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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin
2019-06-26 20:17:19 +00:00
parent b58b2c5ae8 24790e4061
commit b95e93c20a
43 changed files with 895 additions and 355 deletions
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modules/imgproc/src/filter.dispatch.cpp
+18 -6
View File
@@ -1112,6 +1112,7 @@ static bool ippFilter2D(int stype, int dtype, int kernel_type,
static bool dftFilter2D(int stype, int dtype, int kernel_type,
uchar * src_data, size_t src_step,
uchar * dst_data, size_t dst_step,
int width, int height,
int full_width, int full_height,
int offset_x, int offset_y,
uchar * kernel_data, size_t kernel_step,
@@ -1125,13 +1126,23 @@ static bool dftFilter2D(int stype, int dtype, int kernel_type,
int dft_filter_size = checkHardwareSupport(CV_CPU_SSE3) && ((sdepth == CV_8U && (ddepth == CV_8U || ddepth == CV_16S)) || (sdepth == CV_32F && ddepth == CV_32F)) ? 130 : 50;
if (kernel_width * kernel_height < dft_filter_size)
return false;
// detect roi case
if( (offset_x != 0) || (offset_y != 0) )
{
return false;
}
if( (width != full_width) || (height != full_height) )
{
return false;
}
}
Point anchor = Point(anchor_x, anchor_y);
Mat kernel = Mat(Size(kernel_width, kernel_height), kernel_type, kernel_data, kernel_step);
Mat src(Size(full_width-offset_x, full_height-offset_y), stype, src_data, src_step);
Mat dst(Size(full_width, full_height), dtype, dst_data, dst_step);
Mat src(Size(width, height), stype, src_data, src_step);
Mat dst(Size(width, height), dtype, dst_data, dst_step);
Mat temp;
int src_channels = CV_MAT_CN(stype);
int dst_channels = CV_MAT_CN(dtype);
@@ -1144,10 +1155,10 @@ static bool dftFilter2D(int stype, int dtype, int kernel_type,
// we just use that.
int corrDepth = ddepth;
if ((ddepth == CV_32F || ddepth == CV_64F) && src_data != dst_data) {
temp = Mat(Size(full_width, full_height), dtype, dst_data, dst_step);
temp = Mat(Size(width, height), dtype, dst_data, dst_step);
} else {
corrDepth = ddepth == CV_64F ? CV_64F : CV_32F;
temp.create(Size(full_width, full_height), CV_MAKETYPE(corrDepth, dst_channels));
temp.create(Size(width, height), CV_MAKETYPE(corrDepth, dst_channels));
}
crossCorr(src, kernel, temp, src.size(),
CV_MAKETYPE(corrDepth, src_channels),
@@ -1158,9 +1169,9 @@ static bool dftFilter2D(int stype, int dtype, int kernel_type,
}
} else {
if (src_data != dst_data)
temp = Mat(Size(full_width, full_height), dtype, dst_data, dst_step);
temp = Mat(Size(width, height), dtype, dst_data, dst_step);
else
temp.create(Size(full_width, full_height), dtype);
temp.create(Size(width, height), dtype);
crossCorr(src, kernel, temp, src.size(),
CV_MAKETYPE(ddepth, src_channels),
anchor, delta, borderType);
@@ -1293,6 +1304,7 @@ void filter2D(int stype, int dtype, int kernel_type,
res = dftFilter2D(stype, dtype, kernel_type,
src_data, src_step,
dst_data, dst_step,
width, height,
full_width, full_height,
offset_x, offset_y,
kernel_data, kernel_step,
modules/imgproc/src/grabcut.cpp
+18 -6
View File
@@ -46,6 +46,8 @@
using namespace cv;
using namespace detail;
namespace {
/*
This is implementation of image segmentation algorithm GrabCut described in
"GrabCut - Interactive Foreground Extraction using Iterated Graph Cuts".
@@ -229,6 +231,8 @@ void GMM::calcInverseCovAndDeterm(int ci, const double singularFix)
}
}
} // namespace
/*
Calculate beta - parameter of GrabCut algorithm.
beta = 1/(2*avg(sqr(||color[i] - color[j]||)))
@@ -380,12 +384,20 @@ static void initGMMs( const Mat& img, const Mat& mask, GMM& bgdGMM, GMM& fgdGMM
}
}
CV_Assert( !bgdSamples.empty() && !fgdSamples.empty() );
Mat _bgdSamples( (int)bgdSamples.size(), 3, CV_32FC1, &bgdSamples[0][0] );
kmeans( _bgdSamples, GMM::componentsCount, bgdLabels,
TermCriteria( CV_TERMCRIT_ITER, kMeansItCount, 0.0), 0, kMeansType );
Mat _fgdSamples( (int)fgdSamples.size(), 3, CV_32FC1, &fgdSamples[0][0] );
kmeans( _fgdSamples, GMM::componentsCount, fgdLabels,
TermCriteria( CV_TERMCRIT_ITER, kMeansItCount, 0.0), 0, kMeansType );
{
Mat _bgdSamples( (int)bgdSamples.size(), 3, CV_32FC1, &bgdSamples[0][0] );
int num_clusters = GMM::componentsCount;
num_clusters = std::min(num_clusters, (int)bgdSamples.size());
kmeans( _bgdSamples, num_clusters, bgdLabels,
TermCriteria( CV_TERMCRIT_ITER, kMeansItCount, 0.0), 0, kMeansType );
}
{
Mat _fgdSamples( (int)fgdSamples.size(), 3, CV_32FC1, &fgdSamples[0][0] );
int num_clusters = GMM::componentsCount;
num_clusters = std::min(num_clusters, (int)fgdSamples.size());
kmeans( _fgdSamples, num_clusters, fgdLabels,
TermCriteria( CV_TERMCRIT_ITER, kMeansItCount, 0.0), 0, kMeansType );
}
bgdGMM.initLearning();
for( int i = 0; i < (int)bgdSamples.size(); i++ )