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[move sift.cpp] Multithreading findScaleSpaceExtremaComputer. Sort the keypoints afterwards to make the output stable

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original commit: 6be2945abb
This commit is contained in:
Woody Chow 2017-05-31 10:02:09 +09:00
parent 4b64955a12
commit b6d636214e
1 changed files with 137 additions and 69 deletions
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106
modules/features2d/src/sift.cpp
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@ -534,31 +534,53 @@ static bool adjustLocalExtrema( const std::vector<Mat>& dog_pyr, KeyPoint& kpt,
}
//
// Detects features at extrema in DoG scale space. Bad features are discarded
// based on contrast and ratio of principal curvatures.
void SIFT_Impl::findScaleSpaceExtrema( const std::vector<Mat>& gauss_pyr, const std::vector<Mat>& dog_pyr,
std::vector<KeyPoint>& keypoints ) const
class findScaleSpaceExtremaComputer : public ParallelLoopBody
{
int nOctaves = (int)gauss_pyr.size()/(nOctaveLayers + 3);
int threshold = cvFloor(0.5 * contrastThreshold / nOctaveLayers * 255 * SIFT_FIXPT_SCALE);
const int n = SIFT_ORI_HIST_BINS;
float hist[n];
KeyPoint kpt;
public:
findScaleSpaceExtremaComputer(
int _o,
int _i,
int _threshold,
int _idx,
int _step,
int _cols,
int _nOctaveLayers,
double _contrastThreshold,
double _edgeThreshold,
double _sigma,
const std::vector<Mat>& _gauss_pyr,
const std::vector<Mat>& _dog_pyr,
std::vector<KeyPoint>& _keypoints,
Mutex &_mutex)
keypoints.clear();
for( int o = 0; o < nOctaves; o++ )
for( int i = 1; i <= nOctaveLayers; i++ )
: o(_o),
i(_i),
threshold(_threshold),
idx(_idx),
step(_step),
cols(_cols),
nOctaveLayers(_nOctaveLayers),
contrastThreshold(_contrastThreshold),
edgeThreshold(_edgeThreshold),
sigma(_sigma),
gauss_pyr(_gauss_pyr),
dog_pyr(_dog_pyr),
keypoints(_keypoints),
mutex(_mutex) { }
void operator()( const cv::Range& range ) const
{
int idx = o*(nOctaveLayers+2)+i;
const int begin = range.start;
const int end = range.end;
static const int n = SIFT_ORI_HIST_BINS;
float hist[n];
const Mat& img = dog_pyr[idx];
const Mat& prev = dog_pyr[idx-1];
const Mat& next = dog_pyr[idx+1];
int step = (int)img.step1();
int rows = img.rows, cols = img.cols;
for( int r = SIFT_IMG_BORDER; r < rows-SIFT_IMG_BORDER; r++)
KeyPoint kpt;
for( int r = begin; r < end; r++)
{
const sift_wt* currptr = img.ptr<sift_wt>(r);
const sift_wt* prevptr = prev.ptr<sift_wt>(r);
@ -613,6 +635,8 @@ void SIFT_Impl::findScaleSpaceExtrema( const std::vector<Mat>& gauss_pyr, const
kpt.angle = 360.f - (float)((360.f/n) * bin);
if(std::abs(kpt.angle - 360.f) < FLT_EPSILON)
kpt.angle = 0.f;
{
AutoLock autoLock(mutex);
keypoints.push_back(kpt);
}
}
@ -620,6 +644,50 @@ void SIFT_Impl::findScaleSpaceExtrema( const std::vector<Mat>& gauss_pyr, const
}
}
}
}
private:
int o, i;
int threshold;
int idx, step, cols;
int nOctaveLayers;
double contrastThreshold;
double edgeThreshold;
double sigma;
const std::vector<Mat>& gauss_pyr;
const std::vector<Mat>& dog_pyr;
std::vector<KeyPoint>& keypoints;
Mutex &mutex;
};
//
// Detects features at extrema in DoG scale space. Bad features are discarded
// based on contrast and ratio of principal curvatures.
void SIFT_Impl::findScaleSpaceExtrema( const std::vector<Mat>& gauss_pyr, const std::vector<Mat>& dog_pyr,
std::vector<KeyPoint>& keypoints ) const
{
const int nOctaves = (int)gauss_pyr.size()/(nOctaveLayers + 3);
const int threshold = cvFloor(0.5 * contrastThreshold / nOctaveLayers * 255 * SIFT_FIXPT_SCALE);
keypoints.clear();
Mutex mutex;
for( int o = 0; o < nOctaves; o++ )
for( int i = 1; i <= nOctaveLayers; i++ )
{
const int idx = o*(nOctaveLayers+2)+i;
const Mat& img = dog_pyr[idx];
const int step = (int)img.step1();
const int rows = img.rows, cols = img.cols;
parallel_for_(Range(SIFT_IMG_BORDER, rows-SIFT_IMG_BORDER),
findScaleSpaceExtremaComputer(
o, i, threshold, idx, step, cols,
nOctaveLayers,
contrastThreshold,
edgeThreshold,
sigma,
gauss_pyr, dog_pyr, keypoints, mutex));
}
}
@ -1046,7 +1114,7 @@ void SIFT_Impl::detectAndCompute(InputArray _image, InputArray _mask,
{
//t = (double)getTickCount();
findScaleSpaceExtrema(gpyr, dogpyr, keypoints);
KeyPointsFilter::removeDuplicated( keypoints );
KeyPointsFilter::removeDuplicatedSorted( keypoints );
if( nfeatures > 0 )
KeyPointsFilter::retainBest(keypoints, nfeatures);