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diff --git a/modules/line_descriptor/doc/BinaryDescriptor.rst~ b/modules/line_descriptor/doc/BinaryDescriptor.rst~ deleted file mode 100644 index 5e73aae76..000000000 --- a/modules/line_descriptor/doc/BinaryDescriptor.rst~ +++ /dev/null @@ -1,217 +0,0 @@ -BinaryDescriptor Class -====================== - -.. highlight:: cpp - -BinaryDescriptor Class implements both functionalities for detection of lines and computation of their binary descriptor. Class' interface is mainly based on the ones of classical detectors and extractors, such as Feature2d's `FeatureDetector `_ and `DescriptorExtractor `_. -Retrieved information about lines is stored in *KeyLine* objects. - - -BinaryDescriptor::Params ------------------------------------------------------------------------ - -.. ocv:struct:: BinaryDescriptor::Params - -List of BinaryDescriptor parameters:: - - struct CV_EXPORTS_W_SIMPLE Params{ - CV_WRAP Params(); - - /* the number of image octaves (default = 5) */ - CV_PROP_RW int numOfOctave_; - - /* the width of band; (default: 7) */ - CV_PROP_RW int widthOfBand_; - - /* image's reduction ratio in construction of Gaussian pyramids */ - CV_PROP_RW int reductionRatio; - - /* read parameters from a FileNode object and store them (struct function) */ - void read( const FileNode& fn ); - - /* store parameters to a FileStorage object (struct function) */ - void write( FileStorage& fs ) const; - - }; - - -BinaryDescriptor::BinaryDescriptor ----------------------------------- - -Constructor - -.. ocv:function:: bool BinaryDescriptor::BinaryDescriptor( const BinaryDescriptor::Params ¶meters = BinaryDescriptor::Params() ) - - :param parameters: configuration parameters :ocv:struct:`BinaryDescriptor::Params` - -If no argument is provided, constructor sets default values (see comments in the code snippet in previous section). Default values are strongly reccomended. - - -BinaryDescriptor::getNumOfOctaves ---------------------------------- - -Get current number of octaves - -.. ocv:function:: int BinaryDescriptor::getNumOfOctaves() - - -BinaryDescriptor::setNumOfOctaves ---------------------------------- - -Set number of octaves - -.. ocv:function:: void BinaryDescriptor::setNumOfOctaves( int octaves ) - - :param octaves: number of octaves - - -BinaryDescriptor::getWidthOfBand --------------------------------- - -Get current width of bands - -.. ocv:function:: int BinaryDescriptor::getWidthOfBand() - - -BinaryDescriptor::setWidthOfBand --------------------------------- - -Set width of bands - -.. ocv:function:: void BinaryDescriptor::setWidthOfBand( int width ) - - :param width: width of bands - -BinaryDescriptor::getReductionRatio ------------------------------------ - -Get current reduction ratio (used in Gaussian pyramids) - -.. ocv:function:: int BinaryDescriptor::getReductionRatio() - - -BinaryDescriptor::setReductionRatio ------------------------------------ - -Set reduction ratio (used in Gaussian pyramids) - -.. ocv:function:: void BinaryDescriptor::setReductionRatio( int rRatio ) - - :param rRatio: reduction ratio - - -BinaryDescriptor::createBinaryDescriptor ----------------------------------------- - -Create a BinaryDescriptor object with default parameters (or with the ones provided) and return a smart pointer to it - -.. ocv:function:: Ptr BinaryDescriptor::createBinaryDescriptor() -.. ocv:function:: Ptr BinaryDescriptor::createBinaryDescriptor( Params parameters ) - - -BinaryDescriptor::operator() ----------------------------- - -Define operator '()' to perform detection of KeyLines and computation of descriptors in a row. - -.. ocv:function:: void BinaryDescriptor::operator()( InputArray image, InputArray mask, vector& keylines, OutputArray descriptors, bool useProvidedKeyLines=false ) const - - :param image: input image - - :param mask: mask matrix to select which lines in KeyLines must be accepted among the ones extracted (used when *keylines* is not empty) - - :param keylines: vector that contains input lines (when filled, the detection part will be skipped and input lines will be passed as input to the algorithm computing descriptors) - - :param descriptors: matrix that will store final descriptors - - :param useProvidedKeyLines: flag (when set to true, detection phase will be skipped and only computation of descriptors will be executed, using lines provided in *keylines*) - - -BinaryDescriptor::read ----------------------- - -Read parameters from a FileNode object and store them - -.. ocv:function:: void BinaryDescriptor::read( const FileNode& fn ) - - :param fn: source FileNode file - - -BinaryDescriptor::write ------------------------ - -Store parameters to a FileStorage object - -.. ocv:function:: void BinaryDescriptor::write( FileStorage& fs ) const - - :param fs: output FileStorage file - - -BinaryDescriptor::defaultNorm ------------------------------ - -Return norm mode - -.. ocv:function:: int BinaryDescriptor::defaultNorm() const - - -BinaryDescriptor::descriptorType --------------------------------- - -Return data type - -.. ocv:function:: int BinaryDescriptor::descriptorType() const - - -BinaryDescriptor::descriptorSize --------------------------------- - -Return descriptor size - -.. ocv:function:: int BinaryDescriptor::descriptorSize() const - - -BinaryDescriptor::empty ------------------------ - -Check whether Gaussian pyramids were created - -.. ocv:function:: bool BinaryDescriptor::empty() const - - -BinaryDescriptor::detect ------------------------- - -Requires line detection (for one or more images) - -.. ocv:function:: void detect( const Mat& image, vector& keylines, Mat& mask=Mat() ) -.. ocv:function:: void detect( const vector& images, vector >& keylines, vector& masks=vector() ) const - - :param image: input image - - :param images: input images - - :param keylines: vector or set of vectors that will store extracted lines for one or more images - - :param mask: mask matrix to detect only KeyLines of interest - - :param masks: vector of mask matrices to detect only KeyLines of interest from each input image - - -BinaryDescriptor::compute -------------------------- - -Requires descriptors computation (for one or more images) - -.. ocv:function:: void compute( const Mat& image, vector& keylines, Mat& descriptors ) const -.. ocv:function:: void compute( const vector& images, vector >& keylines, vector& descriptors ) const - - :param image: input image - - :param images: input images - - :param keylines: vector or set of vectors containing lines for which descriptors must be computed - - :param mask: mask to select for which lines, among the ones provided in input, descriptors must be computed - - :param masks: set of masks to select for which lines, among the ones provided in input, descriptors must be computed diff --git a/modules/line_descriptor/doc/BinaryDescriptor.rst b/modules/line_descriptor/doc/binary_descriptor.rst similarity index 95% rename from modules/line_descriptor/doc/BinaryDescriptor.rst rename to modules/line_descriptor/doc/binary_descriptor.rst index 329fc7678..1f9d62bbf 100644 --- a/modules/line_descriptor/doc/BinaryDescriptor.rst +++ b/modules/line_descriptor/doc/binary_descriptor.rst @@ -17,7 +17,7 @@ List of BinaryDescriptor parameters:: struct CV_EXPORTS_W_SIMPLE Params{ CV_WRAP Params(); - /* the number of image octaves (default = 5) */ + /* the number of image octaves (default = 1) */ CV_PROP_RW int numOfOctave_; /* the width of band; (default = 7) */ @@ -215,3 +215,11 @@ Requires descriptors computation (for one or more images) :param mask: mask to select for which lines, among the ones provided in input, descriptors must be computed :param masks: set of masks to select for which lines, among the ones provided in input, descriptors must be computed + + +Related pages +------------- + +* `Binary descriptors for lines extracted from an image `_ +* `Matching with binary descriptors `_ +* `Drawing Function of Keylines and Matches `_ diff --git a/modules/line_descriptor/doc/drawing_functions.rst b/modules/line_descriptor/doc/drawing_functions.rst new file mode 100644 index 000000000..93b93841e --- /dev/null +++ b/modules/line_descriptor/doc/drawing_functions.rst @@ -0,0 +1,68 @@ +Drawing Function of Keylines and Matches +======================================== + +.. highlight:: cpp + +drawLineMatches +--------------- + +Draws the found matches of keylines from two images. + +.. ocv:function:: void drawLineMatches( const Mat& img1, const std::vector& keylines1, const Mat& img2, const std::vector& keylines2, const std::vector& matches1to2, Mat& outImg, const Scalar& matchColor=Scalar::all(-1), const Scalar& singleLineColor=Scalar::all(-1), const std::vector& matchesMask=std::vector(), int flags=DrawLinesMatchesFlags::DEFAULT ) + + :param img1: first image + :param keylines1: keylines extracted from first image + :param img2: second image + :param keylines2: keylines extracted from second image + :param matches1to2: vector of matches + :param outImg: output matrix to draw on + :param matchColor: drawing color for matches (chosen randomly in case of default value) + :param singleLineColor: drawing color for keylines (chosen randomly in case of default value) + :param matchesMask: mask to indicate which matches must be drawn + :param flags: drawing flags + +.. note:: If both *matchColor* and *singleLineColor* are set to their default values, function draws matched lines and line connecting them with same color + +The structure of drawing flags is shown in the following: + +.. code-block:: cpp + + /* struct for drawing options */ + struct CV_EXPORTS DrawLinesMatchesFlags + { + enum + { + DEFAULT = 0, // Output image matrix will be created (Mat::create), + // i.e. existing memory of output image may be reused. + // Two source images, matches, and single keylines + // will be drawn. + DRAW_OVER_OUTIMG = 1, // Output image matrix will not be + // created (using Mat::create). Matches will be drawn + // on existing content of output image. + NOT_DRAW_SINGLE_LINES = 2 // Single keylines will not be drawn. + }; + }; + +.. + + +drawKeylines +------------ + +Draws keylines. + +.. ocv:function:: void drawKeylines( const Mat& image, const std::vector& keylines, Mat& outImage, const Scalar& color=Scalar::all(-1), int flags=DrawLinesMatchesFlags::DEFAULT ) + + :param image: input image + :param keylines: keylines to be drawn + :param outImage: output image to draw on + :param color: color of lines to be drawn (if set to defaul value, color is chosen randomly) + :param flags: drawing flags + + +Related pages +------------- + +* `Binary descriptors for lines extracted from an image `_ +* `BinaryDescriptor Class `_ +* `Matching with binary descriptors `_ diff --git a/modules/line_descriptor/doc/line_descriptor.rst b/modules/line_descriptor/doc/line_descriptor.rst index aac76359d..0d60b31cb 100644 --- a/modules/line_descriptor/doc/line_descriptor.rst +++ b/modules/line_descriptor/doc/line_descriptor.rst @@ -131,7 +131,9 @@ Once the LBD has been obtained, it must be converted into a binary form. For suc Related Pages ------------- -* `BinaryDescriptor Class `_ +* `BinaryDescriptor Class `_ +* `Matching with binary descriptors `_ +* `Drawing Function of Keylines and Matches `_ References ---------- diff --git a/modules/line_descriptor/doc/line_descriptor.rst~ b/modules/line_descriptor/doc/line_descriptor.rst~ deleted file mode 100644 index cfd1bf74f..000000000 --- a/modules/line_descriptor/doc/line_descriptor.rst~ +++ /dev/null @@ -1,139 +0,0 @@ -Binary descriptors for lines extracted from an image -==================================================== - -.. highlight:: cpp - - -Introduction ------------- - - -One of the most challenging activities in computer vision is the extraction of useful information from a given image. Such information, usually comes in the form of points that preserve some kind of property (for instance, they are scale-invariant) and are actually representative of input image. - -The goal of this module is seeking a new kind of representative information inside an image and providing the functionalities for its extraction and representation. In particular, differently from previous methods for detection of relevant elements inside an image, lines are extracted in place of points; a new class is defined ad hoc to summarize a line's properties, for reuse and plotting purposes. - - -A class to represent a line: KeyLine ------------------------------------- - -As aformentioned, it is been necessary to design a class that fully stores the information needed to characterize completely a line and plot it on image it was extracted from, when required. - -*KeyLine* class has been created for such goal; it is mainly inspired to Feature2d's KeyPoint class, since KeyLine shares some of *KeyPoint*'s fields, even if a part of them assumes a different meaning, when speaking about lines. -In particular: - -* the *class_id* field is used to gather lines extracted from different octaves which refer to same line inside original image (such lines and the one they represent in original image share the same *class_id* value) -* the *angle* field represents line's slope with respect to (positive) X axis -* the *pt* field represents line's midpoint -* the *response* field is computed as the ratio between the line's length and maximum between image's width and height -* the *size* field is the area of the smallest rectangle containing line - -Apart from fields inspired to KeyPoint class, KeyLines stores information about extremes of line in original image and in octave it was extracted from, about line's length and number of pixels it covers. Code relative to KeyLine class is reported in the following snippet: - -.. ocv:class:: KeyLine - -:: - - class CV_EXPORTS_W KeyLine - { - public: - /* orientation of the line */ - float angle; - - /* object ID, that can be used to cluster keylines by the line they represent */ - int class_id; - - /* octave (pyramid layer), from which the keyline has been extracted */ - int octave; - - /* coordinates of the middlepoint */ - Point pt; - - /* the response, by which the strongest keylines have been selected. - It's represented by the ratio between line's length and maximum between - image's width and height */ - float response; - - /* minimum area containing line */ - float size; - - /* lines's extremes in original image */ - float startPointX; - float startPointY; - float endPointX; - float endPointY; - - /* line's extremes in image it was extracted from */ - float sPointInOctaveX; - float sPointInOctaveY; - float ePointInOctaveX; - float ePointInOctaveY; - - /* the length of line */ - float lineLength; - - /* number of pixels covered by the line */ - unsigned int numOfPixels; - - /* constructor */ - KeyLine(){} - }; - - -Lines extraction methodology ----------------------------- - -The lines extraction methodology described in the following is mainly based on [LBD]_. -The extraction starts with a Gaussian pyramid generated from an original image, downsampled N-1 times, blurred N times, to obtain N layers (one for each octave), with layer 0 corresponding to input image. Then, from each layer (octave) in the pyramid, lines are extracted using LSD algorithm. - -Differently from EDLine lines extractor used in original article, LSD furnishes information only about lines extremes; thus, additional information regarding slope and equation of line are computed via analytic methods. The number of pixels is obtained using `LineIterator `_. Later on, all extracted lines are arranged in buckets: two lines fall in the same bucket if they represent the same line in different octave (they have the same direction and belong to same region of original image). The set of buckets becomes the input for descriptors computation. - - -Computation of binary descriptors ---------------------------------- - -To obtatin a binary descriptor representing a certain line detected from a certain octave of an image, we first compute a non-binary descriptor as described in [LBD]_. Given a line, we consider a rectangular region centered at it and called *line support region (LSR)*. Such region is divided into a set of bands :math:`\{B_1, B_2, ..., B_m\}`, whose length equals the one of line. - -If we indicate with :math:`\bf{d}_L` the direction of line, the orthogonal and clockwise direction to line :math:`\bf{d}_{\perp}` can be determined; these two directions, are used to construct a reference frame centered in the middle point of line. The gradients of pixels :math:`\bf{g'}` inside LSR can be projected to the newly determined frame, obtaining their local equivalent :math:`\bf{g'} = (\bf{g}^T \cdot \bf{d}_{\perp}, \bf{g}^T \cdot \bf{d}_L)^T \triangleq (\bf{g'}_{d_{\perp}}, \bf{g'}_{d_L})^T`. - -Later on, a Gaussian function is applied to all LSR's pixels along :math:`\bf{d}_\perp` direction; first, we assign a global weighting coefficient :math:`f_g(i) = (1/\sqrt{2\pi}\sigma_g)e^{-d^2_i/2\sigma^2_g}` to *i*-th row in LSR, where :math:`d_i` is the distance of *i*-th row from the center row in LSR, :math:`\sigma_g = 0.5(m \cdot w - 1)` and :math:`w` is the width of bands (the same for every band). Secondly, considering a band :math:`B_j` and its neighbor bands :math:`B_{j-1}, B_{j+1}`, we assign a local weighting :math:`F_l(k) = (1/\sqrt{2\pi}\sigma_l)e^{-d'^2_k/2\sigma_l^2}`, where :math:`d'_k` is the distance of *k*-th row from the center row in :math:`B_j` and :math:`\sigma_l = w`. Using the global and local weights, we obtain, at the same time, the reduction of role played by gradients far from line and of boundary effect, respectively. - -Each band :math:`B_j` in LSR has an associated *band descriptor(BD)* which is computed considering previous and next band (top and bottom bands are ignored when computing descriptor for first and last band). Once each band has been assignen its BD, the LBD descriptor of line is simply given by - -.. math:: - LBD = (BD_1^T, BD_2^T, ... , BD^T_m)^T. - -To compute a band descriptor :math:`B_j`, each *k*-th row in it is considered and the gradients in such row are accumulated: - -.. math:: - \begin{matrix} \bf{V1}^k_j = \lambda \sum\limits_{\bf{g}'_{d_\perp}>0}\bf{g}'_{d_\perp}, & \bf{V2}^k_j = \lambda \sum\limits_{\bf{g}'_{d_\perp}<0} -\bf{g}'_{d_\perp}, \\ \bf{V3}^k_j = \lambda \sum\limits_{\bf{g}'_{d_L}>0}\bf{g}'_{d_L}, & \bf{V4}^k_j = \lambda \sum\limits_{\bf{g}'_{d_L}<0} -\bf{g}'_{d_L}\end{matrix}. - -with :math:`\lambda = f_g(k)f_l(k)`. - -By stacking previous results, we obtain the *band description matrix (BDM)* - -.. math:: - BDM_j = \left(\begin{matrix} \bf{V1}_j^1 & \bf{V1}_j^2 & \ldots & \bf{V1}_j^n \\ \bf{V2}_j^1 & \bf{V2}_j^2 & \ldots & \bf{V2}_j^n \\ \bf{V3}_j^1 & \bf{V3}_j^2 & \ldots & \bf{V3}_j^n \\ \bf{V4}_j^1 & \bf{V4}_j^2 & \ldots & \bf{V4}_j^n \end{matrix} \right) \in \mathbb{R}^{4\times n}, - -with :math:`n` the number of rows in band :math:`B_j`: - -.. math:: - n = \begin{cases} 2w, & j = 1||m; \\ 3w, & \mbox{else}. \end{cases} - -Each :math:`BD_j` can be obtained using the standard deviation vector :math:`S_j` and mean vector :math:`M_j` of :math:`BDM_J`. Thus, finally: - -.. math:: - LBD = (M_1^T, S_1^T, M_2^T, S_2^T, \ldots, M_m^T, S_m^T)^T \in \mathbb{R}^{8m} - - -Once the LBD has been obtained, it must be converted into a binary form. For such purpose, we consider 32 possible pairs of BD inside it; each couple of BD is compared bit by bit and comparison generates an 8 bit string. Concatenating 32 comparison strings, we get the 256-bit final binary representation of a single LBD. - - -Related Pages -------------- - -* `BinaryDescriptor ` - -References ----------- - -.. [LBD] Zhang, Lilian, and Reinhard Koch. *An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency*, Journal of Visual Communication and Image Representation 24.7 (2013): 794-805. diff --git a/modules/line_descriptor/doc/matching.rst b/modules/line_descriptor/doc/matching.rst new file mode 100644 index 000000000..ed8837e24 --- /dev/null +++ b/modules/line_descriptor/doc/matching.rst @@ -0,0 +1,141 @@ +Matching with binary descriptors +================================ + +.. highlight:: cpp + +Once descriptors have been extracted from an image (both they represent lines and points), it becomes interesting to be able to match a descriptor with another one extracted from a different image and representing the same line or point, seen from a differente perspective or on a different scale. +In reaching such goal, the main headache is designing an efficient search algorithm to associate a query descriptor to one extracted from a dataset. +In the following, a matching modality based on *Multi-Index Hashing (MiHashing)* will be described. + + +Multi-Index Hashing +------------------- + +The theory described in this section is based on [MIH]_. +Given a dataset populated with binary codes, each code is indexed *m* times into *m* different hash tables, according to *m* substrings it has been divided into. Thus, given a query code, all the entries close to it at least in one substring are returned by search as *neighbor candidates*. Returned entries are then checked for validity by verifying that their full codes are not distant (in Hamming space) more than *r* bits from query code. +In details, each binary code **h** composed of *b* bits is divided into *m* disjoint substrings :math:`\mathbf{h}^{(1)}, ..., \mathbf{h}^{(m)}`, each with length :math:`\lfloor b/m \rfloor` or :math:`\lceil b/m \rceil` bits. Formally, when two codes **h** and **g** differ by at the most *r* bits, in at the least one of their *m* substrings they differ by at the most :math:`\lfloor r/m \rfloor` bits. In particular, when :math:`||\mathbf{h}-\mathbf{g}||_H \le r` (where :math:`||.||_H` is the Hamming norm), there must exist a substring *k* (with :math:`1 \le k \le m`) such that + +.. math:: + ||\mathbf{h}^{(k)} - \mathbf{g}^{(k)}||_H \le \left\lfloor \frac{r}{m} \right\rfloor . + +That means that if Hamming distance between each of the *m* substring is strictly greater than :math:`\lfloor r/m \rfloor`, then :math:`||\mathbf{h}-\mathbf{g}||_H` must be larger that *r* and that is a contradiction. +If the codes in dataset are divided into *m* substrings, then *m* tables will be built. Given a query **q** with substrings :math:`\{\mathbf{q}^{(i)}\}^m_{i=1}`, *i*-th hash table is searched for entries distant at the most :math:`\lfloor r/m \rfloor` from :math:`\mathbf{q}^{(i)}` and a set of candidates :math:`\mathcal{N}_i(\mathbf{q})` is obtained. +The union of sets :math:`\mathcal{N}(\mathbf{q}) = \bigcup_i \mathcal{N}_i(\mathbf{q})` is a superset of the *r*-neighbors of **q**. Then, last step of algorithm is computing the Hamming distance between **q** and each element in :math:`\mathcal{N}(\mathbf{q})`, deleting the codes that are distant more that *r* from **q**. + + +BinaryDescriptorMatcher Class +============================= + +BinaryDescriptorMatcher Class furnishes all functionalities for querying a dataset provided by user or internal to class (that user must, anyway, populate) on the model of Feature2d's `DescriptorMatcher `_. + + +BinaryDescriptorMatcher::BinaryDescriptorMatcher +-------------------------------------------------- + +Constructor. + +.. ocv:function:: BinaryDescriptorMatcher::BinaryDescriptorMatcher() + +The BinaryDescriptorMatcher constructed is able to store and manage 256-bits long entries. + + +BinaryDescriptorMatcher::createBinaryDescriptorMatcher +------------------------------------------------------ + +Create a BinaryDescriptorMatcher object and return a smart pointer to it. + +.. ocv:function:: Ptr BinaryDescriptorMatcher::createBinaryDescriptorMatcher() + + +BinaryDescriptorMatcher::add +---------------------------- + +Store locally new descriptors to be inserted in dataset, without updating dataset. + +.. ocv:function:: void BinaryDescriptorMatcher::add( const std::vector& descriptors ) + + :param descriptors: matrices containing descriptors to be inserted into dataset + +.. note:: Each matrix *i* in **descriptors** should contain descriptors relative to lines extracted from *i*-th image. + + +BinaryDescriptorMatcher::train +------------------------------ + +Update dataset by inserting into it all descriptors that were stored locally by *add* function. + +.. ocv:function:: void BinaryDescriptorMatcher::train() + +.. note:: Every time this function is invoked, current dataset is deleted and locally stored descriptors are inserted into dataset. The locally stored copy of just inserted descriptors is then removed. + + +BinaryDescriptorMatcher::clear +------------------------------ + +Clear dataset and internal data + +.. ocv:function:: void BinaryDescriptorMatcher::clear() + + +BinaryDescriptorMatcher::match +------------------------------ + +For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class + +.. ocv:function:: void BinaryDescriptorMatcher::match( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector& matches, const Mat& mask=Mat() ) const +.. ocv:function:: void BinaryDescriptorMatcher::match( const Mat& queryDescriptors, std::vector& matches, const std::vector& masks=std::vector() ) + + :param queryDescriptors: query descriptors + :param trainDescriptors: dataset of descriptors furnished by user + :param matches: vector to host retrieved matches + :param mask: mask to select which input descriptors must be matched to one in dataset + :param masks: vector of masks to select which input descriptors must be matched to one in dataset (the *i*-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to *i*-th image) + + +BinaryDescriptorMatcher::knnMatch +--------------------------------- + +For every input query descriptor, retrieve the best *k* matching ones from a dataset provided from user or from the one internal to class + +.. ocv:function:: void BinaryDescriptorMatcher::knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector >& matches, int k, const Mat& mask=Mat(), bool compactResult=false ) const +.. ocv:function:: void BinaryDescriptorMatcher::knnMatch( const Mat& queryDescriptors, std::vector >& matches, int k, const std::vector& masks=std::vector(), bool compactResult=false ) + + :param queryDescriptors: query descriptors + :param trainDescriptors: dataset of descriptors furnished by user + :param matches: vector to host retrieved matches + :param k: number of the closest descriptors to be returned for every input query + :param mask: mask to select which input descriptors must be matched to ones in dataset + :param masks: vector of masks to select which input descriptors must be matched to ones in dataset (the *i*-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to *i*-th image) + :param compactResult: flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result) + + +BinaryDescriptorMatcher::radiusMatch +------------------------------------ + +For every input query descriptor, retrieve, from a dataset provided from user or from the one internal to class, all the descriptors that are not further than *maxDist* from input query + +.. ocv:function:: void BinaryDescriptorMatcher::radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector >& matches, float maxDistance, const Mat& mask=Mat(), bool compactResult=false ) const +.. ocv:function:: void BinaryDescriptorMatcher::radiusMatch( const Mat& queryDescriptors, std::vector >& matches, float maxDistance, const std::vector& masks=std::vector(), bool compactResult=false ) + + :param queryDescriptors: query descriptors + :param trainDescriptors: dataset of descriptors furnished by user + :param matches: vector to host retrieved matches + :param maxDist: search radius + :param mask: mask to select which input descriptors must be matched to ones in dataset + :param masks: vector of masks to select which input descriptors must be matched to ones in dataset (the *i*-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to *i*-th image) + :param compactResult: flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result) + + + +Related pages +------------- + +* `Binary descriptors for lines extracted from an image `_ +* `BinaryDescriptor Class `_ +* `Drawing Function of Keylines and Matches `_ + + +References +---------- + +.. [MIH] Norouzi, Mohammad, Ali Punjani, and David J. Fleet. *Fast search in hamming space with multi-index hashing*, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012. diff --git a/modules/line_descriptor/include/opencv2/line_descriptor.hpp b/modules/line_descriptor/include/opencv2/line_descriptor.hpp index 9ed31a256..0ed5e0b22 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor.hpp @@ -47,7 +47,7 @@ namespace cv { -CV_EXPORTS bool initModule_line_descriptor(void); +CV_EXPORTS bool initModule_line_descriptor( void ); } diff --git a/modules/line_descriptor/include/opencv2/line_descriptor/array32.hpp b/modules/line_descriptor/include/opencv2/line_descriptor/array32.hpp index 41f65e33d..816d032cc 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor/array32.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor/array32.hpp @@ -45,52 +45,52 @@ #include "types.hpp" -class Array32 { +class Array32 +{ private: - static double ARRAY_RESIZE_FACTOR; - static double ARRAY_RESIZE_ADD_FACTOR; + static double ARRAY_RESIZE_FACTOR; + static double ARRAY_RESIZE_ADD_FACTOR; public: - /* set ARRAY_RESIZE_FACTOR */ - static void setArrayResizeFactor(double arf); + /* set ARRAY_RESIZE_FACTOR */ + static void setArrayResizeFactor( double arf ); - /* constructor */ - Array32(); + /* constructor */ + Array32(); - /* destructor */ - ~Array32(); + /* destructor */ + ~Array32(); - /* cleaning function used in destructor */ - void cleanup(); + /* cleaning function used in destructor */ + void cleanup(); - /* push data */ - void push(UINT32 data); + /* push data */ + void push( UINT32 data ); - /* insert data at given index */ - void insert(UINT32 index, UINT32 data); + /* insert data at given index */ + void insert( UINT32 index, UINT32 data ); - /* return data */ - UINT32* data(); + /* return data */ + UINT32* data(); - /* return data size */ - UINT32 size(); + /* return data size */ + UINT32 size(); - /* return capacity */ - UINT32 capacity(); + /* return capacity */ + UINT32 capacity(); - /* definition of operator = */ - void operator= (const Array32&); + /* definition of operator = */ + void operator=( const Array32& ); - /* print data */ - void print(); + /* print data */ + void print(); - /* initializer */ - void init(int size); - - /* data */ - UINT32 *arr; + /* initializer */ + void init( int size ); + /* data */ + UINT32 *arr; }; diff --git a/modules/line_descriptor/include/opencv2/line_descriptor/bitarray.hpp b/modules/line_descriptor/include/opencv2/line_descriptor/bitarray.hpp index 86472e0e5..7f1eb9b45 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor/bitarray.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor/bitarray.hpp @@ -49,59 +49,62 @@ #include /* class defining a sequence of bits */ -class bitarray { +class bitarray +{ public: - /* pointer to bits sequence and sequence's length */ - UINT32 *arr; - UINT32 length; - - /* constructor setting default values */ - bitarray() - { - arr = NULL; - length = 0; - } - - /* constructor setting sequence's length */ - bitarray(UINT64 _bits) { - init(_bits); - } - - /* initializer of private fields */ - void init(UINT64 _bits) - { - length = (UINT32)ceil(_bits/32.00); - arr = new UINT32[length]; - erase(); - } - - /* destructor */ - ~bitarray() { - if (arr) - delete[] arr; - } - - inline void flip(UINT64 index) - { - arr[index >> 5] ^= ((UINT32)0x01) << (index % 32); - } + /* pointer to bits sequence and sequence's length */ + UINT32 *arr; + UINT32 length; - inline void set(UINT64 index) - { - arr[index >> 5] |= ((UINT32)0x01) << (index % 32); - } - - inline UINT8 get(UINT64 index) - { - return (arr[index >> 5] & (((UINT32)0x01) << (index % 32))) != 0; - } - - /* reserve menory for an UINT32 */ - inline void erase() - { - memset(arr, 0, sizeof(UINT32) * length); - } + /* constructor setting default values */ + bitarray() + { + arr = NULL; + length = 0; + } + + /* constructor setting sequence's length */ + bitarray( UINT64 _bits ) + { + init( _bits ); + } + + /* initializer of private fields */ + void init( UINT64 _bits ) + { + length = (UINT32) ceil( _bits / 32.00 ); + arr = new UINT32[length]; + erase(); + } + + /* destructor */ + ~bitarray() + { + if( arr ) + delete[] arr; + } + + inline void flip( UINT64 index ) + { + arr[index >> 5] ^= ( (UINT32) 0x01 ) << ( index % 32 ); + } + + inline void set( UINT64 index ) + { + arr[index >> 5] |= ( (UINT32) 0x01 ) << ( index % 32 ); + } + + inline UINT8 get( UINT64 index ) + { + return ( arr[index >> 5] & ( ( (UINT32) 0x01 ) << ( index % 32 ) ) ) != 0; + } + + /* reserve menory for an UINT32 */ + inline void erase() + { + memset( arr, 0, sizeof(UINT32) * length ); + } }; diff --git a/modules/line_descriptor/include/opencv2/line_descriptor/bitops.hpp b/modules/line_descriptor/include/opencv2/line_descriptor/bitops.hpp index 1a3ab5f79..dbc9d4474 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor/bitops.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor/bitops.hpp @@ -49,129 +49,114 @@ #include "precomp.hpp" /* LUT */ -const int lookup [] = {0,1,1,2,1,2,2,3,1,2,2,3,2,3,3,4,1,2, - 2,3,2,3,3,4,2,3,3,4,3,4,4,5,1,2,2,3, - 2,3,3,4,2,3,3,4,3,4,4,5,2,3,3,4,3,4, - 4,5,3,4,4,5,4,5,5,6,1,2,2,3,2,3,3,4, - 2,3,3,4,3,4,4,5,2,3,3,4,3,4,4,5,3,4, - 4,5,4,5,5,6,2,3,3,4,3,4,4,5,3,4,4,5, - 4,5,5,6,3,4,4,5,4,5,5,6,4,5,5,6,5,6, - 6,7,1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5, - 2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,2,3, - 3,4,3,4,4,5,3,4,4,5,4,5,5,6,3,4,4,5, - 4,5,5,6,4,5,5,6,5,6,6,7,2,3,3,4,3,4, - 4,5,3,4,4,5,4,5,5,6,3,4,4,5,4,5,5,6, - 4,5,5,6,5,6,6,7,3,4,4,5,4,5,5,6,4,5, - 5,6,5,6,6,7,4,5,5,6,5,6,6,7,5,6,6,7, - 6,7,7,8}; +const int lookup[] = +{ 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, + 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 2, 3, + 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, + 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 2, 3, 3, 4, + 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, + 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8 }; /*matching function */ -inline int match(UINT8*P, UINT8*Q, int codelb) +inline int match( UINT8*P, UINT8*Q, int codelb ) { - switch(codelb) - { - case 4: // 32 bit - return popcnt(*(UINT32*)P ^ *(UINT32*)Q); - break; - case 8: // 64 bit - return popcntll(((UINT64*)P)[0] ^ ((UINT64*)Q)[0]); - break; - case 16: // 128 bit - return popcntll(((UINT64*)P)[0] ^ ((UINT64*)Q)[0]) \ - + popcntll(((UINT64*)P)[1] ^ ((UINT64*)Q)[1]); - break; - case 32: // 256 bit - return popcntll(((UINT64*)P)[0] ^ ((UINT64*)Q)[0]) \ - + popcntll(((UINT64*)P)[1] ^ ((UINT64*)Q)[1]) \ - + popcntll(((UINT64*)P)[2] ^ ((UINT64*)Q)[2]) \ - + popcntll(((UINT64*)P)[3] ^ ((UINT64*)Q)[3]); - break; - case 64: // 512 bit - return popcntll(((UINT64*)P)[0] ^ ((UINT64*)Q)[0]) \ - + popcntll(((UINT64*)P)[1] ^ ((UINT64*)Q)[1]) \ - + popcntll(((UINT64*)P)[2] ^ ((UINT64*)Q)[2]) \ - + popcntll(((UINT64*)P)[3] ^ ((UINT64*)Q)[3]) \ - + popcntll(((UINT64*)P)[4] ^ ((UINT64*)Q)[4]) \ - + popcntll(((UINT64*)P)[5] ^ ((UINT64*)Q)[5]) \ - + popcntll(((UINT64*)P)[6] ^ ((UINT64*)Q)[6]) \ - + popcntll(((UINT64*)P)[7] ^ ((UINT64*)Q)[7]); - break; - default: - int output = 0; - for (int i=0; i> b; + nbits -= b; - for (int i=0; i> b; - nbits -= b; - - if (i == mplus-1) - { - b--; /* b <= 63 */ - mask = ((UINT64_1 << b) - UINT64_1); - } + b--; /* b <= 63 */ + mask = ( ( UINT64_1 << b ) - UINT64_1 ); } + } } /* generates the next binary code (in alphabetical order) with the - same number of ones as the input x. Taken from - http://www.geeksforgeeks.org/archives/10375 */ -inline UINT64 next_set_of_n_elements(UINT64 x) + same number of ones as the input x. Taken from + http://www.geeksforgeeks.org/archives/10375 */ +inline UINT64 next_set_of_n_elements( UINT64 x ) { - UINT64 smallest, ripple, new_smallest; - - smallest = x & -x; - ripple = x + smallest; - new_smallest = x ^ ripple; - new_smallest = new_smallest / smallest; - new_smallest >>= 2; - return ripple | new_smallest; + UINT64 smallest, ripple, new_smallest; + + smallest = x & -x; + ripple = x + smallest; + new_smallest = x ^ ripple; + new_smallest = new_smallest / smallest; + new_smallest >>= 2; + return ripple | new_smallest; } /* print code */ -inline void print_code(UINT64 tmp, int b) +inline void print_code( UINT64 tmp, int b ) { - for (int j=(b-1); j>=0; j--) - { - printf("%llu", (long long int) tmp/(1 << j)); - tmp = tmp - (tmp/(1 << j)) * (1 << j); - } + for ( int j = ( b - 1 ); j >= 0; j-- ) + { + printf( "%llu", (long long int) tmp / ( 1 << j ) ); + tmp = tmp - ( tmp / ( 1 << j ) ) * ( 1 << j ); + } - printf("\n"); + printf( "\n" ); } -inline UINT64 choose(int n, int r) +inline UINT64 choose( int n, int r ) { - UINT64 nchooser = 1; - for (int k=0; k < r; k++) - { - nchooser *= n-k; - nchooser /= k+1; - } - - return nchooser; + UINT64 nchooser = 1; + for ( int k = 0; k < r; k++ ) + { + nchooser *= n - k; + nchooser /= k + 1; + } + + return nchooser; } #endif diff --git a/modules/line_descriptor/include/opencv2/line_descriptor/bucket_group.hpp b/modules/line_descriptor/include/opencv2/line_descriptor/bucket_group.hpp index 5374c31a5..e71c2223c 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor/bucket_group.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor/bucket_group.hpp @@ -47,24 +47,25 @@ #include "array32.hpp" #include "bitarray.hpp" -class BucketGroup { +class BucketGroup +{ public: - /* constructor */ - BucketGroup(); + /* constructor */ + BucketGroup(); - /* destructor */ - ~BucketGroup(); + /* destructor */ + ~BucketGroup(); - /* insert data into the bucket */ - void insert(int subindex, UINT32 data); + /* insert data into the bucket */ + void insert( int subindex, UINT32 data ); - /* perform a query to the bucket */ - UINT32* query(int subindex, int *size); + /* perform a query to the bucket */ + UINT32* query( int subindex, int *size ); - /* data fields */ - UINT32 empty; - Array32 *group; + /* data fields */ + UINT32 empty; + Array32 *group; }; diff --git a/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp b/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp index b66da3a91..bd7155a83 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp @@ -39,7 +39,6 @@ // //M*/ - #ifndef __OPENCV_DESCRIPTOR_HPP__ #define __OPENCV_DESCRIPTOR_HPP__ @@ -55,342 +54,303 @@ namespace cv { - class CV_EXPORTS_W KeyLine - { - public: - /* orientation of the line */ - float angle; +class CV_EXPORTS_W KeyLine +{ + public: + /* orientation of the line */ + float angle; + + /* object ID, that can be used to cluster keylines by the line they represent */ + int class_id; + + /* octave (pyramid layer), from which the keyline has been extracted */ + int octave; + + /* coordinates of the middlepoint */ + Point pt; + + /* the response, by which the strongest keylines have been selected. + It's represented by the ratio between line's length and maximum between + image's width and height */ + float response; + + /* minimum area containing line */ + float size; + + /* lines's extremes in original image */ + float startPointX; + float startPointY; + float endPointX; + float endPointY; + + /* line's extremes in image it was extracted from */ + float sPointInOctaveX; + float sPointInOctaveY; + float ePointInOctaveX; + float ePointInOctaveY; - /* object ID, that can be used to cluster keylines by the line they represent */ - int class_id; + /* the length of line */ + float lineLength; - /* octave (pyramid layer), from which the keyline has been extracted */ - int octave; + /* number of pixels covered by the line */ + unsigned int numOfPixels; - /* coordinates of the middlepoint */ - Point pt; + /* constructor */ + KeyLine() + { + } +}; - /* the response, by which the strongest keylines have been selected. - It's represented by the ratio between line's length and maximum between - image's width and height */ - float response; +class CV_EXPORTS_W BinaryDescriptor : public Algorithm +{ - /* minimum area containing line */ - float size; + public: + struct CV_EXPORTS_W_SIMPLE Params + { + CV_WRAP + Params(); - /* lines's extremes in original image */ - float startPointX; - float startPointY; - float endPointX; - float endPointY; + /* the number of image octaves (default = 1) */ + CV_PROP_RW + int numOfOctave_; - /* line's extremes in image it was extracted from */ - float sPointInOctaveX; - float sPointInOctaveY; - float ePointInOctaveX; - float ePointInOctaveY; + /* the width of band; (default: 7) */ + CV_PROP_RW + int widthOfBand_; - /* the length of line */ - float lineLength; + /* image's reduction ratio in construction of Gaussian pyramids */ + CV_PROP_RW + int reductionRatio; - /* number of pixels covered by the line */ - unsigned int numOfPixels; + /* read parameters from a FileNode object and store them (struct function) */ + void read( const FileNode& fn ); - /* constructor */ - KeyLine(){} - }; + /* store parameters to a FileStorage object (struct function) */ + void write( FileStorage& fs ) const; - class CV_EXPORTS_W BinaryDescriptor: public Algorithm - { + }; - public: - struct CV_EXPORTS_W_SIMPLE Params{ - CV_WRAP Params(); + /* constructor */ + CV_WRAP + BinaryDescriptor( const BinaryDescriptor::Params ¶meters = BinaryDescriptor::Params() ); - /* the number of image octaves (default = 5) */ - CV_PROP_RW int numOfOctave_; + /* constructors with smart pointers */ + static Ptr createBinaryDescriptor(); + static Ptr createBinaryDescriptor( Params parameters ); - /* the width of band; (default: 7) */ - CV_PROP_RW int widthOfBand_; + /* destructor */ + ~BinaryDescriptor(); - /* image's reduction ratio in construction of Gaussian pyramids */ - CV_PROP_RW int reductionRatio; + /* setters and getters */ + int getNumOfOctaves(); + void setNumOfOctaves( int octaves ); + int getWidthOfBand(); + void setWidthOfBand( int width ); + int getReductionRatio(); + void setReductionRatio( int rRatio ); - /* read parameters from a FileNode object and store them (struct function) */ - void read( const FileNode& fn ); + /* read parameters from a FileNode object and store them (class function ) */ + virtual void read( const cv::FileNode& fn ); - /* store parameters to a FileStorage object (struct function) */ - void write( FileStorage& fs ) const; + /* store parameters to a FileStorage object (class function) */ + virtual void write( cv::FileStorage& fs ) const; - }; + /* requires line detection (only one image) */ + CV_WRAP + void detect( const Mat& image, CV_OUT std::vector& keypoints, const Mat& mask = Mat() ); - /* constructor */ - CV_WRAP BinaryDescriptor(const BinaryDescriptor::Params ¶meters = - BinaryDescriptor::Params()); + /* requires line detection (more than one image) */ + void detect( const std::vector& images, std::vector >& keylines, const std::vector& masks = + std::vector() ) const; - /* constructors with smart pointers */ - static Ptr createBinaryDescriptor(); - static Ptr createBinaryDescriptor(Params parameters); + /* requires descriptors computation (only one image) */ + CV_WRAP + void compute( const Mat& image, CV_OUT CV_IN_OUT std::vector& keylines, CV_OUT Mat& descriptors ) const; - /* destructor */ - ~BinaryDescriptor(); + /* requires descriptors computation (more than one image) */ + void compute( const std::vector& images, std::vector >& keylines, std::vector& descriptors ) const; - /* setters and getters */ - int getNumOfOctaves(); - void setNumOfOctaves(int octaves); - int getWidthOfBand(); - void setWidthOfBand(int width); - int getReductionRatio(); - void setReductionRatio(int rRatio); + /*return descriptor size */ + int descriptorSize() const; - /* read parameters from a FileNode object and store them (class function ) */ - virtual void read( const cv::FileNode& fn ); + /* return data type */ + int descriptorType() const; - /* store parameters to a FileStorage object (class function) */ - virtual void write( cv::FileStorage& fs ) const; + /* return norm mode */ + int defaultNorm() const; - /* requires line detection (only one image) */ - CV_WRAP void detect( const Mat& image, - CV_OUT std::vector& keypoints, - const Mat& mask=Mat() ); + /* check whether Gaussian pyramids were created */ + bool empty() const; - /* requires line detection (more than one image) */ - void detect( const std::vector& images, - std::vector >& keylines, - const std::vector& masks=std::vector() ) const; + /* definition of operator () */ + CV_WRAP_AS(detectAndCompute) + virtual void operator()( InputArray image, InputArray mask, CV_OUT std::vector& keylines, OutputArray descriptors, + bool useProvidedKeyLines = false ) const; - /* requires descriptors computation (only one image) */ - CV_WRAP void compute( const Mat& image, - CV_OUT CV_IN_OUT std::vector& keylines, - CV_OUT Mat& descriptors ) const; + protected: + /* implementation of line detection */ + virtual void detectImpl( const Mat& imageSrc, std::vector& keylines, const Mat& mask = Mat() ) const; - /* requires descriptors computation (more than one image) */ - void compute( const std::vector& images, - std::vector >& keylines, - std::vector& descriptors ) const; + /* implementation of descriptors' computation */ + virtual void computeImpl( const Mat& imageSrc, std::vector& keylines, Mat& descriptors ) const; - /*return descriptor size */ - int descriptorSize() const; + /* function inherited from Algorithm */ + AlgorithmInfo* info() const; - /* return data type */ - int descriptorType() const; + private: + /* conversion of an LBD descriptor to its binary representation */ + unsigned char binaryConversion( float* f1, float* f2 ); - /* return norm mode */ - int defaultNorm() const; + /* compute LBD descriptors */ + int computeLBD( ScaleLines &keyLines ); - /* check whether Gaussian pyramids were created */ - bool empty() const; + /* compute Gaussian pyramid of input image */ + void computeGaussianPyramid( const Mat& image ); - /* definition of operator () */ - CV_WRAP_AS(detectAndCompute) virtual void operator()( InputArray image, - InputArray mask, - CV_OUT std::vector& keylines, - OutputArray descriptors, - bool useProvidedKeyLines=false ) const; + /* gather lines in groups. + Each group contains the same line, detected in different octaves */ + int OctaveKeyLines( ScaleLines &keyLines ); + /* get coefficients of line passing by two points (in line_extremes) */ + void getLineParameters( cv::Vec4i &line_extremes, cv::Vec3i &lineParams ); - protected: - /* implementation of line detection */ - virtual void detectImpl( const Mat& imageSrc, - std::vector& keylines, - const Mat& mask=Mat() ) const; + /* compute the angle between line and X axis */ + float getLineDirection( cv::Vec3i &lineParams ); + /* the local gaussian coefficient applied to the orthogonal line direction within each band */ + std::vector gaussCoefL_; - /* implementation of descriptors' computation */ - virtual void computeImpl( const Mat& imageSrc, - std::vector& keylines, - Mat& descriptors ) const; + /* the global gaussian coefficient applied to each Row within line support region */ + std::vector gaussCoefG_; - /* function inherited from Algorithm */ - AlgorithmInfo* info() const; + /* vector to store horizontal and vertical derivatives of octave images */ + std::vector dxImg_vector, dyImg_vector; - private: - /* conversion of an LBD descriptor to its binary representation */ - unsigned char binaryConversion(float* f1, float* f2); + /* vectot to store sizes of octave images */ + std::vector images_sizes; - /* compute LBD descriptors */ - int computeLBD(ScaleLines &keyLines); + /* structure to store lines extracted from each octave image */ + std::vector > extractedLines; - /* compute Gaussian pyramid of input image */ - void computeGaussianPyramid(const Mat& image); + /* descriptor parameters */ + Params params; - /* gather lines in groups. - Each group contains the same line, detected in different octaves */ - int OctaveKeyLines(ScaleLines &keyLines); + /* vector to store the Gaussian pyramid od an input image */ + std::vector octaveImages; - /* get coefficients of line passing by two points (in line_extremes) */ - void getLineParameters(cv::Vec4i &line_extremes, cv::Vec3i &lineParams); +}; - /* compute the angle between line and X axis */ - float getLineDirection(cv::Vec3i &lineParams); - - /* the local gaussian coefficient applied to the orthogonal line direction within each band */ - std::vector gaussCoefL_; - - /* the global gaussian coefficient applied to each Row within line support region */ - std::vector gaussCoefG_; - - /* vector to store horizontal and vertical derivatives of octave images */ - std::vector dxImg_vector, dyImg_vector; - - /* vectot to store sizes of octave images */ - std::vector images_sizes; - - /* structure to store lines extracted from each octave image */ - std::vector > extractedLines; - - /* descriptor parameters */ - Params params; +class CV_EXPORTS_W BinaryDescriptorMatcher : public Algorithm +{ - /* vector to store the Gaussian pyramid od an input image */ - std::vector octaveImages; + public: + /* for every input descriptor, + find the best matching one (for a pair of images) */ + void match( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector& matches, const Mat& mask = Mat() ) const; - }; + /* for every input descriptor, + find the best matching one (from one image to a set) */ + void match( const Mat& queryDescriptors, std::vector& matches, const std::vector& masks = std::vector() ); - class CV_EXPORTS_W BinaryDescriptorMatcher: public Algorithm - { + /* for every input descriptor, + find the best k matching descriptors (for a pair of images) */ + void knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector >& matches, int k, const Mat& mask = Mat(), + bool compactResult = false ) const; - public: - /* for every input descriptor, - find the best matching one (for a pair of images) */ - void match( const Mat& queryDescriptors, - const Mat& trainDescriptors, - std::vector& matches, - const Mat& mask=Mat() ) const; + /* for every input descriptor, + find the best k matching descriptors (from one image to a set) */ + void knnMatch( const Mat& queryDescriptors, std::vector >& matches, int k, const std::vector& masks = std::vector(), + bool compactResult = false ); - /* for every input descriptor, - find the best matching one (from one image to a set) */ - void match( const Mat& queryDescriptors, - std::vector& matches, - const std::vector& masks=std::vector() ); + /* for every input desciptor, find all the ones falling in a + certaing atching radius (for a pair of images) */ + void radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector >& matches, float maxDistance, + const Mat& mask = Mat(), bool compactResult = false ) const; - /* for every input descriptor, - find the best k matching descriptors (for a pair of images) */ - void knnMatch( const Mat& queryDescriptors, - const Mat& trainDescriptors, - std::vector >& matches, - int k, - const Mat& mask=Mat(), - bool compactResult=false ) const; + /* for every input desciptor, find all the ones falling in a + certaing atching radius (from one image to a set) */ + void radiusMatch( const Mat& queryDescriptors, std::vector >& matches, float maxDistance, const std::vector& masks = + std::vector(), + bool compactResult = false ); - /* for every input descriptor, - find the best k matching descriptors (from one image to a set) */ - void knnMatch( const Mat& queryDescriptors, - std::vector >& matches, - int k, - const std::vector& masks=std::vector(), - bool compactResult=false ); + /* store new descriptors to be inserted in dataset */ + void add( const std::vector& descriptors ); - /* for every input desciptor, find all the ones falling in a - certaing atching radius (for a pair of images) */ - void radiusMatch( const Mat& queryDescriptors, - const Mat& trainDescriptors, - std::vector >& matches, - float maxDistance, - const Mat& mask=Mat(), - bool compactResult=false ) const; + /* store new descriptors into dataset */ + void train(); - /* for every input desciptor, find all the ones falling in a - certaing atching radius (from one image to a set) */ - void radiusMatch( const Mat& queryDescriptors, - std::vector >& matches, - float maxDistance, - const std::vector& masks=std::vector(), - bool compactResult=false ); + /* constructor with smart pointer */ + static Ptr createBinaryDescriptorMatcher(); - /* store new descriptors to be inserted in dataset */ - void add( const std::vector& descriptors ); + /* clear dataset and internal data */ + void clear(); - /* store new descriptors into dataset */ - void train(); + /* constructor */ + BinaryDescriptorMatcher(); - /* constructor with smart pointer */ - static Ptr createBinaryDescriptorMatcher(); + /* desctructor */ + ~BinaryDescriptorMatcher() + { + } - /* clear dataset and internal data */ - void clear(); + protected: + /* function inherited from Algorithm */ + AlgorithmInfo* info() const; - /* constructor */ - BinaryDescriptorMatcher(); + private: + /* retrieve Hamming distances */ + void checkKDistances( UINT32 * numres, int k, std::vector& k_distances, int row, int string_length ) const; - /* desctructor */ - ~BinaryDescriptorMatcher(){} + /* matrix to store new descriptors */ + Mat descriptorsMat; - protected: - /* function inherited from Algorithm */ - AlgorithmInfo* info() const; + /* map storing where each bunch of descriptors benins in DS */ + std::map indexesMap; - private: - /* retrieve Hamming distances */ - void checkKDistances(UINT32 * numres, - int k, - std::vector& k_distances, - int row, - int string_length) const; + /* internal MiHaser representing dataset */ + Mihasher* dataset; - /* matrix to store new descriptors */ - Mat descriptorsMat; + /* index from which next added descriptors' bunch must begin */ + int nextAddedIndex; - /* map storing where each bunch of descriptors benins in DS */ - std::map indexesMap; - - /* internal MiHaser representing dataset */ - Mihasher* dataset; - - /* index from which next added descriptors' bunch must begin */ - int nextAddedIndex; - - /* number of images whose descriptors are stored in DS */ - int numImages; - - /* number of descriptors in dataset */ - int descrInDS; - - }; + /* number of images whose descriptors are stored in DS */ + int numImages; + /* number of descriptors in dataset */ + int descrInDS; + +}; /* -------------------------------------------------------------------------------------------- - UTILITY FUNCTIONS - -------------------------------------------------------------------------------------------- */ + UTILITY FUNCTIONS + -------------------------------------------------------------------------------------------- */ /* struct for drawing options */ struct CV_EXPORTS DrawLinesMatchesFlags { - enum - { - DEFAULT = 0, // Output image matrix will be created (Mat::create), - // i.e. existing memory of output image may be reused. - // Two source images, matches, and single keylines - // will be drawn. - DRAW_OVER_OUTIMG = 1, // Output image matrix will not be - // created (using Mat::create). Matches will be drawn - // on existing content of output image. - NOT_DRAW_SINGLE_LINES = 2 // Single keylines will not 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 images, matches, and single keylines + // will be drawn. + DRAW_OVER_OUTIMG = 1, // Output image matrix will not be + // created (using Mat::create). Matches will be drawn + // on existing content of output image. + NOT_DRAW_SINGLE_LINES = 2 // Single keylines will not be drawn. + }; }; /* draw matches between two images */ -CV_EXPORTS_W void drawLineMatches( const Mat& img1, - const std::vector& keylines1, - const Mat& img2, - const std::vector& keylines2, - const std::vector& matches1to2, - Mat& outImg, - const Scalar& matchColor=Scalar::all(-1), - const Scalar& singleLineColor=Scalar::all(-1), - const std::vector& matchesMask=std::vector(), - int flags=DrawLinesMatchesFlags::DEFAULT ); +CV_EXPORTS_W void drawLineMatches( const Mat& img1, const std::vector& keylines1, const Mat& img2, const std::vector& keylines2, + const std::vector& matches1to2, Mat& outImg, const Scalar& matchColor = Scalar::all( -1 ), + const Scalar& singleLineColor = Scalar::all( -1 ), const std::vector& matchesMask = std::vector(), + int flags = DrawLinesMatchesFlags::DEFAULT ); /* draw extracted lines on original image */ -CV_EXPORTS_W void drawKeylines( const Mat& image, - const std::vector& keylines, - Mat& outImage, - const Scalar& color=Scalar::all(-1), - int flags=DrawLinesMatchesFlags::DEFAULT ); +CV_EXPORTS_W void drawKeylines( const Mat& image, const std::vector& keylines, Mat& outImage, const Scalar& color = Scalar::all( -1 ), + int flags = DrawLinesMatchesFlags::DEFAULT ); } - - - #endif diff --git a/modules/line_descriptor/include/opencv2/line_descriptor/line_structure.hpp b/modules/line_descriptor/include/opencv2/line_descriptor/line_structure.hpp index bfb859ca2..78f4db3db 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor/line_structure.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor/line_structure.hpp @@ -5,38 +5,38 @@ copy or use the software. - License Agreement - For Open Source Computer Vision Library - -Copyright (C) 2011-2012, Lilian Zhang, all rights reserved. -Copyright (C) 2013, Manuele Tamburrano, Stefano Fabri, all rights reserved. -Copyright (C) 2014, Biagio Montesano, 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. - - * The name of the copyright holders may not be used to endorse or promote products - derived from this software without specific prior written permission. - -This software is provided by the copyright holders and contributors "as is" and -any express or implied warranties, including, but not limited to, the implied -warranties of merchantability and fitness for a particular purpose are disclaimed. -In no event shall the Intel Corporation or contributors be liable for any direct, -indirect, incidental, special, exemplary, or consequential damages -(including, but not limited to, procurement of substitute goods or services; -loss of use, data, or profits; or business interruption) however caused -and on any theory of liability, whether in contract, strict liability, -or tort (including negligence or otherwise) arising in any way out of -the use of this software, even if advised of the possibility of such damage. -*/ + License Agreement + For Open Source Computer Vision Library + + Copyright (C) 2011-2012, Lilian Zhang, all rights reserved. + Copyright (C) 2013, Manuele Tamburrano, Stefano Fabri, all rights reserved. + Copyright (C) 2014, Biagio Montesano, 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. + + * The name of the copyright holders may not be used to endorse or promote products + derived from this software without specific prior written permission. + + This software is provided by the copyright holders and contributors "as is" and + any express or implied warranties, including, but not limited to, the implied + warranties of merchantability and fitness for a particular purpose are disclaimed. + In no event shall the Intel Corporation or contributors be liable for any direct, + indirect, incidental, special, exemplary, or consequential damages + (including, but not limited to, procurement of substitute goods or services; + loss of use, data, or profits; or business interruption) however caused + and on any theory of liability, whether in contract, strict liability, + or tort (including negligence or otherwise) arising in any way out of + the use of this software, even if advised of the possibility of such damage. + */ #ifndef __OPENCV_LINE_STRUCTURE_HH_ #define __OPENCV_LINE_STRUCTURE_HH_ @@ -44,42 +44,43 @@ the use of this software, even if advised of the possibility of such damage. #include /* struct to represent lines extracted from an octave */ -struct OctaveLine{ - unsigned int octaveCount;//the octave which this line is detected - unsigned int lineIDInOctave;//the line ID in that octave image - unsigned int lineIDInScaleLineVec;//the line ID in Scale line vector - float lineLength; //the length of line in original image scale +struct OctaveLine +{ + unsigned int octaveCount; //the octave which this line is detected + unsigned int lineIDInOctave; //the line ID in that octave image + unsigned int lineIDInScaleLineVec; //the line ID in Scale line vector + float lineLength; //the length of line in original image scale }; // A 2D line (normal equation parameters). struct SingleLine { - //note: rho and theta are based on coordinate origin, i.e. the top-left corner of image - double rho;//unit: pixel length - double theta;//unit: rad - double linePointX;// = rho * cos(theta); - double linePointY;// = rho * sin(theta); - //for EndPoints, the coordinate origin is the top-left corner of image. - double startPointX; - double startPointY; - double endPointX; - double endPointY; - //direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis. - double direction; - //mean gradient magnitude - double gradientMagnitude; - //mean gray value of pixels in dark side of line - double darkSideGrayValue; - //mean gray value of pixels in light side of line - double lightSideGrayValue; - //the length of line - double lineLength; - //the width of line; - double width; - //number of pixels - int numOfPixels; - //the decriptor of line - std::vector descriptor; + //note: rho and theta are based on coordinate origin, i.e. the top-left corner of image + double rho; //unit: pixel length + double theta; //unit: rad + double linePointX; // = rho * cos(theta); + double linePointY; // = rho * sin(theta); + //for EndPoints, the coordinate origin is the top-left corner of image. + double startPointX; + double startPointY; + double endPointX; + double endPointY; + //direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis. + double direction; + //mean gradient magnitude + double gradientMagnitude; + //mean gray value of pixels in dark side of line + double darkSideGrayValue; + //mean gray value of pixels in light side of line + double lightSideGrayValue; + //the length of line + double lineLength; + //the width of line; + double width; + //number of pixels + int numOfPixels; + //the decriptor of line + std::vector descriptor; }; // Specifies a vector of lines. @@ -87,29 +88,29 @@ typedef std::vector Lines_list; struct OctaveSingleLine { - /*endPoints, the coordinate origin is the top-left corner of the original image. - *startPointX = sPointInOctaveX * (factor)^octaveCount; */ - float startPointX; - float startPointY; - float endPointX; - float endPointY; - //endPoints, the coordinate origin is the top-left corner of the octave image. - float sPointInOctaveX; - float sPointInOctaveY; - float ePointInOctaveX; - float ePointInOctaveY; - //direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis. - float direction; - //the summation of gradient magnitudes of pixels on lines - float salience; - //the length of line - float lineLength; - //number of pixels - unsigned int numOfPixels; - //the octave which this line is detected - unsigned int octaveCount; - //the decriptor of line - std::vector descriptor; + /*endPoints, the coordinate origin is the top-left corner of the original image. + *startPointX = sPointInOctaveX * (factor)^octaveCount; */ + float startPointX; + float startPointY; + float endPointX; + float endPointY; + //endPoints, the coordinate origin is the top-left corner of the octave image. + float sPointInOctaveX; + float sPointInOctaveY; + float ePointInOctaveX; + float ePointInOctaveY; + //direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis. + float direction; + //the summation of gradient magnitudes of pixels on lines + float salience; + //the length of line + float lineLength; + //number of pixels + unsigned int numOfPixels; + //the octave which this line is detected + unsigned int octaveCount; + //the decriptor of line + std::vector descriptor; }; // Specifies a vector of lines. diff --git a/modules/line_descriptor/include/opencv2/line_descriptor/mihasher.hpp b/modules/line_descriptor/include/opencv2/line_descriptor/mihasher.hpp index ea55b1b67..01696db33 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor/mihasher.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor/mihasher.hpp @@ -51,76 +51,76 @@ #include #include - -class Mihasher { +class Mihasher +{ private: - /* Bits per code */ - int B; + /* Bits per code */ + int B; + + /* B/8 */ + int B_over_8; + + /* Bits per chunk (must be less than 64) */ + int b; - /* B/8 */ - int B_over_8; + /* Number of chunks */ + int m; - /* Bits per chunk (must be less than 64) */ - int b; + /* Number of chunks with b bits (have 1 bit more than others) */ + int mplus; - /* Number of chunks */ - int m; + /* Maximum hamming search radius (we use B/2 by default) */ + int D; - /* Number of chunks with b bits (have 1 bit more than others) */ - int mplus; + /* Maximum hamming search radius per substring */ + int d; - /* Maximum hamming search radius (we use B/2 by default) */ - int D; + /* Maximum results to return */ + int K; - /* Maximum hamming search radius per substring */ - int d; + /* Number of codes */ + UINT64 N; - /* Maximum results to return */ - int K; + /* Table of original full-length codes */ + cv::Mat codes; - /* Number of codes */ - UINT64 N; - - /* Table of original full-length codes */ - cv::Mat codes; + /* Counter for eliminating duplicate results (it is not thread safe) */ + bitarray *counter; - /* Counter for eliminating duplicate results (it is not thread safe) */ - bitarray *counter; - - /* Array of m hashtables */ - SparseHashtable *H; - - /* Volume of a b-bit Hamming ball with radius s (for s = 0 to d) */ - UINT32 *xornum; + /* Array of m hashtables */ + SparseHashtable *H; - /* Used within generation of binary codes at a certain Hamming distance */ - int power[100]; + /* Volume of a b-bit Hamming ball with radius s (for s = 0 to d) */ + UINT32 *xornum; + + /* Used within generation of binary codes at a certain Hamming distance */ + int power[100]; public: - - /* constructor */ - Mihasher(); - /* desctructor */ - ~Mihasher(); + /* constructor */ + Mihasher(); + + /* desctructor */ + ~Mihasher(); + + /* constructor 2 */ + Mihasher( int B, int m ); - /* constructor 2 */ - Mihasher(int B, int m); + /* K setter */ + void setK( int K ); - /* K setter */ - void setK(int K); + /* populate tables */ + void populate( cv::Mat & codes, UINT32 N, int dim1codes ); - /* populate tables */ - void populate(cv::Mat & codes, UINT32 N, int dim1codes); + /* execute a batch query */ + void batchquery( UINT32 * results, UINT32 *numres/*, qstat *stats*/, const cv::Mat & q, UINT32 numq, int dim1queries ); - /* execute a batch query */ - void batchquery (UINT32 * results, UINT32 *numres/*, qstat *stats*/,const cv::Mat & q, UINT32 numq, int dim1queries); - private: - /* execute a single query */ - void query(UINT32 * results, UINT32* numres/*, qstat *stats*/, UINT8 *q, UINT64 * chunks, UINT32 * res); + /* execute a single query */ + void query( UINT32 * results, UINT32* numres/*, qstat *stats*/, UINT8 *q, UINT64 * chunks, UINT32 * res ); }; #endif diff --git a/modules/line_descriptor/include/opencv2/line_descriptor/sparse_hashtable.hpp b/modules/line_descriptor/include/opencv2/line_descriptor/sparse_hashtable.hpp index 09b5c9c64..ee754c42b 100644 --- a/modules/line_descriptor/include/opencv2/line_descriptor/sparse_hashtable.hpp +++ b/modules/line_descriptor/include/opencv2/line_descriptor/sparse_hashtable.hpp @@ -51,34 +51,34 @@ class SparseHashtable private: - /* Maximum bits per key before folding the table */ - static const int MAX_B; + /* Maximum bits per key before folding the table */ + static const int MAX_B; - /* Bins (each bin is an Array object for duplicates of the same key) */ - BucketGroup *table; + /* Bins (each bin is an Array object for duplicates of the same key) */ + BucketGroup *table; public: - /* constructor */ - SparseHashtable(); + /* constructor */ + SparseHashtable(); - /* destructor */ - ~SparseHashtable(); + /* destructor */ + ~SparseHashtable(); - /* initializer */ - int init(int _b); - - /* insert data */ - void insert(UINT64 index, UINT32 data); + /* initializer */ + int init( int _b ); - /* query data */ - UINT32* query(UINT64 index, int* size); + /* insert data */ + void insert( UINT64 index, UINT32 data ); - /* Bits per index */ - int b; + /* query data */ + UINT32* query( UINT64 index, int* size ); - /* Number of bins */ - UINT64 size; + /* Bits per index */ + int b; + + /* Number of bins */ + UINT64 size; }; diff --git a/modules/line_descriptor/samples/bak.txt b/modules/line_descriptor/samples/bak.txt deleted file mode 100644 index 0f3bfe7d8..000000000 --- a/modules/line_descriptor/samples/bak.txt +++ /dev/null @@ -1,9 +0,0 @@ -cmake_minimum_required(VERSION 2.8) -project(line_descriptor) -find_package(OpenCV REQUIRED) - -set(SOURCES lines_extraction.cpp compute_descriptors.cpp ../src/BinaryDescriptor.cpp) - -include_directories(${OpenCV_INCLUDE_DIRS}) -add_executable(lines_extraction compute_descriptors ${SOURCES} ${HEADERS}) -target_link_libraries(lines_extraction compute_descriptors ${OpenCV_LIBS}) diff --git a/modules/line_descriptor/samples/compute_descriptors.cpp b/modules/line_descriptor/samples/compute_descriptors.cpp index c9cc1994d..81eb376bd 100644 --- a/modules/line_descriptor/samples/compute_descriptors.cpp +++ b/modules/line_descriptor/samples/compute_descriptors.cpp @@ -41,7 +41,6 @@ #include - #include "opencv2/core/utility.hpp" #include "opencv2/core/private.hpp" #include @@ -53,72 +52,55 @@ using namespace cv; static const char* keys = -{ - "{@image_path | | Image path }" -}; +{ "{@image_path | | Image path }" }; static void help() { - std::cout << "\nThis example shows the functionalities of lines extraction " << - "and descriptors computation furnished by BinaryDescriptor class\n" << - "Please, run this sample using a command in the form\n" << - "./example_line_descriptor_compute_descriptors " - << std::endl; -} - -inline void writeMat(cv::Mat m, std::string name, int n) -{ - std::stringstream ss; - std::string s; - ss << n; - ss >> s; - std::string fileNameConf = name + s; - cv::FileStorage fsConf(fileNameConf, cv::FileStorage::WRITE); - fsConf << "m" << m; - - fsConf.release(); + std::cout << "\nThis example shows the functionalities of lines extraction " << "and descriptors computation furnished by BinaryDescriptor class\n" + << "Please, run this sample using a command in the form\n" << "./example_line_descriptor_compute_descriptors " + << std::endl; } int main( int argc, char** argv ) { - /* get parameters from comand line */ - CommandLineParser parser( argc, argv, keys ); - String image_path = parser.get( 0 ); - - if(image_path.empty()) - { - help(); - return -1; - } - - /* load image */ - cv::Mat imageMat = imread(image_path, 1); - if(imageMat.data == NULL) - { - std::cout << "Error, image could not be loaded. Please, check its path" << std::endl; - } - - /* create a random binary mask */ - cv::Mat mask = Mat::ones(imageMat.size(), CV_8UC1); - - /* create a pointer to a BinaryDescriptor object with default parameters */ - Ptr bd = BinaryDescriptor::createBinaryDescriptor(); - - /* compute lines */ - std::vector keylines; - bd->detect(imageMat, keylines, mask); - - std::vector octave0; - for(size_t i = 0; icompute(imageMat, octave0, descriptors); - writeMat(descriptors, "old_code", 0); + /* get parameters from command line */ + CommandLineParser parser( argc, argv, keys ); + String image_path = parser.get( 0 ); + + if( image_path.empty() ) + { + help(); + return -1; + } + + /* load image */ + cv::Mat imageMat = imread( image_path, 1 ); + if( imageMat.data == NULL ) + { + std::cout << "Error, image could not be loaded. Please, check its path" << std::endl; + } + + /* create a binary mask */ + cv::Mat mask = Mat::ones( imageMat.size(), CV_8UC1 ); + + /* create a pointer to a BinaryDescriptor object with default parameters */ + Ptr bd = BinaryDescriptor::createBinaryDescriptor(); + + /* compute lines */ + std::vector keylines; + bd->detect( imageMat, keylines, mask ); + + /* select only lines from first octave */ + std::vector octave0; + for ( size_t i = 0; i < keylines.size(); i++ ) + { + if( keylines[i].octave == 0 ) + octave0.push_back( keylines[i] ); + } + + /* compute descriptors */ + cv::Mat descriptors; + + bd->compute( imageMat, octave0, descriptors ); } diff --git a/modules/line_descriptor/samples/knn_matching.cpp b/modules/line_descriptor/samples/knn_matching.cpp index 745919f20..f220e7116 100644 --- a/modules/line_descriptor/samples/knn_matching.cpp +++ b/modules/line_descriptor/samples/knn_matching.cpp @@ -53,158 +53,151 @@ using namespace cv; static const char* keys = -{ - "{@image_path1 | | Image path 1 }" - "{@image_path2 | | Image path 2 }" -}; +{ "{@image_path1 | | Image path 1 }" + "{@image_path2 | | Image path 2 }" }; static void help() { - std::cout << "\nThis example shows the functionalities of descriptors matching\n" << - "Please, run this sample using a command in the form\n" << - "./example_line_descriptor_matching " - << "" << std::endl; + std::cout << "\nThis example shows the functionalities of descriptors matching\n" << "Please, run this sample using a command in the form\n" + << "./example_line_descriptor_matching " << "" << std::endl; } /* invert numBits bits in input char */ -uchar invertSingleBits (uchar dividend_char, int numBits) +uchar invertSingleBits( uchar dividend_char, int numBits ) { - std::vector bin_vector; - long dividend; - long bin_num; + std::vector bin_vector; + long dividend; + long bin_num; - /* convert input char to a long */ - dividend = (long)dividend_char; + /* convert input char to a long */ + dividend = (long) dividend_char; - /*if a 0 has been obtained, just generate a 8-bit long vector of zeros */ - if(dividend == 0) - bin_vector = std::vector(8, 0); + /*if a 0 has been obtained, just generate a 8-bit long vector of zeros */ + if( dividend == 0 ) + bin_vector = std::vector( 8, 0 ); - /* else, apply classic decimal to binary conversion */ - else + /* else, apply classic decimal to binary conversion */ + else + { + while ( dividend >= 1 ) { - while ( dividend >= 1 ) - { - bin_num = dividend % 2; - dividend /= 2; - bin_vector.push_back(bin_num); - } + bin_num = dividend % 2; + dividend /= 2; + bin_vector.push_back( bin_num ); } + } - /* ensure that binary vector always has length 8 */ - if(bin_vector.size()<8){ - std::vector zeros (8-bin_vector.size(), 0); - bin_vector.insert(bin_vector.end(), zeros.begin(), zeros.end()); - } + /* ensure that binary vector always has length 8 */ + if( bin_vector.size() < 8 ) + { + std::vector zeros( 8 - bin_vector.size(), 0 ); + bin_vector.insert( bin_vector.end(), zeros.begin(), zeros.end() ); + } - /* invert numBits bits */ - for(int index = 0; index=0; i--) - result += bin_vector[i]*pow(2, i); + /* reconvert to decimal */ + uchar result; + for ( int i = (int) bin_vector.size() - 1; i >= 0; i-- ) + result += bin_vector[i] * pow( 2, i ); - return result; + return result; } int main( int argc, char** argv ) { - /* get parameters from comand line */ - CommandLineParser parser( argc, argv, keys ); - String image_path1 = parser.get( 0 ); - String image_path2 = parser.get( 1 ); - - if(image_path1.empty() || image_path2.empty()) + /* get parameters from comand line */ + CommandLineParser parser( argc, argv, keys ); + String image_path1 = parser.get( 0 ); + String image_path2 = parser.get( 1 ); + + if( image_path1.empty() || image_path2.empty() ) + { + help(); + return -1; + } + + /* load image */ + cv::Mat imageMat1 = imread( image_path1, 1 ); + cv::Mat imageMat2 = imread( image_path2, 1 ); + + if( imageMat1.data == NULL || imageMat2.data == NULL ) + { + std::cout << "Error, images could not be loaded. Please, check their paths" << std::endl; + } + + /* create binary masks */ + cv::Mat mask1 = Mat::ones( imageMat1.size(), CV_8UC1 ); + cv::Mat mask2 = Mat::ones( imageMat2.size(), CV_8UC1 ); + + /* create a pointer to a BinaryDescriptor object with default parameters */ + Ptr bd = BinaryDescriptor::createBinaryDescriptor(); + + /* compute lines */ + std::vector keylines1, keylines2; + bd->detect( imageMat1, keylines1, mask1 ); + bd->detect( imageMat2, keylines2, mask2 ); + + /* compute descriptors */ + cv::Mat descr1, descr2; + bd->compute( imageMat1, keylines1, descr1 ); + bd->compute( imageMat2, keylines2, descr2 ); + + /* create a BinaryDescriptorMatcher object */ + Ptr bdm = BinaryDescriptorMatcher::createBinaryDescriptorMatcher(); + + /* make a copy of descr2 mat */ + Mat descr2Copy = descr1.clone(); + + /* randomly change some bits in original descriptors */ + srand( time( NULL ) ); + + for ( int j = 0; j < descr1.rows; j++ ) + { + /* select a random column */ + int randCol = rand() % 32; + + /* get correspondent data */ + uchar u = descr1.at( j, randCol ); + + /* change bits */ + for ( int k = 1; k <= 5; k++ ) { - help(); - return -1; - } - + /* copy current row to train matrix */ + descr2Copy.push_back( descr1.row( j ) ); - /* load image */ - cv::Mat imageMat1 = imread(image_path1, 1); - cv::Mat imageMat2 = imread(image_path2, 1); + /* invert k bits */ + uchar uc = invertSingleBits( u, k ); - if(imageMat1.data == NULL || imageMat2.data == NULL) - { - std::cout << "Error, images could not be loaded. Please, check their paths" - << std::endl; + /* update current row in train matrix */ + descr2Copy.at( descr2Copy.rows - 1, randCol ) = uc; } + } - /* create binary masks */ - cv::Mat mask1 = Mat::ones(imageMat1.size(), CV_8UC1); - cv::Mat mask2 = Mat::ones(imageMat2.size(), CV_8UC1); - - /* create a pointer to a BinaryDescriptor object with default parameters */ - Ptr bd = BinaryDescriptor::createBinaryDescriptor(); - - /* compute lines */ - std::vector keylines1, keylines2; - bd->detect(imageMat1, keylines1, mask1); - bd->detect(imageMat2, keylines2, mask2); - - /* compute descriptors */ - cv::Mat descr1, descr2; - bd->compute(imageMat1, keylines1, descr1); - bd->compute(imageMat2, keylines2, descr2); + /* prepare a structure to host matches */ + std::vector > matches; - /* create a BinaryDescriptorMatcher object */ - Ptr bdm = BinaryDescriptorMatcher::createBinaryDescriptorMatcher(); + /* require knn match */ + bdm->knnMatch( descr1, descr2, matches, 6 ); - /* make a copy of descr2 mat */ - Mat descr2Copy = descr1.clone(); - - /* randomly change some bits in original descriptors */ - srand (time(NULL)); - - for(int j = 0; j(j, randCol); - - /* change bits */ - for(int k = 1; k<=5; k++) - { - /* copy current row to train matrix */ - descr2Copy.push_back(descr1.row(j)); - - /* invert k bits */ - uchar uc = invertSingleBits(u, k); - - /* update current row in train matrix */ - descr2Copy.at(descr2Copy.rows-1, randCol) = uc; - } - } - - /* prepare a structure to host matches */ - std::vector > matches; - - /* require knn match */ - bdm->knnMatch(descr1, descr2, matches, 6); - - /* visualize matches and Hamming distances */ - for(size_t v = 0; v" << endl; + cout << "\nThis example shows the functionalities of lines extraction " << "furnished by BinaryDescriptor class\n" + << "Please, run this sample using a command in the form\n" << "./example_line_descriptor_lines_extraction " << endl; } int main( int argc, char** argv ) { - /* get parameters from comand line */ - CommandLineParser parser( argc, argv, keys ); - String image_path = parser.get( 0 ); - - if(image_path.empty()) + /* get parameters from comand line */ + CommandLineParser parser( argc, argv, keys ); + String image_path = parser.get( 0 ); + + if( image_path.empty() ) + { + help(); + return -1; + } + + /* load image */ + cv::Mat imageMat = imread( image_path, 1 ); + if( imageMat.data == NULL ) + { + std::cout << "Error, image could not be loaded. Please, check its path" << std::endl; + } + + /* create a ramdom binary mask */ + cv::Mat mask = Mat::ones( imageMat.size(), CV_8UC1 ); + + /* create a pointer to a BinaryDescriptor object with deafult parameters */ + Ptr bd = BinaryDescriptor::createBinaryDescriptor(); + + /* create a structure to store extracted lines */ + vector lines; + + /* extract lines */ + bd->detect( imageMat, lines, mask ); + + /* draw lines extracted from octave 0 */ + cv::Mat output = imageMat.clone(); + if( output.channels() == 1 ) + cvtColor( output, output, COLOR_GRAY2BGR ); + for ( size_t i = 0; i < lines.size(); i++ ) + { + KeyLine kl = lines[i]; + if( kl.octave == 0 ) { - help(); - return -1; - } - - /* load image */ - cv::Mat imageMat = imread(image_path, 1); - if(imageMat.data == NULL) - { - std::cout << "Error, image could not be loaded. Please, check its path" << std::endl; - } + /* get a random color */ + int R = ( rand() % (int) ( 255 + 1 ) ); + int G = ( rand() % (int) ( 255 + 1 ) ); + int B = ( rand() % (int) ( 255 + 1 ) ); - /* create a ramdom binary mask */ - cv::Mat mask = Mat::ones(imageMat.size(), CV_8UC1); - - /* create a pointer to a BinaryDescriptor object with deafult parameters */ - Ptr bd = BinaryDescriptor::createBinaryDescriptor(); - - /* create a structure to store extracted lines */ - vector lines; - - /* extract lines */ - bd->detect(imageMat, lines, mask); - std::cout << lines.size() << std::endl; - - /* draw lines extracted from octave 0 */ - cv::Mat output = imageMat.clone(); - if(output.channels() == 1) - cvtColor(output, output, COLOR_GRAY2BGR); - for(size_t i = 0; i" - << "" << std::endl; + std::cout << "\nThis example shows the functionalities of lines extraction " << "and descriptors computation furnished by BinaryDescriptor class\n" + << "Please, run this sample using a command in the form\n" << "./example_line_descriptor_compute_descriptors " + << "" << std::endl; } int main( int argc, char** argv ) { - /* get parameters from comand line */ - CommandLineParser parser( argc, argv, keys ); - String image_path1 = parser.get( 0 ); - String image_path2 = parser.get( 1 ); - - if(image_path1.empty() || image_path2.empty()) - { - help(); - return -1; - } - - - /* load image */ - cv::Mat imageMat1 = imread(image_path1, 1); - cv::Mat imageMat2 = imread(image_path2, 1); - - waitKey(); - if(imageMat1.data == NULL || imageMat2.data == NULL) - { - std::cout << "Error, images could not be loaded. Please, check their path" - << std::endl; - } - - /* create binary masks */ - cv::Mat mask1 = Mat::ones(imageMat1.size(), CV_8UC1); - cv::Mat mask2 = Mat::ones(imageMat2.size(), CV_8UC1); - - /* create a pointer to a BinaryDescriptor object with default parameters */ - Ptr bd = BinaryDescriptor::createBinaryDescriptor(); - - /* compute lines */ - std::vector keylines1, keylines2; - bd->detect(imageMat1, keylines1, mask1); - bd->detect(imageMat2, keylines2, mask2); - - /* compute descriptors */ - cv::Mat descr1, descr2; - bd->compute(imageMat1, keylines1, descr1); - bd->compute(imageMat2, keylines2, descr2); - - /* create a BinaryDescriptorMatcher object */ - Ptr bdm = BinaryDescriptorMatcher::createBinaryDescriptorMatcher(); - - /* require match */ - std::vector matches; - bdm->match(descr1, descr2, matches); - - /* plot matches */ - cv::Mat outImg; - std::vector mask (matches.size(), 1); - drawLineMatches(imageMat1, keylines1, imageMat2, keylines2, matches, - outImg, Scalar::all(-1), Scalar::all(-1), mask, - DrawLinesMatchesFlags::DEFAULT); - - std::cout << "num dmatch " << matches.size() << std::endl; - imshow("Matches", outImg); - waitKey(); + /* get parameters from comand line */ + CommandLineParser parser( argc, argv, keys ); + String image_path1 = parser.get( 0 ); + String image_path2 = parser.get( 1 ); + + if( image_path1.empty() || image_path2.empty() ) + { + help(); + return -1; + } + + /* load image */ + cv::Mat imageMat1 = imread( image_path1, 1 ); + cv::Mat imageMat2 = imread( image_path2, 1 ); + + waitKey(); + if( imageMat1.data == NULL || imageMat2.data == NULL ) + { + std::cout << "Error, images could not be loaded. Please, check their path" << std::endl; + } + + /* create binary masks */ + cv::Mat mask1 = Mat::ones( imageMat1.size(), CV_8UC1 ); + cv::Mat mask2 = Mat::ones( imageMat2.size(), CV_8UC1 ); + + /* create a pointer to a BinaryDescriptor object with default parameters */ + Ptr bd = BinaryDescriptor::createBinaryDescriptor(); + + /* compute lines */ + std::vector keylines1, keylines2; + bd->detect( imageMat1, keylines1, mask1 ); + bd->detect( imageMat2, keylines2, mask2 ); + + /* compute descriptors */ + cv::Mat descr1, descr2; + bd->compute( imageMat1, keylines1, descr1 ); + bd->compute( imageMat2, keylines2, descr2 ); + + /* create a BinaryDescriptorMatcher object */ + Ptr bdm = BinaryDescriptorMatcher::createBinaryDescriptorMatcher(); + + /* require match */ + std::vector matches; + bdm->match( descr1, descr2, matches ); + + /* plot matches */ + cv::Mat outImg; + std::vector mask( matches.size(), 1 ); + drawLineMatches( imageMat1, keylines1, imageMat2, keylines2, matches, outImg, Scalar::all( -1 ), Scalar::all( -1 ), mask, + DrawLinesMatchesFlags::DEFAULT ); + + imshow( "Matches", outImg ); + waitKey(); } - diff --git a/modules/line_descriptor/src/array32.cpp b/modules/line_descriptor/src/array32.cpp index 31db5055f..62be91bf5 100644 --- a/modules/line_descriptor/src/array32.cpp +++ b/modules/line_descriptor/src/array32.cpp @@ -42,151 +42,148 @@ #include "precomp.hpp" - /* no need for the static keyword in the definition */ double Array32::ARRAY_RESIZE_FACTOR = 1.1; // minimum is 1.0 double Array32::ARRAY_RESIZE_ADD_FACTOR = 4; // minimum is 1 /* set ARRAY_RESIZE_FACTOR */ -void Array32::setArrayResizeFactor(double arf) +void Array32::setArrayResizeFactor( double arf ) { - ARRAY_RESIZE_FACTOR = arf; + ARRAY_RESIZE_FACTOR = arf; } /* constructor */ -Array32::Array32 () +Array32::Array32() { - arr = NULL; + arr = NULL; } /* definition of operator = - Array32& Array32::operator = (const Array32 &rhs) */ -void Array32::operator = (const Array32 &rhs) + Array32& Array32::operator = (const Array32 &rhs) */ +void Array32::operator =( const Array32 &rhs ) { - if (&rhs != this) + if( &rhs != this ) this->arr = rhs.arr; } /* destructor */ -Array32::~Array32 () +Array32::~Array32() { - cleanup(); + cleanup(); } /* cleaning function used in destructor */ -void Array32::cleanup () +void Array32::cleanup() { - free(arr); + free( arr ); } /* push data */ -void Array32::push(UINT32 Data) +void Array32::push( UINT32 Data ) { - if (arr) + if( arr ) + { + if( arr[0] == arr[1] ) { - if (arr[0] == arr[1]) - { - arr[1] = std::max(ceil(arr[1]*ARRAY_RESIZE_FACTOR), - arr[1]+ARRAY_RESIZE_ADD_FACTOR); - UINT32* new_Data = static_cast - (realloc (arr, sizeof(UINT32)*(2 + arr[1]))); - if (new_Data == NULL) - { - /* could not realloc, but orig still valid */ - std::cout << "ALERT!!!! Not enough memory, operation aborted!" - << std::endl; - exit(0); - } - else - { - arr = new_Data; - } - - } - - arr[2 + arr[0]] = Data; - arr[0]++; + arr[1] = std::max( ceil( arr[1] * ARRAY_RESIZE_FACTOR ), arr[1] + ARRAY_RESIZE_ADD_FACTOR ); + UINT32* new_Data = static_cast( realloc( arr, sizeof(UINT32) * ( 2 + arr[1] ) ) ); + if( new_Data == NULL ) + { + /* could not realloc, but orig still valid */ + std::cout << "ALERT!!!! Not enough memory, operation aborted!" << std::endl; + exit( 0 ); + } + else + { + arr = new_Data; + } } - else - { - arr = (UINT32*) malloc ((2+ARRAY_RESIZE_ADD_FACTOR)*sizeof(UINT32)); - arr[0] = 1; - arr[1] = 1; - arr[2] = Data; - } + arr[2 + arr[0]] = Data; + arr[0]++; + + } + + else + { + arr = (UINT32*) malloc( ( 2 + ARRAY_RESIZE_ADD_FACTOR ) * sizeof(UINT32) ); + arr[0] = 1; + arr[1] = 1; + arr[2] = Data; + } } /* insert data at given index */ -void Array32::insert(UINT32 index, UINT32 Data) { - if (arr) { - if (arr[0] == arr[1]) { - arr[1] = ceil(arr[0]*1.1); - UINT32* new_data = static_cast - (realloc (arr, sizeof(UINT32)*(2 + arr[1]))); - if (new_data == NULL) - { - // could not realloc, but orig still valid - std::cout << "ALERT!!!! Not enough memory, operation aborted!" - << std::endl; - exit(0); - } - else - { - arr = new_data; - } - } - - memmove(arr+(2+index)+1, arr+(2+index), (arr[0]-index)*sizeof(*arr)); - - arr[2+index] = Data; - arr[0]++; - } - - else +void Array32::insert( UINT32 index, UINT32 Data ) +{ + if( arr ) + { + if( arr[0] == arr[1] ) { - arr = (UINT32*) malloc (3*sizeof(UINT32)); - arr[0] = 1; - arr[1] = 1; - arr[2] = Data; + arr[1] = ceil( arr[0] * 1.1 ); + UINT32* new_data = static_cast( realloc( arr, sizeof(UINT32) * ( 2 + arr[1] ) ) ); + if( new_data == NULL ) + { + // could not realloc, but orig still valid + std::cout << "ALERT!!!! Not enough memory, operation aborted!" << std::endl; + exit( 0 ); + } + else + { + arr = new_data; + } } -} + memmove( arr + ( 2 + index ) + 1, arr + ( 2 + index ), ( arr[0] - index ) * sizeof ( *arr ) ); + + arr[2 + index] = Data; + arr[0]++; + } + + else + { + arr = (UINT32*) malloc( 3 * sizeof(UINT32) ); + arr[0] = 1; + arr[1] = 1; + arr[2] = Data; + } +} /* return data */ UINT32* Array32::data() { - return arr? arr + 2 : NULL; + return arr ? arr + 2 : NULL; } /* return data size */ -UINT32 Array32::size () +UINT32 Array32::size() { - return arr ? arr[0] : 0; + return arr ? arr[0] : 0; } /* return capacity */ -UINT32 Array32::capacity () +UINT32 Array32::capacity() { - return arr ? arr[1] : 0; + return arr ? arr[1] : 0; } /* print data */ -void Array32::print() { - for (int i=0; i<(int)size(); i++) - printf("%d, ", arr[i+2]); +void Array32::print() +{ + for ( int i = 0; i < (int) size(); i++ ) + printf( "%d, ", arr[i + 2] ); - printf("\n"); + printf( "\n" ); } /* initializer */ -void Array32::init(int Size) +void Array32::init( int Size ) { - if (arr == NULL) - { - arr = (UINT32*) malloc ((2+Size)*sizeof(UINT32)); - arr[0] = 0; - arr[1] = Size; - } + if( arr == NULL ) + { + arr = (UINT32*) malloc( ( 2 + Size ) * sizeof(UINT32) ); + arr[0] = 0; + arr[1] = Size; + } } diff --git a/modules/line_descriptor/src/binary_descriptor.cpp b/modules/line_descriptor/src/binary_descriptor.cpp index cea39d69a..19dfb0359 100644 --- a/modules/line_descriptor/src/binary_descriptor.cpp +++ b/modules/line_descriptor/src/binary_descriptor.cpp @@ -352,6 +352,35 @@ void BinaryDescriptor::computeGaussianPyramid( const Mat& image ) } } +/* check lines' extremes */ +inline void checkLineExtremes( cv::Vec4i& extremes, cv::Size imageSize ) +{ + + if( extremes[0] < 0 ) + extremes[0] = 0; + + if( extremes[0] >= imageSize.width ) + extremes[0] = imageSize.width - 1; + + if( extremes[2] < 0 ) + extremes[2] = 0; + + if( extremes[2] >= imageSize.width ) + extremes[2] = imageSize.width - 1; + + if( extremes[1] < 0 ) + extremes[1] = 0; + + if( extremes[1] >= imageSize.height ) + extremes[1] = imageSize.height - 1; + + if( extremes[3] < 0 ) + extremes[3] = 0; + + if( extremes[3] >= imageSize.height ) + extremes[3] = imageSize.height - 1; +} + /* requires line detection (only one image) */ void BinaryDescriptor::detect( const Mat& image, CV_OUT std::vector& keylines, const Mat& mask ) { @@ -389,7 +418,7 @@ void BinaryDescriptor::detectImpl( const Mat& imageSrc, std::vector& ke { cv::Mat image; - if(imageSrc.channels() != 1) + if( imageSrc.channels() != 1 ) cvtColor( imageSrc, image, COLOR_BGR2GRAY ); else image = imageSrc.clone(); @@ -422,11 +451,15 @@ void BinaryDescriptor::detectImpl( const Mat& imageSrc, std::vector& ke /* create a KeyLine object */ KeyLine kl; + /* check data validity */ + cv::Vec4i extremes( osl.startPointX, osl.startPointY, osl.endPointX, osl.endPointY ); + checkLineExtremes( extremes, image.size() ); + /* fill KeyLine's fields */ - kl.startPointX = osl.startPointX; - kl.startPointY = osl.startPointY; - kl.endPointX = osl.endPointX; - kl.endPointY = osl.endPointY; + kl.startPointX = extremes[0]; + kl.startPointY = extremes[1]; + kl.endPointX = extremes[2]; + kl.endPointY = extremes[3]; kl.sPointInOctaveX = osl.sPointInOctaveX; kl.sPointInOctaveY = osl.sPointInOctaveY; kl.ePointInOctaveX = osl.ePointInOctaveX; @@ -448,6 +481,7 @@ void BinaryDescriptor::detectImpl( const Mat& imageSrc, std::vector& ke } /* delete undesired KeyLines, according to input mask */ + std::cout << "Mask size " << mask.rows << " " << mask.cols << std::endl; if( !mask.empty() ) { for ( size_t keyCounter = 0; keyCounter < keylines.size(); keyCounter++ ) @@ -478,7 +512,7 @@ void BinaryDescriptor::computeImpl( const Mat& imageSrc, std::vector& k { /* convert input image to gray scale */ cv::Mat image; - if(imageSrc.channels() != 1) + if( imageSrc.channels() != 1 ) cvtColor( imageSrc, image, COLOR_BGR2GRAY ); else image = imageSrc.clone(); @@ -861,7 +895,7 @@ int BinaryDescriptor::OctaveKeyLines( ScaleLines &keyLines ) singleLine.octaveCount = octaveID; singleLine.lineLength = octaveLines[lineID].lineLength; - // decide the start point and end point +// decide the start point and end point shouldChange = false; s1 = ( extractedLines[octaveID][lineIDInOctave] )[0]; //sx diff --git a/modules/line_descriptor/src/binary_descriptor_matcher.cpp b/modules/line_descriptor/src/binary_descriptor_matcher.cpp index 723f70189..d9e465e05 100644 --- a/modules/line_descriptor/src/binary_descriptor_matcher.cpp +++ b/modules/line_descriptor/src/binary_descriptor_matcher.cpp @@ -46,554 +46,505 @@ using namespace cv; /* constructor */ BinaryDescriptorMatcher::BinaryDescriptorMatcher() { - dataset = new Mihasher(256, 32); - nextAddedIndex = 0; - numImages = 0; - descrInDS = 0; + dataset = new Mihasher( 256, 32 ); + nextAddedIndex = 0; + numImages = 0; + descrInDS = 0; } /* constructor with smart pointer */ Ptr BinaryDescriptorMatcher::createBinaryDescriptorMatcher() { - return Ptr(new BinaryDescriptorMatcher()); + return Ptr( new BinaryDescriptorMatcher() ); } /* store new descriptors to be inserted in dataset */ void BinaryDescriptorMatcher::add( const std::vector& descriptors ) { - for(size_t i = 0; i(nextAddedIndex, numImages)); - nextAddedIndex += descriptors[i].rows; - numImages++; - } + for ( size_t i = 0; i < descriptors.size(); i++ ) + { + descriptorsMat.push_back( descriptors[i] ); + + indexesMap.insert( std::pair( nextAddedIndex, numImages ) ); + nextAddedIndex += descriptors[i].rows; + numImages++; + } } /* store new descriptors into dataset */ void BinaryDescriptorMatcher::train() { - if(!dataset) - dataset = new Mihasher(256, 32); + if( !dataset ) + dataset = new Mihasher( 256, 32 ); - if(descriptorsMat.rows >0) - dataset->populate(descriptorsMat, - descriptorsMat.rows, - descriptorsMat.cols); + if( descriptorsMat.rows > 0 ) + dataset->populate( descriptorsMat, descriptorsMat.rows, descriptorsMat.cols ); - descrInDS = descriptorsMat.rows; - descriptorsMat.release(); + descrInDS = descriptorsMat.rows; + descriptorsMat.release(); } /* clear dataset and internal data */ void BinaryDescriptorMatcher::clear() { - descriptorsMat.release(); - indexesMap.clear(); - dataset = 0; - nextAddedIndex = 0; - numImages = 0; - descrInDS = 0; + descriptorsMat.release(); + indexesMap.clear(); + dataset = 0; + nextAddedIndex = 0; + numImages = 0; + descrInDS = 0; } /* retrieve Hamming distances */ -void BinaryDescriptorMatcher::checkKDistances(UINT32 * numres, int k, std::vector & k_distances, int row, int string_length) const +void BinaryDescriptorMatcher::checkKDistances( UINT32 * numres, int k, std::vector & k_distances, int row, int string_length ) const { - int k_to_found = k; + int k_to_found = k; - UINT32 * numres_tmp = numres + ((string_length+1) * row); - for(int j = 0; j<(string_length+1) && k_to_found > 0; j++) + UINT32 * numres_tmp = numres + ( ( string_length + 1 ) * row ); + for ( int j = 0; j < ( string_length + 1 ) && k_to_found > 0; j++ ) + { + if( ( * ( numres_tmp + j ) ) > 0 ) { - if((*(numres_tmp+j))>0) - { - for(int i = 0; i<(*(numres_tmp+j)) && k_to_found > 0; i++) - { - k_distances.push_back(j); - k_to_found--; - } - } + for ( int i = 0; i < ( * ( numres_tmp + j ) ) && k_to_found > 0; i++ ) + { + k_distances.push_back( j ); + k_to_found--; + } } + } } /* for every input descriptor, - find the best matching one (from one image to a set) */ -void BinaryDescriptorMatcher::match( const Mat& queryDescriptors, - std::vector& matches, - const std::vector& masks ) + find the best matching one (from one image to a set) */ +void BinaryDescriptorMatcher::match( const Mat& queryDescriptors, std::vector& matches, const std::vector& masks ) { - /* check data validity */ - if(masks.size() !=0 && (int)masks.size() != numImages) - { - std::cout << "Error: the number of images in dataset is " << - numImages << " but match function received " << - masks.size() << " masks. Program will be terminated" - << std::endl; + /* check data validity */ + if( masks.size() != 0 && (int) masks.size() != numImages ) + { + std::cout << "Error: the number of images in dataset is " << numImages << " but match function received " << masks.size() + << " masks. Program will be terminated" << std::endl; - return; - } + return; + } - /* add new descriptors to dataset, if needed */ - train(); + /* add new descriptors to dataset, if needed */ + train(); - /* set number of requested matches to return for each query */ - dataset->setK(1); + /* set number of requested matches to return for each query */ + dataset->setK( 1 ); - /* prepare structures for query */ - UINT32 *results = new UINT32[queryDescriptors.rows]; - UINT32 * numres = new UINT32[(256+1)*(queryDescriptors.rows)]; + /* prepare structures for query */ + UINT32 *results = new UINT32[queryDescriptors.rows]; + UINT32 * numres = new UINT32[ ( 256 + 1 ) * ( queryDescriptors.rows )]; - /* execute query */ - dataset->batchquery(results, numres, queryDescriptors, - queryDescriptors.rows, queryDescriptors.cols); + /* execute query */ + dataset->batchquery( results, numres, queryDescriptors, queryDescriptors.rows, queryDescriptors.cols ); - /* compose matches */ - for(int counter = 0; counter::iterator itup; + + /* get info about original image of each returned descriptor */ + itup = indexesMap.upper_bound( results[counter] - 1 ); + itup--; + + /* data validity check */ + if( !masks.empty() && ( masks[itup->second].rows != queryDescriptors.rows || masks[itup->second].cols != 1 ) ) + { + std::cout << "Error: mask " << itup->second << " in knnMatch function " << "should have " << queryDescriptors.rows << " and " + << "1 column. Program will be terminated" << std::endl; + + CV_Assert( false ); + } + + /* create a DMatch object if required by mask of if there is + no mask at all */ + else if( masks.empty() || masks[itup->second].at( counter ) != 0 ) { - /* create a map iterator */ - std::map::iterator itup; - - /* get info about original image of each returned descriptor */ - itup = indexesMap.upper_bound(results[counter] - 1); - itup--; - - /* data validity check */ - if(!masks.empty() && (masks[itup->second].rows != queryDescriptors.rows - || masks[itup->second].cols !=1)) - { - std::cout << "Error: mask " << itup->second << " in knnMatch function " - << "should have " << queryDescriptors.rows << " and " - << "1 column. Program will be terminated" - << std::endl; - - CV_Assert(false); - } - - /* create a DMatch object if required by mask of if there is - no mask at all */ - else if(masks.empty() || masks[itup->second].at(counter) !=0) - { - std::vector k_distances; - checkKDistances(numres, 1, k_distances, counter, 256); - - DMatch dm; - dm.queryIdx = counter; - dm.trainIdx = results[counter] - 1; - dm.imgIdx = itup->second; - dm.distance = k_distances[0]; - - matches.push_back(dm); - } + std::vector k_distances; + checkKDistances( numres, 1, k_distances, counter, 256 ); + DMatch dm; + dm.queryIdx = counter; + dm.trainIdx = results[counter] - 1; + dm.imgIdx = itup->second; + dm.distance = k_distances[0]; + + matches.push_back( dm ); } - /* delete data */ - delete results; - delete numres; + } + + /* delete data */ + delete results; + delete numres; } /* for every input descriptor, find the best matching one (for a pair of images) */ -void BinaryDescriptorMatcher::match( const Mat& queryDescriptors, - const Mat& trainDescriptors, - std::vector& matches, - const Mat& mask ) const +void BinaryDescriptorMatcher::match( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector& matches, const Mat& mask ) const { - /* check data validity */ - if(!mask.empty() && (mask.rows != queryDescriptors.rows && mask.cols != 1)) - { - std::cout << "Error: input mask should have " << - queryDescriptors.rows << " rows and 1 column. " << - "Program will be terminated" << std::endl; + /* check data validity */ + if( !mask.empty() && ( mask.rows != queryDescriptors.rows && mask.cols != 1 ) ) + { + std::cout << "Error: input mask should have " << queryDescriptors.rows << " rows and 1 column. " << "Program will be terminated" << std::endl; - return; - } + return; + } - /* create a new mihasher object */ - Mihasher *mh = new Mihasher(256, 32); + /* create a new mihasher object */ + Mihasher *mh = new Mihasher( 256, 32 ); - /* populate mihasher */ - cv::Mat copy = trainDescriptors.clone(); - mh->populate(copy, copy.rows, copy.cols); - mh->setK(1); + /* populate mihasher */ + cv::Mat copy = trainDescriptors.clone(); + mh->populate( copy, copy.rows, copy.cols ); + mh->setK( 1 ); - /* prepare structures for query */ - UINT32 *results = new UINT32[queryDescriptors.rows]; - UINT32 * numres = new UINT32[(256+1)*(queryDescriptors.rows)]; + /* prepare structures for query */ + UINT32 *results = new UINT32[queryDescriptors.rows]; + UINT32 * numres = new UINT32[ ( 256 + 1 ) * ( queryDescriptors.rows )]; - /* execute query */ - mh->batchquery(results, numres, queryDescriptors, - queryDescriptors.rows, queryDescriptors.cols); + /* execute query */ + mh->batchquery( results, numres, queryDescriptors, queryDescriptors.rows, queryDescriptors.cols ); - /* compose matches */ - for(int counter = 0; counter( counter ) != 0 ) ) { - /* create a DMatch object if required by mask of if there is - no mask at all */ - if( mask.empty() || (!mask.empty() && mask.at(counter)!=0)) - { - std::vector k_distances; - checkKDistances(numres, 1, k_distances, counter, 256); + std::vector k_distances; + checkKDistances( numres, 1, k_distances, counter, 256 ); - DMatch dm; - dm.queryIdx = counter; - dm.trainIdx = results[counter] - 1; - dm.imgIdx = 0; - dm.distance = k_distances[0]; + DMatch dm; + dm.queryIdx = counter; + dm.trainIdx = results[counter] - 1; + dm.imgIdx = 0; + dm.distance = k_distances[0]; - matches.push_back(dm); - } + matches.push_back( dm ); } + } - /* delete data */ - delete mh; - delete results; - delete numres; - + /* delete data */ + delete mh; + delete results; + delete numres; } /* for every input descriptor, - find the best k matching descriptors (for a pair of images) */ -void BinaryDescriptorMatcher::knnMatch( const Mat& queryDescriptors, - const Mat& trainDescriptors, - std::vector >& matches, - int k, - const Mat& mask, - bool compactResult ) const + find the best k matching descriptors (for a pair of images) */ +void BinaryDescriptorMatcher::knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector >& matches, int k, + const Mat& mask, bool compactResult ) const { - /* check data validity */ - if(!mask.empty() && (mask.rows != queryDescriptors.rows || mask.cols != 1)) - { - std::cout << "Error: input mask should have " << - queryDescriptors.rows << " rows and 1 column. " << - "Program will be terminated" << std::endl; - - return; - } + /* check data validity */ + if( !mask.empty() && ( mask.rows != queryDescriptors.rows || mask.cols != 1 ) ) + { + std::cout << "Error: input mask should have " << queryDescriptors.rows << " rows and 1 column. " << "Program will be terminated" << std::endl; - /* create a new mihasher object */ - Mihasher *mh = new Mihasher(256, 32); + return; + } - /* populate mihasher */ - cv::Mat copy = trainDescriptors.clone(); - mh->populate(copy, copy.rows, copy.cols); + /* create a new mihasher object */ + Mihasher *mh = new Mihasher( 256, 32 ); - /* set K */ - mh->setK(k); + /* populate mihasher */ + cv::Mat copy = trainDescriptors.clone(); + mh->populate( copy, copy.rows, copy.cols ); - /* prepare structures for query */ - UINT32 *results = new UINT32[k*queryDescriptors.rows]; - UINT32 * numres = new UINT32[(256+1)*(queryDescriptors.rows)]; + /* set K */ + mh->setK( k ); - /* execute query */ - mh->batchquery(results, numres, queryDescriptors, - queryDescriptors.rows, queryDescriptors.cols); + /* prepare structures for query */ + UINT32 *results = new UINT32[k * queryDescriptors.rows]; + UINT32 * numres = new UINT32[ ( 256 + 1 ) * ( queryDescriptors.rows )]; - /* compose matches */ - int index = 0; - for(int counter = 0; counter tempVec; + /* execute query */ + mh->batchquery( results, numres, queryDescriptors, queryDescriptors.rows, queryDescriptors.cols ); - /* chech whether query should be ignored */ - if(!mask.empty() && mask.at(counter) == 0) - { - /* if compact result is not requested, add an empty vector */ - if(!compactResult) - matches.push_back(tempVec); - } + /* compose matches */ + int index = 0; + for ( int counter = 0; counter < queryDescriptors.rows; counter++ ) + { + /* initialize a vector of matches */ + std::vector tempVec; - /* query matches must be considered */ - else - { - std::vector k_distances; - checkKDistances(numres, k, k_distances, counter, 256); - for(int j = index; j( counter ) == 0 ) + { + /* if compact result is not requested, add an empty vector */ + if( !compactResult ) + matches.push_back( tempVec ); + } - /* increment pointer */ - index += k; + /* query matches must be considered */ + else + { + std::vector k_distances; + checkKDistances( numres, k, k_distances, counter, 256 ); + for ( int j = index; j < index + k; j++ ) + { + DMatch dm; + dm.queryIdx = counter; + dm.trainIdx = results[j] - 1; + dm.imgIdx = 0; + dm.distance = k_distances[j - index]; + + tempVec.push_back( dm ); + } + + matches.push_back( tempVec ); } - /* delete data */ - delete mh; - delete results; - delete numres; + /* increment pointer */ + index += k; + } + + /* delete data */ + delete mh; + delete results; + delete numres; } /* for every input descriptor, - find the best k matching descriptors (from one image to a set) */ -void BinaryDescriptorMatcher::knnMatch( const Mat& queryDescriptors, - std::vector >& matches, - int k, - const std::vector& masks, + find the best k matching descriptors (from one image to a set) */ +void BinaryDescriptorMatcher::knnMatch( const Mat& queryDescriptors, std::vector >& matches, int k, const std::vector& masks, bool compactResult ) { - /* check data validity */ - if(masks.size() !=0 && (int)masks.size() != numImages) - { - std::cout << "Error: the number of images in dataset is " << - numImages << " but knnMatch function received " << - masks.size() << " masks. Program will be terminated" - << std::endl; + /* check data validity */ + if( masks.size() != 0 && (int) masks.size() != numImages ) + { + std::cout << "Error: the number of images in dataset is " << numImages << " but knnMatch function received " << masks.size() + << " masks. Program will be terminated" << std::endl; - return; - } + return; + } - /* add new descriptors to dataset, if needed */ - train(); + /* add new descriptors to dataset, if needed */ + train(); - /* set number of requested matches to return for each query */ - dataset->setK(k); + /* set number of requested matches to return for each query */ + dataset->setK( k ); - /* prepare structures for query */ - UINT32 *results = new UINT32[k*queryDescriptors.rows]; - UINT32 * numres = new UINT32[(256+1)*(queryDescriptors.rows)]; + /* prepare structures for query */ + UINT32 *results = new UINT32[k * queryDescriptors.rows]; + UINT32 * numres = new UINT32[ ( 256 + 1 ) * ( queryDescriptors.rows )]; - /* execute query */ - dataset->batchquery(results, numres, queryDescriptors, - queryDescriptors.rows, queryDescriptors.cols); + /* execute query */ + dataset->batchquery( results, numres, queryDescriptors, queryDescriptors.rows, queryDescriptors.cols ); - /* compose matches */ - int index = 0; - for(int counter = 0; counter tempVector; + + /* loop over k results returned for every query */ + for ( int j = index; j < index + k; j++ ) { - /* create a void vector of matches */ - std::vector tempVector; + /* retrieve which image returned index refers to */ + int currentIndex = results[j] - 1; + std::map::iterator itup; + itup = indexesMap.upper_bound( currentIndex ); + itup--; + + /* data validity check */ + if( !masks.empty() && ( masks[itup->second].rows != queryDescriptors.rows || masks[itup->second].cols != 1 ) ) + { + std::cout << "Error: mask " << itup->second << " in knnMatch function " << "should have " << queryDescriptors.rows << " and " + << "1 column. Program will be terminated" << std::endl; - /* loop over k results returned for every query */ - for(int j = index; j::iterator itup; - itup = indexesMap.upper_bound(currentIndex); - itup--; - - /* data validity check */ - if(!masks.empty() && (masks[itup->second].rows != queryDescriptors.rows - || masks[itup->second].cols != 1)) - { - std::cout << "Error: mask " << itup->second << " in knnMatch function " - << "should have " << queryDescriptors.rows << " and " - << "1 column. Program will be terminated" - << std::endl; - - return; - } - - /* decide if, according to relative mask, returned match should be - considered */ - else if(masks.size() == 0 || masks[itup->second].at(counter) != 0) - { - std::vector k_distances; - checkKDistances(numres, k, k_distances, counter, 256); - - DMatch dm; - dm.queryIdx = counter; - dm.trainIdx = results[j] - 1; - dm.imgIdx = itup->second; - dm.distance = k_distances[j-index]; - - tempVector.push_back(dm); - } - } + return; + } + + /* decide if, according to relative mask, returned match should be + considered */ + else if( masks.size() == 0 || masks[itup->second].at( counter ) != 0 ) + { + std::vector k_distances; + checkKDistances( numres, k, k_distances, counter, 256 ); - /* decide whether temporary vector should be saved */ - if((tempVector.size() == 0 && !compactResult) || tempVector.size()>0) - matches.push_back(tempVector); + DMatch dm; + dm.queryIdx = counter; + dm.trainIdx = results[j] - 1; + dm.imgIdx = itup->second; + dm.distance = k_distances[j - index]; - /* increment pointer */ - index += k; + tempVector.push_back( dm ); + } } - /* delete data */ - delete results; - delete numres; + /* decide whether temporary vector should be saved */ + if( ( tempVector.size() == 0 && !compactResult ) || tempVector.size() > 0 ) + matches.push_back( tempVector ); + + /* increment pointer */ + index += k; + } + + /* delete data */ + delete results; + delete numres; } /* for every input desciptor, find all the ones falling in a - certaing matching radius (for a pair of images) */ -void BinaryDescriptorMatcher::radiusMatch( const Mat& queryDescriptors, - const Mat& trainDescriptors, - std::vector >& matches, - float maxDistance, - const Mat& mask, - bool compactResult ) const + certaing matching radius (for a pair of images) */ +void BinaryDescriptorMatcher::radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector >& matches, + float maxDistance, const Mat& mask, bool compactResult ) const { - /* check data validity */ - if(!mask.empty() && (mask.rows != queryDescriptors.rows && mask.cols != 1)) - { - std::cout << "Error: input mask should have " << - queryDescriptors.rows << " rows and 1 column. " << - "Program will be terminated" << std::endl; + /* check data validity */ + if( !mask.empty() && ( mask.rows != queryDescriptors.rows && mask.cols != 1 ) ) + { + std::cout << "Error: input mask should have " << queryDescriptors.rows << " rows and 1 column. " << "Program will be terminated" << std::endl; - return; - } + return; + } - /* create a new Mihasher */ - Mihasher* mh = new Mihasher(256, 32); + /* create a new Mihasher */ + Mihasher* mh = new Mihasher( 256, 32 ); - /* populate Mihasher */ - Mat copy = queryDescriptors.clone(); - mh->populate(copy, copy.rows, copy.cols); + /* populate Mihasher */ + Mat copy = queryDescriptors.clone(); + mh->populate( copy, copy.rows, copy.cols ); - /* set K */ - mh->setK(trainDescriptors.rows); + /* set K */ + mh->setK( trainDescriptors.rows ); - /* prepare structures for query */ - UINT32 *results = new UINT32[trainDescriptors.rows*queryDescriptors.rows]; - UINT32 * numres = new UINT32[(256+1)*(queryDescriptors.rows)]; + /* prepare structures for query */ + UINT32 *results = new UINT32[trainDescriptors.rows * queryDescriptors.rows]; + UINT32 * numres = new UINT32[ ( 256 + 1 ) * ( queryDescriptors.rows )]; - /* execute query */ - mh->batchquery(results, numres, queryDescriptors, - queryDescriptors.rows, queryDescriptors.cols); + /* execute query */ + mh->batchquery( results, numres, queryDescriptors, queryDescriptors.rows, queryDescriptors.cols ); - /* compose matches */ - int index = 0; - for (int i = 0; i k_distances; - checkKDistances(numres, trainDescriptors.rows, k_distances, i, 256); + /* compose matches */ + int index = 0; + for ( int i = 0; i < queryDescriptors.rows; i++ ) + { + std::vector k_distances; + checkKDistances( numres, trainDescriptors.rows, k_distances, i, 256 ); - std::vector tempVector; - for(int j = 0; j tempVector; + for ( int j = 0; j < index + trainDescriptors.rows; j++ ) + { + if( numres[j] <= maxDistance ) + { + if( mask.empty() || mask.at( i ) != 0 ) { - if(numres[j] <= maxDistance) - { - if(mask.empty() || mask.at(i) != 0){ - DMatch dm; - dm.queryIdx = i; - dm.trainIdx = results[j] - 1; - dm.imgIdx = 0; - dm.distance = k_distances[j-index]; - - tempVector.push_back(dm); - } - } + DMatch dm; + dm.queryIdx = i; + dm.trainIdx = results[j] - 1; + dm.imgIdx = 0; + dm.distance = k_distances[j - index]; + + tempVector.push_back( dm ); } + } + } - /* decide whether temporary vector should be saved */ - if((tempVector.size() == 0 && !compactResult) || tempVector.size()>0) - matches.push_back(tempVector); + /* decide whether temporary vector should be saved */ + if( ( tempVector.size() == 0 && !compactResult ) || tempVector.size() > 0 ) + matches.push_back( tempVector ); - /* increment pointer */ - index += trainDescriptors.rows; + /* increment pointer */ + index += trainDescriptors.rows; - } + } - /* delete data */ - delete mh; - delete results; - delete numres; + /* delete data */ + delete mh; + delete results; + delete numres; } /* for every input desciptor, find all the ones falling in a - certaing atching radius (from one image to a set) */ -void BinaryDescriptorMatcher::radiusMatch( const Mat& queryDescriptors, - std::vector >& matches, - float maxDistance, - const std::vector& masks, - bool compactResult ) + certaing atching radius (from one image to a set) */ +void BinaryDescriptorMatcher::radiusMatch( const Mat& queryDescriptors, std::vector >& matches, float maxDistance, + const std::vector& masks, bool compactResult ) { - /* check data validity */ - if(masks.size() !=0 && (int)masks.size() != numImages) - { - std::cout << "Error: the number of images in dataset is " << - numImages << " but radiusMatch function received " << - masks.size() << " masks. Program will be terminated" - << std::endl; + /* check data validity */ + if( masks.size() != 0 && (int) masks.size() != numImages ) + { + std::cout << "Error: the number of images in dataset is " << numImages << " but radiusMatch function received " << masks.size() + << " masks. Program will be terminated" << std::endl; - return; - } + return; + } - /* populate dataset */ - train(); + /* populate dataset */ + train(); - /* set K */ - dataset->setK(descrInDS); + /* set K */ + dataset->setK( descrInDS ); - /* prepare structures for query */ - UINT32 *results = new UINT32[descrInDS*queryDescriptors.rows]; - UINT32 * numres = new UINT32[(256+1)*(queryDescriptors.rows)]; + /* prepare structures for query */ + UINT32 *results = new UINT32[descrInDS * queryDescriptors.rows]; + UINT32 * numres = new UINT32[ ( 256 + 1 ) * ( queryDescriptors.rows )]; - /* execute query */ - dataset->batchquery(results, numres, queryDescriptors, - queryDescriptors.rows, queryDescriptors.cols); + /* execute query */ + dataset->batchquery( results, numres, queryDescriptors, queryDescriptors.rows, queryDescriptors.cols ); - /* compose matches */ - int index = 0; - for(int counter = 0; counter tempVector; + for ( int j = index; j < index + descrInDS; j++ ) { - std::vector tempVector; - for(int j = index; j k_distances; + checkKDistances( numres, descrInDS, k_distances, counter, 256 ); + + if( k_distances[j - index] <= maxDistance ) + { + int currentIndex = results[j] - 1; + std::map::iterator itup; + itup = indexesMap.upper_bound( currentIndex ); + itup--; + + /* data validity check */ + if( !masks.empty() && ( masks[itup->second].rows != queryDescriptors.rows || masks[itup->second].cols != 1 ) ) { - std::vector k_distances; - checkKDistances(numres, descrInDS, k_distances, counter, 256); - - if(k_distances[j-index] <= maxDistance) - { - int currentIndex = results[j] - 1; - std::map::iterator itup; - itup = indexesMap.upper_bound(currentIndex); - itup--; - - /* data validity check */ - if(!masks.empty() && (masks[itup->second].rows != queryDescriptors.rows - || masks[itup->second].cols !=1)) - { - std::cout << "Error: mask " << itup->second << " in radiusMatch function " - << "should have " << queryDescriptors.rows << " and " - << "1 column. Program will be terminated" - << std::endl; - - return; - } - - /* add match if necessary */ - else if(masks.empty() || masks[itup->second].at(counter) !=0) - { - - - DMatch dm; - dm.queryIdx = counter; - dm.trainIdx = results[j] - 1; - dm.imgIdx = itup->second; - dm.distance = k_distances[j-index]; - - tempVector.push_back(dm); - } - } + std::cout << "Error: mask " << itup->second << " in radiusMatch function " << "should have " << queryDescriptors.rows << " and " + << "1 column. Program will be terminated" << std::endl; + + return; } - /* decide whether temporary vector should be saved */ - if((tempVector.size() == 0 && !compactResult) || tempVector.size()>0) - matches.push_back(tempVector); + /* add match if necessary */ + else if( masks.empty() || masks[itup->second].at( counter ) != 0 ) + { + + DMatch dm; + dm.queryIdx = counter; + dm.trainIdx = results[j] - 1; + dm.imgIdx = itup->second; + dm.distance = k_distances[j - index]; - /* increment pointer */ - index += descrInDS; + tempVector.push_back( dm ); + } + } } - /* delete data */ - delete results; - delete numres; + /* decide whether temporary vector should be saved */ + if( ( tempVector.size() == 0 && !compactResult ) || tempVector.size() > 0 ) + matches.push_back( tempVector ); + + /* increment pointer */ + index += descrInDS; + } + + /* delete data */ + delete results; + delete numres; } diff --git a/modules/line_descriptor/src/bucket_group.cpp b/modules/line_descriptor/src/bucket_group.cpp index 748d8d135..ed44b0ea4 100644 --- a/modules/line_descriptor/src/bucket_group.cpp +++ b/modules/line_descriptor/src/bucket_group.cpp @@ -45,56 +45,56 @@ /* constructor */ BucketGroup::BucketGroup() { - empty = 0; - group = NULL; + empty = 0; + group = NULL; } /* destructor */ BucketGroup::~BucketGroup() { - if (group != NULL) - delete group; + if( group != NULL ) + delete group; } /* insert data into the bucket */ -void BucketGroup::insert(int subindex, UINT32 data) +void BucketGroup::insert( int subindex, UINT32 data ) { - if (group == NULL) - { - group = new Array32(); - group->push(0); - } + if( group == NULL ) + { + group = new Array32(); + group->push( 0 ); + } - UINT32 lowerbits = ((UINT32)1 << subindex) - 1; - int end = popcnt(empty & lowerbits); + UINT32 lowerbits = ( (UINT32) 1 << subindex ) - 1; + int end = popcnt( empty & lowerbits ); - if (!(empty & ((UINT32)1 << subindex))) - { - group->insert(end, group->arr[end+2]); - empty |= (UINT32)1 << subindex; - } + if( ! ( empty & ( (UINT32) 1 << subindex ) ) ) + { + group->insert( end, group->arr[end + 2] ); + empty |= (UINT32) 1 << subindex; + } - int totones = popcnt(empty); - group->insert(totones+1+group->arr[2+end+1], data); - for (int i=end+1; iarr[2+i]++; + int totones = popcnt( empty ); + group->insert( totones + 1 + group->arr[2 + end + 1], data ); + for ( int i = end + 1; i < totones + 1; i++ ) + group->arr[2 + i]++; } /* perform a query to the bucket */ -UINT32* BucketGroup::query(int subindex, int *size) +UINT32* BucketGroup::query( int subindex, int *size ) { - if (empty & ((UINT32)1 << subindex)) - { - UINT32 lowerbits = ((UINT32)1 << subindex) - 1; - int end = popcnt(empty & lowerbits); - int totones = popcnt(empty); - *size = group->arr[2+end+1]-group->arr[2+end]; - return group->arr + 2 + totones+1 + group->arr[2+end]; - } + if( empty & ( (UINT32) 1 << subindex ) ) + { + UINT32 lowerbits = ( (UINT32) 1 << subindex ) - 1; + int end = popcnt( empty & lowerbits ); + int totones = popcnt( empty ); + *size = group->arr[2 + end + 1] - group->arr[2 + end]; + return group->arr + 2 + totones + 1 + group->arr[2 + end]; + } - else - { - *size = 0; - return NULL; - } + else + { + *size = 0; + return NULL; + } } diff --git a/modules/line_descriptor/src/draw.cpp b/modules/line_descriptor/src/draw.cpp index 71e60d511..d3a380c41 100644 --- a/modules/line_descriptor/src/draw.cpp +++ b/modules/line_descriptor/src/draw.cpp @@ -43,141 +43,129 @@ namespace cv { - /* draw matches between two images */ - void drawLineMatches( const Mat& img1, const std::vector& keylines1, - const Mat& img2, const std::vector& keylines2, - const std::vector& matches1to2, - Mat& outImg, const Scalar& matchColor, - const Scalar& singleLineColor, +/* draw matches between two images */ +void drawLineMatches( const Mat& img1, const std::vector& keylines1, const Mat& img2, const std::vector& keylines2, + const std::vector& matches1to2, Mat& outImg, const Scalar& matchColor, const Scalar& singleLineColor, const std::vector& matchesMask, int flags ) +{ + + /* initialize output matrix (if necessary) */ + if( flags == DrawLinesMatchesFlags::DEFAULT ) + { + /* check how many rows are necessary for output matrix */ + int totalRows = img1.rows >= img2.rows ? img1.rows : img2.rows; + + /* initialize output matrix */ + outImg = Mat::zeros( totalRows, img1.cols + img2.cols, img1.type() ); + + } + + /* initialize random seed: */ + srand( time( NULL ) ); + + Scalar singleLineColorRGB; + if( singleLineColor == Scalar::all( -1 ) ) + { + int R = ( rand() % (int) ( 255 + 1 ) ); + int G = ( rand() % (int) ( 255 + 1 ) ); + int B = ( rand() % (int) ( 255 + 1 ) ); + + singleLineColorRGB = Scalar( R, G, B ); + } + + else + singleLineColorRGB = singleLineColor; + + /* copy input images to output images */ + Mat roi_left( outImg, Rect( 0, 0, img1.cols, img1.rows ) ); + Mat roi_right( outImg, Rect( img1.cols, 0, img2.cols, img2.rows ) ); + img1.copyTo( roi_left ); + img2.copyTo( roi_right ); + + /* get columns offset */ + int offset = img1.cols; + + /* if requested, draw lines from both images */ + if( flags != DrawLinesMatchesFlags::NOT_DRAW_SINGLE_LINES ) + { + for ( size_t i = 0; i < keylines1.size(); i++ ) + { + KeyLine k1 = keylines1[i]; + line( outImg, Point( k1.startPointX, k1.startPointY ), Point( k1.endPointX, k1.endPointY ), singleLineColorRGB, 2 ); + } + + for ( size_t j = 0; j < keylines2.size(); j++ ) + { + KeyLine k2 = keylines2[j]; + line( outImg, Point( k2.startPointX + offset, k2.startPointY ), Point( k2.endPointX + offset, k2.endPointY ), singleLineColorRGB, 2 ); + } + } + + /* draw matches */ + for ( size_t counter = 0; counter < matches1to2.size(); counter++ ) + { + if( matchesMask[counter] != 0 ) { + DMatch dm = matches1to2[counter]; + KeyLine left = keylines1[dm.queryIdx]; + KeyLine right = keylines2[dm.trainIdx]; + + Scalar matchColorRGB; + if( matchColor == Scalar::all( -1 ) ) + { + int R = ( rand() % (int) ( 255 + 1 ) ); + int G = ( rand() % (int) ( 255 + 1 ) ); + int B = ( rand() % (int) ( 255 + 1 ) ); + + matchColorRGB = Scalar( R, G, B ); + + if( singleLineColor == Scalar::all( -1 ) ) + singleLineColorRGB = matchColorRGB; + } + + else + matchColorRGB = matchColor; + + /* draw lines if necessary */ + line( outImg, Point( left.startPointX, left.startPointY ), Point( left.endPointX, left.endPointY ), singleLineColorRGB, 2 ); + + line( outImg, Point( right.startPointX + offset, right.startPointY ), Point( right.endPointX + offset, right.endPointY ), singleLineColorRGB, + 2 ); - /* initialize output matrix (if necessary) */ - if(flags == DrawLinesMatchesFlags::DEFAULT) - { - /* check how many rows are necessary for output matrix */ - int totalRows = img1.rows >= img2.rows?img1.rows:img2.rows; - - /* initialize output matrix */ - outImg = Mat::zeros(totalRows, img1.cols+img2.cols, img1.type()); - - } - - /* initialize random seed: */ - srand (time(NULL)); - - Scalar singleLineColorRGB; - if(singleLineColor == Scalar::all(-1)) - { - int R = (rand() % (int)(255 + 1)); - int G = (rand() % (int)(255 + 1)); - int B = (rand() % (int)(255 + 1)); - - singleLineColorRGB = Scalar(R, G, B); - } - - else - singleLineColorRGB = singleLineColor; - - /* copy input images to output images */ - Mat roi_left(outImg, Rect(0,0,img1.cols,img1.rows)); - Mat roi_right(outImg, Rect(img1.cols,0,img2.cols,img2.rows)); - img1.copyTo(roi_left); - img2.copyTo(roi_right); - - /* get columns offset */ - int offset = img1.cols; - - /* if requested, draw lines from both images */ - if(flags != DrawLinesMatchesFlags::NOT_DRAW_SINGLE_LINES) - { - for(size_t i = 0; i& keylines, - Mat& outImage, - const Scalar& color, - int flags ) +/* draw extracted lines on original image */ +void drawKeylines( const Mat& image, const std::vector& keylines, Mat& outImage, const Scalar& color, int flags ) +{ + if( flags == DrawLinesMatchesFlags::DEFAULT ) + outImage = image.clone(); + + for ( size_t i = 0; i < keylines.size(); i++ ) + { + /* decide lines' color */ + Scalar lineColor; + if( color != Scalar::all( -1 ) ) { - if(flags == DrawLinesMatchesFlags::DEFAULT) - outImage = image.clone(); - - for(size_t i = 0; iinit(N); + /* create and initialize a bitarray */ + counter = new bitarray; + counter->init( N ); - UINT32 *res = new UINT32[K*(D+1)]; - UINT64 *chunks = new UINT64[m]; - UINT32 * presults = results; - UINT32 *pnumres = numres; + UINT32 *res = new UINT32[K * ( D + 1 )]; + UINT64 *chunks = new UINT64[m]; + UINT32 * presults = results; + UINT32 *pnumres = numres; - /* make a copy of input queries */ - cv::Mat queries_clone = queries.clone(); + /* make a copy of input queries */ + cv::Mat queries_clone = queries.clone(); - /* set a pointer to first query (row) */ - UINT8 *pq = queries_clone.ptr(); + /* set a pointer to first query (row) */ + UINT8 *pq = queries_clone.ptr(); - /* loop over number of descriptors */ - for (size_t i=0; ierase(); + memset( numres, 0, ( B + 1 ) * sizeof ( *numres ) ); - counter->erase(); - memset(numres, 0, (B+1)*sizeof(*numres)); + split( chunks, Query, m, mplus, b ); - split(chunks, Query, m, mplus, b); - - /* the growing search radius per substring */ - int s; + /* the growing search radius per substring */ + int s; - /* current b: for the first mplus substrings it is b, for the rest it is (b-1) */ - int curb = b; + /* current b: for the first mplus substrings it is b, for the rest it is (b-1) */ + int curb = b; - for (s = 0; s <= d && n < maxres; s++) + for ( s = 0; s <= d && n < maxres; s++ ) + { + for ( int k = 0; k < m; k++ ) { - for (int k=0; kget(index)) - { /* if it is not a duplicate */ - counter->set(index); - hammd = match(codes.ptr() + (UINT64)index*(B_over_8), Query, B_over_8); - - nc++; - if (hammd <= D && numres[hammd] < maxres) - res[hammd * K + numres[hammd]] = index+1; - - numres[hammd]++; - } - } - } - - /* end of processing */ - while (++bit < s && power[bit] == power[bit+1]-1) { - bitstr ^= (UINT64)1 << (power[bit]-1); - power[bit] = bit; - } - if (bit == s) - break; - } + index = arr[c]; + if( !counter->get( index ) ) + { /* if it is not a duplicate */ + counter->set( index ); + hammd = match( codes.ptr() + (UINT64) index * ( B_over_8 ), Query, B_over_8 ); + + nc++; + if( hammd <= D && numres[hammd] < maxres ) + res[hammd * K + numres[hammd]] = index + 1; + + numres[hammd]++; + } } - - n = n + numres[s*m+k]; - if (n >= maxres) + } + + /* end of processing */ + while ( ++bit < s && power[bit] == power[bit + 1] - 1 ) + { + bitstr ^= (UINT64) 1 << ( power[bit] - 1 ); + power[bit] = bit; + } + if( bit == s ) break; } + } + + n = n + numres[s * m + k]; + if( n >= maxres ) + break; } - - n = 0; - for (s = 0; s <= D && (int)n < K; s++ ) - { - for (int c = 0; c < (int)numres[s] && (int)n < K; c++) - results[n++] = res[s*K + c]; - } + } + + n = 0; + for ( s = 0; s <= D && (int) n < K; s++ ) + { + for ( int c = 0; c < (int) numres[s] && (int) n < K; c++ ) + results[n++] = res[s * K + c]; + } } /* constructor 2 */ -Mihasher::Mihasher(int _B, int _m) +Mihasher::Mihasher( int _B, int _m ) { - B = _B; - B_over_8 = B/8; - m = _m; - b = ceil((double)B/m); - - /* assuming that B/2 is large enough radius to include - all of the k nearest neighbors */ - D = ceil(B/2.0); - d = ceil((double)D/m); - - /* mplus is the number of chunks with b bits - (m-mplus) is the number of chunks with (b-1) bits */ - mplus = B - m * (b-1); - - xornum = new UINT32 [d+2]; - xornum[0] = 0; - for (int i=0; i<=d; i++) - xornum[i+1] = xornum[i] + choose(b, i); - - H = new SparseHashtable[m]; - - /* H[i].init might fail */ - for (int i=0; i MAX_B || b > (int)(sizeof(UINT64)*8)) - return 1; - - size = UINT64_1 << (b-5); // size = 2 ^ b - table = (BucketGroup*) calloc(size, sizeof(BucketGroup)); + b = _b; - return 0; + if( b < 5 || b > MAX_B || b > (int) ( sizeof(UINT64) * 8 ) ) + return 1; + + size = UINT64_1 << ( b - 5 ); // size = 2 ^ b + table = (BucketGroup*) calloc( size, sizeof(BucketGroup) ); + + return 0; } /* destructor */ -SparseHashtable::~SparseHashtable () { - free(table); +SparseHashtable::~SparseHashtable() +{ + free( table ); } /* insert data */ -void SparseHashtable::insert(UINT64 index, UINT32 data) { - table[index >> 5].insert((int)(index % 32), data); +void SparseHashtable::insert( UINT64 index, UINT32 data ) +{ + table[index >> 5].insert( (int) ( index % 32 ), data ); } /* query data */ -UINT32* SparseHashtable::query(UINT64 index, int *Size) { - return table[index >> 5].query((int)(index % 32), Size); +UINT32* SparseHashtable::query( UINT64 index, int *Size ) +{ + return table[index >> 5].query( (int) ( index % 32 ), Size ); }