#!/usr/bin/env python3
# prep_sequence_resampled.py
# Build a fixed-length (N frames) multiclass dataset from sequences/<split>/<CLASS>/clip_*.npz
import argparse, os, glob, json
from pathlib import Path
import numpy as np

def resample_sequence(X, N=32):
    # X: (T,63)  -> (N,63) by linear interpolation along frame index
    T = len(X)
    if T == 0:
        return np.zeros((N, X.shape[1]), np.float32)
    if T == 1:
        return np.repeat(X, N, axis=0)
    src = np.linspace(0, T-1, num=T)
    dst = np.linspace(0, T-1, num=N)
    out = np.zeros((N, X.shape[1]), np.float32)
    for d in range(X.shape[1]):
        out[:, d] = np.interp(dst, src, X[:, d])
    return out.astype(np.float32)

def load_classes(seq_root: Path):
    # classes are subdirs in sequences/train/
    classes = sorted([p.name for p in (seq_root/"train").iterdir() if p.is_dir()])
    classes = [c for c in classes if len(c)==1 and "A"<=c<="Z"]
    if not classes:
        raise SystemExit("No letter classes found in sequences/train/")
    return classes

def collect_split(seq_root: Path, split: str, classes, N):
    Xs, ys = [], []
    for ci, cls in enumerate(classes):
        for f in sorted(glob.glob(str(seq_root/split/cls/"clip_*.npz"))):
            d = np.load(f)
            Xi = d["X"].astype(np.float32)   # (T,63)
            XiN = resample_sequence(Xi, N)   # (N,63)
            Xs.append(XiN); ys.append(ci)
    if Xs:
        X = np.stack(Xs, 0)
        y = np.array(ys, np.int64)
    else:
        X = np.zeros((0, N, 63), np.float32); y = np.zeros((0,), np.int64)
    return X, y

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--in", dest="in_dir", default="sequences", help="Root sequences/ with train/ and val/")
    ap.add_argument("--out", default="landmarks_seq32", help="Output folder with npy files")
    ap.add_argument("--frames", type=int, default=32, help="Frames per clip after resampling (default: 32)")
    args = ap.parse_args()

    seq_root = Path(args.in_dir)
    outdir = Path(args.out)
    outdir.mkdir(parents=True, exist_ok=True)

    classes = load_classes(seq_root)
    trX, trY = collect_split(seq_root, "train", classes, args.frames)
    vaX, vaY = collect_split(seq_root, "val", classes, args.frames)

    np.save(outdir/"train_X.npy", trX)
    np.save(outdir/"train_y.npy", trY)
    np.save(outdir/"val_X.npy", vaX)
    np.save(outdir/"val_y.npy", vaY)
    json.dump(classes, open(outdir/"class_names.json", "w"))
    json.dump({"frames": args.frames, "input_dim": 63}, open(outdir/"meta.json","w"))

    print(f"Saved dataset → {outdir}")
    print(f"  train {trX.shape}, val {vaX.shape}, classes={classes}")

if __name__ == "__main__":
    main()