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Added Line Segmen Detector.

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This commit is contained in:
Daniel Angelov 2013-07-13 00:21:02 +03:00
parent 68a992b075
commit 5350a2f1d9
2 changed files with 1244 additions and 0 deletions
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226
modules/imgproc/include/opencv2/imgproc.hpp
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@ -191,6 +191,13 @@ enum { HOUGH_STANDARD = 0,
HOUGH_GRADIENT = 3
};
//! Variants of Line Segment Detector
enum lsd_refine_lvl
{ LSD_REFINE_NONE = 0,
LSD_REFINE_STD = 1,
LSD_REFINE_ADV = 2
};
//! Histogram comparison methods
enum { HISTCMP_CORREL = 0,
HISTCMP_CHISQR = 1,
@ -829,6 +836,225 @@ protected:
Point2f bottomRight;
};
class CV_EXPORTS_W LSD
{
public:
/**
* Create an LSD object. Specifying scale, number of subdivisions for the image, should the lines be refined and other constants as follows:
*
* @param _refine How should the lines found be refined?
* NO_REFINE - No refinement applied.
* REFINE_STD - Standard refinement is applied. E.g. breaking arches into smaller line approximations.
* REFINE_ADV - Advanced refinement. Number of false alarms is calculated,
* lines are refined through increase of precision, decrement in size, etc.
* @param _scale The scale of the image that will be used to find the lines. Range (0..1].
* @param _sigma_scale Sigma for Gaussian filter is computed as sigma = _sigma_scale/_scale.
* @param _quant Bound to the quantization error on the gradient norm.
* @param _ang_th Gradient angle tolerance in degrees.
* @param _log_eps Detection threshold: -log10(NFA) > _log_eps
* @param _density_th Minimal density of aligned region points in rectangle.
* @param _n_bins Number of bins in pseudo-ordering of gradient modulus.
*/
LSD(lsd_refine_lvl _refine = LSD_REFINE_STD, double _scale = 0.8,
double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5,
double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024);
/**
* Detect lines in the input image with the specified ROI.
*
* @param _image A grayscale(CV_8UC1) input image.
* @param _lines Return: A vector of Vec4i elements specifying the beginning and ending point of a line.
* Where Vec4i is (x1, y1, x2, y2), point 1 is the start, point 2 - end.
* Returned lines are strictly oriented depending on the gradient.
* @param _roi Return: ROI of the image, where lines are to be found. If specified, the returning
* lines coordinates are image wise.
* @param width Return: Vector of widths of the regions, where the lines are found. E.g. Width of line.
* @param prec Return: Vector of precisions with which the lines are found.
* @param nfa Return: Vector containing number of false alarms in the line region, with precision of 10%.
* The bigger the value, logarithmically better the detection.
* * -1 corresponds to 10 mean false alarms
* * 0 corresponds to 1 mean false alarm
* * 1 corresponds to 0.1 mean false alarms
*/
void detect(const cv::InputArray _image, cv::OutputArray _lines, cv::Rect _roi = cv::Rect(),
cv::OutputArray width = cv::noArray(), cv::OutputArray prec = cv::noArray(),
cv::OutputArray nfa = cv::noArray());
/**
* Draw lines on the given canvas.
*
* @param image The image, where lines will be drawn.
* Should have the size of the image, where the lines were found
* @param lines The lines that need to be drawn
*/
static void drawSegments(cv::Mat& image, const std::vector<cv::Vec4i>& lines);
/**
* Draw both vectors on the image canvas. Uses blue for lines 1 and red for lines 2.
*
* @param image The image, where lines will be drawn.
* Should have the size of the image, where the lines were found
* @param lines1 The first lines that need to be drawn. Color - Blue.
* @param lines2 The second lines that need to be drawn. Color - Red.
* @return The number of mismatching pixels between lines1 and lines2.
*/
static int compareSegments(cv::Size& size, const std::vector<cv::Vec4i>& lines1, const std::vector<cv::Vec4i> lines2, cv::Mat* image = 0);
private:
cv::Mat image;
cv::Mat_<double> scaled_image;
double *scaled_image_data;
cv::Mat_<double> angles; // in rads
double *angles_data;
cv::Mat_<double> modgrad;
double *modgrad_data;
cv::Mat_<uchar> used;
int img_width;
int img_height;
double LOG_NT;
cv::Rect roi;
int roix, roiy;
const double SCALE;
const lsd_refine_lvl doRefine;
const double SIGMA_SCALE;
const double QUANT;
const double ANG_TH;
const double LOG_EPS;
const double DENSITY_TH;
const int N_BINS;
struct RegionPoint {
int x;
int y;
uchar* used;
double angle;
double modgrad;
};
struct coorlist
{
cv::Point2i p;
struct coorlist* next;
};
struct rect
{
double x1, y1, x2, y2; // first and second point of the line segment
double width; // rectangle width
double x, y; // center of the rectangle
double theta; // angle
double dx,dy; // (dx,dy) is vector oriented as the line segment
double prec; // tolerance angle
double p; // probability of a point with angle within 'prec'
};
/**
* Detect lines in the whole input image.
*
* @param lines Return: A vector of Vec4i elements specifying the beginning and ending point of a line.
* Where Vec4i is (x1, y1, x2, y2), point 1 is the start, point 2 - end.
* Returned lines are strictly oriented depending on the gradient.
* @param widths Return: Vector of widths of the regions, where the lines are found. E.g. Width of line.
* @param precisions Return: Vector of precisions with which the lines are found.
* @param nfas Return: Vector containing number of false alarms in the line region, with precision of 10%.
* The bigger the value, logarithmically better the detection.
* * -1 corresponds to 10 mean false alarms
* * 0 corresponds to 1 mean false alarm
* * 1 corresponds to 0.1 mean false alarms
*/
void flsd(std::vector<cv::Vec4i>& lines,
std::vector<double>* widths, std::vector<double>* precisions,
std::vector<double>* nfas);
/**
* Finds the angles and the gradients of the image. Generates a list of pseudo ordered points.
*
* @param threshold The minimum value of the angle that is considered defined, otherwise NOTDEF
* @param n_bins The number of bins with which gradients are ordered by, using bucket sort.
* @param list Return: Vector of coordinate points that are pseudo ordered by magnitude.
* Pixels would be ordered by norm value, up to a precision given by max_grad/n_bins.
*/
void ll_angle(const double& threshold, const unsigned int& n_bins, std::vector<coorlist>& list);
/**
* Grow a region starting from point s with a defined precision,
* returning the containing points size and the angle of the gradients.
*
* @param s Starting point for the region.
* @param reg Return: Vector of points, that are part of the region
* @param reg_size Return: The size of the region.
* @param reg_angle Return: The mean angle of the region.
* @param prec The precision by which each region angle should be aligned to the mean.
*/
void region_grow(const cv::Point2i& s, std::vector<RegionPoint>& reg,
int& reg_size, double& reg_angle, const double& prec);
/**
* Finds the bounding rotated rectangle of a region.
*
* @param reg The region of points, from which the rectangle to be constructed from.
* @param reg_size The number of points in the region.
* @param reg_angle The mean angle of the region.
* @param prec The precision by which points were found.
* @param p Probability of a point with angle within 'prec'.
* @param rec Return: The generated rectangle.
*/
void region2rect(const std::vector<RegionPoint>& reg, const int reg_size, const double reg_angle,
const double prec, const double p, rect& rec) const;
/**
* Compute region's angle as the principal inertia axis of the region.
* @return Regions angle.
*/
double get_theta(const std::vector<RegionPoint>& reg, const int& reg_size, const double& x,
const double& y, const double& reg_angle, const double& prec) const;
/**
* An estimation of the angle tolerance is performed by the standard deviation of the angle at points
* near the region's starting point. Then, a new region is grown starting from the same point, but using the
* estimated angle tolerance. If this fails to produce a rectangle with the right density of region points,
* 'reduce_region_radius' is called to try to satisfy this condition.
*/
bool refine(std::vector<RegionPoint>& reg, int& reg_size, double reg_angle,
const double prec, double p, rect& rec, const double& density_th);
/**
* Reduce the region size, by elimination the points far from the starting point, until that leads to
* rectangle with the right density of region points or to discard the region if too small.
*/
bool reduce_region_radius(std::vector<RegionPoint>& reg, int& reg_size, double reg_angle,
const double prec, double p, rect& rec, double density, const double& density_th);
/**
* Try some rectangles variations to improve NFA value. Only if the rectangle is not meaningful (i.e., log_nfa <= log_eps).
* @return The new NFA value.
*/
double rect_improve(rect& rec) const;
/**
* Calculates the number of correctly aligned points within the rectangle.
* @return The new NFA value.
*/
double rect_nfa(const rect& rec) const;
/**
* Computes the NFA values based on the total number of points, points that agree.
* n, k, p are the binomial parameters.
* @return The new NFA value.
*/
double nfa(const int& n, const int& k, const double& p) const;
/**
* Is the point at place 'address' aligned to angle theta, up to precision 'prec'?
* @return Whether the point is aligned.
*/
bool isAligned(const int& address, const double& theta, const double& prec) const;
};
//! returns type (one of KERNEL_*) of 1D or 2D kernel specified by its coefficients.

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modules/imgproc/src/lsd.cpp Normal file
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