Nvidia posted a video about first

[Munchskull]

Originally Posted by marshall

You're in luck! It already is:

https://github.com/FRC900/2015VisionCode

Contrary to the YouTube comments it is running on the Jetson using OpenCV and the Nvidia libraries that enable support for CUDA... though it is Tegra CUDA and not Geforce CUDA so take that as you will. Not all CUDA cores are created equal.

That is awesome. Just making sure that I am understanding this correctly as I am not a programer. It looks to me from the notes on github, that this vision code is one that you teach. Am I correct?

Also from a more hardware perspective, would you be able to runs this on other micro controllers such a raspberry pi or is the Jetson required?

[marshall]

Originally Posted by Munchskull

That is awesome. Just making sure that I am understanding this correctly as I am not a programer. It looks to me from the notes on github, that this vision code is one that you teach. Am I correct?

Also from a more hardware perspective, would you be able to runs this on other micro controllers such a raspberry pi or is the Jetson required?

It is indeed code that you teach or train. You must provide it positive and negative images of the items you are seeking to recognize. The white paper we are working on uses the balls from the 2014 game as an example. I've been told that white paper is still being worked on and to expect a draft this Friday.

You do not need a Jetson to run this type of code but you do need one to run this specific code. In fact, a lot of our prototype work was done on PCs. That being said, we're fans of the Jetson. A raspberry pi should work as well.

Also, the code is using a technique known as cascade classification. It's pretty clever but there are even more cleverer ways to do this using neural networks but that is going to become an off season project for us.

[Munchskull]

Originally Posted by marshall

It is indeed code that you teach or train. You must provide it positive and negative images of the items you are seeking to recognize. The white paper we are working on uses the balls from the 2014 game as an example. I've been told that white paper is still being worked on and to expect a draft this Friday.

You do not need a Jetson to run this type of code but you do need one to run this specific code. In fact, a lot of our prototype work was done on PCs. That being said, we're fans of the Jetson. A raspberry pi should work as well.

Also, the code is using a technique known as cascade classification. It's pretty clever but there are even more cleverer ways to do this using neural networks but that is going to become an off season project for us.

Would you be able to post the computer version of the code?

And if you can, would you be able to give directions on how to run it? (Ex. What environment I would need? Required libraries? Required camera? Ect.)

[marshall]

Originally Posted by Munchskull

Would you be able to post the computer version of the code?

And if you can, would you be able to give directions on how to run it? (Ex. What environment I would need? Required libraries? Required camera? Ect.)

I will see what I can do about getting a student to post some examples once the white paper is up. The computer versions were never meant to run beyond POC from what I recall. I don't think we ever had a complete version built for PC but I could be wrong. I'll see what I can do though.

[Munchskull]

Originally Posted by marshall

I will see what I can do about getting a student to post some examples once the white paper is up. The computer versions were never meant to run beyond POC from what I recall. I don't think we ever had a complete version built for PC but I could be wrong. I'll see what I can do though.

May i suggest that you make a independent thread for this discussion. That would allow for a more formal place to talk about this awesome piece of software.

[KJaget]

Originally Posted by Munchskull

Would you be able to post the computer version of the code?

And if you can, would you be able to give directions on how to run it? (Ex. What environment I would need? Required libraries? Required camera? Ect.)

I'll take a quick shot at this, from memory. Give it a try and ask questions if you run into problems. This will eventually morph into a readme in our code, but feedback will help us debug it.

The code in our git repo (https://github.com/FRC900/2015VisionCode) will build and run on Linux or Windows. The training code will require either Linux or cygwin for some of the scripts. The C++ code will build on everything we've tried, which includes X86 windows, X86 Linux, ARM Linux (for the Jetson) and so on.

You'll need OpenCV 2.4.x installed. On Linux this is typically an apt-get thing or the equivalent. For windows, the OpenCV page is good - http://docs.opencv.org/doc/tutorials...s_install.html.

For cygwin, we've had luck with the tarball at http://hvrl.ics.keio.ac.jp/kimura/op...cv-2.4.11.html. I think we had to move the files extracted into /lib, /share, and so on for the compiler to find them.

The code works with any camera we've thrown at it. It will also run on still images or on video files. For example, for testing we ran the code against video we've downloaded from youtube. We have special code in place for Logitech C920s under Linux since that's what we used, but it wasn't as critical as we thought to use that particular camera.

The detection code itself is in the subdir bindetection. Steps to build :
1. cd bindetection/galib247
2. make
3. cd ..
4. cmake .
5. make

We've hit a weird bug where occasionally you get a weird link error the first time through. If so, repeat the "cmake ." and make.

This will produce the creatively named binary "test", which is the recycle bin detector.

Most of the options to those code can be controlled from the command line. One thing to edit is line 25 of classifierio.cpp. Change the initial /home/ubuntu to the directory the code has been downloaded to. This will require a recompile to take effect.

To run using a camera, run test. This will open the default camera and start detecting. Adding a number to the command line to pick another camera.
To run against a video, add the video name to the command line (e.g. "test video.avi").

I'm sure I'm missing something but that's a start.