.. ximgproc:

Structured forest training
**************************

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

Training pipeline
-----------------

1. Download "Piotr's Toolbox" from `link <http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html>`_
   and put it into separate directory, e.g. PToolbox

2. 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

3. Add both directory and their subdirectories to Matlab path.

4. 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 ::

    .
        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

5. Rename models/forest/modelFinal.mat to models/forest/modelFinal.mat.backup

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

    shrink=opts.shrink;
    chns = single(getFeatures( im2double(I) ));

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

  .. code-block:: 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);
    }

8. 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/.

9. 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/doc/tutorials/ximpgroc/training/modelConvert(model, "model.yml")

How to use your model
---------------------

Just use expanded constructor with above defined class NewRFFeatureGetter

  .. code-block:: cpp

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