diff --git a/modules/imgproc/include/opencv2/imgproc.hpp b/modules/imgproc/include/opencv2/imgproc.hpp
index 6d61088724..dd849ef4c2 100644
--- a/modules/imgproc/include/opencv2/imgproc.hpp
+++ b/modules/imgproc/include/opencv2/imgproc.hpp
@@ -191,6 +191,12 @@ enum { HOUGH_STANDARD = 0,
HOUGH_GRADIENT = 3
};
+//! Variants of Line Segment Detector
+enum { LSD_REFINE_NONE = 0,
+ LSD_REFINE_STD = 1,
+ LSD_REFINE_ADV = 2
+ };
+
//! Histogram comparison methods
enum { HISTCMP_CORREL = 0,
HISTCMP_CHISQR = 1,
@@ -829,7 +835,62 @@ protected:
Point2f bottomRight;
};
+class LineSegmentDetector : public Algorithm
+{
+public:
+/**
+ * Detect lines in the input image with the specified ROI.
+ *
+ * @param _image A grayscale(CV_8UC1) input image.
+ * If only a roi needs to be selected, use
+ * lsd_ptr->detect(image(roi), ..., lines);
+ * lines += Scalar(roi.x, roi.y, roi.x, roi.y);
+ * @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
+ * This vector will be calculated _only_ when the objects type is REFINE_ADV
+ */
+ virtual void detect(const InputArray _image, OutputArray _lines,
+ OutputArray width = noArray(), OutputArray prec = noArray(),
+ OutputArray nfa = noArray()) = 0;
+
+/**
+ * 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
+ */
+ virtual void drawSegments(InputOutputArray image, const InputArray lines) = 0;
+
+/**
+ * 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.
+ */
+ virtual int compareSegments(const Size& size, const InputArray lines1, const InputArray lines2, Mat* image = 0) = 0;
+
+ virtual ~LineSegmentDetector() {};
+};
+//! Returns a pointer to a LineSegmentDetector class.
+CV_EXPORTS Ptr<LineSegmentDetector> createLineSegmentDetectorPtr(
+ int _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);
//! returns type (one of KERNEL_*) of 1D or 2D kernel specified by its coefficients.
CV_EXPORTS int getKernelType(InputArray kernel, Point anchor);
diff --git a/modules/imgproc/src/lsd.cpp b/modules/imgproc/src/lsd.cpp
new file mode 100644
index 0000000000..b4f228a382
--- /dev/null
+++ b/modules/imgproc/src/lsd.cpp
@@ -0,0 +1,1249 @@
+/*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) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2011, Willow Garage Inc., 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.
+//
+//M*/
+
+#include "precomp.hpp"
+#include <vector>
+
+/////////////////////////////////////////////////////////////////////////////////////////
+// Default LSD parameters
+// SIGMA_SCALE 0.6 - Sigma for Gaussian filter is computed as sigma = sigma_scale/scale.
+// QUANT 2.0 - Bound to the quantization error on the gradient norm.
+// ANG_TH 22.5 - Gradient angle tolerance in degrees.
+// LOG_EPS 0.0 - Detection threshold: -log10(NFA) > log_eps
+// DENSITY_TH 0.7 - Minimal density of region points in rectangle.
+// N_BINS 1024 - Number of bins in pseudo-ordering of gradient modulus.
+
+#define M_3_2_PI (3 * CV_PI) / 2 // 3/2 pi
+#define M_2__PI (2 * CV_PI) // 2 pi
+
+#ifndef M_LN10
+#define M_LN10 2.30258509299404568402
+#endif
+
+#define NOTDEF double(-1024.0) // Label for pixels with undefined gradient.
+
+#define NOTUSED 0 // Label for pixels not used in yet.
+#define USED 1 // Label for pixels already used in detection.
+
+#define RELATIVE_ERROR_FACTOR 100.0
+
+const double DEG_TO_RADS = CV_PI / 180;
+
+#define log_gamma(x) ((x)>15.0?log_gamma_windschitl(x):log_gamma_lanczos(x))
+
+struct edge
+{
+ cv::Point p;
+ bool taken;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////
+
+inline double distSq(const double x1, const double y1,
+ const double x2, const double y2)
+{
+ return (x2 - x1)*(x2 - x1) + (y2 - y1)*(y2 - y1);
+}
+
+inline double dist(const double x1, const double y1,
+ const double x2, const double y2)
+{
+ return sqrt(distSq(x1, y1, x2, y2));
+}
+
+// Signed angle difference
+inline double angle_diff_signed(const double& a, const double& b)
+{
+ double diff = a - b;
+ while(diff <= -CV_PI) diff += M_2__PI;
+ while(diff > CV_PI) diff -= M_2__PI;
+ return diff;
+}
+
+// Absolute value angle difference
+inline double angle_diff(const double& a, const double& b)
+{
+ return std::fabs(angle_diff_signed(a, b));
+}
+
+// Compare doubles by relative error.
+inline bool double_equal(const double& a, const double& b)
+{
+ // trivial case
+ if(a == b) return true;
+
+ double abs_diff = fabs(a - b);
+ double aa = fabs(a);
+ double bb = fabs(b);
+ double abs_max = (aa > bb)? aa : bb;
+
+ if(abs_max < DBL_MIN) abs_max = DBL_MIN;
+
+ return (abs_diff / abs_max) <= (RELATIVE_ERROR_FACTOR * DBL_EPSILON);
+}
+
+inline bool AsmallerB_XoverY(const edge& a, const edge& b)
+{
+ if (a.p.x == b.p.x) return a.p.y < b.p.y;
+ else return a.p.x < b.p.x;
+}
+
+/**
+ * Computes the natural logarithm of the absolute value of
+ * the gamma function of x using Windschitl method.
+ * See http://www.rskey.org/gamma.htm
+ */
+inline double log_gamma_windschitl(const 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 the natural logarithm of the absolute value of
+ * the gamma function of x using the Lanczos approximation.
+ * See http://www.rskey.org/gamma.htm
+ */
+inline double log_gamma_lanczos(const 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;
+ for(int n = 0; n < 7; ++n)
+ {
+ a -= log(x + double(n));
+ b += q[n] * pow(x, double(n));
+ }
+ return a + log(b);
+}
+///////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cv{
+
+class LineSegmentDetectorImpl : public LineSegmentDetector
+{
+public:
+
+/**
+ * Create a LineSegmentDetectorImpl 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?
+ * LSD_REFINE_NONE - No refinement applied.
+ * LSD_REFINE_STD - Standard refinement is applied. E.g. breaking arches into smaller line approximations.
+ * LSD_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.
+ */
+ LineSegmentDetectorImpl(int _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.
+ * If only a roi needs to be selected, use
+ * lsd_ptr->detect(image(roi), ..., lines);
+ * lines += Scalar(roi.x, roi.y, roi.x, roi.y);
+ * @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
+ * This vector will be calculated _only_ when the objects type is REFINE_ADV
+ */
+ void detect(const InputArray _image, OutputArray _lines,
+ OutputArray width = noArray(), OutputArray prec = noArray(),
+ OutputArray nfa = 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
+ */
+ void drawSegments(InputOutputArray image, const InputArray 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.
+ */
+ int compareSegments(const Size& size, const InputArray lines1, const InputArray lines2, Mat* image = 0);
+
+private:
+ Mat image;
+ Mat_<double> scaled_image;
+ double *scaled_image_data;
+ Mat_<double> angles; // in rads
+ double *angles_data;
+ Mat_<double> modgrad;
+ double *modgrad_data;
+ Mat_<uchar> used;
+
+ int img_width;
+ int img_height;
+ double LOG_NT;
+
+ bool w_needed;
+ bool p_needed;
+ bool n_needed;
+
+ const double SCALE;
+ const int 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
+ {
+ 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'
+ };
+
+ LineSegmentDetectorImpl& operator= (const LineSegmentDetectorImpl&); // to quiet MSVC
+
+/**
+ * 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<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 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;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////
+
+CV_EXPORTS Ptr<LineSegmentDetector> createLineSegmentDetectorPtr(
+ int _refine, double _scale, double _sigma_scale, double _quant, double _ang_th,
+ double _log_eps, double _density_th, int _n_bins)
+{
+ return Ptr<LineSegmentDetector>(new LineSegmentDetectorImpl(
+ _refine, _scale, _sigma_scale, _quant, _ang_th,
+ _log_eps, _density_th, _n_bins));
+}
+
+/////////////////////////////////////////////////////////////////////////////////////////
+
+LineSegmentDetectorImpl::LineSegmentDetectorImpl(int _refine, double _scale, double _sigma_scale, double _quant,
+ double _ang_th, double _log_eps, double _density_th, int _n_bins)
+ :SCALE(_scale), doRefine(_refine), SIGMA_SCALE(_sigma_scale), QUANT(_quant),
+ ANG_TH(_ang_th), LOG_EPS(_log_eps), DENSITY_TH(_density_th), N_BINS(_n_bins)
+{
+ CV_Assert(_scale > 0 && _sigma_scale > 0 && _quant >= 0 &&
+ _ang_th > 0 && _ang_th < 180 && _density_th >= 0 && _density_th < 1 &&
+ _n_bins > 0);
+}
+
+void LineSegmentDetectorImpl::detect(const InputArray _image, OutputArray _lines,
+ OutputArray _width, OutputArray _prec, OutputArray _nfa)
+{
+ Mat_<double> img = _image.getMat();
+ CV_Assert(!img.empty() && img.channels() == 1);
+
+ // Convert image to double
+ img.convertTo(image, CV_64FC1);
+
+ std::vector<Vec4i> lines;
+ std::vector<double> w, p, n;
+ w_needed = _width.needed();
+ p_needed = _prec.needed();
+ n_needed = _nfa.needed();
+
+ CV_Assert((!_nfa.needed()) || // NFA InputArray will be filled _only_ when
+ (_nfa.needed() && doRefine >= LSD_REFINE_ADV)); // REFINE_ADV type LineSegmentDetectorImpl object is created.
+
+ flsd(lines, w, p, n);
+
+ Mat(lines).copyTo(_lines);
+ if(w_needed) Mat(w).copyTo(_width);
+ if(p_needed) Mat(p).copyTo(_prec);
+ if(n_needed) Mat(n).copyTo(_nfa);
+}
+
+void LineSegmentDetectorImpl::flsd(std::vector<Vec4i>& lines,
+ std::vector<double>& widths, std::vector<double>& precisions,
+ std::vector<double>& nfas)
+{
+ // Angle tolerance
+ const double prec = CV_PI * ANG_TH / 180;
+ const double p = ANG_TH / 180;
+ const double rho = QUANT / sin(prec); // gradient magnitude threshold
+
+ std::vector<coorlist> list;
+ if(SCALE != 1)
+ {
+ Mat gaussian_img;
+ const double sigma = (SCALE < 1)?(SIGMA_SCALE / SCALE):(SIGMA_SCALE);
+ const double sprec = 3;
+ const unsigned int h = (unsigned int)(ceil(sigma * sqrt(2 * sprec * log(10.0))));
+ Size ksize(1 + 2 * h, 1 + 2 * h); // kernel size
+ GaussianBlur(image, gaussian_img, ksize, sigma);
+ // Scale image to needed size
+ resize(gaussian_img, scaled_image, Size(), SCALE, SCALE);
+ ll_angle(rho, N_BINS, list);
+ }
+ else
+ {
+ scaled_image = image;
+ ll_angle(rho, N_BINS, list);
+ }
+
+ LOG_NT = 5 * (log10(double(img_width)) + log10(double(img_height))) / 2 + log10(11.0);
+ const int min_reg_size = int(-LOG_NT/log10(p)); // minimal number of points in region that can give a meaningful event
+
+ // // Initialize region only when needed
+ // Mat region = Mat::zeros(scaled_image.size(), CV_8UC1);
+ used = Mat_<uchar>::zeros(scaled_image.size()); // zeros = NOTUSED
+ std::vector<RegionPoint> reg(img_width * img_height);
+
+ // Search for line segments
+ unsigned int ls_count = 0;
+ unsigned int list_size = list.size();
+ for(unsigned int i = 0; i < list_size; ++i)
+ {
+ unsigned int adx = list[i].p.x + list[i].p.y * img_width;
+ if((used.data[adx] == NOTUSED) && (angles_data[adx] != NOTDEF))
+ {
+ int reg_size;
+ double reg_angle;
+ region_grow(list[i].p, reg, reg_size, reg_angle, prec);
+
+ // Ignore small regions
+ if(reg_size < min_reg_size) { continue; }
+
+ // Construct rectangular approximation for the region
+ rect rec;
+ region2rect(reg, reg_size, reg_angle, prec, p, rec);
+
+ double log_nfa = -1;
+ if(doRefine > LSD_REFINE_NONE)
+ {
+ // At least REFINE_STANDARD lvl.
+ if(!refine(reg, reg_size, reg_angle, prec, p, rec, DENSITY_TH)) { continue; }
+
+ if(doRefine >= LSD_REFINE_ADV)
+ {
+ // Compute NFA
+ log_nfa = rect_improve(rec);
+ if(log_nfa <= LOG_EPS) { continue; }
+ }
+ }
+ // Found new line
+ ++ls_count;
+
+ // Add the offset
+ rec.x1 += 0.5; rec.y1 += 0.5;
+ rec.x2 += 0.5; rec.y2 += 0.5;
+
+ // scale the result values if a sub-sampling was performed
+ if(SCALE != 1)
+ {
+ rec.x1 /= SCALE; rec.y1 /= SCALE;
+ rec.x2 /= SCALE; rec.y2 /= SCALE;
+ rec.width /= SCALE;
+ }
+
+ //Store the relevant data
+ lines.push_back(Vec4i(int(rec.x1), int(rec.y1), int(rec.x2), int(rec.y2)));
+ if(w_needed) widths.push_back(rec.width);
+ if(p_needed) precisions.push_back(rec.p);
+ if(n_needed && doRefine >= LSD_REFINE_ADV) nfas.push_back(log_nfa);
+
+
+ // //Add the linesID to the region on the image
+ // for(unsigned int el = 0; el < reg_size; el++)
+ // {
+ // region.data[reg[i].x + reg[i].y * width] = ls_count;
+ // }
+ }
+ }
+}
+
+void LineSegmentDetectorImpl::ll_angle(const double& threshold,
+ const unsigned int& n_bins,
+ std::vector<coorlist>& list)
+{
+ //Initialize data
+ angles = Mat_<double>(scaled_image.size());
+ modgrad = Mat_<double>(scaled_image.size());
+
+ angles_data = angles.ptr<double>(0);
+ modgrad_data = modgrad.ptr<double>(0);
+ scaled_image_data = scaled_image.ptr<double>(0);
+
+ img_width = scaled_image.cols;
+ img_height = scaled_image.rows;
+
+ // Undefined the down and right boundaries
+ angles.row(img_height - 1).setTo(NOTDEF);
+ angles.col(img_width - 1).setTo(NOTDEF);
+
+ // Computing gradient for remaining pixels
+ CV_Assert(scaled_image.isContinuous() &&
+ modgrad.isContinuous() &&
+ angles.isContinuous()); // Accessing image data linearly
+
+ double max_grad = -1;
+ for(int y = 0; y < img_height - 1; ++y)
+ {
+ for(int addr = y * img_width, addr_end = addr + img_width - 1; addr < addr_end; ++addr)
+ {
+ double DA = scaled_image_data[addr + img_width + 1] - scaled_image_data[addr];
+ double BC = scaled_image_data[addr + 1] - scaled_image_data[addr + img_width];
+ double gx = DA + BC; // gradient x component
+ double gy = DA - BC; // gradient y component
+ double norm = std::sqrt((gx * gx + gy * gy) / 4); // gradient norm
+
+ modgrad_data[addr] = norm; // store gradient
+
+ if (norm <= threshold) // norm too small, gradient no defined
+ {
+ angles_data[addr] = NOTDEF;
+ }
+ else
+ {
+ angles_data[addr] = fastAtan2(float(gx), float(-gy)) * DEG_TO_RADS; // gradient angle computation
+ if (norm > max_grad) { max_grad = norm; }
+ }
+
+ }
+ }
+
+ // Compute histogram of gradient values
+ list = std::vector<coorlist>(img_width * img_height);
+ std::vector<coorlist*> range_s(n_bins);
+ std::vector<coorlist*> range_e(n_bins);
+ unsigned int count = 0;
+ double bin_coef = (max_grad > 0) ? double(n_bins - 1) / max_grad : 0; // If all image is smooth, max_grad <= 0
+
+ for(int y = 0; y < img_height - 1; ++y)
+ {
+ const double* norm = modgrad_data + y * img_width;
+ for(int x = 0; x < img_width - 1; ++x, ++norm)
+ {
+ // Store the point in the right bin according to its norm
+ int i = int((*norm) * bin_coef);
+ if(!range_e[i])
+ {
+ range_e[i] = range_s[i] = &list[count];
+ ++count;
+ }
+ else
+ {
+ range_e[i]->next = &list[count];
+ range_e[i] = &list[count];
+ ++count;
+ }
+ range_e[i]->p = Point(x, y);
+ range_e[i]->next = 0;
+ }
+ }
+
+ // Sort
+ int idx = n_bins - 1;
+ for(;idx > 0 && !range_s[idx]; --idx);
+ coorlist* start = range_s[idx];
+ coorlist* end = range_e[idx];
+ if(start)
+ {
+ while(idx > 0)
+ {
+ --idx;
+ if(range_s[idx])
+ {
+ end->next = range_s[idx];
+ end = range_e[idx];
+ }
+ }
+ }
+}
+
+void LineSegmentDetectorImpl::region_grow(const Point2i& s, std::vector<RegionPoint>& reg,
+ int& reg_size, double& reg_angle, const double& prec)
+{
+ // Point to this region
+ reg_size = 1;
+ reg[0].x = s.x;
+ reg[0].y = s.y;
+ int addr = s.x + s.y * img_width;
+ reg[0].used = used.data + addr;
+ reg_angle = angles_data[addr];
+ reg[0].angle = reg_angle;
+ reg[0].modgrad = modgrad_data[addr];
+
+ float sumdx = float(std::cos(reg_angle));
+ float sumdy = float(std::sin(reg_angle));
+ *reg[0].used = USED;
+
+ //Try neighboring regions
+ for(int i = 0; i < reg_size; ++i)
+ {
+ const RegionPoint& rpoint = reg[i];
+ int xx_min = std::max(rpoint.x - 1, 0), xx_max = std::min(rpoint.x + 1, img_width - 1);
+ int yy_min = std::max(rpoint.y - 1, 0), yy_max = std::min(rpoint.y + 1, img_height - 1);
+ for(int yy = yy_min; yy <= yy_max; ++yy)
+ {
+ int c_addr = xx_min + yy * img_width;
+ for(int xx = xx_min; xx <= xx_max; ++xx, ++c_addr)
+ {
+ if((used.data[c_addr] != USED) &&
+ (isAligned(c_addr, reg_angle, prec)))
+ {
+ // Add point
+ used.data[c_addr] = USED;
+ RegionPoint& region_point = reg[reg_size];
+ region_point.x = xx;
+ region_point.y = yy;
+ region_point.used = &(used.data[c_addr]);
+ region_point.modgrad = modgrad_data[c_addr];
+ const double& angle = angles_data[c_addr];
+ region_point.angle = angle;
+ ++reg_size;
+
+ // Update region's angle
+ sumdx += cos(float(angle));
+ sumdy += sin(float(angle));
+ // reg_angle is used in the isAligned, so it needs to be updates?
+ reg_angle = fastAtan2(sumdy, sumdx) * DEG_TO_RADS;
+ }
+ }
+ }
+ }
+}
+
+void LineSegmentDetectorImpl::region2rect(const std::vector<RegionPoint>& reg, const int reg_size,
+ const double reg_angle, const double prec, const double p, rect& rec) const
+{
+ double x = 0, y = 0, sum = 0;
+ for(int i = 0; i < reg_size; ++i)
+ {
+ const RegionPoint& pnt = reg[i];
+ const double& weight = pnt.modgrad;
+ x += double(pnt.x) * weight;
+ y += double(pnt.y) * weight;
+ sum += weight;
+ }
+
+ // Weighted sum must differ from 0
+ CV_Assert(sum > 0);
+
+ x /= sum;
+ y /= sum;
+
+ double theta = get_theta(reg, reg_size, x, y, reg_angle, prec);
+
+ // Find length and width
+ double dx = cos(theta);
+ double dy = sin(theta);
+ double l_min = 0, l_max = 0, w_min = 0, w_max = 0;
+
+ for(int i = 0; i < reg_size; ++i)
+ {
+ double regdx = double(reg[i].x) - x;
+ double regdy = double(reg[i].y) - y;
+
+ double l = regdx * dx + regdy * dy;
+ double w = -regdx * dy + regdy * dx;
+
+ if(l > l_max) l_max = l;
+ else if(l < l_min) l_min = l;
+ if(w > w_max) w_max = w;
+ else if(w < w_min) w_min = w;
+ }
+
+ // Store values
+ rec.x1 = x + l_min * dx;
+ rec.y1 = y + l_min * dy;
+ rec.x2 = x + l_max * dx;
+ rec.y2 = y + l_max * dy;
+ rec.width = w_max - w_min;
+ rec.x = x;
+ rec.y = y;
+ rec.theta = theta;
+ rec.dx = dx;
+ rec.dy = dy;
+ rec.prec = prec;
+ rec.p = p;
+
+ // Min width of 1 pixel
+ if(rec.width < 1.0) rec.width = 1.0;
+}
+
+double LineSegmentDetectorImpl::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
+{
+ double Ixx = 0.0;
+ double Iyy = 0.0;
+ double Ixy = 0.0;
+
+ // Compute inertia matrix
+ for(int i = 0; i < reg_size; ++i)
+ {
+ const double& regx = reg[i].x;
+ const double& regy = reg[i].y;
+ const double& weight = reg[i].modgrad;
+ double dx = regx - x;
+ double dy = regy - y;
+ Ixx += dy * dy * weight;
+ Iyy += dx * dx * weight;
+ Ixy -= dx * dy * weight;
+ }
+
+ // Check if inertia matrix is null
+ CV_Assert(!(double_equal(Ixx, 0) && double_equal(Iyy, 0) && double_equal(Ixy, 0)));
+
+ // Compute smallest eigenvalue
+ double lambda = 0.5 * (Ixx + Iyy - sqrt((Ixx - Iyy) * (Ixx - Iyy) + 4.0 * Ixy * Ixy));
+
+ // Compute angle
+ double theta = (fabs(Ixx)>fabs(Iyy))?
+ double(fastAtan2(float(lambda - Ixx), float(Ixy))):
+ double(fastAtan2(float(Ixy), float(lambda - Iyy))); // in degs
+ theta *= DEG_TO_RADS;
+
+ // Correct angle by 180 deg if necessary
+ if(angle_diff(theta, reg_angle) > prec) { theta += CV_PI; }
+
+ return theta;
+}
+
+bool LineSegmentDetectorImpl::refine(std::vector<RegionPoint>& reg, int& reg_size, double reg_angle,
+ const double prec, double p, rect& rec, const double& density_th)
+{
+ double density = double(reg_size) / (dist(rec.x1, rec.y1, rec.x2, rec.y2) * rec.width);
+
+ if (density >= density_th) { return true; }
+
+ // Try to reduce angle tolerance
+ double xc = double(reg[0].x);
+ double yc = double(reg[0].y);
+ const double& ang_c = reg[0].angle;
+ double sum = 0, s_sum = 0;
+ int n = 0;
+
+ for (int i = 0; i < reg_size; ++i)
+ {
+ *(reg[i].used) = NOTUSED;
+ if (dist(xc, yc, reg[i].x, reg[i].y) < rec.width)
+ {
+ const double& angle = reg[i].angle;
+ double ang_d = angle_diff_signed(angle, ang_c);
+ sum += ang_d;
+ s_sum += ang_d * ang_d;
+ ++n;
+ }
+ }
+ double mean_angle = sum / double(n);
+ // 2 * standard deviation
+ double tau = 2.0 * sqrt((s_sum - 2.0 * mean_angle * sum) / double(n) + mean_angle * mean_angle);
+
+ // Try new region
+ region_grow(Point(reg[0].x, reg[0].y), reg, reg_size, reg_angle, tau);
+
+ if (reg_size < 2) { return false; }
+
+ region2rect(reg, reg_size, reg_angle, prec, p, rec);
+ density = double(reg_size) / (dist(rec.x1, rec.y1, rec.x2, rec.y2) * rec.width);
+
+ if (density < density_th)
+ {
+ return reduce_region_radius(reg, reg_size, reg_angle, prec, p, rec, density, density_th);
+ }
+ else
+ {
+ return true;
+ }
+}
+
+bool LineSegmentDetectorImpl::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)
+{
+ // Compute region's radius
+ double xc = double(reg[0].x);
+ double yc = double(reg[0].y);
+ double radSq1 = distSq(xc, yc, rec.x1, rec.y1);
+ double radSq2 = distSq(xc, yc, rec.x2, rec.y2);
+ double radSq = radSq1 > radSq2 ? radSq1 : radSq2;
+
+ while(density < density_th)
+ {
+ radSq *= 0.75*0.75; // Reduce region's radius to 75% of its value
+ // Remove points from the region and update 'used' map
+ for(int i = 0; i < reg_size; ++i)
+ {
+ if(distSq(xc, yc, double(reg[i].x), double(reg[i].y)) > radSq)
+ {
+ // Remove point from the region
+ *(reg[i].used) = NOTUSED;
+ std::swap(reg[i], reg[reg_size - 1]);
+ --reg_size;
+ --i; // To avoid skipping one point
+ }
+ }
+
+ if(reg_size < 2) { return false; }
+
+ // Re-compute rectangle
+ region2rect(reg, reg_size ,reg_angle, prec, p, rec);
+
+ // Re-compute region points density
+ density = double(reg_size) /
+ (dist(rec.x1, rec.y1, rec.x2, rec.y2) * rec.width);
+ }
+
+ return true;
+}
+
+double LineSegmentDetectorImpl::rect_improve(rect& rec) const
+{
+ double delta = 0.5;
+ double delta_2 = delta / 2.0;
+
+ double log_nfa = rect_nfa(rec);
+
+ if(log_nfa > LOG_EPS) return log_nfa; // Good rectangle
+
+ // Try to improve
+ // Finer precision
+ rect r = rect(rec); // Copy
+ for(int n = 0; n < 5; ++n)
+ {
+ r.p /= 2;
+ r.prec = r.p * CV_PI;
+ double log_nfa_new = rect_nfa(r);
+ if(log_nfa_new > log_nfa)
+ {
+ log_nfa = log_nfa_new;
+ rec = rect(r);
+ }
+ }
+ if(log_nfa > LOG_EPS) return log_nfa;
+
+ // Try to reduce width
+ r = rect(rec);
+ for(unsigned int n = 0; n < 5; ++n)
+ {
+ if((r.width - delta) >= 0.5)
+ {
+ r.width -= delta;
+ double log_nfa_new = rect_nfa(r);
+ if(log_nfa_new > log_nfa)
+ {
+ rec = rect(r);
+ log_nfa = log_nfa_new;
+ }
+ }
+ }
+ if(log_nfa > LOG_EPS) return log_nfa;
+
+ // Try to reduce one side of rectangle
+ r = rect(rec);
+ for(unsigned int n = 0; n < 5; ++n)
+ {
+ if((r.width - delta) >= 0.5)
+ {
+ r.x1 += -r.dy * delta_2;
+ r.y1 += r.dx * delta_2;
+ r.x2 += -r.dy * delta_2;
+ r.y2 += r.dx * delta_2;
+ r.width -= delta;
+ double log_nfa_new = rect_nfa(r);
+ if(log_nfa_new > log_nfa)
+ {
+ rec = rect(r);
+ log_nfa = log_nfa_new;
+ }
+ }
+ }
+ if(log_nfa > LOG_EPS) return log_nfa;
+
+ // Try to reduce other side of rectangle
+ r = rect(rec);
+ for(unsigned int n = 0; n < 5; ++n)
+ {
+ if((r.width - delta) >= 0.5)
+ {
+ r.x1 -= -r.dy * delta_2;
+ r.y1 -= r.dx * delta_2;
+ r.x2 -= -r.dy * delta_2;
+ r.y2 -= r.dx * delta_2;
+ r.width -= delta;
+ double log_nfa_new = rect_nfa(r);
+ if(log_nfa_new > log_nfa)
+ {
+ rec = rect(r);
+ log_nfa = log_nfa_new;
+ }
+ }
+ }
+ if(log_nfa > LOG_EPS) return log_nfa;
+
+ // Try finer precision
+ r = rect(rec);
+ for(unsigned int n = 0; n < 5; ++n)
+ {
+ if((r.width - delta) >= 0.5)
+ {
+ r.p /= 2;
+ r.prec = r.p * CV_PI;
+ double log_nfa_new = rect_nfa(r);
+ if(log_nfa_new > log_nfa)
+ {
+ rec = rect(r);
+ log_nfa = log_nfa_new;
+ }
+ }
+ }
+
+ return log_nfa;
+}
+
+double LineSegmentDetectorImpl::rect_nfa(const rect& rec) const
+{
+ int total_pts = 0, alg_pts = 0;
+ double half_width = rec.width / 2.0;
+ double dyhw = rec.dy * half_width;
+ double dxhw = rec.dx * half_width;
+
+ std::vector<edge> ordered_x(4);
+ edge* min_y = &ordered_x[0];
+ edge* max_y = &ordered_x[0]; // Will be used for loop range
+
+ ordered_x[0].p.x = int(rec.x1 - dyhw); ordered_x[0].p.y = int(rec.y1 + dxhw); ordered_x[0].taken = false;
+ ordered_x[1].p.x = int(rec.x2 - dyhw); ordered_x[1].p.y = int(rec.y2 + dxhw); ordered_x[1].taken = false;
+ ordered_x[2].p.x = int(rec.x2 + dyhw); ordered_x[2].p.y = int(rec.y2 - dxhw); ordered_x[2].taken = false;
+ ordered_x[3].p.x = int(rec.x1 + dyhw); ordered_x[3].p.y = int(rec.y1 - dxhw); ordered_x[3].taken = false;
+
+ std::sort(ordered_x.begin(), ordered_x.end(), AsmallerB_XoverY);
+
+ // Find min y. And mark as taken. find max y.
+ for(unsigned int i = 1; i < 4; ++i)
+ {
+ if(min_y->p.y > ordered_x[i].p.y) {min_y = &ordered_x[i]; }
+ if(max_y->p.y < ordered_x[i].p.y) {max_y = &ordered_x[i]; }
+ }
+ min_y->taken = true;
+
+ // Find leftmost untaken point;
+ edge* leftmost = 0;
+ for(unsigned int i = 0; i < 4; ++i)
+ {
+ if(!ordered_x[i].taken)
+ {
+ if(!leftmost) // if uninitialized
+ {
+ leftmost = &ordered_x[i];
+ }
+ else if (leftmost->p.x > ordered_x[i].p.x)
+ {
+ leftmost = &ordered_x[i];
+ }
+ }
+ }
+ leftmost->taken = true;
+
+ // Find rightmost untaken point;
+ edge* rightmost = 0;
+ for(unsigned int i = 0; i < 4; ++i)
+ {
+ if(!ordered_x[i].taken)
+ {
+ if(!rightmost) // if uninitialized
+ {
+ rightmost = &ordered_x[i];
+ }
+ else if (rightmost->p.x < ordered_x[i].p.x)
+ {
+ rightmost = &ordered_x[i];
+ }
+ }
+ }
+ rightmost->taken = true;
+
+ // Find last untaken point;
+ edge* tailp = 0;
+ for(unsigned int i = 0; i < 4; ++i)
+ {
+ if(!ordered_x[i].taken)
+ {
+ if(!tailp) // if uninitialized
+ {
+ tailp = &ordered_x[i];
+ }
+ else if (tailp->p.x > ordered_x[i].p.x)
+ {
+ tailp = &ordered_x[i];
+ }
+ }
+ }
+ tailp->taken = true;
+
+ double flstep = (min_y->p.y != leftmost->p.y) ?
+ (min_y->p.x - leftmost->p.x) / (min_y->p.y - leftmost->p.y) : 0; //first left step
+ double slstep = (leftmost->p.y != tailp->p.x) ?
+ (leftmost->p.x - tailp->p.x) / (leftmost->p.y - tailp->p.x) : 0; //second left step
+
+ double frstep = (min_y->p.y != rightmost->p.y) ?
+ (min_y->p.x - rightmost->p.x) / (min_y->p.y - rightmost->p.y) : 0; //first right step
+ double srstep = (rightmost->p.y != tailp->p.x) ?
+ (rightmost->p.x - tailp->p.x) / (rightmost->p.y - tailp->p.x) : 0; //second right step
+
+ double lstep = flstep, rstep = frstep;
+
+ double left_x = min_y->p.x, right_x = min_y->p.x;
+
+ // Loop around all points in the region and count those that are aligned.
+ int min_iter = std::max(min_y->p.y, 0);
+ int max_iter = std::min(max_y->p.y, img_height - 1);
+ for(int y = min_iter; y <= max_iter; ++y)
+ {
+ int adx = y * img_width + int(left_x);
+ for(int x = int(left_x); x <= int(right_x); ++x, ++adx)
+ {
+ ++total_pts;
+ if(isAligned(adx, rec.theta, rec.prec))
+ {
+ ++alg_pts;
+ }
+ }
+
+ if(y >= leftmost->p.y) { lstep = slstep; }
+ if(y >= rightmost->p.y) { rstep = srstep; }
+
+ left_x += lstep;
+ right_x += rstep;
+ }
+
+ return nfa(total_pts, alg_pts, rec.p);
+}
+
+double LineSegmentDetectorImpl::nfa(const int& n, const int& k, const double& p) const
+{
+ // Trivial cases
+ if(n == 0 || k == 0) { return -LOG_NT; }
+ if(n == k) { return -LOG_NT - double(n) * log10(p); }
+
+ double p_term = p / (1 - p);
+
+ double log1term = (double(n) + 1) - log_gamma(double(k) + 1)
+ - log_gamma(double(n-k) + 1)
+ + double(k) * log(p) + double(n-k) * log(1.0 - p);
+ double term = exp(log1term);
+
+ if(double_equal(term, 0))
+ {
+ if(k > n * p) return -log1term / M_LN10 - LOG_NT;
+ else return -LOG_NT;
+ }
+
+ // Compute more terms if needed
+ double bin_tail = term;
+ double tolerance = 0.1; // an error of 10% in the result is accepted
+ for(int i = k + 1; i <= n; ++i)
+ {
+ double bin_term = double(n - i + 1) / double(i);
+ double mult_term = bin_term * p_term;
+ term *= mult_term;
+ bin_tail += term;
+ if(bin_term < 1)
+ {
+ double err = term * ((1 - pow(mult_term, double(n-i+1))) / (1 - mult_term) - 1);
+ if(err < tolerance * fabs(-log10(bin_tail) - LOG_NT) * bin_tail) break;
+ }
+
+ }
+ return -log10(bin_tail) - LOG_NT;
+}
+
+inline bool LineSegmentDetectorImpl::isAligned(const int& address, const double& theta, const double& prec) const
+{
+ if(address < 0) { return false; }
+ const double& a = angles_data[address];
+ if(a == NOTDEF) { return false; }
+
+ // It is assumed that 'theta' and 'a' are in the range [-pi,pi]
+ double n_theta = theta - a;
+ if(n_theta < 0) { n_theta = -n_theta; }
+ if(n_theta > M_3_2_PI)
+ {
+ n_theta -= M_2__PI;
+ if(n_theta < 0) n_theta = -n_theta;
+ }
+
+ return n_theta <= prec;
+}
+
+
+void LineSegmentDetectorImpl::drawSegments(InputOutputArray _image, const InputArray lines)
+{
+ CV_Assert(!_image.empty() && (_image.channels() == 1 || _image.channels() == 3));
+
+ Mat gray;
+ if (_image.channels() == 1)
+ {
+ gray = _image.getMatRef();
+ }
+ else if (_image.channels() == 3)
+ {
+ cvtColor(_image, gray, CV_BGR2GRAY);
+ }
+
+ // Create a 3 channel image in order to draw colored lines
+ std::vector<Mat> planes;
+ planes.push_back(gray);
+ planes.push_back(gray);
+ planes.push_back(gray);
+
+ merge(planes, _image);
+
+ Mat _lines;
+ _lines = lines.getMat();
+
+ // Draw segments
+ for(int i = 0; i < _lines.size().width; ++i)
+ {
+ const Vec4i& v = _lines.at<Vec4i>(i);
+ Point b(v[0], v[1]);
+ Point e(v[2], v[3]);
+ line(_image.getMatRef(), b, e, Scalar(0, 0, 255), 1);
+ }
+}
+
+
+int LineSegmentDetectorImpl::compareSegments(const Size& size, const InputArray lines1, const InputArray lines2, Mat* _image)
+{
+ Size sz = size;
+ if (_image && _image->size() != size) sz = _image->size();
+ CV_Assert(sz.area());
+
+ Mat_<uchar> I1 = Mat_<uchar>::zeros(sz);
+ Mat_<uchar> I2 = Mat_<uchar>::zeros(sz);
+
+ Mat _lines1;
+ Mat _lines2;
+ _lines1 = lines1.getMat();
+ _lines2 = lines2.getMat();
+ // Draw segments
+ std::vector<Mat> _lines;
+ for(int i = 0; i < _lines1.size().width; ++i)
+ {
+ Point b(_lines1.at<Vec4i>(i)[0], _lines1.at<Vec4i>(i)[1]);
+ Point e(_lines1.at<Vec4i>(i)[2], _lines1.at<Vec4i>(i)[3]);
+ line(I1, b, e, Scalar::all(255), 1);
+ }
+ for(int i = 0; i < _lines2.size().width; ++i)
+ {
+ Point b(_lines2.at<Vec4i>(i)[0], _lines2.at<Vec4i>(i)[1]);
+ Point e(_lines2.at<Vec4i>(i)[2], _lines2.at<Vec4i>(i)[3]);
+ line(I2, b, e, Scalar::all(255), 1);
+ }
+
+ // Count the pixels that don't agree
+ Mat Ixor;
+ bitwise_xor(I1, I2, Ixor);
+ int N = countNonZero(Ixor);
+
+ if (_image)
+ {
+ Mat Ig;
+ if (_image->channels() == 1)
+ {
+ cvtColor(*_image, *_image, CV_GRAY2BGR);
+ }
+ CV_Assert(_image->isContinuous() && I1.isContinuous() && I2.isContinuous());
+
+ for (unsigned int i = 0; i < I1.total(); ++i)
+ {
+ uchar i1 = I1.data[i];
+ uchar i2 = I2.data[i];
+ if (i1 || i2)
+ {
+ _image->data[3*i + 1] = 0;
+ if (i1) _image->data[3*i] = 255;
+ else _image->data[3*i] = 0;
+ if (i2) _image->data[3*i + 2] = 255;
+ else _image->data[3*i + 2] = 0;
+ }
+ }
+ }
+
+ return N;
+}
+
+} // namespace cv
diff --git a/modules/imgproc/test/test_lsd.cpp b/modules/imgproc/test/test_lsd.cpp
new file mode 100644
index 0000000000..e18f32fa82
--- /dev/null
+++ b/modules/imgproc/test/test_lsd.cpp
@@ -0,0 +1,265 @@
+#include "test_precomp.hpp"
+
+#include <vector>
+
+using namespace cv;
+using namespace std;
+
+const Size img_size(640, 480);
+const int LSD_TEST_SEED = 0x134679;
+const int EPOCHS = 20;
+
+class LSDBase : public testing::Test
+{
+public:
+ LSDBase() {};
+
+protected:
+ Mat test_image;
+ vector<Vec4i> lines;
+ RNG rng;
+ int passedtests;
+
+ void GenerateWhiteNoise(Mat& image);
+ void GenerateConstColor(Mat& image);
+ void GenerateLines(Mat& image, const unsigned int numLines);
+ void GenerateRotatedRect(Mat& image);
+ virtual void SetUp();
+};
+
+class Imgproc_LSD_ADV: public LSDBase
+{
+public:
+ Imgproc_LSD_ADV() {};
+protected:
+
+};
+
+class Imgproc_LSD_STD: public LSDBase
+{
+public:
+ Imgproc_LSD_STD() {};
+protected:
+
+};
+
+class Imgproc_LSD_NONE: public LSDBase
+{
+public:
+ Imgproc_LSD_NONE() {};
+protected:
+
+};
+
+void LSDBase::GenerateWhiteNoise(Mat& image)
+{
+ image = Mat(img_size, CV_8UC1);
+ rng.fill(image, RNG::UNIFORM, 0, 256);
+}
+
+void LSDBase::GenerateConstColor(Mat& image)
+{
+ image = Mat(img_size, CV_8UC1, Scalar::all(rng.uniform(0, 256)));
+}
+
+void LSDBase::GenerateLines(Mat& image, const unsigned int numLines)
+{
+ image = Mat(img_size, CV_8UC1, Scalar::all(rng.uniform(0, 128)));
+
+ for(unsigned int i = 0; i < numLines; ++i)
+ {
+ int y = rng.uniform(10, img_size.width - 10);
+ Point p1(y, 10);
+ Point p2(y, img_size.height - 10);
+ line(image, p1, p2, Scalar(255), 3);
+ }
+}
+
+void LSDBase::GenerateRotatedRect(Mat& image)
+{
+ image = Mat::zeros(img_size, CV_8UC1);
+
+ Point center(rng.uniform(img_size.width/4, img_size.width*3/4),
+ rng.uniform(img_size.height/4, img_size.height*3/4));
+ Size rect_size(rng.uniform(img_size.width/8, img_size.width/6),
+ rng.uniform(img_size.height/8, img_size.height/6));
+ float angle = rng.uniform(0.f, 360.f);
+
+ Point2f vertices[4];
+
+ RotatedRect rRect = RotatedRect(center, rect_size, angle);
+
+ rRect.points(vertices);
+ for (int i = 0; i < 4; i++)
+ {
+ line(image, vertices[i], vertices[(i + 1) % 4], Scalar(255), 3);
+ }
+}
+
+void LSDBase::SetUp()
+{
+ lines.clear();
+ test_image = Mat();
+ rng = RNG(LSD_TEST_SEED);
+ passedtests = 0;
+}
+
+
+TEST_F(Imgproc_LSD_ADV, whiteNoise)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ GenerateWhiteNoise(test_image);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_ADV);
+ detector->detect(test_image, lines);
+
+ if(uint(40) >= lines.size()) ++passedtests;
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
+
+TEST_F(Imgproc_LSD_ADV, constColor)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ GenerateConstColor(test_image);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_ADV);
+ detector->detect(test_image, lines);
+
+ if(uint(0) == lines.size()) ++passedtests;
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
+
+TEST_F(Imgproc_LSD_ADV, lines)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ const unsigned int numOfLines = 1;
+ GenerateLines(test_image, numOfLines);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_ADV);
+ detector->detect(test_image, lines);
+
+ if(numOfLines * 2 == lines.size()) ++passedtests; // * 2 because of Gibbs effect
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
+
+TEST_F(Imgproc_LSD_ADV, rotatedRect)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ GenerateRotatedRect(test_image);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_ADV);
+ detector->detect(test_image, lines);
+
+ if(uint(2) <= lines.size()) ++passedtests;
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
+
+TEST_F(Imgproc_LSD_STD, whiteNoise)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ GenerateWhiteNoise(test_image);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_STD);
+ detector->detect(test_image, lines);
+
+ if(uint(50) >= lines.size()) ++passedtests;
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
+
+TEST_F(Imgproc_LSD_STD, constColor)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ GenerateConstColor(test_image);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_STD);
+ detector->detect(test_image, lines);
+
+ if(uint(0) == lines.size()) ++passedtests;
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
+
+TEST_F(Imgproc_LSD_STD, lines)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ const unsigned int numOfLines = 1;
+ GenerateLines(test_image, numOfLines);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_STD);
+ detector->detect(test_image, lines);
+
+ if(numOfLines * 2 == lines.size()) ++passedtests; // * 2 because of Gibbs effect
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
+
+TEST_F(Imgproc_LSD_STD, rotatedRect)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ GenerateRotatedRect(test_image);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_STD);
+ detector->detect(test_image, lines);
+
+ if(uint(4) <= lines.size()) ++passedtests;
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
+
+TEST_F(Imgproc_LSD_NONE, whiteNoise)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ GenerateWhiteNoise(test_image);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_STD);
+ detector->detect(test_image, lines);
+
+ if(uint(50) >= lines.size()) ++passedtests;
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
+
+TEST_F(Imgproc_LSD_NONE, constColor)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ GenerateConstColor(test_image);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_NONE);
+ detector->detect(test_image, lines);
+
+ if(uint(0) == lines.size()) ++passedtests;
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
+
+TEST_F(Imgproc_LSD_NONE, lines)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ const unsigned int numOfLines = 1;
+ GenerateLines(test_image, numOfLines);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_NONE);
+ detector->detect(test_image, lines);
+
+ if(numOfLines * 2 == lines.size()) ++passedtests; // * 2 because of Gibbs effect
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
+
+TEST_F(Imgproc_LSD_NONE, rotatedRect)
+{
+ for (int i = 0; i < EPOCHS; ++i)
+ {
+ GenerateRotatedRect(test_image);
+ Ptr<LineSegmentDetector> detector = createLineSegmentDetectorPtr(LSD_REFINE_NONE);
+ detector->detect(test_image, lines);
+
+ if(uint(8) <= lines.size()) ++passedtests;
+ }
+ ASSERT_EQ(EPOCHS, passedtests);
+}
diff --git a/samples/cpp/lsd_lines.cpp b/samples/cpp/lsd_lines.cpp
new file mode 100644
index 0000000000..62692f70bd
--- /dev/null
+++ b/samples/cpp/lsd_lines.cpp
@@ -0,0 +1,54 @@
+#include <iostream>
+#include <string>
+
+#include "opencv2/core/core.hpp"
+#include "opencv2/core/utility.hpp"
+#include "opencv2/imgproc/imgproc.hpp"
+#include "opencv2/highgui/highgui.hpp"
+
+using namespace std;
+using namespace cv;
+
+int main(int argc, char** argv)
+{
+ std::string in;
+ if (argc != 2)
+ {
+ std::cout << "Usage: lsd_lines [input image]. Now loading building.jpg" << std::endl;
+ in = "building.jpg";
+ }
+ else
+ {
+ in = argv[1];
+ }
+
+ Mat image = imread(in, IMREAD_GRAYSCALE);
+
+#if 0
+ Canny(image, image, 50, 200, 3); // Apply canny edge
+#endif
+
+ // Create and LSD detector with standard or no refinement.
+#if 1
+ Ptr<LineSegmentDetector> ls = createLineSegmentDetectorPtr(LSD_REFINE_STD);
+#else
+ Ptr<LineSegmentDetector> ls = createLineSegmentDetectorPtr(LSD_REFINE_NONE);
+#endif
+
+ double start = double(getTickCount());
+ vector<Vec4i> lines_std;
+
+ // Detect the lines
+ ls->detect(image, lines_std);
+
+ double duration_ms = (double(getTickCount()) - start) * 1000 / getTickFrequency();
+ std::cout << "It took " << duration_ms << " ms." << std::endl;
+
+ // Show found lines
+ Mat drawnLines(image);
+ ls->drawSegments(drawnLines, lines_std);
+ imshow("Standard refinement", drawnLines);
+
+ waitKey();
+ return 0;
+}