face recognition

app/Hackathon_setup/face_recognition.py

@@ -102,7 +102,63 @@ def get_similarity(img1, img2):
 #4) Perform necessary transformations to the input(detected face using the above function).
 #5) Along with the siamese, you need the classifier as well, which is to be finetuned with the faces that you are training
 ##Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function
+# def get_face_class(img1):
+#     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+#     BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+#
+#     # 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_5.sav")
+#     clf = joblib.load(clf_path)
+#
+#     scaler_path = os.path.join(BASE_DIR, "standar_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")
+#     ckpt = torch.load(ckpt_path, map_location=device)
+#     myModel.load_state_dict(ckpt['net_dict'])
+#     myModel.eval()
+#
+#     # 3 Face detection (if available)
+#     # det_img1 = detected_face(img1)   # returns cropped face or 0 if not detected
+#     # if det_img1 == 0:
+#         # fallback: use original image
+#         # det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
+#
+#     # 4 Transform the face
+#     img_tensor = trnscm(img1).unsqueeze(0)
+#
+#     # 5 Extract embeddings
+#     with torch.no_grad():
+#         embedding = myModel.forward_once(img_tensor)
+#         embedding = embedding.view(embedding.size(0), -1).cpu().numpy()  # shape (1, embedding_dim)
+#
+#     # 6 Predict class using Decision Tree
+#     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 f"{class_names[pred_label]} {pred_label}"
+
+
 def get_face_class(img1):
+
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 
@@ -121,38 +177,13 @@ def get_face_class(img1):
     myModel.load_state_dict(ckpt['net_dict'])
     myModel.eval()
 
-    # 3 Face detection (if available)
-    # det_img1 = detected_face(img1)   # returns cropped face or 0 if not detected
-    # if det_img1 == 0:
-        # fallback: use original image
-        # det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
-
-    # 4 Transform the face
     img_tensor = trnscm(img1).unsqueeze(0)
 
-    # 5 Extract embeddings
     with torch.no_grad():
         embedding = myModel.forward_once(img_tensor)
-        embedding = embedding.view(embedding.size(0), -1).cpu().numpy()  # shape (1, embedding_dim)
-
-    # 6 Predict class using Decision Tree
+    embedding = embedding.view(embedding.size(0), -1).cpu().numpy()  # shape (1, embedding_dim)
     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 f"{class_names[pred_label]} {pred_label}"
 

app/main.py

@@ -93,7 +93,7 @@ async def create_upload_files(request: Request, file3: UploadFile = File(...)):
             f.write(contents)
     
     img1 = Image.open(face_rec_filename)
-    img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
+    # img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
         
     result = face_recognition.get_face_class(img1)
     print(result)