Summary

Note

This document assumes you already have Darknet installed, and you have a functioning neural network.
If you're not already at that step, you'll want to look up some tutorials like this one: https://www.ccoderun.ca/programming/2019-08-17_Darknet_summary/.

Once you have successfully trained a neural network, the next question becomes: how do you embed it into your C++ application?! Perhaps you've already looked into using Darknet's legacy C API, functions like load_network_custom(), do_nms_sort(), and get_network_boxes(). That API is not easy to work with, and there isn't much documentation nor example code.

(In case it helps, I did put together a blog post with a few details in late August 2019: https://www.ccoderun.ca/programming/2019-08-25_Darknet_C_CPP/.)

DarkHelp lets you skip those C function calls, and simplifies things with an extremely simple-to-use C++ API. You load the neural network and the weight files, then call DarkHelp::predict() once per image you'd like analyzed. Each time you get back a std::vector of predictions.

Since annotating pictures is something that many applications want – especially during debugging – DarkHelp::annotate() is provided to easily mark up images with the detection results. To ease integrating this into larger projects, DarkHelp uses OpenCV's standard cv::Mat images, not Darknet's internal image structure. This is an example of what DarkHelp::annotate() can do with an image and a neural network that detects barcodes:

If you're looking for some sample code to get started, this example loads a network and then loops through several image files:

DarkHelp darkhelp("mynetwork.cfg", "mynetwork.weights", "mynetwork.names");

const auto image_filenames = {"image_0.jpg", "image_1.jpg", "image_2.jpg"};

for (const auto & filename : image_filenames)
{
    // these next two lines is where DarkHelp calls into Darknet to do all the hard work
    darkhelp.predict(filename);
    cv::Mat mat = darkhelp.annotate(); // annotates the most recent image seen by predict()

    // use standard OpenCV calls to show the image results in a window
    cv::imshow("prediction", mat);
    cv::waitKey();
}

The predictions are stored in a std::vector of structures. (See DarkHelp::PredictionResults.) You can get this vector and iterate through the results like this:

DarkHelp darkhelp("mynetwork.cfg", "mynetwork.weights", "mynetwork.names");

const auto results = darkhelp.predict("test_image_01.jpg");

for (const auto & det : results)
{
    std::cout << det.name << " (" << 100.0 * det.best_probability << "% chance that this is class #" << det.best_class << ")" << std::endl;
}

If you have multiple classes defined in your network, then you may want to look at DarkHelp::PredictionResult::all_probabilities, not only DarkHelp::PredictionResult::best_class and DarkHelp::PredictionResult::best_probability.