机器学习之-利用svm(支持向量机)分类（opencv3）

int main(int argc, char** argv)
{
// visual representation
int width = 512;
int height = 512;
cv::Mat image = cv::Mat::zeros(height, width, CV_8UC3);

// training data
int labels[4] = { 1, -1, -1, -1 };
float trainingData[4][2] = { { 501, 10 },{ 255, 10 },{ 501, 255 },{ 10, 501 } };
cv::Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
cv::Mat labelsMat(4, 1, CV_32SC1, labels);

// initial SVM
cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::create();
svm->setType(cv::ml::SVM::Types::C_SVC);
svm->setKernel(cv::ml::SVM::KernelTypes::LINEAR);
svm->setTermCriteria(cv::TermCriteria(cv::TermCriteria::MAX_ITER, 100, 1e-6));

// train operation
svm->train(trainingDataMat, cv::ml::SampleTypes::ROW_SAMPLE, labelsMat);

// prediction
cv::Vec3b green(0, 255, 0);
cv::Vec3b blue(255, 0, 0);
for (int i = 0; i < image.rows; i++)
{
for (int j = 0; j < image.cols; j++)
{
cv::Mat sampleMat = (cv::Mat_<float>(1, 2) << j, i);
float respose = svm->predict(sampleMat);
if (respose == 1)
image.at<cv::Vec3b>(i, j) = green;
else if (respose == -1)
image.at<cv::Vec3b>(i, j) = blue;
}
}

int thickness = -1;
int lineType = cv::LineTypes::LINE_8;

cv::circle(image, cv::Point(501, 10), 5, cv::Scalar(0, 0, 0), thickness, lineType);
cv::circle(image, cv::Point(255, 10), 5, cv::Scalar(255, 255, 255), thickness, lineType);
cv::circle(image, cv::Point(501, 255), 5, cv::Scalar(255, 255, 255), thickness, lineType);
cv::circle(image, cv::Point(10, 501), 5, cv::Scalar(255, 255, 255), thickness, lineType);

thickness = 2;
lineType = cv::LineTypes::LINE_8;

cv::Mat sv = svm->getSupportVectors();
for (int i = 0; i < sv.rows; i++)
{
const float* v = sv.ptr<float>(i);
cv::circle(image, cv::Point((int)v[0], (int)v[1]), 6, cv::Scalar(128, 128, 128), thickness, lineType);
}

cv::imshow("SVM Simple Example", image);

cv::waitKey(0);
return 0;
}