Let me introduce pixelstitch: simple correspondence annotator based on @matplotlib + jupyter notebook.
Just propvide list of image_fnames and
import CorrespondenceAnnotator.
You can add and erase points, zoom, move, and visualize epipolar geometry, induced by correspondences. 1/
You can install it by
pip install pixelstitch
and the documentation is here:
ducha-aiki.github.io/pixelstitch/
Ofc, powered by nbdev.
All suggestions are welcomed
2/3
Why did I do this? Well, the built-in labelling tools in SfM apps like RealityCapture are nice, but one cannot quickly go through unrelated image pairs.
New artisan datasets for WxBS from me are coming soon,
specifically WxBS-relabeled and EVD 2.0.
3/3
And the repo it here
github.com/ducha-aiki/pix…
4/3

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More from @ducha_aiki

23 Nov 20
I know, you all have dreamed about the conversion functions between #kornia @kornia_foss and OpenCV. Here they are.

github.com/ducha-aiki/kor…

pip install kornia_moons

Docs are here: ducha-aiki.github.io/kornia-moons/

Powered by @fastdotai and @HamelHusain nbdev
1/3
For example, you can detect ORB features is OpenCV and convert them to kornia lafs with

lafs = laf_from_opencv_ORB_kpts(kps)

Or you can match descriptors on GPU with kornia.feature.match_snn() and transfer to OpenCV with cv2_matches_from_kornia 2/3
The repo is not @kornia_foss "official" in the terms that we don't promise to maintain everything in a timely manner. However, it should work for most of the times :) 3/3
Read 4 tweets
22 Nov 20
While I am selecting nice pictures for my PhD thesis, let's do a fun visual benchmark of local features on the photos I took myself.

SIFT-HardNet, R2D2, SuperPoint, SuperGlue
1/
Nothing works here, ha :)

SIFT-HardNet, R2D2, SuperPoint, SuperGlue
2/
SIFT-HardNet, R2D2, SuperPoint, SuperGlue

As in 1, only SuperGlue works :)
3/
Read 13 tweets

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