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Doxygen documentation: calib3d and features2d modules

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modules/calib3d/include/opencv2/calib3d.hpp
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modules/calib3d/include/opencv2/calib3d/calib3d_c.h
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@ -50,6 +50,10 @@
extern "C" {
#endif
/** @addtogroup calib3d_c
@{
*/
/****************************************************************************************\
* Camera Calibration, Pose Estimation and Stereo *
\****************************************************************************************/
@ -371,6 +375,8 @@ CVAPI(void) cvReprojectImageTo3D( const CvArr* disparityImage,
CvArr* _3dImage, const CvMat* Q,
int handleMissingValues CV_DEFAULT(0) );
/** @} calib3d_c */
#ifdef __cplusplus
} // extern "C"

679
modules/features2d/include/opencv2/features2d.hpp
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@ -46,18 +46,54 @@
#include "opencv2/core.hpp"
#include "opencv2/flann/miniflann.hpp"
/**
@defgroup features2d 2D Features Framework
@{
@defgroup features2d_main Feature Detection and Description
@defgroup features2d_match Descriptor Matchers
Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to
easily switch between different algorithms solving the same problem. This section is devoted to
matching descriptors that are represented as vectors in a multidimensional space. All objects that
implement vector descriptor matchers inherit the DescriptorMatcher interface.
@note
- An example explaining keypoint matching can be found at
opencv\_source\_code/samples/cpp/descriptor\_extractor\_matcher.cpp
- An example on descriptor matching evaluation can be found at
opencv\_source\_code/samples/cpp/detector\_descriptor\_matcher\_evaluation.cpp
- An example on one to many image matching can be found at
opencv\_source\_code/samples/cpp/matching\_to\_many\_images.cpp
@defgroup features2d_draw Drawing Function of Keypoints and Matches
@defgroup features2d_category Object Categorization
This section describes approaches based on local 2D features and used to categorize objects.
@note
- A complete Bag-Of-Words sample can be found at
opencv\_source\_code/samples/cpp/bagofwords\_classification.cpp
- (Python) An example using the features2D framework to perform object categorization can be
found at opencv\_source\_code/samples/python2/find\_obj.py
@}
*/
namespace cv
{
//! @addtogroup features2d
//! @{
// //! 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
// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector<KeyPoint>& keypoints);
/*
* A class filters a vector of keypoints.
* Because now it is difficult to provide a convenient interface for all usage scenarios of the keypoints filter class,
* it has only several needed by now static methods.
/** @brief A class filters a vector of keypoints.
Because now it is difficult to provide a convenient interface for all usage scenarios of the
keypoints filter class, it has only several needed by now static methods.
*/
class CV_EXPORTS KeyPointsFilter
{
@ -91,44 +127,66 @@ public:
/************************************ Base Classes ************************************/
/*
* Abstract base class for 2D image feature detectors and descriptor extractors
*/
/** @brief Abstract base class for 2D image feature detectors and descriptor extractors
*/
class CV_EXPORTS_W Feature2D : public virtual Algorithm
{
public:
virtual ~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.
/** @brief Detects keypoints in an image (first variant) or image set (second variant).
@param image Image.
@param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
of keypoints detected in images[i] .
@param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
matrix with non-zero values in the region of interest.
*/
CV_WRAP virtual void detect( InputArray image,
CV_OUT std::vector<KeyPoint>& keypoints,
InputArray mask=noArray() );
/** @overload
@param images Image set.
@param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
of keypoints detected in images[i] .
@param masks Masks for each input image specifying where to look for keypoints (optional).
masks[i] is a mask for images[i].
*/
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.
* 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.
/** @brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set
(second variant).
@param image Image.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.
*/
CV_WRAP virtual void compute( InputArray image,
CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints,
OutputArray descriptors );
/** @overload
@param images Image set.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.
*/
virtual void compute( InputArrayOfArrays images,
std::vector<std::vector<KeyPoint> >& keypoints,
OutputArrayOfArrays descriptors );
/* Detects keypoints and computes the descriptors */
/** Detects keypoints and computes the descriptors */
CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask,
CV_OUT std::vector<KeyPoint>& keypoints,
OutputArray descriptors,
@ -138,33 +196,96 @@ public:
CV_WRAP virtual int descriptorType() const;
CV_WRAP virtual int defaultNorm() const;
// Return true if detector object is empty
//! Return true if detector object is empty
CV_WRAP virtual bool empty() const;
};
/** Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch
between different algorithms solving the same problem. All objects that implement keypoint detectors
inherit the FeatureDetector interface. */
typedef Feature2D FeatureDetector;
/** Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you
to easily switch between different algorithms solving the same problem. This section is devoted to
computing descriptors represented as vectors in a multidimensional space. All objects that implement
the vector descriptor extractors inherit the DescriptorExtractor interface.
*/
typedef Feature2D DescriptorExtractor;
/*!
BRISK implementation
*/
//! @addtogroup features2d_main
//! @{
/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11.
*/
class CV_EXPORTS_W BRISK : public Feature2D
{
public:
/** @brief The BRISK constructor
@param thresh FAST/AGAST detection threshold score.
@param octaves detection octaves. Use 0 to do single scale.
@param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
keypoint.
*/
CV_WRAP static Ptr<BRISK> create(int thresh=30, int octaves=3, float patternScale=1.0f);
// custom setup
/** @brief The BRISK constructor for a custom pattern
@param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
keypoint scale 1).
@param numberList defines the number of sampling points on the sampling circle. Must be the same
size as radiusList..
@param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
scale 1).
@param dMin threshold for the long pairings used for orientation determination (in pixels for
keypoint scale 1).
@param indexChange index remapping of the bits. */
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.
*/
/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor
described in @cite RRKB11. The algorithm uses FAST in pyramids to detect stable keypoints, selects
the strongest features using FAST or Harris response, finds their orientation using first-order
moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or
k-tuples) are rotated according to the measured orientation).
*/
class CV_EXPORTS_W ORB : public Feature2D
{
public:
enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 };
/** @brief The ORB constructor
@param nfeatures The maximum number of features to retain.
@param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.
@param nlevels The number of pyramid levels. The smallest level will have linear size equal to
input\_image\_linear\_size/pow(scaleFactor, nlevels).
@param edgeThreshold This is size of the border where the features are not detected. It should
roughly match the patchSize parameter.
@param firstLevel It should be 0 in the current implementation.
@param WTA\_K The number of points that produce each element of the oriented BRIEF descriptor. The
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM\_HAMMING2 (2 bits per bin). When WTA\_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
@param scoreType The default HARRIS\_SCORE means that Harris algorithm is used to rank features
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST\_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
@param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
pyramid layers the perceived image area covered by a feature will be larger.
@param fastThreshold
*/
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);
@ -196,15 +317,16 @@ public:
CV_WRAP virtual int getFastThreshold() const = 0;
};
/*!
Maximal Stable Extremal Regions class.
/** @brief Maximally stable extremal region extractor. :
The class implements MSER algorithm introduced by J. Matas.
Unlike SIFT, SURF and many other detectors in OpenCV, this is salient region detector,
not the salient point detector.
The class encapsulates all the parameters of the MSER extraction algorithm (see
<http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions>). Also see
<http://code.opencv.org/projects/opencv/wiki/MSER> for useful comments and parameters description.
It returns the regions, each of those is encoded as a contour.
*/
@note
- (Python) A complete example showing the use of the MSER detector can be found at
opencv\_source\_code/samples/python2/mser.py
*/
class CV_EXPORTS_W MSER : public Feature2D
{
public:
@ -231,13 +353,38 @@ public:
CV_WRAP virtual bool getPass2Only() const = 0;
};
//! detects corners using FAST algorithm by E. Rosten
/** @overload */
CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
int threshold, bool nonmaxSuppression=true );
/** @brief Detects corners using the FAST algorithm
@param image grayscale image where keypoints (corners) are detected.
@param keypoints keypoints detected on the image.
@param threshold threshold on difference between intensity of the central pixel and pixels of a
circle around this pixel.
@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
(keypoints).
@param type one of the three neighborhoods as defined in the paper:
FastFeatureDetector::TYPE\_9\_16, FastFeatureDetector::TYPE\_7\_12,
FastFeatureDetector::TYPE\_5\_8
Detects corners using the FAST algorithm by @cite Rosten06.
@note In Python API, types are given as cv2.FAST\_FEATURE\_DETECTOR\_TYPE\_5\_8,
cv2.FAST\_FEATURE\_DETECTOR\_TYPE\_7\_12 and cv2.FAST\_FEATURE\_DETECTOR\_TYPE\_9\_16. For corner
detection, use cv2.FAST.detect() method.
*/
CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
int threshold, bool nonmaxSuppression, int type );
//! @} features2d_main
//! @addtogroup features2d_main
//! @{
/** @brief Wrapping class for feature detection using the FAST method. :
*/
class CV_EXPORTS_W FastFeatureDetector : public Feature2D
{
public:
@ -261,7 +408,8 @@ public:
CV_WRAP virtual int getType() const = 0;
};
/** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. :
*/
class CV_EXPORTS_W GFTTDetector : public Feature2D
{
public:
@ -286,7 +434,37 @@ public:
CV_WRAP virtual double getK() const = 0;
};
/** @brief Class for extracting blobs from an image. :
The class implements a simple algorithm for extracting blobs from an image:
1. Convert the source image to binary images by applying thresholding with several thresholds from
minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between
neighboring thresholds.
2. Extract connected components from every binary image by findContours and calculate their
centers.
3. Group centers from several binary images by their coordinates. Close centers form one group that
corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter.
4. From the groups, estimate final centers of blobs and their radiuses and return as locations and
sizes of keypoints.
This class performs several filtrations of returned blobs. You should set filterBy\* to true/false
to turn on/off corresponding filtration. Available filtrations:
- **By color**. This filter compares the intensity of a binary image at the center of a blob to
blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs
and blobColor = 255 to extract light blobs.
- **By area**. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive).
- **By circularity**. Extracted blobs have circularity
(\f$\frac{4*\pi*Area}{perimeter * perimeter}\f$) between minCircularity (inclusive) and
maxCircularity (exclusive).
- **By ratio of the minimum inertia to maximum inertia**. Extracted blobs have this ratio
between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive).
- **By convexity**. Extracted blobs have convexity (area / area of blob convex hull) between
minConvexity (inclusive) and maxConvexity (exclusive).
Default values of parameters are tuned to extract dark circular blobs.
*/
class CV_EXPORTS_W SimpleBlobDetector : public Feature2D
{
public:
@ -322,9 +500,16 @@ public:
create(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
};
//! @} features2d_main
/*!
KAZE implementation
//! @addtogroup features2d_main
//! @{
/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12.
@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo
F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision
(ECCV), Fiorenze, Italy, October 2012.
*/
class CV_EXPORTS_W KAZE : public Feature2D
{
@ -337,6 +522,16 @@ public:
DIFF_CHARBONNIER = 3
};
/** @brief The KAZE constructor
@param extended Set to enable extraction of extended (128-byte) descriptor.
@param upright Set to enable use of upright descriptors (non rotation-invariant).
@param threshold Detector response threshold to accept point
@param nOctaves Maximum octave evolution of the image
@param nOctaveLayers Default number of sublevels per scale level
@param diffusivity Diffusivity type. DIFF\_PM\_G1, DIFF\_PM\_G2, DIFF\_WEICKERT or
DIFF\_CHARBONNIER
*/
CV_WRAP static Ptr<KAZE> create(bool extended=false, bool upright=false,
float threshold = 0.001f,
int nOctaves = 4, int nOctaveLayers = 4,
@ -361,9 +556,13 @@ public:
CV_WRAP virtual int getDiffusivity() const = 0;
};
/*!
AKAZE implementation
*/
/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13. :
@note AKAZE descriptors can only be used with KAZE or AKAZE keypoints. Try to avoid using *extract*
and *detect* instead of *operator()* due to performance reasons. .. [ANB13] Fast Explicit Diffusion
for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien
Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013.
*/
class CV_EXPORTS_W AKAZE : public Feature2D
{
public:
@ -376,6 +575,18 @@ public:
DESCRIPTOR_MLDB = 5
};
/** @brief The AKAZE constructor
@param descriptor\_type Type of the extracted descriptor: DESCRIPTOR\_KAZE,
DESCRIPTOR\_KAZE\_UPRIGHT, DESCRIPTOR\_MLDB or DESCRIPTOR\_MLDB\_UPRIGHT.
@param descriptor\_size Size of the descriptor in bits. 0 -\> Full size
@param descriptor\_channels Number of channels in the descriptor (1, 2, 3)
@param threshold Detector response threshold to accept point
@param nOctaves Maximum octave evolution of the image
@param nOctaveLayers Default number of sublevels per scale level
@param diffusivity Diffusivity type. DIFF\_PM\_G1, DIFF\_PM\_G2, DIFF\_WEICKERT or
DIFF\_CHARBONNIER
*/
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 nOctaves = 4,
@ -403,6 +614,8 @@ public:
CV_WRAP virtual int getDiffusivity() const = 0;
};
//! @} features2d_main
/****************************************************************************************\
* Distance *
\****************************************************************************************/
@ -501,76 +714,153 @@ template<int cellsize> struct HammingMultilevel
/****************************************************************************************\
* DescriptorMatcher *
\****************************************************************************************/
/*
* Abstract base class for matching two sets of descriptors.
//! @addtogroup features2d_match
//! @{
/** @brief Abstract base class for matching keypoint descriptors.
It has two groups of match methods: for matching descriptors of an image with another image or with
an image set.
*/
class CV_EXPORTS_W DescriptorMatcher : public Algorithm
{
public:
virtual ~DescriptorMatcher();
/*
* Add descriptors to train descriptor collection.
* descriptors Descriptors to add. Each descriptors[i] is a descriptors set from one image.
/** @brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor
collection.
If the collection is not empty, the new descriptors are added to existing train descriptors.
@param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same
train image.
*/
CV_WRAP virtual void add( InputArrayOfArrays descriptors );
/*
* Get train descriptors collection.
/** @brief Returns a constant link to the train descriptor collection trainDescCollection .
*/
CV_WRAP const std::vector<Mat>& getTrainDescriptors() const;
/*
* Clear train descriptors collection.
/** @brief Clears the train descriptor collections.
*/
CV_WRAP virtual void clear();
/*
* Return true if there are not train descriptors in collection.
/** @brief Returns true if there are no train descriptors in the both collections.
*/
CV_WRAP virtual bool empty() const;
/*
* Return true if the matcher supports mask in match methods.
/** @brief Returns true if the descriptor matcher supports masking permissible matches.
*/
CV_WRAP virtual bool isMaskSupported() const = 0;
/*
* Train matcher (e.g. train flann index).
* In all methods to match the method train() is run every time before matching.
* Some descriptor matchers (e.g. BruteForceMatcher) have empty implementation
* of this method, other matchers really train their inner structures
* (e.g. FlannBasedMatcher trains flann::Index). So nonempty implementation
* of train() should check the class object state and do traing/retraining
* only if the state requires that (e.g. FlannBasedMatcher trains flann::Index
* if it has not trained yet or if new descriptors have been added to the train
* collection).
/** @brief Trains a descriptor matcher
Trains a descriptor matcher (for example, the flann index). In all methods to match, the method
train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher)
have an empty implementation of this method. Other matchers really train their inner structures (for
example, FlannBasedMatcher trains flann::Index ).
*/
CV_WRAP virtual void train();
/*
* Group of methods to match descriptors from image pair.
* Method train() is run in this methods.
/** @brief Finds the best match for each descriptor from a query set.
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
descriptor. So, matches size may be smaller than the query descriptors count.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
In the first variant of this method, the train descriptors are passed as an input argument. In the
second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
mask.at\<uchar\>(i,j) is non-zero.
*/
// Find one best match for each query descriptor (if mask is empty).
CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors,
CV_OUT std::vector<DMatch>& matches, InputArray mask=noArray() ) const;
// Find k best matches for each query descriptor (in increasing order of distances).
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
/** @brief Finds the k best matches for each descriptor from a query set.
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
These extended variants of DescriptorMatcher::match methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
for the details about query and train descriptors.
*/
CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,
CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
InputArray mask=noArray(), bool compactResult=false ) const;
// Find best matches for each query descriptor which have distance less than
// maxDistance (in increasing order of distances).
/** @brief For each query descriptor, finds the training descriptors not farther than the specified distance.
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Found matches.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.
*/
void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors,
std::vector<std::vector<DMatch> >& matches, float maxDistance,
InputArray mask=noArray(), bool compactResult=false ) const;
/*
* Group of methods to match descriptors from one image to image set.
* See description of similar methods for matching image pair above.
*/
/** @overload
@param queryDescriptors Query set of descriptors.
@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
descriptor. So, matches size may be smaller than the query descriptors count.
@param masks Set of masks. Each masks[i] specifies permissible matches between the input query
descriptors and stored train descriptors from the i-th image trainDescCollection[i].
*/
CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector<DMatch>& matches,
InputArrayOfArrays masks=noArray() );
/** @overload
@param queryDescriptors Query set of descriptors.
@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param masks Set of masks. Each masks[i] specifies permissible matches between the input query
descriptors and stored train descriptors from the i-th image trainDescCollection[i].
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
*/
CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
InputArrayOfArrays masks=noArray(), bool compactResult=false );
/** @overload
@param queryDescriptors Query set of descriptors.
@param matches Found matches.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param masks Set of masks. Each masks[i] specifies permissible matches between the input query
descriptors and stored train descriptors from the i-th image trainDescCollection[i].
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
*/
void radiusMatch( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
InputArrayOfArrays masks=noArray(), bool compactResult=false );
@ -579,14 +869,28 @@ public:
// Writes matcher object to a file storage
virtual void write( FileStorage& ) const;
// Clone the matcher. If emptyTrainData is false the method create deep copy of the object, i.e. copies
// both parameters and train data. If emptyTrainData is true the method create object copy with current parameters
// but with empty train data.
/** @brief Clones the matcher.
@param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object,
that is, copies both parameters and train data. If emptyTrainData is true, the method creates an
object copy with the current parameters but with empty train data.
*/
virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
/** @brief Creates a descriptor matcher of a given type with the default parameters (using default
constructor).
@param descriptorMatcherType Descriptor matcher type. Now the following matcher types are
supported:
- `BruteForce` (it uses L2 )
- `BruteForce-L1`
- `BruteForce-Hamming`
- `BruteForce-Hamming(2)`
- `FlannBased`
*/
CV_WRAP static Ptr<DescriptorMatcher> create( const String& descriptorMatcherType );
protected:
/*
/**
* Class to work with descriptors from several images as with one merged matrix.
* It is used e.g. in FlannBasedMatcher.
*/
@ -613,9 +917,9 @@ protected:
std::vector<int> startIdxs;
};
// In fact the matching is implemented only by the following two methods. These methods suppose
// that the class object has been trained already. Public match methods call these methods
// after calling train().
//! In fact the matching is implemented only by the following two methods. These methods suppose
//! that the class object has been trained already. Public match methods call these methods
//! after calling train().
virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0;
virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
@ -627,23 +931,33 @@ protected:
static Mat clone_op( Mat m ) { return m.clone(); }
void checkMasks( InputArrayOfArrays masks, int queryDescriptorsCount ) const;
// Collection of descriptors from train images.
//! Collection of descriptors from train images.
std::vector<Mat> trainDescCollection;
std::vector<UMat> utrainDescCollection;
};
/*
* Brute-force descriptor matcher.
*
* For each descriptor in the first set, this matcher finds the closest
* descriptor in the second set by trying each one.
*
* For efficiency, BruteForceMatcher is templated on the distance metric.
* For float descriptors, a common choice would be cv::L2<float>.
/** @brief Brute-force descriptor matcher.
For each descriptor in the first set, this matcher finds the closest descriptor in the second set
by trying each one. This descriptor matcher supports masking permissible matches of descriptor
sets.
*/
class CV_EXPORTS_W BFMatcher : public DescriptorMatcher
{
public:
/** @brief Brute-force matcher constructor.
@param normType One of NORM\_L1, NORM\_L2, NORM\_HAMMING, NORM\_HAMMING2. L1 and L2 norms are
preferable choices for SIFT and SURF descriptors, NORM\_HAMMING should be used with ORB, BRISK and
BRIEF, NORM\_HAMMING2 should be used with ORB when WTA\_K==3 or 4 (see ORB::ORB constructor
description).
@param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k
nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with
k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the
matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent
pairs. Such technique usually produces best results with minimal number of outliers when there are
enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
*/
CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false );
virtual ~BFMatcher() {}
@ -661,8 +975,12 @@ protected:
};
/*
* Flann based matcher
/** @brief Flann-based descriptor matcher.
This matcher trains flann::Index\_ on a train descriptor collection and calls its nearest search
methods to find the best matches. So, this matcher may be faster when matching a large train
collection than the brute force matcher. FlannBasedMatcher does not support masking permissible
matches of descriptor sets because flann::Index does not support this. :
*/
class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher
{
@ -700,42 +1018,85 @@ protected:
int addedDescCount;
};
//! @} features2d_match
/****************************************************************************************\
* Drawing functions *
\****************************************************************************************/
//! @addtogroup features2d_draw
//! @{
struct CV_EXPORTS DrawMatchesFlags
{
enum{ DEFAULT = 0, // Output image matrix will be created (Mat::create),
// i.e. existing memory of output image may be reused.
// Two source image, matches and single keypoints will be drawn.
// For each keypoint only the center point will be drawn (without
// the circle around keypoint with keypoint size and orientation).
DRAW_OVER_OUTIMG = 1, // Output image matrix will not be created (Mat::create).
// Matches will be drawn on existing content of output image.
NOT_DRAW_SINGLE_POINTS = 2, // Single keypoints will not be drawn.
DRAW_RICH_KEYPOINTS = 4 // For each keypoint the circle around keypoint with keypoint size and
// orientation will be drawn.
enum{ DEFAULT = 0, //!< Output image matrix will be created (Mat::create),
//!< i.e. existing memory of output image may be reused.
//!< Two source image, matches and single keypoints will be drawn.
//!< For each keypoint only the center point will be drawn (without
//!< the circle around keypoint with keypoint size and orientation).
DRAW_OVER_OUTIMG = 1, //!< Output image matrix will not be created (Mat::create).
//!< Matches will be drawn on existing content of output image.
NOT_DRAW_SINGLE_POINTS = 2, //!< Single keypoints will not be drawn.
DRAW_RICH_KEYPOINTS = 4 //!< For each keypoint the circle around keypoint with keypoint size and
//!< orientation will be drawn.
};
};
// Draw keypoints.
/** @brief Draws keypoints.
@param image Source image.
@param keypoints Keypoints from the source image.
@param outImage Output image. Its content depends on the flags value defining what is drawn in the
output image. See possible flags bit values below.
@param color Color of keypoints.
@param flags Flags setting drawing features. Possible flags bit values are defined by
DrawMatchesFlags. See details above in drawMatches .
@note
For Python API, flags are modified as cv2.DRAW\_MATCHES\_FLAGS\_DEFAULT,
cv2.DRAW\_MATCHES\_FLAGS\_DRAW\_RICH\_KEYPOINTS, cv2.DRAW\_MATCHES\_FLAGS\_DRAW\_OVER\_OUTIMG,
cv2.DRAW\_MATCHES\_FLAGS\_NOT\_DRAW\_SINGLE\_POINTS
*/
CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector<KeyPoint>& keypoints, InputOutputArray outImage,
const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT );
// Draws matches of keypints from two images on output image.
/** @brief Draws the found matches of keypoints from two images.
@param img1 First source image.
@param keypoints1 Keypoints from the first source image.
@param img2 Second source image.
@param keypoints2 Keypoints from the second source image.
@param matches1to2 Matches from the first image to the second one, which means that keypoints1[i]
has a corresponding point in keypoints2[matches[i]] .
@param outImg Output image. Its content depends on the flags value defining what is drawn in the
output image. See possible flags bit values below.
@param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1)
, the color is generated randomly.
@param singlePointColor Color of single keypoints (circles), which means that keypoints do not
have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly.
@param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are
drawn.
@param flags Flags setting drawing features. Possible flags bit values are defined by
DrawMatchesFlags.
This function draws matches of keypoints from two images in the output image. Match is a line
connecting two keypoints (circles). See cv::DrawMatchesFlags.
*/
CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
InputArray img2, const std::vector<KeyPoint>& keypoints2,
const std::vector<DMatch>& matches1to2, InputOutputArray outImg,
const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
const std::vector<char>& matchesMask=std::vector<char>(), int flags=DrawMatchesFlags::DEFAULT );
/** @overload */
CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
InputArray img2, const std::vector<KeyPoint>& keypoints2,
const std::vector<std::vector<DMatch> >& matches1to2, InputOutputArray outImg,
const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
const std::vector<std::vector<char> >& matchesMask=std::vector<std::vector<char> >(), int flags=DrawMatchesFlags::DEFAULT );
//! @} features2d_draw
/****************************************************************************************\
* Functions to evaluate the feature detectors and [generic] descriptor extractors *
\****************************************************************************************/
@ -755,8 +1116,14 @@ CV_EXPORTS int getNearestPoint( const std::vector<Point2f>& recallPrecisionCurve
/****************************************************************************************\
* Bag of visual words *
\****************************************************************************************/
/*
* Abstract base class for training of a 'bag of visual words' vocabulary from a set of descriptors
//! @addtogroup features2d_category
//! @{
/** @brief Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors.
For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka,
Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. :
*/
class CV_EXPORTS_W BOWTrainer
{
@ -764,20 +1131,37 @@ public:
BOWTrainer();
virtual ~BOWTrainer();
/** @brief Adds descriptors to a training set.
@param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a
descriptor.
The training set is clustered using clustermethod to construct the vocabulary.
*/
CV_WRAP void add( const Mat& descriptors );
/** @brief Returns a training set of descriptors.
*/
CV_WRAP const std::vector<Mat>& getDescriptors() const;
/** @brief Returns the count of all descriptors stored in the training set.
*/
CV_WRAP int descriptorsCount() const;
CV_WRAP virtual void clear();
/*
* Train visual words vocabulary, that is cluster training descriptors and
* compute cluster centers.
* Returns cluster centers.
*
* descriptors Training descriptors computed on images keypoints.
*/
/** @overload */
CV_WRAP virtual Mat cluster() const = 0;
/** @brief Clusters train descriptors.
@param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor.
Descriptors are not added to the inner train descriptor set.
The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first
variant of the method, train descriptors stored in the object are clustered. In the second variant,
input descriptors are clustered.
*/
CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0;
protected:
@ -785,12 +1169,15 @@ protected:
int size;
};
/*
* This is BOWTrainer using cv::kmeans to get vocabulary.
/** @brief kmeans -based class to train visual vocabulary using the *bag of visual words* approach. :
*/
class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer
{
public:
/** @brief The constructor.
@see cv::kmeans
*/
CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
int attempts=3, int flags=KMEANS_PP_CENTERS );
virtual ~BOWKMeansTrainer();
@ -807,21 +1194,62 @@ protected:
int flags;
};
/*
* Class to compute image descriptor using bag of visual words.
/** @brief Class to compute an image descriptor using the *bag of visual words*.
Such a computation consists of the following steps:
1. Compute descriptors for a given image and its keypoints set.
2. Find the nearest visual words from the vocabulary for each keypoint descriptor.
3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words
encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the
vocabulary in the given image.
*/
class CV_EXPORTS_W BOWImgDescriptorExtractor
{
public:
/** @brief The constructor.
@param dextractor Descriptor extractor that is used to compute descriptors for an input image and
its keypoints.
@param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary
for each keypoint descriptor of the image.
*/
CV_WRAP BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
const Ptr<DescriptorMatcher>& dmatcher );
/** @overload */
BOWImgDescriptorExtractor( const Ptr<DescriptorMatcher>& dmatcher );
virtual ~BOWImgDescriptorExtractor();
/** @brief Sets a visual vocabulary.
@param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the
vocabulary is a visual word (cluster center).
*/
CV_WRAP void setVocabulary( const Mat& vocabulary );
/** @brief Returns the set vocabulary.
*/
CV_WRAP const Mat& getVocabulary() const;
/** @brief Computes an image descriptor using the set visual vocabulary.
@param image Image, for which the descriptor is computed.
@param keypoints Keypoints detected in the input image.
@param imgDescriptor Computed output image descriptor.
@param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
returned if it is non-zero.
@param descriptors Descriptors of the image keypoints that are returned if they are non-zero.
*/
void compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray imgDescriptor,
std::vector<std::vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 );
/** @overload
@param keypointDescriptors Computed descriptors to match with vocabulary.
@param imgDescriptor Computed output image descriptor.
@param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
returned if it is non-zero.
*/
void compute( InputArray keypointDescriptors, OutputArray imgDescriptor,
std::vector<std::vector<int> >* pointIdxsOfClusters=0 );
// compute() is not constant because DescriptorMatcher::match is not constant
@ -829,7 +1257,12 @@ public:
CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector<KeyPoint>& keypoints, CV_OUT Mat& imgDescriptor )
{ compute(image,keypoints,imgDescriptor); }
/** @brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0.
*/
CV_WRAP int descriptorSize() const;
/** @brief Returns an image descriptor type.
*/
CV_WRAP int descriptorType() const;
protected:
@ -838,6 +1271,10 @@ protected:
Ptr<DescriptorMatcher> dmatcher;
};
//! @} features2d_category
//! @} features2d
} /* namespace cv */
#endif