Update app.py

app.py

@@ -7,7 +7,7 @@ from PIL import Image
 import sqlite3
 from huggingface_hub import HfApi
 from datetime import datetime
-import face_recognition
+from sklearn.preprocessing import LabelEncoder
 
 # Constants
 KNOWN_FACES_DIR = "known_faces"  # Directory to save user images
@@ -136,18 +136,49 @@ if st.checkbox("Show registered students"):
         st.write(f"**Name:** {name}, **Roll No:** {roll_no}, **Timestamp:** {timestamp}")
         st.image(image_path, caption=f"{name} ({roll_no})", use_column_width=True)
 
+# Initialize OpenCV's face detector (Haar Cascade)
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+# Initialize LBPH face recognizer
+face_recognizer = cv2.face_LBPHFaceRecognizer_create()
+
+# Function to load and train face recognizer
+def train_face_recognizer():
+    faces = []
+    labels = []
+    label_encoder = LabelEncoder()
+
+    # Load known faces
+    for filename in os.listdir(KNOWN_FACES_DIR):
+        if filename.endswith(".jpg"):
+            image_path = os.path.join(KNOWN_FACES_DIR, filename)
+            image = cv2.imread(image_path)
+            gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+            # Detect face(s)
+            faces_detected = face_cascade.detectMultiScale(gray_image, 1.3, 5)
+            for (x, y, w, h) in faces_detected:
+                face = gray_image[y:y+h, x:x+w]
+                faces.append(face)
+                labels.append(filename.split('_')[0])  # Assuming name is in the filename
+            
+    labels = label_encoder.fit_transform(labels)
+    face_recognizer.train(faces, np.array(labels))
+    st.success("Face recognizer trained successfully!")
+
+train_face_recognizer()
+
 # Face and Emotion Detection Function
 def detect_faces_and_emotions(image):
-    # Convert the image to RGB (as OpenCV works with BGR by default)
-    rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    # Convert the image to grayscale for face detection
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
     # Detect faces using OpenCV's Haar Cascade
-    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-    faces = face_cascade.detectMultiScale(rgb_image, scaleFactor=1.3, minNeighbors=5)
+    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=5)
 
-    # If faces are detected, predict emotions
+    # If faces are detected, predict emotions and recognize faces
     for (x, y, w, h) in faces:
-        face = rgb_image[y:y+h, x:x+w]
+        face = gray_image[y:y+h, x:x+w]
         resized_face = cv2.resize(face, (48, 48))  # Resize face to 48x48
         rgb_face = cv2.cvtColor(resized_face, cv2.COLOR_BGR2RGB)
         normalized_face = rgb_face / 255.0
@@ -156,9 +187,14 @@ def detect_faces_and_emotions(image):
         # Predict the emotion
         emotion_prediction = emotion_model.predict(reshaped_face)
         emotion_label = np.argmax(emotion_prediction)
-        return EMOTION_LABELS[emotion_label]
+        
+        # Recognize the face
+        label, confidence = face_recognizer.predict(face)
+        recognized_label = label_encoder.inverse_transform([label])[0]
+        
+        return EMOTION_LABELS[emotion_label], recognized_label
     
-    return None
+    return None, None
 
 # UI for Emotion Detection (Only using webcam now)
 if st.sidebar.selectbox("Menu", ["Register Student", "Face Recognition and Emotion Detection", "View Attendance"]) == "Face Recognition and Emotion Detection":
@@ -170,9 +206,11 @@ if st.sidebar.selectbox("Menu", ["Register Student", "Face Recognition and Emoti
         img = Image.open(camera_image)
         img_array = np.array(img)
 
-        # Detect emotion in the captured image
-        emotion_label = detect_faces_and_emotions(img_array)
+        # Detect emotion and recognize face in the captured image
+        emotion_label, recognized_label = detect_faces_and_emotions(img_array)
+        
         if emotion_label:
             st.success(f"Emotion Detected: {emotion_label}")
+            st.success(f"Face Recognized as: {recognized_label}")
         else:
             st.warning("No face detected.")