count
    # crop=2048x1500 -> scale 0.25 -> 512x375
    w = int(round(2048 * scale))
    h = hw // w
    if h * w * count != len(buf):
        raise RuntimeError(f"{video.name}: cannot reshape {len(buf)} as {count}x?x{w}")
    return buf.reshape(count, h, w), scale


def hand_speed(cap: Capture, cam: str, frames: list[int]) -> np.ndarray:
    """Mean projected 2D speed of the hand joints, px/frame, per fit frame."""
    c = cap.cameras[cam]
    uv = np.stack([c.project(cap.joints(f)[HAND_JOINTS]) for f in frames])
    d = np.linalg.norm(np.diff(uv, axis=0), axis=2).mean(1)
    return np.r_[d[0], d]


def hit_rate(cap: Capture, cam: str, probe: list[int], stack: np.ndarray, scale: float,
             v_first: int, offsets: range) -> dict[int, float]:
    """For each offset d: fraction of hand joints landing on foreground alpha."""
    c = cap.cameras[cam]
    n, h, w = stack.shape
    out = {}
    for d in offsets:
        hits, tot = 0, 0
        for f in probe:
            idx = (f + d) - v_first
            if not (0 <= idx < n):
                continue
            m = stack[idx] > 12
            uv = c.project(cap.joints(f)[HAND_JOINTS]) * scale
            u = np.round(uv[:, 0]).astype(int)
            v = np.round(uv[:, 1]).astype(int)
            ok = (u >= 0) & (u < w) & (v >= 0) & (v < h)
            if not ok.any():
                continue
            hits += int(m[v[ok], u[ok]].sum())
            tot += int(ok.sum())
        out[d] = hits / tot if tot else float("nan")
    return out


def main() -> int:
    ap = argparse.ArgumentParser(description=__doc__,
                                 formatter_class=argparse.RawDescriptionHelpFormatter)
    ap.add_argument("capture_dir", type=Path)
    ap.add_argument("--cams", type=int, nargs="*", default=[6, 12])
    ap.add_argument("--start", type=int, default=None, help="first FIT frame of the window")
    ap.add_argument("--count", type=int, default=300)
    ap.add_argument("--lags", type=int, nargs=2, default=[-3, 3])
    ap.add_argument("--n-probe", type=int, default=40,
                    help="how many of the fastest-hand frames to score")
    ap.add_argument("--min-margin", type=float, default=0.05,
                    help="required hit-rate gap to the runner-up; below this = INCONCLUSIVE")
    ap.add_argument("--expect", type=int, default=None,
                    help="fail (exit 1) if the measured offset differs from this")
    a = ap.parse_args()

    cap = Capture(a.capture_dir)
    print(cap)
    frames = [int(f) for f in cap.frames]
    if a.start is not None:
        frames = [f for f in frames if f >= a.start]
    frames = frames[:a.count]
    lo, hi = a.lags
    print(f"window: fit frames {frames[0]}..{frames[-1]} ({len(frames)})")

    cam0 = f"cam{a.cams[0]:02d}"
    spd = hand_speed(cap, cam0, frames)
    order = np.argsort(-spd)[:a.n_probe]
    probe = sorted(frames[i] for i in order)
    print(f"hand speed in window: median {np.median(spd):.1f} px/frame, "
          f"probe frames use {spd[order].min():.1f}..{spd[order].max():.1f} px/frame")
    if spd[order].min() < 3.0:
        print("WARNING: probe frames are slow; +-1 frame may be unresolvable here")

    v_first = frames[0] + lo
    n_dec = len(frames) + (hi - lo) + 1
    print(f"decoding alpha window: video frames {v_first}..{v_first + n_dec - 1}")

    per_cam = {}
    for ci in a.cams:
        cam = f"cam{ci:02d}"
        stack, scale = decode_alpha_window(cap.video_dir / f"{cam}.mp4", v_first, n_dec)
        r = hit_rate(cap, cam, probe, stack, scale, v_first, range(lo, hi + 1))
        per_cam[cam] = r
        best = max(r, key=r.get)
        print(f"  {cam} ({stack.shape[2]}x{stack.shape[1]}): "
              + "  ".join(f"d={k:+d}:{v:.3f}" for k, v in sorted(r.items()))
              + f"   -> best d={best:+d}")

    total = {}
    for r in per_cam.values():
        for k, v in r.items():
            total[k] = total.get(k, 0.0) + v / len(per_cam)
    ranked = sorted(total.items(), key=lambda kv: -kv[1])
    best, margin = ranked[0][0], ranked[0][1] - ranked[1][1]
    print("\nhand-on-silhouette hit rate by offset: "
          + "  ".join(f"d={k:+d}:{v:.3f}" for k, v in sorted(total.items())))
    print(f"MEASURED: video_frame = fit_frame + ({best:+d})  "
          f"(margin over d={ranked[1][0]:+d}: {margin:.3f})")

    if margin < a.min_margin:
        print(f"INCONCLUSIVE: margin {margin:.3f} < --min-margin {a.min_margin}. "
              f"This window cannot resolve the offset; do NOT act on it. "
              f"Retry on a higher-motion window or more cameras.")
        return 2
    if a.expect is not None:
        if best != a.expect:
            print(f"FAIL: expected {a.expect:+d}, measured {best:+d}")
            return 1
        print(f"PASS: matches expected {a.expect:+d}")
    return 0


if __name__ == "__main__":
    sys.exit(main())