changes

app/Hackathon_setup/face_recognition.py

@@ -95,7 +95,7 @@ def get_similarity(img1, img2):
         siamese_net.load_state_dict(torch.load(SIAMESE_MODEL_PATH, map_location=device))
         siamese_net.eval()
     except Exception as e:
-        print(f"Error loading Siamese Model: {e}")
+        print(f"Error loading Siamese Model get_similarity: {e}")
         return -1 # Return error code
 
     # 2. Get Features (Embeddings)
@@ -141,7 +141,7 @@ def get_face_class(img1):
         siamese_net.load_state_dict(torch.load(SIAMESE_MODEL_PATH, map_location=device))
         siamese_net.eval()
     except Exception as e:
-        return f"Error loading Siamese Model: {e}"
+        return f"Error loading Siamese Model get_face_class: {e}"
 
     # 2. Extract Embedding
     with torch.no_grad():

app/Hackathon_setup/face_recognition_model.py

@@ -81,4 +81,49 @@ def get_similarity(img1, img2):
     # Option B: Cosine Similarity (Higher is better) -> Recommended
     similarity = cosine_similarity(embed1, embed2)[0][0]
     
-    return float(similarity)
+    return float(similarity)
+    
+def get_face_class(img1):
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    
+    det_img1 = detected_face(img1)
+    if(det_img1 == 0):
+        det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
+    ##YOUR CODE HERE, return face class here
+    ##Hint: you need a classifier finetuned for your classes, it takes o/p of siamese as i/p to it
+    ##Better Hint: Siamese experiment is covered in one of the labs
+    face1_tensor = trnscm(det_img1).unsqueeze(0).to(device)
+
+    # 1. Load Siamese Network (Feature Extractor)
+    try:
+        siamese_net = SiameseNetwork().to(device)
+        siamese_net.load_state_dict(torch.load(SIAMESE_MODEL_PATH, map_location=device))
+        siamese_net.eval()
+    except Exception as e:
+        return f"Error loading Siamese Model get_face_class: {e}"
+
+    # 2. Extract Embedding
+    with torch.no_grad():
+        embedding_np = siamese_net.forward_once(face1_tensor).cpu().numpy()
+    
+    # 3. Load Sklearn Scaler and Classifier (Joblib)
+    try:
+        knn_classifier = joblib.load(KNN_CLASSIFIER_PATH)
+        scaler = joblib.load(SCALER_PATH)
+    except Exception as e:
+        return f"Error loading Sklearn models: {e}"
+    
+    # 4. Preprocess Embedding and Predict
+    # The embedding must be reshaped to (1, N_features) for the scaler
+    embedding_scaled = scaler.transform(embedding_np.reshape(1, -1))
+    
+    # Perform prediction (returns a NumPy array with the predicted label index)
+    predicted_label_index = knn_classifier.predict(embedding_scaled)[0]
+    
+    # 5. Map index to Class Name
+    if predicted_label_index < len(CLASS_NAMES):
+        predicted_class_name = CLASS_NAMES[predicted_label_index]
+    else:
+        predicted_class_name = "UNKNOWN_CLASS"
+
+    return predicted_class_name