Update app.py

app.py

@@ -24,12 +24,7 @@ def predict_emotion(frame):
                                             minNeighbors=5, minSize=(30, 30),
                                             flags=cv2.CASCADE_SCALE_IMAGE)
 
-    frame_clone = frame.copy()
-    if len(faces) > 0:
-        faces = sorted(faces, reverse=True,
-                       key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
-        (fX, fY, fW, fH) = faces
-        
+    for (fX, fY, fW, fH) in faces:
         # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
         # the ROI for classification via the CNN
         roi = gray[fY:fY + fH, fX:fX + fW]
@@ -42,15 +37,13 @@ def predict_emotion(frame):
         label = EMOTIONS[preds.argmax()]
 
         # Overlay a box over the detected face
-        cv2.putText(frame_clone, label, (fX, fY - 10),
-                    cv2.FONT_HERSHEY_DUPLEX, 0.5, (238, 164, 64), 1, cv2.LINE_AA)
-        cv2.rectangle(frame_clone, (fX, fY), (fX + fW, fY + fH),
+        cv2.putText(frame, label, (fX, fY - 10),
+                    cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1)
+        cv2.rectangle(frame, (fX, fY), (fX + fW, fY + fH),
                       (238, 164, 64), 2)
 
-    else:
-        label = "Can't find your face"
+    return frame
 
-    return frame_clone