Open in Colab

Image anti-alias with local featuresΒΆ

In this example we will show the benefits of using anti-aliased patch extraction with kornia.

!pip install kornia seaborn

First, lets load some image.

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import PIL
import torch
import seaborn as sns
import kornia.feature as KF
import kornia as K
import cv2
img_original = cv2.cvtColor(cv2.imread('drslump.jpg'), cv2.COLOR_BGR2RGB)
H,W,CH = img_original.shape

img_small = cv2.resize(img_original, (W//DOWNSAMPLE, H//DOWNSAMPLE), interpolation = cv2.INTER_AREA)
<matplotlib.image.AxesImage at 0x7f9d262dced0>
_images/aliased_and_not_aliased_patch_extraction_5_1.png _images/aliased_and_not_aliased_patch_extraction_5_2.png

Now, lets define a keypoint with a large support region.

def show_lafs(img, lafs, idx=0, color='r', figsize = (10,7)):
    x,y = KF.laf.get_laf_pts_to_draw(lafs, idx)
    if (type(img) is torch.tensor):
        img_show = K.tensor_to_image(img)
        img_show = img
    plt.plot(x, y, color)

device = torch.device('cpu')

laf_orig  = torch.tensor([[150., 0, 180],
                     [0, 150, 280]]).float().view(1,1,2,3)
laf_small = laf_orig / float(DOWNSAMPLE)

show_lafs(img_original, laf_orig, figsize=(6,4))
show_lafs(img_small, laf_small, figsize=(6,4))
_images/aliased_and_not_aliased_patch_extraction_7_0.png _images/aliased_and_not_aliased_patch_extraction_7_1.png

Now lets compare how extracted patch would look like when extracted in a naive way and from scale pyramid.

PS = 32
with torch.no_grad():
    timg_original = K.image_to_tensor(img_original, False).float().to(device) / 255.
    patches_pyr_orig = KF.extract_patches_from_pyramid(timg_original,, PS)
    patches_simple_orig = KF.extract_patches_simple(timg_original,, PS)
    timg_small = K.image_to_tensor(img_small, False).float().to(device)/255.
    patches_pyr_small = KF.extract_patches_from_pyramid(timg_small,, PS)
    patches_simple_small = KF.extract_patches_simple(timg_small,, PS)
# Now we will glue all the patches together:

def vert_cat_with_margin(p1, p2, margin=3):
    b,n,ch,h,w = p1.size()
    return[p1, torch.ones(b, n, ch, h, margin).to(device), p2], dim=4)

def horiz_cat_with_margin(p1, p2, margin=3):
    b,n,ch,h,w = p1.size()
    return[p1, torch.ones(b, n, ch, margin, w).to(device), p2], dim=3)

patches_pyr = vert_cat_with_margin(patches_pyr_orig, patches_pyr_small)
patches_naive = vert_cat_with_margin(patches_simple_orig, patches_simple_small)

patches_all = horiz_cat_with_margin(patches_naive, patches_pyr)

Now lets show the result. Top row is what you get if you are extracting patches without any antialiasing - note how the patches extracted from the images of different sizes differ.

Bottom row is patches, which are extracted from images of different sizes using a scale pyramid. They are not yet exactly the same, but the difference is much smaller.

<matplotlib.image.AxesImage at 0x7f9d224b4950>

Lets check how much it influences local descriptor performance such as HardNet

hardnet = KF.HardNet(True).eval()
all_patches =[patches_pyr_orig,
                         patches_simple_small], dim=0).squeeze(1).mean(dim=1,keepdim=True)
with torch.no_grad():
    descs = hardnet(all_patches)
    distances = torch.cdist(descs, descs)
    print (distances.cpu().detach().numpy())
Downloading: "" to /home/docs/.cache/torch/hub/checkpoints/checkpoint_liberty_with_aug.pth
[[0.         0.09264608 0.81557477 0.5459373 ]
 [0.09264608 0.         0.78336656 0.50684917]
 [0.81557477 0.78336656 0.         0.44813338]
 [0.5459373  0.50684917 0.44813338 0.        ]]

So the descriptor difference between antialiased patches is 0.09 and between naively extracted – 0.44