116 lines
3.7 KiB
Markdown
116 lines
3.7 KiB
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](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
|