rookfighter / nvision-cpp Goto Github PK

Fix the Windows build on appveyor.

Add a template parameter Score for the FAST detectors.
It should support for now Harris scores and FAST scores.

Implement jpeg loading and saving routines.

Use the libjpeg interface. See libjpeg-turbo.

Currently FAST and ORB only detect integer keypoints.

Especially ORB can also produce subpixel accurate keypoints through its image pyramid.

First, thanks for the great work. I have some questions though:

1. I think the example code for Gauss filter in the main page is outdated (probably for old cve namespace I guess) because my compiler complains that there's no type Image8. From the file (https://github.com/Rookfighter/nvision/blob/master/include/nvision/src/core/image_type.h) I am not able to find neither the type Image8 nor Imagef for FAST example.
   It would be great the examples are updated according to the current library format.

2. When I installed nvision library using cmake, I ran into the problem of hierarchy issue of directories, e.g. #include "nvision/src/core/pixel.h" instead of #include "src/core/pixel.h" in https://github.com/Rookfighter/nvision/blob/master/include/nvision/core.h
   So I had to manually adjust all those things. I guess this is not a problem for build2? or Do you have better suggestions for that matter?

3. Do you have plan to provide other optic flow algorithms such as (https://link.springer.com/chapter/10.1007/978-3-540-24673-2_3)?

4. Let's say I want to extend some boundary conditions as flexibly as I can, e.g. central difference with 5 points instead of 3 points, using 5 points boundaries instead of three points or less and set different values at boundaries for customized derivative filters etc. What would you recommend to approach to the task?

Implement the FREAK feature descriptor. See https://gilscvblog.com/2013/12/09/a-tutorial-on-binary-descriptors-part-5-the-freak-descriptor/.

Hi,

Thank you for such a wonderful & lightweight computer vision library. I am detecting fast keypoints on camera stream by fetching camera feed using opencv then I am converting that cv::mat to cve::imagef and detecting fast keypoint using cve also getting hs optical flow using the same imagef. Now I am willing to track those detected keypoints with hs of. Is it possible as I can see it's kind of dense optical flow? Although with opencv fernback optical flow i can track sparse points but can't figure out how to do it here. Can you please suggest me?

Implement the BRISK descriptor. See BRISK.

Add a GUI module with minimal dependency.

Potential toolkits:

• FLTK
• Qt