Troubleshooting (-15:Bad number of channels) for face landmarks detection

Hello I am doing a project using LBF landmark detector using the sample as a starting point.

the detector is able to successfully detect faces on the 512 x 512 image of lena as well as a custom 640 x 477 image. however when I tried to run the detector on a 320 x 240 image I get the below error.

 img = array([[[171, 169, 163],
        [170, 170, 164],
        [170, 170, 164],
        [160, 160, 161],
     ...[151, 152, 148],
        [142, 143, 167],
        [143, 144, 168],
        [144, 144, 164]]], dtype=uint8)  

     def get_face_landmarks(img):
        faces = FACE_DETECTOR.detectMultiScale(img,
                                               minSize=(30, 30),
       _, landmarks =, faces=faces)
E     cv2.error: OpenCV(4.8.0) D:\a\opencv-python\opencv-python\opencv\modules\core\src\matrix.cpp:1193: error: (-15:Bad number of channels) The total width is not divisible by the new number of channels in function 'cv::Mat::reshape'

I know it has something to do with my image resolution but I am unsure how to diagnose it further.

My questions are:

  • what is causing this exception?
  • is there a parameter I can set in the function?
  • Is there any way to resolve this issue without having to resize the image? (I want to have low latency and reshaping the image would probably add computational delays.)

Turned out the problem wasn’t with the image size but whether or not the image contained a face. If no faces are detected faces is an empty tuple and does not handle that being empty.

I verified this by modifying the script with a non-face image and was able to reproduce the issue.

I ended up fixing this by putting a conditional before the fit in case no faces are detected.

1 Like

nice catch !
i’d even argue, that you found a bug in the sample,
(it MUST check, if there ARE any faces !)

time to make a pr and change this !

Hi berak, I’ve made a pr here. I’m pretty new to contributing so please let me know if there’s anything missing. Thank you.

1 Like

looks great ! thank you :wink: