Update app.py

app.py

@@ -30,6 +30,7 @@ emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
 # Load known faces (from images in a folder)
 known_faces = []
 known_names = []
+face_recognizer = cv2.face.LBPHFaceRecognizer_create()
 
 def load_known_faces():
     folder_path = "known_faces"  # Place your folder with known faces here
@@ -38,12 +39,18 @@ def load_known_faces():
             image_path = os.path.join(folder_path, image_name)
             image = cv2.imread(image_path)
             gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-            faces = cv2.face.LBPHFaceRecognizer_create()
-            faces.train([gray], np.array([0]))  # This is simplified, train with multiple images
-            encoding = faces.getHistograms()
-            if encoding:
-                known_faces.append(encoding)
+            # Detect face in the image
+            faces = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml').detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+            for (x, y, w, h) in faces:
+                roi_gray = gray[y:y+h, x:x+w]
+                # We only need the face, so we crop it and store it for training
+                known_faces.append(roi_gray)
                 known_names.append(image_name.split('.')[0])  # Assuming file name is the person's name
+
+    # Train the recognizer with the known faces
+    face_recognizer.train(known_faces, np.array([i for i in range(len(known_faces))]))
+
 load_known_faces()
 
 # Face detection using OpenCV
@@ -66,7 +73,7 @@ def process_frame(frame):
         emotion = emotion_labels[np.argmax(predictions[0])]
 
         # Face recognition using LBPH
-        label, confidence = cv2.face.LBPHFaceRecognizer_create().predict(roi_gray)
+        label, confidence = face_recognizer.predict(roi_gray)
         name = "Unknown"
         if confidence < 100:
             name = known_names[label]