ptrblck:

 image = np.concatenate((rgb_image, dep_image), axis=-1)


My code and result
def normalize_depth_image(image):
  min_depth = np.min(image)
  return (image.astype(float) - min_depth) / (np.max(image) - min_depth)
#RGB image
original = Image.open('31.png')
RGB_image=np.array(original)
#Depthmap of face
my_data = np.load('31.npy')
NN=normalize_depth_image(my_data)
output_data_N = (NN * 255).astype(np.uint8)
dep_image = np.expand_dims(output_data_N, axis=-1)

print('My RGB data:',RGB_image)
print('My depthmap data of face :',dep_image)


Screenshot from 2021-07-06 10-48-24731×378 14.1 KB


Screenshot from 2021-07-06 10-49-08762×779 19.7 KB

I concate got result:

Screenshot from 2021-07-06 10-53-23831×455 23.4 KB

My picture to train:

How can i finetune inception  to train with 4 input channels instead 3 channel.
Thank for your help.