Initial commit: ASL handshape recognition project

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

eval_val.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+"""
+Evaluate the trained per-letter model on the saved val split.
+Prints confusion matrix and a classification report.
+
+Usage:
+  python eval_val.py --letter A
+"""
+import argparse, json
+import numpy as np
+from sklearn.metrics import confusion_matrix, classification_report
+import torch
+import torch.nn as nn
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, num_classes):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(in_dim,128), nn.ReLU(), nn.Dropout(0.2),
+            nn.Linear(128,64), nn.ReLU(), nn.Dropout(0.1),
+            nn.Linear(64,num_classes),
+        )
+    def forward(self, x): return self.net(x)
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--letter", required=True, help="Target letter (A–Z)")
+    args = ap.parse_args()
+    L = args.letter.upper()
+
+    # Load val split and classes
+    X = np.load(f"landmarks_{L}/val_X.npy")
+    y = np.load(f"landmarks_{L}/val_y.npy")
+    classes = json.load(open(f"landmarks_{L}/class_names.json"))
+
+    # Load checkpoint (disable weights-only safety; handle tensor/ndarray)
+    state = torch.load(f"asl_{L}_mlp.pt", map_location="cpu", weights_only=False)
+    X_mean = state["X_mean"]
+    X_std  = state["X_std"]
+    if isinstance(X_mean, torch.Tensor): X_mean = X_mean.cpu().numpy()
+    if isinstance(X_std,  torch.Tensor): X_std  = X_std.cpu().numpy()
+    X_mean = np.asarray(X_mean, dtype=np.float32)
+    X_std  = np.asarray(X_std, dtype=np.float32) + 1e-6
+
+    model = MLP(X.shape[1], len(classes))
+    model.load_state_dict(state["model"])
+    model.eval()
+
+    # Normalize and predict
+    Xn = (X - X_mean) / X_std
+    with torch.no_grad():
+        probs = torch.softmax(model(torch.from_numpy(Xn).float()), dim=1).numpy()
+    pred = probs.argmax(axis=1)
+
+    print("Classes:", classes)  # e.g., ['Not_A','A']
+    print("\nConfusion matrix (rows=true, cols=pred):")
+    print(confusion_matrix(y, pred))
+    print("\nReport:")
+    print(classification_report(y, pred, target_names=classes, digits=3))
+
+if __name__ == "__main__":
+    main()