train_svm_opencv.cpp

#include <iostream>

#include <opencv2/opencv.hpp>
#include <opencv2/ml.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/core.hpp>

using namespace cv;

const std::string TRAINING_DATA_ROOT = "E:\\SVM_Training\\";

std::string acquire_training_data_color(const std::string col);

std::string acquire_training_data_color(const std::string col) {
	return TRAINING_DATA_ROOT + col + "\\";
}

int main()
{
	cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::create();

	svm->setType(cv::ml::SVM::C_SVC);
	svm->setKernel(cv::ml::SVM::LINEAR); // Or POLY
	svm->setGamma(1); // Or 1
	svm->setDegree(0);
	svm->setCoef0(0);
	svm->setC(1);
	svm->setNu(0);
	svm->setP(0);
	svm->setTermCriteria(cv::TermCriteria(CV_TERMCRIT_ITER, 1000, 0.01));

	cv::Mat imr = cv::imread(acquire_training_data_color("red") + "1.bmp", cv::IMREAD_COLOR);

	Mat bir = imr.reshape(0, 1);

	cv::Mat imb = cv::imread(acquire_training_data_color("blue") + "1.bmp", cv::IMREAD_COLOR);

	Mat bib = imb.reshape(0, 1);

	cv::Mat trainData, labels;
	for (int i = 0; i < 10; i++)
	{
		trainData.push_back(bir);
		labels.push_back(1);
		trainData.push_back(bib);
		labels.push_back(2);
	}

	cv::Ptr<cv::ml::TrainData> trainD;

	try
	{
		trainD = cv::ml::TrainData::create(trainData, cv::ml::ROW_SAMPLE, labels);
	}
	catch (const std::exception& ex)
	{
		std::cerr << ex.what();
	}

	/*svm->train(trainD);

	std::cout << svm->predict(bib);*/

	std::cout << trainData.rows << " x " << trainData.cols << std::endl;

	std::cout << labels.rows << " x " << labels.cols << std::endl;
	
	getchar();

	return 0;
}