biagio montesano
11 years ago
parent
79b8b2342f
commit
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74 changed files with 1894 additions and 4263 deletions
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FILE(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE SHARED_LIBRARY FILES |
||||
"/home/ubisum/src/opencv/lib/libopencv_line_descriptor.so.3.0.0" |
||||
"/home/ubisum/src/opencv/lib/libopencv_line_descriptor.so.3.0" |
||||
"/home/ubisum/src/opencv/lib/libopencv_line_descriptor.so" |
||||
) |
||||
FOREACH(file |
||||
"$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libopencv_line_descriptor.so.3.0.0" |
||||
"$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libopencv_line_descriptor.so.3.0" |
||||
"$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libopencv_line_descriptor.so" |
||||
) |
||||
IF(EXISTS "${file}" AND |
||||
NOT IS_SYMLINK "${file}") |
||||
FILE(RPATH_CHANGE |
||||
FILE "${file}" |
||||
OLD_RPATH "/home/ubisum/src/opencv/lib:" |
||||
NEW_RPATH "/usr/local/lib") |
||||
IF(CMAKE_INSTALL_DO_STRIP) |
||||
EXECUTE_PROCESS(COMMAND "/usr/bin/strip" "${file}") |
||||
ENDIF(CMAKE_INSTALL_DO_STRIP) |
||||
ENDIF() |
||||
ENDFOREACH() |
||||
ENDIF(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "main") |
||||
|
||||
IF(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "main") |
||||
FILE(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/include/opencv2" TYPE FILE FILES "/home/ubisum/src/opencv/modules/line_descriptor/include/opencv2/line_descriptor.hpp") |
||||
ENDIF(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "main") |
||||
|
||||
IF(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "main") |
||||
FILE(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/include/opencv2/line_descriptor" TYPE FILE FILES "/home/ubisum/src/opencv/modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp") |
||||
ENDIF(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "main") |
||||
|
||||
IF(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "main") |
||||
FILE(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/include/opencv2/line_descriptor" TYPE FILE FILES "/home/ubisum/src/opencv/modules/line_descriptor/include/opencv2/line_descriptor/LineStructure.hpp") |
||||
ENDIF(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "main") |
||||
|
||||
@ -1,217 +0,0 @@ |
||||
BinaryDescriptor Class |
||||
====================== |
||||
|
||||
.. highlight:: cpp |
||||
|
||||
BinaryDescriptor Class implements both functionalities for detection of lines and computation of their binary descriptor. Class' interface is mainly based on the ones of classical detectors and extractors, such as Feature2d's `FeatureDetector <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 ¶meters = BinaryDescriptor::Params() ) |
||||
|
||||
:param parameters: configuration parameters :ocv:struct:`BinaryDescriptor::Params` |
||||
|
||||
If no argument is provided, constructor sets default values (see comments in the code snippet in previous section). Default values are strongly reccomended. |
||||
|
||||
|
||||
BinaryDescriptor::getNumOfOctaves |
||||
--------------------------------- |
||||
|
||||
Get current number of octaves |
||||
|
||||
.. ocv:function:: int BinaryDescriptor::getNumOfOctaves() |
||||
|
||||
|
||||
BinaryDescriptor::setNumOfOctaves |
||||
--------------------------------- |
||||
|
||||
Set number of octaves |
||||
|
||||
.. ocv:function:: void BinaryDescriptor::setNumOfOctaves( int octaves ) |
||||
|
||||
:param octaves: number of octaves |
||||
|
||||
|
||||
BinaryDescriptor::getWidthOfBand |
||||
-------------------------------- |
||||
|
||||
Get current width of bands |
||||
|
||||
.. ocv:function:: int BinaryDescriptor::getWidthOfBand() |
||||
|
||||
|
||||
BinaryDescriptor::setWidthOfBand |
||||
-------------------------------- |
||||
|
||||
Set width of bands |
||||
|
||||
.. ocv:function:: void BinaryDescriptor::setWidthOfBand( int width ) |
||||
|
||||
:param width: width of bands |
||||
|
||||
BinaryDescriptor::getReductionRatio |
||||
----------------------------------- |
||||
|
||||
Get current reduction ratio (used in Gaussian pyramids) |
||||
|
||||
.. ocv:function:: int BinaryDescriptor::getReductionRatio() |
||||
|
||||
|
||||
BinaryDescriptor::setReductionRatio |
||||
----------------------------------- |
||||
|
||||
Set reduction ratio (used in Gaussian pyramids) |
||||
|
||||
.. ocv:function:: void BinaryDescriptor::setReductionRatio( int rRatio ) |
||||
|
||||
:param rRatio: reduction ratio |
||||
|
||||
|
||||
BinaryDescriptor::createBinaryDescriptor |
||||
---------------------------------------- |
||||
|
||||
Create a BinaryDescriptor object with default parameters (or with the ones provided) and return a smart pointer to it |
||||
|
||||
.. ocv:function:: Ptr<BinaryDescriptor> 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 |
||||
@ -0,0 +1,68 @@ |
||||
Drawing Function of Keylines and Matches |
||||
======================================== |
||||
|
||||
.. highlight:: cpp |
||||
|
||||
drawLineMatches |
||||
--------------- |
||||
|
||||
Draws the found matches of keylines from two images. |
||||
|
||||
.. ocv:function:: void drawLineMatches( const Mat& img1, const std::vector<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>`_ |
||||
@ -1,139 +0,0 @@ |
||||
Binary descriptors for lines extracted from an image |
||||
==================================================== |
||||
|
||||
.. highlight:: cpp |
||||
|
||||
|
||||
Introduction |
||||
------------ |
||||
|
||||
|
||||
One of the most challenging activities in computer vision is the extraction of useful information from a given image. Such information, usually comes in the form of points that preserve some kind of property (for instance, they are scale-invariant) and are actually representative of input image. |
||||
|
||||
The goal of this module is seeking a new kind of representative information inside an image and providing the functionalities for its extraction and representation. In particular, differently from previous methods for detection of relevant elements inside an image, lines are extracted in place of points; a new class is defined ad hoc to summarize a line's properties, for reuse and plotting purposes. |
||||
|
||||
|
||||
A class to represent a line: KeyLine |
||||
------------------------------------ |
||||
|
||||
As aformentioned, it is been necessary to design a class that fully stores the information needed to characterize completely a line and plot it on image it was extracted from, when required. |
||||
|
||||
*KeyLine* class has been created for such goal; it is mainly inspired to Feature2d's KeyPoint class, since KeyLine shares some of *KeyPoint*'s fields, even if a part of them assumes a different meaning, when speaking about lines. |
||||
In particular: |
||||
|
||||
* the *class_id* field is used to gather lines extracted from different octaves which refer to same line inside original image (such lines and the one they represent in original image share the same *class_id* value) |
||||
* the *angle* field represents line's slope with respect to (positive) X axis |
||||
* the *pt* field represents line's midpoint |
||||
* the *response* field is computed as the ratio between the line's length and maximum between image's width and height |
||||
* the *size* field is the area of the smallest rectangle containing line |
||||
|
||||
Apart from fields inspired to KeyPoint class, KeyLines stores information about extremes of line in original image and in octave it was extracted from, about line's length and number of pixels it covers. Code relative to KeyLine class is reported in the following snippet: |
||||
|
||||
.. ocv:class:: KeyLine |
||||
|
||||
:: |
||||
|
||||
class CV_EXPORTS_W KeyLine |
||||
{ |
||||
public: |
||||
/* orientation of the line */ |
||||
float angle; |
||||
|
||||
/* object ID, that can be used to cluster keylines by the line they represent */ |
||||
int class_id; |
||||
|
||||
/* octave (pyramid layer), from which the keyline has been extracted */ |
||||
int octave; |
||||
|
||||
/* coordinates of the middlepoint */ |
||||
Point pt; |
||||
|
||||
/* the response, by which the strongest keylines have been selected. |
||||
It's represented by the ratio between line's length and maximum between |
||||
image's width and height */ |
||||
float response; |
||||
|
||||
/* minimum area containing line */ |
||||
float size; |
||||
|
||||
/* lines's extremes in original image */ |
||||
float startPointX; |
||||
float startPointY; |
||||
float endPointX; |
||||
float endPointY; |
||||
|
||||
/* line's extremes in image it was extracted from */ |
||||
float sPointInOctaveX; |
||||
float sPointInOctaveY; |
||||
float ePointInOctaveX; |
||||
float ePointInOctaveY; |
||||
|
||||
/* the length of line */ |
||||
float lineLength; |
||||
|
||||
/* number of pixels covered by the line */ |
||||
unsigned int numOfPixels; |
||||
|
||||
/* constructor */ |
||||
KeyLine(){} |
||||
}; |
||||
|
||||
|
||||
Lines extraction methodology |
||||
---------------------------- |
||||
|
||||
The lines extraction methodology described in the following is mainly based on [LBD]_. |
||||
The extraction starts with a Gaussian pyramid generated from an original image, downsampled N-1 times, blurred N times, to obtain N layers (one for each octave), with layer 0 corresponding to input image. Then, from each layer (octave) in the pyramid, lines are extracted using LSD algorithm. |
||||
|
||||
Differently from EDLine lines extractor used in original article, LSD furnishes information only about lines extremes; thus, additional information regarding slope and equation of line are computed via analytic methods. The number of pixels is obtained using `LineIterator <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. |
||||
@ -0,0 +1,141 @@ |
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Matching with binary descriptors |
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================================ |
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|
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.. highlight:: cpp |
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|
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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. |
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In reaching such goal, the main headache is designing an efficient search algorithm to associate a query descriptor to one extracted from a dataset. |
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In the following, a matching modality based on *Multi-Index Hashing (MiHashing)* will be described. |
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|
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Multi-Index Hashing |
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------------------- |
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|
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The theory described in this section is based on [MIH]_. |
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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. |
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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 |
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|
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.. math:: |
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||\mathbf{h}^{(k)} - \mathbf{g}^{(k)}||_H \le \left\lfloor \frac{r}{m} \right\rfloor . |
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|
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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. |
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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. |
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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**. |
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|
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|
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BinaryDescriptorMatcher Class |
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============================= |
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|
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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>`_. |
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|
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|
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BinaryDescriptorMatcher::BinaryDescriptorMatcher |
||||
-------------------------------------------------- |
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|
||||
Constructor. |
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|
||||
.. ocv:function:: BinaryDescriptorMatcher::BinaryDescriptorMatcher() |
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|
||||
The BinaryDescriptorMatcher constructed is able to store and manage 256-bits long entries. |
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|
||||
|
||||
BinaryDescriptorMatcher::createBinaryDescriptorMatcher |
||||
------------------------------------------------------ |
||||
|
||||
Create a BinaryDescriptorMatcher object and return a smart pointer to it. |
||||
|
||||
.. ocv:function:: Ptr<BinaryDescriptorMatcher> BinaryDescriptorMatcher::createBinaryDescriptorMatcher() |
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|
||||
|
||||
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. |
||||
@ -1,9 +0,0 @@ |
||||
cmake_minimum_required(VERSION 2.8) |
||||
project(line_descriptor) |
||||
find_package(OpenCV REQUIRED) |
||||
|
||||
set(SOURCES lines_extraction.cpp compute_descriptors.cpp ../src/BinaryDescriptor.cpp) |
||||
|
||||
include_directories(${OpenCV_INCLUDE_DIRS}) |
||||
add_executable(lines_extraction compute_descriptors ${SOURCES} ${HEADERS}) |
||||
target_link_libraries(lines_extraction compute_descriptors ${OpenCV_LIBS}) |
||||
@ -1,42 +0,0 @@ |
||||
/*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.
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||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
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||||
// indirect, incidental, special, exemplary, or consequential damages
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||||
// (including, but not limited to, procurement of substitute goods or services;
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||||
// loss of use, data, or profits; or business interruption) however caused
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||||
// and on any theory of liability, whether in contract, strict liability,
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||||
// 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" |
||||
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