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yet another attempt to refactor features2d; the first commit, features2d does not even compile

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This commit is contained in:
Vadim Pisarevsky
2014-10-13 23:01:45 +04:00
parent 83ef276697
commit 1796a26fc7
13 changed files with 414 additions and 783 deletions
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74
modules/features2d/src/orb.cpp
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@@ -645,38 +645,70 @@ static inline float getScale(int level, int firstLevel, double scaleFactor)
return (float)std::pow(scaleFactor, (double)(level - firstLevel));
}
/** Constructor
* @param detector_params parameters to use
*/
ORB::ORB(int _nfeatures, float _scaleFactor, int _nlevels, int _edgeThreshold,
int _firstLevel, int _WTA_K, int _scoreType, int _patchSize, int _fastThreshold) :
nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(_WTA_K),
scoreType(_scoreType), patchSize(_patchSize), fastThreshold(_fastThreshold)
{}
class ORB_Impl : public ORB
{
public:
explicit ORB_Impl(int _nfeatures, float _scaleFactor, int _nlevels, int _edgeThreshold,
int _firstLevel, int _WTA_K, int _scoreType, int _patchSize, int _fastThreshold) :
nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(_WTA_K),
scoreType(_scoreType), patchSize(_patchSize), fastThreshold(_fastThreshold)
{}
int ORB::descriptorSize() const
// returns the descriptor size in bytes
int descriptorSize() const;
// returns the descriptor type
int descriptorType() const;
// returns the default norm type
int defaultNorm() const;
// Compute the ORB_Impl features and descriptors on an image
void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
// Compute the ORB_Impl features and descriptors on an image
void operator()( InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
OutputArray descriptors, bool useProvidedKeypoints=false ) const;
AlgorithmInfo* info() const;
protected:
void computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
int nfeatures;
double scaleFactor;
int nlevels;
int edgeThreshold;
int firstLevel;
int WTA_K;
int scoreType;
int patchSize;
int fastThreshold;
};
int ORB_Impl::descriptorSize() const
{
return kBytes;
}
int ORB::descriptorType() const
int ORB_Impl::descriptorType() const
{
return CV_8U;
}
int ORB::defaultNorm() const
int ORB_Impl::defaultNorm() const
{
return NORM_HAMMING;
}
/** Compute the ORB features and descriptors on an image
/** Compute the ORB_Impl features and descriptors on an image
* @param img the image to compute the features and descriptors on
* @param mask the mask to apply
* @param keypoints the resulting keypoints
*/
void ORB::operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const
void ORB_Impl::operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const
{
(*this)(image, mask, keypoints, noArray(), false);
}
@@ -716,7 +748,7 @@ static void uploadORBKeypoints(const std::vector<KeyPoint>& src,
}
/** Compute the ORB keypoints on an image
/** Compute the ORB_Impl keypoints on an image
* @param image_pyramid the image pyramid to compute the features and descriptors on
* @param mask_pyramid the masks to apply at every level
* @param keypoints the resulting keypoints, clustered per level
@@ -788,7 +820,7 @@ static void computeKeyPoints(const Mat& imagePyramid,
KeyPointsFilter::runByImageBorder(keypoints, img.size(), edgeThreshold);
// Keep more points than necessary as FAST does not give amazing corners
KeyPointsFilter::retainBest(keypoints, scoreType == ORB::HARRIS_SCORE ? 2 * featuresNum : featuresNum);
KeyPointsFilter::retainBest(keypoints, scoreType == ORB_Impl::HARRIS_SCORE ? 2 * featuresNum : featuresNum);
nkeypoints = (int)keypoints.size();
counters[level] = nkeypoints;
@@ -814,7 +846,7 @@ static void computeKeyPoints(const Mat& imagePyramid,
UMat ukeypoints, uresponses(1, nkeypoints, CV_32F);
// Select best features using the Harris cornerness (better scoring than FAST)
if( scoreType == ORB::HARRIS_SCORE )
if( scoreType == ORB_Impl::HARRIS_SCORE )
{
if( useOCL )
{
@@ -886,7 +918,7 @@ static void computeKeyPoints(const Mat& imagePyramid,
}
/** Compute the ORB features and descriptors on an image
/** Compute the ORB_Impl features and descriptors on an image
* @param img the image to compute the features and descriptors on
* @param mask the mask to apply
* @param keypoints the resulting keypoints
@@ -894,7 +926,7 @@ static void computeKeyPoints(const Mat& imagePyramid,
* @param do_keypoints if true, the keypoints are computed, otherwise used as an input
* @param do_descriptors if true, also computes the descriptors
*/
void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>& keypoints,
void ORB_Impl::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>& keypoints,
OutputArray _descriptors, bool useProvidedKeypoints ) const
{
CV_Assert(patchSize >= 2);
@@ -1121,12 +1153,12 @@ void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>
}
}
void ORB::detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
void ORB_Impl::detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
{
(*this)(image.getMat(), mask.getMat(), keypoints, noArray(), false);
}
void ORB::computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
void ORB_Impl::computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
{
(*this)(image, Mat(), keypoints, descriptors, true);
}