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merged all the latest changes from 2.4 to trunk

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This commit is contained in:
Vadim Pisarevsky
2012-04-13 21:50:59 +00:00
parent 020f9a6047
commit 2fd1e2ea57
416 changed files with 12852 additions and 6070 deletions
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2
samples/cpp/CMakeLists.txt
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@@ -20,7 +20,7 @@ if(BUILD_EXAMPLES AND OCV_DEPENDENCIES_FOUND)
ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpu/include")
endif()
if(CMAKE_COMPILER_IS_GNUCXX)
if(CMAKE_COMPILER_IS_GNUCXX AND NOT ENABLE_NOISY_WARNINGS)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wno-unused-function")
endif()

3
samples/cpp/detector_descriptor_matcher_evaluation.cpp
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@@ -273,7 +273,8 @@ int main(int argc, char** argv)
if( dir[dir.size()-1] != '\\' && dir[dir.size()-1] != '/' )
dir += "/";
system(("mkdir " + dir).c_str());
int result = system(("mkdir " + dir).c_str());
CV_Assert(result == 0);
for( int i = 0; ddms[i*4] != 0; i++ )
{

2
samples/cpp/em.cpp
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@@ -1,4 +1,4 @@
#include "opencv2/ml/ml.hpp"
#include "opencv2/legacy/legacy.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace cv;

6
samples/cpp/hybridtrackingsample.cpp
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@@ -82,7 +82,8 @@ int main(int argc, char** argv)
sprintf(test_file, "%s", argv[1]);
f = fopen(test_file, "r");
char vid[20];
fscanf(f, "%s\n", vid);
int values_read = fscanf(f, "%s\n", vid);
CV_Assert(values_read == 1);
cout << "Benchmarking against " << vid << endl;
live = 0;
}
@@ -133,7 +134,8 @@ int main(int argc, char** argv)
}
else
{
fscanf(f, "%d %f %f %f %f\n", &i, &w[0], &w[1], &w[2], &w[3]);
int values_read = fscanf(f, "%d %f %f %f %f\n", &i, &w[0], &w[1], &w[2], &w[3]);
CV_Assert(values_read == 5);
sprintf(img_file, "seqG/%04d.png", i);
image = imread(img_file, CV_LOAD_IMAGE_COLOR);
if (image.empty())

28
samples/cpp/opencv_version.cpp Normal file
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@@ -0,0 +1,28 @@
#include "opencv2/core/core.hpp"
#include <iostream>
const char* keys =
{
"{ b |build |false | print complete build info }"
"{ h |help |false | print this help }"
};
int main(int argc, const char* argv[])
{
cv::CommandLineParser parser(argc, argv, keys);
if (parser.get<bool>("help"))
{
parser.printParams();
}
else if (parser.get<bool>("build"))
{
std::cout << cv::getBuildInformation() << std::endl;
}
else
{
std::cout << "OpenCV " << CV_VERSION << std::endl;
}
return 0;
}

82
samples/cpp/points_classifier.cpp
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@@ -11,7 +11,6 @@ const Scalar WHITE_COLOR = CV_RGB(255,255,255);
const string winName = "points";
const int testStep = 5;
Mat img, imgDst;
RNG rng;
@@ -19,16 +18,16 @@ vector<Point> trainedPoints;
vector<int> trainedPointsMarkers;
vector<Scalar> classColors;
#define NBC 0 // normal Bayessian classifier
#define KNN 0 // k nearest neighbors classifier
#define SVM 0 // support vectors machine
#define DT 1 // decision tree
#define BT 0 // ADA Boost
#define GBT 0 // gradient boosted trees
#define RF 0 // random forest
#define ERT 0 // extremely randomized trees
#define ANN 0 // artificial neural networks
#define EM 0 // expectation-maximization
#define _NBC_ 0 // normal Bayessian classifier
#define _KNN_ 0 // k nearest neighbors classifier
#define _SVM_ 0 // support vectors machine
#define _DT_ 1 // decision tree
#define _BT_ 0 // ADA Boost
#define _GBT_ 0 // gradient boosted trees
#define _RF_ 0 // random forest
#define _ERT_ 0 // extremely randomized trees
#define _ANN_ 0 // artificial neural networks
#define _EM_ 0 // expectation-maximization
void on_mouse( int event, int x, int y, int /*flags*/, void* )
{
@@ -48,13 +47,13 @@ void on_mouse( int event, int x, int y, int /*flags*/, void* )
}
else if( event == CV_EVENT_RBUTTONUP )
{
#if BT
#if _BT_
if( classColors.size() < 2 )
{
#endif
classColors.push_back( Scalar((uchar)rng(256), (uchar)rng(256), (uchar)rng(256)) );
updateFlag = true;
#if BT
#if _BT_
}
else
cout << "New class can not be added, because CvBoost can only be used for 2-class classification" << endl;
@@ -98,7 +97,7 @@ void prepare_train_data( Mat& samples, Mat& classes )
samples.convertTo( samples, CV_32FC1 );
}
#if NBC
#if _NBC_
void find_decision_boundary_NBC()
{
img.copyTo( imgDst );
@@ -125,7 +124,7 @@ void find_decision_boundary_NBC()
#endif
#if KNN
#if _KNN_
void find_decision_boundary_KNN( int K )
{
img.copyTo( imgDst );
@@ -151,7 +150,7 @@ void find_decision_boundary_KNN( int K )
}
#endif
#if SVM
#if _SVM_
void find_decision_boundary_SVM( CvSVMParams params )
{
img.copyTo( imgDst );
@@ -185,7 +184,7 @@ void find_decision_boundary_SVM( CvSVMParams params )
}
#endif
#if DT
#if _DT_
void find_decision_boundary_DT()
{
img.copyTo( imgDst );
@@ -225,7 +224,7 @@ void find_decision_boundary_DT()
}
#endif
#if BT
#if _BT_
void find_decision_boundary_BT()
{
img.copyTo( imgDst );
@@ -265,7 +264,7 @@ void find_decision_boundary_BT()
#endif
#if GBT
#if _GBT_
void find_decision_boundary_GBT()
{
img.copyTo( imgDst );
@@ -305,7 +304,7 @@ void find_decision_boundary_GBT()
#endif
#if RF
#if _RF_
void find_decision_boundary_RF()
{
img.copyTo( imgDst );
@@ -346,7 +345,7 @@ void find_decision_boundary_RF()
#endif
#if ERT
#if _ERT_
void find_decision_boundary_ERT()
{
img.copyTo( imgDst );
@@ -390,7 +389,7 @@ void find_decision_boundary_ERT()
}
#endif
#if ANN
#if _ANN_
void find_decision_boundary_ANN( const Mat& layer_sizes )
{
img.copyTo( imgDst );
@@ -435,7 +434,7 @@ void find_decision_boundary_ANN( const Mat& layer_sizes )
}
#endif
#if EM
#if _EM_
void find_decision_boundary_EM()
{
img.copyTo( imgDst );
@@ -443,19 +442,12 @@ void find_decision_boundary_EM()
Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses );
CvEM em;
CvEMParams params;
params.covs = NULL;
params.means = NULL;
params.weights = NULL;
params.probs = NULL;
cv::EM em;
cv::EM::Params params;
params.nclusters = classColors.size();
params.cov_mat_type = CvEM::COV_MAT_GENERIC;
params.start_step = CvEM::START_AUTO_STEP;
params.term_crit.max_iter = 10;
params.term_crit.epsilon = 0.1;
params.term_crit.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;
params.covMatType = cv::EM::COV_MAT_GENERIC;
params.startStep = cv::EM::START_AUTO_STEP;
params.termCrit = cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::COUNT, 10, 0.1);
// learn classifier
em.train( trainSamples, Mat(), params, &trainClasses );
@@ -509,12 +501,12 @@ int main()
if( key == 'r' ) // run
{
#if NBC
#if _NBC_
find_decision_boundary_NBC();
cvNamedWindow( "NormalBayesClassifier", WINDOW_AUTOSIZE );
imshow( "NormalBayesClassifier", imgDst );
#endif
#if KNN
#if _KNN_
int K = 3;
find_decision_boundary_KNN( K );
namedWindow( "kNN", WINDOW_AUTOSIZE );
@@ -526,7 +518,7 @@ int main()
imshow( "kNN2", imgDst );
#endif
#if SVM
#if _SVM_
//(1)-(2)separable and not sets
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
@@ -549,37 +541,37 @@ int main()
imshow( "classificationSVM2", imgDst );
#endif
#if DT
#if _DT_
find_decision_boundary_DT();
namedWindow( "DT", WINDOW_AUTOSIZE );
imshow( "DT", imgDst );
#endif
#if BT
#if _BT_
find_decision_boundary_BT();
namedWindow( "BT", WINDOW_AUTOSIZE );
imshow( "BT", imgDst);
#endif
#if GBT
#if _GBT_
find_decision_boundary_GBT();
namedWindow( "GBT", WINDOW_AUTOSIZE );
imshow( "GBT", imgDst);
#endif
#if RF
#if _RF_
find_decision_boundary_RF();
namedWindow( "RF", WINDOW_AUTOSIZE );
imshow( "RF", imgDst);
#endif
#if ERT
#if _ERT_
find_decision_boundary_ERT();
namedWindow( "ERT", WINDOW_AUTOSIZE );
imshow( "ERT", imgDst);
#endif
#if ANN
#if _ANN_
Mat layer_sizes1( 1, 3, CV_32SC1 );
layer_sizes1.at<int>(0) = 2;
layer_sizes1.at<int>(1) = 5;
@@ -589,7 +581,7 @@ int main()
imshow( "ANN", imgDst );
#endif
#if EM
#if _EM_
find_decision_boundary_EM();
namedWindow( "EM", WINDOW_AUTOSIZE );
imshow( "EM", imgDst );

3
samples/cpp/select3dobj.cpp
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@@ -491,7 +491,8 @@ int main(int argc, char** argv)
if( outbarename )
{
cmd[6 + outbarename - outprefix] = '\0';
system(cmd);
int result = system(cmd);
CV_Assert(result == 0);
outbarename++;
}
else