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Hey all --

I took a class on ML a long time ago in college, and now I'm trying to use OpenCV's ML library to re-teach it to myself. I wanted to through a simple classification example to teach myself how it works, but so far it isn't going as expected.

I wanted to:

• create two groups of points -- points below the line y = x (labelled -1), and points above it (labeled 1),
• Feed the set of points into a SVM,
• Predict the classification of an obvious test point as a test case, and
• Draw the support vectors

My code is at the bottom of the post (using Cinder as a graphical wrapper).

The code seems to run fine, but the simple validations I'm running are both wrong:

• When asked to classify a point at y = 0, the SVM incorrectly returns 1 as the classification.
• It returns the support vectors at approximately (0, 0) -- my understanding is that it should return at least two support vectors (one on each side of the classification hyperplane), and the support vector should be an actual point...

There doesn't seem to be a lot of tutorials for how to use the OpenCV3 SVM class; does anyone have any experience / advice to share on how to get this to work?

#include "cinder/app/App.h"
#include "cinder/app/RendererGl.h"
#include "cinder/gl/gl.h"
#include "cinder/Rand.h"

#include <opencv2\core\core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2\ml\ml.hpp>

using namespace ci;
using namespace ci::app;
using namespace std;

class exampleClassificationApp : public App {
  public:
    void setup() override;
    void update() override;
    void draw() override;

    std::vector<cv::Point> mPoints;
    std::vector<int> mLabels;
    cv::Ptr<cv::ml::SVM> mSVM;
};

void exampleClassificationApp::setup() {
    for (int i = 0; i < 100; i++) {
        float x = Rand::randFloat(0, ci::app::getWindowWidth());
        float y = Rand::randFloat(0, x);
        mPoints.push_back(cv::Point(x, y));
        mLabels.push_back(-1.0);
    }
    for (int i = 0; i < 100; i++) {
        float x = Rand::randFloat(0, ci::app::getWindowWidth());
        float y = Rand::randFloat(x, ci::app::getWindowHeight());
        mPoints.push_back(cv::Point(x, y));
        mLabels.push_back(1.0);
    }
    cv::Mat trainingData(mPoints.size(), 2, CV_32FC1, mPoints.data());
    cv::Mat trainingLabels(mLabels.size(), 1, CV_32SC1, mLabels.data());

    std::printf("Created SVM\n");
    mSVM = cv::ml::SVM::create();

    std::printf("Setting parameters...\n");
    mSVM->setType(cv::ml::SVM::C_SVC);
    mSVM->setKernel(cv::ml::SVM::LINEAR);
    mSVM->setC(1.0);

    std::printf("Training...\n");
    mSVM->train(trainingData, cv::ml::ROW_SAMPLE, trainingLabels);

    float data[2] = { 0, ci::app::getWindowWidth() / 2.0 };
    cv::Mat query(1, 2, CV_32F, data);
    int response = mSVM->predict(query);

    std::printf("Asked SVM where to classify %f, %f -- result was %d\n", query.at<float>(0,0), query.at<float>(0, 1), response);
}

void exampleClassificationApp::update() {

}

void exampleClassificationApp::draw() {
    gl::clear( Color( 0, 0, 0 ) );
    for (int i = 0; i < mPoints.size(); i++) {
        if (mLabels[i] == -1.0) {
            gl::color(Color(1, 0, 0));
        }
        else {
            gl::color(Color(0, 0, 1));
        }
        ci::gl::drawSolidCircle(ci::vec2(mPoints[i].x, mPoints[i].y), 5);
    }
    cv::Mat supports;
    if (mSVM->isTrained()) {
        supports = mSVM->getSupportVectors();
        std::cout << "Suuport Vectors are " << supports << std::endl;
    }

}

CINDER_APP( exampleClassificationApp, RendererGl, [](App::Settings* settings) { settings->setConsoleWindowEnabled(); })