Update app.py

app.py

@@ -81,39 +81,41 @@ def mark_attendance(name):
 def generate_frames():
     camera = cv2.VideoCapture(0)
     
-    while True:
-        success, frame = camera.read()
-        if not success:
-            break
-        else:
-            small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
-            rgb_small_frame = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)
-
-            face_locations = face_recognition.face_locations(rgb_small_frame)
-            face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
-
-            for face_encoding, face_location in zip(face_encodings, face_locations):
-                matches = face_recognition.compare_faces(known_encodings, face_encoding, tolerance=0.5)
-                face_distances = face_recognition.face_distance(known_encodings, face_encoding)
+    try:
+        while True:
+            success, frame = camera.read()
+            if not success:
+                break
+            else:
+                small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
+                rgb_small_frame = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)
+                face_locations = face_recognition.face_locations(rgb_small_frame)
+                face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
                 
-                name = "Unknown"
-                if len(face_distances) > 0:
-                    best_match_index = np.argmin(face_distances)
-                    if matches[best_match_index]:
-                        name = known_names[best_match_index]
-                        mark_attendance(name)
-
-                top, right, bottom, left = face_location
-                top, right, bottom, left = top*4, right*4, bottom*4, left*4
-
-                cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
-                cv2.putText(frame, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2)
-
-            ret, buffer = cv2.imencode('.jpg', frame)
-            frame = buffer.tobytes()
-
-            yield (b'--frame\r\n'
-                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
+                for face_encoding, face_location in zip(face_encodings, face_locations):
+                    matches = face_recognition.compare_faces(known_encodings, face_encoding, tolerance=0.5)
+                    face_distances = face_recognition.face_distance(known_encodings, face_encoding)
+                    
+                    name = "Unknown"
+                    if len(face_distances) > 0:
+                        best_match_index = np.argmin(face_distances)
+                        if matches[best_match_index]:
+                            name = known_names[best_match_index]
+                            mark_attendance(name)
+                    
+                    top, right, bottom, left = face_location
+                    top, right, bottom, left = top*4, right*4, bottom*4, left*4
+                    cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
+                    cv2.putText(frame, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2)
+                
+                ret, buffer = cv2.imencode('.jpg', frame)
+                frame = buffer.tobytes()
+                yield (b'--frame\r\n'
+                       b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
+    finally:
+        # This executes when the frontend clears the image src, 
+        # disconnecting the stream and releasing the webcam hardware.
+        camera.release()
 
 
 # ROUTES