Image stitching example with LoFTR

A show case of how to do image stitching using LoFTR from Kornia.

First, we will install everything needed:

• fresh version of kornia for LoFTR
• fresh version of kornia for ImageStitcher

%%capture
!pip install kornia
!pip install kornia-rs

Now let’s download an image pair

import io

import requests


def download_image(url: str, filename: str = "") -> str:
    filename = url.split("/")[-1].split("?")[0] if len(filename) == 0 else filename
    # Download
    bytesio = io.BytesIO(requests.get(url).content)
    # Save file
    with open(filename, "wb") as outfile:
        outfile.write(bytesio.getbuffer())

    return filename


download_image("https://github.com/kornia/data/raw/main/prtn00.jpg")
download_image("https://github.com/kornia/data/raw/main/prtn01.jpg")

'prtn01.jpg'

%%capture
import kornia as K
import kornia.feature as KF
import matplotlib.pyplot as plt
import numpy as np
import torch


def load_images(fnames):
    return [K.io.load_image(fn, K.io.ImageLoadType.RGB32)[None, ...] for fn in fnames]


imgs = load_images(["prtn00.jpg", "prtn01.jpg"])

Stitch them together

from kornia.contrib import ImageStitcher

IS = ImageStitcher(KF.LoFTR(pretrained="outdoor"), estimator="ransac")

with torch.no_grad():
    out = IS(*imgs)

plt.imshow(K.tensor_to_image(out))
plt.show()

Another example

download_image("https://github.com/daeyun/Image-Stitching/blob/master/img/hill/1.JPG?raw=true")
download_image("https://github.com/daeyun/Image-Stitching/blob/master/img/hill/2.JPG?raw=true")
download_image("https://github.com/daeyun/Image-Stitching/blob/master/img/hill/3.JPG?raw=true")

'3.JPG'

imgs = load_images(["1.JPG", "2.JPG", "3.JPG"])

f, axarr = plt.subplots(1, 3, figsize=(16, 6))

axarr[0].imshow(K.tensor_to_image(imgs[0]))
axarr[0].tick_params(left=False, right=False, labelleft=False, labelbottom=False, bottom=False)
axarr[1].imshow(K.tensor_to_image(imgs[1]))
axarr[1].tick_params(left=False, right=False, labelleft=False, labelbottom=False, bottom=False)
axarr[2].imshow(K.tensor_to_image(imgs[2]))
axarr[2].tick_params(left=False, right=False, labelleft=False, labelbottom=False, bottom=False)

matcher = KF.LocalFeatureMatcher(KF.GFTTAffNetHardNet(100), KF.DescriptorMatcher("snn", 0.8))
IS = ImageStitcher(matcher, estimator="ransac")

with torch.no_grad():
    out = IS(*imgs)

plt.figure(figsize=(16, 16))
plt.imshow(K.tensor_to_image(out))