1 /*M///////////////////////////////////////////////////////////////////////////////////////
3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
5 // By downloading, copying, installing or using the software you agree to this license.
6 // If you do not agree to this license, do not download, install,
7 // copy or use the software.
11 // For Open Source Computer Vision Library
13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
14 // Copyright (C) 2009, Willow Garage Inc., all rights reserved.
15 // Third party copyrights are property of their respective owners.
17 // Redistribution and use in source and binary forms, with or without modification,
18 // are permitted provided that the following conditions are met:
20 // * Redistribution's of source code must retain the above copyright notice,
21 // this list of conditions and the following disclaimer.
23 // * Redistribution's in binary form must reproduce the above copyright notice,
24 // this list of conditions and the following disclaimer in the documentation
25 // and/or other materials provided with the distribution.
27 // * The name of the copyright holders may not be used to endorse or promote products
28 // derived from this software without specific prior written permission.
30 // This software is provided by the copyright holders and contributors "as is" and
31 // any express or implied warranties, including, but not limited to, the implied
32 // warranties of merchantability and fitness for a particular purpose are disclaimed.
33 // In no event shall the Intel Corporation or contributors be liable for any direct,
34 // indirect, incidental, special, exemplary, or consequential damages
35 // (including, but not limited to, procurement of substitute goods or services;
36 // loss of use, data, or profits; or business interruption) however caused
37 // and on any theory of liability, whether in contract, strict liability,
38 // or tort (including negligence or otherwise) arising in any way out of
39 // the use of this software, even if advised of the possibility of such damage.
43 #ifndef __OPENCV_FEATURES_2D_HPP__
44 #define __OPENCV_FEATURES_2D_HPP__
46 #include "opencv2/core.hpp"
47 #include "opencv2/flann/miniflann.hpp"
52 CV_EXPORTS bool initModule_features2d();
54 // //! writes vector of keypoints to the file storage
55 // CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector<KeyPoint>& keypoints);
56 // //! reads vector of keypoints from the specified file storage node
57 // CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector<KeyPoint>& keypoints);
60 * A class filters a vector of keypoints.
61 * Because now it is difficult to provide a convenient interface for all usage scenarios of the keypoints filter class,
62 * it has only several needed by now static methods.
64 class CV_EXPORTS KeyPointsFilter
70 * Remove keypoints within borderPixels of an image edge.
72 static void runByImageBorder( std::vector<KeyPoint>& keypoints, Size imageSize, int borderSize );
74 * Remove keypoints of sizes out of range.
76 static void runByKeypointSize( std::vector<KeyPoint>& keypoints, float minSize,
77 float maxSize=FLT_MAX );
79 * Remove keypoints from some image by mask for pixels of this image.
81 static void runByPixelsMask( std::vector<KeyPoint>& keypoints, const Mat& mask );
83 * Remove duplicated keypoints.
85 static void removeDuplicated( std::vector<KeyPoint>& keypoints );
88 * Retain the specified number of the best keypoints (according to the response)
90 static void retainBest( std::vector<KeyPoint>& keypoints, int npoints );
94 /************************************ Base Classes ************************************/
97 * Abstract base class for 2D image feature detectors.
99 class CV_EXPORTS_W FeatureDetector : public virtual Algorithm
102 virtual ~FeatureDetector();
105 * Detect keypoints in an image.
107 * keypoints The detected keypoints.
108 * mask Mask specifying where to look for keypoints (optional). Must be a char
109 * matrix with non-zero values in the region of interest.
111 CV_WRAP void detect( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
114 * Detect keypoints in an image set.
115 * images Image collection.
116 * keypoints Collection of keypoints detected in an input images. keypoints[i] is a set of keypoints detected in an images[i].
117 * masks Masks for image set. masks[i] is a mask for images[i].
119 void detect( InputArrayOfArrays images, std::vector<std::vector<KeyPoint> >& keypoints, InputArrayOfArrays masks=noArray() ) const;
121 // Return true if detector object is empty
122 CV_WRAP virtual bool empty() const;
124 // Create feature detector by detector name.
125 CV_WRAP static Ptr<FeatureDetector> create( const String& detectorType );
128 virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const = 0;
131 * Remove keypoints that are not in the mask.
132 * Helper function, useful when wrapping a library call for keypoint detection that
133 * does not support a mask argument.
135 static void removeInvalidPoints( const Mat & mask, std::vector<KeyPoint>& keypoints );
140 * Abstract base class for computing descriptors for image keypoints.
142 * In this interface we assume a keypoint descriptor can be represented as a
143 * dense, fixed-dimensional vector of some basic type. Most descriptors used
144 * in practice follow this pattern, as it makes it very easy to compute
145 * distances between descriptors. Therefore we represent a collection of
146 * descriptors as a Mat, where each row is one keypoint descriptor.
148 class CV_EXPORTS_W DescriptorExtractor : public virtual Algorithm
151 virtual ~DescriptorExtractor();
154 * Compute the descriptors for a set of keypoints in an image.
156 * keypoints The input keypoints. Keypoints for which a descriptor cannot be computed are removed.
157 * descriptors Copmputed descriptors. Row i is the descriptor for keypoint i.
159 CV_WRAP void compute( InputArray image, CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
162 * Compute the descriptors for a keypoints collection detected in image collection.
163 * images Image collection.
164 * keypoints Input keypoints collection. keypoints[i] is keypoints detected in images[i].
165 * Keypoints for which a descriptor cannot be computed are removed.
166 * descriptors Descriptor collection. descriptors[i] are descriptors computed for set keypoints[i].
168 void compute( InputArrayOfArrays images, std::vector<std::vector<KeyPoint> >& keypoints, OutputArrayOfArrays descriptors ) const;
170 CV_WRAP virtual int descriptorSize() const = 0;
171 CV_WRAP virtual int descriptorType() const = 0;
172 CV_WRAP virtual int defaultNorm() const = 0;
174 CV_WRAP virtual bool empty() const;
176 CV_WRAP static Ptr<DescriptorExtractor> create( const String& descriptorExtractorType );
179 virtual void computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const = 0;
182 * Remove keypoints within borderPixels of an image edge.
184 static void removeBorderKeypoints( std::vector<KeyPoint>& keypoints,
185 Size imageSize, int borderSize );
191 * Abstract base class for simultaneous 2D feature detection descriptor extraction.
193 class CV_EXPORTS_W Feature2D : public FeatureDetector, public DescriptorExtractor
197 * Detect keypoints in an image.
199 * keypoints The detected keypoints.
200 * mask Mask specifying where to look for keypoints (optional). Must be a char
201 * matrix with non-zero values in the region of interest.
202 * useProvidedKeypoints If true, the method will skip the detection phase and will compute
203 * descriptors for the provided keypoints
205 CV_WRAP_AS(detectAndCompute) virtual void operator()( InputArray image, InputArray mask,
206 CV_OUT std::vector<KeyPoint>& keypoints,
207 OutputArray descriptors,
208 bool useProvidedKeypoints=false ) const = 0;
210 CV_WRAP void compute( InputArray image, CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
212 // Create feature detector and descriptor extractor by name.
213 CV_WRAP static Ptr<Feature2D> create( const String& name );
219 class CV_EXPORTS_W BRISK : public Feature2D
222 CV_WRAP explicit BRISK(int thresh=30, int octaves=3, float patternScale=1.0f);
226 // returns the descriptor size in bytes
227 int descriptorSize() const;
228 // returns the descriptor type
229 int descriptorType() const;
230 // returns the default norm type
231 int defaultNorm() const;
233 // Compute the BRISK features on an image
234 void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
236 // Compute the BRISK features and descriptors on an image
237 void operator()( InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
238 OutputArray descriptors, bool useProvidedKeypoints=false ) const;
240 AlgorithmInfo* info() const;
243 CV_WRAP explicit BRISK(std::vector<float> &radiusList, std::vector<int> &numberList,
244 float dMax=5.85f, float dMin=8.2f, std::vector<int> indexChange=std::vector<int>());
246 // call this to generate the kernel:
247 // circle of radius r (pixels), with n points;
248 // short pairings with dMax, long pairings with dMin
249 CV_WRAP void generateKernel(std::vector<float> &radiusList,
250 std::vector<int> &numberList, float dMax=5.85f, float dMin=8.2f,
251 std::vector<int> indexChange=std::vector<int>());
255 void computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
256 void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
258 void computeKeypointsNoOrientation(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
259 void computeDescriptorsAndOrOrientation(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
260 OutputArray descriptors, bool doDescriptors, bool doOrientation,
261 bool useProvidedKeypoints) const;
263 // Feature parameters
264 CV_PROP_RW int threshold;
265 CV_PROP_RW int octaves;
267 // some helper structures for the Brisk pattern representation
268 struct BriskPatternPoint{
269 float x; // x coordinate relative to center
270 float y; // x coordinate relative to center
271 float sigma; // Gaussian smoothing sigma
273 struct BriskShortPair{
274 unsigned int i; // index of the first pattern point
275 unsigned int j; // index of other pattern point
277 struct BriskLongPair{
278 unsigned int i; // index of the first pattern point
279 unsigned int j; // index of other pattern point
280 int weighted_dx; // 1024.0/dx
281 int weighted_dy; // 1024.0/dy
283 inline int smoothedIntensity(const cv::Mat& image,
284 const cv::Mat& integral,const float key_x,
285 const float key_y, const unsigned int scale,
286 const unsigned int rot, const unsigned int point) const;
287 // pattern properties
288 BriskPatternPoint* patternPoints_; //[i][rotation][scale]
289 unsigned int points_; // total number of collocation points
290 float* scaleList_; // lists the scaling per scale index [scale]
291 unsigned int* sizeList_; // lists the total pattern size per scale index [scale]
292 static const unsigned int scales_; // scales discretization
293 static const float scalerange_; // span of sizes 40->4 Octaves - else, this needs to be adjusted...
294 static const unsigned int n_rot_; // discretization of the rotation look-up
297 int strings_; // number of uchars the descriptor consists of
298 float dMax_; // short pair maximum distance
299 float dMin_; // long pair maximum distance
300 BriskShortPair* shortPairs_; // d<_dMax
301 BriskLongPair* longPairs_; // d>_dMin
302 unsigned int noShortPairs_; // number of shortParis
303 unsigned int noLongPairs_; // number of longParis
306 static const float basicSize_;
313 class CV_EXPORTS_W ORB : public Feature2D
316 // the size of the signature in bytes
317 enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 };
319 CV_WRAP explicit ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31,
320 int firstLevel = 0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31 );
322 // returns the descriptor size in bytes
323 int descriptorSize() const;
324 // returns the descriptor type
325 int descriptorType() const;
326 // returns the default norm type
327 int defaultNorm() const;
329 // Compute the ORB features and descriptors on an image
330 void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
332 // Compute the ORB features and descriptors on an image
333 void operator()( InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
334 OutputArray descriptors, bool useProvidedKeypoints=false ) const;
336 AlgorithmInfo* info() const;
340 void computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
341 void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
343 CV_PROP_RW int nfeatures;
344 CV_PROP_RW double scaleFactor;
345 CV_PROP_RW int nlevels;
346 CV_PROP_RW int edgeThreshold;
347 CV_PROP_RW int firstLevel;
348 CV_PROP_RW int WTA_K;
349 CV_PROP_RW int scoreType;
350 CV_PROP_RW int patchSize;
353 typedef ORB OrbFeatureDetector;
354 typedef ORB OrbDescriptorExtractor;
359 class CV_EXPORTS FREAK : public DescriptorExtractor
363 * @param orientationNormalized enable orientation normalization
364 * @param scaleNormalized enable scale normalization
365 * @param patternScale scaling of the description pattern
366 * @param nbOctave number of octaves covered by the detected keypoints
367 * @param selectedPairs (optional) user defined selected pairs
369 explicit FREAK( bool orientationNormalized = true,
370 bool scaleNormalized = true,
371 float patternScale = 22.0f,
373 const std::vector<int>& selectedPairs = std::vector<int>());
374 FREAK( const FREAK& rhs );
375 FREAK& operator=( const FREAK& );
379 /** returns the descriptor length in bytes */
380 virtual int descriptorSize() const;
382 /** returns the descriptor type */
383 virtual int descriptorType() const;
385 /** returns the default norm type */
386 virtual int defaultNorm() const;
388 /** select the 512 "best description pairs"
389 * @param images grayscale images set
390 * @param keypoints set of detected keypoints
391 * @param corrThresh correlation threshold
392 * @param verbose print construction information
393 * @return list of best pair indexes
395 std::vector<int> selectPairs( const std::vector<Mat>& images, std::vector<std::vector<KeyPoint> >& keypoints,
396 const double corrThresh = 0.7, bool verbose = true );
398 AlgorithmInfo* info() const;
402 NB_SCALES = 64, NB_PAIRS = 512, NB_ORIENPAIRS = 45
406 virtual void computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
409 template <typename imgType, typename iiType>
410 imgType meanIntensity( InputArray image, InputArray integral, const float kp_x, const float kp_y,
411 const unsigned int scale, const unsigned int rot, const unsigned int point ) const;
413 template <typename srcMatType, typename iiMatType>
414 void computeDescriptors( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
416 template <typename srcMatType>
417 void extractDescriptor(srcMatType *pointsValue, void ** ptr) const;
419 bool orientationNormalized; //true if the orientation is normalized, false otherwise
420 bool scaleNormalized; //true if the scale is normalized, false otherwise
421 double patternScale; //scaling of the pattern
422 int nOctaves; //number of octaves
423 bool extAll; // true if all pairs need to be extracted for pairs selection
425 double patternScale0;
427 std::vector<int> selectedPairs0;
431 float x; // x coordinate relative to center
432 float y; // x coordinate relative to center
433 float sigma; // Gaussian smoothing sigma
436 struct DescriptionPair
438 uchar i; // index of the first point
439 uchar j; // index of the second point
442 struct OrientationPair
444 uchar i; // index of the first point
445 uchar j; // index of the second point
446 int weight_dx; // dx/(norm_sq))*4096
447 int weight_dy; // dy/(norm_sq))*4096
450 std::vector<PatternPoint> patternLookup; // look-up table for the pattern points (position+sigma of all points at all scales and orientation)
451 int patternSizes[NB_SCALES]; // size of the pattern at a specific scale (used to check if a point is within image boundaries)
452 DescriptionPair descriptionPairs[NB_PAIRS];
453 OrientationPair orientationPairs[NB_ORIENPAIRS];
458 Maximal Stable Extremal Regions class.
460 The class implements MSER algorithm introduced by J. Matas.
461 Unlike SIFT, SURF and many other detectors in OpenCV, this is salient region detector,
462 not the salient point detector.
464 It returns the regions, each of those is encoded as a contour.
466 class CV_EXPORTS_W MSER : public FeatureDetector
469 //! the full constructor
470 CV_WRAP explicit MSER( int _delta=5, int _min_area=60, int _max_area=14400,
471 double _max_variation=0.25, double _min_diversity=.2,
472 int _max_evolution=200, double _area_threshold=1.01,
473 double _min_margin=0.003, int _edge_blur_size=5 );
475 //! the operator that extracts the MSERs from the image or the specific part of it
476 CV_WRAP_AS(detect) void operator()( InputArray image, CV_OUT std::vector<std::vector<Point> >& msers,
477 InputArray mask=noArray() ) const;
478 AlgorithmInfo* info() const;
481 void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
489 double areaThreshold;
494 typedef MSER MserFeatureDetector;
499 The class implements the keypoint detector introduced by K. Konolige.
501 class CV_EXPORTS_W StarDetector : public FeatureDetector
504 //! the full constructor
505 CV_WRAP StarDetector(int _maxSize=45, int _responseThreshold=30,
506 int _lineThresholdProjected=10,
507 int _lineThresholdBinarized=8,
508 int _suppressNonmaxSize=5);
510 //! finds the keypoints in the image
511 CV_WRAP_AS(detect) void operator()(const Mat& image,
512 CV_OUT std::vector<KeyPoint>& keypoints) const;
514 AlgorithmInfo* info() const;
517 void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
520 int responseThreshold;
521 int lineThresholdProjected;
522 int lineThresholdBinarized;
523 int suppressNonmaxSize;
526 //! detects corners using FAST algorithm by E. Rosten
527 CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
528 int threshold, bool nonmaxSuppression=true );
530 CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
531 int threshold, bool nonmaxSuppression, int type );
533 class CV_EXPORTS_W FastFeatureDetector : public FeatureDetector
538 TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2
541 CV_WRAP FastFeatureDetector( int threshold=10, bool nonmaxSuppression=true);
542 CV_WRAP FastFeatureDetector( int threshold, bool nonmaxSuppression, int type);
543 AlgorithmInfo* info() const;
546 virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
549 bool nonmaxSuppression;
554 class CV_EXPORTS_W GFTTDetector : public FeatureDetector
557 CV_WRAP GFTTDetector( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1,
558 int blockSize=3, bool useHarrisDetector=false, double k=0.04 );
559 AlgorithmInfo* info() const;
562 virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
568 bool useHarrisDetector;
572 typedef GFTTDetector GoodFeaturesToTrackDetector;
573 typedef StarDetector StarFeatureDetector;
575 class CV_EXPORTS_W SimpleBlobDetector : public FeatureDetector
578 struct CV_EXPORTS_W_SIMPLE Params
581 CV_PROP_RW float thresholdStep;
582 CV_PROP_RW float minThreshold;
583 CV_PROP_RW float maxThreshold;
584 CV_PROP_RW size_t minRepeatability;
585 CV_PROP_RW float minDistBetweenBlobs;
587 CV_PROP_RW bool filterByColor;
588 CV_PROP_RW uchar blobColor;
590 CV_PROP_RW bool filterByArea;
591 CV_PROP_RW float minArea, maxArea;
593 CV_PROP_RW bool filterByCircularity;
594 CV_PROP_RW float minCircularity, maxCircularity;
596 CV_PROP_RW bool filterByInertia;
597 CV_PROP_RW float minInertiaRatio, maxInertiaRatio;
599 CV_PROP_RW bool filterByConvexity;
600 CV_PROP_RW float minConvexity, maxConvexity;
602 void read( const FileNode& fn );
603 void write( FileStorage& fs ) const;
606 CV_WRAP SimpleBlobDetector(const SimpleBlobDetector::Params ¶meters = SimpleBlobDetector::Params());
608 virtual void read( const FileNode& fn );
609 virtual void write( FileStorage& fs ) const;
612 struct CV_EXPORTS Center
619 virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
620 virtual void findBlobs(InputArray image, InputArray binaryImage, std::vector<Center> ¢ers) const;
623 AlgorithmInfo* info() const;
627 class CV_EXPORTS_W DenseFeatureDetector : public FeatureDetector
630 CV_WRAP explicit DenseFeatureDetector( float initFeatureScale=1.f, int featureScaleLevels=1,
631 float featureScaleMul=0.1f,
632 int initXyStep=6, int initImgBound=0,
633 bool varyXyStepWithScale=true,
634 bool varyImgBoundWithScale=false );
635 AlgorithmInfo* info() const;
638 virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
640 double initFeatureScale;
641 int featureScaleLevels;
642 double featureScaleMul;
647 bool varyXyStepWithScale;
648 bool varyImgBoundWithScale;
652 * Adapts a detector to partition the source image into a grid and detect
653 * points in each cell.
655 class CV_EXPORTS_W GridAdaptedFeatureDetector : public FeatureDetector
659 * detector Detector that will be adapted.
660 * maxTotalKeypoints Maximum count of keypoints detected on the image. Only the strongest keypoints
662 * gridRows Grid rows count.
663 * gridCols Grid column count.
665 CV_WRAP GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector=Ptr<FeatureDetector>(),
666 int maxTotalKeypoints=1000,
667 int gridRows=4, int gridCols=4 );
669 // TODO implement read/write
670 virtual bool empty() const;
672 AlgorithmInfo* info() const;
675 virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
677 Ptr<FeatureDetector> detector;
678 int maxTotalKeypoints;
684 * Adapts a detector to detect points over multiple levels of a Gaussian
685 * pyramid. Useful for detectors that are not inherently scaled.
687 class CV_EXPORTS_W PyramidAdaptedFeatureDetector : public FeatureDetector
690 // maxLevel - The 0-based index of the last pyramid layer
691 CV_WRAP PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int maxLevel=2 );
693 // TODO implement read/write
694 virtual bool empty() const;
697 virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
699 Ptr<FeatureDetector> detector;
703 /** \brief A feature detector parameter adjuster, this is used by the DynamicAdaptedFeatureDetector
704 * and is a wrapper for FeatureDetector that allow them to be adjusted after a detection
706 class CV_EXPORTS AdjusterAdapter: public FeatureDetector
709 /** pure virtual interface
711 virtual ~AdjusterAdapter() {}
712 /** too few features were detected so, adjust the detector params accordingly
713 * \param min the minimum number of desired features
714 * \param n_detected the number previously detected
716 virtual void tooFew(int min, int n_detected) = 0;
717 /** too many features were detected so, adjust the detector params accordingly
718 * \param max the maximum number of desired features
719 * \param n_detected the number previously detected
721 virtual void tooMany(int max, int n_detected) = 0;
722 /** are params maxed out or still valid?
723 * \return false if the parameters can't be adjusted any more
725 virtual bool good() const = 0;
727 virtual Ptr<AdjusterAdapter> clone() const = 0;
729 static Ptr<AdjusterAdapter> create( const String& detectorType );
731 /** \brief an adaptively adjusting detector that iteratively detects until the desired number
732 * of features are detected.
733 * Beware that this is not thread safe - as the adjustment of parameters breaks the const
734 * of the detection routine...
735 * /TODO Make this const correct and thread safe
738 //will create a detector that attempts to find 100 - 110 FAST Keypoints, and will at most run
739 //FAST feature detection 10 times until that number of keypoints are found
740 Ptr<FeatureDetector> detector(new DynamicAdaptedFeatureDetector(new FastAdjuster(20,true),100, 110, 10));
743 class CV_EXPORTS DynamicAdaptedFeatureDetector: public FeatureDetector
747 /** \param adjuster an AdjusterAdapter that will do the detection and parameter adjustment
748 * \param max_features the maximum desired number of features
749 * \param max_iters the maximum number of times to try to adjust the feature detector params
750 * for the FastAdjuster this can be high, but with Star or Surf this can get time consuming
751 * \param min_features the minimum desired features
753 DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster, int min_features=400, int max_features=500, int max_iters=5 );
755 virtual bool empty() const;
758 virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
761 DynamicAdaptedFeatureDetector& operator=(const DynamicAdaptedFeatureDetector&);
762 DynamicAdaptedFeatureDetector(const DynamicAdaptedFeatureDetector&);
765 int min_features_, max_features_;
766 const Ptr<AdjusterAdapter> adjuster_;
769 /**\brief an adjust for the FAST detector. This will basically decrement or increment the
772 class CV_EXPORTS FastAdjuster: public AdjusterAdapter
775 /**\param init_thresh the initial threshold to start with, default = 20
776 * \param nonmax whether to use non max or not for fast feature detection
778 FastAdjuster(int init_thresh=20, bool nonmax=true, int min_thresh=1, int max_thresh=200);
780 virtual void tooFew(int minv, int n_detected);
781 virtual void tooMany(int maxv, int n_detected);
782 virtual bool good() const;
784 virtual Ptr<AdjusterAdapter> clone() const;
787 virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
791 int init_thresh_, min_thresh_, max_thresh_;
795 /** An adjuster for StarFeatureDetector, this one adjusts the responseThreshold for now
796 * TODO find a faster way to converge the parameters for Star - use CvStarDetectorParams
798 class CV_EXPORTS StarAdjuster: public AdjusterAdapter
801 StarAdjuster(double initial_thresh=30.0, double min_thresh=2., double max_thresh=200.);
803 virtual void tooFew(int minv, int n_detected);
804 virtual void tooMany(int maxv, int n_detected);
805 virtual bool good() const;
807 virtual Ptr<AdjusterAdapter> clone() const;
810 virtual void detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
812 double thresh_, init_thresh_, min_thresh_, max_thresh_;
815 class CV_EXPORTS SurfAdjuster: public AdjusterAdapter
818 SurfAdjuster( double initial_thresh=400.f, double min_thresh=2, double max_thresh=1000 );
820 virtual void tooFew(int minv, int n_detected);
821 virtual void tooMany(int maxv, int n_detected);
822 virtual bool good() const;
824 virtual Ptr<AdjusterAdapter> clone() const;
827 virtual void detectImpl( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const;
829 double thresh_, init_thresh_, min_thresh_, max_thresh_;
832 CV_EXPORTS Mat windowedMatchingMask( const std::vector<KeyPoint>& keypoints1, const std::vector<KeyPoint>& keypoints2,
833 float maxDeltaX, float maxDeltaY );
838 * OpponentColorDescriptorExtractor
840 * Adapts a descriptor extractor to compute descriptors in Opponent Color Space
841 * (refer to van de Sande et al., CGIV 2008 "Color Descriptors for Object Category Recognition").
842 * Input RGB image is transformed in Opponent Color Space. Then unadapted descriptor extractor
843 * (set in constructor) computes descriptors on each of the three channel and concatenate
844 * them into a single color descriptor.
846 class CV_EXPORTS OpponentColorDescriptorExtractor : public DescriptorExtractor
849 OpponentColorDescriptorExtractor( const Ptr<DescriptorExtractor>& descriptorExtractor );
851 virtual void read( const FileNode& );
852 virtual void write( FileStorage& ) const;
854 virtual int descriptorSize() const;
855 virtual int descriptorType() const;
856 virtual int defaultNorm() const;
858 virtual bool empty() const;
861 virtual void computeImpl( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const;
863 Ptr<DescriptorExtractor> descriptorExtractor;
869 class CV_EXPORTS BriefDescriptorExtractor : public DescriptorExtractor
872 static const int PATCH_SIZE = 48;
873 static const int KERNEL_SIZE = 9;
875 // bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.
876 BriefDescriptorExtractor( int bytes = 32 );
878 virtual void read( const FileNode& );
879 virtual void write( FileStorage& ) const;
881 virtual int descriptorSize() const;
882 virtual int descriptorType() const;
883 virtual int defaultNorm() const;
885 /// @todo read and write for brief
887 AlgorithmInfo* info() const;
890 virtual void computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const;
892 typedef void(*PixelTestFn)(InputArray, const std::vector<KeyPoint>&, OutputArray);
895 PixelTestFn test_fn_;
898 // KAZE/AKAZE diffusivity
906 // AKAZE descriptor type
908 DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation
910 DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation
917 class CV_EXPORTS_W KAZE : public Feature2D
921 CV_WRAP explicit KAZE(bool extended, bool upright, float threshold = 0.001f,
922 int octaves = 4, int sublevels = 4, int diffusivity = DIFF_PM_G2);
926 // returns the descriptor size in bytes
927 int descriptorSize() const;
928 // returns the descriptor type
929 int descriptorType() const;
930 // returns the default norm type
931 int defaultNorm() const;
933 AlgorithmInfo* info() const;
935 // Compute the KAZE features on an image
936 void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
938 // Compute the KAZE features and descriptors on an image
939 void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
940 OutputArray descriptors, bool useProvidedKeypoints = false) const;
943 void detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const;
944 void computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const;
946 CV_PROP bool extended;
947 CV_PROP bool upright;
948 CV_PROP float threshold;
950 CV_PROP int sublevels;
951 CV_PROP int diffusivity;
957 class CV_EXPORTS_W AKAZE : public Feature2D
961 CV_WRAP explicit AKAZE(int descriptor_type, int descriptor_size = 0, int descriptor_channels = 3,
962 float threshold = 0.001f, int octaves = 4, int sublevels = 4, int diffusivity = DIFF_PM_G2);
966 // returns the descriptor size in bytes
967 int descriptorSize() const;
968 // returns the descriptor type
969 int descriptorType() const;
970 // returns the default norm type
971 int defaultNorm() const;
973 // Compute the AKAZE features on an image
974 void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
976 // Compute the AKAZE features and descriptors on an image
977 void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
978 OutputArray descriptors, bool useProvidedKeypoints = false) const;
980 AlgorithmInfo* info() const;
984 void computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const;
985 void detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask = noArray()) const;
987 CV_PROP int descriptor;
988 CV_PROP int descriptor_channels;
989 CV_PROP int descriptor_size;
990 CV_PROP float threshold;
992 CV_PROP int sublevels;
993 CV_PROP int diffusivity;
995 /****************************************************************************************\
997 \****************************************************************************************/
1000 struct CV_EXPORTS Accumulator
1005 template<> struct Accumulator<unsigned char> { typedef float Type; };
1006 template<> struct Accumulator<unsigned short> { typedef float Type; };
1007 template<> struct Accumulator<char> { typedef float Type; };
1008 template<> struct Accumulator<short> { typedef float Type; };
1011 * Squared Euclidean distance functor
1014 struct CV_EXPORTS SL2
1016 enum { normType = NORM_L2SQR };
1017 typedef T ValueType;
1018 typedef typename Accumulator<T>::Type ResultType;
1020 ResultType operator()( const T* a, const T* b, int size ) const
1022 return normL2Sqr<ValueType, ResultType>(a, b, size);
1027 * Euclidean distance functor
1030 struct CV_EXPORTS L2
1032 enum { normType = NORM_L2 };
1033 typedef T ValueType;
1034 typedef typename Accumulator<T>::Type ResultType;
1036 ResultType operator()( const T* a, const T* b, int size ) const
1038 return (ResultType)std::sqrt((double)normL2Sqr<ValueType, ResultType>(a, b, size));
1043 * Manhattan distance (city block distance) functor
1046 struct CV_EXPORTS L1
1048 enum { normType = NORM_L1 };
1049 typedef T ValueType;
1050 typedef typename Accumulator<T>::Type ResultType;
1052 ResultType operator()( const T* a, const T* b, int size ) const
1054 return normL1<ValueType, ResultType>(a, b, size);
1059 * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
1060 * bit count of A exclusive XOR'ed with B
1062 struct CV_EXPORTS Hamming
1064 enum { normType = NORM_HAMMING };
1065 typedef unsigned char ValueType;
1066 typedef int ResultType;
1068 /** this will count the bits in a ^ b
1070 ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const
1072 return normHamming(a, b, size);
1076 typedef Hamming HammingLUT;
1078 template<int cellsize> struct HammingMultilevel
1080 enum { normType = NORM_HAMMING + (cellsize>1) };
1081 typedef unsigned char ValueType;
1082 typedef int ResultType;
1084 ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const
1086 return normHamming(a, b, size, cellsize);
1090 /****************************************************************************************\
1091 * DescriptorMatcher *
1092 \****************************************************************************************/
1094 * Abstract base class for matching two sets of descriptors.
1096 class CV_EXPORTS_W DescriptorMatcher : public Algorithm
1099 virtual ~DescriptorMatcher();
1102 * Add descriptors to train descriptor collection.
1103 * descriptors Descriptors to add. Each descriptors[i] is a descriptors set from one image.
1105 CV_WRAP virtual void add( InputArrayOfArrays descriptors );
1107 * Get train descriptors collection.
1109 CV_WRAP const std::vector<Mat>& getTrainDescriptors() const;
1111 * Clear train descriptors collection.
1113 CV_WRAP virtual void clear();
1116 * Return true if there are not train descriptors in collection.
1118 CV_WRAP virtual bool empty() const;
1120 * Return true if the matcher supports mask in match methods.
1122 CV_WRAP virtual bool isMaskSupported() const = 0;
1125 * Train matcher (e.g. train flann index).
1126 * In all methods to match the method train() is run every time before matching.
1127 * Some descriptor matchers (e.g. BruteForceMatcher) have empty implementation
1128 * of this method, other matchers really train their inner structures
1129 * (e.g. FlannBasedMatcher trains flann::Index). So nonempty implementation
1130 * of train() should check the class object state and do traing/retraining
1131 * only if the state requires that (e.g. FlannBasedMatcher trains flann::Index
1132 * if it has not trained yet or if new descriptors have been added to the train
1135 CV_WRAP virtual void train();
1137 * Group of methods to match descriptors from image pair.
1138 * Method train() is run in this methods.
1140 // Find one best match for each query descriptor (if mask is empty).
1141 CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors,
1142 CV_OUT std::vector<DMatch>& matches, InputArray mask=noArray() ) const;
1143 // Find k best matches for each query descriptor (in increasing order of distances).
1144 // compactResult is used when mask is not empty. If compactResult is false matches
1145 // vector will have the same size as queryDescriptors rows. If compactResult is true
1146 // matches vector will not contain matches for fully masked out query descriptors.
1147 CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,
1148 CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
1149 InputArray mask=noArray(), bool compactResult=false ) const;
1150 // Find best matches for each query descriptor which have distance less than
1151 // maxDistance (in increasing order of distances).
1152 void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors,
1153 std::vector<std::vector<DMatch> >& matches, float maxDistance,
1154 InputArray mask=noArray(), bool compactResult=false ) const;
1156 * Group of methods to match descriptors from one image to image set.
1157 * See description of similar methods for matching image pair above.
1159 CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector<DMatch>& matches,
1160 InputArrayOfArrays masks=noArray() );
1161 CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
1162 InputArrayOfArrays masks=noArray(), bool compactResult=false );
1163 void radiusMatch( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
1164 InputArrayOfArrays masks=noArray(), bool compactResult=false );
1166 // Reads matcher object from a file node
1167 virtual void read( const FileNode& );
1168 // Writes matcher object to a file storage
1169 virtual void write( FileStorage& ) const;
1171 // Clone the matcher. If emptyTrainData is false the method create deep copy of the object, i.e. copies
1172 // both parameters and train data. If emptyTrainData is true the method create object copy with current parameters
1173 // but with empty train data.
1174 virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
1176 CV_WRAP static Ptr<DescriptorMatcher> create( const String& descriptorMatcherType );
1179 * Class to work with descriptors from several images as with one merged matrix.
1180 * It is used e.g. in FlannBasedMatcher.
1182 class CV_EXPORTS DescriptorCollection
1185 DescriptorCollection();
1186 DescriptorCollection( const DescriptorCollection& collection );
1187 virtual ~DescriptorCollection();
1189 // Vector of matrices "descriptors" will be merged to one matrix "mergedDescriptors" here.
1190 void set( const std::vector<Mat>& descriptors );
1191 virtual void clear();
1193 const Mat& getDescriptors() const;
1194 const Mat getDescriptor( int imgIdx, int localDescIdx ) const;
1195 const Mat getDescriptor( int globalDescIdx ) const;
1196 void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const;
1201 Mat mergedDescriptors;
1202 std::vector<int> startIdxs;
1205 // In fact the matching is implemented only by the following two methods. These methods suppose
1206 // that the class object has been trained already. Public match methods call these methods
1207 // after calling train().
1208 virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
1209 InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0;
1210 virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
1211 InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0;
1213 static bool isPossibleMatch( InputArray mask, int queryIdx, int trainIdx );
1214 static bool isMaskedOut( InputArrayOfArrays masks, int queryIdx );
1216 static Mat clone_op( Mat m ) { return m.clone(); }
1217 void checkMasks( InputArrayOfArrays masks, int queryDescriptorsCount ) const;
1219 // Collection of descriptors from train images.
1220 std::vector<Mat> trainDescCollection;
1221 std::vector<UMat> utrainDescCollection;
1225 * Brute-force descriptor matcher.
1227 * For each descriptor in the first set, this matcher finds the closest
1228 * descriptor in the second set by trying each one.
1230 * For efficiency, BruteForceMatcher is templated on the distance metric.
1231 * For float descriptors, a common choice would be cv::L2<float>.
1233 class CV_EXPORTS_W BFMatcher : public DescriptorMatcher
1236 CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false );
1237 virtual ~BFMatcher() {}
1239 virtual bool isMaskSupported() const { return true; }
1241 virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
1243 AlgorithmInfo* info() const;
1245 virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
1246 InputArrayOfArrays masks=noArray(), bool compactResult=false );
1247 virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
1248 InputArrayOfArrays masks=noArray(), bool compactResult=false );
1256 * Flann based matcher
1258 class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher
1261 CV_WRAP FlannBasedMatcher( const Ptr<flann::IndexParams>& indexParams=makePtr<flann::KDTreeIndexParams>(),
1262 const Ptr<flann::SearchParams>& searchParams=makePtr<flann::SearchParams>() );
1264 virtual void add( InputArrayOfArrays descriptors );
1265 virtual void clear();
1267 // Reads matcher object from a file node
1268 virtual void read( const FileNode& );
1269 // Writes matcher object to a file storage
1270 virtual void write( FileStorage& ) const;
1272 virtual void train();
1273 virtual bool isMaskSupported() const;
1275 virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
1277 AlgorithmInfo* info() const;
1279 static void convertToDMatches( const DescriptorCollection& descriptors,
1280 const Mat& indices, const Mat& distances,
1281 std::vector<std::vector<DMatch> >& matches );
1283 virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
1284 InputArrayOfArrays masks=noArray(), bool compactResult=false );
1285 virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
1286 InputArrayOfArrays masks=noArray(), bool compactResult=false );
1288 Ptr<flann::IndexParams> indexParams;
1289 Ptr<flann::SearchParams> searchParams;
1290 Ptr<flann::Index> flannIndex;
1292 DescriptorCollection mergedDescriptors;
1296 /****************************************************************************************\
1297 * GenericDescriptorMatcher *
1298 \****************************************************************************************/
1300 * Abstract interface for a keypoint descriptor and matcher
1302 class GenericDescriptorMatcher;
1303 typedef GenericDescriptorMatcher GenericDescriptorMatch;
1305 class CV_EXPORTS GenericDescriptorMatcher
1308 GenericDescriptorMatcher();
1309 virtual ~GenericDescriptorMatcher();
1312 * Add train collection: images and keypoints from them.
1313 * images A set of train images.
1314 * ketpoints Keypoint collection that have been detected on train images.
1316 * Keypoints for which a descriptor cannot be computed are removed. Such keypoints
1317 * must be filtered in this method befor adding keypoints to train collection "trainPointCollection".
1318 * If inheritor class need perform such prefiltering the method add() must be overloaded.
1319 * In the other class methods programmer has access to the train keypoints by a constant link.
1321 virtual void add( InputArrayOfArrays images,
1322 std::vector<std::vector<KeyPoint> >& keypoints );
1324 const std::vector<Mat>& getTrainImages() const;
1325 const std::vector<std::vector<KeyPoint> >& getTrainKeypoints() const;
1328 * Clear images and keypoints storing in train collection.
1330 virtual void clear();
1332 * Returns true if matcher supports mask to match descriptors.
1334 virtual bool isMaskSupported() = 0;
1336 * Train some inner structures (e.g. flann index or decision trees).
1337 * train() methods is run every time in matching methods. So the method implementation
1338 * should has a check whether these inner structures need be trained/retrained or not.
1340 virtual void train();
1343 * Classifies query keypoints.
1344 * queryImage The query image
1345 * queryKeypoints Keypoints from the query image
1346 * trainImage The train image
1347 * trainKeypoints Keypoints from the train image
1349 // Classify keypoints from query image under one train image.
1350 void classify( InputArray queryImage, std::vector<KeyPoint>& queryKeypoints,
1351 InputArray trainImage, std::vector<KeyPoint>& trainKeypoints ) const;
1352 // Classify keypoints from query image under train image collection.
1353 void classify( InputArray queryImage, std::vector<KeyPoint>& queryKeypoints );
1356 * Group of methods to match keypoints from image pair.
1357 * Keypoints for which a descriptor cannot be computed are removed.
1358 * train() method is called here.
1360 // Find one best match for each query descriptor (if mask is empty).
1361 void match( InputArray queryImage, std::vector<KeyPoint>& queryKeypoints,
1362 InputArray trainImage, std::vector<KeyPoint>& trainKeypoints,
1363 std::vector<DMatch>& matches, InputArray mask=noArray() ) const;
1364 // Find k best matches for each query keypoint (in increasing order of distances).
1365 // compactResult is used when mask is not empty. If compactResult is false matches
1366 // vector will have the same size as queryDescriptors rows.
1367 // If compactResult is true matches vector will not contain matches for fully masked out query descriptors.
1368 void knnMatch( InputArray queryImage, std::vector<KeyPoint>& queryKeypoints,
1369 InputArray trainImage, std::vector<KeyPoint>& trainKeypoints,
1370 std::vector<std::vector<DMatch> >& matches, int k,
1371 InputArray mask=noArray(), bool compactResult=false ) const;
1372 // Find best matches for each query descriptor which have distance less than maxDistance (in increasing order of distances).
1373 void radiusMatch( InputArray queryImage, std::vector<KeyPoint>& queryKeypoints,
1374 InputArray trainImage, std::vector<KeyPoint>& trainKeypoints,
1375 std::vector<std::vector<DMatch> >& matches, float maxDistance,
1376 InputArray mask=noArray(), bool compactResult=false ) const;
1378 * Group of methods to match keypoints from one image to image set.
1379 * See description of similar methods for matching image pair above.
1381 void match( InputArray queryImage, std::vector<KeyPoint>& queryKeypoints,
1382 std::vector<DMatch>& matches, InputArrayOfArrays masks=noArray() );
1383 void knnMatch( InputArray queryImage, std::vector<KeyPoint>& queryKeypoints,
1384 std::vector<std::vector<DMatch> >& matches, int k,
1385 InputArrayOfArrays masks=noArray(), bool compactResult=false );
1386 void radiusMatch(InputArray queryImage, std::vector<KeyPoint>& queryKeypoints,
1387 std::vector<std::vector<DMatch> >& matches, float maxDistance,
1388 InputArrayOfArrays masks=noArray(), bool compactResult=false );
1390 // Reads matcher object from a file node
1391 virtual void read( const FileNode& fn );
1392 // Writes matcher object to a file storage
1393 virtual void write( FileStorage& fs ) const;
1395 // Return true if matching object is empty (e.g. feature detector or descriptor matcher are empty)
1396 virtual bool empty() const;
1398 // Clone the matcher. If emptyTrainData is false the method create deep copy of the object, i.e. copies
1399 // both parameters and train data. If emptyTrainData is true the method create object copy with current parameters
1400 // but with empty train data.
1401 virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
1403 static Ptr<GenericDescriptorMatcher> create( const String& genericDescritptorMatcherType,
1404 const String ¶msFilename=String() );
1407 // In fact the matching is implemented only by the following two methods. These methods suppose
1408 // that the class object has been trained already. Public match methods call these methods
1409 // after calling train().
1410 virtual void knnMatchImpl( InputArray queryImage, std::vector<KeyPoint>& queryKeypoints,
1411 std::vector<std::vector<DMatch> >& matches, int k,
1412 InputArrayOfArrays masks, bool compactResult ) = 0;
1413 virtual void radiusMatchImpl( InputArray queryImage, std::vector<KeyPoint>& queryKeypoints,
1414 std::vector<std::vector<DMatch> >& matches, float maxDistance,
1415 InputArrayOfArrays masks, bool compactResult ) = 0;
1417 * A storage for sets of keypoints together with corresponding images and class IDs
1419 class CV_EXPORTS KeyPointCollection
1422 KeyPointCollection();
1423 KeyPointCollection( const KeyPointCollection& collection );
1424 void add( const std::vector<Mat>& images, const std::vector<std::vector<KeyPoint> >& keypoints );
1427 // Returns the total number of keypoints in the collection
1428 size_t keypointCount() const;
1429 size_t imageCount() const;
1431 const std::vector<std::vector<KeyPoint> >& getKeypoints() const;
1432 const std::vector<KeyPoint>& getKeypoints( int imgIdx ) const;
1433 const KeyPoint& getKeyPoint( int imgIdx, int localPointIdx ) const;
1434 const KeyPoint& getKeyPoint( int globalPointIdx ) const;
1435 void getLocalIdx( int globalPointIdx, int& imgIdx, int& localPointIdx ) const;
1437 const std::vector<Mat>& getImages() const;
1438 const Mat& getImage( int imgIdx ) const;
1443 std::vector<Mat> images;
1444 std::vector<std::vector<KeyPoint> > keypoints;
1445 // global indices of the first points in each image, startIndices.size() = keypoints.size()
1446 std::vector<int> startIndices;
1449 static Mat clone_op( Mat m ) { return m.clone(); }
1452 KeyPointCollection trainPointCollection;
1456 /****************************************************************************************\
1457 * VectorDescriptorMatcher *
1458 \****************************************************************************************/
1461 * A class used for matching descriptors that can be described as vectors in a finite-dimensional space
1463 class VectorDescriptorMatcher;
1464 typedef VectorDescriptorMatcher VectorDescriptorMatch;
1466 class CV_EXPORTS VectorDescriptorMatcher : public GenericDescriptorMatcher
1469 VectorDescriptorMatcher( const Ptr<DescriptorExtractor>& extractor, const Ptr<DescriptorMatcher>& matcher );
1470 virtual ~VectorDescriptorMatcher();
1472 virtual void add( InputArrayOfArrays imgCollection,
1473 std::vector<std::vector<KeyPoint> >& pointCollection );
1475 virtual void clear();
1477 virtual void train();
1479 virtual bool isMaskSupported();
1481 virtual void read( const FileNode& fn );
1482 virtual void write( FileStorage& fs ) const;
1483 virtual bool empty() const;
1485 virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
1488 virtual void knnMatchImpl( InputArray queryImage, std::vector<KeyPoint>& queryKeypoints,
1489 std::vector<std::vector<DMatch> >& matches, int k,
1490 InputArrayOfArrays masks, bool compactResult );
1491 virtual void radiusMatchImpl( InputArray queryImage, std::vector<KeyPoint>& queryKeypoints,
1492 std::vector<std::vector<DMatch> >& matches, float maxDistance,
1493 InputArrayOfArrays masks, bool compactResult );
1495 Ptr<DescriptorExtractor> extractor;
1496 Ptr<DescriptorMatcher> matcher;
1499 /****************************************************************************************\
1500 * Drawing functions *
1501 \****************************************************************************************/
1502 struct CV_EXPORTS DrawMatchesFlags
1504 enum{ DEFAULT = 0, // Output image matrix will be created (Mat::create),
1505 // i.e. existing memory of output image may be reused.
1506 // Two source image, matches and single keypoints will be drawn.
1507 // For each keypoint only the center point will be drawn (without
1508 // the circle around keypoint with keypoint size and orientation).
1509 DRAW_OVER_OUTIMG = 1, // Output image matrix will not be created (Mat::create).
1510 // Matches will be drawn on existing content of output image.
1511 NOT_DRAW_SINGLE_POINTS = 2, // Single keypoints will not be drawn.
1512 DRAW_RICH_KEYPOINTS = 4 // For each keypoint the circle around keypoint with keypoint size and
1513 // orientation will be drawn.
1518 CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector<KeyPoint>& keypoints, InputOutputArray outImage,
1519 const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT );
1521 // Draws matches of keypints from two images on output image.
1522 CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
1523 InputArray img2, const std::vector<KeyPoint>& keypoints2,
1524 const std::vector<DMatch>& matches1to2, InputOutputArray outImg,
1525 const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
1526 const std::vector<char>& matchesMask=std::vector<char>(), int flags=DrawMatchesFlags::DEFAULT );
1528 CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
1529 InputArray img2, const std::vector<KeyPoint>& keypoints2,
1530 const std::vector<std::vector<DMatch> >& matches1to2, InputOutputArray outImg,
1531 const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
1532 const std::vector<std::vector<char> >& matchesMask=std::vector<std::vector<char> >(), int flags=DrawMatchesFlags::DEFAULT );
1534 /****************************************************************************************\
1535 * Functions to evaluate the feature detectors and [generic] descriptor extractors *
1536 \****************************************************************************************/
1538 CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2,
1539 std::vector<KeyPoint>* keypoints1, std::vector<KeyPoint>* keypoints2,
1540 float& repeatability, int& correspCount,
1541 const Ptr<FeatureDetector>& fdetector=Ptr<FeatureDetector>() );
1543 CV_EXPORTS void computeRecallPrecisionCurve( const std::vector<std::vector<DMatch> >& matches1to2,
1544 const std::vector<std::vector<uchar> >& correctMatches1to2Mask,
1545 std::vector<Point2f>& recallPrecisionCurve );
1547 CV_EXPORTS float getRecall( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );
1548 CV_EXPORTS int getNearestPoint( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );
1550 CV_EXPORTS void evaluateGenericDescriptorMatcher( const Mat& img1, const Mat& img2, const Mat& H1to2,
1551 std::vector<KeyPoint>& keypoints1, std::vector<KeyPoint>& keypoints2,
1552 std::vector<std::vector<DMatch> >* matches1to2, std::vector<std::vector<uchar> >* correctMatches1to2Mask,
1553 std::vector<Point2f>& recallPrecisionCurve,
1554 const Ptr<GenericDescriptorMatcher>& dmatch=Ptr<GenericDescriptorMatcher>() );
1557 /****************************************************************************************\
1558 * Bag of visual words *
1559 \****************************************************************************************/
1561 * Abstract base class for training of a 'bag of visual words' vocabulary from a set of descriptors
1563 class CV_EXPORTS_W BOWTrainer
1567 virtual ~BOWTrainer();
1569 CV_WRAP void add( const Mat& descriptors );
1570 CV_WRAP const std::vector<Mat>& getDescriptors() const;
1571 CV_WRAP int descriptorsCount() const;
1573 CV_WRAP virtual void clear();
1576 * Train visual words vocabulary, that is cluster training descriptors and
1577 * compute cluster centers.
1578 * Returns cluster centers.
1580 * descriptors Training descriptors computed on images keypoints.
1582 CV_WRAP virtual Mat cluster() const = 0;
1583 CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0;
1586 std::vector<Mat> descriptors;
1591 * This is BOWTrainer using cv::kmeans to get vocabulary.
1593 class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer
1596 CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
1597 int attempts=3, int flags=KMEANS_PP_CENTERS );
1598 virtual ~BOWKMeansTrainer();
1600 // Returns trained vocabulary (i.e. cluster centers).
1601 CV_WRAP virtual Mat cluster() const;
1602 CV_WRAP virtual Mat cluster( const Mat& descriptors ) const;
1607 TermCriteria termcrit;
1613 * Class to compute image descriptor using bag of visual words.
1615 class CV_EXPORTS_W BOWImgDescriptorExtractor
1618 CV_WRAP BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
1619 const Ptr<DescriptorMatcher>& dmatcher );
1620 BOWImgDescriptorExtractor( const Ptr<DescriptorMatcher>& dmatcher );
1621 virtual ~BOWImgDescriptorExtractor();
1623 CV_WRAP void setVocabulary( const Mat& vocabulary );
1624 CV_WRAP const Mat& getVocabulary() const;
1625 void compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray imgDescriptor,
1626 std::vector<std::vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 );
1627 void compute( InputArray keypointDescriptors, OutputArray imgDescriptor,
1628 std::vector<std::vector<int> >* pointIdxsOfClusters=0 );
1629 // compute() is not constant because DescriptorMatcher::match is not constant
1631 CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector<KeyPoint>& keypoints, CV_OUT Mat& imgDescriptor )
1632 { compute(image,keypoints,imgDescriptor); }
1634 CV_WRAP int descriptorSize() const;
1635 CV_WRAP int descriptorType() const;
1639 Ptr<DescriptorExtractor> dextractor;
1640 Ptr<DescriptorMatcher> dmatcher;
1643 } /* namespace cv */