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Merge pull request #2176 from SpecLad:merge-2.4

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This commit is contained in:
Roman Donchenko 2014-01-20 18:04:55 +04:00 committed by OpenCV Buildbot
commit 9dc93ce328
10 changed files with 116 additions and 15 deletions
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2
3rdparty/include/opencl/1.2/CL/cl.hpp vendored
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@ -210,7 +210,7 @@
#include <string>
#endif
#if defined(linux) || defined(__APPLE__) || defined(__MACOSX)
#if defined(__linux__) || defined(__APPLE__) || defined(__MACOSX)
#include <alloca.h>
#include <emmintrin.h>

6
modules/core/src/copy.cpp
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@ -247,10 +247,7 @@ void Mat::copyTo( OutputArray _dst ) const
const uchar* sptr = data;
uchar* dptr = dst.data;
// to handle the copying 1xn matrix => nx1 std vector.
Size sz = size() == dst.size() ?
getContinuousSize(*this, dst) :
getContinuousSize(*this);
Size sz = getContinuousSize(*this, dst);
size_t len = sz.width*elemSize();
for( ; sz.height--; sptr += step, dptr += dst.step )
@ -301,6 +298,7 @@ void Mat::copyTo( OutputArray _dst, InputArray _mask ) const
if( dims <= 2 )
{
CV_Assert( size() == mask.size() );
Size sz = getContinuousSize(*this, dst, mask, mcn);
copymask(data, step, mask.data, mask.step, dst.data, dst.step, sz, &esz);
return;

2
modules/core/src/opencl/runtime/opencl_clamdblas.cpp
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@ -67,7 +67,7 @@
#define CV_CL_GET_PROC_ADDRESS(name) WinGetProcAddress(name)
#endif // _WIN32
#if defined(linux)
#if defined(__linux__)
#include <dlfcn.h>
#include <stdio.h>

2
modules/core/src/opencl/runtime/opencl_clamdfft.cpp
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@ -67,7 +67,7 @@
#define CV_CL_GET_PROC_ADDRESS(name) WinGetProcAddress(name)
#endif // _WIN32
#if defined(linux)
#if defined(__linux__)
#include <dlfcn.h>
#include <stdio.h>

2
modules/core/src/opencl/runtime/opencl_core.cpp
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@ -125,7 +125,7 @@ static void* WinGetProcAddress(const char* name)
#define CV_CL_GET_PROC_ADDRESS(name) WinGetProcAddress(name)
#endif // _WIN32
#if defined(linux)
#if defined(__linux__)
#include <dlfcn.h>
#include <stdio.h>

21
modules/core/test/test_mat.cpp
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@ -1139,3 +1139,24 @@ TEST(Core_Mat, reshape_1942)
);
ASSERT_EQ(1, cn);
}
TEST(Core_Mat, copyNx1ToVector)
{
cv::Mat_<uchar> src(5, 1);
cv::Mat_<uchar> ref_dst8;
cv::Mat_<ushort> ref_dst16;
std::vector<uchar> dst8;
std::vector<ushort> dst16;
src << 1, 2, 3, 4, 5;
src.copyTo(ref_dst8);
src.copyTo(dst8);
ASSERT_PRED_FORMAT2(cvtest::MatComparator(0, 0), ref_dst8, cv::Mat_<uchar>(dst8));
src.convertTo(ref_dst16, CV_16U);
src.convertTo(dst16, CV_16U);
ASSERT_PRED_FORMAT2(cvtest::MatComparator(0, 0), ref_dst16, cv::Mat_<ushort>(dst16));
}

4
modules/imgproc/doc/miscellaneous_transformations.rst
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@ -110,6 +110,8 @@ But in case of a non-linear transformation, an input RGB image should be normali
If you use ``cvtColor`` with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back.
If conversion adds the alpha channel, its value will set to the maximum of corresponding channel range: 255 for ``CV_8U``, 65535 for ``CV_16U``, 1 for ``CV_32F``.
The function can do the following transformations:
*
@ -124,7 +126,7 @@ The function can do the following transformations:
.. math::
\text{Gray to RGB[A]:} \quad R \leftarrow Y, G \leftarrow Y, B \leftarrow Y, A \leftarrow 0
\text{Gray to RGB[A]:} \quad R \leftarrow Y, G \leftarrow Y, B \leftarrow Y, A \leftarrow \max (ChannelRange)
The conversion from a RGB image to gray is done with:

18
modules/ml/src/svm.cpp
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@ -2347,14 +2347,24 @@ void CvSVM::write_params( CvFileStorage* fs ) const
}
static bool isSvmModelApplicable(int sv_total, int var_all, int var_count, int class_count)
{
return (sv_total > 0 && var_count > 0 && var_count <= var_all && class_count >= 0);
}
void CvSVM::write( CvFileStorage* fs, const char* name ) const
{
CV_FUNCNAME( "CvSVM::write" );
__BEGIN__;
int i, var_count = get_var_count(), df_count, class_count;
int i, var_count = get_var_count(), df_count;
int class_count = class_labels ? class_labels->cols :
params.svm_type == CvSVM::ONE_CLASS ? 1 : 0;
const CvSVMDecisionFunc* df = decision_func;
if( !isSvmModelApplicable(sv_total, var_all, var_count, class_count) )
CV_ERROR( CV_StsParseError, "SVM model data is invalid, check sv_count, var_* and class_count tags" );
cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_SVM );
@ -2363,9 +2373,6 @@ void CvSVM::write( CvFileStorage* fs, const char* name ) const
cvWriteInt( fs, "var_all", var_all );
cvWriteInt( fs, "var_count", var_count );
class_count = class_labels ? class_labels->cols :
params.svm_type == CvSVM::ONE_CLASS ? 1 : 0;
if( class_count )
{
cvWriteInt( fs, "class_count", class_count );
@ -2503,7 +2510,6 @@ void CvSVM::read_params( CvFileStorage* fs, CvFileNode* svm_node )
__END__;
}
void CvSVM::read( CvFileStorage* fs, CvFileNode* svm_node )
{
const double not_found_dbl = DBL_MAX;
@ -2532,7 +2538,7 @@ void CvSVM::read( CvFileStorage* fs, CvFileNode* svm_node )
var_count = cvReadIntByName( fs, svm_node, "var_count", var_all );
class_count = cvReadIntByName( fs, svm_node, "class_count", 0 );
if( sv_total <= 0 || var_all <= 0 || var_count <= 0 || var_count > var_all || class_count < 0 )
if( !isSvmModelApplicable(sv_total, var_all, var_count, class_count) )
CV_ERROR( CV_StsParseError, "SVM model data is invalid, check sv_count, var_* and class_count tags" );
CV_CALL( class_labels = (CvMat*)cvReadByName( fs, svm_node, "class_labels" ));

8
modules/ml/test/test_save_load.cpp
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@ -155,6 +155,14 @@ TEST(ML_RTrees, save_load) { CV_SLMLTest test( CV_RTREES ); test.safe_run(); }
TEST(ML_ERTrees, save_load) { CV_SLMLTest test( CV_ERTREES ); test.safe_run(); }
TEST(ML_SVM, throw_exception_when_save_untrained_model)
{
SVM svm;
string filename = tempfile("svm.xml");
ASSERT_THROW(svm.save(filename.c_str()), Exception);
remove(filename.c_str());
}
TEST(DISABLED_ML_SVM, linear_save_load)
{
CvSVM svm1, svm2, svm3;

66
modules/ocl/perf/perf_haar.cpp
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@ -85,3 +85,69 @@ PERF_TEST(HaarFixture, Haar)
else
OCL_PERF_ELSE
}
using namespace std;
using namespace cv;
using namespace perf;
using std::tr1::make_tuple;
using std::tr1::get;
typedef std::tr1::tuple<std::string, std::string, int> OCL_Cascade_Image_MinSize_t;
typedef perf::TestBaseWithParam<OCL_Cascade_Image_MinSize_t> OCL_Cascade_Image_MinSize;
PERF_TEST_P( OCL_Cascade_Image_MinSize, CascadeClassifier,
testing::Combine(
testing::Values( string("cv/cascadeandhog/cascades/haarcascade_frontalface_alt.xml") ),
testing::Values( string("cv/shared/lena.png"),
string("cv/cascadeandhog/images/bttf301.png"),
string("cv/cascadeandhog/images/class57.png") ),
testing::Values(30, 64, 90) ) )
{
const string cascasePath = get<0>(GetParam());
const string imagePath = get<1>(GetParam());
const int min_size = get<2>(GetParam());
Size minSize(min_size, min_size);
vector<Rect> faces;
Mat img = imread(getDataPath(imagePath), IMREAD_GRAYSCALE);
ASSERT_TRUE(!img.empty()) << "Can't load source image: " << getDataPath(imagePath);
equalizeHist(img, img);
declare.in(img);
if (RUN_PLAIN_IMPL)
{
CascadeClassifier cc;
ASSERT_TRUE(cc.load(getDataPath(cascasePath))) << "Can't load cascade file: " << getDataPath(cascasePath);
while (next())
{
faces.clear();
startTimer();
cc.detectMultiScale(img, faces, 1.1, 3, 0, minSize);
stopTimer();
}
}
else if (RUN_OCL_IMPL)
{
ocl::oclMat uimg(img);
ocl::OclCascadeClassifier cc;
ASSERT_TRUE(cc.load(getDataPath(cascasePath))) << "Can't load cascade file: " << getDataPath(cascasePath);
while (next())
{
faces.clear();
ocl::finish();
startTimer();
cc.detectMultiScale(uimg, faces, 1.1, 3, 0, minSize);
stopTimer();
}
}
else
OCL_PERF_ELSE
//sort(faces.begin(), faces.end(), comparators::RectLess());
SANITY_CHECK_NOTHING();//(faces, min_size/5);
// using SANITY_CHECK_NOTHING() since OCL and PLAIN version may find different faces number
}