face recognition

app/Hackathon_setup/face_recognition.py

@@ -108,9 +108,12 @@ def get_face_class(img1):
 
     # 1 Load the Decision Tree classifier
     # clf_path = os.path.join(BASE_DIR, "decision_tree_model.sav")
-    clf_path = os.path.join(BASE_DIR, "logistic_regression_1.sav")
+    clf_path = os.path.join(BASE_DIR, "logistic_regression_2.sav")
     clf = joblib.load(clf_path)
 
+    scaler_path = os.path.join(BASE_DIR, "standard_scaler.sav")
+    scaler = joblib.load(scaler_path)
+
     # 2 Load the Siamese feature extractor
     myModel = Siamese().to(device)
     ckpt_path = os.path.join(BASE_DIR, "siamese_model.t7")
@@ -133,11 +136,22 @@ def get_face_class(img1):
         embedding = embedding.view(embedding.size(0), -1).cpu().numpy()  # shape (1, embedding_dim)
 
     # 6 Predict class using Decision Tree
-    pred_label = clf.predict(embedding)[0]
+    # pred_label = clf.predict(scaler.transform(embedding))[0]
+
+
+    # --- Predict ---
+    scaled_emb = scaler.transform(embedding)
+    probs = clf.predict_proba(scaled_emb)
+    pred_label = np.argmax(probs)
+    confidence = probs[0, pred_label]
+
+
 
     # 7 Optional: return class name (if available)
     # If you have the dataset available:
     # class_names = finalClassifierDset.classes
     # return class_names[pred_label]
+    # class_names = ['Aayush', 'Aditya', 'Vikram']
+    # return class_names[pred_label] + " " + str(pred_label)
     class_names = ['Aayush', 'Aditya', 'Vikram']
-    return class_names[pred_label] + " " + str(pred_label)
+    return f"{class_names[pred_label]} ({confidence:.2f})"