Initial commit: handshapes multiclass project

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

doc/prep_sequence_resampled.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+# Build fixed-length (N frames) dataset from sequences/<split>/<CLASS>/clip_*.npz
+
+import argparse, os, glob, json                   # stdlib utilities
+from pathlib import Path
+import numpy as np                                 # arrays
+
+def resample_sequence(X, N=32):
+    """
+    Linearly resample a variable-length sequence (T,F) to exactly (N,F) over the frame index.
+    This preserves temporal order and distributes frames evenly across the clip.
+    """
+    T = len(X)                                     # original number of frames
+    if T == 0: return np.zeros((N, X.shape[1]), np.float32)  # empty → zeros
+    if T == 1: return np.repeat(X, N, axis=0)                 # single frame → tile N times
+    src = np.linspace(0, T-1, num=T, dtype=np.float32)        # original frame positions
+    dst = np.linspace(0, T-1, num=N, dtype=np.float32)        # desired positions
+    out = np.zeros((N, X.shape[1]), np.float32)               # allocate result
+    for d in range(X.shape[1]):                               # interpolate each feature independently
+        out[:, d] = np.interp(dst, src, X[:, d])             # linear interpolation
+    return out
+
+def load_classes(seq_root: Path):
+    """
+    Discover class subfolders under sequences/train/.
+    Ignores hidden/system directories. Returns sorted list of class names.
+    """
+    train_dir = seq_root / "train"
+    if not train_dir.exists():
+        raise SystemExit(f"Missing folder: {train_dir}")
+    classes = sorted([
+        p.name for p in train_dir.iterdir()
+        if p.is_dir() and not p.name.startswith(".")
+    ])
+    if not classes:
+        raise SystemExit("No classes found in sequences/train/ (folders should be class names like Mother, Father, etc.)")
+    return classes
+
+def collect_split(seq_root: Path, split: str, classes, N):
+    """
+    Collect all clips for a given split ('train' or 'val'):
+      - Load each clip_*.npz
+      - Resample to (N,F)
+      - Stack into X (num_clips, N, F) and y (num_clips,)
+    """
+    Xs, ys = [], []
+    for ci, cls in enumerate(classes):                            # class index, name
+        for f in sorted(glob.glob(str(seq_root / split / cls / "clip_*.npz"))):  # iterate clips
+            d = np.load(f)                                        # load .npz
+            Xi = d["X"].astype(np.float32)                        # (T,F) features
+            XiN = resample_sequence(Xi, N)                        # (N,F) resampled
+            Xs.append(XiN); ys.append(ci)                         # add to lists
+    if Xs:
+        X = np.stack(Xs, 0); y = np.array(ys, np.int64)           # stack arrays
+    else:
+        X = np.zeros((0, N, 1), np.float32); y = np.zeros((0,), np.int64)  # empty split guard
+    return X, y
+
+def main():
+    """
+    CLI: read sequences/*/*/clip_*.npz, resample to --frames, and write dataset arrays and metadata.
+    """
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--in", dest="in_dir", default="sequences")     # source root
+    ap.add_argument("--out", default="landmarks_seq32")             # destination folder
+    ap.add_argument("--frames", type=int, default=32)               # target frames per clip
+    args = ap.parse_args()
+
+    seq_root = Path(args.in_dir)                                    # resolve input root
+    outdir = Path(args.out); outdir.mkdir(parents=True, exist_ok=True)
+
+    classes = load_classes(seq_root)                                # discover class names
+    trX, trY = collect_split(seq_root, "train", classes, args.frames)  # build train split
+    vaX, vaY = collect_split(seq_root, "val",   classes, args.frames)  # build val split
+
+    if trX.size == 0 and vaX.size == 0:                             # sanity check
+        raise SystemExit("Found no clips. Did you run capture and save any clip_*.npz files?")
+
+    np.save(outdir/"train_X.npy", trX)                              # save arrays
+    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"))        # save labels
+
+    # Detect true feature dimension from data (in case it changes)
+    input_dim = int(trX.shape[-1] if trX.size else vaX.shape[-1])
+    json.dump({"frames": args.frames, "input_dim": input_dim}, open(outdir/"meta.json","w"))
+
+    print(f"Saved dataset → {outdir}")
+    print(f"  train {trX.shape}, val {vaX.shape}, classes={classes}, input_dim={input_dim}")
+
+if __name__ == "__main__":
+    main()