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Fix issue #6473

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Enable "useProvidedKeypoints" flag on cuda::ORB's detectAndCompute function in order to able to describe provided keypoints.

* additional author : Ender Tunç <endertunc@yahoo.com>

resolves #6473

 * Enable "useProvidedKeypoints" flag on cuda::ORB's detectAndCompute function in order to able to describe provided keypoints.

   Update for using 'const' reference to avoid array copy on each iteration.

resolves #6473

	Update for 'const' reference to avoid array copy on each iteration.
This commit is contained in:
Özgür Can 2016-05-19 19:59:41 +03:00
parent c3d1f94ee6
commit 4951543441
1 changed files with 74 additions and 12 deletions
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86
modules/cudafeatures2d/src/orb.cpp
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@ -570,30 +570,92 @@ namespace
blurFilter_ = cuda::createGaussianFilter(CV_8UC1, -1, Size(7, 7), 2, 2, BORDER_REFLECT_101);
}
static float getScale(float scaleFactor, int firstLevel, int level)
{
return pow(scaleFactor, level - firstLevel);
}
void ORB_Impl::detectAndCompute(InputArray _image, InputArray _mask, std::vector<KeyPoint>& keypoints, OutputArray _descriptors, bool useProvidedKeypoints)
{
CV_Assert( useProvidedKeypoints == false );
using namespace cv::cuda::device::orb;
if (useProvidedKeypoints)
{
d_keypoints_.release();
keyPointsPyr_.clear();
detectAndComputeAsync(_image, _mask, d_keypoints_, _descriptors, false, Stream::Null());
convert(d_keypoints_, keypoints);
int j, level, nkeypoints = (int)keypoints.size();
nLevels_ = 0;
for( j = 0; j < nkeypoints; j++ )
{
level = keypoints[j].octave;
CV_Assert(level >= 0);
nLevels_ = std::max(nLevels_, level);
}
nLevels_ ++;
std::vector<std::vector<KeyPoint> > oKeypoints(nLevels_);
for( j = 0; j < nkeypoints; j++ )
{
level = keypoints[j].octave;
oKeypoints[level].push_back(keypoints[j]);
}
if (!keypoints.empty())
{
keyPointsPyr_.resize(nLevels_);
keyPointsCount_.resize(nLevels_);
int t;
for(t = 0; t < nLevels_; t++) {
const std::vector<KeyPoint>& ks = oKeypoints[t];
if (!ks.empty()){
Mat h_keypoints(ROWS_COUNT, static_cast<int>(ks.size()), CV_32FC1);
float sf = getScale(scaleFactor_, firstLevel_, t);
float locScale = t != firstLevel_ ? sf : 1.0f;
float scale = 1.f/locScale;
short2* x_loc_row = h_keypoints.ptr<short2>(0);
float* x_kp_hessian = h_keypoints.ptr<float>(1);
float* x_kp_dir = h_keypoints.ptr<float>(2);
for (size_t i = 0, size = ks.size(); i < size; ++i)
{
const KeyPoint& kp = ks[i];
x_kp_hessian[i] = kp.response;
x_loc_row[i].x = cvRound(kp.pt.x * scale);
x_loc_row[i].y = cvRound(kp.pt.y * scale);
x_kp_dir[i] = kp.angle;
}
keyPointsPyr_[t].upload(h_keypoints.rowRange(0,3));
keyPointsCount_[t] = h_keypoints.cols;
}
}
}
}
detectAndComputeAsync(_image, _mask, d_keypoints_, _descriptors, useProvidedKeypoints, Stream::Null());
if (!useProvidedKeypoints) {
convert(d_keypoints_, keypoints);
}
}
void ORB_Impl::detectAndComputeAsync(InputArray _image, InputArray _mask, OutputArray _keypoints, OutputArray _descriptors, bool useProvidedKeypoints, Stream& stream)
{
CV_Assert( useProvidedKeypoints == false );
buildScalePyramids(_image, _mask, stream);
computeKeyPointsPyramid(stream);
if (!useProvidedKeypoints)
{
computeKeyPointsPyramid(stream);
}
if (_descriptors.needed())
{
computeDescriptors(_descriptors, stream);
}
mergeKeyPoints(_keypoints, stream);
}
static float getScale(float scaleFactor, int firstLevel, int level)
{
return pow(scaleFactor, level - firstLevel);
if (!useProvidedKeypoints)
{
mergeKeyPoints(_keypoints, stream);
}
}
void ORB_Impl::buildScalePyramids(InputArray _image, InputArray _mask, Stream& stream)