Update app.py

app.py

@@ -7,6 +7,7 @@ import cv2
 from keras.models import load_model
 from datetime import datetime
 from huggingface_hub import HfApi
+import face_recognition
 
 # Constants
 KNOWN_FACES_DIR = "known_faces"  # Directory to save user images
@@ -88,30 +89,65 @@ def save_image_to_hugging_face(image, name, roll_no):
     
     return local_path
 
-# Process each frame for emotion detection
-def process_frame(frame):
+# Process each frame for emotion detection and face recognition
+def process_frame(frame, known_face_encodings, known_face_names):
     gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
     faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
 
+    face_locations = []
+    face_encodings = []
+    face_names = []
     for (x, y, w, h) in faces:
-        roi_gray = gray_frame[y:y+h, x:x+w]
         roi_color = frame[y:y+h, x:x+w]
+        # Face recognition
+        rgb_frame = frame[:, :, ::-1]  # Convert to RGB (face_recognition expects RGB)
+        face_encoding = face_recognition.face_encodings(rgb_frame, [(y, x + w, y + h, x)])[0]
 
+        # Check if the detected face matches any known face
+        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+        name = "Unknown"
+
+        if True in matches:
+            first_match_index = matches.index(True)
+            name = known_face_names[first_match_index]
+
+        # Emotion detection
         face_roi = cv2.resize(roi_color, (48, 48))
         face_roi = np.expand_dims(face_roi, axis=0)
         face_roi = face_roi / float(48)
         predictions = model.predict(face_roi)
         emotion = emotion_labels[np.argmax(predictions[0])]
 
-        # Display emotion text on face
+        # Display name and emotion text on face
         cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-        cv2.putText(frame, emotion, (x, y+h), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.putText(frame, f"{name} - {emotion}", (x, y+h), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 
     return frame
 
 # User Interface for registration
 st.title("Student Registration and Attendance")
 
+# Initialize the database and create the table if it does not exist
+initialize_database()
+
+# Load known faces from the database
+known_face_encodings = []
+known_face_names = []
+
+conn = sqlite3.connect(DATABASE)
+cursor = conn.cursor()
+cursor.execute("SELECT name, image_path FROM students")
+rows = cursor.fetchall()
+
+for row in rows:
+    name, image_path = row
+    image = face_recognition.load_image_file(image_path)
+    encoding = face_recognition.face_encodings(image)[0]
+    known_face_encodings.append(encoding)
+    known_face_names.append(name)
+
+conn.close()
+
 # Choose input method for the image (webcam or file upload)
 capture_mode = st.radio("Choose an option to upload your image", ["Use Webcam", "Upload File"])
 
@@ -144,22 +180,28 @@ if st.button("Register"):
             # Save user data to the database
             save_to_database(name, roll_no, image_path)
 
-            # Detect faces and emotions
-            cap = cv2.VideoCapture(0)
-            while True:
-                ret, frame = cap.read()
-                if not ret:
-                    break
-
-                frame = process_frame(frame)
-                st.image(frame, channels="BGR", use_column_width=True)
-                break  # Stop after capturing one frame
+            # Update the known faces list
+            known_face_encodings.append(face_recognition.face_encodings(image)[0])
+            known_face_names.append(name)
 
-            cap.release()
+            st.success(f"Student {name} registered successfully!")
 
         except Exception as e:
             st.error(f"An error occurred: {e}")
 
+# Detect faces and emotions from webcam
+cap = cv2.VideoCapture(0)
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    frame = process_frame(frame, known_face_encodings, known_face_names)
+    st.image(frame, channels="BGR", use_column_width=True)
+    break  # Stop after capturing one frame
+
+cap.release()
+
 # Display registered students and attendance history
 if st.checkbox("Show registered students"):
     conn = sqlite3.connect(DATABASE)