opencv/modules/features2d/src/feature2d.cpp
Jiri Horner 1ba7c728a6 Merge pull request #12827 from hrnr:stitching_4
[evolution] Stitching for OpenCV 4.0

* stitching: wrap Stitcher::create for bindings

* provide method for consistent stitcher usage across languages

* samples: add python stitching sample

* port cpp stitching sample to python

* stitching: consolidate Stitcher create methods

* remove Stitcher::createDefault, it returns Stitcher, not Ptr<Stitcher> -> inconsistent API
* deprecate cv::createStitcher and cv::createStitcherScans in favor of Stitcher::create

* stitching: avoid anonymous enum in Stitcher

* ORIG_RESOL should be double
* add documentatiton

* stitching: improve documentation in Stitcher

* stitching: expose estimator in Stitcher

* remove ABI hack

* stitching: drop try_use_gpu flag

* OCL will be used automatically through T-API in OCL-enable paths
* CUDA won't be used unless user sets CUDA-enabled classes manually

* stitching: drop FeaturesFinder

* use Feature2D instead of FeaturesFinder
* interoperability with features2d module
* detach from dependency on xfeatures2d

* features2d: fix compute and detect to work with UMat vectors

* correctly pass UMats as UMats to allow OCL paths
* support vector of UMats as output arg

* stitching: use nearest interpolation for resizing masks

* fix warnings
2018-11-10 19:53:48 +03:00

225 lines
6.2 KiB
C++

/*M///////////////////////////////////////////////////////////////////////////////////////
//
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#include "precomp.hpp"
namespace cv
{
using std::vector;
Feature2D::~Feature2D() {}
/*
* Detect keypoints in an image.
* image The image.
* keypoints The detected keypoints.
* mask Mask specifying where to look for keypoints (optional). Must be a char
* matrix with non-zero values in the region of interest.
*/
void Feature2D::detect( InputArray image,
std::vector<KeyPoint>& keypoints,
InputArray mask )
{
CV_INSTRUMENT_REGION();
if( image.empty() )
{
keypoints.clear();
return;
}
detectAndCompute(image, mask, keypoints, noArray(), false);
}
void Feature2D::detect( InputArrayOfArrays images,
std::vector<std::vector<KeyPoint> >& keypoints,
InputArrayOfArrays masks )
{
CV_INSTRUMENT_REGION();
int nimages = (int)images.total();
if (!masks.empty())
{
CV_Assert(masks.total() == (size_t)nimages);
}
keypoints.resize(nimages);
if (images.isMatVector())
{
for (int i = 0; i < nimages; i++)
{
detect(images.getMat(i), keypoints[i], masks.empty() ? noArray() : masks.getMat(i));
}
}
else
{
// assume UMats
for (int i = 0; i < nimages; i++)
{
detect(images.getUMat(i), keypoints[i], masks.empty() ? noArray() : masks.getUMat(i));
}
}
}
/*
* Compute the descriptors for a set of keypoints in an image.
* image The image.
* keypoints The input keypoints. Keypoints for which a descriptor cannot be computed are removed.
* descriptors Copmputed descriptors. Row i is the descriptor for keypoint i.
*/
void Feature2D::compute( InputArray image,
std::vector<KeyPoint>& keypoints,
OutputArray descriptors )
{
CV_INSTRUMENT_REGION();
if( image.empty() )
{
descriptors.release();
return;
}
detectAndCompute(image, noArray(), keypoints, descriptors, true);
}
void Feature2D::compute( InputArrayOfArrays images,
std::vector<std::vector<KeyPoint> >& keypoints,
OutputArrayOfArrays descriptors )
{
CV_INSTRUMENT_REGION();
if( !descriptors.needed() )
return;
int nimages = (int)images.total();
CV_Assert( keypoints.size() == (size_t)nimages );
// resize descriptors to appropriate size and compute
if (descriptors.isMatVector())
{
vector<Mat>& vec = *(vector<Mat>*)descriptors.getObj();
vec.resize(nimages);
for (int i = 0; i < nimages; i++)
{
compute(images.getMat(i), keypoints[i], vec[i]);
}
}
else if (descriptors.isUMatVector())
{
vector<UMat>& vec = *(vector<UMat>*)descriptors.getObj();
vec.resize(nimages);
for (int i = 0; i < nimages; i++)
{
compute(images.getUMat(i), keypoints[i], vec[i]);
}
}
else
{
CV_Error(Error::StsBadArg, "descriptors must be vector<Mat> or vector<UMat>");
}
}
/* Detects keypoints and computes the descriptors */
void Feature2D::detectAndCompute( InputArray, InputArray,
std::vector<KeyPoint>&,
OutputArray,
bool )
{
CV_INSTRUMENT_REGION();
CV_Error(Error::StsNotImplemented, "");
}
void Feature2D::write( const String& fileName ) const
{
FileStorage fs(fileName, FileStorage::WRITE);
write(fs);
}
void Feature2D::read( const String& fileName )
{
FileStorage fs(fileName, FileStorage::READ);
read(fs.root());
}
void Feature2D::write( FileStorage&) const
{
}
void Feature2D::read( const FileNode&)
{
}
int Feature2D::descriptorSize() const
{
return 0;
}
int Feature2D::descriptorType() const
{
return CV_32F;
}
int Feature2D::defaultNorm() const
{
int tp = descriptorType();
return tp == CV_8U ? NORM_HAMMING : NORM_L2;
}
// Return true if detector object is empty
bool Feature2D::empty() const
{
return true;
}
String Feature2D::getDefaultName() const
{
return "Feature2D";
}
}