face recognition

app/Hackathon_setup/face_recognition.py

@@ -102,63 +102,7 @@ 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" if torch.cuda.is_available() else "cpu")
     BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 
@@ -177,18 +121,75 @@ def get_face_class(img1):
     myModel.load_state_dict(ckpt['net_dict'])
     myModel.eval()
     myModel = myModel.float()
+    # 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))
 
-    img_tensor = transform1(img1).unsqueeze(0).to(device).float()
+    # 4 Transform the face
+    img_tensor = trnscm(det_img1).unsqueeze(0).float()
 
+    # 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)
+        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} {embedding}"
 
 
+# def get_face_class(img1):
+#
+#     device = torch.device("cuda" 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, "SVC_3.sav")
+#     clf = joblib.load(clf_path)
+#
+#     scaler_path = os.path.join(BASE_DIR, "scaler.joblib")
+#     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()
+#     myModel = myModel.float()
+#
+#     img_tensor = transform1(img1).unsqueeze(0).to(device).float()
+#
+#     with torch.no_grad():
+#         embedding = myModel.forward_once(img_tensor)
+#     embedding = embedding.view(embedding.size(0), -1).cpu().numpy()  # shape (1, embedding_dim)
+#     pred_label = clf.predict(scaler.transform(embedding))[0]
+#
+#     class_names = ['Aayush', 'Aditya', 'Vikram']
+#     return f"{class_names[pred_label]} {pred_label} {embedding}"
+
+
 # def get_face_class(img1):
 #     """
 #     img1: BGR image as numpy array (from cv2) OR path string accepted by detected_face.

app/main.py

@@ -91,7 +91,7 @@ async def create_upload_files(request: Request, file3: UploadFile = File(...)):
         face_rec_filename = 'app/static/' + file3.filename
         with open(face_rec_filename, 'wb') as f:
             f.write(contents)
-    
+    print(face_rec_filename)
     img1 = Image.open(face_rec_filename)
     # img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)