#!/venv/main/bin/python3
"""
multiview.py — synthesize teleop-style 3-camera views for the retargeted
LeRobot v2.1 dataset (method A': keypoint-guided crops), writing the videos
STRAIGHT INTO the dataset's videos/ tree so it slots in on the go.

LOCKED conventions (per user, 2026-07-24):
  observation.images.top         = full egocentric frame, square center-crop -> 224
  observation.images.left_wrist  = zoom crop tracking the human LEFT-hand grasp point
  observation.images.right_wrist = zoom crop tracking the human RIGHT-hand grasp point
  L/R is ANATOMICAL / as-is: rightThumbTip.. -> right_wrist ; leftThumbTip.. -> left_wrist.
  BOTH wrist views are ALWAYS rendered (both viewpoints must be present) — each
  crop tracks its hand's grasp point every frame; only when a hand genuinely
  leaves the image does crop_at zero-pad that region. No forced all-black views.

Output matches abc-teleop EXACTLY: 224x224, h264, yuv420p, 30 fps.

Episode E (v2.1) -> EgoDex source (category, file-YYY), reconstructed with the
same ordering to_lerobot_v21.discover() used (sort by category, file index):
  keypoints/intrinsics/pose <- /workspace/ego/<cat>/data/chunk-*/file-YYY.parquet
  frames                    <- /workspace/ego/<cat>/videos/observation.images.camera/chunk-*/file-YYY.mp4
Frames 0..T-1 (T = episode length) are prefix-aligned t=0 — verified 1:1 with the
already-shipped observation.images.camera (parquet_rows == video_frames == T).

Resumable (results.jsonl = source of truth, carries per-episode image stats used
by finalize_multiview.py), logged (--log tees to <ds>/multiview.log), pooled.

Usage:
  /venv/main/bin/python3 multiview.py --ds /workspace/retargeted_lerobot \
      --ego /workspace/ego --retgt /workspace/retargeted \
      --workers 48 --log [--limit N] [--categories a,b]
"""
import os, sys, re, io, glob, json, time, argparse, subprocess, signal
os.environ.setdefault("OMP_NUM_THREADS", "1")
os.environ.setdefault("OPENBLAS_NUM_THREADS", "1")
from pathlib import Path
import numpy as np
import pyarrow.parquet as pq
from scipy.ndimage import map_coordinates, gaussian_filter1d
from PIL import Image
import multiprocessing as mp

# ── constants ────────────────────────────────────────────────────────────────
FW, FH = 1920, 1080          # source frame size
OUT    = 224                 # output view size
FPS    = 30
CHUNK  = 1000
WRIST_CROP = 620             # px window @1080p tracking the grasp point
KEYS = ["observation.images.top",
        "observation.images.left_wrist",
        "observation.images.right_wrist"]
SIDES = {"R": ("rightThumbTip", "rightIndexFingerTip", "rightMiddleFingerTip"),
         "L": ("leftThumbTip",  "leftIndexFingerTip",  "leftMiddleFingerTip")}
# per-clip activity gate for a wrist view
MOTION_MIN   = 0.06          # m of total grasp-path travel to count as "used"
INBOUNDS_MIN = 0.15          # fraction of frames the hand must be inside the frame
CENTER_SIGMA = 2.0           # gaussian smoothing (frames) of the crop-center track

# per-view fixed lens (virtual camera) — geometry is content-independent, so the
# barrel+tilt remap coords are precomputed ONCE below and reused every frame.
VIEW = {  # key: (tilt_deg, barrel_k, color_gain, color_bias)
    "observation.images.top":         ( 3.0, 0.00, 1.05,  3.0),
    "observation.images.left_wrist":  ( 5.0, 0.12, 1.08,  5.0),
    "observation.images.right_wrist": (-5.0, 0.12, 0.96, -3.0),
}

DS = EGO = RETGT = ""        # set in main


# ── precomputed remap coords (barrel∘tilt), one (YS,XS) per view ─────────────
def _remap_coords(tilt_deg, barrel_k, n=OUT):
    """Source-sampling coords for output pixel (y,x) after resize->barrel->tilt.
    Pixel flow in make_view is R --barrel--> B --tilt--> final, so we compose:
      final[y,x] = B[y, sx_t(x)] = R[ barrel(sx_t(x), y) ].  Returns (YS, XS)."""
    c = (n - 1) / 2.0
    yy, xx = np.mgrid[0:n, 0:n].astype(np.float64)
    # tilt: horizontal perspective shear, sx depends only on x
    d = np.tan(np.radians(tilt_deg)) * n * 0.5
    Xt = (xx - n / 2) * (1 + d / n * (xx / n - 0.5) * 2) + n / 2      # intermediate x
    Yt = yy                                                            # tilt keeps y
    # barrel evaluated at (Xt, Yt)
    xn = (Xt - c) / c
    yn = (Yt - c) / c
    f = 1 + barrel_k * (xn * xn + yn * yn)
    XS = xn * f * c + c
    YS = yn * f * c + c
    return YS.astype(np.float32), XS.astype(np.float32)

REMAP = {k: _remap_coords(VIEW[k][0], VIEW[k][1]) for k in KEYS}


# ── image ops ────────────────────────────────────────────────────────────────
def square_center(img):
    h, w = img.shape[:2]; s = min(h, w)
    return img[(h - s) // 2:(h - s) // 2 + s, (w - s) // 2:(w - s) // 2 + s]

def crop_at(img, cx, cy, size):
    """Zero-padded square crop centered at (cx,cy). Out-of-frame -> black pixels."""
    h, w = img.shape[:2]; half = size // 2
    x0 = int(round(cx - half)); y0 = int(round(cy - half))
    out = np.zeros((size, size, 3), np.uint8)
    sx0, sy0 = max(0, x0), max(0, y0)
    sx1, sy1 = min(w, x0 + size), min(h, y0 + size)
    if sx1 > sx0 and sy1 > sy0:
        out[sy0 - y0:sy0 - y0 + (sy1 - sy0), sx0 - x0:sx0 - x0 + (sx1 - sx0)] = img[sy0:sy1, sx0:sx1]
    return out

def resize224(img):
    return np.asarray(Image.fromarray(img).resize((OUT, OUT), Image.LANCZOS))

def apply_lens(img224, key, gain, bias):
    ys, xs = REMAP[key]
    out = np.empty_like(img224)
    for c in range(3):
        out[..., c] = map_coordinates(img224[..., c], (ys, xs), order=3, mode="nearest")
    if gain != 1.0 or bias != 0.0:
        out = np.clip(out.astype(np.float32) * gain + bias, 0, 255).astype(np.uint8)
    return out


# ── geometry ─────────────────────────────────────────────────────────────────
def _mat(col, fr):
    return np.array(col[fr].as_py(), dtype=np.float64).reshape(4, 4)

def grasp_and_project(t, T):
    """Return dict side-> (u[T], v[T], world_g[T,3]) and per-frame K,cam already
    applied. Projection uses EgoDex's per-frame intrinsics + camera pose."""
    Kc  = t.column("camera_intrinsics")
    Cc  = t.column("observation.state.camera")
    cols = {s: [t.column("observation.state." + n) for n in names]
            for s, names in SIDES.items()}
    out = {}
    for s in SIDES:
        th, ix, md = cols[s]
        g = np.empty((T, 3)); u = np.empty(T); v = np.empty(T)
        for fr in range(T):
            pt = 0.5 * (_mat(th, fr)[:3, 3]
                        + 0.7 * _mat(ix, fr)[:3, 3]
                        + 0.3 * _mat(md, fr)[:3, 3])
            g[fr] = pt
            K   = np.array(Kc[fr].as_py()).reshape(3, 3)
            cam = _mat(Cc, fr)
            Xc  = (np.linalg.inv(cam) @ np.append(pt, 1))[:3]
            z   = -Xc[2] if Xc[2] != 0 else 1e-6
            u[fr] = K[0, 0] * Xc[0] / z + K[0, 2]
            v[fr] = -K[1, 1] * Xc[1] / z + K[1, 2]
        out[s] = (u, v, g)
    return out

HALF = WRIST_CROP // 2       # crop-center bounds so the window stays fully in-frame

def hand_track(u, v, g):
    """(active, cu[T], cv[T]). The crop-center track is smoothed AND clamped so the
    WRIST_CROP window always lies fully inside the frame -> the wrist view is ALWAYS
    real scene content, never black. When the hand is in FOV the crop tracks it; when
    the hand leaves FOV the center rides the nearest edge (hand-adjacent workspace).
    `active` is informational only (logging) now that no view is blacked."""
    T = len(u)
    inb = (u >= 0) & (u < FW) & (v >= 0) & (v < FH)
    motion = float(np.linalg.norm(np.diff(g, axis=0), axis=1).sum()) if T > 1 else 0.0
    active = (motion >= MOTION_MIN) and (inb.mean() >= INBOUNDS_MIN)
    cu = np.nan_to_num(u, nan=FW / 2, posinf=FW, neginf=0.0)
    cv = np.nan_to_num(v, nan=FH / 2, posinf=FH, neginf=0.0)
    cu = np.clip(cu, HALF, FW - HALF).astype(np.float64)
    cv = np.clip(cv, HALF, FH - HALF).astype(np.float64)
    if T >= 3:
        cu = gaussian_filter1d(cu, CENTER_SIGMA, mode="nearest")
        cv = gaussian_filter1d(cv, CENTER_SIGMA, mode="nearest")
        cu = np.clip(cu, HALF, FW - HALF)
        cv = np.clip(cv, HALF, FH - HALF)
    return active, cu, cv, inb


# ── running per-view image stats (LeRobot: per-channel, [0,1], shape (3,1,1)) ─
class Stat:
    def __init__(self):
        self.n = 0
        self.s = np.zeros(3); self.ss = np.zeros(3)
        self.mn = np.full(3, np.inf); self.mx = np.full(3, -np.inf)
    def add(self, frame):                    # frame uint8 (H,W,3)
        a = frame.reshape(-1, 3).astype(np.float64) / 255.0
        self.n += a.shape[0]
        self.s += a.sum(0); self.ss += (a * a).sum(0)
        self.mn = np.minimum(self.mn, a.min(0)); self.mx = np.maximum(self.mx, a.max(0))
    def finalize(self, T):
        if self.n == 0:                      # all-black safety
            z = np.zeros((3, 1, 1))
            return {"min": z.tolist(), "max": z.tolist(),
                    "mean": z.tolist(), "std": z.tolist(), "count": [T]}
        mean = self.s / self.n
        var = np.maximum(self.ss / self.n - mean * mean, 0.0)
        r = lambda a: a.reshape(3, 1, 1).tolist()
        return {"min": r(self.mn), "max": r(self.mx),
                "mean": r(mean), "std": r(np.sqrt(var)), "count": [T]}


# ── ffmpeg streaming ─────────────────────────────────────────────────────────
def _readn(f, n):
    buf = bytearray()
    while len(buf) < n:
        chunk = f.read(n - len(buf))
        if not chunk:
            break
        buf += chunk
    return bytes(buf)

def _fferr():
    return open(os.environ.get("FFMPEG_LOG", os.devnull), "a")


def open_decoder(vid, T):
    return subprocess.Popen(
        ["ffmpeg", "-v", "error", "-i", vid, "-frames:v", str(T),
         "-f", "rawvideo", "-pix_fmt", "rgb24", "-s", f"{FW}x{FH}", "-"],
        stdout=subprocess.PIPE, stderr=_fferr(), bufsize=FW * FH * 3 * 2)

def open_encoder(path):
    Path(path).parent.mkdir(parents=True, exist_ok=True)
    return subprocess.Popen(
        ["ffmpeg", "-y", "-v", "error", "-f", "rawvideo", "-pix_fmt", "rgb24",
         "-s", f"{OUT}x{OUT}", "-r", str(FPS), "-i", "-",
         "-c:v", "libx264", "-pix_fmt", "yuv420p", "-crf", "23",
         "-g", str(FPS), "-preset", "veryfast", "-threads", "1",
         "-f", "mp4", path],
        stdin=subprocess.PIPE, stderr=_fferr())


# ── per-episode work ─────────────────────────────────────────────────────────
def src_paths(cat, fyy):
    p = glob.glob(f"{EGO}/{cat}/data/chunk-*/{fyy}.parquet")
    v = glob.glob(f"{EGO}/{cat}/videos/observation.images.camera/chunk-*/{fyy}.mp4")
    return (p[0] if p else None), (v[0] if v else None)

def out_paths(eidx):
    ci = eidx // CHUNK
    return {k: f"{DS}/videos/chunk-{ci:03d}/{k}/episode_{eidx:06d}.mp4" for k in KEYS}

def process(e):
    eidx, cat, fyy, T = e["episode_index"], e["category"], e["fyy"], e["length"]
    t0 = time.time()
    pqf, vid = src_paths(cat, fyy)
    if not pqf or not vid:
        return {"episode_index": eidx, "error": f"missing source ({cat}/{fyy})"}
    outs = out_paths(eidx)
    try:
        cols = ["camera_intrinsics", "observation.state.camera"] + \
               [f"observation.state.{n}" for s in SIDES.values() for n in s]
        t = pq.read_table(pqf, columns=cols)
        Tsrc = t.num_rows
        Tn = min(T, Tsrc)
        proj = grasp_and_project(t, Tn)
        tracks = {}
        for s in SIDES:
            u, v, g = proj[s]
            tracks[s] = hand_track(u, v, g)

        stats = {k: Stat() for k in KEYS}
        dec = open_decoder(vid, T)
        enc = {k: open_encoder(outs[k] + ".tmp") for k in KEYS}
        frame_bytes = FW * FH * 3
        last = None
        for fr in range(T):
            raw = _readn(dec.stdout, frame_bytes)
            if len(raw) < frame_bytes:
                img = last if last is not None else np.zeros((FH, FW, 3), np.uint8)
            else:
                img = np.frombuffer(raw, np.uint8).reshape(FH, FW, 3)
                last = img
            fi = min(fr, Tn - 1)             # keypoints index (prefix-aligned)

            # top
            g_top, b_top = VIEW[KEYS[0]][2], VIEW[KEYS[0]][3]
            top = apply_lens(resize224(square_center(img)), KEYS[0], g_top, b_top)

            views = {KEYS[0]: top}
            for key, s in ((KEYS[1], "L"), (KEYS[2], "R")):
                _active, cu, cv, _ = tracks[s]
                # ALWAYS render both wrist views (both viewpoints present) —
                # crop tracks the hand's grasp point every frame; if the hand is
                # genuinely off-frame, crop_at zero-pads only that region.
                crop = crop_at(img, cu[fi], cv[fi], WRIST_CROP)
                gn, bs = VIEW[key][2], VIEW[key][3]
                views[key] = apply_lens(resize224(crop), key, gn, bs)

            for k in KEYS:
                stats[k].add(views[k])
                enc[k].stdin.write(views[k].tobytes())

        dec.stdout.close(); dec.wait()
        for k in KEYS:
            enc[k].stdin.close(); enc[k].wait()
            os.replace(outs[k] + ".tmp", outs[k])       # atomic publish

        return {"episode_index": eidx, "category": cat, "length": T,
                "active": {"L": bool(tracks["L"][0]), "R": bool(tracks["R"][0])},
                "stats": {k: stats[k].finalize(T) for k in KEYS},
                "sec": round(time.time() - t0, 2)}
    except Exception as ex:
        for k in KEYS:
            for p in (outs[k] + ".tmp",):
                try: os.remove(p)
                except OSError: pass
        return {"episode_index": eidx, "error": f"{type(ex).__name__}: {ex}"}


# ── mapping (replicate to_lerobot_v21.discover ordering) ─────────────────────
def build_episodes():
    lengths = {}
    with open(f"{DS}/meta/episodes.jsonl") as f:
        for line in f:
            d = json.loads(line); lengths[d["episode_index"]] = d["length"]
    rows = []
    for h in sorted(glob.glob(f"{RETGT}/*/clip_*/retargeted.hdf5")):
        parts = h.split("/")
        cat, name = parts[-3], parts[-2]
        m = re.search(r"(file-\d+)_ep", name)
        if not m:
            continue
        fyy = m.group(1)
        rows.append({"category": cat, "fyy": fyy, "fileidx": int(fyy.split("-")[1])})
    rows.sort(key=lambda r: (r["category"], r["fileidx"]))
    eps = []
    for i, r in enumerate(rows):
        if i not in lengths:            # dataset has fewer episodes than clips
            continue
        eps.append({"episode_index": i, "category": r["category"],
                    "fyy": r["fyy"], "length": lengths[i]})
    return eps


def main():
    print('>>> MULTIVIEW main() entered', flush=True)
    global DS, EGO, RETGT
    ap = argparse.ArgumentParser()
    ap.add_argument("--ds", default="/workspace/retargeted_lerobot")
    ap.add_argument("--ego", default="/workspace/ego")
    ap.add_argument("--retgt", default="/workspace/retargeted")
    ap.add_argument("--workers", type=int, default=48)
    ap.add_argument("--limit", type=int, default=0)
    ap.add_argument("--categories", default="")
    ap.add_argument("--log", action="store_true")
    args = ap.parse_args()
    DS, EGO, RETGT = args.ds, args.ego, args.retgt

    logf = None
    if args.log:
        logf = open(f"{DS}/multiview.log", "a", buffering=1)
    def emit(msg):
        print(msg, flush=True)
        if logf: logf.write(msg + "\n")

    eps = build_episodes()
    if args.categories:
        keep = set(args.categories.split(","))
        eps = [e for e in eps if e["category"] in keep]
    if args.limit:
        eps = eps[:args.limit]

    # resume: results.jsonl is the source of truth (carries stats for finalize)
    res_path = f"{DS}/multiview_results.jsonl"
    done = set()
    if os.path.exists(res_path):
        with open(res_path) as f:
            for line in f:
                try:
                    d = json.loads(line)
                    if "stats" in d:
                        done.add(d["episode_index"])
                except Exception:
                    pass
    todo = [e for e in eps if e["episode_index"] not in done]

    emit(f"\n=== multiview run {time.strftime('%Y-%m-%d %H:%M:%S')} ===")
    emit(f"episodes={len(eps)}  resumed={len(done)}  todo={len(todo)}  "
         f"workers={args.workers}  keys={len(KEYS)}  out={OUT}x{OUT} h264")
    if not todo:
        emit("nothing to do — all episodes already have results.")
        return

    res_f = open(res_path, "a", buffering=1)
    t_start = time.time()
    n_ok = n_err = 0
    errs = []
    with mp.Pool(args.workers, maxtasksperchild=200) as pool:
        for i, r in enumerate(pool.imap_unordered(process, todo, chunksize=1), 1):
            res_f.write(json.dumps(r) + "\n")
            eidx = r["episode_index"]
            if "error" in r:
                n_err += 1; errs.append((eidx, r["error"]))
                emit(f"  [ERR {r['error'][:40]:40s}] ep{eidx:06d}  {i}/{len(todo)}")
            else:
                n_ok += 1
                el = time.time() - t_start
                eta = el / i * (len(todo) - i) / 3600
                a = r.get("active", {})
                tag = ("LR" if a.get("L") and a.get("R") else
                       "L-" if a.get("L") else "-R" if a.get("R") else "--")
                emit(f"  [ok {tag}] ep{eidx:06d} {r['category'][:14]:14s} "
                     f"{r['sec']:5.1f}s  {i}/{len(todo)}  ETA {eta:4.2f}h")
    res_f.close()
    emit(f"\nDONE: ok={n_ok} err={n_err} in {(time.time()-t_start)/3600:.2f}h")
    for eidx, msg in errs[:15]:
        emit(f"  ERR ep{eidx:06d}: {msg}")
    emit("next: /venv/main/bin/python3 finalize_multiview.py --ds " + DS)


if __name__ == "__main__":
    signal.signal(signal.SIGINT, signal.SIG_DFL)
    import traceback
    try:
        main()
    except BaseException:
        print(">>> MULTIVIEW CRASHED:", flush=True); traceback.print_exc(); raise