I finally finished the python -> C++ code conversion, and I am happy to say that the code is ready to be demoed! The things that I converted this week were the dataset parser file (which currently reads from images in a folder), the individual component classifier file, and the main file that classifies the entire circuit.
Tomorrow I will be experimenting with running the code on either Jaden or Devan’s laptops for the demo. I have been working on a Linux machine, and the problem with that is I don’t have a GUI installed that can show images, which I want to do for the demonstration (so I am not just showing std output). Also it would be much better if we could just run all of our code off of one machine anyways instead of switching computers every time someone presents their part.
The steps for next week will to be to start working on testing and improving the accuracy of the circuit and component detection. The other thing is to also start working on integration with the mobile application. This will require creating a bridge file such that the C++ classes I made can be used in Swift. I also need to do a little bit of redesigning of the code, such as with the dataset parser. Right now I have it so that every time you run the program, the dataset parser will take each image in the dataset directory and generate the keypoints and descriptors. What we want to do is have dataset be represented by just one file that already has the keypoints and descriptors. This will honestly be a pretty easy change and coding this function will probably only take an hour or two max. I will also probably make this one of the last things I do because the dataset is not set yet.
I am on schedule. I foresee that maybe the improvement of the accuracy can potentially overflow into next next week as well, but we have given a slack period in our schedule that can account for this.