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diff --git a/thirdparty1/linux/include/opencv2/xfeatures2d.hpp b/thirdparty1/linux/include/opencv2/xfeatures2d.hpp new file mode 100644 index 0000000..1185de0 --- /dev/null +++ b/thirdparty1/linux/include/opencv2/xfeatures2d.hpp @@ -0,0 +1,842 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2013, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#ifndef __OPENCV_XFEATURES2D_HPP__ +#define __OPENCV_XFEATURES2D_HPP__ + +#include "opencv2/features2d.hpp" +#include "opencv2/xfeatures2d/nonfree.hpp" + +/** @defgroup xfeatures2d Extra 2D Features Framework +@{ + @defgroup xfeatures2d_experiment Experimental 2D Features Algorithms + +This section describes experimental algorithms for 2d feature detection. + + @defgroup xfeatures2d_nonfree Non-free 2D Features Algorithms + +This section describes two popular algorithms for 2d feature detection, SIFT and SURF, that are +known to be patented. Use them at your own risk. + +@} +*/ + +namespace cv +{ +namespace xfeatures2d +{ + +//! @addtogroup xfeatures2d_experiment +//! @{ + +/** @brief Class implementing the FREAK (*Fast Retina Keypoint*) keypoint descriptor, described in @cite AOV12 . + +The algorithm propose a novel keypoint descriptor inspired by the human visual system and more +precisely the retina, coined Fast Retina Key- point (FREAK). A cascade of binary strings is +computed by efficiently comparing image intensities over a retinal sampling pattern. FREAKs are in +general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. +They are competitive alternatives to existing keypoints in particular for embedded applications. + +@note + - An example on how to use the FREAK descriptor can be found at + opencv_source_code/samples/cpp/freak_demo.cpp + */ +class CV_EXPORTS_W FREAK : public Feature2D +{ +public: + + enum + { + NB_SCALES = 64, NB_PAIRS = 512, NB_ORIENPAIRS = 45 + }; + + /** + @param orientationNormalized Enable orientation normalization. + @param scaleNormalized Enable scale normalization. + @param patternScale Scaling of the description pattern. + @param nOctaves Number of octaves covered by the detected keypoints. + @param selectedPairs (Optional) user defined selected pairs indexes, + */ + CV_WRAP static Ptr<FREAK> create(bool orientationNormalized = true, + bool scaleNormalized = true, + float patternScale = 22.0f, + int nOctaves = 4, + const std::vector<int>& selectedPairs = std::vector<int>()); +}; + + +/** @brief The class implements the keypoint detector introduced by @cite Agrawal08, synonym of StarDetector. : + */ +class CV_EXPORTS_W StarDetector : public Feature2D +{ +public: + //! the full constructor + CV_WRAP static Ptr<StarDetector> create(int maxSize=45, int responseThreshold=30, + int lineThresholdProjected=10, + int lineThresholdBinarized=8, + int suppressNonmaxSize=5); +}; + +/* + * BRIEF Descriptor + */ + +/** @brief Class for computing BRIEF descriptors described in @cite calon2010 . + +@param bytes legth of the descriptor in bytes, valid values are: 16, 32 (default) or 64 . +@param use_orientation sample patterns using keypoints orientation, disabled by default. + + */ +class CV_EXPORTS_W BriefDescriptorExtractor : public Feature2D +{ +public: + CV_WRAP static Ptr<BriefDescriptorExtractor> create( int bytes = 32, bool use_orientation = false ); +}; + +/** @brief Class implementing the locally uniform comparison image descriptor, described in @cite LUCID + +An image descriptor that can be computed very fast, while being +about as robust as, for example, SURF or BRIEF. + +@note It requires a color image as input. + */ +class CV_EXPORTS_W LUCID : public Feature2D +{ +public: + /** + * @param lucid_kernel kernel for descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth + * @param blur_kernel kernel for blurring image prior to descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth + */ + CV_WRAP static Ptr<LUCID> create(const int lucid_kernel = 1, const int blur_kernel = 2); +}; + + +/* +* LATCH Descriptor +*/ + +/** latch Class for computing the LATCH descriptor. +If you find this code useful, please add a reference to the following paper in your work: +Gil Levi and Tal Hassner, "LATCH: Learned Arrangements of Three Patch Codes", arXiv preprint arXiv:1501.03719, 15 Jan. 2015 + +LATCH is a binary descriptor based on learned comparisons of triplets of image patches. + +* bytes is the size of the descriptor - can be 64, 32, 16, 8, 4, 2 or 1 +* rotationInvariance - whether or not the descriptor should compansate for orientation changes. +* half_ssd_size - the size of half of the mini-patches size. For example, if we would like to compare triplets of patches of size 7x7x + then the half_ssd_size should be (7-1)/2 = 3. + +Note: the descriptor can be coupled with any keypoint extractor. The only demand is that if you use set rotationInvariance = True then + you will have to use an extractor which estimates the patch orientation (in degrees). Examples for such extractors are ORB and SIFT. + +Note: a complete example can be found under /samples/cpp/tutorial_code/xfeatures2D/latch_match.cpp + +*/ +class CV_EXPORTS_W LATCH : public Feature2D +{ +public: + CV_WRAP static Ptr<LATCH> create(int bytes = 32, bool rotationInvariance = true, int half_ssd_size=3); +}; + +/** @brief Class implementing DAISY descriptor, described in @cite Tola10 + +@param radius radius of the descriptor at the initial scale +@param q_radius amount of radial range division quantity +@param q_theta amount of angular range division quantity +@param q_hist amount of gradient orientations range division quantity +@param norm choose descriptors normalization type, where +DAISY::NRM_NONE will not do any normalization (default), +DAISY::NRM_PARTIAL mean that histograms are normalized independently for L2 norm equal to 1.0, +DAISY::NRM_FULL mean that descriptors are normalized for L2 norm equal to 1.0, +DAISY::NRM_SIFT mean that descriptors are normalized for L2 norm equal to 1.0 but no individual one is bigger than 0.154 as in SIFT +@param H optional 3x3 homography matrix used to warp the grid of daisy but sampling keypoints remains unwarped on image +@param interpolation switch to disable interpolation for speed improvement at minor quality loss +@param use_orientation sample patterns using keypoints orientation, disabled by default. + + */ +class CV_EXPORTS_W DAISY : public Feature2D +{ +public: + enum + { + NRM_NONE = 100, NRM_PARTIAL = 101, NRM_FULL = 102, NRM_SIFT = 103, + }; + CV_WRAP static Ptr<DAISY> create( float radius = 15, int q_radius = 3, int q_theta = 8, + int q_hist = 8, int norm = DAISY::NRM_NONE, InputArray H = noArray(), + bool interpolation = true, bool use_orientation = false ); + + /** @overload + * @param image image to extract descriptors + * @param keypoints of interest within image + * @param descriptors resulted descriptors array + */ + virtual void compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) = 0; + + virtual void compute( InputArrayOfArrays images, + std::vector<std::vector<KeyPoint> >& keypoints, + OutputArrayOfArrays descriptors ); + + /** @overload + * @param image image to extract descriptors + * @param roi region of interest within image + * @param descriptors resulted descriptors array for roi image pixels + */ + virtual void compute( InputArray image, Rect roi, OutputArray descriptors ) = 0; + + /**@overload + * @param image image to extract descriptors + * @param descriptors resulted descriptors array for all image pixels + */ + virtual void compute( InputArray image, OutputArray descriptors ) = 0; + + /** + * @param y position y on image + * @param x position x on image + * @param orientation orientation on image (0->360) + * @param descriptor supplied array for descriptor storage + */ + virtual void GetDescriptor( double y, double x, int orientation, float* descriptor ) const = 0; + + /** + * @param y position y on image + * @param x position x on image + * @param orientation orientation on image (0->360) + * @param descriptor supplied array for descriptor storage + * @param H homography matrix for warped grid + */ + virtual bool GetDescriptor( double y, double x, int orientation, float* descriptor, double* H ) const = 0; + + /** + * @param y position y on image + * @param x position x on image + * @param orientation orientation on image (0->360) + * @param descriptor supplied array for descriptor storage + */ + virtual void GetUnnormalizedDescriptor( double y, double x, int orientation, float* descriptor ) const = 0; + + /** + * @param y position y on image + * @param x position x on image + * @param orientation orientation on image (0->360) + * @param descriptor supplied array for descriptor storage + * @param H homography matrix for warped grid + */ + virtual bool GetUnnormalizedDescriptor( double y, double x, int orientation, float* descriptor , double *H ) const = 0; + +}; + +/** @brief Class implementing the MSD (*Maximal Self-Dissimilarity*) keypoint detector, described in @cite Tombari14. + +The algorithm implements a novel interest point detector stemming from the intuition that image patches +which are highly dissimilar over a relatively large extent of their surroundings hold the property of +being repeatable and distinctive. This concept of "contextual self-dissimilarity" reverses the key +paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local +Means filter, which build upon the presence of similar - rather than dissimilar - patches. Moreover, +it extends to contextual information the local self-dissimilarity notion embedded in established +detectors of corner-like interest points, thereby achieving enhanced repeatability, distinctiveness and +localization accuracy. + +*/ + +class CV_EXPORTS_W MSDDetector : public Feature2D { + +public: + + static Ptr<MSDDetector> create(int m_patch_radius = 3, int m_search_area_radius = 5, + int m_nms_radius = 5, int m_nms_scale_radius = 0, float m_th_saliency = 250.0f, int m_kNN = 4, + float m_scale_factor = 1.25f, int m_n_scales = -1, bool m_compute_orientation = false); +}; + +/** @brief Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end +using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in @cite Simonyan14. + +@param desc type of descriptor to use, VGG::VGG_120 is default (120 dimensions float) +Available types are VGG::VGG_120, VGG::VGG_80, VGG::VGG_64, VGG::VGG_48 +@param isigma gaussian kernel value for image blur (default is 1.4f) +@param img_normalize use image sample intensity normalization (enabled by default) +@param use_orientation sample patterns using keypoints orientation, enabled by default +@param scale_factor adjust the sampling window of detected keypoints to 64.0f (VGG sampling window) +6.25f is default and fits for KAZE, SURF detected keypoints window ratio +6.75f should be the scale for SIFT detected keypoints window ratio +5.00f should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints window ratio +0.75f should be the scale for ORB keypoints ratio + +@param dsc_normalize clamp descriptors to 255 and convert to uchar CV_8UC1 (disabled by default) + + */ +class CV_EXPORTS_W VGG : public Feature2D +{ +public: + + CV_WRAP enum + { + VGG_120 = 100, VGG_80 = 101, VGG_64 = 102, VGG_48 = 103, + }; + + CV_WRAP static Ptr<VGG> create( int desc = VGG::VGG_120, float isigma = 1.4f, + bool img_normalize = true, bool use_scale_orientation = true, + float scale_factor = 6.25f, bool dsc_normalize = false ); + /** + * @param image image to extract descriptors + * @param keypoints of interest within image + * @param descriptors resulted descriptors array + */ + CV_WRAP virtual void compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) = 0; + +}; + +/** @brief Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in +@cite Trzcinski13a and @cite Trzcinski13b. + +@param desc type of descriptor to use, BoostDesc::BINBOOST_256 is default (256 bit long dimension) +Available types are: BoostDesc::BGM, BoostDesc::BGM_HARD, BoostDesc::BGM_BILINEAR, BoostDesc::LBGM, +BoostDesc::BINBOOST_64, BoostDesc::BINBOOST_128, BoostDesc::BINBOOST_256 +@param use_orientation sample patterns using keypoints orientation, enabled by default +@param scale_factor adjust the sampling window of detected keypoints +6.25f is default and fits for KAZE, SURF detected keypoints window ratio +6.75f should be the scale for SIFT detected keypoints window ratio +5.00f should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints window ratio +0.75f should be the scale for ORB keypoints ratio +1.50f was the default in original implementation + +@note BGM is the base descriptor where each binary dimension is computed as the output of a single weak learner. +BGM_HARD and BGM_BILINEAR refers to same BGM but use different type of gradient binning. In the BGM_HARD that +use ASSIGN_HARD binning type the gradient is assigned to the nearest orientation bin. In the BGM_BILINEAR that use +ASSIGN_BILINEAR binning type the gradient is assigned to the two neighbouring bins. In the BGM and all other modes that use +ASSIGN_SOFT binning type the gradient is assigned to 8 nearest bins according to the cosine value between the gradient +angle and the bin center. LBGM (alias FP-Boost) is the floating point extension where each dimension is computed +as a linear combination of the weak learner responses. BINBOOST and subvariants are the binary extensions of LBGM +where each bit is computed as a thresholded linear combination of a set of weak learners. +BoostDesc header files (boostdesc_*.i) was exported from original binaries with export-boostdesc.py script from +samples subfolder. + +*/ + +class CV_EXPORTS_W BoostDesc : public Feature2D +{ +public: + + CV_WRAP enum + { + BGM = 100, BGM_HARD = 101, BGM_BILINEAR = 102, LBGM = 200, + BINBOOST_64 = 300, BINBOOST_128 = 301, BINBOOST_256 = 302 + }; + + CV_WRAP static Ptr<BoostDesc> create( int desc = BoostDesc::BINBOOST_256, + bool use_scale_orientation = true, float scale_factor = 6.25f ); +}; + + +/* +* Position-Color-Texture signatures +*/ + +/** +* @brief Class implementing PCT (position-color-texture) signature extraction +* as described in @cite KrulisLS16. +* The algorithm is divided to a feature sampler and a clusterizer. +* Feature sampler produces samples at given set of coordinates. +* Clusterizer then produces clusters of these samples using k-means algorithm. +* Resulting set of clusters is the signature of the input image. +* +* A signature is an array of SIGNATURE_DIMENSION-dimensional points. +* Used dimensions are: +* weight, x, y position; lab color, contrast, entropy. +* @cite KrulisLS16 +* @cite BeecksUS10 +*/ +class CV_EXPORTS_W PCTSignatures : public Algorithm +{ +public: + /** + * @brief Lp distance function selector. + */ + enum DistanceFunction + { + L0_25, L0_5, L1, L2, L2SQUARED, L5, L_INFINITY + }; + + /** + * @brief Point distributions supported by random point generator. + */ + enum PointDistribution + { + UNIFORM, //!< Generate numbers uniformly. + REGULAR, //!< Generate points in a regular grid. + NORMAL //!< Generate points with normal (gaussian) distribution. + }; + + /** + * @brief Similarity function selector. + * @see + * Christian Beecks, Merih Seran Uysal, Thomas Seidl. + * Signature quadratic form distance. + * In Proceedings of the ACM International Conference on Image and Video Retrieval, pages 438-445. + * ACM, 2010. + * @cite BeecksUS10 + * @note For selected distance function: \f[ d(c_i, c_j) \f] and parameter: \f[ \alpha \f] + */ + enum SimilarityFunction + { + MINUS, //!< \f[ -d(c_i, c_j) \f] + GAUSSIAN, //!< \f[ e^{ -\alpha * d^2(c_i, c_j)} \f] + HEURISTIC //!< \f[ \frac{1}{\alpha + d(c_i, c_j)} \f] + }; + + + /** + * @brief Creates PCTSignatures algorithm using sample and seed count. + * It generates its own sets of sampling points and clusterization seed indexes. + * @param initSampleCount Number of points used for image sampling. + * @param initSeedCount Number of initial clusterization seeds. + * Must be lower or equal to initSampleCount + * @param pointDistribution Distribution of generated points. Default: UNIFORM. + * Available: UNIFORM, REGULAR, NORMAL. + * @return Created algorithm. + */ + CV_WRAP static Ptr<PCTSignatures> create( + const int initSampleCount = 2000, + const int initSeedCount = 400, + const int pointDistribution = 0); + + /** + * @brief Creates PCTSignatures algorithm using pre-generated sampling points + * and number of clusterization seeds. It uses the provided + * sampling points and generates its own clusterization seed indexes. + * @param initSamplingPoints Sampling points used in image sampling. + * @param initSeedCount Number of initial clusterization seeds. + * Must be lower or equal to initSamplingPoints.size(). + * @return Created algorithm. + */ + CV_WRAP static Ptr<PCTSignatures> create( + const std::vector<Point2f>& initSamplingPoints, + const int initSeedCount); + + /** + * @brief Creates PCTSignatures algorithm using pre-generated sampling points + * and clusterization seeds indexes. + * @param initSamplingPoints Sampling points used in image sampling. + * @param initClusterSeedIndexes Indexes of initial clusterization seeds. + * Its size must be lower or equal to initSamplingPoints.size(). + * @return Created algorithm. + */ + CV_WRAP static Ptr<PCTSignatures> create( + const std::vector<Point2f>& initSamplingPoints, + const std::vector<int>& initClusterSeedIndexes); + + + + /** + * @brief Computes signature of given image. + * @param image Input image of CV_8U type. + * @param signature Output computed signature. + */ + CV_WRAP virtual void computeSignature( + InputArray image, + OutputArray signature) const = 0; + + /** + * @brief Computes signatures for multiple images in parallel. + * @param images Vector of input images of CV_8U type. + * @param signatures Vector of computed signatures. + */ + CV_WRAP virtual void computeSignatures( + const std::vector<Mat>& images, + std::vector<Mat>& signatures) const = 0; + + /** + * @brief Draws signature in the source image and outputs the result. + * Signatures are visualized as a circle + * with radius based on signature weight + * and color based on signature color. + * Contrast and entropy are not visualized. + * @param source Source image. + * @param signature Image signature. + * @param result Output result. + * @param radiusToShorterSideRatio Determines maximal radius of signature in the output image. + * @param borderThickness Border thickness of the visualized signature. + */ + CV_WRAP static void drawSignature( + InputArray source, + InputArray signature, + OutputArray result, + float radiusToShorterSideRatio = 1.0 / 8, + int borderThickness = 1); + + /** + * @brief Generates initial sampling points according to selected point distribution. + * @param initPoints Output vector where the generated points will be saved. + * @param count Number of points to generate. + * @param pointDistribution Point distribution selector. + * Available: UNIFORM, REGULAR, NORMAL. + * @note Generated coordinates are in range [0..1) + */ + CV_WRAP static void generateInitPoints( + std::vector<Point2f>& initPoints, + const int count, + int pointDistribution); + + + /**** sampler ****/ + + /** + * @brief Number of initial samples taken from the image. + */ + CV_WRAP virtual int getSampleCount() const = 0; + + /** + * @brief Color resolution of the greyscale bitmap represented in allocated bits + * (i.e., value 4 means that 16 shades of grey are used). + * The greyscale bitmap is used for computing contrast and entropy values. + */ + CV_WRAP virtual int getGrayscaleBits() const = 0; + /** + * @brief Color resolution of the greyscale bitmap represented in allocated bits + * (i.e., value 4 means that 16 shades of grey are used). + * The greyscale bitmap is used for computing contrast and entropy values. + */ + CV_WRAP virtual void setGrayscaleBits(int grayscaleBits) = 0; + + /** + * @brief Size of the texture sampling window used to compute contrast and entropy + * (center of the window is always in the pixel selected by x,y coordinates + * of the corresponding feature sample). + */ + CV_WRAP virtual int getWindowRadius() const = 0; + /** + * @brief Size of the texture sampling window used to compute contrast and entropy + * (center of the window is always in the pixel selected by x,y coordinates + * of the corresponding feature sample). + */ + CV_WRAP virtual void setWindowRadius(int radius) = 0; + + + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual float getWeightX() const = 0; + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual void setWeightX(float weight) = 0; + + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual float getWeightY() const = 0; + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual void setWeightY(float weight) = 0; + + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual float getWeightL() const = 0; + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual void setWeightL(float weight) = 0; + + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual float getWeightA() const = 0; + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual void setWeightA(float weight) = 0; + + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual float getWeightB() const = 0; + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual void setWeightB(float weight) = 0; + + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual float getWeightConstrast() const = 0; + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual void setWeightContrast(float weight) = 0; + + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual float getWeightEntropy() const = 0; + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space + * (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy) + */ + CV_WRAP virtual void setWeightEntropy(float weight) = 0; + + /** + * @brief Initial samples taken from the image. + * These sampled features become the input for clustering. + */ + CV_WRAP virtual std::vector<Point2f> getSamplingPoints() const = 0; + + + + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space. + * @param idx ID of the weight + * @param value Value of the weight + * @note + * WEIGHT_IDX = 0; + * X_IDX = 1; + * Y_IDX = 2; + * L_IDX = 3; + * A_IDX = 4; + * B_IDX = 5; + * CONTRAST_IDX = 6; + * ENTROPY_IDX = 7; + */ + CV_WRAP virtual void setWeight(int idx, float value) = 0; + /** + * @brief Weights (multiplicative constants) that linearly stretch individual axes of the feature space. + * @param weights Values of all weights. + * @note + * WEIGHT_IDX = 0; + * X_IDX = 1; + * Y_IDX = 2; + * L_IDX = 3; + * A_IDX = 4; + * B_IDX = 5; + * CONTRAST_IDX = 6; + * ENTROPY_IDX = 7; + */ + CV_WRAP virtual void setWeights(const std::vector<float>& weights) = 0; + + /** + * @brief Translations of the individual axes of the feature space. + * @param idx ID of the translation + * @param value Value of the translation + * @note + * WEIGHT_IDX = 0; + * X_IDX = 1; + * Y_IDX = 2; + * L_IDX = 3; + * A_IDX = 4; + * B_IDX = 5; + * CONTRAST_IDX = 6; + * ENTROPY_IDX = 7; + */ + CV_WRAP virtual void setTranslation(int idx, float value) = 0; + /** + * @brief Translations of the individual axes of the feature space. + * @param translations Values of all translations. + * @note + * WEIGHT_IDX = 0; + * X_IDX = 1; + * Y_IDX = 2; + * L_IDX = 3; + * A_IDX = 4; + * B_IDX = 5; + * CONTRAST_IDX = 6; + * ENTROPY_IDX = 7; + */ + CV_WRAP virtual void setTranslations(const std::vector<float>& translations) = 0; + + /** + * @brief Sets sampling points used to sample the input image. + * @param samplingPoints Vector of sampling points in range [0..1) + * @note Number of sampling points must be greater or equal to clusterization seed count. + */ + CV_WRAP virtual void setSamplingPoints(std::vector<Point2f> samplingPoints) = 0; + + + + /**** clusterizer ****/ + /** + * @brief Initial seeds (initial number of clusters) for the k-means algorithm. + */ + CV_WRAP virtual std::vector<int> getInitSeedIndexes() const = 0; + /** + * @brief Initial seed indexes for the k-means algorithm. + */ + CV_WRAP virtual void setInitSeedIndexes(std::vector<int> initSeedIndexes) = 0; + /** + * @brief Number of initial seeds (initial number of clusters) for the k-means algorithm. + */ + CV_WRAP virtual int getInitSeedCount() const = 0; + + /** + * @brief Number of iterations of the k-means clustering. + * We use fixed number of iterations, since the modified clustering is pruning clusters + * (not iteratively refining k clusters). + */ + CV_WRAP virtual int getIterationCount() const = 0; + /** + * @brief Number of iterations of the k-means clustering. + * We use fixed number of iterations, since the modified clustering is pruning clusters + * (not iteratively refining k clusters). + */ + CV_WRAP virtual void setIterationCount(int iterationCount) = 0; + + /** + * @brief Maximal number of generated clusters. If the number is exceeded, + * the clusters are sorted by their weights and the smallest clusters are cropped. + */ + CV_WRAP virtual int getMaxClustersCount() const = 0; + /** + * @brief Maximal number of generated clusters. If the number is exceeded, + * the clusters are sorted by their weights and the smallest clusters are cropped. + */ + CV_WRAP virtual void setMaxClustersCount(int maxClustersCount) = 0; + + /** + * @brief This parameter multiplied by the index of iteration gives lower limit for cluster size. + * Clusters containing fewer points than specified by the limit have their centroid dismissed + * and points are reassigned. + */ + CV_WRAP virtual int getClusterMinSize() const = 0; + /** + * @brief This parameter multiplied by the index of iteration gives lower limit for cluster size. + * Clusters containing fewer points than specified by the limit have their centroid dismissed + * and points are reassigned. + */ + CV_WRAP virtual void setClusterMinSize(int clusterMinSize) = 0; + + /** + * @brief Threshold euclidean distance between two centroids. + * If two cluster centers are closer than this distance, + * one of the centroid is dismissed and points are reassigned. + */ + CV_WRAP virtual float getJoiningDistance() const = 0; + /** + * @brief Threshold euclidean distance between two centroids. + * If two cluster centers are closer than this distance, + * one of the centroid is dismissed and points are reassigned. + */ + CV_WRAP virtual void setJoiningDistance(float joiningDistance) = 0; + + /** + * @brief Remove centroids in k-means whose weight is lesser or equal to given threshold. + */ + CV_WRAP virtual float getDropThreshold() const = 0; + /** + * @brief Remove centroids in k-means whose weight is lesser or equal to given threshold. + */ + CV_WRAP virtual void setDropThreshold(float dropThreshold) = 0; + + /** + * @brief Distance function selector used for measuring distance between two points in k-means. + */ + CV_WRAP virtual int getDistanceFunction() const = 0; + /** + * @brief Distance function selector used for measuring distance between two points in k-means. + * Available: L0_25, L0_5, L1, L2, L2SQUARED, L5, L_INFINITY. + */ + CV_WRAP virtual void setDistanceFunction(int distanceFunction) = 0; + +}; + +/** +* @brief Class implementing Signature Quadratic Form Distance (SQFD). +* @see Christian Beecks, Merih Seran Uysal, Thomas Seidl. +* Signature quadratic form distance. +* In Proceedings of the ACM International Conference on Image and Video Retrieval, pages 438-445. +* ACM, 2010. +* @cite BeecksUS10 +*/ +class CV_EXPORTS_W PCTSignaturesSQFD : public Algorithm +{ +public: + + /** + * @brief Creates the algorithm instance using selected distance function, + * similarity function and similarity function parameter. + * @param distanceFunction Distance function selector. Default: L2 + * Available: L0_25, L0_5, L1, L2, L2SQUARED, L5, L_INFINITY + * @param similarityFunction Similarity function selector. Default: HEURISTIC + * Available: MINUS, GAUSSIAN, HEURISTIC + * @param similarityParameter Parameter of the similarity function. + */ + CV_WRAP static Ptr<PCTSignaturesSQFD> create( + const int distanceFunction = 3, + const int similarityFunction = 2, + const float similarityParameter = 1.0f); + + /** + * @brief Computes Signature Quadratic Form Distance of two signatures. + * @param _signature0 The first signature. + * @param _signature1 The second signature. + */ + CV_WRAP virtual float computeQuadraticFormDistance( + InputArray _signature0, + InputArray _signature1) const = 0; + + /** + * @brief Computes Signature Quadratic Form Distance between the reference signature + * and each of the other image signatures. + * @param sourceSignature The signature to measure distance of other signatures from. + * @param imageSignatures Vector of signatures to measure distance from the source signature. + * @param distances Output vector of measured distances. + */ + CV_WRAP virtual void computeQuadraticFormDistances( + const Mat& sourceSignature, + const std::vector<Mat>& imageSignatures, + std::vector<float>& distances) const = 0; + +}; + +//! @} + +} +} + +#endif |