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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](http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html)
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
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