Need help in optimizing the following code

Python
1

I am currently looking into some work related to skin tone change
Basically for an image containing a person,we first obtain its mask using standard semantic seg algorithm,and then all pixels in the image that belong to the mask are multiplied by a skin tone adjustment factor.

The code for skin tone adjustment is shown below along with the test image and its mask.

Original image

male_caucasian_1_000
male_caucasian_1_000720×480 127 KB

Mask of person
subject_mask

Code for skin tone adjustment

skin_tones =[-0.2,-0.1,0.0,0.1,0.2]
n = len(test_data)
col = len(skin_tones)
for images, _ in testloader:
  plt.figure(figsize=(12,20))
  image_arr = images[0].numpy()
  image_arr = np.transpose(image_arr, (1,2,0)) 
  images = images.to(device=device, dtype=torch.float)
  prediction = model(images)
  prediction = prediction[0].cpu().detach().numpy()
  prediction = np.transpose(prediction, (1,2,0)) 
  prediction = prediction > prediction_threshold
  images = [np.zeros(shape=image_arr.shape)]*n

 1 for i,skin_tone in enumerate(skin_tones):
  2  for h in range(image_arr.shape[0]):
    3  for w in range(image_arr.shape[1]):
      4  if prediction[h][w][0]:
        5  for c in range(3):
         6  images[i][h][w][c] = min(image_arr[h][w][c]*(1+skin_tone),1)
        7 else:
         8 images[i][h][w][:] = image_arr[h][w][:]
    plt.subplot(1,col,i+1)
    plt.imshow(images[i])
    plt.title(f'{skin_tone}')
  plt.tight_layout()
  plt.show()

As we can see here for all skin tone values ranging from -0.2 to 0.2 we are using a nesed for loop to go through all the pixels in the image,those pixels that belong to the mask are then set to image[i][j]*(1+skin_tone)

So is there a better way to optimize the code from line 1-8.
Also should I apply a binary threshold to the mask first before moving into the for loop?Would that be useful?