Created nested classes. Removed some useless headers.

pull/38/head
biagio montesano 11 years ago
parent e39065bf77
commit 9a52f4626b
  1. 101
      modules/line_descriptor/include/opencv2/line_descriptor/array32.hpp
  2. 76
      modules/line_descriptor/include/opencv2/line_descriptor/bucket_group.hpp
  3. 1003
      modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp
  4. 476
      modules/line_descriptor/include/opencv2/line_descriptor/ed_line_detector.hpp
  5. 123
      modules/line_descriptor/include/opencv2/line_descriptor/line_structure.hpp
  6. 130
      modules/line_descriptor/include/opencv2/line_descriptor/mihasher.hpp
  7. 89
      modules/line_descriptor/include/opencv2/line_descriptor/sparse_hashtable.hpp
  8. 18
      modules/line_descriptor/samples/radius_matching.cpp
  9. 189
      modules/line_descriptor/src/array32.cpp
  10. 1400
      modules/line_descriptor/src/binary_descriptor.cpp
  11. 498
      modules/line_descriptor/src/binary_descriptor_matcher.cpp
  12. 0
      modules/line_descriptor/src/bitarray.hpp
  13. 8
      modules/line_descriptor/src/bitops.hpp
  14. 100
      modules/line_descriptor/src/bucket_group.cpp
  15. 1432
      modules/line_descriptor/src/ed_line_detector.cpp
  16. 270
      modules/line_descriptor/src/mihasher.cpp
  17. 4
      modules/line_descriptor/src/precomp.hpp
  18. 85
      modules/line_descriptor/src/sparse_hashtable.cpp
  19. 0
      modules/line_descriptor/src/types.hpp

@ -1,101 +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, Mohammad Norouzi, Ali Punjani, David J. Fleet,
// 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*/
#ifndef __OPENCV_ARRAY32_HPP
#define __OPENCV_ARRAY32_HPP
#ifdef _WIN32
#pragma warning( disable : 4267 )
#endif
#include "types.hpp"
class Array32
{
private:
static double ARRAY_RESIZE_FACTOR;
static double ARRAY_RESIZE_ADD_FACTOR;
public:
/* set ARRAY_RESIZE_FACTOR */
static void setArrayResizeFactor( double arf );
/* constructor */
Array32();
/* destructor */
~Array32();
/* cleaning function used in destructor */
void cleanup();
/* push data */
void push( UINT32 data );
/* insert data at given index */
void insert( UINT32 index, UINT32 data );
/* return data */
UINT32* data();
/* return data size */
UINT32 size();
/* return capacity */
UINT32 capacity();
/* definition of operator = */
void operator=( const Array32& );
/* print data */
void print();
/* initializer */
void init( int size );
/* data */
UINT32 *arr;
};
#endif

@ -1,76 +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, Mohammad Norouzi, Ali Punjani, David J. Fleet,
// 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*/
#ifndef __OPENCV_BUCKET_GROUP_HPP
#define __OPENCV_BUCKET_GROUP_HPP
#ifdef _WIN32
#pragma warning( disable : 4267 )
#endif
#include "types.hpp"
#include "array32.hpp"
#include "bitarray.hpp"
class BucketGroup
{
public:
/* constructor */
BucketGroup();
/* destructor */
~BucketGroup();
/* insert data into the bucket */
void insert( int subindex, UINT32 data );
/* perform a query to the bucket */
UINT32* query( int subindex, int *size );
/* data fields */
UINT32 empty;
Array32 *group;
};
#endif

@ -1,476 +0,0 @@
/*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) 2011-2012, Lilian Zhang, all rights reserved.
Copyright (C) 2013, Manuele Tamburrano, Stefano Fabri, all rights reserved.
Copyright (C) 2014, Biagio Montesano, all rights reserved.
Third party copyrights are property of their respective owners.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* The name of the copyright holders may not be used to endorse or promote products
derived from this software without specific prior written permission.
This software is provided by the copyright holders and contributors "as is" and
any express or implied warranties, including, but not limited to, the implied
warranties of merchantability and fitness for a particular purpose are disclaimed.
In no event shall the Intel Corporation or contributors be liable for any direct,
indirect, incidental, special, exemplary, or consequential damages
(including, but not limited to, procurement of substitute goods or services;
loss of use, data, or profits; or business interruption) however caused
and on any theory of liability, whether in contract, strict liability,
or tort (including negligence or otherwise) arising in any way out of
the use of this software, even if advised of the possibility of such damage.
*/
#ifndef __OPENCV_ED_LINE_DETECTOR_HH_
#define __OPENCV_ED_LINE_DETECTOR_HH_
#ifdef _WIN32
#pragma warning( disable : 4267 )
#endif
#include <vector>
#include <iostream>
#include <list>
#include <opencv2/core.hpp>
#include <opencv2/features2d.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/core/utility.hpp>
struct Pixel
{
unsigned int x; //X coordinate
unsigned int y; //Y coordinate
};
struct EdgeChains
{
std::vector<unsigned int> xCors; //all the x coordinates of edge points
std::vector<unsigned int> yCors; //all the y coordinates of edge points
std::vector<unsigned int> sId; //the start index of each edge in the coordinate arrays
unsigned int numOfEdges; //the number of edges whose length are larger than minLineLen; numOfEdges < sId.size;
};
struct LineChains
{
std::vector<unsigned int> xCors; //all the x coordinates of line points
std::vector<unsigned int> yCors; //all the y coordinates of line points
std::vector<unsigned int> sId; //the start index of each line in the coordinate arrays
unsigned int numOfLines; //the number of lines whose length are larger than minLineLen; numOfLines < sId.size;
};
typedef std::list<Pixel> PixelChain; //each edge is a pixel chain
struct EDLineParam
{
int ksize;
float sigma;
float gradientThreshold;
float anchorThreshold;
int scanIntervals;
int minLineLen;
double lineFitErrThreshold;
};
#define RELATIVE_ERROR_FACTOR 100.0
#define MLN10 2.30258509299404568402
#define log_gamma(x) ((x)>15.0?log_gamma_windschitl(x):log_gamma_lanczos(x))
/* This class is used to detect lines from input image.
* First, edges are extracted from input image following the method presented in Cihan Topal and
* Cuneyt Akinlar's paper:"Edge Drawing: A Heuristic Approach to Robust Real-Time Edge Detection", 2010.
* Then, lines are extracted from the edge image following the method presented in Cuneyt Akinlar and
* Cihan Topal's paper:"EDLines: A real-time line segment detector with a false detection control", 2011
* PS: The linking step of edge detection has a little bit difference with the Edge drawing algorithm
* described in the paper. The edge chain doesn't stop when the pixel direction is changed.
*/
class EDLineDetector
{
public:
EDLineDetector();
EDLineDetector( EDLineParam param );
~EDLineDetector();
/*extract edges from image
*image: In, gray image;
*edges: Out, store the edges, each edge is a pixel chain
*return -1: error happen
*/
int EdgeDrawing( cv::Mat &image, EdgeChains &edgeChains );
/*extract lines from image
*image: In, gray image;
*lines: Out, store the extracted lines,
*return -1: error happen
*/
int EDline( cv::Mat &image, LineChains &lines );
/* extract line from image, and store them */
int EDline( cv::Mat &image );
cv::Mat dxImg_; //store the dxImg;
cv::Mat dyImg_; //store the dyImg;
cv::Mat gImgWO_; //store the gradient image without threshold;
LineChains lines_; //store the detected line chains;
//store the line Equation coefficients, vec3=[w1,w2,w3] for line w1*x + w2*y + w3=0;
std::vector<std::vector<double> > lineEquations_;
//store the line endpoints, [x1,y1,x2,y3]
std::vector<std::vector<float> > lineEndpoints_;
//store the line direction
std::vector<float> lineDirection_;
//store the line salience, which is the summation of gradients of pixels on line
std::vector<float> lineSalience_;
// image sizes
unsigned int imageWidth;
unsigned int imageHeight;
/*The threshold of line fit error;
*If lineFitErr is large than this threshold, then
*the pixel chain is not accepted as a single line segment.*/
double lineFitErrThreshold_;
/*the threshold of pixel gradient magnitude.
*Only those pixel whose gradient magnitude are larger than this threshold will be
*taken as possible edge points. Default value is 36*/
short gradienThreshold_;
/*If the pixel's gradient value is bigger than both of its neighbors by a
*certain threshold (ANCHOR_THRESHOLD), the pixel is marked to be an anchor.
*Default value is 8*/
unsigned char anchorThreshold_;
/*anchor testing can be performed at different scan intervals, i.e.,
*every row/column, every second row/column etc.
*Default value is 2*/
unsigned int scanIntervals_;
int minLineLen_; //minimal acceptable line length
private:
void InitEDLine_();
/*For an input edge chain, find the best fit line, the default chain length is minLineLen_
*xCors: In, pointer to the X coordinates of pixel chain;
*yCors: In, pointer to the Y coordinates of pixel chain;
*offsetS:In, start index of this chain in vector;
*lineEquation: Out, [a,b] which are the coefficient of lines y=ax+b(horizontal) or x=ay+b(vertical);
*return: line fit error; -1:error happens;
*/
double LeastSquaresLineFit_( unsigned int *xCors, unsigned int *yCors, unsigned int offsetS, std::vector<double> &lineEquation );
/*For an input pixel chain, find the best fit line. Only do the update based on new points.
*For A*x=v, Least square estimation of x = Inv(A^T * A) * (A^T * v);
*If some new observations are added, i.e, [A; A'] * x = [v; v'],
*then x' = Inv(A^T * A + (A')^T * A') * (A^T * v + (A')^T * v');
*xCors: In, pointer to the X coordinates of pixel chain;
*yCors: In, pointer to the Y coordinates of pixel chain;
*offsetS:In, start index of this chain in vector;
*newOffsetS: In, start index of extended part;
*offsetE:In, end index of this chain in vector;
*lineEquation: Out, [a,b] which are the coefficient of lines y=ax+b(horizontal) or x=ay+b(vertical);
*return: line fit error; -1:error happens;
*/
double LeastSquaresLineFit_( unsigned int *xCors, unsigned int *yCors, unsigned int offsetS, unsigned int newOffsetS, unsigned int offsetE,
std::vector<double> &lineEquation );
/* Validate line based on the Helmholtz principle, which basically states that
* for a structure to be perceptually meaningful, the expectation of this structure
* by chance must be very low.
*/
bool LineValidation_( unsigned int *xCors, unsigned int *yCors, unsigned int offsetS, unsigned int offsetE, std::vector<double> &lineEquation,
float &direction );
bool bValidate_; //flag to decide whether line will be validated
int ksize_; //the size of Gaussian kernel: ksize X ksize, default value is 5.
float sigma_; //the sigma of Gaussian kernal, default value is 1.0.
/*For example, there two edges in the image:
*edge1 = [(7,4), (8,5), (9,6),| (10,7)|, (11, 8), (12,9)] and
*edge2 = [(14,9), (15,10), (16,11), (17,12),| (18, 13)|, (19,14)] ; then we store them as following:
*pFirstPartEdgeX_ = [10, 11, 12, 18, 19];//store the first part of each edge[from middle to end]
*pFirstPartEdgeY_ = [7, 8, 9, 13, 14];
*pFirstPartEdgeS_ = [0,3,5];// the index of start point of first part of each edge
*pSecondPartEdgeX_ = [10, 9, 8, 7, 18, 17, 16, 15, 14];//store the second part of each edge[from middle to front]
*pSecondPartEdgeY_ = [7, 6, 5, 4, 13, 12, 11, 10, 9];//anchor points(10, 7) and (18, 13) are stored again
*pSecondPartEdgeS_ = [0, 4, 9];// the index of start point of second part of each edge
*This type of storage order is because of the order of edge detection process.
*For each edge, start from one anchor point, first go right, then go left or first go down, then go up*/
//store the X coordinates of the first part of the pixels for chains
unsigned int *pFirstPartEdgeX_;
//store the Y coordinates of the first part of the pixels for chains
unsigned int *pFirstPartEdgeY_;
//store the start index of every edge chain in the first part arrays
unsigned int *pFirstPartEdgeS_;
//store the X coordinates of the second part of the pixels for chains
unsigned int *pSecondPartEdgeX_;
//store the Y coordinates of the second part of the pixels for chains
unsigned int *pSecondPartEdgeY_;
//store the start index of every edge chain in the second part arrays
unsigned int *pSecondPartEdgeS_;
//store the X coordinates of anchors
unsigned int *pAnchorX_;
//store the Y coordinates of anchors
unsigned int *pAnchorY_;
//edges
cv::Mat edgeImage_;
cv::Mat gImg_; //store the gradient image;
cv::Mat dirImg_; //store the direction image
double logNT_;
cv::Mat_<float> ATA; //the previous matrix of A^T * A;
cv::Mat_<float> ATV; //the previous vector of A^T * V;
cv::Mat_<float> fitMatT; //the matrix used in line fit function;
cv::Mat_<float> fitVec; //the vector used in line fit function;
cv::Mat_<float> tempMatLineFit; //the matrix used in line fit function;
cv::Mat_<float> tempVecLineFit; //the vector used in line fit function;
/** Compare doubles by relative error.
The resulting rounding error after floating point computations
depend on the specific operations done. The same number computed by
different algorithms could present different rounding errors. For a
useful comparison, an estimation of the relative rounding error
should be considered and compared to a factor times EPS. The factor
should be related to the accumulated rounding error in the chain of
computation. Here, as a simplification, a fixed factor is used.
*/
static int double_equal( double a, double b )
{
double abs_diff, aa, bb, abs_max;
/* trivial case */
if( a == b )
return true;
abs_diff = fabs( a - b );
aa = fabs( a );
bb = fabs( b );
abs_max = aa > bb ? aa : bb;
/* DBL_MIN is the smallest normalized number, thus, the smallest
number whose relative error is bounded by DBL_EPSILON. For
smaller numbers, the same quantization steps as for DBL_MIN
are used. Then, for smaller numbers, a meaningful "relative"
error should be computed by dividing the difference by DBL_MIN. */
if( abs_max < DBL_MIN )
abs_max = DBL_MIN;
/* equal if relative error <= factor x eps */
return ( abs_diff / abs_max ) <= ( RELATIVE_ERROR_FACTOR * DBL_EPSILON );
}
/** Computes the natural logarithm of the absolute value of
the gamma function of x using the Lanczos approximation.
See http://www.rskey.org/gamma.htm
The formula used is
@f[
\Gamma(x) = \frac{ \sum_{n=0}^{N} q_n x^n }{ \Pi_{n=0}^{N} (x+n) }
(x+5.5)^{x+0.5} e^{-(x+5.5)}
@f]
so
@f[
\log\Gamma(x) = \log\left( \sum_{n=0}^{N} q_n x^n \right)
+ (x+0.5) \log(x+5.5) - (x+5.5) - \sum_{n=0}^{N} \log(x+n)
@f]
and
q0 = 75122.6331530,
q1 = 80916.6278952,
q2 = 36308.2951477,
q3 = 8687.24529705,
q4 = 1168.92649479,
q5 = 83.8676043424,
q6 = 2.50662827511.
*/
static double log_gamma_lanczos( double x )
{
static double q[7] =
{ 75122.6331530, 80916.6278952, 36308.2951477, 8687.24529705, 1168.92649479, 83.8676043424, 2.50662827511 };
double a = ( x + 0.5 ) * log( x + 5.5 ) - ( x + 5.5 );
double b = 0.0;
int n;
for ( n = 0; n < 7; n++ )
{
a -= log( x + (double) n );
b += q[n] * pow( x, (double) n );
}
return a + log( b );
}
/** Computes the natural logarithm of the absolute value of
the gamma function of x using Windschitl method.
See http://www.rskey.org/gamma.htm
The formula used is
@f[
\Gamma(x) = \sqrt{\frac{2\pi}{x}} \left( \frac{x}{e}
\sqrt{ x\sinh(1/x) + \frac{1}{810x^6} } \right)^x
@f]
so
@f[
\log\Gamma(x) = 0.5\log(2\pi) + (x-0.5)\log(x) - x
+ 0.5x\log\left( x\sinh(1/x) + \frac{1}{810x^6} \right).
@f]
This formula is a good approximation when x > 15.
*/
static double log_gamma_windschitl( double x )
{
return 0.918938533204673 + ( x - 0.5 ) * log( x ) - x + 0.5 * x * log( x * sinh( 1 / x ) + 1 / ( 810.0 * pow( x, 6.0 ) ) );
}
/** Computes -log10(NFA).
NFA stands for Number of False Alarms:
@f[
\mathrm{NFA} = NT \cdot B(n,k,p)
@f]
- NT - number of tests
- B(n,k,p) - tail of binomial distribution with parameters n,k and p:
@f[
B(n,k,p) = \sum_{j=k}^n
\left(\begin{array}{c}n\\j\end{array}\right)
p^{j} (1-p)^{n-j}
@f]
The value -log10(NFA) is equivalent but more intuitive than NFA:
- -1 corresponds to 10 mean false alarms
- 0 corresponds to 1 mean false alarm
- 1 corresponds to 0.1 mean false alarms
- 2 corresponds to 0.01 mean false alarms
- ...
Used this way, the bigger the value, better the detection,
and a logarithmic scale is used.
@param n,k,p binomial parameters.
@param logNT logarithm of Number of Tests
The computation is based in the gamma function by the following
relation:
@f[
\left(\begin{array}{c}n\\k\end{array}\right)
= \frac{ \Gamma(n+1) }{ \Gamma(k+1) \cdot \Gamma(n-k+1) }.
@f]
We use efficient algorithms to compute the logarithm of
the gamma function.
To make the computation faster, not all the sum is computed, part
of the terms are neglected based on a bound to the error obtained
(an error of 10% in the result is accepted).
*/
static double nfa( int n, int k, double p, double logNT )
{
double tolerance = 0.1; /* an error of 10% in the result is accepted */
double log1term, term, bin_term, mult_term, bin_tail, err, p_term;
int i;
/* check parameters */
if( n < 0 || k < 0 || k > n || p <= 0.0 || p >= 1.0 )
{
std::cout << "nfa: wrong n, k or p values." << std::endl;
exit( 0 );
}
/* trivial cases */
if( n == 0 || k == 0 )
return -logNT;
if( n == k )
return -logNT - (double) n * log10( p );
/* probability term */
p_term = p / ( 1.0 - p );
/* compute the first term of the series */
/*
binomial_tail(n,k,p) = sum_{i=k}^n bincoef(n,i) * p^i * (1-p)^{n-i}
where bincoef(n,i) are the binomial coefficients.
But
bincoef(n,k) = gamma(n+1) / ( gamma(k+1) * gamma(n-k+1) ).
We use this to compute the first term. Actually the log of it.
*/
log1term = log_gamma( (double) n + 1.0 ) - log_gamma( (double ) k + 1.0 ) - log_gamma( (double ) ( n - k ) + 1.0 ) + (double) k * log( p )
+ (double) ( n - k ) * log( 1.0 - p );
term = exp( log1term );
/* in some cases no more computations are needed */
if( double_equal( term, 0.0 ) )
{ /* the first term is almost zero */
if( (double) k > (double) n * p ) /* at begin or end of the tail? */
return -log1term / MLN10 - logNT; /* end: use just the first term */
else
return -logNT; /* begin: the tail is roughly 1 */
}
/* compute more terms if needed */
bin_tail = term;
for ( i = k + 1; i <= n; i++ )
{
/* As
term_i = bincoef(n,i) * p^i * (1-p)^(n-i)
and
bincoef(n,i)/bincoef(n,i-1) = n-i+1 / i,
then,
term_i / term_i-1 = (n-i+1)/i * p/(1-p)
and
term_i = term_i-1 * (n-i+1)/i * p/(1-p).
p/(1-p) is computed only once and stored in 'p_term'.
*/
bin_term = (double) ( n - i + 1 ) / (double) i;
mult_term = bin_term * p_term;
term *= mult_term;
bin_tail += term;
if( bin_term < 1.0 )
{
/* When bin_term<1 then mult_term_j<mult_term_i for j>i.
Then, the error on the binomial tail when truncated at
the i term can be bounded by a geometric series of form
term_i * sum mult_term_i^j. */
err = term * ( ( 1.0 - pow( mult_term, (double) ( n - i + 1 ) ) ) / ( 1.0 - mult_term ) - 1.0 );
/* One wants an error at most of tolerance*final_result, or:
tolerance * abs(-log10(bin_tail)-logNT).
Now, the error that can be accepted on bin_tail is
given by tolerance*final_result divided by the derivative
of -log10(x) when x=bin_tail. that is:
tolerance * abs(-log10(bin_tail)-logNT) / (1/bin_tail)
Finally, we truncate the tail if the error is less than:
tolerance * abs(-log10(bin_tail)-logNT) * bin_tail */
if( err < tolerance * fabs( -log10( bin_tail ) - logNT ) * bin_tail )
break;
}
}
return -log10( bin_tail ) - logNT;
}
};
#endif /* EDLINEDETECTOR_HH_ */

@ -1,123 +0,0 @@
/*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) 2011-2012, Lilian Zhang, all rights reserved.
Copyright (C) 2013, Manuele Tamburrano, Stefano Fabri, all rights reserved.
Copyright (C) 2014, Biagio Montesano, all rights reserved.
Third party copyrights are property of their respective owners.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* The name of the copyright holders may not be used to endorse or promote products
derived from this software without specific prior written permission.
This software is provided by the copyright holders and contributors "as is" and
any express or implied warranties, including, but not limited to, the implied
warranties of merchantability and fitness for a particular purpose are disclaimed.
In no event shall the Intel Corporation or contributors be liable for any direct,
indirect, incidental, special, exemplary, or consequential damages
(including, but not limited to, procurement of substitute goods or services;
loss of use, data, or profits; or business interruption) however caused
and on any theory of liability, whether in contract, strict liability,
or tort (including negligence or otherwise) arising in any way out of
the use of this software, even if advised of the possibility of such damage.
*/
#ifndef __OPENCV_LINE_STRUCTURE_HH_
#define __OPENCV_LINE_STRUCTURE_HH_
#include <vector>
/* struct to represent lines extracted from an octave */
struct OctaveLine
{
unsigned int octaveCount; //the octave which this line is detected
unsigned int lineIDInOctave; //the line ID in that octave image
unsigned int lineIDInScaleLineVec; //the line ID in Scale line vector
float lineLength; //the length of line in original image scale
};
// A 2D line (normal equation parameters).
struct SingleLine
{
//note: rho and theta are based on coordinate origin, i.e. the top-left corner of image
double rho; //unit: pixel length
double theta; //unit: rad
double linePointX; // = rho * cos(theta);
double linePointY; // = rho * sin(theta);
//for EndPoints, the coordinate origin is the top-left corner of image.
double startPointX;
double startPointY;
double endPointX;
double endPointY;
//direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis.
double direction;
//mean gradient magnitude
double gradientMagnitude;
//mean gray value of pixels in dark side of line
double darkSideGrayValue;
//mean gray value of pixels in light side of line
double lightSideGrayValue;
//the length of line
double lineLength;
//the width of line;
double width;
//number of pixels
int numOfPixels;
//the decriptor of line
std::vector<double> descriptor;
};
// Specifies a vector of lines.
typedef std::vector<SingleLine> Lines_list;
struct OctaveSingleLine
{
/*endPoints, the coordinate origin is the top-left corner of the original image.
*startPointX = sPointInOctaveX * (factor)^octaveCount; */
float startPointX;
float startPointY;
float endPointX;
float endPointY;
//endPoints, the coordinate origin is the top-left corner of the octave image.
float sPointInOctaveX;
float sPointInOctaveY;
float ePointInOctaveX;
float ePointInOctaveY;
//direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis.
float direction;
//the summation of gradient magnitudes of pixels on lines
float salience;
//the length of line
float lineLength;
//number of pixels
unsigned int numOfPixels;
//the octave which this line is detected
unsigned int octaveCount;
//the decriptor of line
std::vector<float> descriptor;
};
// Specifies a vector of lines.
typedef std::vector<OctaveSingleLine> LinesVec;
// each element in ScaleLines is a vector of lines
// which corresponds the same line detected in different octave images.
typedef std::vector<LinesVec> ScaleLines;
#endif

@ -1,130 +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, Mohammad Norouzi, Ali Punjani, David J. Fleet,
// 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*/
#ifndef __OPENCV_MIHASHER_HPP
#define __OPENCV_MIHASHER_HPP
#ifdef _WIN32
#pragma warning( disable : 4267 )
#endif
#include "types.hpp"
#include "bitops.hpp"
#include "sparse_hashtable.hpp"
#include "bitarray.hpp"
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
class Mihasher
{
private:
/* Bits per code */
int B;
/* B/8 */
int B_over_8;
/* Bits per chunk (must be less than 64) */
int b;
/* Number of chunks */
int m;
/* Number of chunks with b bits (have 1 bit more than others) */
int mplus;
/* Maximum hamming search radius (we use B/2 by default) */
int D;
/* Maximum hamming search radius per substring */
int d;
/* Maximum results to return */
int K;
/* Number of codes */
UINT64 N;
/* Table of original full-length codes */
cv::Mat codes;
/* Counter for eliminating duplicate results (it is not thread safe) */
bitarray *counter;
/* Array of m hashtables */
SparseHashtable *H;
/* Volume of a b-bit Hamming ball with radius s (for s = 0 to d) */
UINT32 *xornum;
/* Used within generation of binary codes at a certain Hamming distance */
int power[100];
public:
/* constructor */
Mihasher();
/* desctructor */
~Mihasher();
/* constructor 2 */
Mihasher( int B, int m );
/* K setter */
void setK( int K );
/* populate tables */
void populate( cv::Mat & codes, UINT32 N, int dim1codes );
/* execute a batch query */
void batchquery( UINT32 * results, UINT32 *numres/*, qstat *stats*/, const cv::Mat & q, UINT32 numq, int dim1queries );
private:
/* execute a single query */
void query( UINT32 * results, UINT32* numres/*, qstat *stats*/, UINT8 *q, UINT64 * chunks, UINT32 * res );
};
#endif

@ -1,89 +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, Mohammad Norouzi, Ali Punjani, David J. Fleet,
// 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*/
#ifndef __OPENCV_SPARSE_HASHTABLE_HPP
#define __OPENCV_SPARSE_HASHTABLE_HPP
#ifdef _WIN32
#pragma warning( disable : 4267 )
#endif
#include "types.hpp"
#include "bucket_group.hpp"
class SparseHashtable
{
private:
/* Maximum bits per key before folding the table */
static const int MAX_B;
/* Bins (each bin is an Array object for duplicates of the same key) */
BucketGroup *table;
public:
/* constructor */
SparseHashtable();
/* destructor */
~SparseHashtable();
/* initializer */
int init( int _b );
/* insert data */
void insert( UINT64 index, UINT32 data );
/* query data */
UINT32* query( UINT64 index, int* size );
/* Bits per index */
int b;
/* Number of bins */
UINT64 size;
};
#endif

@ -69,15 +69,15 @@ int main( int argc, char** argv )
{ {
/* get parameters from comand line */ /* get parameters from comand line */
CommandLineParser parser( argc, argv, keys ); CommandLineParser parser( argc, argv, keys );
String pathToImages = parser.get<String>( 0 ); String pathToImages = parser.get < String > ( 0 );
/* create structures for hosting KeyLines and descriptors */ /* create structures for hosting KeyLines and descriptors */
int num_elements = sizeof ( images ) / sizeof ( images[0] ); int num_elements = sizeof ( images ) / sizeof ( images[0] );
std::vector<Mat> descriptorsMat; std::vector < Mat > descriptorsMat;
std::vector<std::vector<KeyLine> > linesMat; std::vector < std::vector<KeyLine> > linesMat;
/*create a pointer to a BinaryDescriptor object */ /*create a pointer to a BinaryDescriptor object */
Ptr<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor(); Ptr < BinaryDescriptor > bd = BinaryDescriptor::createBinaryDescriptor();
/* compute lines and descriptors */ /* compute lines and descriptors */
for ( int i = 0; i < num_elements; i++ ) for ( int i = 0; i < num_elements; i++ )
@ -97,7 +97,7 @@ int main( int argc, char** argv )
} }
/* compute lines and descriptors */ /* compute lines and descriptors */
std::vector<KeyLine> lines; std::vector < KeyLine > lines;
Mat computedDescr; Mat computedDescr;
bd->detect( loadedImage, lines ); bd->detect( loadedImage, lines );
bd->compute( loadedImage, lines, computedDescr ); bd->compute( loadedImage, lines, computedDescr );
@ -121,19 +121,19 @@ int main( int argc, char** argv )
std::cout << "It has been generated a matrix of " << queries.rows << " descriptors" << std::endl; std::cout << "It has been generated a matrix of " << queries.rows << " descriptors" << std::endl;
/* create a BinaryDescriptorMatcher object */ /* create a BinaryDescriptorMatcher object */
Ptr<BinaryDescriptorMatcher> bdm = BinaryDescriptorMatcher::createBinaryDescriptorMatcher(); Ptr < BinaryDescriptorMatcher > bdm = BinaryDescriptorMatcher::createBinaryDescriptorMatcher();
/* populate matcher */ /* populate matcher */
bdm->add( descriptorsMat ); bdm->add( descriptorsMat );
/* compute matches */ /* compute matches */
std::vector<std::vector<DMatch> > matches; std::vector < std::vector<DMatch> > matches;
bdm->radiusMatch( queries, matches, 30 ); bdm->radiusMatch( queries, matches, 30 );
std::cout << "size matches sample " << matches.size() << std::endl; std::cout << "size matches sample " << matches.size() << std::endl;
for ( int i = 0; i < matches.size(); i++ ) for ( int i = 0; i < (int) matches.size(); i++ )
{ {
for ( int j = 0; j < matches[i].size(); j++ ) for ( int j = 0; j < (int) matches[i].size(); j++ )
{ {
std::cout << "match: " << matches[i][j].queryIdx << " " << matches[i][j].trainIdx << " " << matches[i][j].distance << std::endl; std::cout << "match: " << matches[i][j].queryIdx << " " << matches[i][j].trainIdx << " " << matches[i][j].distance << std::endl;
} }

@ -1,189 +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, Mohammad Norouzi, Ali Punjani, David J. Fleet,
// 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"
/* 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 )
{
ARRAY_RESIZE_FACTOR = arf;
}
/* constructor */
Array32::Array32()
{
arr = NULL;
}
/* definition of operator =
Array32& Array32::operator = (const Array32 &rhs) */
void Array32::operator =( const Array32 &rhs )
{
if( &rhs != this )
this->arr = rhs.arr;
}
/* destructor */
Array32::~Array32()
{
cleanup();
}
/* cleaning function used in destructor */
void Array32::cleanup()
{
free( arr );
}
/* push data */
void Array32::push( UINT32 Data )
{
if( arr )
{
if( arr[0] == arr[1] )
{
arr[1] = (UINT32) std::max( ceil( arr[1] * ARRAY_RESIZE_FACTOR ), arr[1] + ARRAY_RESIZE_ADD_FACTOR );
UINT32* new_Data = static_cast<UINT32*>( realloc( arr, sizeof(UINT32) * ( 2 + arr[1] ) ) );
if( new_Data == NULL )
{
/* could not realloc, but orig still valid */
std::cout << "ALERT!!!! Not enough memory, operation aborted!" << std::endl;
exit( 0 );
}
else
{
arr = new_Data;
}
}
arr[2 + arr[0]] = Data;
arr[0]++;
}
else
{
arr = (UINT32*) malloc( (size_t) ( 2 + ARRAY_RESIZE_ADD_FACTOR ) * sizeof(UINT32) );
arr[0] = 1;
arr[1] = 1;
arr[2] = Data;
}
}
/* insert data at given index */
void Array32::insert( UINT32 index, UINT32 Data )
{
if( arr )
{
if( arr[0] == arr[1] )
{
arr[1] = (UINT32) ceil( arr[0] * 1.1 );
UINT32* new_data = static_cast<UINT32*>( realloc( arr, sizeof(UINT32) * ( 2 + arr[1] ) ) );
if( new_data == NULL )
{
// could not realloc, but orig still valid
std::cout << "ALERT!!!! Not enough memory, operation aborted!" << std::endl;
exit( 0 );
}
else
{
arr = new_data;
}
}
memmove( arr + ( 2 + index ) + 1, arr + ( 2 + index ), ( arr[0] - index ) * sizeof ( *arr ) );
arr[2 + index] = Data;
arr[0]++;
}
else
{
arr = (UINT32*) malloc( 3 * sizeof(UINT32) );
arr[0] = 1;
arr[1] = 1;
arr[2] = Data;
}
}
/* return data */
UINT32* Array32::data()
{
return arr ? arr + 2 : NULL;
}
/* return data size */
UINT32 Array32::size()
{
return arr ? arr[0] : 0;
}
/* return 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] );
printf( "\n" );
}
/* initializer */
void Array32::init( int Size )
{
if( arr == NULL )
{
arr = (UINT32*) malloc( ( 2 + Size ) * sizeof(UINT32) );
arr[0] = 0;
arr[1] = Size;
}
}

File diff suppressed because it is too large Load Diff

@ -41,6 +41,10 @@
#include "precomp.hpp" #include "precomp.hpp"
#define MAX_B 37
double ARRAY_RESIZE_FACTOR = 1.1; // minimum is 1.0
double ARRAY_RESIZE_ADD_FACTOR = 4; // minimum is 1
//using namespace cv; //using namespace cv;
namespace cv namespace cv
{ {
@ -59,7 +63,7 @@ BinaryDescriptorMatcher::BinaryDescriptorMatcher()
/* constructor with smart pointer */ /* constructor with smart pointer */
Ptr<BinaryDescriptorMatcher> BinaryDescriptorMatcher::createBinaryDescriptorMatcher() Ptr<BinaryDescriptorMatcher> BinaryDescriptorMatcher::createBinaryDescriptorMatcher()
{ {
return Ptr<BinaryDescriptorMatcher>( new BinaryDescriptorMatcher() ); return Ptr < BinaryDescriptorMatcher > ( new BinaryDescriptorMatcher() );
} }
/* store new descriptors to be inserted in dataset */ /* store new descriptors to be inserted in dataset */
@ -168,7 +172,7 @@ void BinaryDescriptorMatcher::match( const Mat& queryDescriptors, std::vector<DM
} }
/* create a DMatch object if required by mask or if there is /* create a DMatch object if required by mask or if there is
no mask at all */ no mask at all */
else if( masks.empty() || masks[itup->second].at<uchar>( counter ) != 0 ) else if( masks.empty() || masks[itup->second].at < uchar > ( counter ) != 0 )
{ {
std::vector<int> k_distances; std::vector<int> k_distances;
checkKDistances( numres, 1, k_distances, counter, 256 ); checkKDistances( numres, 1, k_distances, counter, 256 );
@ -227,7 +231,7 @@ void BinaryDescriptorMatcher::match( const Mat& queryDescriptors, const Mat& tra
{ {
/* create a DMatch object if required by mask or if there is /* create a DMatch object if required by mask or if there is
no mask at all */ no mask at all */
if( mask.empty() || ( !mask.empty() && mask.at<uchar>( counter ) != 0 ) ) if( mask.empty() || ( !mask.empty() && mask.at < uchar > ( counter ) != 0 ) )
{ {
std::vector<int> k_distances; std::vector<int> k_distances;
checkKDistances( numres, 1, k_distances, counter, 256 ); checkKDistances( numres, 1, k_distances, counter, 256 );
@ -291,10 +295,10 @@ void BinaryDescriptorMatcher::knnMatch( const Mat& queryDescriptors, const Mat&
for ( int counter = 0; counter < queryDescriptors.rows; counter++ ) for ( int counter = 0; counter < queryDescriptors.rows; counter++ )
{ {
/* initialize a vector of matches */ /* initialize a vector of matches */
std::vector<DMatch> tempVec; std::vector < DMatch > tempVec;
/* chech whether query should be ignored */ /* chech whether query should be ignored */
if( !mask.empty() && mask.at<uchar>( counter ) == 0 ) if( !mask.empty() && mask.at < uchar > ( counter ) == 0 )
{ {
/* if compact result is not requested, add an empty vector */ /* if compact result is not requested, add an empty vector */
if( !compactResult ) if( !compactResult )
@ -369,7 +373,7 @@ void BinaryDescriptorMatcher::knnMatch( const Mat& queryDescriptors, std::vector
for ( int counter = 0; counter < queryDescriptors.rows; counter++ ) for ( int counter = 0; counter < queryDescriptors.rows; counter++ )
{ {
/* create a void vector of matches */ /* create a void vector of matches */
std::vector<DMatch> tempVector; std::vector < DMatch > tempVector;
/* loop over k results returned for every query */ /* loop over k results returned for every query */
for ( int j = index; j < index + k; j++ ) for ( int j = index; j < index + k; j++ )
@ -391,7 +395,7 @@ void BinaryDescriptorMatcher::knnMatch( const Mat& queryDescriptors, std::vector
/* decide if, according to relative mask, returned match should be /* decide if, according to relative mask, returned match should be
considered */ considered */
else if( masks.size() == 0 || masks[itup->second].at<uchar>( counter ) != 0 ) else if( masks.size() == 0 || masks[itup->second].at < uchar > ( counter ) != 0 )
{ {
std::vector<int> k_distances; std::vector<int> k_distances;
checkKDistances( numres, k, k_distances, counter, 256 ); checkKDistances( numres, k, k_distances, counter, 256 );
@ -465,13 +469,13 @@ void BinaryDescriptorMatcher::radiusMatch( const Mat& queryDescriptors, const Ma
std::vector<int> k_distances; std::vector<int> k_distances;
checkKDistances( numres, trainDescriptors.rows, k_distances, i, 256 ); checkKDistances( numres, trainDescriptors.rows, k_distances, i, 256 );
std::vector<DMatch> tempVector; std::vector < DMatch > tempVector;
for ( int j = index; j < index + trainDescriptors.rows; j++ ) for ( int j = index; j < index + trainDescriptors.rows; j++ )
{ {
// if( numres[j] <= maxDistance ) // if( numres[j] <= maxDistance )
if( k_distances[j - index] <= maxDistance ) if( k_distances[j - index] <= maxDistance )
{ {
if( mask.empty() || mask.at<uchar>( i ) != 0 ) if( mask.empty() || mask.at < uchar > ( i ) != 0 )
{ {
DMatch dm; DMatch dm;
dm.queryIdx = i; dm.queryIdx = i;
@ -537,7 +541,7 @@ void BinaryDescriptorMatcher::radiusMatch( const Mat& queryDescriptors, std::vec
int index = 0; int index = 0;
for ( int counter = 0; counter < queryDescriptors.rows; counter++ ) for ( int counter = 0; counter < queryDescriptors.rows; counter++ )
{ {
std::vector<DMatch> tempVector; std::vector < DMatch > tempVector;
for ( int j = index; j < index + descrInDS; j++ ) for ( int j = index; j < index + descrInDS; j++ )
{ {
std::vector<int> k_distances; std::vector<int> k_distances;
@ -560,7 +564,7 @@ void BinaryDescriptorMatcher::radiusMatch( const Mat& queryDescriptors, std::vec
} }
/* add match if necessary */ /* add match if necessary */
else if( masks.empty() || masks[itup->second].at<uchar>( counter ) != 0 ) else if( masks.empty() || masks[itup->second].at < uchar > ( counter ) != 0 )
{ {
DMatch dm; DMatch dm;
@ -587,5 +591,477 @@ void BinaryDescriptorMatcher::radiusMatch( const Mat& queryDescriptors, std::vec
delete numres; delete numres;
} }
/* execute a batch query */
void BinaryDescriptorMatcher::Mihasher::batchquery( UINT32 * results, UINT32 *numres, const cv::Mat & queries, UINT32 numq, int dim1queries )
{
/* create and initialize a bitarray */
counter = new bitarray;
counter->init( N );
UINT32 *res = new UINT32[K * ( D + 1 )];
UINT64 *chunks = new UINT64[m];
UINT32 * presults = results;
UINT32 *pnumres = numres;
/* make a copy of input queries */
cv::Mat queries_clone = queries.clone();
/* set a pointer to first query (row) */
UINT8 *pq = queries_clone.ptr();
/* loop over number of descriptors */
for ( size_t i = 0; i < numq; i++ )
{
/* for every descriptor, query database */
query( presults, pnumres, pq, chunks, res );
/* move pointer to write next K indeces */
presults += K;
pnumres += B + 1;
/* move forward pointer to current row in descriptors matrix */
pq += dim1queries;
}
delete[] res;
delete[] chunks;
delete counter;
}
/* execute a single query */
void BinaryDescriptorMatcher::Mihasher::query( UINT32* results, UINT32* numres, UINT8 * Query, UINT64 *chunks, UINT32 *res )
{
/* if K == 0 that means we want everything to be processed.
So maxres = N in that case. Otherwise K limits the results processed */
UINT32 maxres = K ? K : (UINT32) N;
/* number of results so far obtained (up to a distance of s per chunk) */
UINT32 n = 0;
/* number of candidates tested with full codes (not counting duplicates) */
UINT32 nc = 0;
/* counting everything retrieved (duplicates are counted multiple times)
number of lookups (and xors) */
UINT32 nl = 0;
UINT32 nd = 0;
UINT32 *arr;
int size = 0;
UINT32 index;
int hammd;
counter->erase();
memset( numres, 0, ( B + 1 ) * sizeof ( *numres ) );
split( chunks, Query, m, mplus, b );
/* the growing search radius per substring */
int s;
/* current b: for the first mplus substrings it is b, for the rest it is (b-1) */
int curb = b;
for ( s = 0; s <= d && n < maxres; s++ )
{
for ( int k = 0; k < m; k++ )
{
if( k < mplus )
curb = b;
else
curb = b - 1;
UINT64 chunksk = chunks[k];
/* number of bit-strings with s number of 1s */
nl += xornum[s + 1] - xornum[s];
/* the bit-string with s number of 1s */
UINT64 bitstr = 0;
for ( int i = 0; i < s; i++ )
/* power[i] stores the location of the i'th 1 */
power[i] = i;
/* used for stopping criterion (location of (s+1)th 1) */
power[s] = curb + 1;
/* bit determines the 1 that should be moving to the left */
int bit = s - 1;
/* start from the left-most 1, and move it to the left until
it touches another one */
/* the loop for changing bitstr */
bool infiniteWhile = true;
while ( infiniteWhile )
{
if( bit != -1 )
{
bitstr ^= ( power[bit] == bit ) ? (UINT64) 1 << power[bit] : (UINT64) 3 << ( power[bit] - 1 );
power[bit]++;
bit--;
}
else
{ /* bit == -1 */
/* the binary code bitstr is available for processing */
arr = H[k].query( chunksk ^ bitstr, &size ); // lookup
if( size )
{ /* the corresponding bucket is not empty */
nd += size;
for ( int c = 0; c < size; c++ )
{
index = arr[c];
if( !counter->get( index ) )
{ /* if it is not a duplicate */
counter->set( index );
hammd = cv::line_descriptor::match( codes.ptr() + (UINT64) index * ( B_over_8 ), Query, B_over_8 );
nc++;
if( hammd <= D && numres[hammd] < maxres )
res[hammd * K + numres[hammd]] = index + 1;
numres[hammd]++;
}
}
}
/* end of processing */
while ( ++bit < s && power[bit] == power[bit + 1] - 1 )
{
bitstr ^= (UINT64) 1 << ( power[bit] - 1 );
power[bit] = bit;
}
if( bit == s )
break;
}
}
n = n + numres[s * m + k];
if( n >= maxres )
break;
}
}
n = 0;
for ( s = 0; s <= D && (int) n < K; s++ )
{
for ( int c = 0; c < (int) numres[s] && (int) n < K; c++ )
results[n++] = res[s * K + c];
}
}
/* constructor 2 */
BinaryDescriptorMatcher::Mihasher::Mihasher( int _B, int _m )
{
B = _B;
B_over_8 = B / 8;
m = _m;
b = (int) ceil( (double) B / m );
/* assuming that B/2 is large enough radius to include
all of the k nearest neighbors */
D = (int) ceil( B / 2.0 );
d = (int) ceil( (double) D / m );
/* mplus is the number of chunks with b bits
(m-mplus) is the number of chunks with (b-1) bits */
mplus = B - m * ( b - 1 );
xornum = new UINT32[d + 2];
xornum[0] = 0;
for ( int i = 0; i <= d; i++ )
xornum[i + 1] = xornum[i] + (UINT32) choose( b, i );
H = new SparseHashtable[m];
/* H[i].init might fail */
for ( int i = 0; i < mplus; i++ )
H[i].init( b );
for ( int i = mplus; i < m; i++ )
H[i].init( b - 1 );
}
/* K setter */
void BinaryDescriptorMatcher::Mihasher::setK( int _K )
{
K = _K;
}
/* desctructor */
BinaryDescriptorMatcher::Mihasher::~Mihasher()
{
delete[] xornum;
delete[] H;
}
/* populate tables */
void BinaryDescriptorMatcher::Mihasher::populate( cv::Mat & _codes, UINT32 _N, int dim1codes )
{
N = _N;
codes = _codes;
UINT64 * chunks = new UINT64[m];
UINT8 * pcodes = codes.ptr();
for ( UINT64 i = 0; i < N; i++, pcodes += dim1codes )
{
split( chunks, pcodes, m, mplus, b );
for ( int k = 0; k < m; k++ )
H[k].insert( chunks[k], (UINT32) i );
if( i % (int) ceil( N / 1000.0 ) == 0 )
fflush (stdout);
}
delete[] chunks;
}
/* constructor */
BinaryDescriptorMatcher::SparseHashtable::SparseHashtable()
{
table = NULL;
size = 0;
b = 0;
}
/* initializer */
int BinaryDescriptorMatcher::SparseHashtable::init( int _b )
{
b = _b;
if( b < 5 || b > MAX_B || b > (int) ( sizeof(UINT64) * 8 ) )
return 1;
size = UINT64_1 << ( b - 5 ); // size = 2 ^ b
table = (BucketGroup*) calloc( size, sizeof(BucketGroup) );
return 0;
}
/* destructor */
BinaryDescriptorMatcher::SparseHashtable::~SparseHashtable()
{
free( table );
}
/* insert data */
void BinaryDescriptorMatcher::SparseHashtable::insert( UINT64 index, UINT32 data )
{
table[index >> 5].insert( (int) ( index % 32 ), data );
}
/* query data */
UINT32* BinaryDescriptorMatcher::SparseHashtable::query( UINT64 index, int *Size )
{
return table[index >> 5].query( (int) ( index % 32 ), Size );
}
/* constructor */
BinaryDescriptorMatcher::BucketGroup::BucketGroup()
{
empty = 0;
group = NULL;
}
/* destructor */
BinaryDescriptorMatcher::BucketGroup::~BucketGroup()
{
if( group != NULL )
delete group;
}
/* insert data into the bucket */
void BinaryDescriptorMatcher::BucketGroup::insert( int subindex, UINT32 data )
{
if( group == NULL )
{
group = new Array32();
group->push( 0 );
}
UINT32 lowerbits = ( (UINT32) 1 << subindex ) - 1;
int end = popcnt( empty & lowerbits );
if( ! ( empty & ( (UINT32) 1 << subindex ) ) )
{
group->insert( end, group->arr[end + 2] );
empty |= (UINT32) 1 << subindex;
}
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* BinaryDescriptorMatcher::BucketGroup::query( int subindex, int *size )
{
if( empty & ( (UINT32) 1 << subindex ) )
{
UINT32 lowerbits = ( (UINT32) 1 << subindex ) - 1;
int end = popcnt( empty & lowerbits );
int totones = popcnt( empty );
*size = group->arr[2 + end + 1] - group->arr[2 + end];
return group->arr + 2 + totones + 1 + group->arr[2 + end];
}
else
{
*size = 0;
return NULL;
}
}
/* set ARRAY_RESIZE_FACTOR */
void BinaryDescriptorMatcher::Array32::setArrayResizeFactor( double arf )
{
ARRAY_RESIZE_FACTOR = arf;
}
/* constructor */
BinaryDescriptorMatcher::Array32::Array32()
{
arr = NULL;
ARRAY_RESIZE_FACTOR = 1.1; // minimum is 1.0
ARRAY_RESIZE_ADD_FACTOR = 4; // minimum is 1
}
/* definition of operator =
Array32& Array32::operator = (const Array32 &rhs) */
void BinaryDescriptorMatcher::Array32::operator =( const Array32 &rhs )
{
if( &rhs != this )
this->arr = rhs.arr;
}
/* destructor */
BinaryDescriptorMatcher::Array32::~Array32()
{
cleanup();
}
/* cleaning function used in destructor */
void BinaryDescriptorMatcher::Array32::cleanup()
{
free( arr );
}
/* push data */
void BinaryDescriptorMatcher::Array32::push( UINT32 Data )
{
if( arr )
{
if( arr[0] == arr[1] )
{
arr[1] = (UINT32) std::max( ceil( arr[1] * ARRAY_RESIZE_FACTOR ), arr[1] + ARRAY_RESIZE_ADD_FACTOR );
UINT32* new_Data = static_cast<UINT32*>( realloc( arr, sizeof(UINT32) * ( 2 + arr[1] ) ) );
if( new_Data == NULL )
{
/* could not realloc, but orig still valid */
std::cout << "ALERT!!!! Not enough memory, operation aborted!" << std::endl;
exit( 0 );
}
else
{
arr = new_Data;
}
}
arr[2 + arr[0]] = Data;
arr[0]++;
}
else
{
arr = (UINT32*) malloc( ( size_t )( 2 + ARRAY_RESIZE_ADD_FACTOR ) * sizeof(UINT32) );
arr[0] = 1;
arr[1] = 1;
arr[2] = Data;
}
}
/* insert data at given index */
void BinaryDescriptorMatcher::Array32::insert( UINT32 index, UINT32 Data )
{
if( arr )
{
if( arr[0] == arr[1] )
{
arr[1] = (UINT32) ceil( arr[0] * 1.1 );
UINT32* new_data = static_cast<UINT32*>( realloc( arr, sizeof(UINT32) * ( 2 + arr[1] ) ) );
if( new_data == NULL )
{
// could not realloc, but orig still valid
std::cout << "ALERT!!!! Not enough memory, operation aborted!" << std::endl;
exit( 0 );
}
else
{
arr = new_data;
}
}
memmove( arr + ( 2 + index ) + 1, arr + ( 2 + index ), ( arr[0] - index ) * sizeof ( *arr ) );
arr[2 + index] = Data;
arr[0]++;
}
else
{
arr = (UINT32*) malloc( 3 * sizeof(UINT32) );
arr[0] = 1;
arr[1] = 1;
arr[2] = Data;
}
}
/* return data */
UINT32* BinaryDescriptorMatcher::Array32::data()
{
return arr ? arr + 2 : NULL;
}
/* return data size */
UINT32 BinaryDescriptorMatcher::Array32::size()
{
return arr ? arr[0] : 0;
}
/* return capacity */
UINT32 BinaryDescriptorMatcher::Array32::capacity()
{
return arr ? arr[1] : 0;
}
/* print data */
void BinaryDescriptorMatcher::Array32::print()
{
for ( int i = 0; i < (int) size(); i++ )
printf( "%d, ", arr[i + 2] );
printf( "\n" );
}
/* initializer */
void BinaryDescriptorMatcher::Array32::init( int Size )
{
if( arr == NULL )
{
arr = (UINT32*) malloc( ( 2 + Size ) * sizeof(UINT32) );
arr[0] = 0;
arr[1] = Size;
}
}
} }
} }

@ -43,6 +43,8 @@
#ifndef __OPENCV_BITOPTS_HPP #ifndef __OPENCV_BITOPTS_HPP
#define __OPENCV_BITOPTS_HPP #define __OPENCV_BITOPTS_HPP
#include "precomp.hpp"
#ifdef _WIN32 #ifdef _WIN32
# include <intrin.h> # include <intrin.h>
# define popcnt __popcnt # define popcnt __popcnt
@ -63,6 +65,10 @@ const int lookup[] =
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, 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 }; 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8 };
namespace cv
{
namespace line_descriptor
{
/*matching function */ /*matching function */
inline int match( UINT8*P, UINT8*Q, int codelb ) inline int match( UINT8*P, UINT8*Q, int codelb )
{ {
@ -163,5 +169,7 @@ inline UINT64 choose( int n, int r )
return nchooser; return nchooser;
} }
}
}
#endif #endif

@ -1,100 +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, Mohammad Norouzi, Ali Punjani, David J. Fleet,
// 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"
/* constructor */
BucketGroup::BucketGroup()
{
empty = 0;
group = NULL;
}
/* destructor */
BucketGroup::~BucketGroup()
{
if( group != NULL )
delete group;
}
/* insert data into the bucket */
void BucketGroup::insert( int subindex, UINT32 data )
{
if( group == NULL )
{
group = new Array32();
group->push( 0 );
}
UINT32 lowerbits = ( (UINT32) 1 << subindex ) - 1;
int end = popcnt( empty & lowerbits );
if( ! ( empty & ( (UINT32) 1 << subindex ) ) )
{
group->insert( end, group->arr[end + 2] );
empty |= (UINT32) 1 << subindex;
}
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 )
{
if( empty & ( (UINT32) 1 << subindex ) )
{
UINT32 lowerbits = ( (UINT32) 1 << subindex ) - 1;
int end = popcnt( empty & lowerbits );
int totones = popcnt( empty );
*size = group->arr[2 + end + 1] - group->arr[2 + end];
return group->arr + 2 + totones + 1 + group->arr[2 + end];
}
else
{
*size = 0;
return NULL;
}
}

File diff suppressed because it is too large Load Diff

@ -1,270 +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, Mohammad Norouzi, Ali Punjani, David J. Fleet,
// 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"
/* execute a batch query */
void Mihasher::batchquery( UINT32 * results, UINT32 *numres, const cv::Mat & queries, UINT32 numq, int dim1queries )
{
/* create and initialize a bitarray */
counter = new bitarray;
counter->init( N );
UINT32 *res = new UINT32[K * ( D + 1 )];
UINT64 *chunks = new UINT64[m];
UINT32 * presults = results;
UINT32 *pnumres = numres;
/* make a copy of input queries */
cv::Mat queries_clone = queries.clone();
/* set a pointer to first query (row) */
UINT8 *pq = queries_clone.ptr();
/* loop over number of descriptors */
for ( size_t i = 0; i < numq; i++ )
{
/* for every descriptor, query database */
query( presults, pnumres, pq, chunks, res );
/* move pointer to write next K indeces */
presults += K;
pnumres += B + 1;
/* move forward pointer to current row in descriptors matrix */
pq += dim1queries;
}
delete[] res;
delete[] chunks;
delete counter;
}
/* execute a single query */
void Mihasher::query( UINT32* results, UINT32* numres, UINT8 * Query, UINT64 *chunks, UINT32 *res )
{
/* if K == 0 that means we want everything to be processed.
So maxres = N in that case. Otherwise K limits the results processed */
UINT32 maxres = K ? K : (UINT32) N;
/* number of results so far obtained (up to a distance of s per chunk) */
UINT32 n = 0;
/* number of candidates tested with full codes (not counting duplicates) */
UINT32 nc = 0;
/* counting everything retrieved (duplicates are counted multiple times)
number of lookups (and xors) */
UINT32 nl = 0;
UINT32 nd = 0;
UINT32 *arr;
int size = 0;
UINT32 index;
int hammd;
counter->erase();
memset( numres, 0, ( B + 1 ) * sizeof ( *numres ) );
split( chunks, Query, m, mplus, b );
/* the growing search radius per substring */
int s;
/* current b: for the first mplus substrings it is b, for the rest it is (b-1) */
int curb = b;
for ( s = 0; s <= d && n < maxres; s++ )
{
for ( int k = 0; k < m; k++ )
{
if( k < mplus )
curb = b;
else
curb = b - 1;
UINT64 chunksk = chunks[k];
/* number of bit-strings with s number of 1s */
nl += xornum[s + 1] - xornum[s];
/* the bit-string with s number of 1s */
UINT64 bitstr = 0;
for ( int i = 0; i < s; i++ )
/* power[i] stores the location of the i'th 1 */
power[i] = i;
/* used for stopping criterion (location of (s+1)th 1) */
power[s] = curb + 1;
/* bit determines the 1 that should be moving to the left */
int bit = s - 1;
/* start from the left-most 1, and move it to the left until
it touches another one */
/* the loop for changing bitstr */
bool infiniteWhile = true;
while ( infiniteWhile )
{
if( bit != -1 )
{
bitstr ^= ( power[bit] == bit ) ? (UINT64) 1 << power[bit] : (UINT64) 3 << ( power[bit] - 1 );
power[bit]++;
bit--;
}
else
{ /* bit == -1 */
/* the binary code bitstr is available for processing */
arr = H[k].query( chunksk ^ bitstr, &size ); // lookup
if( size )
{ /* the corresponding bucket is not empty */
nd += size;
for ( int c = 0; c < size; c++ )
{
index = arr[c];
if( !counter->get( index ) )
{ /* if it is not a duplicate */
counter->set( index );
hammd = match( codes.ptr() + (UINT64) index * ( B_over_8 ), Query, B_over_8 );
nc++;
if( hammd <= D && numres[hammd] < maxres )
res[hammd * K + numres[hammd]] = index + 1;
numres[hammd]++;
}
}
}
/* end of processing */
while ( ++bit < s && power[bit] == power[bit + 1] - 1 )
{
bitstr ^= (UINT64) 1 << ( power[bit] - 1 );
power[bit] = bit;
}
if( bit == s )
break;
}
}
n = n + numres[s * m + k];
if( n >= maxres )
break;
}
}
n = 0;
for ( s = 0; s <= D && (int) n < K; s++ )
{
for ( int c = 0; c < (int) numres[s] && (int) n < K; c++ )
results[n++] = res[s * K + c];
}
}
/* constructor 2 */
Mihasher::Mihasher( int _B, int _m )
{
B = _B;
B_over_8 = B / 8;
m = _m;
b = (int) ceil( (double) B / m );
/* assuming that B/2 is large enough radius to include
all of the k nearest neighbors */
D = (int) ceil( B / 2.0 );
d = (int) ceil( (double) D / m );
/* mplus is the number of chunks with b bits
(m-mplus) is the number of chunks with (b-1) bits */
mplus = B - m * ( b - 1 );
xornum = new UINT32[d + 2];
xornum[0] = 0;
for ( int i = 0; i <= d; i++ )
xornum[i + 1] = xornum[i] + (UINT32) choose( b, i );
H = new SparseHashtable[m];
/* H[i].init might fail */
for ( int i = 0; i < mplus; i++ )
H[i].init( b );
for ( int i = mplus; i < m; i++ )
H[i].init( b - 1 );
}
/* K setter */
void Mihasher::setK( int _K )
{
K = _K;
}
/* desctructor */
Mihasher::~Mihasher()
{
delete[] xornum;
delete[] H;
}
/* populate tables */
void Mihasher::populate( cv::Mat & _codes, UINT32 _N, int dim1codes )
{
N = _N;
codes = _codes;
UINT64 * chunks = new UINT64[m];
UINT8 * pcodes = codes.ptr();
for ( UINT64 i = 0; i < N; i++, pcodes += dim1codes )
{
split( chunks, pcodes, m, mplus, b );
for ( int k = 0; k < m; k++ )
H[k].insert( chunks[k], (UINT32) i );
if( i % (int) ceil( N / 1000.0 ) == 0 )
fflush( stdout );
}
delete[] chunks;
}

@ -68,6 +68,10 @@
#include <sstream> #include <sstream>
#include <vector> #include <vector>
#include "bitarray.hpp"
#include "bitops.hpp"
#include "types.hpp"
#include "opencv2/line_descriptor.hpp" #include "opencv2/line_descriptor.hpp"
#endif #endif

@ -1,85 +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.
// 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"
const int SparseHashtable::MAX_B = 37;
/* constructor */
SparseHashtable::SparseHashtable()
{
table = NULL;
size = 0;
b = 0;
}
/* initializer */
int SparseHashtable::init( int _b )
{
b = _b;
if( b < 5 || b > MAX_B || b > (int) ( sizeof(UINT64) * 8 ) )
return 1;
size = UINT64_1 << ( b - 5 ); // size = 2 ^ b
table = (BucketGroup*) calloc( size, sizeof(BucketGroup) );
return 0;
}
/* destructor */
SparseHashtable::~SparseHashtable()
{
free( table );
}
/* insert 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 );
}
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