#include "opencv2/imgcodecs.hpp"
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/ml/ml.hpp>
using namespace cv::ml;
#define NTRAINING_SAMPLES 100
#define FRAC_LINEAR_SEP 0.9f
using namespace cv;
using namespace std;
int main() {
const int WIDTH = 512, HEIGHT = 512;
Mat I = Mat::zeros(HEIGHT, WIDTH, CV_8UC3);
Mat trainData(2 * NTRAINING_SAMPLES, 2, CV_32FC1);
Mat labels(2 * NTRAINING_SAMPLES, 1, CV_32SC1);
RNG rng(100);
int nLinearSamples = (int)(FRAC_LINEAR_SEP * NTRAINING_SAMPLES);
Mat trainClass = trainData.rowRange(0, nLinearSamples);
Mat c = trainClass.colRange(0, 1);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * WIDTH));
c = trainClass.colRange(1, 2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
trainClass = trainData.rowRange(2 * NTRAINING_SAMPLES - nLinearSamples, 2 * NTRAINING_SAMPLES);
c = trainClass.colRange(0, 1);
rng.fill(c, RNG::UNIFORM, Scalar(0.6 * WIDTH), Scalar(WIDTH));
c = trainClass.colRange(1, 2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
trainClass = trainData.rowRange(nLinearSamples, 2 * NTRAINING_SAMPLES - nLinearSamples);
c = trainClass.colRange(0, 1);
rng.fill(c, RNG::UNIFORM, Scalar(0.4 * WIDTH), Scalar(0.6 * WIDTH));
c = trainClass.colRange(1, 2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
labels.rowRange(0, NTRAINING_SAMPLES).setTo(1);
labels.rowRange(NTRAINING_SAMPLES, 2 * NTRAINING_SAMPLES).setTo(2);
Ptr<ml::SVM> svm = ml::SVM::create();
svm->setC(0.1);
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::LINEAR);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER,(int)1e7,1e-6));
cout << "Starting training process" << endl;
Ptr<ml::TrainData> tdata = ml::TrainData::create(trainData, ROW_SAMPLE,labels);
svm->train(tdata);
cout << "Finished training process" << endl;
Vec3b green(0, 100, 0), blue(100, 0, 0);
for (int i = 0; i < I.rows; ++i)
for (int j = 0; j < I.cols; ++j) {
Mat sampleMat = (Mat_<float>(1, 2) << i, j);
float response = svm->predict(sampleMat);
if (response == 1)
I.at<Vec3b>(j, i) = green;
else if (response == 2)
I.at<Vec3b>(j, i) = blue;
}
int thick = -1;
int lineType = 8;
float px, py;
for (int i = 0; i < NTRAINING_SAMPLES; ++i) {
px = trainData.at<float>(i, 0);
py = trainData.at<float>(i, 1);
circle(I, Point((int)px, (int)py), 3, Scalar(0, 255, 0), thick, lineType);
}
for (int i = NTRAINING_SAMPLES; i < 2 * NTRAINING_SAMPLES; ++i) {
px = trainData.at<float>(i, 0);
py = trainData.at<float>(i, 1);
circle(I, Point((int)px, (int)py), 3, Scalar(255, 0, 0), thick, lineType);
}
thick = 2;
lineType = 8;
Mat sv = svm->getSupportVectors();
for (int i = 0; i < sv.rows; ++i) {
const float *v = sv.ptr<float>(i);
circle(I, Point((int)v[ 0 ], (int)v[ 1 ]), 6, Scalar(128, 128, 128), thick, lineType);
}
imwrite("result.png", I);
imshow("SVM for Non-Linear Training Data", I);
waitKey(0);
}