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Merge pull request #3339 from vpisarev:refactor_features2d_take4

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Vadim Pisarevsky 2014-10-17 14:07:06 +00:00
commit 22ff1e8826
57 changed files with 2822 additions and 3417 deletions
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17
doc/tutorials/features2d/detection_of_planar_objects/detection_of_planar_objects.rst
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@ -16,25 +16,16 @@ The goal of this tutorial is to learn how to use *features2d* and *calib3d* modu
Mat img2 = imread(argv[2], IMREAD_GRAYSCALE);
#.
Detect keypoints in both images. ::
Detect keypoints in both images and compute descriptors for each of the keypoints. ::
// detecting keypoints
FastFeatureDetector detector(15);
Ptr<Feature2D> surf = SURF::create();
vector<KeyPoint> keypoints1;
detector.detect(img1, keypoints1);
Mat descriptors1;
surf->detectAndCompute(img1, Mat(), keypoints1, descriptors1);
... // do the same for the second image
#.
Compute descriptors for each of the keypoints. ::
// computing descriptors
SurfDescriptorExtractor extractor;
Mat descriptors1;
extractor.compute(img1, keypoints1, descriptors1);
... // process keypoints from the second image as well
#.
Now, find the closest matches between descriptors from the first image to the second: ::

12
doc/user_guide/ug_features2d.rst
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@ -65,18 +65,18 @@ Let us break the code down. ::
We load two images and check if they are loaded correctly.::
// detecting keypoints
FastFeatureDetector detector(15);
Ptr<FeatureDetector> detector = FastFeatureDetector::create(15);
vector<KeyPoint> keypoints1, keypoints2;
detector.detect(img1, keypoints1);
detector.detect(img2, keypoints2);
detector->detect(img1, keypoints1);
detector->detect(img2, keypoints2);
First, we create an instance of a keypoint detector. All detectors inherit the abstract ``FeatureDetector`` interface, but the constructors are algorithm-dependent. The first argument to each detector usually controls the balance between the amount of keypoints and their stability. The range of values is different for different detectors (For instance, *FAST* threshold has the meaning of pixel intensity difference and usually varies in the region *[0,40]*. *SURF* threshold is applied to a Hessian of an image and usually takes on values larger than *100*), so use defaults in case of doubt. ::
// computing descriptors
SurfDescriptorExtractor extractor;
Ptr<SURF> extractor = SURF::create();
Mat descriptors1, descriptors2;
extractor.compute(img1, keypoints1, descriptors1);
extractor.compute(img2, keypoints2, descriptors2);
extractor->compute(img1, keypoints1, descriptors1);
extractor->compute(img2, keypoints2, descriptors2);
We create an instance of descriptor extractor. The most of OpenCV descriptors inherit ``DescriptorExtractor`` abstract interface. Then we compute descriptors for each of the keypoints. The output ``Mat`` of the ``DescriptorExtractor::compute`` method contains a descriptor in a row *i* for each *i*-th keypoint. Note that the method can modify the keypoints vector by removing the keypoints such that a descriptor for them is not defined (usually these are the keypoints near image border). The method makes sure that the ouptut keypoints and descriptors are consistent with each other (so that the number of keypoints is equal to the descriptors row count). ::

2
modules/calib3d/include/opencv2/calib3d.hpp
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@ -185,7 +185,7 @@ CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSiz
//! finds circles' grid pattern of the specified size in the image
CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize,
OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID,
const Ptr<FeatureDetector> &blobDetector = makePtr<SimpleBlobDetector>());
const Ptr<FeatureDetector> &blobDetector = SimpleBlobDetector::create());
//! finds intrinsic and extrinsic camera parameters from several fews of a known calibration pattern.
CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints,

5
modules/calib3d/src/solvepnp.cpp
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@ -115,7 +115,10 @@ bool cv::solvePnP( InputArray _opoints, InputArray _ipoints,
cv::Mat R, rvec = _rvec.getMat(), tvec = _tvec.getMat();
double f = PnP.compute_pose(R, tvec);
cv::Rodrigues(R, rvec);
cameraMatrix.at<double>(0,0) = cameraMatrix.at<double>(1,1) = f;
if(cameraMatrix.type() == CV_32F)
cameraMatrix.at<float>(0,0) = cameraMatrix.at<float>(1,1) = (float)f;
else
cameraMatrix.at<double>(0,0) = cameraMatrix.at<double>(1,1) = f;
return true;
}
else

10
modules/calib3d/src/upnp.h
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@ -73,12 +73,12 @@ private:
{
for(int i = 0; i < number_of_correspondences; i++)
{
pws[3 * i ] = opoints.at<OpointType>(0,i).x;
pws[3 * i + 1] = opoints.at<OpointType>(0,i).y;
pws[3 * i + 2] = opoints.at<OpointType>(0,i).z;
pws[3 * i ] = opoints.at<OpointType>(i).x;
pws[3 * i + 1] = opoints.at<OpointType>(i).y;
pws[3 * i + 2] = opoints.at<OpointType>(i).z;
us[2 * i ] = ipoints.at<IpointType>(0,i).x;
us[2 * i + 1] = ipoints.at<IpointType>(0,i).y;
us[2 * i ] = ipoints.at<IpointType>(i).x;
us[2 * i + 1] = ipoints.at<IpointType>(i).y;
}
}

3
modules/core/include/opencv2/core.hpp
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@ -874,6 +874,9 @@ public:
virtual ~Algorithm();
String name() const;
virtual void set(int, double);
virtual double get(int) const;
template<typename _Tp> typename ParamType<_Tp>::member_type get(const String& name) const;
template<typename _Tp> typename ParamType<_Tp>::member_type get(const char* name) const;

3
modules/core/src/algorithm.cpp
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@ -179,6 +179,9 @@ String Algorithm::name() const
return info()->name();
}
void Algorithm::set(int, double) {}
double Algorithm::get(int) const { return 0.; }
void Algorithm::set(const String& parameter, int value)
{
info()->set(this, parameter.c_str(), ParamType<int>::type, &value);

106
modules/features2d/doc/common_interfaces_of_feature_detectors.rst
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@ -59,100 +59,60 @@ Detects keypoints in an image (first variant) or image set (second variant).
:param masks: Masks for each input image specifying where to look for keypoints (optional). ``masks[i]`` is a mask for ``images[i]``.
FeatureDetector::create
-----------------------
Creates a feature detector by its name.
.. ocv:function:: Ptr<FeatureDetector> FeatureDetector::create( const String& detectorType )
.. ocv:pyfunction:: cv2.FeatureDetector_create(detectorType) -> retval
:param detectorType: Feature detector type.
The following detector types are supported:
* ``"FAST"`` -- :ocv:class:`FastFeatureDetector`
* ``"ORB"`` -- :ocv:class:`ORB`
* ``"BRISK"`` -- :ocv:class:`BRISK`
* ``"MSER"`` -- :ocv:class:`MSER`
* ``"GFTT"`` -- :ocv:class:`GoodFeaturesToTrackDetector`
* ``"HARRIS"`` -- :ocv:class:`GoodFeaturesToTrackDetector` with Harris detector enabled
* ``"SimpleBlob"`` -- :ocv:class:`SimpleBlobDetector`
FastFeatureDetector
-------------------
.. ocv:class:: FastFeatureDetector : public FeatureDetector
.. ocv:class:: FastFeatureDetector : public Feature2D
Wrapping class for feature detection using the
:ocv:func:`FAST` method. ::
class FastFeatureDetector : public FeatureDetector
class FastFeatureDetector : public Feature2D
{
public:
FastFeatureDetector( int threshold=1, bool nonmaxSuppression=true, type=FastFeatureDetector::TYPE_9_16 );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
...
static Ptr<FastFeatureDetector> create( int threshold=1, bool nonmaxSuppression=true, type=FastFeatureDetector::TYPE_9_16 );
};
GoodFeaturesToTrackDetector
GFTTDetector
---------------------------
.. ocv:class:: GoodFeaturesToTrackDetector : public FeatureDetector
.. ocv:class:: GFTTDetector : public FeatureDetector
Wrapping class for feature detection using the
:ocv:func:`goodFeaturesToTrack` function. ::
class GoodFeaturesToTrackDetector : public FeatureDetector
class GFTTDetector : public Feature2D
{
public:
class Params
{
public:
Params( int maxCorners=1000, double qualityLevel=0.01,
double minDistance=1., int blockSize=3,
bool useHarrisDetector=false, double k=0.04 );
void read( const FileNode& fn );
void write( FileStorage& fs ) const;
int maxCorners;
double qualityLevel;
double minDistance;
int blockSize;
bool useHarrisDetector;
double k;
};
GoodFeaturesToTrackDetector( const GoodFeaturesToTrackDetector::Params& params=
GoodFeaturesToTrackDetector::Params() );
GoodFeaturesToTrackDetector( int maxCorners, double qualityLevel,
double minDistance, int blockSize=3,
bool useHarrisDetector=false, double k=0.04 );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
...
enum { USE_HARRIS_DETECTOR=10000 };
static Ptr<GFTTDetector> create( int maxCorners=1000, double qualityLevel=0.01,
double minDistance=1, int blockSize=3,
bool useHarrisDetector=false, double k=0.04 );
};
MserFeatureDetector
MSER
-------------------
.. ocv:class:: MserFeatureDetector : public FeatureDetector
.. ocv:class:: MSER : public Feature2D
Wrapping class for feature detection using the
:ocv:class:`MSER` class. ::
Maximally stable region detector ::
class MserFeatureDetector : public FeatureDetector
class MSER : public Feature2D
{
public:
MserFeatureDetector( CvMSERParams params=cvMSERParams() );
MserFeatureDetector( int delta, int minArea, int maxArea,
double maxVariation, double minDiversity,
int maxEvolution, double areaThreshold,
double minMargin, int edgeBlurSize );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
...
enum
{
DELTA=10000, MIN_AREA=10001, MAX_AREA=10002, PASS2_ONLY=10003,
MAX_EVOLUTION=10004, AREA_THRESHOLD=10005,
MIN_MARGIN=10006, EDGE_BLUR_SIZE=10007
};
//! the full constructor
static Ptr<MSER> create( int _delta=5, int _min_area=60, int _max_area=14400,
double _max_variation=0.25, double _min_diversity=.2,
int _max_evolution=200, double _area_threshold=1.01,
double _min_margin=0.003, int _edge_blur_size=5 );
virtual void detectRegions( InputArray image,
std::vector<std::vector<Point> >& msers,
std::vector<Rect>& bboxes ) = 0;
};
SimpleBlobDetector
@ -189,10 +149,8 @@ Class for extracting blobs from an image. ::
float minConvexity, maxConvexity;
};
SimpleBlobDetector(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
protected:
...
static Ptr<SimpleBlobDetector> create(const SimpleBlobDetector::Params
&parameters = SimpleBlobDetector::Params());
};
The class implements a simple algorithm for extracting blobs from an image:

7
modules/features2d/doc/feature_detection_and_description.rst
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@ -14,11 +14,6 @@ Detects corners using the FAST algorithm
.. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSuppression=true )
.. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSuppression, int type )
.. ocv:pyfunction:: cv2.FastFeatureDetector([, threshold[, nonmaxSuppression]]) -> <FastFeatureDetector object>
.. ocv:pyfunction:: cv2.FastFeatureDetector(threshold, nonmaxSuppression, type) -> <FastFeatureDetector object>
.. ocv:pyfunction:: cv2.FastFeatureDetector.detect(image[, mask]) -> keypoints
:param image: grayscale image where keypoints (corners) are detected.
:param keypoints: keypoints detected on the image.
@ -55,7 +50,7 @@ Maximally stable extremal region extractor. ::
// runs the extractor on the specified image; returns the MSERs,
// each encoded as a contour (vector<Point>, see findContours)
// the optional mask marks the area where MSERs are searched for
void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
void detectRegions( InputArray image, vector<vector<Point> >& msers, vector<Rect>& bboxes ) const;
};
The class encapsulates all the parameters of the MSER extraction algorithm (see

456
modules/features2d/include/opencv2/features2d.hpp
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@ -49,8 +49,6 @@
namespace cv
{
CV_EXPORTS bool initModule_features2d(void);
// //! writes vector of keypoints to the file storage
// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector<KeyPoint>& keypoints);
// //! reads vector of keypoints from the specified file storage node
@ -94,12 +92,12 @@ public:
/************************************ Base Classes ************************************/
/*
* Abstract base class for 2D image feature detectors.
* Abstract base class for 2D image feature detectors and descriptor extractors
*/
class CV_EXPORTS_W FeatureDetector : public virtual Algorithm
class CV_EXPORTS_W Feature2D : public virtual Algorithm
{
public:
virtual ~FeatureDetector();
virtual ~Feature2D();
/*
* Detect keypoints in an image.
@ -108,47 +106,13 @@ public:
* mask Mask specifying where to look for keypoints (optional). Must be a char
* matrix with non-zero values in the region of interest.
*/
CV_WRAP void detect( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
CV_WRAP virtual void detect( InputArray image,
CV_OUT std::vector<KeyPoint>& keypoints,
InputArray mask=noArray() );
/*
* Detect keypoints in an image set.
* images Image collection.
* keypoints Collection of keypoints detected in an input images. keypoints[i] is a set of keypoints detected in an images[i].
* masks Masks for image set. masks[i] is a mask for images[i].
*/
void detect( InputArrayOfArrays images, std::vector<std::vector<KeyPoint> >& keypoints, InputArrayOfArrays masks=noArray() ) const;
// Return true if detector object is empty
CV_WRAP virtual bool empty() const;
// Create feature detector by detector name.
CV_WRAP static Ptr<FeatureDetector> create( const String& detectorType );
protected:
virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const = 0;
/*
* Remove keypoints that are not in the mask.
* Helper function, useful when wrapping a library call for keypoint detection that
* does not support a mask argument.
*/
static void removeInvalidPoints( const Mat & mask, std::vector<KeyPoint>& keypoints );
};
/*
* Abstract base class for computing descriptors for image keypoints.
*
* In this interface we assume a keypoint descriptor can be represented as a
* dense, fixed-dimensional vector of some basic type. Most descriptors used
* in practice follow this pattern, as it makes it very easy to compute
* distances between descriptors. Therefore we represent a collection of
* descriptors as a Mat, where each row is one keypoint descriptor.
*/
class CV_EXPORTS_W DescriptorExtractor : public virtual Algorithm
{
public:
virtual ~DescriptorExtractor();
virtual void detect( InputArrayOfArrays images,
std::vector<std::vector<KeyPoint> >& keypoints,
InputArrayOfArrays masks=noArray() );
/*
* Compute the descriptors for a set of keypoints in an image.
@ -156,62 +120,30 @@ public:
* 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.
*/
CV_WRAP void compute( InputArray image, CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
CV_WRAP virtual void compute( InputArray image,
CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints,
OutputArray descriptors );
/*
* Compute the descriptors for a keypoints collection detected in image collection.
* images Image collection.
* keypoints Input keypoints collection. keypoints[i] is keypoints detected in images[i].
* Keypoints for which a descriptor cannot be computed are removed.
* descriptors Descriptor collection. descriptors[i] are descriptors computed for set keypoints[i].
*/
void compute( InputArrayOfArrays images, std::vector<std::vector<KeyPoint> >& keypoints, OutputArrayOfArrays descriptors ) const;
virtual void compute( InputArrayOfArrays images,
std::vector<std::vector<KeyPoint> >& keypoints,
OutputArrayOfArrays descriptors );
CV_WRAP virtual int descriptorSize() const = 0;
CV_WRAP virtual int descriptorType() const = 0;
CV_WRAP virtual int defaultNorm() const = 0;
/* Detects keypoints and computes the descriptors */
CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask,
CV_OUT std::vector<KeyPoint>& keypoints,
OutputArray descriptors,
bool useProvidedKeypoints=false );
CV_WRAP virtual int descriptorSize() const;
CV_WRAP virtual int descriptorType() const;
CV_WRAP virtual int defaultNorm() const;
// Return true if detector object is empty
CV_WRAP virtual bool empty() const;
CV_WRAP static Ptr<DescriptorExtractor> create( const String& descriptorExtractorType );
protected:
virtual void computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const = 0;
/*
* Remove keypoints within borderPixels of an image edge.
*/
static void removeBorderKeypoints( std::vector<KeyPoint>& keypoints,
Size imageSize, int borderSize );
};
/*
* Abstract base class for simultaneous 2D feature detection descriptor extraction.
*/
class CV_EXPORTS_W Feature2D : public FeatureDetector, public DescriptorExtractor
{
public:
/*
* 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.
* useProvidedKeypoints If true, the method will skip the detection phase and will compute
* descriptors for the provided keypoints
*/
CV_WRAP_AS(detectAndCompute) virtual void operator()( InputArray image, InputArray mask,
CV_OUT std::vector<KeyPoint>& keypoints,
OutputArray descriptors,
bool useProvidedKeypoints=false ) const = 0;
CV_WRAP void compute( InputArray image, CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
// Create feature detector and descriptor extractor by name.
CV_WRAP static Ptr<Feature2D> create( const String& name );
};
typedef Feature2D FeatureDetector;
typedef Feature2D DescriptorExtractor;
/*!
BRISK implementation
@ -219,94 +151,12 @@ public:
class CV_EXPORTS_W BRISK : public Feature2D
{
public:
CV_WRAP explicit BRISK(int thresh=30, int octaves=3, float patternScale=1.0f);
virtual ~BRISK();
// 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 BRISK features on an image
void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
// Compute the BRISK 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;
CV_WRAP static Ptr<BRISK> create(int thresh=30, int octaves=3, float patternScale=1.0f);
// custom setup
CV_WRAP explicit BRISK(std::vector<float> &radiusList, std::vector<int> &numberList,
float dMax=5.85f, float dMin=8.2f, std::vector<int> indexChange=std::vector<int>());
// call this to generate the kernel:
// circle of radius r (pixels), with n points;
// short pairings with dMax, long pairings with dMin
CV_WRAP void generateKernel(std::vector<float> &radiusList,
std::vector<int> &numberList, float dMax=5.85f, float dMin=8.2f,
std::vector<int> indexChange=std::vector<int>());
protected:
void computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
void computeKeypointsNoOrientation(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
void computeDescriptorsAndOrOrientation(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
OutputArray descriptors, bool doDescriptors, bool doOrientation,
bool useProvidedKeypoints) const;
// Feature parameters
CV_PROP_RW int threshold;
CV_PROP_RW int octaves;
// some helper structures for the Brisk pattern representation
struct BriskPatternPoint{
float x; // x coordinate relative to center
float y; // x coordinate relative to center
float sigma; // Gaussian smoothing sigma
};
struct BriskShortPair{
unsigned int i; // index of the first pattern point
unsigned int j; // index of other pattern point
};
struct BriskLongPair{
unsigned int i; // index of the first pattern point
unsigned int j; // index of other pattern point
int weighted_dx; // 1024.0/dx
int weighted_dy; // 1024.0/dy
};
inline int smoothedIntensity(const cv::Mat& image,
const cv::Mat& integral,const float key_x,
const float key_y, const unsigned int scale,
const unsigned int rot, const unsigned int point) const;
// pattern properties
BriskPatternPoint* patternPoints_; //[i][rotation][scale]
unsigned int points_; // total number of collocation points
float* scaleList_; // lists the scaling per scale index [scale]
unsigned int* sizeList_; // lists the total pattern size per scale index [scale]
static const unsigned int scales_; // scales discretization
static const float scalerange_; // span of sizes 40->4 Octaves - else, this needs to be adjusted...
static const unsigned int n_rot_; // discretization of the rotation look-up
// pairs
int strings_; // number of uchars the descriptor consists of
float dMax_; // short pair maximum distance
float dMin_; // long pair maximum distance
BriskShortPair* shortPairs_; // d<_dMax
BriskLongPair* longPairs_; // d>_dMin
unsigned int noShortPairs_; // number of shortParis
unsigned int noLongPairs_; // number of longParis
// general
static const float basicSize_;
CV_WRAP static Ptr<BRISK> create(const std::vector<float> &radiusList, const std::vector<int> &numberList,
float dMax=5.85f, float dMin=8.2f, const std::vector<int>& indexChange=std::vector<int>());
};
/*!
ORB implementation.
*/
@ -314,46 +164,18 @@ class CV_EXPORTS_W ORB : public Feature2D
{
public:
// the size of the signature in bytes
enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 };
enum
{
kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1,
NFEATURES=10000, SCALE_FACTOR=10001, NLEVELS=10002,
EDGE_THRESHOLD=10003, FIRST_LEVEL=10004, WTA_K=10005,
SCORE_TYPE=10006, PATCH_SIZE=10007, FAST_THRESHOLD=10008
};
CV_WRAP explicit ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31,
CV_WRAP static Ptr<ORB> create(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31,
int firstLevel = 0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold = 20);
// 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 features and descriptors on an image
void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
// Compute the ORB 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;
CV_PROP_RW int nfeatures;
CV_PROP_RW double scaleFactor;
CV_PROP_RW int nlevels;
CV_PROP_RW int edgeThreshold;
CV_PROP_RW int firstLevel;
CV_PROP_RW int WTA_K;
CV_PROP_RW int scoreType;
CV_PROP_RW int patchSize;
CV_PROP_RW int fastThreshold;
};
typedef ORB OrbFeatureDetector;
typedef ORB OrbDescriptorExtractor;
/*!
Maximal Stable Extremal Regions class.
@ -363,36 +185,27 @@ typedef ORB OrbDescriptorExtractor;
It returns the regions, each of those is encoded as a contour.
*/
class CV_EXPORTS_W MSER : public FeatureDetector
class CV_EXPORTS_W MSER : public Feature2D
{
public:
enum
{
DELTA=10000, MIN_AREA=10001, MAX_AREA=10002, PASS2_ONLY=10003,
MAX_EVOLUTION=10004, AREA_THRESHOLD=10005,
MIN_MARGIN=10006, EDGE_BLUR_SIZE=10007
};
//! the full constructor
CV_WRAP explicit MSER( int _delta=5, int _min_area=60, int _max_area=14400,
CV_WRAP static Ptr<MSER> create( int _delta=5, int _min_area=60, int _max_area=14400,
double _max_variation=0.25, double _min_diversity=.2,
int _max_evolution=200, double _area_threshold=1.01,
double _min_margin=0.003, int _edge_blur_size=5 );
//! the operator that extracts the MSERs from the image or the specific part of it
CV_WRAP_AS(detect) void operator()( InputArray image, CV_OUT std::vector<std::vector<Point> >& msers,
InputArray mask=noArray() ) const;
AlgorithmInfo* info() const;
protected:
void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
int delta;
int minArea;
int maxArea;
double maxVariation;
double minDiversity;
int maxEvolution;
double areaThreshold;
double minMargin;
int edgeBlurSize;
CV_WRAP virtual void detectRegions( InputArray image,
std::vector<std::vector<Point> >& msers,
std::vector<Rect>& bboxes ) = 0;
};
typedef MSER MserFeatureDetector;
//! detects corners using FAST algorithm by E. Rosten
CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
int threshold, bool nonmaxSuppression=true );
@ -400,48 +213,31 @@ CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
int threshold, bool nonmaxSuppression, int type );
class CV_EXPORTS_W FastFeatureDetector : public FeatureDetector
class CV_EXPORTS_W FastFeatureDetector : public Feature2D
{
public:
enum Type
enum
{
TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2
TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2,
THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002,
};
CV_WRAP FastFeatureDetector( int threshold=10, bool nonmaxSuppression=true);
CV_WRAP FastFeatureDetector( int threshold, bool nonmaxSuppression, int type);
AlgorithmInfo* info() const;
protected:
virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
int threshold;
bool nonmaxSuppression;
int type;
CV_WRAP static Ptr<FastFeatureDetector> create( int threshold=10,
bool nonmaxSuppression=true,
int type=FastFeatureDetector::TYPE_9_16 );
};
class CV_EXPORTS_W GFTTDetector : public FeatureDetector
class CV_EXPORTS_W GFTTDetector : public Feature2D
{
public:
CV_WRAP GFTTDetector( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1,
int blockSize=3, bool useHarrisDetector=false, double k=0.04 );
AlgorithmInfo* info() const;
protected:
virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
int nfeatures;
double qualityLevel;
double minDistance;
int blockSize;
bool useHarrisDetector;
double k;
enum { USE_HARRIS_DETECTOR=10000 };
CV_WRAP static Ptr<GFTTDetector> create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1,
int blockSize=3, bool useHarrisDetector=false, double k=0.04 );
};
typedef GFTTDetector GoodFeaturesToTrackDetector;
class CV_EXPORTS_W SimpleBlobDetector : public FeatureDetector
class CV_EXPORTS_W SimpleBlobDetector : public Feature2D
{
public:
struct CV_EXPORTS_W_SIMPLE Params
@ -472,81 +268,29 @@ public:
void write( FileStorage& fs ) const;
};
CV_WRAP SimpleBlobDetector(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
struct CV_EXPORTS Center
{
Point2d location;
double radius;
double confidence;
};
virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
virtual void findBlobs(InputArray image, InputArray binaryImage, std::vector<Center> &centers) const;
Params params;
AlgorithmInfo* info() const;
CV_WRAP static Ptr<SimpleBlobDetector>
create(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
};
// KAZE/AKAZE diffusivity
enum {
DIFF_PM_G1 = 0,
DIFF_PM_G2 = 1,
DIFF_WEICKERT = 2,
DIFF_CHARBONNIER = 3
};
// AKAZE descriptor type
enum {
DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation
DESCRIPTOR_KAZE = 3,
DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation
DESCRIPTOR_MLDB = 5
};
/*!
KAZE implementation
*/
class CV_EXPORTS_W KAZE : public Feature2D
{
public:
CV_WRAP KAZE();
CV_WRAP explicit KAZE(bool extended, bool upright, float threshold = 0.001f,
int octaves = 4, int sublevels = 4, int diffusivity = DIFF_PM_G2);
enum
{
DIFF_PM_G1 = 0,
DIFF_PM_G2 = 1,
DIFF_WEICKERT = 2,
DIFF_CHARBONNIER = 3
};
virtual ~KAZE();
// returns the descriptor size in bytes
int descriptorSize() const;
// returns the descriptor type
int descriptorType() const;
// returns the default norm type
int defaultNorm() const;
AlgorithmInfo* info() const;
// Compute the KAZE features on an image
void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
// Compute the KAZE features and descriptors on an image
void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
OutputArray descriptors, bool useProvidedKeypoints = false) const;
protected:
void detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const;
void computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const;
CV_PROP bool extended;
CV_PROP bool upright;
CV_PROP float threshold;
CV_PROP int octaves;
CV_PROP int sublevels;
CV_PROP int diffusivity;
CV_WRAP static Ptr<KAZE> create(bool extended=false, bool upright=false,
float threshold = 0.001f,
int octaves = 4, int sublevels = 4,
int diffusivity = KAZE::DIFF_PM_G2);
};
/*!
@ -555,41 +299,21 @@ AKAZE implementation
class CV_EXPORTS_W AKAZE : public Feature2D
{
public:
CV_WRAP AKAZE();
CV_WRAP explicit AKAZE(int descriptor_type, int descriptor_size = 0, int descriptor_channels = 3,
float threshold = 0.001f, int octaves = 4, int sublevels = 4, int diffusivity = DIFF_PM_G2);
// AKAZE descriptor type
enum
{
DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation
DESCRIPTOR_KAZE = 3,
DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation
DESCRIPTOR_MLDB = 5
};
virtual ~AKAZE();
// 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 AKAZE features on an image
void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
// Compute the AKAZE 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;
CV_PROP int descriptor;
CV_PROP int descriptor_channels;
CV_PROP int descriptor_size;
CV_PROP float threshold;
CV_PROP int octaves;
CV_PROP int sublevels;
CV_PROP int diffusivity;
CV_WRAP static Ptr<AKAZE> create(int descriptor_type=AKAZE::DESCRIPTOR_MLDB,
int descriptor_size = 0, int descriptor_channels = 3,
float threshold = 0.001f, int octaves = 4,
int sublevels = 4, int diffusivity = KAZE::DIFF_PM_G2);
};
/****************************************************************************************\
* Distance *
\****************************************************************************************/
@ -837,8 +561,6 @@ public:
virtual bool isMaskSupported() const { return true; }
virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
AlgorithmInfo* info() const;
protected:
virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
InputArrayOfArrays masks=noArray(), bool compactResult=false );
@ -871,8 +593,6 @@ public:
virtual bool isMaskSupported() const;
virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
AlgorithmInfo* info() const;
protected:
static void convertToDMatches( const DescriptorCollection& descriptors,
const Mat& indices, const Mat& distances,

5
modules/features2d/perf/opencl/perf_fast.cpp
View File

@ -32,11 +32,8 @@ OCL_PERF_TEST_P(FASTFixture, FastDetect, testing::Combine(
mframe.copyTo(frame);
declare.in(frame);
Ptr<FeatureDetector> fd = Algorithm::create<FeatureDetector>("Feature2D.FAST");
Ptr<FeatureDetector> fd = FastFeatureDetector::create(20, true, type);
ASSERT_FALSE( fd.empty() );
fd->set("threshold", 20);
fd->set("nonmaxSuppression", true);
fd->set("type", type);
vector<KeyPoint> points;
OCL_TEST_CYCLE() fd->detect(frame, points);

14
modules/features2d/perf/opencl/perf_orb.cpp
View File

@ -22,10 +22,10 @@ OCL_PERF_TEST_P(ORBFixture, ORB_Detect, ORB_IMAGES)
mframe.copyTo(frame);
declare.in(frame);
ORB detector(1500, 1.3f, 1);
Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
vector<KeyPoint> points;
OCL_TEST_CYCLE() detector(frame, mask, points);
OCL_TEST_CYCLE() detector->detect(frame, points, mask);
std::sort(points.begin(), points.end(), comparators::KeypointGreater());
SANITY_CHECK_KEYPOINTS(points, 1e-5);
@ -44,14 +44,14 @@ OCL_PERF_TEST_P(ORBFixture, ORB_Extract, ORB_IMAGES)
declare.in(frame);
ORB detector(1500, 1.3f, 1);
Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
vector<KeyPoint> points;
detector(frame, mask, points);
detector->detect(frame, points, mask);
std::sort(points.begin(), points.end(), comparators::KeypointGreater());
UMat descriptors;
OCL_TEST_CYCLE() detector(frame, mask, points, descriptors, true);
OCL_TEST_CYCLE() detector->compute(frame, points, descriptors);
SANITY_CHECK(descriptors);
}
@ -68,12 +68,12 @@ OCL_PERF_TEST_P(ORBFixture, ORB_Full, ORB_IMAGES)
mframe.copyTo(frame);
declare.in(frame);
ORB detector(1500, 1.3f, 1);
Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
vector<KeyPoint> points;
UMat descriptors;
OCL_TEST_CYCLE() detector(frame, mask, points, descriptors, false);
OCL_TEST_CYCLE() detector->detectAndCompute(frame, mask, points, descriptors, false);
::perf::sort(points, descriptors);
SANITY_CHECK_KEYPOINTS(points, 1e-5);

5
modules/features2d/perf/perf_fast.cpp
View File

@ -30,11 +30,8 @@ PERF_TEST_P(fast, detect, testing::Combine(
declare.in(frame);
Ptr<FeatureDetector> fd = Algorithm::create<FeatureDetector>("Feature2D.FAST");
Ptr<FeatureDetector> fd = FastFeatureDetector::create(20, true, type);
ASSERT_FALSE( fd.empty() );
fd->set("threshold", 20);
fd->set("nonmaxSuppression", true);
fd->set("type", type);
vector<KeyPoint> points;
TEST_CYCLE() fd->detect(frame, points);

14
modules/features2d/perf/perf_orb.cpp
View File

@ -22,10 +22,10 @@ PERF_TEST_P(orb, detect, testing::Values(ORB_IMAGES))
Mat mask;
declare.in(frame);
ORB detector(1500, 1.3f, 1);
Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
vector<KeyPoint> points;
TEST_CYCLE() detector(frame, mask, points);
TEST_CYCLE() detector->detect(frame, points, mask);
sort(points.begin(), points.end(), comparators::KeypointGreater());
SANITY_CHECK_KEYPOINTS(points, 1e-5);
@ -42,14 +42,14 @@ PERF_TEST_P(orb, extract, testing::Values(ORB_IMAGES))
Mat mask;
declare.in(frame);
ORB detector(1500, 1.3f, 1);
Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
vector<KeyPoint> points;
detector(frame, mask, points);
detector->detect(frame, points, mask);
sort(points.begin(), points.end(), comparators::KeypointGreater());
Mat descriptors;
TEST_CYCLE() detector(frame, mask, points, descriptors, true);
TEST_CYCLE() detector->compute(frame, points, descriptors);
SANITY_CHECK(descriptors);
}
@ -64,12 +64,12 @@ PERF_TEST_P(orb, full, testing::Values(ORB_IMAGES))
Mat mask;
declare.in(frame);
ORB detector(1500, 1.3f, 1);
Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
vector<KeyPoint> points;
Mat descriptors;
TEST_CYCLE() detector(frame, mask, points, descriptors, false);
TEST_CYCLE() detector->detectAndCompute(frame, mask, points, descriptors, false);
perf::sort(points, descriptors);
SANITY_CHECK_KEYPOINTS(points, 1e-5);

299
modules/features2d/src/akaze.cpp
View File

@ -52,22 +52,15 @@ http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla13bmvc.pd
#include "kaze/AKAZEFeatures.h"
#include <iostream>
using namespace std;
namespace cv
{
AKAZE::AKAZE()
: descriptor(DESCRIPTOR_MLDB)
, descriptor_channels(3)
, descriptor_size(0)
, threshold(0.001f)
, octaves(4)
, sublevels(4)
, diffusivity(DIFF_PM_G2)
{
}
using namespace std;
AKAZE::AKAZE(int _descriptor_type, int _descriptor_size, int _descriptor_channels,
class AKAZE_Impl : public AKAZE
{
public:
AKAZE_Impl(int _descriptor_type, int _descriptor_size, int _descriptor_channels,
float _threshold, int _octaves, int _sublevels, int _diffusivity)
: descriptor(_descriptor_type)
, descriptor_channels(_descriptor_channels)
@ -76,181 +69,161 @@ namespace cv
, octaves(_octaves)
, sublevels(_sublevels)
, diffusivity(_diffusivity)
{
}
AKAZE::~AKAZE()
{
}
// returns the descriptor size in bytes
int AKAZE::descriptorSize() const
{
switch (descriptor)
{
case cv::DESCRIPTOR_KAZE:
case cv::DESCRIPTOR_KAZE_UPRIGHT:
return 64;
case cv::DESCRIPTOR_MLDB:
case cv::DESCRIPTOR_MLDB_UPRIGHT:
// We use the full length binary descriptor -> 486 bits
if (descriptor_size == 0)
{
int t = (6 + 36 + 120) * descriptor_channels;
return (int)ceil(t / 8.);
}
else
{
// We use the random bit selection length binary descriptor
return (int)ceil(descriptor_size / 8.);
}
default:
return -1;
}
}
// returns the descriptor type
int AKAZE::descriptorType() const
{
switch (descriptor)
virtual ~AKAZE_Impl()
{
case cv::DESCRIPTOR_KAZE:
case cv::DESCRIPTOR_KAZE_UPRIGHT:
return CV_32F;
case cv::DESCRIPTOR_MLDB:
case cv::DESCRIPTOR_MLDB_UPRIGHT:
return CV_8U;
}
// returns the descriptor size in bytes
int descriptorSize() const
{
switch (descriptor)
{
case DESCRIPTOR_KAZE:
case DESCRIPTOR_KAZE_UPRIGHT:
return 64;
case DESCRIPTOR_MLDB:
case DESCRIPTOR_MLDB_UPRIGHT:
// We use the full length binary descriptor -> 486 bits
if (descriptor_size == 0)
{
int t = (6 + 36 + 120) * descriptor_channels;
return (int)ceil(t / 8.);
}
else
{
// We use the random bit selection length binary descriptor
return (int)ceil(descriptor_size / 8.);
}
default:
return -1;
}
}
}
// returns the default norm type
int AKAZE::defaultNorm() const
{
switch (descriptor)
// returns the descriptor type
int descriptorType() const
{
case cv::DESCRIPTOR_KAZE:
case cv::DESCRIPTOR_KAZE_UPRIGHT:
return cv::NORM_L2;
switch (descriptor)
{
case DESCRIPTOR_KAZE:
case DESCRIPTOR_KAZE_UPRIGHT:
return CV_32F;
case cv::DESCRIPTOR_MLDB:
case cv::DESCRIPTOR_MLDB_UPRIGHT:
return cv::NORM_HAMMING;
case DESCRIPTOR_MLDB:
case DESCRIPTOR_MLDB_UPRIGHT:
return CV_8U;
default:
return -1;
default:
return -1;
}
}
}
void AKAZE::operator()(InputArray image, InputArray mask,
std::vector<KeyPoint>& keypoints,
OutputArray descriptors,
bool useProvidedKeypoints) const
{
cv::Mat img = image.getMat();
if (img.type() != CV_8UC1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
cv::Mat& desc = descriptors.getMatRef();
AKAZEOptions options;
options.descriptor = descriptor;
options.descriptor_channels = descriptor_channels;
options.descriptor_size = descriptor_size;
options.img_width = img.cols;
options.img_height = img.rows;
options.dthreshold = threshold;
options.omax = octaves;
options.nsublevels = sublevels;
options.diffusivity = diffusivity;
AKAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
if (!useProvidedKeypoints)
// returns the default norm type
int defaultNorm() const
{
impl.Feature_Detection(keypoints);
switch (descriptor)
{
case DESCRIPTOR_KAZE:
case DESCRIPTOR_KAZE_UPRIGHT:
return NORM_L2;
case DESCRIPTOR_MLDB:
case DESCRIPTOR_MLDB_UPRIGHT:
return NORM_HAMMING;
default:
return -1;
}
}
if (!mask.empty())
void detectAndCompute(InputArray image, InputArray mask,
std::vector<KeyPoint>& keypoints,
OutputArray descriptors,
bool useProvidedKeypoints)
{
cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
Mat img = image.getMat();
if (img.type() != CV_8UC1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
AKAZEOptions options;
options.descriptor = descriptor;
options.descriptor_channels = descriptor_channels;
options.descriptor_size = descriptor_size;
options.img_width = img.cols;
options.img_height = img.rows;
options.dthreshold = threshold;
options.omax = octaves;
options.nsublevels = sublevels;
options.diffusivity = diffusivity;
AKAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
if (!useProvidedKeypoints)
{
impl.Feature_Detection(keypoints);
}
if (!mask.empty())
{
KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
}
if( descriptors.needed() )
{
Mat& desc = descriptors.getMatRef();
impl.Compute_Descriptors(keypoints, desc);
CV_Assert((!desc.rows || desc.cols == descriptorSize()));
CV_Assert((!desc.rows || (desc.type() == descriptorType())));
}
}
impl.Compute_Descriptors(keypoints, desc);
CV_Assert((!desc.rows || desc.cols == descriptorSize()));
CV_Assert((!desc.rows || (desc.type() == descriptorType())));
}
void AKAZE::detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
{
cv::Mat img = image.getMat();
if (img.type() != CV_8UC1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
AKAZEOptions options;
options.descriptor = descriptor;
options.descriptor_channels = descriptor_channels;
options.descriptor_size = descriptor_size;
options.img_width = img.cols;
options.img_height = img.rows;
options.dthreshold = threshold;
options.omax = octaves;
options.nsublevels = sublevels;
options.diffusivity = diffusivity;
AKAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
impl.Feature_Detection(keypoints);
if (!mask.empty())
void write(FileStorage& fs) const
{
cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
fs << "descriptor" << descriptor;
fs << "descriptor_channels" << descriptor_channels;
fs << "descriptor_size" << descriptor_size;
fs << "threshold" << threshold;
fs << "octaves" << octaves;
fs << "sublevels" << sublevels;
fs << "diffusivity" << diffusivity;
}
}
void AKAZE::computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
void read(const FileNode& fn)
{
descriptor = (int)fn["descriptor"];
descriptor_channels = (int)fn["descriptor_channels"];
descriptor_size = (int)fn["descriptor_size"];
threshold = (float)fn["threshold"];
octaves = (int)fn["octaves"];
sublevels = (int)fn["sublevels"];
diffusivity = (int)fn["diffusivity"];
}
int descriptor;
int descriptor_channels;
int descriptor_size;
float threshold;
int octaves;
int sublevels;
int diffusivity;
};
Ptr<AKAZE> AKAZE::create(int descriptor_type,
int descriptor_size, int descriptor_channels,
float threshold, int octaves,
int sublevels, int diffusivity)
{
cv::Mat img = image.getMat();
if (img.type() != CV_8UC1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
cv::Mat& desc = descriptors.getMatRef();
AKAZEOptions options;
options.descriptor = descriptor;
options.descriptor_channels = descriptor_channels;
options.descriptor_size = descriptor_size;
options.img_width = img.cols;
options.img_height = img.rows;
options.dthreshold = threshold;
options.omax = octaves;
options.nsublevels = sublevels;
options.diffusivity = diffusivity;
AKAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
impl.Compute_Descriptors(keypoints, desc);
CV_Assert((!desc.rows || desc.cols == descriptorSize()));
CV_Assert((!desc.rows || (desc.type() == descriptorType())));
return makePtr<AKAZE_Impl>(descriptor_type, descriptor_size, descriptor_channels,
threshold, octaves, sublevels, diffusivity);
}
}

43
modules/features2d/src/blobdetector.cpp
View File

@ -55,7 +55,31 @@
# endif
#endif
using namespace cv;
namespace cv
{
class CV_EXPORTS_W SimpleBlobDetectorImpl : public SimpleBlobDetector
{
public:
explicit SimpleBlobDetectorImpl(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
struct CV_EXPORTS Center
{
Point2d location;
double radius;
double confidence;
};
virtual void detect( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() );
virtual void findBlobs(InputArray image, InputArray binaryImage, std::vector<Center> &centers) const;
Params params;
};
/*
* SimpleBlobDetector
@ -148,22 +172,22 @@ void SimpleBlobDetector::Params::write(cv::FileStorage& fs) const
fs << "maxConvexity" << maxConvexity;
}
SimpleBlobDetector::SimpleBlobDetector(const SimpleBlobDetector::Params &parameters) :
SimpleBlobDetectorImpl::SimpleBlobDetectorImpl(const SimpleBlobDetector::Params &parameters) :
params(parameters)
{
}
void SimpleBlobDetector::read( const cv::FileNode& fn )
void SimpleBlobDetectorImpl::read( const cv::FileNode& fn )
{
params.read(fn);
}
void SimpleBlobDetector::write( cv::FileStorage& fs ) const
void SimpleBlobDetectorImpl::write( cv::FileStorage& fs ) const
{
params.write(fs);
}
void SimpleBlobDetector::findBlobs(InputArray _image, InputArray _binaryImage, std::vector<Center> &centers) const
void SimpleBlobDetectorImpl::findBlobs(InputArray _image, InputArray _binaryImage, std::vector<Center> &centers) const
{
Mat image = _image.getMat(), binaryImage = _binaryImage.getMat();
(void)image;
@ -277,7 +301,7 @@ void SimpleBlobDetector::findBlobs(InputArray _image, InputArray _binaryImage, s
#endif
}
void SimpleBlobDetector::detectImpl(InputArray image, std::vector<cv::KeyPoint>& keypoints, InputArray) const
void SimpleBlobDetectorImpl::detect(InputArray image, std::vector<cv::KeyPoint>& keypoints, InputArray)
{
//TODO: support mask
keypoints.clear();
@ -340,3 +364,10 @@ void SimpleBlobDetector::detectImpl(InputArray image, std::vector<cv::KeyPoint>&
keypoints.push_back(kpt);
}
}
Ptr<SimpleBlobDetector> SimpleBlobDetector::create(const SimpleBlobDetector::Params& params)
{
return makePtr<SimpleBlobDetectorImpl>(params);
}
}

190
modules/features2d/src/brisk.cpp
View File

@ -42,9 +42,7 @@
the IEEE International Conference on Computer Vision (ICCV2011).
*/
#include <opencv2/features2d.hpp>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include "precomp.hpp"
#include <fstream>
#include <stdlib.h>
@ -53,6 +51,97 @@
namespace cv
{
class BRISK_Impl : public BRISK
{
public:
explicit BRISK_Impl(int thresh=30, int octaves=3, float patternScale=1.0f);
// custom setup
explicit BRISK_Impl(const std::vector<float> &radiusList, const std::vector<int> &numberList,
float dMax=5.85f, float dMin=8.2f, const std::vector<int> indexChange=std::vector<int>());
virtual ~BRISK_Impl();
int descriptorSize() const
{
return strings_;
}
int descriptorType() const
{
return CV_8U;
}
int defaultNorm() const
{
return NORM_HAMMING;
}
// call this to generate the kernel:
// circle of radius r (pixels), with n points;
// short pairings with dMax, long pairings with dMin
void generateKernel(const std::vector<float> &radiusList,
const std::vector<int> &numberList, float dMax=5.85f, float dMin=8.2f,
const std::vector<int> &indexChange=std::vector<int>());
void detectAndCompute( InputArray image, InputArray mask,
CV_OUT std::vector<KeyPoint>& keypoints,
OutputArray descriptors,
bool useProvidedKeypoints );
protected:
void computeKeypointsNoOrientation(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
void computeDescriptorsAndOrOrientation(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
OutputArray descriptors, bool doDescriptors, bool doOrientation,
bool useProvidedKeypoints) const;
// Feature parameters
CV_PROP_RW int threshold;
CV_PROP_RW int octaves;
// some helper structures for the Brisk pattern representation
struct BriskPatternPoint{
float x; // x coordinate relative to center
float y; // x coordinate relative to center
float sigma; // Gaussian smoothing sigma
};
struct BriskShortPair{
unsigned int i; // index of the first pattern point
unsigned int j; // index of other pattern point
};
struct BriskLongPair{
unsigned int i; // index of the first pattern point
unsigned int j; // index of other pattern point
int weighted_dx; // 1024.0/dx
int weighted_dy; // 1024.0/dy
};
inline int smoothedIntensity(const cv::Mat& image,
const cv::Mat& integral,const float key_x,
const float key_y, const unsigned int scale,
const unsigned int rot, const unsigned int point) const;
// pattern properties
BriskPatternPoint* patternPoints_; //[i][rotation][scale]
unsigned int points_; // total number of collocation points
float* scaleList_; // lists the scaling per scale index [scale]
unsigned int* sizeList_; // lists the total pattern size per scale index [scale]
static const unsigned int scales_; // scales discretization
static const float scalerange_; // span of sizes 40->4 Octaves - else, this needs to be adjusted...
static const unsigned int n_rot_; // discretization of the rotation look-up
// pairs
int strings_; // number of uchars the descriptor consists of
float dMax_; // short pair maximum distance
float dMin_; // long pair maximum distance
BriskShortPair* shortPairs_; // d<_dMax
BriskLongPair* longPairs_; // d>_dMin
unsigned int noShortPairs_; // number of shortParis
unsigned int noLongPairs_; // number of longParis
// general
static const float basicSize_;
};
// a layer in the Brisk detector pyramid
class CV_EXPORTS BriskLayer
{
@ -183,16 +272,16 @@ protected:
static const float basicSize_;
};
const float BRISK::basicSize_ = 12.0f;
const unsigned int BRISK::scales_ = 64;
const float BRISK::scalerange_ = 30.f; // 40->4 Octaves - else, this needs to be adjusted...
const unsigned int BRISK::n_rot_ = 1024; // discretization of the rotation look-up
const float BRISK_Impl::basicSize_ = 12.0f;
const unsigned int BRISK_Impl::scales_ = 64;
const float BRISK_Impl::scalerange_ = 30.f; // 40->4 Octaves - else, this needs to be adjusted...
const unsigned int BRISK_Impl::n_rot_ = 1024; // discretization of the rotation look-up
const float BriskScaleSpace::safetyFactor_ = 1.0f;
const float BriskScaleSpace::basicSize_ = 12.0f;
// constructors
BRISK::BRISK(int thresh, int octaves_in, float patternScale)
BRISK_Impl::BRISK_Impl(int thresh, int octaves_in, float patternScale)
{
threshold = thresh;
octaves = octaves_in;
@ -218,10 +307,12 @@ BRISK::BRISK(int thresh, int octaves_in, float patternScale)
nList[4] = 20;
generateKernel(rList, nList, (float)(5.85 * patternScale), (float)(8.2 * patternScale));
}
BRISK::BRISK(std::vector<float> &radiusList, std::vector<int> &numberList, float dMax, float dMin,
std::vector<int> indexChange)
BRISK_Impl::BRISK_Impl(const std::vector<float> &radiusList,
const std::vector<int> &numberList,
float dMax, float dMin,
const std::vector<int> indexChange)
{
generateKernel(radiusList, numberList, dMax, dMin, indexChange);
threshold = 20;
@ -229,10 +320,12 @@ BRISK::BRISK(std::vector<float> &radiusList, std::vector<int> &numberList, float
}
void
BRISK::generateKernel(std::vector<float> &radiusList, std::vector<int> &numberList, float dMax,
float dMin, std::vector<int> indexChange)
BRISK_Impl::generateKernel(const std::vector<float> &radiusList,
const std::vector<int> &numberList,
float dMax, float dMin,
const std::vector<int>& _indexChange)
{
std::vector<int> indexChange = _indexChange;
dMax_ = dMax;
dMin_ = dMin;
@ -354,7 +447,7 @@ BRISK::generateKernel(std::vector<float> &radiusList, std::vector<int> &numberLi
// simple alternative:
inline int
BRISK::smoothedIntensity(const cv::Mat& image, const cv::Mat& integral, const float key_x,
BRISK_Impl::smoothedIntensity(const cv::Mat& image, const cv::Mat& integral, const float key_x,
const float key_y, const unsigned int scale, const unsigned int rot,
const unsigned int point) const
{
@ -521,8 +614,8 @@ RoiPredicate(const float minX, const float minY, const float maxX, const float m
// computes the descriptor
void
BRISK::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>& keypoints,
OutputArray _descriptors, bool useProvidedKeypoints) const
BRISK_Impl::detectAndCompute( InputArray _image, InputArray _mask, std::vector<KeyPoint>& keypoints,
OutputArray _descriptors, bool useProvidedKeypoints)
{
bool doOrientation=true;
if (useProvidedKeypoints)
@ -536,7 +629,7 @@ BRISK::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>& k
}
void
BRISK::computeDescriptorsAndOrOrientation(InputArray _image, InputArray _mask, std::vector<KeyPoint>& keypoints,
BRISK_Impl::computeDescriptorsAndOrOrientation(InputArray _image, InputArray _mask, std::vector<KeyPoint>& keypoints,
OutputArray _descriptors, bool doDescriptors, bool doOrientation,
bool useProvidedKeypoints) const
{
@ -702,25 +795,8 @@ BRISK::computeDescriptorsAndOrOrientation(InputArray _image, InputArray _mask, s
delete[] _values;
}
int
BRISK::descriptorSize() const
{
return strings_;
}
int
BRISK::descriptorType() const
{
return CV_8U;
}
int
BRISK::defaultNorm() const
{
return NORM_HAMMING;
}
BRISK::~BRISK()
BRISK_Impl::~BRISK_Impl()
{
delete[] patternPoints_;
delete[] shortPairs_;
@ -730,14 +806,7 @@ BRISK::~BRISK()
}
void
BRISK::operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const
{
computeKeypointsNoOrientation(image, mask, keypoints);
computeDescriptorsAndOrOrientation(image, mask, keypoints, cv::noArray(), false, true, true);
}
void
BRISK::computeKeypointsNoOrientation(InputArray _image, InputArray _mask, std::vector<KeyPoint>& keypoints) const
BRISK_Impl::computeKeypointsNoOrientation(InputArray _image, InputArray _mask, std::vector<KeyPoint>& keypoints) const
{
Mat image = _image.getMat(), mask = _mask.getMat();
if( image.type() != CV_8UC1 )
@ -748,20 +817,7 @@ BRISK::computeKeypointsNoOrientation(InputArray _image, InputArray _mask, std::v
briskScaleSpace.getKeypoints(threshold, keypoints);
// remove invalid points
removeInvalidPoints(mask, keypoints);
}
void
BRISK::detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
{
(*this)(image.getMat(), mask.getMat(), keypoints);
}
void
BRISK::computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
{
(*this)(image, Mat(), keypoints, descriptors, true);
KeyPointsFilter::runByPixelsMask(keypoints, mask);
}
// construct telling the octaves number:
@ -2011,7 +2067,7 @@ BriskLayer::BriskLayer(const cv::Mat& img_in, float scale_in, float offset_in)
scale_ = scale_in;
offset_ = offset_in;
// create an agast detector
fast_9_16_ = makePtr<FastFeatureDetector>(1, true, FastFeatureDetector::TYPE_9_16);
fast_9_16_ = FastFeatureDetector::create(1, true, FastFeatureDetector::TYPE_9_16);
makeOffsets(pixel_5_8_, (int)img_.step, 8);
makeOffsets(pixel_9_16_, (int)img_.step, 16);
}
@ -2033,7 +2089,7 @@ BriskLayer::BriskLayer(const BriskLayer& layer, int mode)
offset_ = 0.5f * scale_ - 0.5f;
}
scores_ = cv::Mat::zeros(img_.rows, img_.cols, CV_8U);
fast_9_16_ = makePtr<FastFeatureDetector>(1, false, FastFeatureDetector::TYPE_9_16);
fast_9_16_ = FastFeatureDetector::create(1, false, FastFeatureDetector::TYPE_9_16);
makeOffsets(pixel_5_8_, (int)img_.step, 8);
makeOffsets(pixel_9_16_, (int)img_.step, 16);
}
@ -2043,7 +2099,7 @@ BriskLayer::BriskLayer(const BriskLayer& layer, int mode)
void
BriskLayer::getAgastPoints(int threshold, std::vector<KeyPoint>& keypoints)
{
fast_9_16_->set("threshold", threshold);
fast_9_16_->set(FastFeatureDetector::THRESHOLD, threshold);
fast_9_16_->detect(img_, keypoints);
// also write scores
@ -2245,4 +2301,16 @@ BriskLayer::twothirdsample(const cv::Mat& srcimg, cv::Mat& dstimg)
resize(srcimg, dstimg, dstimg.size(), 0, 0, INTER_AREA);
}
Ptr<BRISK> BRISK::create(int thresh, int octaves, float patternScale)
{
return makePtr<BRISK_Impl>(thresh, octaves, patternScale);
}
// custom setup
Ptr<BRISK> BRISK::create(const std::vector<float> &radiusList, const std::vector<int> &numberList,
float dMax, float dMin, const std::vector<int>& indexChange)
{
return makePtr<BRISK_Impl>(radiusList, numberList, dMax, dMin, indexChange);
}
}

110
modules/features2d/src/descriptors.cpp
View File

@ -1,110 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include <limits>
namespace cv
{
/****************************************************************************************\
* DescriptorExtractor *
\****************************************************************************************/
/*
* DescriptorExtractor
*/
DescriptorExtractor::~DescriptorExtractor()
{}
void DescriptorExtractor::compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const
{
if( image.empty() || keypoints.empty() )
{
descriptors.release();
return;
}
KeyPointsFilter::runByImageBorder( keypoints, image.size(), 0 );
KeyPointsFilter::runByKeypointSize( keypoints, std::numeric_limits<float>::epsilon() );
computeImpl( image, keypoints, descriptors );
}
void DescriptorExtractor::compute( InputArrayOfArrays _imageCollection, std::vector<std::vector<KeyPoint> >& pointCollection, OutputArrayOfArrays _descCollection ) const
{
std::vector<Mat> imageCollection, descCollection;
_imageCollection.getMatVector(imageCollection);
_descCollection.getMatVector(descCollection);
CV_Assert( imageCollection.size() == pointCollection.size() );
descCollection.resize( imageCollection.size() );
for( size_t i = 0; i < imageCollection.size(); i++ )
compute( imageCollection[i], pointCollection[i], descCollection[i] );
}
/*void DescriptorExtractor::read( const FileNode& )
{}
void DescriptorExtractor::write( FileStorage& ) const
{}*/
bool DescriptorExtractor::empty() const
{
return false;
}
void DescriptorExtractor::removeBorderKeypoints( std::vector<KeyPoint>& keypoints,
Size imageSize, int borderSize )
{
KeyPointsFilter::runByImageBorder( keypoints, imageSize, borderSize );
}
Ptr<DescriptorExtractor> DescriptorExtractor::create(const String& descriptorExtractorType)
{
return Algorithm::create<DescriptorExtractor>("Feature2D." + descriptorExtractorType);
}
CV_WRAP void Feature2D::compute( InputArray image, CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const
{
DescriptorExtractor::compute(image, keypoints, descriptors);
}
}

161
modules/features2d/src/detectors.cpp
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@ -1,161 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv
{
/*
* FeatureDetector
*/
FeatureDetector::~FeatureDetector()
{}
void FeatureDetector::detect( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask ) const
{
keypoints.clear();
if( image.empty() )
return;
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()) );
detectImpl( image, keypoints, mask );
}
void FeatureDetector::detect(InputArrayOfArrays _imageCollection, std::vector<std::vector<KeyPoint> >& pointCollection,
InputArrayOfArrays _masks ) const
{
if (_imageCollection.isUMatVector())
{
std::vector<UMat> uimageCollection, umasks;
_imageCollection.getUMatVector(uimageCollection);
_masks.getUMatVector(umasks);
pointCollection.resize( uimageCollection.size() );
for( size_t i = 0; i < uimageCollection.size(); i++ )
detect( uimageCollection[i], pointCollection[i], umasks.empty() ? noArray() : umasks[i] );
return;
}
std::vector<Mat> imageCollection, masks;
_imageCollection.getMatVector(imageCollection);
_masks.getMatVector(masks);
pointCollection.resize( imageCollection.size() );
for( size_t i = 0; i < imageCollection.size(); i++ )
detect( imageCollection[i], pointCollection[i], masks.empty() ? noArray() : masks[i] );
}
/*void FeatureDetector::read( const FileNode& )
{}
void FeatureDetector::write( FileStorage& ) const
{}*/
bool FeatureDetector::empty() const
{
return false;
}
void FeatureDetector::removeInvalidPoints( const Mat& mask, std::vector<KeyPoint>& keypoints )
{
KeyPointsFilter::runByPixelsMask( keypoints, mask );
}
Ptr<FeatureDetector> FeatureDetector::create( const String& detectorType )
{
if( detectorType.compare( "HARRIS" ) == 0 )
{
Ptr<FeatureDetector> fd = FeatureDetector::create("GFTT");
fd->set("useHarrisDetector", true);
return fd;
}
return Algorithm::create<FeatureDetector>("Feature2D." + detectorType);
}
GFTTDetector::GFTTDetector( int _nfeatures, double _qualityLevel,
double _minDistance, int _blockSize,
bool _useHarrisDetector, double _k )
: nfeatures(_nfeatures), qualityLevel(_qualityLevel), minDistance(_minDistance),
blockSize(_blockSize), useHarrisDetector(_useHarrisDetector), k(_k)
{
}
void GFTTDetector::detectImpl( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask) const
{
std::vector<Point2f> corners;
if (_image.isUMat())
{
UMat ugrayImage;
if( _image.type() != CV_8U )
cvtColor( _image, ugrayImage, COLOR_BGR2GRAY );
else
ugrayImage = _image.getUMat();
goodFeaturesToTrack( ugrayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
blockSize, useHarrisDetector, k );
}
else
{
Mat image = _image.getMat(), grayImage = image;
if( image.type() != CV_8U )
cvtColor( image, grayImage, COLOR_BGR2GRAY );
goodFeaturesToTrack( grayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
blockSize, useHarrisDetector, k );
}
keypoints.resize(corners.size());
std::vector<Point2f>::const_iterator corner_it = corners.begin();
std::vector<KeyPoint>::iterator keypoint_it = keypoints.begin();
for( ; corner_it != corners.end(); ++corner_it, ++keypoint_it )
*keypoint_it = KeyPoint( *corner_it, (float)blockSize );
}
}

71
modules/features2d/src/fast.cpp
View File

@ -359,30 +359,63 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
{
FAST(_img, keypoints, threshold, nonmax_suppression, FastFeatureDetector::TYPE_9_16);
}
/*
* FastFeatureDetector
*/
FastFeatureDetector::FastFeatureDetector( int _threshold, bool _nonmaxSuppression )
: threshold(_threshold), nonmaxSuppression(_nonmaxSuppression), type(FastFeatureDetector::TYPE_9_16)
{}
FastFeatureDetector::FastFeatureDetector( int _threshold, bool _nonmaxSuppression, int _type )
: threshold(_threshold), nonmaxSuppression(_nonmaxSuppression), type((short)_type)
{}
void FastFeatureDetector::detectImpl( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask ) const
class FastFeatureDetector_Impl : public FastFeatureDetector
{
Mat mask = _mask.getMat(), grayImage;
UMat ugrayImage;
_InputArray gray = _image;
if( _image.type() != CV_8U )
public:
FastFeatureDetector_Impl( int _threshold, bool _nonmaxSuppression, int _type )
: threshold(_threshold), nonmaxSuppression(_nonmaxSuppression), type((short)_type)
{}
void detect( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask )
{
_OutputArray ogray = _image.isUMat() ? _OutputArray(ugrayImage) : _OutputArray(grayImage);
cvtColor( _image, ogray, COLOR_BGR2GRAY );
gray = ogray;
Mat mask = _mask.getMat(), grayImage;
UMat ugrayImage;
_InputArray gray = _image;
if( _image.type() != CV_8U )
{
_OutputArray ogray = _image.isUMat() ? _OutputArray(ugrayImage) : _OutputArray(grayImage);
cvtColor( _image, ogray, COLOR_BGR2GRAY );
gray = ogray;
}
FAST( gray, keypoints, threshold, nonmaxSuppression, type );
KeyPointsFilter::runByPixelsMask( keypoints, mask );
}
FAST( gray, keypoints, threshold, nonmaxSuppression, type );
KeyPointsFilter::runByPixelsMask( keypoints, mask );
void set(int prop, double value)
{
if(prop == THRESHOLD)
threshold = cvRound(value);
else if(prop == NONMAX_SUPPRESSION)
nonmaxSuppression = value != 0;
else if(prop == FAST_N)
type = cvRound(value);
else
CV_Error(Error::StsBadArg, "");
}
double get(int prop) const
{
if(prop == THRESHOLD)
return threshold;
if(prop == NONMAX_SUPPRESSION)
return nonmaxSuppression;
if(prop == FAST_N)
return type;
CV_Error(Error::StsBadArg, "");
return 0;
}
int threshold;
bool nonmaxSuppression;
int type;
};
Ptr<FastFeatureDetector> FastFeatureDetector::create( int threshold, bool nonmaxSuppression, int type )
{
return makePtr<FastFeatureDetector_Impl>(threshold, nonmaxSuppression, type);
}
}

169
modules/features2d/src/feature2d.cpp Normal file
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@ -0,0 +1,169 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#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 )
{
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 )
{
vector<Mat> images, masks;
_images.getMatVector(images);
size_t i, nimages = images.size();
if( !_masks.empty() )
{
_masks.getMatVector(masks);
CV_Assert(masks.size() == nimages);
}
keypoints.resize(nimages);
for( i = 0; i < nimages; i++ )
{
detect(images[i], keypoints[i], masks.empty() ? Mat() : masks[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 )
{
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 )
{
if( !_descriptors.needed() )
return;
vector<Mat> images;
_images.getMatVector(images);
size_t i, nimages = images.size();
CV_Assert( keypoints.size() == nimages );
CV_Assert( _descriptors.kind() == _InputArray::STD_VECTOR_MAT );
vector<Mat>& descriptors = *(vector<Mat>*)_descriptors.getObj();
descriptors.resize(nimages);
for( i = 0; i < nimages; i++ )
{
compute(images[i], keypoints[i], descriptors[i]);
}
}
/* Detects keypoints and computes the descriptors */
void Feature2D::detectAndCompute( InputArray, InputArray,
std::vector<KeyPoint>&,
OutputArray,
bool )
{
CV_Error(Error::StsNotImplemented, "");
}
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;
}
}

195
modules/features2d/src/features2d_init.cpp
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@ -1,195 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
Ptr<Feature2D> Feature2D::create( const String& feature2DType )
{
return Algorithm::create<Feature2D>("Feature2D." + feature2DType);
}
/////////////////////// AlgorithmInfo for various detector & descriptors ////////////////////////////
/* NOTE!!!
All the AlgorithmInfo-related stuff should be in the same file as initModule_features2d().
Otherwise, linker may throw away some seemingly unused stuff.
*/
CV_INIT_ALGORITHM(BRISK, "Feature2D.BRISK",
obj.info()->addParam(obj, "thres", obj.threshold);
obj.info()->addParam(obj, "octaves", obj.octaves))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(FastFeatureDetector, "Feature2D.FAST",
obj.info()->addParam(obj, "threshold", obj.threshold);
obj.info()->addParam(obj, "nonmaxSuppression", obj.nonmaxSuppression);
obj.info()->addParam(obj, "type", obj.type))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(MSER, "Feature2D.MSER",
obj.info()->addParam(obj, "delta", obj.delta);
obj.info()->addParam(obj, "minArea", obj.minArea);
obj.info()->addParam(obj, "maxArea", obj.maxArea);
obj.info()->addParam(obj, "maxVariation", obj.maxVariation);
obj.info()->addParam(obj, "minDiversity", obj.minDiversity);
obj.info()->addParam(obj, "maxEvolution", obj.maxEvolution);
obj.info()->addParam(obj, "areaThreshold", obj.areaThreshold);
obj.info()->addParam(obj, "minMargin", obj.minMargin);
obj.info()->addParam(obj, "edgeBlurSize", obj.edgeBlurSize))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(ORB, "Feature2D.ORB",
obj.info()->addParam(obj, "nFeatures", obj.nfeatures);
obj.info()->addParam(obj, "scaleFactor", obj.scaleFactor);
obj.info()->addParam(obj, "nLevels", obj.nlevels);
obj.info()->addParam(obj, "firstLevel", obj.firstLevel);
obj.info()->addParam(obj, "edgeThreshold", obj.edgeThreshold);
obj.info()->addParam(obj, "patchSize", obj.patchSize);
obj.info()->addParam(obj, "WTA_K", obj.WTA_K);
obj.info()->addParam(obj, "scoreType", obj.scoreType);
obj.info()->addParam(obj, "fastThreshold", obj.fastThreshold))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(GFTTDetector, "Feature2D.GFTT",
obj.info()->addParam(obj, "nfeatures", obj.nfeatures);
obj.info()->addParam(obj, "qualityLevel", obj.qualityLevel);
obj.info()->addParam(obj, "minDistance", obj.minDistance);
obj.info()->addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
obj.info()->addParam(obj, "k", obj.k))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(KAZE, "Feature2D.KAZE",
obj.info()->addParam(obj, "upright", obj.upright);
obj.info()->addParam(obj, "extended", obj.extended);
obj.info()->addParam(obj, "threshold", obj.threshold);
obj.info()->addParam(obj, "octaves", obj.octaves);
obj.info()->addParam(obj, "sublevels", obj.sublevels);
obj.info()->addParam(obj, "diffusivity", obj.diffusivity))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(AKAZE, "Feature2D.AKAZE",
obj.info()->addParam(obj, "descriptor", obj.descriptor);
obj.info()->addParam(obj, "descriptor_channels", obj.descriptor_channels);
obj.info()->addParam(obj, "descriptor_size", obj.descriptor_size);
obj.info()->addParam(obj, "threshold", obj.threshold);
obj.info()->addParam(obj, "octaves", obj.octaves);
obj.info()->addParam(obj, "sublevels", obj.sublevels);
obj.info()->addParam(obj, "diffusivity", obj.diffusivity))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(SimpleBlobDetector, "Feature2D.SimpleBlob",
obj.info()->addParam(obj, "thresholdStep", obj.params.thresholdStep);
obj.info()->addParam(obj, "minThreshold", obj.params.minThreshold);
obj.info()->addParam(obj, "maxThreshold", obj.params.maxThreshold);
obj.info()->addParam_(obj, "minRepeatability", (sizeof(size_t) == sizeof(uint64))?Param::UINT64 : Param::UNSIGNED_INT, &obj.params.minRepeatability, false, 0, 0);
obj.info()->addParam(obj, "minDistBetweenBlobs", obj.params.minDistBetweenBlobs);
obj.info()->addParam(obj, "filterByColor", obj.params.filterByColor);
obj.info()->addParam(obj, "blobColor", obj.params.blobColor);
obj.info()->addParam(obj, "filterByArea", obj.params.filterByArea);
obj.info()->addParam(obj, "maxArea", obj.params.maxArea);
obj.info()->addParam(obj, "filterByCircularity", obj.params.filterByCircularity);
obj.info()->addParam(obj, "maxCircularity", obj.params.maxCircularity);
obj.info()->addParam(obj, "filterByInertia", obj.params.filterByInertia);
obj.info()->addParam(obj, "maxInertiaRatio", obj.params.maxInertiaRatio);
obj.info()->addParam(obj, "filterByConvexity", obj.params.filterByConvexity);
obj.info()->addParam(obj, "maxConvexity", obj.params.maxConvexity);
)
///////////////////////////////////////////////////////////////////////////////////////////////////////////
class CV_EXPORTS HarrisDetector : public GFTTDetector
{
public:
HarrisDetector( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1,
int blockSize=3, bool useHarrisDetector=true, double k=0.04 );
AlgorithmInfo* info() const;
};
inline HarrisDetector::HarrisDetector( int _maxCorners, double _qualityLevel, double _minDistance,
int _blockSize, bool _useHarrisDetector, double _k )
: GFTTDetector( _maxCorners, _qualityLevel, _minDistance, _blockSize, _useHarrisDetector, _k ) {}
CV_INIT_ALGORITHM(HarrisDetector, "Feature2D.HARRIS",
obj.info()->addParam(obj, "nfeatures", obj.nfeatures);
obj.info()->addParam(obj, "qualityLevel", obj.qualityLevel);
obj.info()->addParam(obj, "minDistance", obj.minDistance);
obj.info()->addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
obj.info()->addParam(obj, "k", obj.k))
////////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(BFMatcher, "DescriptorMatcher.BFMatcher",
obj.info()->addParam(obj, "normType", obj.normType);
obj.info()->addParam(obj, "crossCheck", obj.crossCheck))
CV_INIT_ALGORITHM(FlannBasedMatcher, "DescriptorMatcher.FlannBasedMatcher",)
///////////////////////////////////////////////////////////////////////////////////////////////////////////
bool cv::initModule_features2d(void)
{
bool all = true;
all &= !BRISK_info_auto.name().empty();
all &= !FastFeatureDetector_info_auto.name().empty();
all &= !MSER_info_auto.name().empty();
all &= !ORB_info_auto.name().empty();
all &= !GFTTDetector_info_auto.name().empty();
all &= !KAZE_info_auto.name().empty();
all &= !AKAZE_info_auto.name().empty();
all &= !HarrisDetector_info_auto.name().empty();
all &= !BFMatcher_info_auto.name().empty();
all &= !FlannBasedMatcher_info_auto.name().empty();
return all;
}

126
modules/features2d/src/gftt.cpp Normal file
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@ -0,0 +1,126 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv
{
class GFTTDetector_Impl : public GFTTDetector
{
public:
GFTTDetector_Impl( int _nfeatures, double _qualityLevel,
double _minDistance, int _blockSize,
bool _useHarrisDetector, double _k )
: nfeatures(_nfeatures), qualityLevel(_qualityLevel), minDistance(_minDistance),
blockSize(_blockSize), useHarrisDetector(_useHarrisDetector), k(_k)
{
}
void set(int prop, double value)
{
if( prop == USE_HARRIS_DETECTOR )
useHarrisDetector = value != 0;
else
CV_Error(Error::StsBadArg, "");
}
double get(int prop) const
{
double value = 0;
if( prop == USE_HARRIS_DETECTOR )
value = useHarrisDetector;
else
CV_Error(Error::StsBadArg, "");
return value;
}
void detect( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask )
{
std::vector<Point2f> corners;
if (_image.isUMat())
{
UMat ugrayImage;
if( _image.type() != CV_8U )
cvtColor( _image, ugrayImage, COLOR_BGR2GRAY );
else
ugrayImage = _image.getUMat();
goodFeaturesToTrack( ugrayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
blockSize, useHarrisDetector, k );
}
else
{
Mat image = _image.getMat(), grayImage = image;
if( image.type() != CV_8U )
cvtColor( image, grayImage, COLOR_BGR2GRAY );
goodFeaturesToTrack( grayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
blockSize, useHarrisDetector, k );
}
keypoints.resize(corners.size());
std::vector<Point2f>::const_iterator corner_it = corners.begin();
std::vector<KeyPoint>::iterator keypoint_it = keypoints.begin();
for( ; corner_it != corners.end(); ++corner_it, ++keypoint_it )
*keypoint_it = KeyPoint( *corner_it, (float)blockSize );
}
int nfeatures;
double qualityLevel;
double minDistance;
int blockSize;
bool useHarrisDetector;
double k;
};
Ptr<GFTTDetector> GFTTDetector::create( int _nfeatures, double _qualityLevel,
double _minDistance, int _blockSize,
bool _useHarrisDetector, double _k )
{
return makePtr<GFTTDetector_Impl>(_nfeatures, _qualityLevel,
_minDistance, _blockSize, _useHarrisDetector, _k);
}
}

228
modules/features2d/src/kaze.cpp
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@ -52,153 +52,119 @@ http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla12eccv.pd
namespace cv
{
KAZE::KAZE()
: extended(false)
, upright(false)
, threshold(0.001f)
, octaves(4)
, sublevels(4)
, diffusivity(DIFF_PM_G2)
{
}
KAZE::KAZE(bool _extended, bool _upright, float _threshold, int _octaves,
int _sublevels, int _diffusivity)
class KAZE_Impl : public KAZE
{
public:
KAZE_Impl(bool _extended, bool _upright, float _threshold, int _octaves,
int _sublevels, int _diffusivity)
: extended(_extended)
, upright(_upright)
, threshold(_threshold)
, octaves(_octaves)
, sublevels(_sublevels)
, diffusivity(_diffusivity)
{
}
KAZE::~KAZE()
{
}
// returns the descriptor size in bytes
int KAZE::descriptorSize() const
{
return extended ? 128 : 64;
}
// returns the descriptor type
int KAZE::descriptorType() const
{
return CV_32F;
}
// returns the default norm type
int KAZE::defaultNorm() const
{
return NORM_L2;
}
void KAZE::operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const
{
detectImpl(image, keypoints, mask);
}
void KAZE::operator()(InputArray image, InputArray mask,
std::vector<KeyPoint>& keypoints,
OutputArray descriptors,
bool useProvidedKeypoints) const
{
cv::Mat img = image.getMat();
if (img.type() != CV_8UC1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
cv::Mat& desc = descriptors.getMatRef();
KAZEOptions options;
options.img_width = img.cols;
options.img_height = img.rows;
options.extended = extended;
options.upright = upright;
options.dthreshold = threshold;
options.omax = octaves;
options.nsublevels = sublevels;
options.diffusivity = diffusivity;
KAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
if (!useProvidedKeypoints)
{
impl.Feature_Detection(keypoints);
}
if (!mask.empty())
virtual ~KAZE_Impl() {}
// returns the descriptor size in bytes
int descriptorSize() const
{
cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
return extended ? 128 : 64;
}
impl.Feature_Description(keypoints, desc);
CV_Assert((!desc.rows || desc.cols == descriptorSize()));
CV_Assert((!desc.rows || (desc.type() == descriptorType())));
}
void KAZE::detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
{
Mat img = image.getMat();
if (img.type() != CV_8UC1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
KAZEOptions options;
options.img_width = img.cols;
options.img_height = img.rows;
options.extended = extended;
options.upright = upright;
options.dthreshold = threshold;
options.omax = octaves;
options.nsublevels = sublevels;
options.diffusivity = diffusivity;
KAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
impl.Feature_Detection(keypoints);
if (!mask.empty())
// returns the descriptor type
int descriptorType() const
{
cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
return CV_32F;
}
}
void KAZE::computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
// returns the default norm type
int defaultNorm() const
{
return NORM_L2;
}
void detectAndCompute(InputArray image, InputArray mask,
std::vector<KeyPoint>& keypoints,
OutputArray descriptors,
bool useProvidedKeypoints)
{
cv::Mat img = image.getMat();
if (img.type() != CV_8UC1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
KAZEOptions options;
options.img_width = img.cols;
options.img_height = img.rows;
options.extended = extended;
options.upright = upright;
options.dthreshold = threshold;
options.omax = octaves;
options.nsublevels = sublevels;
options.diffusivity = diffusivity;
KAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
if (!useProvidedKeypoints)
{
impl.Feature_Detection(keypoints);
}
if (!mask.empty())
{
cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
}
if( descriptors.needed() )
{
Mat& desc = descriptors.getMatRef();
impl.Feature_Description(keypoints, desc);
CV_Assert((!desc.rows || desc.cols == descriptorSize()));
CV_Assert((!desc.rows || (desc.type() == descriptorType())));
}
}
void write(FileStorage& fs) const
{
fs << "extended" << (int)extended;
fs << "upright" << (int)upright;
fs << "threshold" << threshold;
fs << "octaves" << octaves;
fs << "sublevels" << sublevels;
fs << "diffusivity" << diffusivity;
}
void read(const FileNode& fn)
{
extended = (int)fn["extended"] != 0;
upright = (int)fn["upright"] != 0;
threshold = (float)fn["threshold"];
octaves = (int)fn["octaves"];
sublevels = (int)fn["sublevels"];
diffusivity = (int)fn["diffusivity"];
}
bool extended;
bool upright;
float threshold;
int octaves;
int sublevels;
int diffusivity;
};
Ptr<KAZE> KAZE::create(bool extended, bool upright,
float threshold,
int octaves, int sublevels,
int diffusivity)
{
cv::Mat img = image.getMat();
if (img.type() != CV_8UC1)
cvtColor(image, img, COLOR_BGR2GRAY);
Mat img1_32;
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
cv::Mat& desc = descriptors.getMatRef();
KAZEOptions options;
options.img_width = img.cols;
options.img_height = img.rows;
options.extended = extended;
options.upright = upright;
options.dthreshold = threshold;
options.omax = octaves;
options.nsublevels = sublevels;
options.diffusivity = diffusivity;
KAZEFeatures impl(options);
impl.Create_Nonlinear_Scale_Space(img1_32);
impl.Feature_Description(keypoints, desc);
CV_Assert((!desc.rows || desc.cols == descriptorSize()));
CV_Assert((!desc.rows || (desc.type() == descriptorType())));
return makePtr<KAZE_Impl>(extended, upright, threshold, octaves, sublevels, diffusivity);
}
}

25
modules/features2d/src/kaze/AKAZEConfig.h
View File

@ -8,23 +8,8 @@
#ifndef __OPENCV_FEATURES_2D_AKAZE_CONFIG_H__
#define __OPENCV_FEATURES_2D_AKAZE_CONFIG_H__
/* ************************************************************************* */
// OpenCV
#include "../precomp.hpp"
#include <opencv2/features2d.hpp>
/* ************************************************************************* */
/// Lookup table for 2d gaussian (sigma = 2.5) where (0,0) is top left and (6,6) is bottom right
const float gauss25[7][7] = {
{ 0.02546481f, 0.02350698f, 0.01849125f, 0.01239505f, 0.00708017f, 0.00344629f, 0.00142946f },
{ 0.02350698f, 0.02169968f, 0.01706957f, 0.01144208f, 0.00653582f, 0.00318132f, 0.00131956f },
{ 0.01849125f, 0.01706957f, 0.01342740f, 0.00900066f, 0.00514126f, 0.00250252f, 0.00103800f },
{ 0.01239505f, 0.01144208f, 0.00900066f, 0.00603332f, 0.00344629f, 0.00167749f, 0.00069579f },
{ 0.00708017f, 0.00653582f, 0.00514126f, 0.00344629f, 0.00196855f, 0.00095820f, 0.00039744f },
{ 0.00344629f, 0.00318132f, 0.00250252f, 0.00167749f, 0.00095820f, 0.00046640f, 0.00019346f },
{ 0.00142946f, 0.00131956f, 0.00103800f, 0.00069579f, 0.00039744f, 0.00019346f, 0.00008024f }
};
namespace cv
{
/* ************************************************************************* */
/// AKAZE configuration options structure
struct AKAZEOptions {
@ -37,12 +22,12 @@ struct AKAZEOptions {
, soffset(1.6f)
, derivative_factor(1.5f)
, sderivatives(1.0)
, diffusivity(cv::DIFF_PM_G2)
, diffusivity(KAZE::DIFF_PM_G2)
, dthreshold(0.001f)
, min_dthreshold(0.00001f)
, descriptor(cv::DESCRIPTOR_MLDB)
, descriptor(AKAZE::DESCRIPTOR_MLDB)
, descriptor_size(0)
, descriptor_channels(3)
, descriptor_pattern_size(10)
@ -75,4 +60,6 @@ struct AKAZEOptions {
int kcontrast_nbins; ///< Number of bins for the contrast factor histogram
};
}
#endif

238
modules/features2d/src/kaze/AKAZEFeatures.cpp
View File

@ -6,6 +6,7 @@
* @author Pablo F. Alcantarilla, Jesus Nuevo
*/
#include "../precomp.hpp"
#include "AKAZEFeatures.h"
#include "fed.h"
#include "nldiffusion_functions.h"
@ -14,9 +15,9 @@
#include <iostream>
// Namespaces
namespace cv
{
using namespace std;
using namespace cv;
using namespace cv::details::kaze;
/* ************************************************************************* */
/**
@ -29,7 +30,7 @@ AKAZEFeatures::AKAZEFeatures(const AKAZEOptions& options) : options_(options) {
ncycles_ = 0;
reordering_ = true;
if (options_.descriptor_size > 0 && options_.descriptor >= cv::DESCRIPTOR_MLDB_UPRIGHT) {
if (options_.descriptor_size > 0 && options_.descriptor >= AKAZE::DESCRIPTOR_MLDB_UPRIGHT) {
generateDescriptorSubsample(descriptorSamples_, descriptorBits_, options_.descriptor_size,
options_.descriptor_pattern_size, options_.descriptor_channels);
}
@ -60,14 +61,14 @@ void AKAZEFeatures::Allocate_Memory_Evolution(void) {
for (int j = 0; j < options_.nsublevels; j++) {
TEvolution step;
step.Lx = cv::Mat::zeros(level_height, level_width, CV_32F);
step.Ly = cv::Mat::zeros(level_height, level_width, CV_32F);
step.Lxx = cv::Mat::zeros(level_height, level_width, CV_32F);
step.Lxy = cv::Mat::zeros(level_height, level_width, CV_32F);
step.Lyy = cv::Mat::zeros(level_height, level_width, CV_32F);
step.Lt = cv::Mat::zeros(level_height, level_width, CV_32F);
step.Ldet = cv::Mat::zeros(level_height, level_width, CV_32F);
step.Lsmooth = cv::Mat::zeros(level_height, level_width, CV_32F);
step.Lx = Mat::zeros(level_height, level_width, CV_32F);
step.Ly = Mat::zeros(level_height, level_width, CV_32F);
step.Lxx = Mat::zeros(level_height, level_width, CV_32F);
step.Lxy = Mat::zeros(level_height, level_width, CV_32F);
step.Lyy = Mat::zeros(level_height, level_width, CV_32F);
step.Lt = Mat::zeros(level_height, level_width, CV_32F);
step.Ldet = Mat::zeros(level_height, level_width, CV_32F);
step.Lsmooth = Mat::zeros(level_height, level_width, CV_32F);
step.esigma = options_.soffset*pow(2.f, (float)(j) / (float)(options_.nsublevels) + i);
step.sigma_size = fRound(step.esigma);
step.etime = 0.5f*(step.esigma*step.esigma);
@ -96,7 +97,7 @@ void AKAZEFeatures::Allocate_Memory_Evolution(void) {
* @param img Input image for which the nonlinear scale space needs to be created
* @return 0 if the nonlinear scale space was created successfully, -1 otherwise
*/
int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img)
int AKAZEFeatures::Create_Nonlinear_Scale_Space(const Mat& img)
{
CV_Assert(evolution_.size() > 0);
@ -106,8 +107,8 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img)
evolution_[0].Lt.copyTo(evolution_[0].Lsmooth);
// Allocate memory for the flow and step images
cv::Mat Lflow = cv::Mat::zeros(evolution_[0].Lt.rows, evolution_[0].Lt.cols, CV_32F);
cv::Mat Lstep = cv::Mat::zeros(evolution_[0].Lt.rows, evolution_[0].Lt.cols, CV_32F);
Mat Lflow = Mat::zeros(evolution_[0].Lt.rows, evolution_[0].Lt.cols, CV_32F);
Mat Lstep = Mat::zeros(evolution_[0].Lt.rows, evolution_[0].Lt.cols, CV_32F);
// First compute the kcontrast factor
options_.kcontrast = compute_k_percentile(img, options_.kcontrast_percentile, 1.0f, options_.kcontrast_nbins, 0, 0);
@ -120,8 +121,8 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img)
options_.kcontrast = options_.kcontrast*0.75f;
// Allocate memory for the resized flow and step images
Lflow = cv::Mat::zeros(evolution_[i].Lt.rows, evolution_[i].Lt.cols, CV_32F);
Lstep = cv::Mat::zeros(evolution_[i].Lt.rows, evolution_[i].Lt.cols, CV_32F);
Lflow = Mat::zeros(evolution_[i].Lt.rows, evolution_[i].Lt.cols, CV_32F);
Lstep = Mat::zeros(evolution_[i].Lt.rows, evolution_[i].Lt.cols, CV_32F);
}
else {
evolution_[i - 1].Lt.copyTo(evolution_[i].Lt);
@ -135,16 +136,16 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img)
// Compute the conductivity equation
switch (options_.diffusivity) {
case cv::DIFF_PM_G1:
case KAZE::DIFF_PM_G1:
pm_g1(evolution_[i].Lx, evolution_[i].Ly, Lflow, options_.kcontrast);
break;
case cv::DIFF_PM_G2:
case KAZE::DIFF_PM_G2:
pm_g2(evolution_[i].Lx, evolution_[i].Ly, Lflow, options_.kcontrast);
break;
case cv::DIFF_WEICKERT:
case KAZE::DIFF_WEICKERT:
weickert_diffusivity(evolution_[i].Lx, evolution_[i].Ly, Lflow, options_.kcontrast);
break;
case cv::DIFF_CHARBONNIER:
case KAZE::DIFF_CHARBONNIER:
charbonnier_diffusivity(evolution_[i].Lx, evolution_[i].Ly, Lflow, options_.kcontrast);
break;
default:
@ -154,7 +155,7 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img)
// Perform FED n inner steps
for (int j = 0; j < nsteps_[i - 1]; j++) {
cv::details::kaze::nld_step_scalar(evolution_[i].Lt, Lflow, Lstep, tsteps_[i - 1][j]);
nld_step_scalar(evolution_[i].Lt, Lflow, Lstep, tsteps_[i - 1][j]);
}
}
@ -166,7 +167,7 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat& img)
* @brief This method selects interesting keypoints through the nonlinear scale space
* @param kpts Vector of detected keypoints
*/
void AKAZEFeatures::Feature_Detection(std::vector<cv::KeyPoint>& kpts)
void AKAZEFeatures::Feature_Detection(std::vector<KeyPoint>& kpts)
{
kpts.clear();
Compute_Determinant_Hessian_Response();
@ -175,7 +176,7 @@ void AKAZEFeatures::Feature_Detection(std::vector<cv::KeyPoint>& kpts)
}
/* ************************************************************************* */
class MultiscaleDerivativesAKAZEInvoker : public cv::ParallelLoopBody
class MultiscaleDerivativesAKAZEInvoker : public ParallelLoopBody
{
public:
explicit MultiscaleDerivativesAKAZEInvoker(std::vector<TEvolution>& ev, const AKAZEOptions& opt)
@ -184,7 +185,7 @@ public:
{
}
void operator()(const cv::Range& range) const
void operator()(const Range& range) const
{
std::vector<TEvolution>& evolution = *evolution_;
@ -218,7 +219,7 @@ private:
*/
void AKAZEFeatures::Compute_Multiscale_Derivatives(void)
{
cv::parallel_for_(cv::Range(0, (int)evolution_.size()),
parallel_for_(Range(0, (int)evolution_.size()),
MultiscaleDerivativesAKAZEInvoker(evolution_, options_));
}
@ -252,7 +253,7 @@ void AKAZEFeatures::Compute_Determinant_Hessian_Response(void) {
* @brief This method finds extrema in the nonlinear scale space
* @param kpts Vector of detected keypoints
*/
void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector<cv::KeyPoint>& kpts)
void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector<KeyPoint>& kpts)
{
float value = 0.0;
@ -260,14 +261,14 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector<cv::KeyPoint>& kpts)
int npoints = 0, id_repeated = 0;
int sigma_size_ = 0, left_x = 0, right_x = 0, up_y = 0, down_y = 0;
bool is_extremum = false, is_repeated = false, is_out = false;
cv::KeyPoint point;
vector<cv::KeyPoint> kpts_aux;
KeyPoint point;
vector<KeyPoint> kpts_aux;
// Set maximum size
if (options_.descriptor == cv::DESCRIPTOR_MLDB_UPRIGHT || options_.descriptor == cv::DESCRIPTOR_MLDB) {
if (options_.descriptor == AKAZE::DESCRIPTOR_MLDB_UPRIGHT || options_.descriptor == AKAZE::DESCRIPTOR_MLDB) {
smax = 10.0f*sqrtf(2.0f);
}
else if (options_.descriptor == cv::DESCRIPTOR_KAZE_UPRIGHT || options_.descriptor == cv::DESCRIPTOR_KAZE) {
else if (options_.descriptor == AKAZE::DESCRIPTOR_KAZE_UPRIGHT || options_.descriptor == AKAZE::DESCRIPTOR_KAZE) {
smax = 12.0f*sqrtf(2.0f);
}
@ -364,7 +365,7 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector<cv::KeyPoint>& kpts)
for (size_t i = 0; i < kpts_aux.size(); i++) {
is_repeated = false;
const cv::KeyPoint& pt = kpts_aux[i];
const KeyPoint& pt = kpts_aux[i];
for (size_t j = i + 1; j < kpts_aux.size(); j++) {
// Compare response with the upper scale
@ -391,7 +392,7 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector<cv::KeyPoint>& kpts)
* @brief This method performs subpixel refinement of the detected keypoints
* @param kpts Vector of detected keypoints
*/
void AKAZEFeatures::Do_Subpixel_Refinement(std::vector<cv::KeyPoint>& kpts)
void AKAZEFeatures::Do_Subpixel_Refinement(std::vector<KeyPoint>& kpts)
{
float Dx = 0.0, Dy = 0.0, ratio = 0.0;
float Dxx = 0.0, Dyy = 0.0, Dxy = 0.0;
@ -432,7 +433,7 @@ void AKAZEFeatures::Do_Subpixel_Refinement(std::vector<cv::KeyPoint>& kpts)
b(0) = -Dx;
b(1) = -Dy;
cv::solve(A, b, dst, DECOMP_LU);
solve(A, b, dst, DECOMP_LU);
if (fabs(dst(0)) <= 1.0f && fabs(dst(1)) <= 1.0f) {
kpts[i].pt.x = x + dst(0);
@ -455,10 +456,10 @@ void AKAZEFeatures::Do_Subpixel_Refinement(std::vector<cv::KeyPoint>& kpts)
/* ************************************************************************* */
class SURF_Descriptor_Upright_64_Invoker : public cv::ParallelLoopBody
class SURF_Descriptor_Upright_64_Invoker : public ParallelLoopBody
{
public:
SURF_Descriptor_Upright_64_Invoker(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc, std::vector<TEvolution>& evolution)
SURF_Descriptor_Upright_64_Invoker(std::vector<KeyPoint>& kpts, Mat& desc, std::vector<TEvolution>& evolution)
: keypoints_(&kpts)
, descriptors_(&desc)
, evolution_(&evolution)
@ -473,18 +474,18 @@ public:
}
}
void Get_SURF_Descriptor_Upright_64(const cv::KeyPoint& kpt, float* desc) const;
void Get_SURF_Descriptor_Upright_64(const KeyPoint& kpt, float* desc) const;
private:
std::vector<cv::KeyPoint>* keypoints_;
cv::Mat* descriptors_;
std::vector<KeyPoint>* keypoints_;
Mat* descriptors_;
std::vector<TEvolution>* evolution_;
};
class SURF_Descriptor_64_Invoker : public cv::ParallelLoopBody
class SURF_Descriptor_64_Invoker : public ParallelLoopBody
{
public:
SURF_Descriptor_64_Invoker(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc, std::vector<TEvolution>& evolution)
SURF_Descriptor_64_Invoker(std::vector<KeyPoint>& kpts, Mat& desc, std::vector<TEvolution>& evolution)
: keypoints_(&kpts)
, descriptors_(&desc)
, evolution_(&evolution)
@ -500,18 +501,18 @@ public:
}
}
void Get_SURF_Descriptor_64(const cv::KeyPoint& kpt, float* desc) const;
void Get_SURF_Descriptor_64(const KeyPoint& kpt, float* desc) const;
private:
std::vector<cv::KeyPoint>* keypoints_;
cv::Mat* descriptors_;
std::vector<KeyPoint>* keypoints_;
Mat* descriptors_;
std::vector<TEvolution>* evolution_;
};
class MSURF_Upright_Descriptor_64_Invoker : public cv::ParallelLoopBody
class MSURF_Upright_Descriptor_64_Invoker : public ParallelLoopBody
{
public:
MSURF_Upright_Descriptor_64_Invoker(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc, std::vector<TEvolution>& evolution)
MSURF_Upright_Descriptor_64_Invoker(std::vector<KeyPoint>& kpts, Mat& desc, std::vector<TEvolution>& evolution)
: keypoints_(&kpts)
, descriptors_(&desc)
, evolution_(&evolution)
@ -526,18 +527,18 @@ public:
}
}
void Get_MSURF_Upright_Descriptor_64(const cv::KeyPoint& kpt, float* desc) const;
void Get_MSURF_Upright_Descriptor_64(const KeyPoint& kpt, float* desc) const;
private:
std::vector<cv::KeyPoint>* keypoints_;
cv::Mat* descriptors_;
std::vector<KeyPoint>* keypoints_;
Mat* descriptors_;
std::vector<TEvolution>* evolution_;
};
class MSURF_Descriptor_64_Invoker : public cv::ParallelLoopBody
class MSURF_Descriptor_64_Invoker : public ParallelLoopBody
{
public:
MSURF_Descriptor_64_Invoker(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc, std::vector<TEvolution>& evolution)
MSURF_Descriptor_64_Invoker(std::vector<KeyPoint>& kpts, Mat& desc, std::vector<TEvolution>& evolution)
: keypoints_(&kpts)
, descriptors_(&desc)
, evolution_(&evolution)
@ -553,18 +554,18 @@ public:
}
}
void Get_MSURF_Descriptor_64(const cv::KeyPoint& kpt, float* desc) const;
void Get_MSURF_Descriptor_64(const KeyPoint& kpt, float* desc) const;
private:
std::vector<cv::KeyPoint>* keypoints_;
cv::Mat* descriptors_;
std::vector<KeyPoint>* keypoints_;
Mat* descriptors_;
std::vector<TEvolution>* evolution_;
};
class Upright_MLDB_Full_Descriptor_Invoker : public cv::ParallelLoopBody
class Upright_MLDB_Full_Descriptor_Invoker : public ParallelLoopBody
{
public:
Upright_MLDB_Full_Descriptor_Invoker(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc, std::vector<TEvolution>& evolution, AKAZEOptions& options)
Upright_MLDB_Full_Descriptor_Invoker(std::vector<KeyPoint>& kpts, Mat& desc, std::vector<TEvolution>& evolution, AKAZEOptions& options)
: keypoints_(&kpts)
, descriptors_(&desc)
, evolution_(&evolution)
@ -580,24 +581,24 @@ public:
}
}
void Get_Upright_MLDB_Full_Descriptor(const cv::KeyPoint& kpt, unsigned char* desc) const;
void Get_Upright_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char* desc) const;
private:
std::vector<cv::KeyPoint>* keypoints_;
cv::Mat* descriptors_;
std::vector<KeyPoint>* keypoints_;
Mat* descriptors_;
std::vector<TEvolution>* evolution_;
AKAZEOptions* options_;
};
class Upright_MLDB_Descriptor_Subset_Invoker : public cv::ParallelLoopBody
class Upright_MLDB_Descriptor_Subset_Invoker : public ParallelLoopBody
{
public:
Upright_MLDB_Descriptor_Subset_Invoker(std::vector<cv::KeyPoint>& kpts,
cv::Mat& desc,
Upright_MLDB_Descriptor_Subset_Invoker(std::vector<KeyPoint>& kpts,
Mat& desc,
std::vector<TEvolution>& evolution,
AKAZEOptions& options,
cv::Mat descriptorSamples,
cv::Mat descriptorBits)
Mat descriptorSamples,
Mat descriptorBits)
: keypoints_(&kpts)
, descriptors_(&desc)
, evolution_(&evolution)
@ -615,22 +616,22 @@ public:
}
}
void Get_Upright_MLDB_Descriptor_Subset(const cv::KeyPoint& kpt, unsigned char* desc) const;
void Get_Upright_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char* desc) const;
private:
std::vector<cv::KeyPoint>* keypoints_;
cv::Mat* descriptors_;
std::vector<KeyPoint>* keypoints_;
Mat* descriptors_;
std::vector<TEvolution>* evolution_;
AKAZEOptions* options_;
cv::Mat descriptorSamples_; // List of positions in the grids to sample LDB bits from.
cv::Mat descriptorBits_;
Mat descriptorSamples_; // List of positions in the grids to sample LDB bits from.
Mat descriptorBits_;
};
class MLDB_Full_Descriptor_Invoker : public cv::ParallelLoopBody
class MLDB_Full_Descriptor_Invoker : public ParallelLoopBody
{
public:
MLDB_Full_Descriptor_Invoker(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc, std::vector<TEvolution>& evolution, AKAZEOptions& options)
MLDB_Full_Descriptor_Invoker(std::vector<KeyPoint>& kpts, Mat& desc, std::vector<TEvolution>& evolution, AKAZEOptions& options)
: keypoints_(&kpts)
, descriptors_(&desc)
, evolution_(&evolution)
@ -647,28 +648,28 @@ public:
}
}
void Get_MLDB_Full_Descriptor(const cv::KeyPoint& kpt, unsigned char* desc) const;
void Get_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char* desc) const;
void MLDB_Fill_Values(float* values, int sample_step, int level,
float xf, float yf, float co, float si, float scale) const;
void MLDB_Binary_Comparisons(float* values, unsigned char* desc,
int count, int& dpos) const;
private:
std::vector<cv::KeyPoint>* keypoints_;
cv::Mat* descriptors_;
std::vector<KeyPoint>* keypoints_;
Mat* descriptors_;
std::vector<TEvolution>* evolution_;
AKAZEOptions* options_;
};
class MLDB_Descriptor_Subset_Invoker : public cv::ParallelLoopBody
class MLDB_Descriptor_Subset_Invoker : public ParallelLoopBody
{
public:
MLDB_Descriptor_Subset_Invoker(std::vector<cv::KeyPoint>& kpts,
cv::Mat& desc,
MLDB_Descriptor_Subset_Invoker(std::vector<KeyPoint>& kpts,
Mat& desc,
std::vector<TEvolution>& evolution,
AKAZEOptions& options,
cv::Mat descriptorSamples,
cv::Mat descriptorBits)
Mat descriptorSamples,
Mat descriptorBits)
: keypoints_(&kpts)
, descriptors_(&desc)
, evolution_(&evolution)
@ -687,16 +688,16 @@ public:
}
}
void Get_MLDB_Descriptor_Subset(const cv::KeyPoint& kpt, unsigned char* desc) const;
void Get_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char* desc) const;
private:
std::vector<cv::KeyPoint>* keypoints_;
cv::Mat* descriptors_;
std::vector<KeyPoint>* keypoints_;
Mat* descriptors_;
std::vector<TEvolution>* evolution_;
AKAZEOptions* options_;
cv::Mat descriptorSamples_; // List of positions in the grids to sample LDB bits from.
cv::Mat descriptorBits_;
Mat descriptorSamples_; // List of positions in the grids to sample LDB bits from.
Mat descriptorBits_;
};
/**
@ -704,7 +705,7 @@ private:
* @param kpts Vector of detected keypoints
* @param desc Matrix to store the descriptors
*/
void AKAZEFeatures::Compute_Descriptors(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc)
void AKAZEFeatures::Compute_Descriptors(std::vector<KeyPoint>& kpts, Mat& desc)
{
for(size_t i = 0; i < kpts.size(); i++)
{
@ -712,47 +713,47 @@ void AKAZEFeatures::Compute_Descriptors(std::vector<cv::KeyPoint>& kpts, cv::Mat
}
// Allocate memory for the matrix with the descriptors
if (options_.descriptor < cv::DESCRIPTOR_MLDB_UPRIGHT) {
desc = cv::Mat::zeros((int)kpts.size(), 64, CV_32FC1);
if (options_.descriptor < AKAZE::DESCRIPTOR_MLDB_UPRIGHT) {
desc = Mat::zeros((int)kpts.size(), 64, CV_32FC1);
}
else {
// We use the full length binary descriptor -> 486 bits
if (options_.descriptor_size == 0) {
int t = (6 + 36 + 120)*options_.descriptor_channels;
desc = cv::Mat::zeros((int)kpts.size(), (int)ceil(t / 8.), CV_8UC1);
desc = Mat::zeros((int)kpts.size(), (int)ceil(t / 8.), CV_8UC1);
}
else {
// We use the random bit selection length binary descriptor
desc = cv::Mat::zeros((int)kpts.size(), (int)ceil(options_.descriptor_size / 8.), CV_8UC1);
desc = Mat::zeros((int)kpts.size(), (int)ceil(options_.descriptor_size / 8.), CV_8UC1);
}
}
switch (options_.descriptor)
{
case cv::DESCRIPTOR_KAZE_UPRIGHT: // Upright descriptors, not invariant to rotation
case AKAZE::DESCRIPTOR_KAZE_UPRIGHT: // Upright descriptors, not invariant to rotation
{
cv::parallel_for_(cv::Range(0, (int)kpts.size()), MSURF_Upright_Descriptor_64_Invoker(kpts, desc, evolution_));
parallel_for_(Range(0, (int)kpts.size()), MSURF_Upright_Descriptor_64_Invoker(kpts, desc, evolution_));
}
break;
case cv::DESCRIPTOR_KAZE:
case AKAZE::DESCRIPTOR_KAZE:
{
cv::parallel_for_(cv::Range(0, (int)kpts.size()), MSURF_Descriptor_64_Invoker(kpts, desc, evolution_));
parallel_for_(Range(0, (int)kpts.size()), MSURF_Descriptor_64_Invoker(kpts, desc, evolution_));
}
break;
case cv::DESCRIPTOR_MLDB_UPRIGHT: // Upright descriptors, not invariant to rotation
case AKAZE::DESCRIPTOR_MLDB_UPRIGHT: // Upright descriptors, not invariant to rotation
{
if (options_.descriptor_size == 0)
cv::parallel_for_(cv::Range(0, (int)kpts.size()), Upright_MLDB_Full_Descriptor_Invoker(kpts, desc, evolution_, options_));
parallel_for_(Range(0, (int)kpts.size()), Upright_MLDB_Full_Descriptor_Invoker(kpts, desc, evolution_, options_));
else
cv::parallel_for_(cv::Range(0, (int)kpts.size()), Upright_MLDB_Descriptor_Subset_Invoker(kpts, desc, evolution_, options_, descriptorSamples_, descriptorBits_));
parallel_for_(Range(0, (int)kpts.size()), Upright_MLDB_Descriptor_Subset_Invoker(kpts, desc, evolution_, options_, descriptorSamples_, descriptorBits_));
}
break;
case cv::DESCRIPTOR_MLDB:
case AKAZE::DESCRIPTOR_MLDB:
{
if (options_.descriptor_size == 0)
cv::parallel_for_(cv::Range(0, (int)kpts.size()), MLDB_Full_Descriptor_Invoker(kpts, desc, evolution_, options_));
parallel_for_(Range(0, (int)kpts.size()), MLDB_Full_Descriptor_Invoker(kpts, desc, evolution_, options_));
else
cv::parallel_for_(cv::Range(0, (int)kpts.size()), MLDB_Descriptor_Subset_Invoker(kpts, desc, evolution_, options_, descriptorSamples_, descriptorBits_));
parallel_for_(Range(0, (int)kpts.size()), MLDB_Descriptor_Subset_Invoker(kpts, desc, evolution_, options_, descriptorSamples_, descriptorBits_));
}
break;
}
@ -765,7 +766,20 @@ void AKAZEFeatures::Compute_Descriptors(std::vector<cv::KeyPoint>& kpts, cv::Mat
* @note The orientation is computed using a similar approach as described in the
* original SURF method. See Bay et al., Speeded Up Robust Features, ECCV 2006
*/
void AKAZEFeatures::Compute_Main_Orientation(cv::KeyPoint& kpt, const std::vector<TEvolution>& evolution_) {
void AKAZEFeatures::Compute_Main_Orientation(KeyPoint& kpt, const std::vector<TEvolution>& evolution_)
{
/* ************************************************************************* */
/// Lookup table for 2d gaussian (sigma = 2.5) where (0,0) is top left and (6,6) is bottom right
static const float gauss25[7][7] =
{
{ 0.02546481f, 0.02350698f, 0.01849125f, 0.01239505f, 0.00708017f, 0.00344629f, 0.00142946f },
{ 0.02350698f, 0.02169968f, 0.01706957f, 0.01144208f, 0.00653582f, 0.00318132f, 0.00131956f },
{ 0.01849125f, 0.01706957f, 0.01342740f, 0.00900066f, 0.00514126f, 0.00250252f, 0.00103800f },
{ 0.01239505f, 0.01144208f, 0.00900066f, 0.00603332f, 0.00344629f, 0.00167749f, 0.00069579f },
{ 0.00708017f, 0.00653582f, 0.00514126f, 0.00344629f, 0.00196855f, 0.00095820f, 0.00039744f },
{ 0.00344629f, 0.00318132f, 0.00250252f, 0.00167749f, 0.00095820f, 0.00046640f, 0.00019346f },
{ 0.00142946f, 0.00131956f, 0.00103800f, 0.00069579f, 0.00039744f, 0.00019346f, 0.00008024f }
};
int ix = 0, iy = 0, idx = 0, s = 0, level = 0;
float xf = 0.0, yf = 0.0, gweight = 0.0, ratio = 0.0;
@ -840,7 +854,7 @@ void AKAZEFeatures::Compute_Main_Orientation(cv::KeyPoint& kpt, const std::vecto
* from Agrawal et al., CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,
* ECCV 2008
*/
void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const cv::KeyPoint& kpt, float *desc) const {
void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const KeyPoint& kpt, float *desc) const {
float dx = 0.0, dy = 0.0, mdx = 0.0, mdy = 0.0, gauss_s1 = 0.0, gauss_s2 = 0.0;
float rx = 0.0, ry = 0.0, len = 0.0, xf = 0.0, yf = 0.0, ys = 0.0, xs = 0.0;
@ -963,7 +977,7 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
* from Agrawal et al., CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,
* ECCV 2008
*/
void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const cv::KeyPoint& kpt, float *desc) const {
void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, float *desc) const {
float dx = 0.0, dy = 0.0, mdx = 0.0, mdy = 0.0, gauss_s1 = 0.0, gauss_s2 = 0.0;
float rx = 0.0, ry = 0.0, rrx = 0.0, rry = 0.0, len = 0.0, xf = 0.0, yf = 0.0, ys = 0.0, xs = 0.0;
@ -1087,7 +1101,7 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const cv::KeyPoint& kp
* @param kpt Input keypoint
* @param desc Descriptor vector
*/
void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(const cv::KeyPoint& kpt, unsigned char *desc) const {
void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char *desc) const {
float di = 0.0, dx = 0.0, dy = 0.0;
float ri = 0.0, rx = 0.0, ry = 0.0, xf = 0.0, yf = 0.0;
@ -1100,9 +1114,9 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
const std::vector<TEvolution>& evolution = *evolution_;
// Matrices for the M-LDB descriptor
cv::Mat values_1 = cv::Mat::zeros(4, options.descriptor_channels, CV_32FC1);
cv::Mat values_2 = cv::Mat::zeros(9, options.descriptor_channels, CV_32FC1);
cv::Mat values_3 = cv::Mat::zeros(16, options.descriptor_channels, CV_32FC1);
Mat values_1 = Mat::zeros(4, options.descriptor_channels, CV_32FC1);
Mat values_2 = Mat::zeros(9, options.descriptor_channels, CV_32FC1);
Mat values_3 = Mat::zeros(16, options.descriptor_channels, CV_32FC1);
// Get the information from the keypoint
ratio = (float)(1 << kpt.octave);
@ -1381,7 +1395,7 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Binary_Comparisons(float* values, unsign
* @param kpt Input keypoint
* @param desc Descriptor vector
*/
void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const cv::KeyPoint& kpt, unsigned char *desc) const {
void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char *desc) const {
const int max_channels = 3;
CV_Assert(options_->descriptor_channels <= max_channels);
@ -1414,7 +1428,7 @@ void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const cv::KeyPoint&
* @param kpt Input keypoint
* @param desc Descriptor vector
*/
void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const cv::KeyPoint& kpt, unsigned char *desc) const {
void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char *desc) const {
float di = 0.f, dx = 0.f, dy = 0.f;
float rx = 0.f, ry = 0.f;
@ -1435,7 +1449,7 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const cv::KeyPoi
float si = sin(angle);
// Allocate memory for the matrix of values
cv::Mat values = cv::Mat_<float>::zeros((4 + 9 + 16)*options.descriptor_channels, 1);
Mat values = Mat_<float>::zeros((4 + 9 + 16)*options.descriptor_channels, 1);
// Sample everything, but only do the comparisons
vector<int> steps(3);
@ -1508,7 +1522,7 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const cv::KeyPoi
* @param kpt Input keypoint
* @param desc Descriptor vector
*/
void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(const cv::KeyPoint& kpt, unsigned char *desc) const {
void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char *desc) const {
float di = 0.0f, dx = 0.0f, dy = 0.0f;
float rx = 0.0f, ry = 0.0f;
@ -1526,7 +1540,7 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
float xf = kpt.pt.x / ratio;
// Allocate memory for the matrix of values
Mat values = cv::Mat_<float>::zeros((4 + 9 + 16)*options.descriptor_channels, 1);
Mat values = Mat_<float>::zeros((4 + 9 + 16)*options.descriptor_channels, 1);
vector<int> steps(3);
steps.at(0) = options.descriptor_pattern_size;
@ -1600,7 +1614,7 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
* @note The function keeps the 18 bits (3-channels by 6 comparisons) of the
* coarser grid, since it provides the most robust estimations
*/
void generateDescriptorSubsample(cv::Mat& sampleList, cv::Mat& comparisons, int nbits,
void generateDescriptorSubsample(Mat& sampleList, Mat& comparisons, int nbits,
int pattern_size, int nchannels) {
int ssz = 0;
@ -1702,3 +1716,5 @@ void generateDescriptorSubsample(cv::Mat& sampleList, cv::Mat& comparisons, int
sampleList = samples.rowRange(0, count).clone();
comparisons = comps.rowRange(0, nbits).clone();
}
}

8
modules/features2d/src/kaze/AKAZEFeatures.h
View File

@ -11,10 +11,12 @@
/* ************************************************************************* */
// Includes
#include "../precomp.hpp"
#include "AKAZEConfig.h"
#include "TEvolution.h"
namespace cv
{
/* ************************************************************************* */
// AKAZE Class Declaration
class AKAZEFeatures {
@ -22,7 +24,7 @@ class AKAZEFeatures {
private:
AKAZEOptions options_; ///< Configuration options for AKAZE
std::vector<TEvolution> evolution_; ///< Vector of nonlinear diffusion evolution
std::vector<TEvolution> evolution_; ///< Vector of nonlinear diffusion evolution
/// FED parameters
int ncycles_; ///< Number of cycles
@ -59,4 +61,6 @@ public:
void generateDescriptorSubsample(cv::Mat& sampleList, cv::Mat& comparisons,
int nbits, int pattern_size, int nchannels);
}
#endif

6
modules/features2d/src/kaze/KAZEConfig.h
View File

@ -12,12 +12,14 @@
#include "../precomp.hpp"
#include <opencv2/features2d.hpp>
namespace cv
{
//*************************************************************************************
struct KAZEOptions {
KAZEOptions()
: diffusivity(cv::DIFF_PM_G2)
: diffusivity(KAZE::DIFF_PM_G2)
, soffset(1.60f)
, omax(4)
@ -49,4 +51,6 @@ struct KAZEOptions {
bool extended;
};
}
#endif

218
modules/features2d/src/kaze/KAZEFeatures.cpp
View File

@ -20,14 +20,15 @@
* @date Jan 21, 2012
* @author Pablo F. Alcantarilla
*/
#include "../precomp.hpp"
#include "KAZEFeatures.h"
#include "utils.h"
namespace cv
{
// Namespaces
using namespace std;
using namespace cv;
using namespace cv::details::kaze;
/* ************************************************************************* */
/**
@ -52,19 +53,20 @@ KAZEFeatures::KAZEFeatures(KAZEOptions& options)
void KAZEFeatures::Allocate_Memory_Evolution(void) {
// Allocate the dimension of the matrices for the evolution
for (int i = 0; i <= options_.omax - 1; i++) {
for (int j = 0; j <= options_.nsublevels - 1; j++) {
for (int i = 0; i <= options_.omax - 1; i++)
{
for (int j = 0; j <= options_.nsublevels - 1; j++)
{
TEvolution aux;
aux.Lx = cv::Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Ly = cv::Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Lxx = cv::Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Lxy = cv::Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Lyy = cv::Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Lt = cv::Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Lsmooth = cv::Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Ldet = cv::Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.esigma = options_.soffset*pow((float)2.0f, (float)(j) / (float)(options_.nsublevels)+i);
aux.Lx = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Ly = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Lxx = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Lxy = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Lyy = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Lt = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Lsmooth = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Ldet = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.esigma = options_.soffset*pow((float)2.0f, (float)(j) / (float)(options_.nsublevels)+i);
aux.etime = 0.5f*(aux.esigma*aux.esigma);
aux.sigma_size = fRound(aux.esigma);
aux.octave = i;
@ -74,7 +76,8 @@ void KAZEFeatures::Allocate_Memory_Evolution(void) {
}
// Allocate memory for the FED number of cycles and time steps
for (size_t i = 1; i < evolution_.size(); i++) {
for (size_t i = 1; i < evolution_.size(); i++)
{
int naux = 0;
vector<float> tau;
float ttime = 0.0;
@ -92,47 +95,43 @@ void KAZEFeatures::Allocate_Memory_Evolution(void) {
* @param img Input image for which the nonlinear scale space needs to be created
* @return 0 if the nonlinear scale space was created successfully. -1 otherwise
*/
int KAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat &img)
int KAZEFeatures::Create_Nonlinear_Scale_Space(const Mat &img)
{
CV_Assert(evolution_.size() > 0);
// Copy the original image to the first level of the evolution
img.copyTo(evolution_[0].Lt);
gaussian_2D_convolution(evolution_[0].Lt, evolution_[0].Lt, 0, 0, options_.soffset);
gaussian_2D_convolution(evolution_[0].Lt, evolution_[0].Lsmooth, 0, 0, options_.sderivatives);
gaussian_2D_convolution(evolution_[0].Lt, evolution_[0].Lt, 0, 0, options_.soffset);
gaussian_2D_convolution(evolution_[0].Lt, evolution_[0].Lsmooth, 0, 0, options_.sderivatives);
// Firstly compute the kcontrast factor
Compute_KContrast(evolution_[0].Lt, options_.kcontrast_percentille);
// Allocate memory for the flow and step images
cv::Mat Lflow = cv::Mat::zeros(evolution_[0].Lt.rows, evolution_[0].Lt.cols, CV_32F);
cv::Mat Lstep = cv::Mat::zeros(evolution_[0].Lt.rows, evolution_[0].Lt.cols, CV_32F);
Mat Lflow = Mat::zeros(evolution_[0].Lt.rows, evolution_[0].Lt.cols, CV_32F);
Mat Lstep = Mat::zeros(evolution_[0].Lt.rows, evolution_[0].Lt.cols, CV_32F);
// Now generate the rest of evolution levels
for (size_t i = 1; i < evolution_.size(); i++) {
for (size_t i = 1; i < evolution_.size(); i++)
{
evolution_[i - 1].Lt.copyTo(evolution_[i].Lt);
gaussian_2D_convolution(evolution_[i - 1].Lt, evolution_[i].Lsmooth, 0, 0, options_.sderivatives);
gaussian_2D_convolution(evolution_[i - 1].Lt, evolution_[i].Lsmooth, 0, 0, options_.sderivatives);
// Compute the Gaussian derivatives Lx and Ly
Scharr(evolution_[i].Lsmooth, evolution_[i].Lx, CV_32F, 1, 0, 1, 0, BORDER_DEFAULT);
Scharr(evolution_[i].Lsmooth, evolution_[i].Ly, CV_32F, 0, 1, 1, 0, BORDER_DEFAULT);
// Compute the conductivity equation
if (options_.diffusivity == cv::DIFF_PM_G1) {
pm_g1(evolution_[i].Lx, evolution_[i].Ly, Lflow, options_.kcontrast);
}
else if (options_.diffusivity == cv::DIFF_PM_G2) {
pm_g2(evolution_[i].Lx, evolution_[i].Ly, Lflow, options_.kcontrast);
}
else if (options_.diffusivity == cv::DIFF_WEICKERT) {
weickert_diffusivity(evolution_[i].Lx, evolution_[i].Ly, Lflow, options_.kcontrast);
}
if (options_.diffusivity == KAZE::DIFF_PM_G1)
pm_g1(evolution_[i].Lx, evolution_[i].Ly, Lflow, options_.kcontrast);
else if (options_.diffusivity == KAZE::DIFF_PM_G2)
pm_g2(evolution_[i].Lx, evolution_[i].Ly, Lflow, options_.kcontrast);
else if (options_.diffusivity == KAZE::DIFF_WEICKERT)
weickert_diffusivity(evolution_[i].Lx, evolution_[i].Ly, Lflow, options_.kcontrast);
// Perform FED n inner steps
for (int j = 0; j < nsteps_[i - 1]; j++) {
for (int j = 0; j < nsteps_[i - 1]; j++)
nld_step_scalar(evolution_[i].Lt, Lflow, Lstep, tsteps_[i - 1][j]);
}
}
return 0;
@ -144,9 +143,9 @@ int KAZEFeatures::Create_Nonlinear_Scale_Space(const cv::Mat &img)
* @param img Input image
* @param kpercentile Percentile of the gradient histogram
*/
void KAZEFeatures::Compute_KContrast(const cv::Mat &img, const float &kpercentile)
void KAZEFeatures::Compute_KContrast(const Mat &img, const float &kpercentile)
{
options_.kcontrast = compute_k_percentile(img, kpercentile, options_.sderivatives, options_.kcontrast_bins, 0, 0);
options_.kcontrast = compute_k_percentile(img, kpercentile, options_.sderivatives, options_.kcontrast_bins, 0, 0);
}
/* ************************************************************************* */
@ -181,7 +180,7 @@ void KAZEFeatures::Compute_Detector_Response(void)
* @brief This method selects interesting keypoints through the nonlinear scale space
* @param kpts Vector of keypoints
*/
void KAZEFeatures::Feature_Detection(std::vector<cv::KeyPoint>& kpts)
void KAZEFeatures::Feature_Detection(std::vector<KeyPoint>& kpts)
{
kpts.clear();
Compute_Detector_Response();
@ -190,14 +189,14 @@ void KAZEFeatures::Feature_Detection(std::vector<cv::KeyPoint>& kpts)
}
/* ************************************************************************* */
class MultiscaleDerivativesKAZEInvoker : public cv::ParallelLoopBody
class MultiscaleDerivativesKAZEInvoker : public ParallelLoopBody
{
public:
explicit MultiscaleDerivativesKAZEInvoker(std::vector<TEvolution>& ev) : evolution_(&ev)
{
}
void operator()(const cv::Range& range) const
void operator()(const Range& range) const
{
std::vector<TEvolution>& evolution = *evolution_;
for (int i = range.start; i < range.end; i++)
@ -226,74 +225,79 @@ private:
*/
void KAZEFeatures::Compute_Multiscale_Derivatives(void)
{
cv::parallel_for_(cv::Range(0, (int)evolution_.size()),
parallel_for_(Range(0, (int)evolution_.size()),
MultiscaleDerivativesKAZEInvoker(evolution_));
}
/* ************************************************************************* */
class FindExtremumKAZEInvoker : public cv::ParallelLoopBody
class FindExtremumKAZEInvoker : public ParallelLoopBody
{
public:
explicit FindExtremumKAZEInvoker(std::vector<TEvolution>& ev, std::vector<std::vector<cv::KeyPoint> >& kpts_par,
explicit FindExtremumKAZEInvoker(std::vector<TEvolution>& ev, std::vector<std::vector<KeyPoint> >& kpts_par,
const KAZEOptions& options) : evolution_(&ev), kpts_par_(&kpts_par), options_(options)
{
}
void operator()(const cv::Range& range) const
void operator()(const Range& range) const
{
std::vector<TEvolution>& evolution = *evolution_;
std::vector<std::vector<cv::KeyPoint> >& kpts_par = *kpts_par_;
std::vector<std::vector<KeyPoint> >& kpts_par = *kpts_par_;
for (int i = range.start; i < range.end; i++)
{
float value = 0.0;
bool is_extremum = false;
for (int ix = 1; ix < options_.img_height - 1; ix++) {
for (int jx = 1; jx < options_.img_width - 1; jx++) {
for (int ix = 1; ix < options_.img_height - 1; ix++)
{
for (int jx = 1; jx < options_.img_width - 1; jx++)
{
is_extremum = false;
value = *(evolution[i].Ldet.ptr<float>(ix)+jx);
is_extremum = false;
value = *(evolution[i].Ldet.ptr<float>(ix)+jx);
// Filter the points with the detector threshold
if (value > options_.dthreshold) {
if (value >= *(evolution[i].Ldet.ptr<float>(ix)+jx - 1)) {
// First check on the same scale
if (check_maximum_neighbourhood(evolution[i].Ldet, 1, value, ix, jx, 1)) {
// Now check on the lower scale
if (check_maximum_neighbourhood(evolution[i - 1].Ldet, 1, value, ix, jx, 0)) {
// Now check on the upper scale
if (check_maximum_neighbourhood(evolution[i + 1].Ldet, 1, value, ix, jx, 0)) {
is_extremum = true;
}
}
}
}
}
// Add the point of interest!!
if (is_extremum == true) {
cv::KeyPoint point;
point.pt.x = (float)jx;
point.pt.y = (float)ix;
point.response = fabs(value);
point.size = evolution[i].esigma;
point.octave = (int)evolution[i].octave;
point.class_id = i;
// We use the angle field for the sublevel value
// Then, we will replace this angle field with the main orientation
point.angle = static_cast<float>(evolution[i].sublevel);
kpts_par[i - 1].push_back(point);
// Filter the points with the detector threshold
if (value > options_.dthreshold)
{
if (value >= *(evolution[i].Ldet.ptr<float>(ix)+jx - 1))
{
// First check on the same scale
if (check_maximum_neighbourhood(evolution[i].Ldet, 1, value, ix, jx, 1))
{
// Now check on the lower scale
if (check_maximum_neighbourhood(evolution[i - 1].Ldet, 1, value, ix, jx, 0))
{
// Now check on the upper scale
if (check_maximum_neighbourhood(evolution[i + 1].Ldet, 1, value, ix, jx, 0))
is_extremum = true;
}
}
}
}
// Add the point of interest!!
if (is_extremum)
{
KeyPoint point;
point.pt.x = (float)jx;
point.pt.y = (float)ix;
point.response = fabs(value);
point.size = evolution[i].esigma;
point.octave = (int)evolution[i].octave;
point.class_id = i;
// We use the angle field for the sublevel value
// Then, we will replace this angle field with the main orientation
point.angle = static_cast<float>(evolution[i].sublevel);
kpts_par[i - 1].push_back(point);
}
}
}
}
}
private:
std::vector<TEvolution>* evolution_;
std::vector<std::vector<cv::KeyPoint> >* kpts_par_;
std::vector<std::vector<KeyPoint> >* kpts_par_;
KAZEOptions options_;
};
@ -304,7 +308,7 @@ private:
* @param kpts Vector of keypoints
* @note We compute features for each of the nonlinear scale space level in a different processing thread
*/
void KAZEFeatures::Determinant_Hessian(std::vector<cv::KeyPoint>& kpts)
void KAZEFeatures::Determinant_Hessian(std::vector<KeyPoint>& kpts)
{
int level = 0;
float dist = 0.0, smax = 3.0;
@ -325,12 +329,14 @@ void KAZEFeatures::Determinant_Hessian(std::vector<cv::KeyPoint>& kpts)
kpts_par_.push_back(aux);
}
cv::parallel_for_(cv::Range(1, (int)evolution_.size()-1),
FindExtremumKAZEInvoker(evolution_, kpts_par_, options_));
parallel_for_(Range(1, (int)evolution_.size()-1),
FindExtremumKAZEInvoker(evolution_, kpts_par_, options_));
// Now fill the vector of keypoints!!!
for (int i = 0; i < (int)kpts_par_.size(); i++) {
for (int j = 0; j < (int)kpts_par_[i].size(); j++) {
for (int i = 0; i < (int)kpts_par_.size(); i++)
{
for (int j = 0; j < (int)kpts_par_[i].size(); j++)
{
level = i + 1;
is_extremum = true;
is_repeated = false;
@ -388,7 +394,7 @@ void KAZEFeatures::Determinant_Hessian(std::vector<cv::KeyPoint>& kpts)
* @brief This method performs subpixel refinement of the detected keypoints
* @param kpts Vector of detected keypoints
*/
void KAZEFeatures::Do_Subpixel_Refinement(std::vector<cv::KeyPoint> &kpts) {
void KAZEFeatures::Do_Subpixel_Refinement(std::vector<KeyPoint> &kpts) {
int step = 1;
int x = 0, y = 0;
@ -482,10 +488,10 @@ void KAZEFeatures::Do_Subpixel_Refinement(std::vector<cv::KeyPoint> &kpts) {
}
/* ************************************************************************* */
class KAZE_Descriptor_Invoker : public cv::ParallelLoopBody
class KAZE_Descriptor_Invoker : public ParallelLoopBody
{
public:
KAZE_Descriptor_Invoker(std::vector<cv::KeyPoint> &kpts, cv::Mat &desc, std::vector<TEvolution>& evolution, const KAZEOptions& options)
KAZE_Descriptor_Invoker(std::vector<KeyPoint> &kpts, Mat &desc, std::vector<TEvolution>& evolution, const KAZEOptions& options)
: kpts_(&kpts)
, desc_(&desc)
, evolution_(&evolution)
@ -497,10 +503,10 @@ public:
{
}
void operator() (const cv::Range& range) const
void operator() (const Range& range) const
{
std::vector<cv::KeyPoint> &kpts = *kpts_;
cv::Mat &desc = *desc_;
std::vector<KeyPoint> &kpts = *kpts_;
Mat &desc = *desc_;
std::vector<TEvolution> &evolution = *evolution_;
for (int i = range.start; i < range.end; i++)
@ -526,13 +532,13 @@ public:
}
}
private:
void Get_KAZE_Upright_Descriptor_64(const cv::KeyPoint& kpt, float* desc) const;
void Get_KAZE_Descriptor_64(const cv::KeyPoint& kpt, float* desc) const;
void Get_KAZE_Upright_Descriptor_128(const cv::KeyPoint& kpt, float* desc) const;
void Get_KAZE_Descriptor_128(const cv::KeyPoint& kpt, float *desc) const;
void Get_KAZE_Upright_Descriptor_64(const KeyPoint& kpt, float* desc) const;
void Get_KAZE_Descriptor_64(const KeyPoint& kpt, float* desc) const;
void Get_KAZE_Upright_Descriptor_128(const KeyPoint& kpt, float* desc) const;
void Get_KAZE_Descriptor_128(const KeyPoint& kpt, float *desc) const;
std::vector<cv::KeyPoint> * kpts_;
cv::Mat * desc_;
std::vector<KeyPoint> * kpts_;
Mat * desc_;
std::vector<TEvolution> * evolution_;
KAZEOptions options_;
};
@ -543,7 +549,7 @@ private:
* @param kpts Vector of keypoints
* @param desc Matrix with the feature descriptors
*/
void KAZEFeatures::Feature_Description(std::vector<cv::KeyPoint> &kpts, cv::Mat &desc)
void KAZEFeatures::Feature_Description(std::vector<KeyPoint> &kpts, Mat &desc)
{
for(size_t i = 0; i < kpts.size(); i++)
{
@ -558,7 +564,7 @@ void KAZEFeatures::Feature_Description(std::vector<cv::KeyPoint> &kpts, cv::Mat
desc = Mat::zeros((int)kpts.size(), 64, CV_32FC1);
}
cv::parallel_for_(cv::Range(0, (int)kpts.size()), KAZE_Descriptor_Invoker(kpts, desc, evolution_, options_));
parallel_for_(Range(0, (int)kpts.size()), KAZE_Descriptor_Invoker(kpts, desc, evolution_, options_));
}
/* ************************************************************************* */
@ -568,7 +574,7 @@ void KAZEFeatures::Feature_Description(std::vector<cv::KeyPoint> &kpts, cv::Mat
* @note The orientation is computed using a similar approach as described in the
* original SURF method. See Bay et al., Speeded Up Robust Features, ECCV 2006
*/
void KAZEFeatures::Compute_Main_Orientation(cv::KeyPoint &kpt, const std::vector<TEvolution>& evolution_, const KAZEOptions& options)
void KAZEFeatures::Compute_Main_Orientation(KeyPoint &kpt, const std::vector<TEvolution>& evolution_, const KAZEOptions& options)
{
int ix = 0, iy = 0, idx = 0, s = 0, level = 0;
float xf = 0.0, yf = 0.0, gweight = 0.0;
@ -647,7 +653,7 @@ void KAZEFeatures::Compute_Main_Orientation(cv::KeyPoint &kpt, const std::vector
* from Agrawal et al., CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,
* ECCV 2008
*/
void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_64(const cv::KeyPoint &kpt, float *desc) const
void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_64(const KeyPoint &kpt, float *desc) const
{
float dx = 0.0, dy = 0.0, mdx = 0.0, mdy = 0.0, gauss_s1 = 0.0, gauss_s2 = 0.0;
float rx = 0.0, ry = 0.0, len = 0.0, xf = 0.0, yf = 0.0, ys = 0.0, xs = 0.0;
@ -775,7 +781,7 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_64(const cv::KeyPoint
* from Agrawal et al., CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,
* ECCV 2008
*/
void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_64(const cv::KeyPoint &kpt, float *desc) const
void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_64(const KeyPoint &kpt, float *desc) const
{
float dx = 0.0, dy = 0.0, mdx = 0.0, mdy = 0.0, gauss_s1 = 0.0, gauss_s2 = 0.0;
float rx = 0.0, ry = 0.0, rrx = 0.0, rry = 0.0, len = 0.0, xf = 0.0, yf = 0.0, ys = 0.0, xs = 0.0;
@ -904,7 +910,7 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_64(const cv::KeyPoint &kpt, fl
* from Agrawal et al., CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,
* ECCV 2008
*/
void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_128(const cv::KeyPoint &kpt, float *desc) const
void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_128(const KeyPoint &kpt, float *desc) const
{
float gauss_s1 = 0.0, gauss_s2 = 0.0;
float rx = 0.0, ry = 0.0, len = 0.0, xf = 0.0, yf = 0.0, ys = 0.0, xs = 0.0;
@ -1056,7 +1062,7 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_128(const cv::KeyPoint
* from Agrawal et al., CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,
* ECCV 2008
*/
void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_128(const cv::KeyPoint &kpt, float *desc) const
void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_128(const KeyPoint &kpt, float *desc) const
{
float gauss_s1 = 0.0, gauss_s2 = 0.0;
float rx = 0.0, ry = 0.0, rrx = 0.0, rry = 0.0, len = 0.0, xf = 0.0, yf = 0.0, ys = 0.0, xs = 0.0;
@ -1202,3 +1208,5 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_128(const cv::KeyPoint &kpt, f
desc[i] /= len;
}
}
}

35
modules/features2d/src/kaze/KAZEFeatures.h
View File

@ -17,43 +17,48 @@
#include "fed.h"
#include "TEvolution.h"
namespace cv
{
/* ************************************************************************* */
// KAZE Class Declaration
class KAZEFeatures {
class KAZEFeatures
{
private:
/// Parameters of the Nonlinear diffusion class
KAZEOptions options_; ///< Configuration options for KAZE
std::vector<TEvolution> evolution_; ///< Vector of nonlinear diffusion evolution
/// Parameters of the Nonlinear diffusion class
KAZEOptions options_; ///< Configuration options for KAZE
std::vector<TEvolution> evolution_; ///< Vector of nonlinear diffusion evolution
/// Vector of keypoint vectors for finding extrema in multiple threads
/// Vector of keypoint vectors for finding extrema in multiple threads
std::vector<std::vector<cv::KeyPoint> > kpts_par_;
/// FED parameters
int ncycles_; ///< Number of cycles
bool reordering_; ///< Flag for reordering time steps
std::vector<std::vector<float > > tsteps_; ///< Vector of FED dynamic time steps
std::vector<int> nsteps_; ///< Vector of number of steps per cycle
/// FED parameters
int ncycles_; ///< Number of cycles
bool reordering_; ///< Flag for reordering time steps
std::vector<std::vector<float > > tsteps_; ///< Vector of FED dynamic time steps
std::vector<int> nsteps_; ///< Vector of number of steps per cycle
public:
/// Constructor
/// Constructor
KAZEFeatures(KAZEOptions& options);
/// Public methods for KAZE interface
/// Public methods for KAZE interface
void Allocate_Memory_Evolution(void);
int Create_Nonlinear_Scale_Space(const cv::Mat& img);
void Feature_Detection(std::vector<cv::KeyPoint>& kpts);
void Feature_Description(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc);
static void Compute_Main_Orientation(cv::KeyPoint& kpt, const std::vector<TEvolution>& evolution_, const KAZEOptions& options);
/// Feature Detection Methods
/// Feature Detection Methods
void Compute_KContrast(const cv::Mat& img, const float& kper);
void Compute_Multiscale_Derivatives(void);
void Compute_Detector_Response(void);
void Determinant_Hessian(std::vector<cv::KeyPoint>& kpts);
void Determinant_Hessian(std::vector<cv::KeyPoint>& kpts);
void Do_Subpixel_Refinement(std::vector<cv::KeyPoint>& kpts);
};
}
#endif

19
modules/features2d/src/kaze/TEvolution.h
View File

@ -8,10 +8,13 @@
#ifndef __OPENCV_FEATURES_2D_TEVOLUTION_H__
#define __OPENCV_FEATURES_2D_TEVOLUTION_H__
namespace cv
{
/* ************************************************************************* */
/// KAZE/A-KAZE nonlinear diffusion filtering evolution
struct TEvolution {
struct TEvolution
{
TEvolution() {
etime = 0.0f;
esigma = 0.0f;
@ -20,11 +23,11 @@ struct TEvolution {
sigma_size = 0;
}
cv::Mat Lx, Ly; ///< First order spatial derivatives
cv::Mat Lxx, Lxy, Lyy; ///< Second order spatial derivatives
cv::Mat Lt; ///< Evolution image
cv::Mat Lsmooth; ///< Smoothed image
cv::Mat Ldet; ///< Detector response
Mat Lx, Ly; ///< First order spatial derivatives
Mat Lxx, Lxy, Lyy; ///< Second order spatial derivatives
Mat Lt; ///< Evolution image
Mat Lsmooth; ///< Smoothed image
Mat Ldet; ///< Detector response
float etime; ///< Evolution time
float esigma; ///< Evolution sigma. For linear diffusion t = sigma^2 / 2
int octave; ///< Image octave
@ -32,4 +35,6 @@ struct TEvolution {
int sigma_size; ///< Integer esigma. For computing the feature detector responses
};
}
#endif

976
modules/features2d/src/kaze/nldiffusion_functions.cpp
View File

@ -22,502 +22,500 @@
* @author Pablo F. Alcantarilla
*/
#include "../precomp.hpp"
#include "nldiffusion_functions.h"
#include <iostream>
// Namespaces
using namespace std;
using namespace cv;
/* ************************************************************************* */
namespace cv {
namespace details {
namespace kaze {
/* ************************************************************************* */
/**
* @brief This function smoothes an image with a Gaussian kernel
* @param src Input image
* @param dst Output image
* @param ksize_x Kernel size in X-direction (horizontal)
* @param ksize_y Kernel size in Y-direction (vertical)
* @param sigma Kernel standard deviation
*/
void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int ksize_y, float sigma) {
int ksize_x_ = 0, ksize_y_ = 0;
// Compute an appropriate kernel size according to the specified sigma
if (sigma > ksize_x || sigma > ksize_y || ksize_x == 0 || ksize_y == 0) {
ksize_x_ = (int)ceil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
ksize_y_ = ksize_x_;
}
// The kernel size must be and odd number
if ((ksize_x_ % 2) == 0) {
ksize_x_ += 1;
}
if ((ksize_y_ % 2) == 0) {
ksize_y_ += 1;
}
// Perform the Gaussian Smoothing with border replication
GaussianBlur(src, dst, Size(ksize_x_, ksize_y_), sigma, sigma, BORDER_REPLICATE);
}
/* ************************************************************************* */
/**
* @brief This function computes image derivatives with Scharr kernel
* @param src Input image
* @param dst Output image
* @param xorder Derivative order in X-direction (horizontal)
* @param yorder Derivative order in Y-direction (vertical)
* @note Scharr operator approximates better rotation invariance than
* other stencils such as Sobel. See Weickert and Scharr,
* A Scheme for Coherence-Enhancing Diffusion Filtering with Optimized Rotation Invariance,
* Journal of Visual Communication and Image Representation 2002
*/
void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder) {
Scharr(src, dst, CV_32F, xorder, yorder, 1.0, 0, BORDER_DEFAULT);
}
/* ************************************************************************* */
/**
* @brief This function computes the Perona and Malik conductivity coefficient g1
* g1 = exp(-|dL|^2/k^2)
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
*/
void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {
Size sz = Lx.size();
float inv_k = 1.0f / (k*k);
for (int y = 0; y < sz.height; y++) {
const float* Lx_row = Lx.ptr<float>(y);
const float* Ly_row = Ly.ptr<float>(y);
float* dst_row = dst.ptr<float>(y);
for (int x = 0; x < sz.width; x++) {
dst_row[x] = (-inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]));
}
}
exp(dst, dst);
}
/* ************************************************************************* */
/**
* @brief This function computes the Perona and Malik conductivity coefficient g2
* g2 = 1 / (1 + dL^2 / k^2)
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
*/
void pm_g2(const cv::Mat &Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {
Size sz = Lx.size();
dst.create(sz, Lx.type());
float k2inv = 1.0f / (k * k);
for(int y = 0; y < sz.height; y++) {
const float *Lx_row = Lx.ptr<float>(y);
const float *Ly_row = Ly.ptr<float>(y);
float* dst_row = dst.ptr<float>(y);
for(int x = 0; x < sz.width; x++) {
dst_row[x] = 1.0f / (1.0f + ((Lx_row[x] * Lx_row[x] + Ly_row[x] * Ly_row[x]) * k2inv));
}
}
}
/* ************************************************************************* */
/**
* @brief This function computes Weickert conductivity coefficient gw
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
* @note For more information check the following paper: J. Weickert
* Applications of nonlinear diffusion in image processing and computer vision,
* Proceedings of Algorithmy 2000
*/
void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {
Size sz = Lx.size();
float inv_k = 1.0f / (k*k);
for (int y = 0; y < sz.height; y++) {
const float* Lx_row = Lx.ptr<float>(y);
const float* Ly_row = Ly.ptr<float>(y);
float* dst_row = dst.ptr<float>(y);
for (int x = 0; x < sz.width; x++) {
float dL = inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]);
dst_row[x] = -3.315f/(dL*dL*dL*dL);
}
}
exp(dst, dst);
dst = 1.0 - dst;
}
/* ************************************************************************* */
/**
* @brief This function computes Charbonnier conductivity coefficient gc
* gc = 1 / sqrt(1 + dL^2 / k^2)
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
* @note For more information check the following paper: J. Weickert
* Applications of nonlinear diffusion in image processing and computer vision,
* Proceedings of Algorithmy 2000
*/
void charbonnier_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {
Size sz = Lx.size();
float inv_k = 1.0f / (k*k);
for (int y = 0; y < sz.height; y++) {
const float* Lx_row = Lx.ptr<float>(y);
const float* Ly_row = Ly.ptr<float>(y);
float* dst_row = dst.ptr<float>(y);
for (int x = 0; x < sz.width; x++) {
float den = sqrt(1.0f+inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]));
dst_row[x] = 1.0f / den;
}
}
}
/* ************************************************************************* */
/**
* @brief This function computes a good empirical value for the k contrast factor
* given an input image, the percentile (0-1), the gradient scale and the number of
* bins in the histogram
* @param img Input image
* @param perc Percentile of the image gradient histogram (0-1)
* @param gscale Scale for computing the image gradient histogram
* @param nbins Number of histogram bins
* @param ksize_x Kernel size in X-direction (horizontal) for the Gaussian smoothing kernel
* @param ksize_y Kernel size in Y-direction (vertical) for the Gaussian smoothing kernel
* @return k contrast factor
*/
float compute_k_percentile(const cv::Mat& img, float perc, float gscale, int nbins, int ksize_x, int ksize_y) {
int nbin = 0, nelements = 0, nthreshold = 0, k = 0;
float kperc = 0.0, modg = 0.0;
float npoints = 0.0;
float hmax = 0.0;
// Create the array for the histogram
std::vector<int> hist(nbins, 0);
// Create the matrices
Mat gaussian = Mat::zeros(img.rows, img.cols, CV_32F);
Mat Lx = Mat::zeros(img.rows, img.cols, CV_32F);
Mat Ly = Mat::zeros(img.rows, img.cols, CV_32F);
// Perform the Gaussian convolution
gaussian_2D_convolution(img, gaussian, ksize_x, ksize_y, gscale);
// Compute the Gaussian derivatives Lx and Ly
Scharr(gaussian, Lx, CV_32F, 1, 0, 1, 0, cv::BORDER_DEFAULT);
Scharr(gaussian, Ly, CV_32F, 0, 1, 1, 0, cv::BORDER_DEFAULT);
// Skip the borders for computing the histogram
for (int i = 1; i < gaussian.rows - 1; i++) {
const float *lx = Lx.ptr<float>(i);
const float *ly = Ly.ptr<float>(i);
for (int j = 1; j < gaussian.cols - 1; j++) {
modg = lx[j]*lx[j] + ly[j]*ly[j];
// Get the maximum
if (modg > hmax) {
hmax = modg;
}
}
}
hmax = sqrt(hmax);
// Skip the borders for computing the histogram
for (int i = 1; i < gaussian.rows - 1; i++) {
const float *lx = Lx.ptr<float>(i);
const float *ly = Ly.ptr<float>(i);
for (int j = 1; j < gaussian.cols - 1; j++) {
modg = lx[j]*lx[j] + ly[j]*ly[j];
// Find the correspondent bin
if (modg != 0.0) {
nbin = (int)floor(nbins*(sqrt(modg) / hmax));
if (nbin == nbins) {
nbin--;
}
hist[nbin]++;
npoints++;
}
}
}
// Now find the perc of the histogram percentile
nthreshold = (int)(npoints*perc);
for (k = 0; nelements < nthreshold && k < nbins; k++) {
nelements = nelements + hist[k];
}
if (nelements < nthreshold) {
kperc = 0.03f;
}
else {
kperc = hmax*((float)(k) / (float)nbins);
}
return kperc;
}
/* ************************************************************************* */
/**
* @brief This function computes Scharr image derivatives
* @param src Input image
* @param dst Output image
* @param xorder Derivative order in X-direction (horizontal)
* @param yorder Derivative order in Y-direction (vertical)
* @param scale Scale factor for the derivative size
*/
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale) {
Mat kx, ky;
compute_derivative_kernels(kx, ky, xorder, yorder, scale);
sepFilter2D(src, dst, CV_32F, kx, ky);
}
/* ************************************************************************* */
/**
* @brief Compute derivative kernels for sizes different than 3
* @param _kx Horizontal kernel ues
* @param _ky Vertical kernel values
* @param dx Derivative order in X-direction (horizontal)
* @param dy Derivative order in Y-direction (vertical)
* @param scale_ Scale factor or derivative size
*/
void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale) {
int ksize = 3 + 2 * (scale - 1);
// The standard Scharr kernel
if (scale == 1) {
getDerivKernels(_kx, _ky, dx, dy, 0, true, CV_32F);
return;
}
_kx.create(ksize, 1, CV_32F, -1, true);
_ky.create(ksize, 1, CV_32F, -1, true);
Mat kx = _kx.getMat();
Mat ky = _ky.getMat();
float w = 10.0f / 3.0f;
float norm = 1.0f / (2.0f*scale*(w + 2.0f));
for (int k = 0; k < 2; k++) {
Mat* kernel = k == 0 ? &kx : &ky;
int order = k == 0 ? dx : dy;
std::vector<float> kerI(ksize, 0.0f);
if (order == 0) {
kerI[0] = norm, kerI[ksize / 2] = w*norm, kerI[ksize - 1] = norm;
}
else if (order == 1) {
kerI[0] = -1, kerI[ksize / 2] = 0, kerI[ksize - 1] = 1;
}
Mat temp(kernel->rows, kernel->cols, CV_32F, &kerI[0]);
temp.copyTo(*kernel);
}
}
class Nld_Step_Scalar_Invoker : public cv::ParallelLoopBody
{
public:
Nld_Step_Scalar_Invoker(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float _stepsize)
: _Ld(&Ld)
, _c(&c)
, _Lstep(&Lstep)
, stepsize(_stepsize)
{
}
virtual ~Nld_Step_Scalar_Invoker()
{
}
void operator()(const cv::Range& range) const
{
cv::Mat& Ld = *_Ld;
const cv::Mat& c = *_c;
cv::Mat& Lstep = *_Lstep;
for (int i = range.start; i < range.end; i++)
{
const float *c_prev = c.ptr<float>(i - 1);
const float *c_curr = c.ptr<float>(i);
const float *c_next = c.ptr<float>(i + 1);
const float *ld_prev = Ld.ptr<float>(i - 1);
const float *ld_curr = Ld.ptr<float>(i);
const float *ld_next = Ld.ptr<float>(i + 1);
float *dst = Lstep.ptr<float>(i);
for (int j = 1; j < Lstep.cols - 1; j++)
{
float xpos = (c_curr[j] + c_curr[j+1])*(ld_curr[j+1] - ld_curr[j]);
float xneg = (c_curr[j-1] + c_curr[j]) *(ld_curr[j] - ld_curr[j-1]);
float ypos = (c_curr[j] + c_next[j]) *(ld_next[j] - ld_curr[j]);
float yneg = (c_prev[j] + c_curr[j]) *(ld_curr[j] - ld_prev[j]);
dst[j] = 0.5f*stepsize*(xpos - xneg + ypos - yneg);
}
}
}
private:
cv::Mat * _Ld;
const cv::Mat * _c;
cv::Mat * _Lstep;
float stepsize;
};
/* ************************************************************************* */
/**
* @brief This function performs a scalar non-linear diffusion step
* @param Ld2 Output image in the evolution
* @param c Conductivity image
* @param Lstep Previous image in the evolution
* @param stepsize The step size in time units
* @note Forward Euler Scheme 3x3 stencil
* The function c is a scalar value that depends on the gradient norm
* dL_by_ds = d(c dL_by_dx)_by_dx + d(c dL_by_dy)_by_dy
*/
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize) {
cv::parallel_for_(cv::Range(1, Lstep.rows - 1), Nld_Step_Scalar_Invoker(Ld, c, Lstep, stepsize), (double)Ld.total()/(1 << 16));
float xneg, xpos, yneg, ypos;
float* dst = Lstep.ptr<float>(0);
const float* cprv = NULL;
const float* ccur = c.ptr<float>(0);
const float* cnxt = c.ptr<float>(1);
const float* ldprv = NULL;
const float* ldcur = Ld.ptr<float>(0);
const float* ldnxt = Ld.ptr<float>(1);
for (int j = 1; j < Lstep.cols - 1; j++) {
xpos = (ccur[j] + ccur[j+1]) * (ldcur[j+1] - ldcur[j]);
xneg = (ccur[j-1] + ccur[j]) * (ldcur[j] - ldcur[j-1]);
ypos = (ccur[j] + cnxt[j]) * (ldnxt[j] - ldcur[j]);
dst[j] = 0.5f*stepsize*(xpos - xneg + ypos);
}
dst = Lstep.ptr<float>(Lstep.rows - 1);
ccur = c.ptr<float>(Lstep.rows - 1);
cprv = c.ptr<float>(Lstep.rows - 2);
ldcur = Ld.ptr<float>(Lstep.rows - 1);
ldprv = Ld.ptr<float>(Lstep.rows - 2);
for (int j = 1; j < Lstep.cols - 1; j++) {
xpos = (ccur[j] + ccur[j+1]) * (ldcur[j+1] - ldcur[j]);
xneg = (ccur[j-1] + ccur[j]) * (ldcur[j] - ldcur[j-1]);
yneg = (cprv[j] + ccur[j]) * (ldcur[j] - ldprv[j]);
dst[j] = 0.5f*stepsize*(xpos - xneg - yneg);
}
ccur = c.ptr<float>(1);
ldcur = Ld.ptr<float>(1);
cprv = c.ptr<float>(0);
ldprv = Ld.ptr<float>(0);
int r0 = Lstep.cols - 1;
int r1 = Lstep.cols - 2;
for (int i = 1; i < Lstep.rows - 1; i++) {
cnxt = c.ptr<float>(i + 1);
ldnxt = Ld.ptr<float>(i + 1);
dst = Lstep.ptr<float>(i);
xpos = (ccur[0] + ccur[1]) * (ldcur[1] - ldcur[0]);
ypos = (ccur[0] + cnxt[0]) * (ldnxt[0] - ldcur[0]);
yneg = (cprv[0] + ccur[0]) * (ldcur[0] - ldprv[0]);
dst[0] = 0.5f*stepsize*(xpos + ypos - yneg);
xneg = (ccur[r1] + ccur[r0]) * (ldcur[r0] - ldcur[r1]);
ypos = (ccur[r0] + cnxt[r0]) * (ldnxt[r0] - ldcur[r0]);
yneg = (cprv[r0] + ccur[r0]) * (ldcur[r0] - ldprv[r0]);
dst[r0] = 0.5f*stepsize*(-xneg + ypos - yneg);
cprv = ccur;
ccur = cnxt;
ldprv = ldcur;
ldcur = ldnxt;
}
Ld += Lstep;
}
/* ************************************************************************* */
/**
* @brief This function downsamples the input image using OpenCV resize
* @param img Input image to be downsampled
* @param dst Output image with half of the resolution of the input image
*/
void halfsample_image(const cv::Mat& src, cv::Mat& dst) {
// Make sure the destination image is of the right size
CV_Assert(src.cols / 2 == dst.cols);
CV_Assert(src.rows / 2 == dst.rows);
resize(src, dst, dst.size(), 0, 0, cv::INTER_AREA);
}
/* ************************************************************************* */
/**
* @brief This function checks if a given pixel is a maximum in a local neighbourhood
* @param img Input image where we will perform the maximum search
* @param dsize Half size of the neighbourhood
* @param value Response value at (x,y) position
* @param row Image row coordinate
* @param col Image column coordinate
* @param same_img Flag to indicate if the image value at (x,y) is in the input image
* @return 1->is maximum, 0->otherwise
*/
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img) {
bool response = true;
for (int i = row - dsize; i <= row + dsize; i++) {
for (int j = col - dsize; j <= col + dsize; j++) {
if (i >= 0 && i < img.rows && j >= 0 && j < img.cols) {
if (same_img == true) {
if (i != row || j != col) {
if ((*(img.ptr<float>(i)+j)) > value) {
response = false;
return response;
}
}
}
else {
if ((*(img.ptr<float>(i)+j)) > value) {
response = false;
return response;
}
}
}
}
}
return response;
}
namespace cv
{
using namespace std;
/* ************************************************************************* */
/**
* @brief This function smoothes an image with a Gaussian kernel
* @param src Input image
* @param dst Output image
* @param ksize_x Kernel size in X-direction (horizontal)
* @param ksize_y Kernel size in Y-direction (vertical)
* @param sigma Kernel standard deviation
*/
void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int ksize_y, float sigma) {
int ksize_x_ = 0, ksize_y_ = 0;
// Compute an appropriate kernel size according to the specified sigma
if (sigma > ksize_x || sigma > ksize_y || ksize_x == 0 || ksize_y == 0) {
ksize_x_ = (int)ceil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
ksize_y_ = ksize_x_;
}
// The kernel size must be and odd number
if ((ksize_x_ % 2) == 0) {
ksize_x_ += 1;
}
if ((ksize_y_ % 2) == 0) {
ksize_y_ += 1;
}
// Perform the Gaussian Smoothing with border replication
GaussianBlur(src, dst, Size(ksize_x_, ksize_y_), sigma, sigma, BORDER_REPLICATE);
}
/* ************************************************************************* */
/**
* @brief This function computes image derivatives with Scharr kernel
* @param src Input image
* @param dst Output image
* @param xorder Derivative order in X-direction (horizontal)
* @param yorder Derivative order in Y-direction (vertical)
* @note Scharr operator approximates better rotation invariance than
* other stencils such as Sobel. See Weickert and Scharr,
* A Scheme for Coherence-Enhancing Diffusion Filtering with Optimized Rotation Invariance,
* Journal of Visual Communication and Image Representation 2002
*/
void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder) {
Scharr(src, dst, CV_32F, xorder, yorder, 1.0, 0, BORDER_DEFAULT);
}
/* ************************************************************************* */
/**
* @brief This function computes the Perona and Malik conductivity coefficient g1
* g1 = exp(-|dL|^2/k^2)
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
*/
void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {
Size sz = Lx.size();
float inv_k = 1.0f / (k*k);
for (int y = 0; y < sz.height; y++) {
const float* Lx_row = Lx.ptr<float>(y);
const float* Ly_row = Ly.ptr<float>(y);
float* dst_row = dst.ptr<float>(y);
for (int x = 0; x < sz.width; x++) {
dst_row[x] = (-inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]));
}
}
exp(dst, dst);
}
/* ************************************************************************* */
/**
* @brief This function computes the Perona and Malik conductivity coefficient g2
* g2 = 1 / (1 + dL^2 / k^2)
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
*/
void pm_g2(const cv::Mat &Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {
Size sz = Lx.size();
dst.create(sz, Lx.type());
float k2inv = 1.0f / (k * k);
for(int y = 0; y < sz.height; y++) {
const float *Lx_row = Lx.ptr<float>(y);
const float *Ly_row = Ly.ptr<float>(y);
float* dst_row = dst.ptr<float>(y);
for(int x = 0; x < sz.width; x++) {
dst_row[x] = 1.0f / (1.0f + ((Lx_row[x] * Lx_row[x] + Ly_row[x] * Ly_row[x]) * k2inv));
}
}
}
/* ************************************************************************* */
/**
* @brief This function computes Weickert conductivity coefficient gw
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
* @note For more information check the following paper: J. Weickert
* Applications of nonlinear diffusion in image processing and computer vision,
* Proceedings of Algorithmy 2000
*/
void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {
Size sz = Lx.size();
float inv_k = 1.0f / (k*k);
for (int y = 0; y < sz.height; y++) {
const float* Lx_row = Lx.ptr<float>(y);
const float* Ly_row = Ly.ptr<float>(y);
float* dst_row = dst.ptr<float>(y);
for (int x = 0; x < sz.width; x++) {
float dL = inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]);
dst_row[x] = -3.315f/(dL*dL*dL*dL);
}
}
exp(dst, dst);
dst = 1.0 - dst;
}
/* ************************************************************************* */
/**
* @brief This function computes Charbonnier conductivity coefficient gc
* gc = 1 / sqrt(1 + dL^2 / k^2)
* @param Lx First order image derivative in X-direction (horizontal)
* @param Ly First order image derivative in Y-direction (vertical)
* @param dst Output image
* @param k Contrast factor parameter
* @note For more information check the following paper: J. Weickert
* Applications of nonlinear diffusion in image processing and computer vision,
* Proceedings of Algorithmy 2000
*/
void charbonnier_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {
Size sz = Lx.size();
float inv_k = 1.0f / (k*k);
for (int y = 0; y < sz.height; y++) {
const float* Lx_row = Lx.ptr<float>(y);
const float* Ly_row = Ly.ptr<float>(y);
float* dst_row = dst.ptr<float>(y);
for (int x = 0; x < sz.width; x++) {
float den = sqrt(1.0f+inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]));
dst_row[x] = 1.0f / den;
}
}
}
/* ************************************************************************* */
/**
* @brief This function computes a good empirical value for the k contrast factor
* given an input image, the percentile (0-1), the gradient scale and the number of
* bins in the histogram
* @param img Input image
* @param perc Percentile of the image gradient histogram (0-1)
* @param gscale Scale for computing the image gradient histogram
* @param nbins Number of histogram bins
* @param ksize_x Kernel size in X-direction (horizontal) for the Gaussian smoothing kernel
* @param ksize_y Kernel size in Y-direction (vertical) for the Gaussian smoothing kernel
* @return k contrast factor
*/
float compute_k_percentile(const cv::Mat& img, float perc, float gscale, int nbins, int ksize_x, int ksize_y) {
int nbin = 0, nelements = 0, nthreshold = 0, k = 0;
float kperc = 0.0, modg = 0.0;
float npoints = 0.0;
float hmax = 0.0;
// Create the array for the histogram
std::vector<int> hist(nbins, 0);
// Create the matrices
Mat gaussian = Mat::zeros(img.rows, img.cols, CV_32F);
Mat Lx = Mat::zeros(img.rows, img.cols, CV_32F);
Mat Ly = Mat::zeros(img.rows, img.cols, CV_32F);
// Perform the Gaussian convolution
gaussian_2D_convolution(img, gaussian, ksize_x, ksize_y, gscale);
// Compute the Gaussian derivatives Lx and Ly
Scharr(gaussian, Lx, CV_32F, 1, 0, 1, 0, cv::BORDER_DEFAULT);
Scharr(gaussian, Ly, CV_32F, 0, 1, 1, 0, cv::BORDER_DEFAULT);
// Skip the borders for computing the histogram
for (int i = 1; i < gaussian.rows - 1; i++) {
const float *lx = Lx.ptr<float>(i);
const float *ly = Ly.ptr<float>(i);
for (int j = 1; j < gaussian.cols - 1; j++) {
modg = lx[j]*lx[j] + ly[j]*ly[j];
// Get the maximum
if (modg > hmax) {
hmax = modg;
}
}
}
hmax = sqrt(hmax);
// Skip the borders for computing the histogram
for (int i = 1; i < gaussian.rows - 1; i++) {
const float *lx = Lx.ptr<float>(i);
const float *ly = Ly.ptr<float>(i);
for (int j = 1; j < gaussian.cols - 1; j++) {
modg = lx[j]*lx[j] + ly[j]*ly[j];
// Find the correspondent bin
if (modg != 0.0) {
nbin = (int)floor(nbins*(sqrt(modg) / hmax));
if (nbin == nbins) {
nbin--;
}
hist[nbin]++;
npoints++;
}
}
}
// Now find the perc of the histogram percentile
nthreshold = (int)(npoints*perc);
for (k = 0; nelements < nthreshold && k < nbins; k++) {
nelements = nelements + hist[k];
}
if (nelements < nthreshold) {
kperc = 0.03f;
}
else {
kperc = hmax*((float)(k) / (float)nbins);
}
return kperc;
}
/* ************************************************************************* */
/**
* @brief This function computes Scharr image derivatives
* @param src Input image
* @param dst Output image
* @param xorder Derivative order in X-direction (horizontal)
* @param yorder Derivative order in Y-direction (vertical)
* @param scale Scale factor for the derivative size
*/
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale) {
Mat kx, ky;
compute_derivative_kernels(kx, ky, xorder, yorder, scale);
sepFilter2D(src, dst, CV_32F, kx, ky);
}
/* ************************************************************************* */
/**
* @brief Compute derivative kernels for sizes different than 3
* @param _kx Horizontal kernel ues
* @param _ky Vertical kernel values
* @param dx Derivative order in X-direction (horizontal)
* @param dy Derivative order in Y-direction (vertical)
* @param scale_ Scale factor or derivative size
*/
void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale) {
int ksize = 3 + 2 * (scale - 1);
// The standard Scharr kernel
if (scale == 1) {
getDerivKernels(_kx, _ky, dx, dy, 0, true, CV_32F);
return;
}
_kx.create(ksize, 1, CV_32F, -1, true);
_ky.create(ksize, 1, CV_32F, -1, true);
Mat kx = _kx.getMat();
Mat ky = _ky.getMat();
float w = 10.0f / 3.0f;
float norm = 1.0f / (2.0f*scale*(w + 2.0f));
for (int k = 0; k < 2; k++) {
Mat* kernel = k == 0 ? &kx : &ky;
int order = k == 0 ? dx : dy;
std::vector<float> kerI(ksize, 0.0f);
if (order == 0) {
kerI[0] = norm, kerI[ksize / 2] = w*norm, kerI[ksize - 1] = norm;
}
else if (order == 1) {
kerI[0] = -1, kerI[ksize / 2] = 0, kerI[ksize - 1] = 1;
}
Mat temp(kernel->rows, kernel->cols, CV_32F, &kerI[0]);
temp.copyTo(*kernel);
}
}
class Nld_Step_Scalar_Invoker : public cv::ParallelLoopBody
{
public:
Nld_Step_Scalar_Invoker(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float _stepsize)
: _Ld(&Ld)
, _c(&c)
, _Lstep(&Lstep)
, stepsize(_stepsize)
{
}
virtual ~Nld_Step_Scalar_Invoker()
{
}
void operator()(const cv::Range& range) const
{
cv::Mat& Ld = *_Ld;
const cv::Mat& c = *_c;
cv::Mat& Lstep = *_Lstep;
for (int i = range.start; i < range.end; i++)
{
const float *c_prev = c.ptr<float>(i - 1);
const float *c_curr = c.ptr<float>(i);
const float *c_next = c.ptr<float>(i + 1);
const float *ld_prev = Ld.ptr<float>(i - 1);
const float *ld_curr = Ld.ptr<float>(i);
const float *ld_next = Ld.ptr<float>(i + 1);
float *dst = Lstep.ptr<float>(i);
for (int j = 1; j < Lstep.cols - 1; j++)
{
float xpos = (c_curr[j] + c_curr[j+1])*(ld_curr[j+1] - ld_curr[j]);
float xneg = (c_curr[j-1] + c_curr[j]) *(ld_curr[j] - ld_curr[j-1]);
float ypos = (c_curr[j] + c_next[j]) *(ld_next[j] - ld_curr[j]);
float yneg = (c_prev[j] + c_curr[j]) *(ld_curr[j] - ld_prev[j]);
dst[j] = 0.5f*stepsize*(xpos - xneg + ypos - yneg);
}
}
}
private:
cv::Mat * _Ld;
const cv::Mat * _c;
cv::Mat * _Lstep;
float stepsize;
};
/* ************************************************************************* */
/**
* @brief This function performs a scalar non-linear diffusion step
* @param Ld2 Output image in the evolution
* @param c Conductivity image
* @param Lstep Previous image in the evolution
* @param stepsize The step size in time units
* @note Forward Euler Scheme 3x3 stencil
* The function c is a scalar value that depends on the gradient norm
* dL_by_ds = d(c dL_by_dx)_by_dx + d(c dL_by_dy)_by_dy
*/
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize) {
cv::parallel_for_(cv::Range(1, Lstep.rows - 1), Nld_Step_Scalar_Invoker(Ld, c, Lstep, stepsize), (double)Ld.total()/(1 << 16));
float xneg, xpos, yneg, ypos;
float* dst = Lstep.ptr<float>(0);
const float* cprv = NULL;
const float* ccur = c.ptr<float>(0);
const float* cnxt = c.ptr<float>(1);
const float* ldprv = NULL;
const float* ldcur = Ld.ptr<float>(0);
const float* ldnxt = Ld.ptr<float>(1);
for (int j = 1; j < Lstep.cols - 1; j++) {
xpos = (ccur[j] + ccur[j+1]) * (ldcur[j+1] - ldcur[j]);
xneg = (ccur[j-1] + ccur[j]) * (ldcur[j] - ldcur[j-1]);
ypos = (ccur[j] + cnxt[j]) * (ldnxt[j] - ldcur[j]);
dst[j] = 0.5f*stepsize*(xpos - xneg + ypos);
}
dst = Lstep.ptr<float>(Lstep.rows - 1);
ccur = c.ptr<float>(Lstep.rows - 1);
cprv = c.ptr<float>(Lstep.rows - 2);
ldcur = Ld.ptr<float>(Lstep.rows - 1);
ldprv = Ld.ptr<float>(Lstep.rows - 2);
for (int j = 1; j < Lstep.cols - 1; j++) {
xpos = (ccur[j] + ccur[j+1]) * (ldcur[j+1] - ldcur[j]);
xneg = (ccur[j-1] + ccur[j]) * (ldcur[j] - ldcur[j-1]);
yneg = (cprv[j] + ccur[j]) * (ldcur[j] - ldprv[j]);
dst[j] = 0.5f*stepsize*(xpos - xneg - yneg);
}
ccur = c.ptr<float>(1);
ldcur = Ld.ptr<float>(1);
cprv = c.ptr<float>(0);
ldprv = Ld.ptr<float>(0);
int r0 = Lstep.cols - 1;
int r1 = Lstep.cols - 2;
for (int i = 1; i < Lstep.rows - 1; i++) {
cnxt = c.ptr<float>(i + 1);
ldnxt = Ld.ptr<float>(i + 1);
dst = Lstep.ptr<float>(i);
xpos = (ccur[0] + ccur[1]) * (ldcur[1] - ldcur[0]);
ypos = (ccur[0] + cnxt[0]) * (ldnxt[0] - ldcur[0]);
yneg = (cprv[0] + ccur[0]) * (ldcur[0] - ldprv[0]);
dst[0] = 0.5f*stepsize*(xpos + ypos - yneg);
xneg = (ccur[r1] + ccur[r0]) * (ldcur[r0] - ldcur[r1]);
ypos = (ccur[r0] + cnxt[r0]) * (ldnxt[r0] - ldcur[r0]);
yneg = (cprv[r0] + ccur[r0]) * (ldcur[r0] - ldprv[r0]);
dst[r0] = 0.5f*stepsize*(-xneg + ypos - yneg);
cprv = ccur;
ccur = cnxt;
ldprv = ldcur;
ldcur = ldnxt;
}
Ld += Lstep;
}
/* ************************************************************************* */
/**
* @brief This function downsamples the input image using OpenCV resize
* @param img Input image to be downsampled
* @param dst Output image with half of the resolution of the input image
*/
void halfsample_image(const cv::Mat& src, cv::Mat& dst) {
// Make sure the destination image is of the right size
CV_Assert(src.cols / 2 == dst.cols);
CV_Assert(src.rows / 2 == dst.rows);
resize(src, dst, dst.size(), 0, 0, cv::INTER_AREA);
}
/* ************************************************************************* */
/**
* @brief This function checks if a given pixel is a maximum in a local neighbourhood
* @param img Input image where we will perform the maximum search
* @param dsize Half size of the neighbourhood
* @param value Response value at (x,y) position
* @param row Image row coordinate
* @param col Image column coordinate
* @param same_img Flag to indicate if the image value at (x,y) is in the input image
* @return 1->is maximum, 0->otherwise
*/
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img) {
bool response = true;
for (int i = row - dsize; i <= row + dsize; i++) {
for (int j = col - dsize; j <= col + dsize; j++) {
if (i >= 0 && i < img.rows && j >= 0 && j < img.cols) {
if (same_img == true) {
if (i != row || j != col) {
if ((*(img.ptr<float>(i)+j)) > value) {
response = false;
return response;
}
}
}
else {
if ((*(img.ptr<float>(i)+j)) > value) {
response = false;
return response;
}
}
}
}
}
return response;
}
}

48
modules/features2d/src/kaze/nldiffusion_functions.h
View File

@ -11,43 +11,37 @@
#ifndef __OPENCV_FEATURES_2D_NLDIFFUSION_FUNCTIONS_H__
#define __OPENCV_FEATURES_2D_NLDIFFUSION_FUNCTIONS_H__
/* ************************************************************************* */
// Includes
#include "../precomp.hpp"
/* ************************************************************************* */
// Declaration of functions
namespace cv {
namespace details {
namespace kaze {
namespace cv
{
// Gaussian 2D convolution
void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int ksize_y, float sigma);
// Gaussian 2D convolution
void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int ksize_y, float sigma);
// Diffusivity functions
void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
void pm_g2(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
void charbonnier_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
// Diffusivity functions
void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
void pm_g2(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
void charbonnier_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
float compute_k_percentile(const cv::Mat& img, float perc, float gscale, int nbins, int ksize_x, int ksize_y);
float compute_k_percentile(const cv::Mat& img, float perc, float gscale, int nbins, int ksize_x, int ksize_y);
// Image derivatives
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale);
void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale);
void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder);
// Image derivatives
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale);
void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale);
void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder);
// Nonlinear diffusion filtering scalar step
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize);
// Nonlinear diffusion filtering scalar step
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize);
// For non-maxima suppresion
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img);
// For non-maxima suppresion
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img);
// Image downsampling
void halfsample_image(const cv::Mat& src, cv::Mat& dst);
// Image downsampling
void halfsample_image(const cv::Mat& src, cv::Mat& dst);
}
}
}
#endif

1671
modules/features2d/src/mser.cpp
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168
modules/features2d/src/orb.cpp
View File

@ -38,6 +38,10 @@
#include "opencl_kernels_features2d.hpp"
#include <iterator>
#ifndef CV_IMPL_ADD
#define CV_IMPL_ADD(x)
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
namespace cv
@ -100,12 +104,12 @@ ocl_ICAngles(const UMat& imgbuf, const UMat& layerinfo,
static bool
ocl_computeOrbDescriptors(const UMat& imgbuf, const UMat& layerInfo,
const UMat& keypoints, UMat& desc, const UMat& pattern,
int nkeypoints, int dsize, int WTA_K)
int nkeypoints, int dsize, int wta_k)
{
size_t globalSize[] = {nkeypoints};
ocl::Kernel desc_ker("ORB_computeDescriptor", ocl::features2d::orb_oclsrc,
format("-D ORB_DESCRIPTORS -D WTA_K=%d", WTA_K));
format("-D ORB_DESCRIPTORS -D WTA_K=%d", wta_k));
if( desc_ker.empty() )
return false;
@ -209,7 +213,7 @@ static void ICAngles(const Mat& img, const std::vector<Rect>& layerinfo,
static void
computeOrbDescriptors( const Mat& imagePyramid, const std::vector<Rect>& layerInfo,
const std::vector<float>& layerScale, std::vector<KeyPoint>& keypoints,
Mat& descriptors, const std::vector<Point>& _pattern, int dsize, int WTA_K )
Mat& descriptors, const std::vector<Point>& _pattern, int dsize, int wta_k )
{
int step = (int)imagePyramid.step;
int j, i, nkeypoints = (int)keypoints.size();
@ -248,7 +252,7 @@ computeOrbDescriptors( const Mat& imagePyramid, const std::vector<Rect>& layerIn
center[iy*step + ix+1]*x*(1-y) + center[(iy+1)*step + ix+1]*x*y))
#endif
if( WTA_K == 2 )
if( wta_k == 2 )
{
for (i = 0; i < dsize; ++i, pattern += 16)
{
@ -273,7 +277,7 @@ computeOrbDescriptors( const Mat& imagePyramid, const std::vector<Rect>& layerIn
desc[i] = (uchar)val;
}
}
else if( WTA_K == 3 )
else if( wta_k == 3 )
{
for (i = 0; i < dsize; ++i, pattern += 12)
{
@ -293,7 +297,7 @@ computeOrbDescriptors( const Mat& imagePyramid, const std::vector<Rect>& layerIn
desc[i] = (uchar)val;
}
}
else if( WTA_K == 4 )
else if( wta_k == 4 )
{
for (i = 0; i < dsize; ++i, pattern += 16)
{
@ -334,7 +338,7 @@ computeOrbDescriptors( const Mat& imagePyramid, const std::vector<Rect>& layerIn
}
}
else
CV_Error( Error::StsBadSize, "Wrong WTA_K. It can be only 2, 3 or 4." );
CV_Error( Error::StsBadSize, "Wrong wta_k. It can be only 2, 3 or 4." );
#undef GET_VALUE
}
}
@ -645,43 +649,106 @@ 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
void set(int prop, double value)
{
if( prop == NFEATURES )
nfeatures = cvRound(value);
else if( prop == SCALE_FACTOR )
scaleFactor = value;
else if( prop == NLEVELS )
nlevels = cvRound(value);
else if( prop == EDGE_THRESHOLD )
edgeThreshold = cvRound(value);
else if( prop == FIRST_LEVEL )
firstLevel = cvRound(value);
else if( prop == WTA_K )
wta_k = cvRound(value);
else if( prop == SCORE_TYPE )
scoreType = cvRound(value);
else if( prop == PATCH_SIZE )
patchSize = cvRound(value);
else if( prop == FAST_THRESHOLD )
fastThreshold = cvRound(value);
else
CV_Error(Error::StsBadArg, "");
}
double get(int prop) const
{
double value = 0;
if( prop == NFEATURES )
value = nfeatures;
else if( prop == SCALE_FACTOR )
value = scaleFactor;
else if( prop == NLEVELS )
value = nlevels;
else if( prop == EDGE_THRESHOLD )
value = edgeThreshold;
else if( prop == FIRST_LEVEL )
value = firstLevel;
else if( prop == WTA_K )
value = wta_k;
else if( prop == SCORE_TYPE )
value = scoreType;
else if( prop == PATCH_SIZE )
value = patchSize;
else if( prop == FAST_THRESHOLD )
value = fastThreshold;
else
CV_Error(Error::StsBadArg, "");
return value;
}
// 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 detectAndCompute( InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
OutputArray descriptors, bool useProvidedKeypoints=false );
protected:
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
* @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
{
(*this)(image, mask, keypoints, noArray(), false);
}
static void uploadORBKeypoints(const std::vector<KeyPoint>& src, std::vector<Vec3i>& buf, OutputArray dst)
{
size_t i, n = src.size();
@ -716,7 +783,7 @@ 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
@ -781,14 +848,16 @@ static void computeKeyPoints(const Mat& imagePyramid,
Mat mask = maskPyramid.empty() ? Mat() : maskPyramid(layerInfo[level]);
// Detect FAST features, 20 is a good threshold
FastFeatureDetector fd(fastThreshold, true);
fd.detect(img, keypoints, mask);
{
Ptr<FastFeatureDetector> fd = FastFeatureDetector::create(fastThreshold, true);
fd->detect(img, keypoints, mask);
}
// Remove keypoints very close to the border
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 +883,7 @@ 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 )
{
@ -888,7 +957,7 @@ 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
@ -896,8 +965,9 @@ 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,
OutputArray _descriptors, bool useProvidedKeypoints ) const
void ORB_Impl::detectAndCompute( InputArray _image, InputArray _mask,
std::vector<KeyPoint>& keypoints,
OutputArray _descriptors, bool useProvidedKeypoints )
{
CV_Assert(patchSize >= 2);
@ -1081,14 +1151,14 @@ void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>
makeRandomPattern(patchSize, patternbuf, npoints);
}
CV_Assert( WTA_K == 2 || WTA_K == 3 || WTA_K == 4 );
CV_Assert( wta_k == 2 || wta_k == 3 || wta_k == 4 );
if( WTA_K == 2 )
if( wta_k == 2 )
std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));
else
{
int ntuples = descriptorSize()*4;
initializeOrbPattern(pattern0, pattern, ntuples, WTA_K, npoints);
initializeOrbPattern(pattern0, pattern, ntuples, wta_k, npoints);
}
for( level = 0; level < nLevels; level++ )
@ -1111,7 +1181,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>
UMat udescriptors = _descriptors.getUMat();
useOCL = ocl_computeOrbDescriptors(uimagePyramid, ulayerInfo,
ukeypoints, udescriptors, upattern,
nkeypoints, dsize, WTA_K);
nkeypoints, dsize, wta_k);
if(useOCL)
{
CV_IMPL_ADD(CV_IMPL_OCL);
@ -1122,20 +1192,16 @@ void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>
{
Mat descriptors = _descriptors.getMat();
computeOrbDescriptors(imagePyramid, layerInfo, layerScale,
keypoints, descriptors, pattern, dsize, WTA_K);
keypoints, descriptors, pattern, dsize, wta_k);
}
}
}
void ORB::detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
Ptr<ORB> ORB::create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold,
int firstLevel, int wta_k, int scoreType, int patchSize, int fastThreshold)
{
(*this)(image.getMat(), mask.getMat(), keypoints, noArray(), false);
return makePtr<ORB_Impl>(nfeatures, scaleFactor, nlevels, edgeThreshold,
firstLevel, wta_k, scoreType, patchSize, fastThreshold);
}
void ORB::computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
{
(*this)(image, Mat(), keypoints, descriptors, true);
}
}

2
modules/features2d/test/test_brisk.cpp
View File

@ -72,7 +72,7 @@ void CV_BRISKTest::run( int )
cvtColor(image1, gray1, COLOR_BGR2GRAY);
cvtColor(image2, gray2, COLOR_BGR2GRAY);
Ptr<FeatureDetector> detector = Algorithm::create<FeatureDetector>("Feature2D.BRISK");
Ptr<FeatureDetector> detector = BRISK::create();
vector<KeyPoint> keypoints1;
vector<KeyPoint> keypoints2;

23
modules/features2d/test/test_descriptors_regression.cpp
View File

@ -106,8 +106,6 @@ public:
~CV_DescriptorExtractorTest()
{
if(!detector.empty())
detector.release();
}
protected:
virtual void createDescriptorExtractor() {}
@ -314,31 +312,34 @@ private:
TEST( Features2d_DescriptorExtractor_BRISK, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk", (CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f,
DescriptorExtractor::create("BRISK") );
CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk",
(CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f,
BRISK::create() );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_ORB, regression )
{
// TODO adjust the parameters below
CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb", (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
DescriptorExtractor::create("ORB") );
CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb",
(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
ORB::create() );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_KAZE, regression )
{
CV_DescriptorExtractorTest< L2<float> > test( "descriptor-kaze", 0.03f,
DescriptorExtractor::create("KAZE"),
L2<float>(), FeatureDetector::create("KAZE"));
KAZE::create(),
L2<float>(), KAZE::create() );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_AKAZE, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze", (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
DescriptorExtractor::create("AKAZE"),
Hamming(), FeatureDetector::create("AKAZE"));
CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",
(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
AKAZE::create(),
Hamming(), AKAZE::create());
test.safe_run();
}

18
modules/features2d/test/test_detectors_regression.cpp
View File

@ -249,48 +249,50 @@ void CV_FeatureDetectorTest::run( int /*start_from*/ )
TEST( Features2d_Detector_BRISK, regression )
{
CV_FeatureDetectorTest test( "detector-brisk", FeatureDetector::create("BRISK") );
CV_FeatureDetectorTest test( "detector-brisk", BRISK::create() );
test.safe_run();
}
TEST( Features2d_Detector_FAST, regression )
{
CV_FeatureDetectorTest test( "detector-fast", FeatureDetector::create("FAST") );
CV_FeatureDetectorTest test( "detector-fast", FastFeatureDetector::create() );
test.safe_run();
}
TEST( Features2d_Detector_GFTT, regression )
{
CV_FeatureDetectorTest test( "detector-gftt", FeatureDetector::create("GFTT") );
CV_FeatureDetectorTest test( "detector-gftt", GFTTDetector::create() );
test.safe_run();
}
TEST( Features2d_Detector_Harris, regression )
{
CV_FeatureDetectorTest test( "detector-harris", FeatureDetector::create("HARRIS") );
Ptr<FeatureDetector> gftt = GFTTDetector::create();
gftt->set(GFTTDetector::USE_HARRIS_DETECTOR, 1);
CV_FeatureDetectorTest test( "detector-harris", gftt);
test.safe_run();
}
TEST( Features2d_Detector_MSER, DISABLED_regression )
{
CV_FeatureDetectorTest test( "detector-mser", FeatureDetector::create("MSER") );
CV_FeatureDetectorTest test( "detector-mser", MSER::create() );
test.safe_run();
}
TEST( Features2d_Detector_ORB, regression )
{
CV_FeatureDetectorTest test( "detector-orb", FeatureDetector::create("ORB") );
CV_FeatureDetectorTest test( "detector-orb", ORB::create() );
test.safe_run();
}
TEST( Features2d_Detector_KAZE, regression )
{
CV_FeatureDetectorTest test( "detector-kaze", FeatureDetector::create("KAZE") );
CV_FeatureDetectorTest test( "detector-kaze", KAZE::create() );
test.safe_run();
}
TEST( Features2d_Detector_AKAZE, regression )
{
CV_FeatureDetectorTest test( "detector-akaze", FeatureDetector::create("AKAZE") );
CV_FeatureDetectorTest test( "detector-akaze", AKAZE::create() );
test.safe_run();
}

23
modules/features2d/test/test_keypoints.cpp
View File

@ -41,6 +41,7 @@
#include "test_precomp.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/core/core_c.h"
using namespace std;
using namespace cv;
@ -61,7 +62,6 @@ public:
protected:
virtual void run(int)
{
cv::initModule_features2d();
CV_Assert(detector);
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
@ -121,51 +121,54 @@ protected:
TEST(Features2d_Detector_Keypoints_BRISK, validation)
{
CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"));
CV_FeatureDetectorKeypointsTest test(BRISK::create());
test.safe_run();
}
TEST(Features2d_Detector_Keypoints_FAST, validation)
{
CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.FAST"));
CV_FeatureDetectorKeypointsTest test(FastFeatureDetector::create());
test.safe_run();
}
TEST(Features2d_Detector_Keypoints_HARRIS, validation)
{
CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.HARRIS"));
CV_FeatureDetectorKeypointsTest test(GFTTDetector::create(1000, 0.01, 1, 3, true, 0.04));
test.safe_run();
}
TEST(Features2d_Detector_Keypoints_GFTT, validation)
{
CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.GFTT"));
Ptr<FeatureDetector> gftt = GFTTDetector::create();
gftt->set(GFTTDetector::USE_HARRIS_DETECTOR, 1);
CV_FeatureDetectorKeypointsTest test(gftt);
test.safe_run();
}
TEST(Features2d_Detector_Keypoints_MSER, validation)
{
CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.MSER"));
CV_FeatureDetectorKeypointsTest test(MSER::create());
test.safe_run();
}
TEST(Features2d_Detector_Keypoints_ORB, validation)
{
CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"));
CV_FeatureDetectorKeypointsTest test(ORB::create());
test.safe_run();
}
TEST(Features2d_Detector_Keypoints_KAZE, validation)
{
CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.KAZE"));
CV_FeatureDetectorKeypointsTest test(KAZE::create());
test.safe_run();
}
TEST(Features2d_Detector_Keypoints_AKAZE, validation)
{
CV_FeatureDetectorKeypointsTest test_kaze(cv::Ptr<FeatureDetector>(new cv::AKAZE(cv::DESCRIPTOR_KAZE)));
CV_FeatureDetectorKeypointsTest test_kaze(AKAZE::create(AKAZE::DESCRIPTOR_KAZE));
test_kaze.safe_run();
CV_FeatureDetectorKeypointsTest test_mldb(cv::Ptr<FeatureDetector>(new cv::AKAZE(cv::DESCRIPTOR_MLDB)));
CV_FeatureDetectorKeypointsTest test_mldb(AKAZE::create(AKAZE::DESCRIPTOR_MLDB));
test_mldb.safe_run();
}

6
modules/features2d/test/test_matchers_algorithmic.cpp
View File

@ -532,12 +532,14 @@ void CV_DescriptorMatcherTest::run( int )
TEST( Features2d_DescriptorMatcher_BruteForce, regression )
{
CV_DescriptorMatcherTest test( "descriptor-matcher-brute-force", Algorithm::create<DescriptorMatcher>("DescriptorMatcher.BFMatcher"), 0.01f );
CV_DescriptorMatcherTest test( "descriptor-matcher-brute-force",
DescriptorMatcher::create("BruteForce"), 0.01f );
test.safe_run();
}
TEST( Features2d_DescriptorMatcher_FlannBased, regression )
{
CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based", Algorithm::create<DescriptorMatcher>("DescriptorMatcher.FlannBasedMatcher"), 0.04f );
CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based",
DescriptorMatcher::create("FlannBased"), 0.04f );
test.safe_run();
}

4
modules/features2d/test/test_mser.cpp
View File

@ -43,6 +43,8 @@
#include "test_precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#if 0
#include <vector>
#include <string>
using namespace std;
@ -205,3 +207,5 @@ void CV_MserTest::run(int)
}
TEST(Features2d_MSER, DISABLED_regression) { CV_MserTest test; test.safe_run(); }
#endif

6
modules/features2d/test/test_orb.cpp
View File

@ -47,10 +47,8 @@ using namespace cv;
TEST(Features2D_ORB, _1996)
{
Ptr<FeatureDetector> fd = FeatureDetector::create("ORB");
fd->set("nFeatures", 10000);//setting a higher maximum to make effect of threshold visible
fd->set("fastThreshold", 20);//more features than the default
Ptr<DescriptorExtractor> de = DescriptorExtractor::create("ORB");
Ptr<FeatureDetector> fd = ORB::create(10000, 1.2f, 8, 31, 0, 2, ORB::HARRIS_SCORE, 31, 20);
Ptr<DescriptorExtractor> de = fd;
Mat image = imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/lena.png");
ASSERT_FALSE(image.empty());

28
modules/features2d/test/test_rotation_and_scale_invariance.cpp
View File

@ -595,7 +595,7 @@ protected:
TEST(Features2d_RotationInvariance_Detector_BRISK, regression)
{
DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"),
DetectorRotationInvarianceTest test(BRISK::create(),
0.32f,
0.76f);
test.safe_run();
@ -603,7 +603,7 @@ TEST(Features2d_RotationInvariance_Detector_BRISK, regression)
TEST(Features2d_RotationInvariance_Detector_ORB, regression)
{
DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
DetectorRotationInvarianceTest test(ORB::create(),
0.47f,
0.76f);
test.safe_run();
@ -615,19 +615,15 @@ TEST(Features2d_RotationInvariance_Detector_ORB, regression)
TEST(Features2d_RotationInvariance_Descriptor_BRISK, regression)
{
DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"),
Algorithm::create<DescriptorExtractor>("Feature2D.BRISK"),
Algorithm::create<DescriptorExtractor>("Feature2D.BRISK")->defaultNorm(),
0.99f);
Ptr<Feature2D> f2d = BRISK::create();
DescriptorRotationInvarianceTest test(f2d, f2d, f2d->defaultNorm(), 0.99f);
test.safe_run();
}
TEST(Features2d_RotationInvariance_Descriptor_ORB, regression)
{
DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
Algorithm::create<DescriptorExtractor>("Feature2D.ORB"),
Algorithm::create<DescriptorExtractor>("Feature2D.ORB")->defaultNorm(),
0.99f);
Ptr<Feature2D> f2d = ORB::create();
DescriptorRotationInvarianceTest test(f2d, f2d, f2d->defaultNorm(), 0.99f);
test.safe_run();
}
@ -646,25 +642,19 @@ TEST(Features2d_RotationInvariance_Descriptor_ORB, regression)
TEST(Features2d_ScaleInvariance_Detector_BRISK, regression)
{
DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"),
0.08f,
0.49f);
DetectorScaleInvarianceTest test(BRISK::create(), 0.08f, 0.49f);
test.safe_run();
}
TEST(Features2d_ScaleInvariance_Detector_KAZE, regression)
{
DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.KAZE"),
0.08f,
0.49f);
DetectorScaleInvarianceTest test(KAZE::create(), 0.08f, 0.49f);
test.safe_run();
}
TEST(Features2d_ScaleInvariance_Detector_AKAZE, regression)
{
DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.AKAZE"),
0.08f,
0.49f);
DetectorScaleInvarianceTest test(AKAZE::create(), 0.08f, 0.49f);
test.safe_run();
}

89
modules/java/generator/src/cpp/features2d_manual.hpp
View File

@ -90,67 +90,69 @@ public:
//not supported: SimpleBlob, Dense
CV_WRAP static javaFeatureDetector* create( int detectorType )
{
String name;
//String name;
if (detectorType > DYNAMICDETECTOR)
{
name = "Dynamic";
//name = "Dynamic";
detectorType -= DYNAMICDETECTOR;
}
if (detectorType > PYRAMIDDETECTOR)
{
name = "Pyramid";
//name = "Pyramid";
detectorType -= PYRAMIDDETECTOR;
}
if (detectorType > GRIDDETECTOR)
{
name = "Grid";
//name = "Grid";
detectorType -= GRIDDETECTOR;
}
Ptr<FeatureDetector> fd;
switch(detectorType)
{
case FAST:
name = name + "FAST";
break;
case STAR:
name = name + "STAR";
break;
case SIFT:
name = name + "SIFT";
break;
case SURF:
name = name + "SURF";
fd = FastFeatureDetector::create();
break;
//case STAR:
// fd = xfeatures2d::StarDetector::create();
// break;
//case SIFT:
// name = name + "SIFT";
// break;
//case SURF:
// name = name + "SURF";
// break;
case ORB:
name = name + "ORB";
fd = ORB::create();
break;
case MSER:
name = name + "MSER";
fd = MSER::create();
break;
case GFTT:
name = name + "GFTT";
fd = GFTTDetector::create();
break;
case HARRIS:
name = name + "HARRIS";
fd = GFTTDetector::create();
fd->set(GFTTDetector::USE_HARRIS_DETECTOR, 1);
break;
case SIMPLEBLOB:
name = name + "SimpleBlob";
break;
case DENSE:
name = name + "Dense";
fd = SimpleBlobDetector::create();
break;
//case DENSE:
// name = name + "Dense";
// break;
case BRISK:
name = name + "BRISK";
fd = BRISK::create();
break;
case AKAZE:
name = name + "AKAZE";
fd = AKAZE::create();
break;
default:
CV_Error( Error::StsBadArg, "Specified feature detector type is not supported." );
break;
}
return new javaFeatureDetector(FeatureDetector::create(name));
return new javaFeatureDetector(fd);
}
CV_WRAP void write( const String& fileName ) const
@ -332,43 +334,44 @@ public:
//not supported: Calonder
CV_WRAP static javaDescriptorExtractor* create( int extractorType )
{
String name;
//String name;
if (extractorType > OPPONENTEXTRACTOR)
{
name = "Opponent";
//name = "Opponent";
extractorType -= OPPONENTEXTRACTOR;
}
Ptr<DescriptorExtractor> de;
switch(extractorType)
{
case SIFT:
name = name + "SIFT";
break;
case SURF:
name = name + "SURF";
break;
//case SIFT:
// name = name + "SIFT";
// break;
//case SURF:
// name = name + "SURF";
// break;
case ORB:
name = name + "ORB";
break;
case BRIEF:
name = name + "BRIEF";
de = ORB::create();
break;
//case BRIEF:
// name = name + "BRIEF";
// break;
case BRISK:
name = name + "BRISK";
break;
case FREAK:
name = name + "FREAK";
de = BRISK::create();
break;
//case FREAK:
// name = name + "FREAK";
// break;
case AKAZE:
name = name + "AKAZE";
de = AKAZE::create();
break;
default:
CV_Error( Error::StsBadArg, "Specified descriptor extractor type is not supported." );
break;
}
return new javaDescriptorExtractor(DescriptorExtractor::create(name));
return new javaDescriptorExtractor(de);
}
CV_WRAP void write( const String& fileName ) const

6
modules/java/generator/src/cpp/jni_part.cpp
View File

@ -24,15 +24,9 @@ JNI_OnLoad(JavaVM* vm, void* )
return -1;
bool init = true;
#ifdef HAVE_OPENCV_FEATURES2D
init &= cv::initModule_features2d();
#endif
#ifdef HAVE_OPENCV_VIDEO
init &= cv::initModule_video();
#endif
#ifdef HAVE_OPENCV_CONTRIB
init &= cv::initModule_contrib();
#endif
if(!init)
return -1;

2
modules/python/test/test.py
View File

@ -91,7 +91,7 @@ class Hackathon244Tests(NewOpenCVTests):
self.assertEqual(cv2.countNonZero(inliers), pattern_size[0]*pattern_size[1])
def test_fast(self):
fd = cv2.FastFeatureDetector(30, True)
fd = cv2.FastFeatureDetector_create(30, True)
img = self.get_sample("samples/cpp/right02.jpg", 0)
img = cv2.medianBlur(img, 3)
imgc = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

37
modules/stitching/src/matchers.cpp
View File

@ -48,8 +48,7 @@ using namespace cv::cuda;
#ifdef HAVE_OPENCV_XFEATURES2D
#include "opencv2/xfeatures2d.hpp"
static bool makeUseOfXfeatures2d = xfeatures2d::initModule_xfeatures2d();
using xfeatures2d::SURF;
#endif
namespace {
@ -321,30 +320,34 @@ void FeaturesFinder::operator ()(InputArray image, ImageFeatures &features, cons
SurfFeaturesFinder::SurfFeaturesFinder(double hess_thresh, int num_octaves, int num_layers,
int num_octaves_descr, int num_layers_descr)
{
#ifdef HAVE_OPENCV_XFEATURES2D
if (num_octaves_descr == num_octaves && num_layers_descr == num_layers)
{
surf = Algorithm::create<Feature2D>("Feature2D.SURF");
surf = SURF::create();
if( !surf )
CV_Error( Error::StsNotImplemented, "OpenCV was built without SURF support" );
surf->set("hessianThreshold", hess_thresh);
surf->set("nOctaves", num_octaves);
surf->set("nOctaveLayers", num_layers);
surf->set(SURF::HESSIAN_THRESHOLD, hess_thresh);
surf->set(SURF::NOCTAVES, num_octaves);
surf->set(SURF::NOCTAVE_LAYERS, num_layers);
}
else
{
detector_ = Algorithm::create<FeatureDetector>("Feature2D.SURF");
extractor_ = Algorithm::create<DescriptorExtractor>("Feature2D.SURF");
detector_ = SURF::create();
extractor_ = SURF::create();
if( !detector_ || !extractor_ )
CV_Error( Error::StsNotImplemented, "OpenCV was built without SURF support" );
detector_->set("hessianThreshold", hess_thresh);
detector_->set("nOctaves", num_octaves);
detector_->set("nOctaveLayers", num_layers);
detector_->set(SURF::HESSIAN_THRESHOLD, hess_thresh);
detector_->set(SURF::NOCTAVES, num_octaves);
detector_->set(SURF::NOCTAVE_LAYERS, num_layers);
extractor_->set("nOctaves", num_octaves_descr);
extractor_->set("nOctaveLayers", num_layers_descr);
extractor_->set(SURF::NOCTAVES, num_octaves_descr);
extractor_->set(SURF::NOCTAVE_LAYERS, num_layers_descr);
}
#else
CV_Error( Error::StsNotImplemented, "OpenCV was built without SURF support" );
#endif
}
void SurfFeaturesFinder::find(InputArray image, ImageFeatures &features)
@ -367,7 +370,7 @@ void SurfFeaturesFinder::find(InputArray image, ImageFeatures &features)
else
{
UMat descriptors;
(*surf)(gray_image, Mat(), features.keypoints, descriptors);
surf->detectAndCompute(gray_image, Mat(), features.keypoints, descriptors);
features.descriptors = descriptors.reshape(1, (int)features.keypoints.size());
}
}
@ -375,7 +378,7 @@ void SurfFeaturesFinder::find(InputArray image, ImageFeatures &features)
OrbFeaturesFinder::OrbFeaturesFinder(Size _grid_size, int n_features, float scaleFactor, int nlevels)
{
grid_size = _grid_size;
orb = makePtr<ORB>(n_features * (99 + grid_size.area())/100/grid_size.area(), scaleFactor, nlevels);
orb = ORB::create(n_features * (99 + grid_size.area())/100/grid_size.area(), scaleFactor, nlevels);
}
void OrbFeaturesFinder::find(InputArray image, ImageFeatures &features)
@ -395,7 +398,7 @@ void OrbFeaturesFinder::find(InputArray image, ImageFeatures &features)
}
if (grid_size.area() == 1)
(*orb)(gray_image, Mat(), features.keypoints, features.descriptors);
orb->detectAndCompute(gray_image, Mat(), features.keypoints, features.descriptors);
else
{
features.keypoints.clear();
@ -425,7 +428,7 @@ void OrbFeaturesFinder::find(InputArray image, ImageFeatures &features)
// << " gray_image_part.dims=" << gray_image_part.dims << ", "
// << " gray_image_part.data=" << ((size_t)gray_image_part.data) << "\n");
(*orb)(gray_image_part, UMat(), points, descriptors);
orb->detectAndCompute(gray_image_part, UMat(), points, descriptors);
features.keypoints.reserve(features.keypoints.size() + points.size());
for (std::vector<KeyPoint>::iterator kp = points.begin(); kp != points.end(); ++kp)

2
modules/videostab/src/global_motion.cpp
View File

@ -671,7 +671,7 @@ Mat ToFileMotionWriter::estimate(const Mat &frame0, const Mat &frame1, bool *ok)
KeypointBasedMotionEstimator::KeypointBasedMotionEstimator(Ptr<MotionEstimatorBase> estimator)
: ImageMotionEstimatorBase(estimator->motionModel()), motionEstimator_(estimator)
{
setDetector(makePtr<GoodFeaturesToTrackDetector>());
setDetector(GFTTDetector::create());
setOpticalFlowEstimator(makePtr<SparsePyrLkOptFlowEstimator>());
setOutlierRejector(makePtr<NullOutlierRejector>());
}

6
modules/world/src/world_init.cpp
View File

@ -47,12 +47,6 @@ bool cv::initAll()
return true
#ifdef HAVE_OPENCV_VIDEO
&& initModule_video()
#endif
#ifdef HAVE_OPENCV_FEATURES2D
&& initModule_features2d()
#endif
#ifdef HAVE_OPENCV_XFEATURES2D
&& xfeatures2d::initModule_xfeatures2d()
#endif
;
}

4
samples/android/tutorial-2-mixedprocessing/jni/jni_part.cpp
View File

@ -16,8 +16,8 @@ JNIEXPORT void JNICALL Java_org_opencv_samples_tutorial2_Tutorial2Activity_FindF
Mat& mRgb = *(Mat*)addrRgba;
vector<KeyPoint> v;
FastFeatureDetector detector(50);
detector.detect(mGr, v);
Ptr<FeatureDetector> detector = FastFeatureDetector::create(50);
detector->detect(mGr, v);
for( unsigned int i = 0; i < v.size(); i++ )
{
const KeyPoint& kp = v[i];

18
samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/RobustMatcher.h
View File

@ -19,23 +19,23 @@ public:
RobustMatcher() : ratio_(0.8f)
{
// ORB is the default feature
detector_ = new cv::OrbFeatureDetector();
extractor_ = new cv::OrbDescriptorExtractor();
detector_ = cv::ORB::create();
extractor_ = cv::ORB::create();
// BruteFroce matcher with Norm Hamming is the default matcher
matcher_ = new cv::BFMatcher(cv::NORM_HAMMING, false);
matcher_ = cv::makePtr<cv::BFMatcher>((int)cv::NORM_HAMMING, false);
}
virtual ~RobustMatcher();
// Set the feature detector
void setFeatureDetector(cv::FeatureDetector * detect) { detector_ = detect; }
void setFeatureDetector(const cv::Ptr<cv::FeatureDetector>& detect) { detector_ = detect; }
// Set the descriptor extractor
void setDescriptorExtractor(cv::DescriptorExtractor * desc) { extractor_ = desc; }
void setDescriptorExtractor(const cv::Ptr<cv::DescriptorExtractor>& desc) { extractor_ = desc; }
// Set the matcher
void setDescriptorMatcher(cv::DescriptorMatcher * match) { matcher_ = match; }
void setDescriptorMatcher(const cv::Ptr<cv::DescriptorMatcher>& match) { matcher_ = match; }
// Compute the keypoints of an image
void computeKeyPoints( const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints);
@ -69,11 +69,11 @@ public:
private:
// pointer to the feature point detector object
cv::FeatureDetector * detector_;
cv::Ptr<cv::FeatureDetector> detector_;
// pointer to the feature descriptor extractor object
cv::DescriptorExtractor * extractor_;
cv::Ptr<cv::DescriptorExtractor> extractor_;
// pointer to the matcher object
cv::DescriptorMatcher * matcher_;
cv::Ptr<cv::DescriptorMatcher> matcher_;
// max ratio between 1st and 2nd NN
float ratio_;
};

173
samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/main_detection.cpp
View File

@ -18,11 +18,14 @@
/** GLOBAL VARIABLES **/
std::string tutorial_path = "../../samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/"; // path to tutorial
using namespace cv;
using namespace std;
std::string video_read_path = tutorial_path + "Data/box.mp4"; // recorded video
std::string yml_read_path = tutorial_path + "Data/cookies_ORB.yml"; // 3dpts + descriptors
std::string ply_read_path = tutorial_path + "Data/box.ply"; // mesh
string tutorial_path = "../../samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/"; // path to tutorial
string video_read_path = tutorial_path + "Data/box.mp4"; // recorded video
string yml_read_path = tutorial_path + "Data/cookies_ORB.yml"; // 3dpts + descriptors
string ply_read_path = tutorial_path + "Data/box.ply"; // mesh
// Intrinsic camera parameters: UVC WEBCAM
double f = 55; // focal length in mm
@ -35,15 +38,15 @@ double params_WEBCAM[] = { width*f/sx, // fx
height/2}; // cy
// Some basic colors
cv::Scalar red(0, 0, 255);
cv::Scalar green(0,255,0);
cv::Scalar blue(255,0,0);
cv::Scalar yellow(0,255,255);
Scalar red(0, 0, 255);
Scalar green(0,255,0);
Scalar blue(255,0,0);
Scalar yellow(0,255,255);
// Robust Matcher parameters
int numKeyPoints = 2000; // number of detected keypoints
float ratio = 0.70f; // ratio test
float ratioTest = 0.70f; // ratio test
bool fast_match = true; // fastRobustMatch() or robustMatch()
// RANSAC parameters
@ -55,16 +58,16 @@ double confidence = 0.95; // ransac successful confidence.
int minInliersKalman = 30; // Kalman threshold updating
// PnP parameters
int pnpMethod = cv::SOLVEPNP_ITERATIVE;
int pnpMethod = SOLVEPNP_ITERATIVE;
/** Functions headers **/
void help();
void initKalmanFilter( cv::KalmanFilter &KF, int nStates, int nMeasurements, int nInputs, double dt);
void updateKalmanFilter( cv::KalmanFilter &KF, cv::Mat &measurements,
cv::Mat &translation_estimated, cv::Mat &rotation_estimated );
void fillMeasurements( cv::Mat &measurements,
const cv::Mat &translation_measured, const cv::Mat &rotation_measured);
void initKalmanFilter( KalmanFilter &KF, int nStates, int nMeasurements, int nInputs, double dt);
void updateKalmanFilter( KalmanFilter &KF, Mat &measurements,
Mat &translation_estimated, Mat &rotation_estimated );
void fillMeasurements( Mat &measurements,
const Mat &translation_measured, const Mat &rotation_measured);
/** Main program **/
@ -73,7 +76,7 @@ int main(int argc, char *argv[])
help();
const cv::String keys =
const String keys =
"{help h | | print this message }"
"{video v | | path to recorded video }"
"{model | | path to yml model }"
@ -87,7 +90,7 @@ int main(int argc, char *argv[])
"{method pnp |0 | PnP method: (0) ITERATIVE - (1) EPNP - (2) P3P - (3) DLS}"
"{fast f |true | use of robust fast match }"
;
cv::CommandLineParser parser(argc, argv, keys);
CommandLineParser parser(argc, argv, keys);
if (parser.has("help"))
{
@ -96,11 +99,11 @@ int main(int argc, char *argv[])
}
else
{
video_read_path = parser.get<std::string>("video").size() > 0 ? parser.get<std::string>("video") : video_read_path;
yml_read_path = parser.get<std::string>("model").size() > 0 ? parser.get<std::string>("model") : yml_read_path;
ply_read_path = parser.get<std::string>("mesh").size() > 0 ? parser.get<std::string>("mesh") : ply_read_path;
video_read_path = parser.get<string>("video").size() > 0 ? parser.get<string>("video") : video_read_path;
yml_read_path = parser.get<string>("model").size() > 0 ? parser.get<string>("model") : yml_read_path;
ply_read_path = parser.get<string>("mesh").size() > 0 ? parser.get<string>("mesh") : ply_read_path;
numKeyPoints = !parser.has("keypoints") ? parser.get<int>("keypoints") : numKeyPoints;
ratio = !parser.has("ratio") ? parser.get<float>("ratio") : ratio;
ratioTest = !parser.has("ratio") ? parser.get<float>("ratio") : ratioTest;
fast_match = !parser.has("fast") ? parser.get<bool>("fast") : fast_match;
iterationsCount = !parser.has("iterations") ? parser.get<int>("iterations") : iterationsCount;
reprojectionError = !parser.has("error") ? parser.get<float>("error") : reprojectionError;
@ -120,45 +123,45 @@ int main(int argc, char *argv[])
RobustMatcher rmatcher; // instantiate RobustMatcher
cv::FeatureDetector * detector = new cv::OrbFeatureDetector(numKeyPoints); // instatiate ORB feature detector
cv::DescriptorExtractor * extractor = new cv::OrbDescriptorExtractor(); // instatiate ORB descriptor extractor
Ptr<FeatureDetector> orb = ORB::create();
rmatcher.setFeatureDetector(detector); // set feature detector
rmatcher.setDescriptorExtractor(extractor); // set descriptor extractor
rmatcher.setFeatureDetector(orb); // set feature detector
rmatcher.setDescriptorExtractor(orb); // set descriptor extractor
cv::Ptr<cv::flann::IndexParams> indexParams = cv::makePtr<cv::flann::LshIndexParams>(6, 12, 1); // instantiate LSH index parameters
cv::Ptr<cv::flann::SearchParams> searchParams = cv::makePtr<cv::flann::SearchParams>(50); // instantiate flann search parameters
Ptr<flann::IndexParams> indexParams = makePtr<flann::LshIndexParams>(6, 12, 1); // instantiate LSH index parameters
Ptr<flann::SearchParams> searchParams = makePtr<flann::SearchParams>(50); // instantiate flann search parameters
cv::DescriptorMatcher * matcher = new cv::FlannBasedMatcher(indexParams, searchParams); // instantiate FlannBased matcher
// instantiate FlannBased matcher
Ptr<DescriptorMatcher> matcher = makePtr<FlannBasedMatcher>(indexParams, searchParams);
rmatcher.setDescriptorMatcher(matcher); // set matcher
rmatcher.setRatio(ratio); // set ratio test parameter
rmatcher.setRatio(ratioTest); // set ratio test parameter
cv::KalmanFilter KF; // instantiate Kalman Filter
KalmanFilter KF; // instantiate Kalman Filter
int nStates = 18; // the number of states
int nMeasurements = 6; // the number of measured states
int nInputs = 0; // the number of control actions
double dt = 0.125; // time between measurements (1/FPS)
initKalmanFilter(KF, nStates, nMeasurements, nInputs, dt); // init function
cv::Mat measurements(nMeasurements, 1, CV_64F); measurements.setTo(cv::Scalar(0));
Mat measurements(nMeasurements, 1, CV_64F); measurements.setTo(Scalar(0));
bool good_measurement = false;
// Get the MODEL INFO
std::vector<cv::Point3f> list_points3d_model = model.get_points3d(); // list with model 3D coordinates
cv::Mat descriptors_model = model.get_descriptors(); // list with descriptors of each 3D coordinate
vector<Point3f> list_points3d_model = model.get_points3d(); // list with model 3D coordinates
Mat descriptors_model = model.get_descriptors(); // list with descriptors of each 3D coordinate
// Create & Open Window
cv::namedWindow("REAL TIME DEMO", cv::WINDOW_KEEPRATIO);
namedWindow("REAL TIME DEMO", WINDOW_KEEPRATIO);
cv::VideoCapture cap; // instantiate VideoCapture
VideoCapture cap; // instantiate VideoCapture
cap.open(video_read_path); // open a recorded video
if(!cap.isOpened()) // check if we succeeded
{
std::cout << "Could not open the camera device" << std::endl;
cout << "Could not open the camera device" << endl;
return -1;
}
@ -175,9 +178,9 @@ int main(int argc, char *argv[])
// start the clock
time(&start);
cv::Mat frame, frame_vis;
Mat frame, frame_vis;
while(cap.read(frame) && cv::waitKey(30) != 27) // capture frame until ESC is pressed
while(cap.read(frame) && waitKey(30) != 27) // capture frame until ESC is pressed
{
frame_vis = frame.clone(); // refresh visualisation frame
@ -185,8 +188,8 @@ int main(int argc, char *argv[])
// -- Step 1: Robust matching between model descriptors and scene descriptors
std::vector<cv::DMatch> good_matches; // to obtain the 3D points of the model
std::vector<cv::KeyPoint> keypoints_scene; // to obtain the 2D points of the scene
vector<DMatch> good_matches; // to obtain the 3D points of the model
vector<KeyPoint> keypoints_scene; // to obtain the 2D points of the scene
if(fast_match)
@ -201,13 +204,13 @@ int main(int argc, char *argv[])
// -- Step 2: Find out the 2D/3D correspondences
std::vector<cv::Point3f> list_points3d_model_match; // container for the model 3D coordinates found in the scene
std::vector<cv::Point2f> list_points2d_scene_match; // container for the model 2D coordinates found in the scene
vector<Point3f> list_points3d_model_match; // container for the model 3D coordinates found in the scene
vector<Point2f> list_points2d_scene_match; // container for the model 2D coordinates found in the scene
for(unsigned int match_index = 0; match_index < good_matches.size(); ++match_index)
{
cv::Point3f point3d_model = list_points3d_model[ good_matches[match_index].trainIdx ]; // 3D point from model
cv::Point2f point2d_scene = keypoints_scene[ good_matches[match_index].queryIdx ].pt; // 2D point from the scene
Point3f point3d_model = list_points3d_model[ good_matches[match_index].trainIdx ]; // 3D point from model
Point2f point2d_scene = keypoints_scene[ good_matches[match_index].queryIdx ].pt; // 2D point from the scene
list_points3d_model_match.push_back(point3d_model); // add 3D point
list_points2d_scene_match.push_back(point2d_scene); // add 2D point
}
@ -216,8 +219,8 @@ int main(int argc, char *argv[])
draw2DPoints(frame_vis, list_points2d_scene_match, red);
cv::Mat inliers_idx;
std::vector<cv::Point2f> list_points2d_inliers;
Mat inliers_idx;
vector<Point2f> list_points2d_inliers;
if(good_matches.size() > 0) // None matches, then RANSAC crashes
{
@ -231,7 +234,7 @@ int main(int argc, char *argv[])
for(int inliers_index = 0; inliers_index < inliers_idx.rows; ++inliers_index)
{
int n = inliers_idx.at<int>(inliers_index); // i-inlier
cv::Point2f point2d = list_points2d_scene_match[n]; // i-inlier point 2D
Point2f point2d = list_points2d_scene_match[n]; // i-inlier point 2D
list_points2d_inliers.push_back(point2d); // add i-inlier to list
}
@ -248,11 +251,11 @@ int main(int argc, char *argv[])
{
// Get the measured translation
cv::Mat translation_measured(3, 1, CV_64F);
Mat translation_measured(3, 1, CV_64F);
translation_measured = pnp_detection.get_t_matrix();
// Get the measured rotation
cv::Mat rotation_measured(3, 3, CV_64F);
Mat rotation_measured(3, 3, CV_64F);
rotation_measured = pnp_detection.get_R_matrix();
// fill the measurements vector
@ -263,8 +266,8 @@ int main(int argc, char *argv[])
}
// Instantiate estimated translation and rotation
cv::Mat translation_estimated(3, 1, CV_64F);
cv::Mat rotation_estimated(3, 3, CV_64F);
Mat translation_estimated(3, 1, CV_64F);
Mat rotation_estimated(3, 3, CV_64F);
// update the Kalman filter with good measurements
updateKalmanFilter( KF, measurements,
@ -288,11 +291,11 @@ int main(int argc, char *argv[])
}
float l = 5;
std::vector<cv::Point2f> pose_points2d;
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(cv::Point3f(0,0,0))); // axis center
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(cv::Point3f(l,0,0))); // axis x
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(cv::Point3f(0,l,0))); // axis y
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(cv::Point3f(0,0,l))); // axis z
vector<Point2f> pose_points2d;
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(Point3f(0,0,0))); // axis center
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(Point3f(l,0,0))); // axis x
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(Point3f(0,l,0))); // axis y
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(Point3f(0,0,l))); // axis z
draw3DCoordinateAxes(frame_vis, pose_points2d); // draw axes
// FRAME RATE
@ -316,49 +319,49 @@ int main(int argc, char *argv[])
// Draw some debug text
int inliers_int = inliers_idx.rows;
int outliers_int = (int)good_matches.size() - inliers_int;
std::string inliers_str = IntToString(inliers_int);
std::string outliers_str = IntToString(outliers_int);
std::string n = IntToString((int)good_matches.size());
std::string text = "Found " + inliers_str + " of " + n + " matches";
std::string text2 = "Inliers: " + inliers_str + " - Outliers: " + outliers_str;
string inliers_str = IntToString(inliers_int);
string outliers_str = IntToString(outliers_int);
string n = IntToString((int)good_matches.size());
string text = "Found " + inliers_str + " of " + n + " matches";
string text2 = "Inliers: " + inliers_str + " - Outliers: " + outliers_str;
drawText(frame_vis, text, green);
drawText2(frame_vis, text2, red);
cv::imshow("REAL TIME DEMO", frame_vis);
imshow("REAL TIME DEMO", frame_vis);
}
// Close and Destroy Window
cv::destroyWindow("REAL TIME DEMO");
destroyWindow("REAL TIME DEMO");
std::cout << "GOODBYE ..." << std::endl;
cout << "GOODBYE ..." << endl;
}
/**********************************************************************************************************/
void help()
{
std::cout
<< "--------------------------------------------------------------------------" << std::endl
cout
<< "--------------------------------------------------------------------------" << endl
<< "This program shows how to detect an object given its 3D textured model. You can choose to "
<< "use a recorded video or the webcam." << std::endl
<< "Usage:" << std::endl
<< "./cpp-tutorial-pnp_detection -help" << std::endl
<< "Keys:" << std::endl
<< "'esc' - to quit." << std::endl
<< "--------------------------------------------------------------------------" << std::endl
<< std::endl;
<< "use a recorded video or the webcam." << endl
<< "Usage:" << endl
<< "./cpp-tutorial-pnp_detection -help" << endl
<< "Keys:" << endl
<< "'esc' - to quit." << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
/**********************************************************************************************************/
void initKalmanFilter(cv::KalmanFilter &KF, int nStates, int nMeasurements, int nInputs, double dt)
void initKalmanFilter(KalmanFilter &KF, int nStates, int nMeasurements, int nInputs, double dt)
{
KF.init(nStates, nMeasurements, nInputs, CV_64F); // init Kalman Filter
cv::setIdentity(KF.processNoiseCov, cv::Scalar::all(1e-5)); // set process noise
cv::setIdentity(KF.measurementNoiseCov, cv::Scalar::all(1e-2)); // set measurement noise
cv::setIdentity(KF.errorCovPost, cv::Scalar::all(1)); // error covariance
setIdentity(KF.processNoiseCov, Scalar::all(1e-5)); // set process noise
setIdentity(KF.measurementNoiseCov, Scalar::all(1e-2)); // set measurement noise
setIdentity(KF.errorCovPost, Scalar::all(1)); // error covariance
/** DYNAMIC MODEL **/
@ -424,15 +427,15 @@ void initKalmanFilter(cv::KalmanFilter &KF, int nStates, int nMeasurements, int
}
/**********************************************************************************************************/
void updateKalmanFilter( cv::KalmanFilter &KF, cv::Mat &measurement,
cv::Mat &translation_estimated, cv::Mat &rotation_estimated )
void updateKalmanFilter( KalmanFilter &KF, Mat &measurement,
Mat &translation_estimated, Mat &rotation_estimated )
{
// First predict, to update the internal statePre variable
cv::Mat prediction = KF.predict();
Mat prediction = KF.predict();
// The "correct" phase that is going to use the predicted value and our measurement
cv::Mat estimated = KF.correct(measurement);
Mat estimated = KF.correct(measurement);
// Estimated translation
translation_estimated.at<double>(0) = estimated.at<double>(0);
@ -440,7 +443,7 @@ void updateKalmanFilter( cv::KalmanFilter &KF, cv::Mat &measurement,
translation_estimated.at<double>(2) = estimated.at<double>(2);
// Estimated euler angles
cv::Mat eulers_estimated(3, 1, CV_64F);
Mat eulers_estimated(3, 1, CV_64F);
eulers_estimated.at<double>(0) = estimated.at<double>(9);
eulers_estimated.at<double>(1) = estimated.at<double>(10);
eulers_estimated.at<double>(2) = estimated.at<double>(11);
@ -451,11 +454,11 @@ void updateKalmanFilter( cv::KalmanFilter &KF, cv::Mat &measurement,
}
/**********************************************************************************************************/
void fillMeasurements( cv::Mat &measurements,
const cv::Mat &translation_measured, const cv::Mat &rotation_measured)
void fillMeasurements( Mat &measurements,
const Mat &translation_measured, const Mat &rotation_measured)
{
// Convert rotation matrix to euler angles
cv::Mat measured_eulers(3, 1, CV_64F);
Mat measured_eulers(3, 1, CV_64F);
measured_eulers = rot2euler(rotation_measured);
// Set measurement to predict

107
samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/main_registration.cpp
View File

@ -13,13 +13,16 @@
#include "ModelRegistration.h"
#include "Utils.h"
using namespace cv;
using namespace std;
/** GLOBAL VARIABLES **/
std::string tutorial_path = "../../samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/"; // path to tutorial
string tutorial_path = "../../samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/"; // path to tutorial
std::string img_path = tutorial_path + "Data/resized_IMG_3875.JPG"; // image to register
std::string ply_read_path = tutorial_path + "Data/box.ply"; // object mesh
std::string write_path = tutorial_path + "Data/cookies_ORB.yml"; // output file
string img_path = tutorial_path + "Data/resized_IMG_3875.JPG"; // image to register
string ply_read_path = tutorial_path + "Data/box.ply"; // object mesh
string write_path = tutorial_path + "Data/cookies_ORB.yml"; // output file
// Boolean the know if the registration it's done
bool end_registration = false;
@ -39,10 +42,10 @@ int n = 8;
int pts[] = {1, 2, 3, 4, 5, 6, 7, 8}; // 3 -> 4
// Some basic colors
cv::Scalar red(0, 0, 255);
cv::Scalar green(0,255,0);
cv::Scalar blue(255,0,0);
cv::Scalar yellow(0,255,255);
Scalar red(0, 0, 255);
Scalar green(0,255,0);
Scalar blue(255,0,0);
Scalar yellow(0,255,255);
/*
* CREATE MODEL REGISTRATION OBJECT
@ -61,13 +64,13 @@ void help();
// Mouse events for model registration
static void onMouseModelRegistration( int event, int x, int y, int, void* )
{
if ( event == cv::EVENT_LBUTTONUP )
if ( event == EVENT_LBUTTONUP )
{
int n_regist = registration.getNumRegist();
int n_vertex = pts[n_regist];
cv::Point2f point_2d = cv::Point2f((float)x,(float)y);
cv::Point3f point_3d = mesh.getVertex(n_vertex-1);
Point2f point_2d = Point2f((float)x,(float)y);
Point3f point_3d = mesh.getVertex(n_vertex-1);
bool is_registrable = registration.is_registrable();
if (is_registrable)
@ -92,23 +95,23 @@ int main()
//Instantiate robust matcher: detector, extractor, matcher
RobustMatcher rmatcher;
cv::FeatureDetector * detector = new cv::OrbFeatureDetector(numKeyPoints);
Ptr<FeatureDetector> detector = ORB::create(numKeyPoints);
rmatcher.setFeatureDetector(detector);
/** GROUND TRUTH OF THE FIRST IMAGE **/
// Create & Open Window
cv::namedWindow("MODEL REGISTRATION", cv::WINDOW_KEEPRATIO);
namedWindow("MODEL REGISTRATION", WINDOW_KEEPRATIO);
// Set up the mouse events
cv::setMouseCallback("MODEL REGISTRATION", onMouseModelRegistration, 0 );
setMouseCallback("MODEL REGISTRATION", onMouseModelRegistration, 0 );
// Open the image to register
cv::Mat img_in = cv::imread(img_path, cv::IMREAD_COLOR);
cv::Mat img_vis = img_in.clone();
Mat img_in = imread(img_path, IMREAD_COLOR);
Mat img_vis = img_in.clone();
if (!img_in.data) {
std::cout << "Could not open or find the image" << std::endl;
cout << "Could not open or find the image" << endl;
return -1;
}
@ -116,18 +119,18 @@ int main()
int num_registrations = n;
registration.setNumMax(num_registrations);
std::cout << "Click the box corners ..." << std::endl;
std::cout << "Waiting ..." << std::endl;
cout << "Click the box corners ..." << endl;
cout << "Waiting ..." << endl;
// Loop until all the points are registered
while ( cv::waitKey(30) < 0 )
while ( waitKey(30) < 0 )
{
// Refresh debug image
img_vis = img_in.clone();
// Current registered points
std::vector<cv::Point2f> list_points2d = registration.get_points2d();
std::vector<cv::Point3f> list_points3d = registration.get_points3d();
vector<Point2f> list_points2d = registration.get_points2d();
vector<Point3f> list_points3d = registration.get_points3d();
// Draw current registered points
drawPoints(img_vis, list_points2d, list_points3d, red);
@ -139,7 +142,7 @@ int main()
// Draw debug text
int n_regist = registration.getNumRegist();
int n_vertex = pts[n_regist];
cv::Point3f current_poin3d = mesh.getVertex(n_vertex-1);
Point3f current_poin3d = mesh.getVertex(n_vertex-1);
drawQuestion(img_vis, current_poin3d, green);
drawCounter(img_vis, registration.getNumRegist(), registration.getNumMax(), red);
@ -153,43 +156,43 @@ int main()
}
// Show the image
cv::imshow("MODEL REGISTRATION", img_vis);
imshow("MODEL REGISTRATION", img_vis);
}
/** COMPUTE CAMERA POSE **/
std::cout << "COMPUTING POSE ..." << std::endl;
cout << "COMPUTING POSE ..." << endl;
// The list of registered points
std::vector<cv::Point2f> list_points2d = registration.get_points2d();
std::vector<cv::Point3f> list_points3d = registration.get_points3d();
vector<Point2f> list_points2d = registration.get_points2d();
vector<Point3f> list_points3d = registration.get_points3d();
// Estimate pose given the registered points
bool is_correspondence = pnp_registration.estimatePose(list_points3d, list_points2d, cv::SOLVEPNP_ITERATIVE);
bool is_correspondence = pnp_registration.estimatePose(list_points3d, list_points2d, SOLVEPNP_ITERATIVE);
if ( is_correspondence )
{
std::cout << "Correspondence found" << std::endl;
cout << "Correspondence found" << endl;
// Compute all the 2D points of the mesh to verify the algorithm and draw it
std::vector<cv::Point2f> list_points2d_mesh = pnp_registration.verify_points(&mesh);
vector<Point2f> list_points2d_mesh = pnp_registration.verify_points(&mesh);
draw2DPoints(img_vis, list_points2d_mesh, green);
} else {
std::cout << "Correspondence not found" << std::endl << std::endl;
cout << "Correspondence not found" << endl << endl;
}
// Show the image
cv::imshow("MODEL REGISTRATION", img_vis);
imshow("MODEL REGISTRATION", img_vis);
// Show image until ESC pressed
cv::waitKey(0);
waitKey(0);
/** COMPUTE 3D of the image Keypoints **/
// Containers for keypoints and descriptors of the model
std::vector<cv::KeyPoint> keypoints_model;
cv::Mat descriptors;
vector<KeyPoint> keypoints_model;
Mat descriptors;
// Compute keypoints and descriptors
rmatcher.computeKeyPoints(img_in, keypoints_model);
@ -197,8 +200,8 @@ int main()
// Check if keypoints are on the surface of the registration image and add to the model
for (unsigned int i = 0; i < keypoints_model.size(); ++i) {
cv::Point2f point2d(keypoints_model[i].pt);
cv::Point3f point3d;
Point2f point2d(keypoints_model[i].pt);
Point3f point3d;
bool on_surface = pnp_registration.backproject2DPoint(&mesh, point2d, point3d);
if (on_surface)
{
@ -219,12 +222,12 @@ int main()
img_vis = img_in.clone();
// The list of the points2d of the model
std::vector<cv::Point2f> list_points_in = model.get_points2d_in();
std::vector<cv::Point2f> list_points_out = model.get_points2d_out();
vector<Point2f> list_points_in = model.get_points2d_in();
vector<Point2f> list_points_out = model.get_points2d_out();
// Draw some debug text
std::string num = IntToString((int)list_points_in.size());
std::string text = "There are " + num + " inliers";
string num = IntToString((int)list_points_in.size());
string text = "There are " + num + " inliers";
drawText(img_vis, text, green);
// Draw some debug text
@ -240,26 +243,26 @@ int main()
draw2DPoints(img_vis, list_points_out, red);
// Show the image
cv::imshow("MODEL REGISTRATION", img_vis);
imshow("MODEL REGISTRATION", img_vis);
// Wait until ESC pressed
cv::waitKey(0);
waitKey(0);
// Close and Destroy Window
cv::destroyWindow("MODEL REGISTRATION");
destroyWindow("MODEL REGISTRATION");
std::cout << "GOODBYE" << std::endl;
cout << "GOODBYE" << endl;
}
/**********************************************************************************************************/
void help()
{
std::cout
<< "--------------------------------------------------------------------------" << std::endl
<< "This program shows how to create your 3D textured model. " << std::endl
<< "Usage:" << std::endl
<< "./cpp-tutorial-pnp_registration" << std::endl
<< "--------------------------------------------------------------------------" << std::endl
<< std::endl;
cout
<< "--------------------------------------------------------------------------" << endl
<< "This program shows how to create your 3D textured model. " << endl
<< "Usage:" << endl
<< "./cpp-tutorial-pnp_registration" << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}

6
samples/cpp/tutorial_code/features2D/AKAZE_match.cpp
View File

@ -22,9 +22,9 @@ int main(void)
vector<KeyPoint> kpts1, kpts2;
Mat desc1, desc2;
AKAZE akaze;
akaze(img1, noArray(), kpts1, desc1);
akaze(img2, noArray(), kpts2, desc2);
Ptr<AKAZE> akaze = AKAZE::create();
akaze->detectAndCompute(img1, noArray(), kpts1, desc1);
akaze->detectAndCompute(img2, noArray(), kpts2, desc2);
BFMatcher matcher(NORM_HAMMING);
vector< vector<DMatch> > nn_matches;

8
samples/cpp/tutorial_code/features2D/AKAZE_tracking/planar_tracking.cpp
View File

@ -41,7 +41,7 @@ protected:
void Tracker::setFirstFrame(const Mat frame, vector<Point2f> bb, string title, Stats& stats)
{
first_frame = frame.clone();
(*detector)(first_frame, noArray(), first_kp, first_desc);
detector->detectAndCompute(first_frame, noArray(), first_kp, first_desc);
stats.keypoints = (int)first_kp.size();
drawBoundingBox(first_frame, bb);
putText(first_frame, title, Point(0, 60), FONT_HERSHEY_PLAIN, 5, Scalar::all(0), 4);
@ -52,7 +52,7 @@ Mat Tracker::process(const Mat frame, Stats& stats)
{
vector<KeyPoint> kp;
Mat desc;
(*detector)(frame, noArray(), kp, desc);
detector->detectAndCompute(frame, noArray(), kp, desc);
stats.keypoints = (int)kp.size();
vector< vector<DMatch> > matches;
@ -135,9 +135,9 @@ int main(int argc, char **argv)
return 1;
}
fs["bounding_box"] >> bb;
Ptr<Feature2D> akaze = Feature2D::create("AKAZE");
Ptr<Feature2D> akaze = AKAZE::create();
akaze->set("threshold", akaze_thresh);
Ptr<Feature2D> orb = Feature2D::create("ORB");
Ptr<Feature2D> orb = ORB::create();
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
Tracker akaze_tracker(akaze, matcher);
Tracker orb_tracker(orb, matcher);

4
samples/cpp/videostab.cpp
View File

@ -227,7 +227,7 @@ public:
#endif
Ptr<KeypointBasedMotionEstimator> kbest = makePtr<KeypointBasedMotionEstimator>(est);
kbest->setDetector(makePtr<GoodFeaturesToTrackDetector>(argi(prefix + "nkps")));
kbest->setDetector(GFTTDetector::create(argi(prefix + "nkps")));
kbest->setOutlierRejector(outlierRejector);
return kbest;
}
@ -268,7 +268,7 @@ public:
#endif
Ptr<KeypointBasedMotionEstimator> kbest = makePtr<KeypointBasedMotionEstimator>(est);
kbest->setDetector(makePtr<GoodFeaturesToTrackDetector>(argi(prefix + "nkps")));
kbest->setDetector(GFTTDetector::create(argi(prefix + "nkps")));
kbest->setOutlierRejector(outlierRejector);
return kbest;
}

4
samples/winrt/OcvImageProcessing/OcvImageProcessing/MainPage.xaml.cpp
View File

@ -129,12 +129,12 @@ cv::Mat OcvImageProcessing::MainPage::ApplyFindFeaturesFilter(const cv::Mat& ima
{
cv::Mat result;
cv::Mat intermediateMat;
cv::FastFeatureDetector detector(50);
cv::Ptr<cv::FeatureDetector> detector = cv::FastFeatureDetector::create(50);
std::vector<cv::KeyPoint> features;
image.copyTo(result);
cv::cvtColor(image, intermediateMat, CV_RGBA2GRAY);
detector.detect(intermediateMat, features);
detector->detect(intermediateMat, features);
for( unsigned int i = 0; i < std::min(features.size(), (size_t)50); i++ )
{