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Commit eb9bb7d5 authored by biagio montesano's avatar biagio montesano
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Reformatting and deletion of useless files. Part of documentation updated

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modules/line_descriptor/doc/BinaryDescriptor.rst~ deleted 100644 → 0
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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
modules/line_descriptor/doc/BinaryDescriptor.rst → modules/line_descriptor/doc/binary_descriptor.rst
View file @ eb9bb7d5
......@@ -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>`_
modules/line_descriptor/doc/drawing_functions.rst 0 → 100644
View file @ eb9bb7d5
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>`_
modules/line_descriptor/doc/line_descriptor.rst
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......@@ -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
----------
......
modules/line_descriptor/doc/line_descriptor.rst~ deleted 100644 → 0
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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.
modules/line_descriptor/doc/matching.rst 0 → 100644
View file @ eb9bb7d5
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.
modules/line_descriptor/include/opencv2/line_descriptor.hpp
View file @ eb9bb7d5
......@@ -47,7 +47,7 @@
namespace cv
{
CV_EXPORTS bool initModule_line_descriptor(void);
CV_EXPORTS bool initModule_line_descriptor( void );
}
......
modules/line_descriptor/include/opencv2/line_descriptor/array32.hpp
View file @ eb9bb7d5
......@@ -45,7 +45,8 @@
#include "types.hpp"
class Array32 {
class Array32
{
private:
static double ARRAY_RESIZE_FACTOR;
......@@ -53,7 +54,7 @@ class Array32 {
public:
/* set ARRAY_RESIZE_FACTOR */
static void setArrayResizeFactor(double arf);
static void setArrayResizeFactor( double arf );
/* constructor */
Array32();
......@@ -65,10 +66,10 @@ class Array32 {
void cleanup();
/* push data */
void push(UINT32 data);
void push( UINT32 data );
/* insert data at given index */
void insert(UINT32 index, UINT32 data);
void insert( UINT32 index, UINT32 data );
/* return data */
UINT32* data();
......@@ -80,18 +81,17 @@ class Array32 {
UINT32 capacity();
/* definition of operator = */
void operator= (const Array32&);
void operator=( const Array32& );
/* print data */
void print();
/* initializer */
void init(int size);
void init( int size );
/* data */
UINT32 *arr;
};
#endif
modules/line_descriptor/include/opencv2/line_descriptor/bitarray.hpp
View file @ eb9bb7d5
......@@ -49,7 +49,8 @@
#include <string.h>
/* class defining a sequence of bits */
class bitarray {
class bitarray
{
public:
/* pointer to bits sequence and sequence's length */
......@@ -64,43 +65,45 @@ class bitarray {
}
/* constructor setting sequence's length */
bitarray(UINT64 _bits) {
init(_bits);
bitarray( UINT64 _bits )
{
init( _bits );
}
/* initializer of private fields */
void init(UINT64 _bits)
void init( UINT64 _bits )
{
length = (UINT32)ceil(_bits/32.00);
length = (UINT32) ceil( _bits / 32.00 );
arr = new UINT32[length];
erase();
}
/* destructor */
~bitarray() {
if (arr)
~bitarray()
{
if( arr )
delete[] arr;
}
inline void flip(UINT64 index)
inline void flip( UINT64 index )
{
arr[index >> 5] ^= ((UINT32)0x01) << (index % 32);
arr[index >> 5] ^= ( (UINT32) 0x01 ) << ( index % 32 );
}
inline void set(UINT64 index)
inline void set( UINT64 index )
{
arr[index >> 5] |= ((UINT32)0x01) << (index % 32);
arr[index >> 5] |= ( (UINT32) 0x01 ) << ( index % 32 );
}
inline UINT8 get(UINT64 index)
inline UINT8 get( UINT64 index )
{
return (arr[index >> 5] & (((UINT32)0x01) << (index % 32))) != 0;
return ( arr[index >> 5] & ( ( (UINT32) 0x01 ) << ( index % 32 ) ) ) != 0;
}
/* reserve menory for an UINT32 */
inline void erase()
{
memset(arr, 0, sizeof(UINT32) * length);
memset( arr, 0, sizeof(UINT32) * length );
}
};
......
modules/line_descriptor/include/opencv2/line_descriptor/bitops.hpp
View file @ eb9bb7d5
......@@ -49,57 +49,42 @@
#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)
switch ( codelb )
{
case 4: // 32 bit
return popcnt(*(UINT32*)P ^ *(UINT32*)Q);
return popcnt( *(UINT32*) P ^ *(UINT32*) Q );
break;
case 8: // 64 bit
return popcntll(((UINT64*)P)[0] ^ ((UINT64*)Q)[0]);
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]);
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]);
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]);
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]];
for ( int i = 0; i < codelb; i++ )
output += lookup[P[i] ^ Q[i]];
return output;
break;
}
......@@ -108,29 +93,29 @@ inline int match(UINT8*P, UINT8*Q, int codelb)
}
/* 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 mask = b == 64 ? 0xFFFFFFFFFFFFFFFFLLU : ( ( UINT64_1 << b ) - UINT64_1 );
for (int i=0; i<m; i++)
for ( int i = 0; i < m; i++ )
{
while (nbits < b)
while ( nbits < b )
{
temp |= ((UINT64)code[nbyte++] << nbits);
temp |= ( (UINT64) code[nbyte++] << nbits );
nbits += 8;
}
chunks[i] = temp & mask;
temp = b==64 ? 0x0 : temp >> b;
temp = b == 64 ? 0x0 : temp >> b;
nbits -= b;
if (i == mplus-1)
if( i == mplus - 1 )
{
b--; /* b <= 63 */
mask = ((UINT64_1 << b) - UINT64_1);
mask = ( ( UINT64_1 << b ) - UINT64_1 );
}
}
}
......@@ -138,7 +123,7 @@ inline void split (UINT64 *chunks, UINT8 *code, int m, int mplus, int b)
/* 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)
inline UINT64 next_set_of_n_elements( UINT64 x )
{
UINT64 smallest, ripple, new_smallest;
......@@ -151,24 +136,24 @@ inline UINT64 next_set_of_n_elements(UINT64 x)
}
/* 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--)
for ( int j = ( b - 1 ); j >= 0; j-- )
{
printf("%llu", (long long int) tmp/(1 << j));
tmp = tmp - (tmp/(1 << j)) * (1 << 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++)
for ( int k = 0; k < r; k++ )
{
nchooser *= n-k;
nchooser /= k+1;
nchooser *= n - k;
nchooser /= k + 1;
}
return nchooser;
......
modules/line_descriptor/include/opencv2/line_descriptor/bucket_group.hpp
View file @ eb9bb7d5
......@@ -47,7 +47,8 @@
#include "array32.hpp"
#include "bitarray.hpp"
class BucketGroup {
class BucketGroup
{
public:
/* constructor */
......@@ -57,10 +58,10 @@ class BucketGroup {
~BucketGroup();
/* insert data into the bucket */
void insert(int subindex, UINT32 data);
void insert( int subindex, UINT32 data );
/* perform a query to the bucket */
UINT32* query(int subindex, int *size);
UINT32* query( int subindex, int *size );
/* data fields */
UINT32 empty;
......
modules/line_descriptor/include/opencv2/line_descriptor/line_structure.hpp
View file @ eb9bb7d5
......@@ -8,13 +8,13 @@
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.
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:
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.
......@@ -26,17 +26,17 @@ are permitted provided that the following conditions are met:
* 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.
*/
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_
......@@ -44,10 +44,11 @@ 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
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
};
......@@ -55,10 +56,10 @@ struct OctaveLine{
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);
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;
......
modules/line_descriptor/include/opencv2/line_descriptor/mihasher.hpp
View file @ eb9bb7d5
......@@ -51,8 +51,8 @@
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
class Mihasher {
class Mihasher
{
private:
/* Bits per code */
......@@ -106,21 +106,21 @@ class Mihasher {
~Mihasher();
/* constructor 2 */
Mihasher(int B, int m);
Mihasher( int B, int m );
/* K setter */
void setK(int K);
void setK( int K );
/* populate tables */
void populate(cv::Mat & codes, UINT32 N, int dim1codes);
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);
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);
void query( UINT32 * results, UINT32* numres/*, qstat *stats*/, UINT8 *q, UINT64 * chunks, UINT32 * res );
};
#endif
modules/line_descriptor/include/opencv2/line_descriptor/sparse_hashtable.hpp
View file @ eb9bb7d5
......@@ -66,13 +66,13 @@ class SparseHashtable
~SparseHashtable();
/* initializer */
int init(int _b);
int init( int _b );
/* insert data */
void insert(UINT64 index, UINT32 data);
void insert( UINT64 index, UINT32 data );
/* query data */
UINT32* query(UINT64 index, int* size);
UINT32* query( UINT64 index, int* size );
/* Bits per index */
int b;
......
modules/line_descriptor/samples/bak.txt deleted 100644 → 0
View file @ 79b8b234
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})
modules/line_descriptor/samples/compute_descriptors.cpp
View file @ eb9bb7d5
......@@ -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::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();
}
int main( int argc, char** argv )
{
/* get parameters from comand line */
/* get parameters from command line */
CommandLineParser parser( argc, argv, keys );
String image_path = parser.get<String>( 0 );
if(image_path.empty())
if( image_path.empty() )
{
help();
return -1;
}
/* load image */
cv::Mat imageMat = imread(image_path, 1);
if(imageMat.data == NULL)
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 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);
bd->detect( imageMat, keylines, mask );
/* select only lines from first octave */
std::vector<KeyLine> octave0;
for(size_t i = 0; i<keylines.size(); i++)
for ( size_t i = 0; i < keylines.size(); i++ )
{
if(keylines[i].octave == 0)
octave0.push_back(keylines[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);
bd->compute( imageMat, octave0, descriptors );
}
modules/line_descriptor/samples/knn_matching.cpp
View file @ eb9bb7d5
......@@ -53,33 +53,29 @@
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;
/* convert input char to a long */
dividend = (long)dividend_char;
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( dividend == 0 )
bin_vector = std::vector<int>( 8, 0 );
/* else, apply classic decimal to binary conversion */
else
......@@ -88,20 +84,21 @@ uchar invertSingleBits (uchar dividend_char, int numBits)
{
bin_num = dividend % 2;
dividend /= 2;
bin_vector.push_back(bin_num);
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());
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++)
for ( int index = 0; index < numBits; index++ )
{
if(bin_vector[index] == 0)
if( bin_vector[index] == 0 )
bin_vector[index] = 1;
else
......@@ -110,8 +107,8 @@ uchar invertSingleBits (uchar dividend_char, int numBits)
/* reconvert to decimal */
uchar result;
for(int i = (int)bin_vector.size()-1; i>=0; i--)
result += bin_vector[i]*pow(2, i);
for ( int i = (int) bin_vector.size() - 1; i >= 0; i-- )
result += bin_vector[i] * pow( 2, i );
return result;
}
......@@ -123,39 +120,37 @@ int main( int argc, char** argv )
String image_path1 = parser.get<String>( 0 );
String image_path2 = parser.get<String>( 1 );
if(image_path1.empty() || image_path2.empty())
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);
cv::Mat imageMat1 = imread( image_path1, 1 );
cv::Mat imageMat2 = imread( image_path2, 1 );
if(imageMat1.data == NULL || imageMat2.data == NULL)
if( imageMat1.data == NULL || imageMat2.data == NULL )
{
std::cout << "Error, images could not be loaded. Please, check their paths"
<< std::endl;
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);
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);
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);
bd->compute( imageMat1, keylines1, descr1 );
bd->compute( imageMat2, keylines2, descr2 );
/* create a BinaryDescriptorMatcher object */
Ptr<BinaryDescriptorMatcher> bdm = BinaryDescriptorMatcher::createBinaryDescriptorMatcher();
......@@ -164,27 +159,27 @@ int main( int argc, char** argv )
Mat descr2Copy = descr1.clone();
/* randomly change some bits in original descriptors */
srand (time(NULL));
srand( time( NULL ) );
for(int j = 0; j<descr1.rows; j++)
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);
uchar u = descr1.at<uchar>( j, randCol );
/* change bits */
for(int k = 1; k<=5; k++)
for ( int k = 1; k <= 5; k++ )
{
/* copy current row to train matrix */
descr2Copy.push_back(descr1.row(j));
descr2Copy.push_back( descr1.row( j ) );
/* invert k bits */
uchar uc = invertSingleBits(u, k);
uchar uc = invertSingleBits( u, k );
/* update current row in train matrix */
descr2Copy.at<uchar>(descr2Copy.rows-1, randCol) = uc;
descr2Copy.at<uchar>( descr2Copy.rows - 1, randCol ) = uc;
}
}
......@@ -192,19 +187,17 @@ int main( int argc, char** argv )
std::vector<std::vector<DMatch> > matches;
/* require knn match */
bdm->knnMatch(descr1, descr2, matches, 6);
bdm->knnMatch( descr1, descr2, matches, 6 );
/* visualize matches and Hamming distances */
for(size_t v = 0; v<matches.size(); v++)
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;
std::cout << dm.queryIdx << " " << dm.trainIdx << " " << dm.distance << std::endl;
}
}
}
modules/line_descriptor/samples/lines_extraction.cpp
View file @ eb9bb7d5
......@@ -53,17 +53,12 @@ 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 )
......@@ -72,21 +67,21 @@ int main( int argc, char** argv )
CommandLineParser parser( argc, argv, keys );
String image_path = parser.get<String>( 0 );
if(image_path.empty())
if( image_path.empty() )
{
help();
return -1;
}
/* load image */
cv::Mat imageMat = imread(image_path, 1);
if(imageMat.data == NULL)
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);
cv::Mat mask = Mat::ones( imageMat.size(), CV_8UC1 );
/* create a pointer to a BinaryDescriptor object with deafult parameters */
Ptr<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor();
......@@ -95,34 +90,33 @@ int main( int argc, char** argv )
vector<KeyLine> lines;
/* extract lines */
bd->detect(imageMat, lines, mask);
std::cout << lines.size() << std::endl;
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++)
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)
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));
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);
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);
line( output, pt1, pt2, Scalar( B, G, R ), 5 );
}
}
/* show lines on image */
imshow("Lines", output);
imshow( "Lines", output );
waitKey();
}
modules/line_descriptor/samples/matching.cpp
View file @ eb9bb7d5
......@@ -52,17 +52,13 @@
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>"
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;
}
......@@ -74,58 +70,53 @@ int main( int argc, char** argv )
String image_path1 = parser.get<String>( 0 );
String image_path2 = parser.get<String>( 1 );
if(image_path1.empty() || image_path2.empty())
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);
cv::Mat imageMat1 = imread( image_path1, 1 );
cv::Mat imageMat2 = imread( image_path2, 1 );
waitKey();
if(imageMat1.data == NULL || imageMat2.data == NULL)
if( imageMat1.data == NULL || imageMat2.data == NULL )
{
std::cout << "Error, images could not be loaded. Please, check their path"
<< std::endl;
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);
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);
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);
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);
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::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);
imshow( "Matches", outImg );
waitKey();
}
modules/line_descriptor/src/array32.cpp
View file @ eb9bb7d5
......@@ -42,60 +42,56 @@
#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;
}
/* constructor */
Array32::Array32 ()
Array32::Array32()
{
arr = NULL;
}
/* definition of operator =
Array32& Array32::operator = (const Array32 &rhs) */
void 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();
}
/* 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)
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);
std::cout << "ALERT!!!! Not enough memory, operation aborted!" << std::endl;
exit( 0 );
}
else
{
......@@ -111,7 +107,7 @@ void Array32::push(UINT32 Data)
else
{
arr = (UINT32*) malloc ((2+ARRAY_RESIZE_ADD_FACTOR)*sizeof(UINT32));
arr = (UINT32*) malloc( ( 2 + ARRAY_RESIZE_ADD_FACTOR ) * sizeof(UINT32) );
arr[0] = 1;
arr[1] = 1;
arr[2] = Data;
......@@ -119,18 +115,19 @@ void Array32::push(UINT32 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)
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);
std::cout << "ALERT!!!! Not enough memory, operation aborted!" << std::endl;
exit( 0 );
}
else
{
......@@ -138,54 +135,54 @@ void Array32::insert(UINT32 index, UINT32 Data) {
}
}
memmove(arr+(2+index)+1, arr+(2+index), (arr[0]-index)*sizeof(*arr));
memmove( arr + ( 2 + index ) + 1, arr + ( 2 + index ), ( arr[0] - index ) * sizeof ( *arr ) );
arr[2+index] = Data;
arr[2 + index] = Data;
arr[0]++;
}
else
{
arr = (UINT32*) malloc (3*sizeof(UINT32));
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 capacity */
UINT32 Array32::capacity ()
UINT32 Array32::capacity()
{
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)
if( arr == NULL )
{
arr = (UINT32*) malloc ((2+Size)*sizeof(UINT32));
arr = (UINT32*) malloc( ( 2 + Size ) * sizeof(UINT32) );
arr[0] = 0;
arr[1] = Size;
}
......
modules/line_descriptor/src/binary_descriptor.cpp
View file @ eb9bb7d5
......@@ -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<KeyLine>& keylines, const Mat& mask )
{
......@@ -389,7 +418,7 @@ void BinaryDescriptor::detectImpl( const Mat& imageSrc, std::vector<KeyLine>& 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<KeyLine>& 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<KeyLine>& 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<KeyLine>& 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
......
modules/line_descriptor/src/bucket_group.cpp
View file @ eb9bb7d5
......@@ -52,44 +52,44 @@ BucketGroup::BucketGroup()
/* destructor */
BucketGroup::~BucketGroup()
{
if (group != NULL)
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)
if( group == NULL )
{
group = new Array32();
group->push(0);
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)))
if( ! ( empty & ( (UINT32) 1 << subindex ) ) )
{
group->insert(end, group->arr[end+2]);
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; i<totones+1; i++)
group->arr[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))
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];
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
......
modules/line_descriptor/src/draw.cpp
View file @ eb9bb7d5
This diff is collapsed.
modules/line_descriptor/src/line_descriptor_init.cpp
View file @ eb9bb7d5
......@@ -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();
......
modules/line_descriptor/src/mihasher.cpp
View file @ eb9bb7d5
This diff is collapsed.
modules/line_descriptor/src/precomp.cpp deleted 100644 → 0
View file @ 79b8b234
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// 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:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's 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.
//
//M*/
#include "precomp.hpp"
modules/line_descriptor/src/sparse_hashtable.cpp
View file @ eb9bb7d5
......@@ -52,31 +52,34 @@ SparseHashtable::SparseHashtable()
}
/* initializer */
int SparseHashtable::init(int _b)
int SparseHashtable::init( int _b )
{
b = _b;
if (b < 5 || b > MAX_B || b > (int)(sizeof(UINT64)*8))
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));
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 );
}
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