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Added edline header

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biagio montesano
2014-07-21 16:11:43 +02:00
parent dc272c06de
commit b2484cfd94
2 changed files with 473 additions and 5 deletions
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11
modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp
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@@ -50,6 +50,7 @@
#include "mihasher.hpp"
#include "sparse_hashtable.hpp"
#include "types.hpp"
#include "ed_line_detector.hpp"
namespace cv
{
@@ -262,13 +263,13 @@ class CV_EXPORTS_W BinaryDescriptorMatcher : public Algorithm
void knnMatch( const Mat& queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k, const std::vector<Mat>& masks = std::vector<Mat>(),
bool compactResult = false );
/* for every input desciptor, find all the ones falling in a
certaing atching radius (for a pair of images) */
/* for every input descriptor, find all the ones falling in a
certain matching radius (for a pair of images) */
void radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
const Mat& mask = Mat(), bool compactResult = false ) const;
/* for every input desciptor, find all the ones falling in a
certaing atching radius (from one image to a set) */
/* for every input descriptor, find all the ones falling in a
certain matching radius (from one image to a set) */
void radiusMatch( const Mat& queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance, const std::vector<Mat>& masks =
std::vector<Mat>(),
bool compactResult = false );
@@ -288,7 +289,7 @@ class CV_EXPORTS_W BinaryDescriptorMatcher : public Algorithm
/* constructor */
BinaryDescriptorMatcher();
/* desctructor */
/* destructor */
~BinaryDescriptorMatcher()
{
}

467
modules/line_descriptor/include/opencv2/line_descriptor/ed_line_detector.hpp Normal file
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@@ -0,0 +1,467 @@
/*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_
#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 M_LN10 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
*smoothed: In, flag to mark whether the image has already been smoothed by Gaussian filter.
*return -1: error happen
*/
int EdgeDrawing(cv::Mat &image, EdgeChains &edgeChains, bool smoothed=false);
/*extract lines from image
*image: In, gray image;
*lines: Out, store the extracted lines,
*smoothed: In, flag to mark whether the image has already been smoothed by Gaussian filter.
*return -1: error happen
*/
int EDline(cv::Mat &image, LineChains &lines, bool smoothed=false);
/* extract line from image, and store them */
int EDline(cv::Mat &image, bool smoothed=false);
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 / M_LN10 - 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_ */