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removed contrib, legacy and softcsscade modules; removed latentsvm and datamatrix detector from objdetect. removed haartraining and sft apps.

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some of the stuff will be moved to opencv_contrib module.
in order to make this PR pass buildbot, please, comment off opencv_legacy, opencv_contrib and opencv_softcascade test runs.
This commit is contained in:
Vadim Pisarevsky
2014-06-24 19:18:51 +04:00
parent 6d4c4dcd94
commit 3858f2291d
398 changed files with 12 additions and 150885 deletions
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37
samples/cpp/em.cpp
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@@ -1,5 +1,5 @@
#include "opencv2/highgui.hpp"
#include "opencv2/legacy.hpp"
#include "opencv2/ml.hpp"
using namespace cv;
@@ -19,8 +19,6 @@ int main( int /*argc*/, char** /*argv*/ )
Mat labels;
Mat img = Mat::zeros( Size( 500, 500 ), CV_8UC3 );
Mat sample( 1, 2, CV_32FC1 );
CvEM em_model;
CvEMParams params;
samples = samples.reshape(2, 0);
for( i = 0; i < N; i++ )
@@ -35,37 +33,10 @@ int main( int /*argc*/, char** /*argv*/ )
}
samples = samples.reshape(1, 0);
// initialize model parameters
params.covs = NULL;
params.means = NULL;
params.weights = NULL;
params.probs = NULL;
params.nclusters = N;
params.cov_mat_type = CvEM::COV_MAT_SPHERICAL;
params.start_step = CvEM::START_AUTO_STEP;
params.term_crit.max_iter = 300;
params.term_crit.epsilon = 0.1;
params.term_crit.type = TermCriteria::COUNT|TermCriteria::EPS;
// cluster the data
em_model.train( samples, Mat(), params, &labels );
EM em_model(N, EM::COV_MAT_SPHERICAL, TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 300, 0.1));
em_model.train( samples, noArray(), labels, noArray() );
#if 0
// the piece of code shows how to repeatedly optimize the model
// with less-constrained parameters
//(COV_MAT_DIAGONAL instead of COV_MAT_SPHERICAL)
// when the output of the first stage is used as input for the second one.
CvEM em_model2;
params.cov_mat_type = CvEM::COV_MAT_DIAGONAL;
params.start_step = CvEM::START_E_STEP;
params.means = em_model.get_means();
params.covs = em_model.get_covs();
params.weights = em_model.get_weights();
em_model2.train( samples, Mat(), params, &labels );
// to use em_model2, replace em_model.predict()
// with em_model2.predict() below
#endif
// classify every image pixel
for( i = 0; i < img.rows; i++ )
{
@@ -73,7 +44,7 @@ int main( int /*argc*/, char** /*argv*/ )
{
sample.at<float>(0) = (float)j;
sample.at<float>(1) = (float)i;
int response = cvRound(em_model.predict( sample ));
int response = cvRound(em_model.predict( sample )[1]);
Scalar c = colors[response];
circle( img, Point(j, i), 1, c*0.75, FILLED );