OpenCV/opencv_contrib/modules/ximgproc/tutorials/training.markdown

Structured forest training {#tutorial_ximgproc_training}

Introduction

In this tutorial we show how to train your own structured forest using author's initial Matlab implementation.

Training pipeline

-# Download "Piotr's Toolbox" from link

and put it into separate directory, e.g. PToolbox

-# Download BSDS500 dataset from

link \<http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/\> and put it into
separate directory named exactly BSR

-# Add both directory and their subdirectories to Matlab path.

-# Download detector code from

link \<http://research.microsoft.com/en-us/downloads/389109f6-b4e8-404c-84bf-239f7cbf4e3d/\> and
put it into root directory. Now you should have :
@code
    .
        BSR
        PToolbox
        models
        private
        Contents.m
        edgesChns.m
        edgesDemo.m
        edgesDemoRgbd.m
        edgesDetect.m
        edgesEval.m
        edgesEvalDir.m
        edgesEvalImg.m
        edgesEvalPlot.m
        edgesSweeps.m
        edgesTrain.m
        license.txt
        readme.txt
@endcode

-# Rename models/forest/modelFinal.mat to models/forest/modelFinal.mat.backup

-# Open edgesChns.m and comment lines 26--41. Add after commented lines the following:

@code{.cpp}
        shrink=opts.shrink;
        chns = single(getFeatures( im2double(I) ));
@endcode

-# Now it is time to compile promised getFeatures. I do with the following code:

@code{.cpp}
#include <cv.h>
#include <highgui.h>

#include <mat.h>
#include <mex.h>

#include "MxArray.hpp" // https://github.com/kyamagu/mexopencv

class NewRFFeatureGetter : public cv::RFFeatureGetter
{
public:
    NewRFFeatureGetter() : name("NewRFFeatureGetter"){}

    virtual void getFeatures(const cv::Mat &src, NChannelsMat &features,
                             const int gnrmRad, const int gsmthRad,
                             const int shrink, const int outNum, const int gradNum) const
    {
        // here your feature extraction code, the default one is:
        // resulting features Mat should be n-channels, floating point matrix
    }

protected:
    cv::String name;
};

MEXFUNCTION_LINKAGE void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
    if (nlhs != 1) mexErrMsgTxt("nlhs != 1");
    if (nrhs != 1) mexErrMsgTxt("nrhs != 1");

    cv::Mat src = MxArray(prhs[0]).toMat();
    src.convertTo(src, cv::DataType<float>::type);

    std::string modelFile = MxArray(prhs[1]).toString();
    NewRFFeatureGetter *pDollar = createNewRFFeatureGetter();

    cv::Mat edges;
    pDollar->getFeatures(src, edges, 4, 0, 2, 13, 4);
    // you can use other numbers here

    edges.convertTo(edges, cv::DataType<double>::type);

    plhs[0] = MxArray(edges);
}
@endcode

-# Place compiled mex file into root dir and run edgesDemo. You will need to wait a couple of hours

after that the new model will appear inside models/forest/.

-# The final step is converting trained model from Matlab binary format to YAML which you can use

with our ocv::StructuredEdgeDetection. For this purpose run
opencv_contrib/ximgproc/tutorials/scripts/modelConvert(model, "model.yml")

How to use your model

Just use expanded constructor with above defined class NewRFFeatureGetter @code{.cpp} cv::StructuredEdgeDetection pDollar

= cv::createStructuredEdgeDetection( modelName, makePtr<NewRFFeatureGetter>() );

@endcode