Update app.py

app.py

@@ -17,51 +17,19 @@ EMOTIONS = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', '
 # face_detector_mtcnn = MTCNN()
 classifier = load_model(emotion_model_path)
 
-# def predict_emotion(frame):
-#     frame = imutils.resize(frame, width=300)
-#     gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
-#     faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
-#                                             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
-        
-#         # 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]
-#         roi = cv2.resize(roi, (48, 48))
-#         roi = roi.astype("float") / 255.0
-#         roi = img_to_array(roi)
-#         roi = np.expand_dims(roi, axis=0)
-
-#         preds = emotion_classifier.predict(roi)[0]
-#         label = EMOTIONS[preds.argmax()]
-
-#         # Overlay a box over the detected face
-#         cv2.putText(frame_clone, label, (fX, fY + 100),
-#                     cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1)
-#         cv2.rectangle(frame_clone, (fX, fY), (fX + fW, fY + fH),
-#                       (238, 164, 64), 2)
-
-#     else:
-#         label = "Can't find your face"
-
-#     return frame_clone
-
-
 def predict_emotion(frame):
     frame = imutils.resize(frame, width=300)
     gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
     faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
                                             minNeighbors=5, minSize=(30, 30),
                                             flags=cv2.CASCADE_SCALE_IMAGE)
-    frame_clone = frame.copy()
 
-    for (fX, fY, fW, fH) in faces:
+    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
+        
         # 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]
@@ -79,7 +47,10 @@ def predict_emotion(frame):
         cv2.rectangle(frame_clone, (fX, fY), (fX + fW, fY + fH),
                       (238, 164, 64), 2)
 
-    return frame
+    else:
+        label = "Can't find your face"
+
+    return frame_clone
 
 
 
@@ -102,7 +73,7 @@ demo = gr.Interface(
         os.path.join(os.path.dirname(__file__), "images/gunther.jpeg")
      
     ],
-    title = "Whatchu feeling?",
+    title = "How are you feeling?",
     theme = "shivi/calm_seafoam"
 )