@@ -49,11 +49,13 @@ def detect_face(img):
4949 if len (faces ) == 0 :
5050 return False
5151 for x , y , w , h in faces :
52- roi_gray = img [y : y + h , x : x + w ]
52+ # Crop the detected face region from the grayscale image and
53+ # prepare it for model inference.
54+ roi = gray [y : y + h , x : x + w ]
5355 cropped_img = np .expand_dims (
54- np .expand_dims (cv2 .resize (roi_gray , (224 , 224 )), - 1 ), 0
56+ np .expand_dims (cv2 .resize (roi , (224 , 224 )), - 1 ), 0
5557 )
56- return roi_gray
58+ return cropped_img
5759
5860
5961def get_font_size (text ):
@@ -151,9 +153,8 @@ def upload_file():
151153 img = add_text_to_image ("noface" , img )
152154 else :
153155 img = cv2 .resize (img , (224 , 224 ))
154- face_img = cv2 .resize (face_img , (224 , 224 ))
155156 interpreter = get_interpreter ("models/memegen1.tflite" )
156- results = predict_emotion (interpreter , LABELS , [ face_img ] )
157+ results = predict_emotion (interpreter , LABELS , face_img )
157158 dominant_emotion = max (results , key = results .get )
158159 img = add_text_to_image (dominant_emotion , img )
159160
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