vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

2026-08-22 00:11:13 +08:00
commit 12022378a3
3872 changed files with 2513409 additions and 0 deletions
@@ -0,0 +1,115 @@
+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