From 9b768727080b3279c244ad595115b1d5126d32ed Mon Sep 17 00:00:00 2001 From: Suleyman TURKMEN Date: Fri, 1 Oct 2021 16:23:16 +0300 Subject: [PATCH] restore LSD --- modules/imgproc/include/opencv2/imgproc.hpp | 10 +- modules/imgproc/src/lsd.cpp | 983 +++++++++++++++++++- modules/imgproc/test/test_lsd.cpp | 4 - samples/cpp/lsd_lines.cpp | 73 ++ 4 files changed, 1055 insertions(+), 15 deletions(-) create mode 100644 samples/cpp/lsd_lines.cpp diff --git a/modules/imgproc/include/opencv2/imgproc.hpp b/modules/imgproc/include/opencv2/imgproc.hpp index f7583c1926..855cbaf159 100644 --- a/modules/imgproc/include/opencv2/imgproc.hpp +++ b/modules/imgproc/include/opencv2/imgproc.hpp @@ -1220,12 +1220,14 @@ protected: //! @addtogroup imgproc_feature //! @{ +/** @example samples/cpp/lsd_lines.cpp +An example using the LineSegmentDetector +\image html building_lsd.png "Sample output image" width=434 height=300 +*/ + /** @brief Line segment detector class following the algorithm described at @cite Rafael12 . - -@note Implementation has been removed due original code license conflict - */ class CV_EXPORTS_W LineSegmentDetector : public Algorithm { @@ -1288,8 +1290,6 @@ to edit those, as to tailor it for their own application. @param log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement is chosen. @param density_th Minimal density of aligned region points in the enclosing rectangle. @param n_bins Number of bins in pseudo-ordering of gradient modulus. - -@note Implementation has been removed due original code license conflict */ CV_EXPORTS_W Ptr createLineSegmentDetector( int refine = LSD_REFINE_STD, double scale = 0.8, diff --git a/modules/imgproc/src/lsd.cpp b/modules/imgproc/src/lsd.cpp index 1ec984d290..d06759c2bb 100644 --- a/modules/imgproc/src/lsd.cpp +++ b/modules/imgproc/src/lsd.cpp @@ -42,7 +42,123 @@ #include "precomp.hpp" #include -namespace cv { +///////////////////////////////////////////////////////////////////////////////////////// +// 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 CV_FINAL : public LineSegmentDetector { @@ -113,7 +229,164 @@ public: int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray()) CV_OVERRIDE; private: + Mat image; + Mat scaled_image; + Mat_ angles; // in rads + Mat_ modgrad; + Mat_ 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 normPoint + { + Point2i p; + int norm; + }; + + std::vector ordered_points; + + 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 Vec4f elements specifying the beginning and ending point of a line. + * Where Vec4f 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& lines, + std::vector& widths, std::vector& precisions, + std::vector& 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 ordered_points 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); + +/** + * 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_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& reg, + 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_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& reg, 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& reg, 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& reg, 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& reg, 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(int x, int y, const double& theta, const double& prec) const; + +public: + // Compare norm + static inline bool compare_norm( const normPoint& n1, const normPoint& n2 ) + { + return (n1.norm > n2.norm); + } }; ///////////////////////////////////////////////////////////////////////////////////////// @@ -131,12 +404,13 @@ CV_EXPORTS Ptr createLineSegmentDetector( LineSegmentDetectorImpl::LineSegmentDetectorImpl(int _refine, double _scale, double _sigma_scale, double _quant, double _ang_th, double _log_eps, double _density_th, int _n_bins) + : img_width(0), img_height(0), LOG_NT(0), w_needed(false), p_needed(false), n_needed(false), + 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); - CV_UNUSED(_refine); CV_UNUSED(_log_eps); - CV_Error(Error::StsNotImplemented, "Implementation has been removed due original code license issues"); } void LineSegmentDetectorImpl::detect(InputArray _image, OutputArray _lines, @@ -144,11 +418,708 @@ void LineSegmentDetectorImpl::detect(InputArray _image, OutputArray _lines, { CV_INSTRUMENT_REGION(); - CV_UNUSED(_image); CV_UNUSED(_lines); - CV_UNUSED(_width); CV_UNUSED(_prec); CV_UNUSED(_nfa); - CV_Error(Error::StsNotImplemented, "Implementation has been removed due original code license issues"); + image = _image.getMat(); + CV_Assert(!image.empty() && image.type() == CV_8UC1); + + std::vector lines; + std::vector w, p, n; + w_needed = _width.needed(); + p_needed = _prec.needed(); + if (doRefine < LSD_REFINE_ADV) + n_needed = false; + else + n_needed = _nfa.needed(); + + 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); + + // Clear used structures + ordered_points.clear(); } +void LineSegmentDetectorImpl::flsd(std::vector& lines, + std::vector& widths, std::vector& precisions, + std::vector& 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 + + 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, INTER_LINEAR_EXACT); + ll_angle(rho, N_BINS); + } + else + { + scaled_image = image; + ll_angle(rho, N_BINS); + } + + LOG_NT = 5 * (log10(double(img_width)) + log10(double(img_height))) / 2 + log10(11.0); + const size_t min_reg_size = size_t(-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_::zeros(scaled_image.size()); // zeros = NOTUSED + std::vector reg; + + // Search for line segments + for(size_t i = 0, points_size = ordered_points.size(); i < points_size; ++i) + { + const Point2i& point = ordered_points[i].p; + if((used.at(point) == NOTUSED) && (angles.at(point) != NOTDEF)) + { + double reg_angle; + region_grow(ordered_points[i].p, reg, reg_angle, prec); + + // Ignore small regions + if(reg.size() < min_reg_size) { continue; } + + // Construct rectangular approximation for the region + rect rec; + region2rect(reg, reg_angle, prec, p, rec); + + double log_nfa = -1; + if(doRefine > LSD_REFINE_NONE) + { + // At least REFINE_STANDARD lvl. + if(!refine(reg, 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 + + // 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(Vec4f(float(rec.x1), float(rec.y1), float(rec.x2), float(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); + } + } +} + +void LineSegmentDetectorImpl::ll_angle(const double& threshold, + const unsigned int& n_bins) +{ + //Initialize data + angles = Mat_(scaled_image.size()); + modgrad = Mat_(scaled_image.size()); + + 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 + double max_grad = -1; + for(int y = 0; y < img_height - 1; ++y) + { + const uchar* scaled_image_row = scaled_image.ptr(y); + const uchar* next_scaled_image_row = scaled_image.ptr(y+1); + double* angles_row = angles.ptr(y); + double* modgrad_row = modgrad.ptr(y); + for(int x = 0; x < img_width-1; ++x) + { + int DA = next_scaled_image_row[x + 1] - scaled_image_row[x]; + int BC = scaled_image_row[x + 1] - next_scaled_image_row[x]; + int gx = DA + BC; // gradient x component + int gy = DA - BC; // gradient y component + double norm = std::sqrt((gx * gx + gy * gy) / 4.0); // gradient norm + + modgrad_row[x] = norm; // store gradient + + if (norm <= threshold) // norm too small, gradient no defined + { + angles_row[x] = NOTDEF; + } + else + { + angles_row[x] = fastAtan2(float(gx), float(-gy)) * DEG_TO_RADS; // gradient angle computation + if (norm > max_grad) { max_grad = norm; } + } + + } + } + + // Compute histogram of gradient values + 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* modgrad_row = modgrad.ptr(y); + for(int x = 0; x < img_width - 1; ++x) + { + normPoint _point; + int i = int(modgrad_row[x] * bin_coef); + _point.p = Point(x, y); + _point.norm = i; + ordered_points.push_back(_point); + } + } + + // Sort + std::sort(ordered_points.begin(), ordered_points.end(), compare_norm); +} + +void LineSegmentDetectorImpl::region_grow(const Point2i& s, std::vector& reg, + double& reg_angle, const double& prec) +{ + reg.clear(); + + // Point to this region + RegionPoint seed; + seed.x = s.x; + seed.y = s.y; + seed.used = &used.at(s); + reg_angle = angles.at(s); + seed.angle = reg_angle; + seed.modgrad = modgrad.at(s); + reg.push_back(seed); + + float sumdx = float(std::cos(reg_angle)); + float sumdy = float(std::sin(reg_angle)); + *seed.used = USED; + + //Try neighboring regions + for (size_t i = 0;i(yy); + const double* angles_row = angles.ptr(yy); + const double* modgrad_row = modgrad.ptr(yy); + for(int xx = xx_min; xx <= xx_max; ++xx) + { + uchar& is_used = used_row[xx]; + if(is_used != USED && + (isAligned(xx, yy, reg_angle, prec))) + { + const double& angle = angles_row[xx]; + // Add point + is_used = USED; + RegionPoint region_point; + region_point.x = xx; + region_point.y = yy; + region_point.used = &is_used; + region_point.modgrad = modgrad_row[xx]; + region_point.angle = angle; + reg.push_back(region_point); + + // 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& reg, + const double reg_angle, const double prec, const double p, rect& rec) const +{ + double x = 0, y = 0, sum = 0; + for(size_t 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, 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(size_t 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& reg, 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(size_t 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& reg, 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 (size_t 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; + } + } + CV_Assert(n > 0); + 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_angle, tau); + + if (reg.size() < 2) { return false; } + + region2rect(reg, 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_angle, prec, p, rec, density, density_th); + } + else + { + return true; + } +} + +bool LineSegmentDetectorImpl::reduce_region_radius(std::vector& reg, 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 (size_t 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.pop_back(); + --i; // To avoid skipping one point + } + } + + if(reg.size() < 2) { return false; } + + // Re-compute rectangle + region2rect(reg ,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; + + 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, ordered_x + 4, 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]; + } + } + } + CV_Assert(leftmost != NULL); + 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]; + } + } + } + CV_Assert(rightmost != NULL); + 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]; + } + } + } + CV_Assert(tailp != NULL); + 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 = min_y->p.y; + int max_iter = max_y->p.y; + for(int y = min_iter; y <= max_iter; ++y) + { + if (y < 0 || y >= img_height) continue; + + for(int x = int(left_x); x <= int(right_x); ++x) + { + if (x < 0 || x >= img_width) continue; + + ++total_pts; + if(isAligned(x, y, 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(int x, int y, const double& theta, const double& prec) const +{ + if(x < 0 || y < 0 || x >= angles.cols || y >= angles.rows) { return false; } + const double& a = angles.at(y, x); + 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, InputArray lines) { CV_INSTRUMENT_REGION(); diff --git a/modules/imgproc/test/test_lsd.cpp b/modules/imgproc/test/test_lsd.cpp index e162a3c6f4..43d00b4928 100644 --- a/modules/imgproc/test/test_lsd.cpp +++ b/modules/imgproc/test/test_lsd.cpp @@ -5,8 +5,6 @@ namespace opencv_test { namespace { -#if 0 // LSD implementation has been removed due original code license issues - const Size img_size(640, 480); const int LSD_TEST_SEED = 0x134679; const int EPOCHS = 20; @@ -404,6 +402,4 @@ TEST_F(Imgproc_LSD_Common, compareSegmentsVec4i) ASSERT_EQ(result2, 11); } -#endif - }} // namespace diff --git a/samples/cpp/lsd_lines.cpp b/samples/cpp/lsd_lines.cpp new file mode 100644 index 0000000000..3feed9cbc2 --- /dev/null +++ b/samples/cpp/lsd_lines.cpp @@ -0,0 +1,73 @@ +#include "opencv2/imgproc.hpp" +#include "opencv2/imgcodecs.hpp" +#include "opencv2/highgui.hpp" +#include + +using namespace std; +using namespace cv; + +int main(int argc, char** argv) +{ + cv::CommandLineParser parser(argc, argv, + "{input i|building.jpg|input image}" + "{refine r|false|if true use LSD_REFINE_STD method, if false use LSD_REFINE_NONE method}" + "{canny c|false|use Canny edge detector}" + "{overlay o|false|show result on input image}" + "{help h|false|show help message}"); + + if (parser.get("help")) + { + parser.printMessage(); + return 0; + } + + parser.printMessage(); + + String filename = samples::findFile(parser.get("input")); + bool useRefine = parser.get("refine"); + bool useCanny = parser.get("canny"); + bool overlay = parser.get("overlay"); + + Mat image = imread(filename, IMREAD_GRAYSCALE); + + if( image.empty() ) + { + cout << "Unable to load " << filename; + return 1; + } + + imshow("Source Image", image); + + if (useCanny) + { + Canny(image, image, 50, 200, 3); // Apply Canny edge detector + } + + // Create and LSD detector with standard or no refinement. + Ptr ls = useRefine ? createLineSegmentDetector(LSD_REFINE_STD) : createLineSegmentDetector(LSD_REFINE_NONE); + + double start = double(getTickCount()); + vector 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 + if (!overlay || useCanny) + { + image = Scalar(0, 0, 0); + } + + ls->drawSegments(image, lines_std); + + String window_name = useRefine ? "Result - standard refinement" : "Result - no refinement"; + window_name += useCanny ? " - Canny edge detector used" : ""; + + imshow(window_name, image); + + waitKey(); + return 0; +}