remove duplicate functions

lpips/__init__.py

@@ -24,13 +24,6 @@ def dssim(p0, p1, range=255.):
  from skimage.measure import compare_ssim
  return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.
 
-def rgb2lab(in_img,mean_cent=False):
- from skimage import color
- img_lab = color.rgb2lab(in_img)
- if(mean_cent):
- img_lab[:,:,0] = img_lab[:,:,0]-50
- return img_lab
-
 def tensor2np(tensor_obj):
  # change dimension of a tensor object into a numpy array
  return tensor_obj[0].cpu().float().numpy().transpose((1,2,0))
@@ -84,9 +77,16 @@ def load_image(path):
 
  return img
 
-def rgb2lab(input):
- from skimage import color
- return color.rgb2lab(input / 255.)
+# def rgb2lab(input):
+# from skimage import color
+# return color.rgb2lab(input / 255.)
+
+# def rgb2lab(in_img,mean_cent=False):
+# from skimage import color
+# img_lab = color.rgb2lab(in_img)
+# if(mean_cent):
+# img_lab[:,:,0] = img_lab[:,:,0]-50
+# return img_lab
 
 def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
  image_numpy = image_tensor[0].cpu().float().numpy()
@@ -101,19 +101,6 @@ def tensor2vec(vector_tensor):
  return vector_tensor.data.cpu().numpy()[:, :, 0, 0]
 
 
-def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
-# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.):
- image_numpy = image_tensor[0].cpu().float().numpy()
- image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
- return image_numpy.astype(imtype)
-
-def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
-# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.):
- return torch.Tensor((image / factor - cent)
- [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
-
-
-
 def voc_ap(rec, prec, use_07_metric=False):
  """ ap = voc_ap(rec, prec, [use_07_metric])
  Compute VOC AP given precision and recall.