Commit eb9bb7d5 authored by biagio montesano's avatar biagio montesano

Reformatting and deletion of useless files. Part of documentation updated

parent 79b8b234
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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 <http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_feature_detectors.html?highlight=featuredetector#featuredetector>`_ and `DescriptorExtractor <http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_descriptor_extractors.html?highlight=extractor#DescriptorExtractor : public Algorithm>`_.
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 &parameters = 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> BinaryDescriptor::createBinaryDescriptor()
.. ocv:function:: Ptr<BinaryDescriptor> 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<KeyLine>& 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<KeyLine>& keylines, Mat& mask=Mat() )
.. ocv:function:: void detect( const vector<Mat>& images, vector<vector<KeyLine> >& keylines, vector<Mat>& masks=vector<Mat>() ) 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<KeyLine>& keylines, Mat& descriptors ) const
.. ocv:function:: void compute( const vector<Mat>& images, vector<vector<KeyLine> >& keylines, vector<Mat>& 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
......@@ -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 <line_descriptor.html>`_
* `Matching with binary descriptors <matching.html>`_
* `Drawing Function of Keylines and Matches <drawing_functions.html>`_
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<KeyLine>& keylines1, const Mat& img2, const std::vector<KeyLine>& keylines2, const std::vector<DMatch>& matches1to2, Mat& outImg, const Scalar& matchColor=Scalar::all(-1), const Scalar& singleLineColor=Scalar::all(-1), const std::vector<char>& matchesMask=std::vector<char>(), 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<KeyLine>& 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 <line_descriptor.html>`_
* `BinaryDescriptor Class <binary_descriptor.html>`_
* `Matching with binary descriptors <matching.html>`_
......@@ -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.html>`_
* `BinaryDescriptor Class <binary_descriptor.html>`_
* `Matching with binary descriptors <matching.html>`_
* `Drawing Function of Keylines and Matches <drawing_functions.html>`_
References
----------
......
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 <http://docs.opencv.org/modules/core/doc/drawing_functions.html#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 <BinaryDescriptor.html>`
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.
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 <http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_descriptor_matchers.html?highlight=bfmatcher#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> BinaryDescriptorMatcher::createBinaryDescriptorMatcher()
BinaryDescriptorMatcher::add
----------------------------
Store locally new descriptors to be inserted in dataset, without updating dataset.
.. ocv:function:: void BinaryDescriptorMatcher::add( const std::vector<Mat>& 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<DMatch>& matches, const Mat& mask=Mat() ) const
.. ocv:function:: void BinaryDescriptorMatcher::match( const Mat& queryDescriptors, std::vector<DMatch>& matches, const std::vector<Mat>& masks=std::vector<Mat>() )
: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<std::vector<DMatch> >& matches, int k, const Mat& mask=Mat(), bool compactResult=false ) const
.. ocv:function:: void BinaryDescriptorMatcher::knnMatch( const Mat& queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k, const std::vector<Mat>& masks=std::vector<Mat>(), 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<std::vector<DMatch> >& matches, float maxDistance, const Mat& mask=Mat(), bool compactResult=false ) const
.. ocv:function:: void BinaryDescriptorMatcher::radiusMatch( const Mat& queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance, const std::vector<Mat>& masks=std::vector<Mat>(), 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 <line_descriptor.html>`_
* `BinaryDescriptor Class <binary_descriptor.html>`_
* `Drawing Function of Keylines and Matches <drawing_functions.html>`_
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.
......@@ -47,7 +47,7 @@
namespace cv
{
CV_EXPORTS bool initModule_line_descriptor(void);
CV_EXPORTS bool initModule_line_descriptor( void );
}
......
......@@ -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;
};
......
......@@ -49,59 +49,62 @@
#include <string.h>
/* 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 );
}
};
......
......@@ -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<codelb; i++)
output+= lookup[P[i] ^ Q[i]];
return output;
break;
}
return -1;
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 < codelb; i++ )
output += lookup[P[i] ^ Q[i]];
return output;
break;
}
return -1;
}
/* splitting function (b <= 64) */
inline void split (UINT64 *chunks, UINT8 *code, int m, int mplus, int b)
inline void split( UINT64 *chunks, UINT8 *code, int m, int mplus, int b )
{
UINT64 temp = 0x0;
int nbits = 0;
int nbyte = 0;
UINT64 mask = b==64 ? 0xFFFFFFFFFFFFFFFFLLU : ((UINT64_1 << b) - UINT64_1);
UINT64 temp = 0x0;
int nbits = 0;
int nbyte = 0;
UINT64 mask = b == 64 ? 0xFFFFFFFFFFFFFFFFLLU : ( ( UINT64_1 << b ) - UINT64_1 );
for ( int i = 0; i < m; i++ )
{
while ( nbits < b )
{
temp |= ( (UINT64) code[nbyte++] << nbits );
nbits += 8;
}
chunks[i] = temp & mask;
temp = b == 64 ? 0x0 : temp >> b;
nbits -= b;
for (int i=0; i<m; i++)
if( i == mplus - 1 )
{
while (nbits < b)
{
temp |= ((UINT64)code[nbyte++] << nbits);
nbits += 8;
}
chunks[i] = temp & mask;
temp = b==64 ? 0x0 : temp >> 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
......@@ -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;
};
......
......@@ -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 <vector>
/* 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<double> 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<double> descriptor;
};
// Specifies a vector of lines.
......@@ -87,29 +88,29 @@ typedef std::vector<SingleLine> 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<float> 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<float> descriptor;
};
// Specifies a vector of lines.
......
......@@ -51,76 +51,76 @@
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
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
......@@ -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;
};
......
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})
......@@ -41,7 +41,6 @@
#include <opencv2/line_descriptor.hpp>
#include "opencv2/core/utility.hpp"
#include "opencv2/core/private.hpp"
#include <opencv2/imgproc.hpp>
......@@ -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 <path_to_input_image>"
<< 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 <path_to_input_image>"
<< std::endl;
}
int main( int argc, char** argv )
{
/* get parameters from comand line */
CommandLineParser parser( argc, argv, keys );
String image_path = parser.get<String>( 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<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor();
/* compute lines */
std::vector<KeyLine> keylines;
bd->detect(imageMat, keylines, mask);
std::vector<KeyLine> 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);
writeMat(descriptors, "old_code", 0);
/* get parameters from command line */
CommandLineParser parser( argc, argv, keys );
String image_path = parser.get<String>( 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<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor();
/* compute lines */
std::vector<KeyLine> keylines;
bd->detect( imageMat, keylines, mask );
/* select only lines from first octave */
std::vector<KeyLine> 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 );
}
......@@ -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 <path_to_input_image 1>"
<< "<path_to_input_image 2>" << 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 <path_to_input_image 1>" << "<path_to_input_image 2>" << std::endl;
}
/* invert numBits bits in input char */
uchar invertSingleBits (uchar dividend_char, int numBits)
uchar invertSingleBits( uchar dividend_char, int numBits )
{
std::vector<int> bin_vector;
long dividend;
long bin_num;
std::vector<int> 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<int>(8, 0);
/*if a 0 has been obtained, just generate a 8-bit long vector of zeros */
if( dividend == 0 )
bin_vector = std::vector<int>( 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<int> 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<int> zeros( 8 - bin_vector.size(), 0 );
bin_vector.insert( bin_vector.end(), zeros.begin(), zeros.end() );
}
/* invert numBits bits */
for(int index = 0; index<numBits; index++)
{
if(bin_vector[index] == 0)
bin_vector[index] = 1;
/* invert numBits bits */
for ( int index = 0; index < numBits; index++ )
{
if( bin_vector[index] == 0 )
bin_vector[index] = 1;
else
bin_vector[index] = 0;
}
else
bin_vector[index] = 0;
}
/* reconvert to decimal */
uchar result;
for(int i = (int)bin_vector.size()-1; i>=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<String>( 0 );
String image_path2 = parser.get<String>( 1 );
if(image_path1.empty() || image_path2.empty())
/* get parameters from comand line */
CommandLineParser parser( argc, argv, keys );
String image_path1 = parser.get<String>( 0 );
String image_path2 = parser.get<String>( 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<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor();
/* compute lines */
std::vector<KeyLine> 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<BinaryDescriptorMatcher> 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<uchar>( 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<uchar>( 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<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor();
/* compute lines */
std::vector<KeyLine> 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<std::vector<DMatch> > matches;
/* create a BinaryDescriptorMatcher object */
Ptr<BinaryDescriptorMatcher> 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<descr1.rows; j++)
{
/* select a random column */
int randCol = rand() % 32;
/* get correspondent data */
uchar u = descr1.at<uchar>(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<uchar>(descr2Copy.rows-1, randCol) = uc;
}
}
/* prepare a structure to host matches */
std::vector<std::vector<DMatch> > matches;
/* require knn match */
bdm->knnMatch(descr1, descr2, matches, 6);
/* visualize matches and Hamming distances */
for(size_t v = 0; v<matches.size(); v++)
/* visualize matches and Hamming distances */
for ( size_t v = 0; v < matches.size(); v++ )
{
for ( size_t m = 0; m < matches[v].size(); m++ )
{
for(size_t m = 0; m<matches[v].size(); m++)
{
DMatch dm = matches[v][m];
std::cout << dm.queryIdx << " " << dm.trainIdx << " "
<< dm.distance << std::endl;
}
DMatch dm = matches[v][m];
std::cout << dm.queryIdx << " " << dm.trainIdx << " " << dm.distance << std::endl;
}
}
}
......@@ -53,76 +53,70 @@ using namespace cv;
using namespace std;
static const char* keys =
{
"{@image_path | | Image path }"
};
{ "{@image_path | | Image path }" };
static void help()
{
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 <path_to_input_image>" << 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 <path_to_input_image>" << endl;
}
int main( int argc, char** argv )
{
/* get parameters from comand line */
CommandLineParser parser( argc, argv, keys );
String image_path = parser.get<String>( 0 );
if(image_path.empty())
/* get parameters from comand line */
CommandLineParser parser( argc, argv, keys );
String image_path = parser.get<String>( 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<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor();
/* create a structure to store extracted lines */
vector<KeyLine> 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<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor();
/* create a structure to store extracted lines */
vector<KeyLine> 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<lines.size(); i++)
{
KeyLine kl = lines[i];
if(kl.octave == 0)
{
/* get a random color */
int R = (rand() % (int)(255 + 1));
int G = (rand() % (int)(255 + 1));
int B = (rand() % (int)(255 + 1));
/* get extremes of line */
Point pt1 = Point(kl.startPointX, kl.startPointY);
Point pt2 = Point(kl.endPointX, kl.endPointY);
/* draw line */
line(output, pt1, pt2, Scalar(B,G,R), 5);
}
/* get extremes of line */
Point pt1 = Point( kl.startPointX, kl.startPointY );
Point pt2 = Point( kl.endPointX, kl.endPointY );
/* draw line */
line( output, pt1, pt2, Scalar( B, G, R ), 5 );
}
/* show lines on image */
imshow("Lines", output);
waitKey();
}
/* show lines on image */
imshow( "Lines", output );
waitKey();
}
......@@ -52,80 +52,71 @@
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 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 <path_to_input_image 1>"
<< "<path_to_input_image 2>" << 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 <path_to_input_image 1>"
<< "<path_to_input_image 2>" << std::endl;
}
int main( int argc, char** argv )
{
/* get parameters from comand line */
CommandLineParser parser( argc, argv, keys );
String image_path1 = parser.get<String>( 0 );
String image_path2 = parser.get<String>( 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<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor();
/* compute lines */
std::vector<KeyLine> 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<BinaryDescriptorMatcher> bdm = BinaryDescriptorMatcher::createBinaryDescriptorMatcher();
/* require match */
std::vector<DMatch> matches;
bdm->match(descr1, descr2, matches);
/* plot matches */
cv::Mat outImg;
std::vector<char> 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<String>( 0 );
String image_path2 = parser.get<String>( 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<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor();
/* compute lines */
std::vector<KeyLine> 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<BinaryDescriptorMatcher> bdm = BinaryDescriptorMatcher::createBinaryDescriptorMatcher();
/* require match */
std::vector<DMatch> matches;
bdm->match( descr1, descr2, matches );
/* plot matches */
cv::Mat outImg;
std::vector<char> mask( matches.size(), 1 );
drawLineMatches( imageMat1, keylines1, imageMat2, keylines2, matches, outImg, Scalar::all( -1 ), Scalar::all( -1 ), mask,
DrawLinesMatchesFlags::DEFAULT );
imshow( "Matches", outImg );
waitKey();
}
......@@ -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<UINT32*>
(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<UINT32*>( 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<UINT32*>
(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<UINT32*>( 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;
}
}
This diff is collapsed.
......@@ -44,10 +44,10 @@
namespace cv
{
CV_INIT_ALGORITHM(BinaryDescriptor, "BINARY.DESCRIPTOR",);
CV_INIT_ALGORITHM(BinaryDescriptorMatcher, "BINARY.DESCRIPTOR.MATCHER",);
CV_INIT_ALGORITHM( BinaryDescriptor, "BINARY.DESCRIPTOR", );
CV_INIT_ALGORITHM( BinaryDescriptorMatcher, "BINARY.DESCRIPTOR.MATCHER", );
bool initModule_line_descriptor(void)
bool initModule_line_descriptor( void )
{
bool all = true;
all &= !BinaryDescriptor_info_auto.name().empty();
......
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