example.cpp

#include <DarkHelp.hpp>

// -----------------------------------------------------------------------------
// Please also see the other source code examples in the ../src-apps/ directory.
// -----------------------------------------------------------------------------

int main()
{
	int rc = 0;

	try
	{
		DarkHelp::Config cfg("example.cfg", "example_best.weights", "example.names");
		cfg.enable_tiles					= false;
		cfg.annotation_auto_hide_labels		= false;
		cfg.annotation_include_duration		= true;
		cfg.annotation_include_timestamp	= false;
		cfg.threshold						= 0.2f;
		// lots of other options, scroll down this page to see what can be done:  https://www.ccoderun.ca/darkhelp/api/classDarkHelp_1_1Config.html#details

		DarkHelp::NN nn(cfg);

		// you can further modify the configuration even after the neural network has been created
		nn.config.annotation_line_thickness	= 1;
		nn.config.annotation_shade_predictions = 0.36f;

		// apply the neural network to an image on disk
		const auto results = nn.predict("example.jpg");

		// print the neural network results on the console
		std::cout << results << std::endl;

		// display both the original image and the annotated image using OpenCV HighGUI
		cv::Mat img1 = nn.original_image;
		cv::Mat img2 = nn.annotate();

		const cv::Size size(1024, 768);
		cv::imshow("original", DarkHelp::resize_keeping_aspect_ratio(img1, size));
		cv::imshow("detected", DarkHelp::resize_keeping_aspect_ratio(img2, size));
		cv::waitKey();
	}
	catch (const std::exception & e)
	{
		std::cout << e.what() << std::endl;
		rc = 1;
	}

	return rc;
}