#!/usr/bin/env python3
"""Run InterFormer (egocentric hand–object parsing) on every frame of a clip.

Does not import third-party/do-as-i-do. Uses reconstruction/modules/InterFormer.

Output (default: <video_dir>/video_segmentation/interformer/):
  masks/frame_XXXXXX_masks/<class>.png
  overlays/frame_XXXXXX.png
  semantic/frame_XXXXXX.png
  tracked_interformer.mp4

6-class EgoHOS names (config_interformer.py num_classes=6):
  background, left_hand, right_hand, left_object, right_object, two_object

Union object mask is also written as <object_id>.png (default: object).
"""

from __future__ import annotations

import argparse
import os
import subprocess
import sys
from pathlib import Path

import cv2
import numpy as np

_RECON_ROOT = Path(__file__).resolve().parents[1]
_IF_DIR = Path(os.environ.get("INTERFORMER_DIR", _RECON_ROOT / "modules" / "InterFormer"))
sys.path.insert(0, str(_IF_DIR))


def _raise_mmdet_mmcv_cap() -> None:
    """mmdet 3.x asserts mmcv<2.2.0; source-built mmcv for torch 2.11 is 2.2.0."""
    try:
        import site

        paths = []
        try:
            paths.extend(site.getsitepackages())
        except Exception:
            pass
        try:
            paths.append(site.getusersitepackages())
        except Exception:
            pass
        for sp in paths:
            init = Path(sp) / "mmdet" / "__init__.py"
            if not init.is_file():
                continue
            text = init.read_text()
            if "mmcv_maximum_version = '2.2.0'" in text:
                init.write_text(
                    text.replace(
                        "mmcv_maximum_version = '2.2.0'",
                        "mmcv_maximum_version = '2.3.0'",
                    )
                )
    except OSError:
        pass


_raise_mmdet_mmcv_cap()

EGOHOS6 = [
    "background",
    "left_hand",
    "right_hand",
    "left_object",
    "right_object",
    "two_object",
]
EGOHOS9 = [
    "background",
    "left_hand",
    "right_hand",
    "left_object",
    "right_object",
    "two_object",
    "left_object2",
    "right_object2",
    "two_object2",
]
PALETTE = np.array(
    [
        [0, 0, 0],
        [255, 0, 0],
        [0, 0, 255],
        [255, 0, 255],
        [0, 255, 255],
        [0, 255, 0],
        [255, 204, 255],
        [204, 255, 255],
        [204, 255, 204],
    ],
    dtype=np.uint8,
)
OBJECT_CLASSES = {
    "left_object",
    "right_object",
    "two_object",
    "left_object2",
    "right_object2",
    "two_object2",
}


def _find_checkpoint(explicit: str | None) -> Path:
    if explicit:
        p = Path(explicit)
        if not p.is_file():
            raise FileNotFoundError(f"checkpoint not found: {p}")
        return p
    env = os.environ.get("INTERFORMER_CHECKPOINT")
    if env:
        p = Path(env)
        if p.is_file():
            return p
    ckpt_dir = Path(
        os.environ.get(
            "INTERFORMER_CKPT_DIR",
            _RECON_ROOT.parent / "checkpoints" / "interformer",
        )
    )
    if ckpt_dir.is_file():
        return ckpt_dir
    hits: list[Path] = []
    if ckpt_dir.is_dir():
        for pat in ("*.pth", "*.pt", "*.ckpt"):
            hits.extend(sorted(ckpt_dir.glob(pat)))
            hits.extend(sorted(ckpt_dir.glob(f"**/{pat}")))
    hits = [h for h in hits if h.is_file() and "backbone" not in h.name.lower()]
    if hits:
        return hits[0]
    raise FileNotFoundError(
        "No InterFormer weights under checkpoints/interformer.\n"
        "Put the trained .pth there (README: Google Drive "
        "https://drive.google.com/file/d/172Hi0jTTijEnbroYSUZgOfpsyue_HHxg )."
    )


def _list_frames(video: str, frames_dir: str | None) -> list[Path]:
    if frames_dir:
        d = Path(frames_dir)
        frames = sorted(d.glob("*.png")) + sorted(d.glob("*.jpg"))
        if not frames:
            raise FileNotFoundError(f"no images in {d}")
        return frames
    vp = Path(video)
    if vp.is_dir():
        frames = sorted(vp.glob("*.png")) + sorted(vp.glob("*.jpg"))
        if frames:
            return frames
        raise FileNotFoundError(f"no images in {vp}")
    sibling = vp.parent / "all_frames"
    if sibling.is_dir():
        frames = sorted(sibling.glob("*.png"))
        if frames:
            return frames
    cap = cv2.VideoCapture(str(vp))
    if not cap.isOpened():
        raise FileNotFoundError(f"cannot open {vp}")
    tmp = vp.parent / "all_frames"
    tmp.mkdir(parents=True, exist_ok=True)
    i = 0
    while True:
        ok, bgr = cap.read()
        if not ok:
            break
        out = tmp / f"{i:06d}.png"
        if not out.exists():
            cv2.imwrite(str(out), bgr)
        i += 1
    cap.release()
    return sorted(tmp.glob("*.png"))


def _colorize(sem: np.ndarray) -> np.ndarray:
    h, w = sem.shape
    rgb = np.zeros((h, w, 3), dtype=np.uint8)
    for i in range(min(len(PALETTE), int(sem.max()) + 1)):
        rgb[sem == i] = PALETTE[i]
    return rgb


def main() -> int:
    p = argparse.ArgumentParser(description="InterFormer HOI segmentation on all frames")
    p.add_argument("--video", type=str, default=None, help="clip.mp4 or a frames directory")
    p.add_argument("--frames-dir", type=str, default=None, help="directory of PNG/JPG frames")
    p.add_argument("--output-dir", type=str, default=None)
    p.add_argument("--checkpoint", type=str, default=None)
    p.add_argument(
        "--config",
        type=str,
        default=None,
        help="default: modules/InterFormer/mmseg/configs/config_interformer.py",
    )
    p.add_argument("--object-id", type=str, default="object", help="filename for union object mask")
    p.add_argument("--device", type=str, default=None)
    p.add_argument("--skip-existing", action="store_true")
    args = p.parse_args()

    if not args.video and not args.frames_dir:
        p.error("pass --video or --frames-dir")

    ckpt = _find_checkpoint(args.checkpoint)
    cfg_path = Path(
        args.config
        or os.environ.get(
            "INTERFORMER_CONFIG",
            _IF_DIR / "mmseg" / "configs" / "config_interformer.py",
        )
    )
    if not cfg_path.is_file():
        raise FileNotFoundError(f"InterFormer config missing: {cfg_path}")

    frames = _list_frames(args.video or "", args.frames_dir)
    if args.output_dir:
        out_root = Path(args.output_dir)
    else:
        base = Path(args.video).resolve().parent if args.video else frames[0].parent.parent
        if (base / "clip.mp4").exists() or (base / "all_frames").exists():
            out_root = base / "video_segmentation" / "interformer"
        else:
            out_root = base / "interformer"
    masks_root = out_root / "masks"
    overlay_dir = out_root / "overlays"
    semantic_dir = out_root / "semantic"
    overlay_dir.mkdir(parents=True, exist_ok=True)
    semantic_dir.mkdir(parents=True, exist_ok=True)

    device = args.device or ("cuda:0" if os.environ.get("CUDA_VISIBLE_DEVICES") is not None else "cuda:0")
    try:
        import torch

        if not torch.cuda.is_available():
            device = "cpu"
    except ImportError:
        pass

    print(f"InterFormer config: {cfg_path}")
    print(f"checkpoint: {ckpt}")
    print(f"frames: {len(frames)}  out: {out_root}")

    from mmseg.apis import inference_model, init_model

    model = init_model(str(cfg_path), str(ckpt), device=device)
    ncls = int(model.decode_head.num_classes)
    names = EGOHOS6 if ncls <= 6 else EGOHOS9
    names = names[:ncls]
    print(f"classes ({ncls}): {names}")

    nonempty = {n: 0 for n in names}
    for i, fpath in enumerate(frames):
        frame_i = i
        stem_digits = "".join(ch for ch in fpath.stem if ch.isdigit())
        if stem_digits:
            frame_i = int(stem_digits)
        frame_dir = masks_root / f"frame_{frame_i:06d}_masks"
        obj_png = frame_dir / f"{args.object_id}.png"
        if args.skip_existing and obj_png.is_file():
            m = cv2.imread(str(obj_png), cv2.IMREAD_GRAYSCALE)
            if m is not None and int(m.max()) > 0:
                print(f"[{i+1}/{len(frames)}] skip {fpath.name}")
                continue

        bgr = cv2.imread(str(fpath), cv2.IMREAD_COLOR)
        if bgr is None:
            print(f"skip unreadable {fpath}", file=sys.stderr)
            continue
        h0, w0 = bgr.shape[:2]
        result = inference_model(model, str(fpath))
        sem = result.pred_sem_seg.data.cpu().numpy()
        if sem.ndim == 3:
            sem = sem[0]
        if sem.shape[0] != h0 or sem.shape[1] != w0:
            sem = cv2.resize(sem.astype(np.uint8), (w0, h0), interpolation=cv2.INTER_NEAREST)

        frame_dir.mkdir(parents=True, exist_ok=True)
        union_obj = np.zeros((h0, w0), dtype=np.uint8)
        for cid, cname in enumerate(names):
            if cname == "background":
                continue
            binm = (sem == cid).astype(np.uint8) * 255
            if binm.max() > 0:
                nonempty[cname] += 1
            cv2.imwrite(str(frame_dir / f"{cname}.png"), binm)
            if cname in OBJECT_CLASSES:
                union_obj = np.maximum(union_obj, binm)
            if cname == "right_hand":
                cv2.imwrite(str(frame_dir / "right_hand_0.png"), binm)
            if cname == "left_hand":
                cv2.imwrite(str(frame_dir / "left_hand_0.png"), binm)
        cv2.imwrite(str(obj_png), union_obj)

        color = _colorize(sem)
        overlay = cv2.addWeighted(cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB), 0.55, color, 0.45, 0)
        cv2.imwrite(
            str(overlay_dir / f"frame_{frame_i:06d}.png"),
            cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR),
        )
        cv2.imwrite(str(semantic_dir / f"frame_{frame_i:06d}.png"), cv2.cvtColor(color, cv2.COLOR_RGB2BGR))
        cov = 100.0 * (union_obj > 0).mean()
        print(f"[{i+1}/{len(frames)}] {fpath.name} object_cov={cov:.2f}%")

    ffmpeg = os.environ.get("FFMPEG") or shutil_which("ffmpeg")
    if ffmpeg and overlay_dir.glob("frame_*.png"):
        mp4 = out_root / "tracked_interformer.mp4"
        subprocess.run(
            [
                ffmpeg, "-y", "-framerate", "24",
                "-i", str(overlay_dir / "frame_%06d.png"),
                "-c:v", "libx264", "-pix_fmt", "yuv420p", str(mp4),
            ],
            check=False,
        )
        print(f"video: {mp4}")

    print("nonempty frames per class:")
    for n, c in nonempty.items():
        if n == "background":
            continue
        print(f"  {n}: {c}/{len(frames)}")
    print(f"done: {out_root}")
    return 0


def shutil_which(name: str) -> str | None:
    import shutil

    return shutil.which(name)


if __name__ == "__main__":
    raise SystemExit(main())