lsd.cpp 42.3 KB
Newer Older
wester committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232
/*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) 2013, OpenCV Foundation, 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.
 *
 * @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 or Vec4f elements specifying the beginning and ending point of a line.
 *                          Where Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end.
 *                          Returned lines are strictly oriented depending on the gradient.
 * @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(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, InputArray lines);

/**
 * Draw both vectors on the image canvas. Uses blue for lines 1 and red for lines 2.
 *
 * @param size      The size of the image, where lines1 and lines2 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.
 * @param image     An optional image, where lines will be drawn.
 *                  Should have the size of the image, where the lines were found
 * @return          The number of mismatching pixels between lines1 and lines2.
 */
    int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray());

private:
    Mat image;
a  
Kai Westerkamp committed
233 234
    Mat_<double> scaled_image;
    double *scaled_image_data;
wester committed
235
    Mat_<double> angles;     // in rads
a  
Kai Westerkamp committed
236
    double *angles_data;
wester committed
237
    Mat_<double> modgrad;
a  
Kai Westerkamp committed
238
    double *modgrad_data;
wester committed
239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311
    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 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<Vec4f>& 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.
 */
a  
Kai Westerkamp committed
312
    void ll_angle(const double& threshold, const unsigned int& n_bins, std::vector<coorlist>& list);
wester committed
313 314 315 316 317 318 319

/**
 * 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
a  
Kai Westerkamp committed
320
 * @param reg_size  Return: The size of the region.
wester committed
321 322 323 324
 * @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,
a  
Kai Westerkamp committed
325
                     int& reg_size, double& reg_angle, const double& prec);
wester committed
326 327 328 329 330

/**
 * Finds the bounding rotated rectangle of a region.
 *
 * @param reg       The region of points, from which the rectangle to be constructed from.
a  
Kai Westerkamp committed
331
 * @param reg_size  The number of points in the region.
wester committed
332 333 334 335 336
 * @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.
 */
a  
Kai Westerkamp committed
337
    void region2rect(const std::vector<RegionPoint>& reg, const int reg_size, const double reg_angle,
wester committed
338 339 340 341 342 343
                     const double prec, const double p, rect& rec) const;

/**
 * Compute region's angle as the principal inertia axis of the region.
 * @return          Regions angle.
 */
a  
Kai Westerkamp committed
344
    double get_theta(const std::vector<RegionPoint>& reg, const int& reg_size, const double& x,
wester committed
345 346 347 348 349 350 351 352
                     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.
 */
a  
Kai Westerkamp committed
353
    bool refine(std::vector<RegionPoint>& reg, int& reg_size, double reg_angle,
wester committed
354 355 356 357 358 359
                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.
 */
a  
Kai Westerkamp committed
360
    bool reduce_region_radius(std::vector<RegionPoint>& reg, int& reg_size, double reg_angle,
wester committed
361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385
                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.
 */
a  
Kai Westerkamp committed
386
    bool isAligned(const int& address, const double& theta, const double& prec) const;
wester committed
387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403
};

/////////////////////////////////////////////////////////////////////////////////////////

CV_EXPORTS Ptr<LineSegmentDetector> createLineSegmentDetector(
        int _refine, double _scale, double _sigma_scale, double _quant, double _ang_th,
        double _log_eps, double _density_th, int _n_bins)
{
    return makePtr<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)
a  
Kai Westerkamp committed
404 405
        :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)
wester committed
406 407 408 409 410 411 412 413 414
{
    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(InputArray _image, OutputArray _lines,
                OutputArray _width, OutputArray _prec, OutputArray _nfa)
{
a  
Kai Westerkamp committed
415 416
    Mat_<double> img = _image.getMat();
    CV_Assert(!img.empty() && img.channels() == 1);
wester committed
417

a  
Kai Westerkamp committed
418 419
    // Convert image to double
    img.convertTo(image, CV_64FC1);
wester committed
420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446

    std::vector<Vec4f> lines;
    std::vector<double> 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);
}

void LineSegmentDetectorImpl::flsd(std::vector<Vec4f>& 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

a  
Kai Westerkamp committed
447
    std::vector<coorlist> list;
wester committed
448 449 450 451 452 453 454 455 456 457
    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);
a  
Kai Westerkamp committed
458
        ll_angle(rho, N_BINS, list);
wester committed
459 460 461 462
    }
    else
    {
        scaled_image = image;
a  
Kai Westerkamp committed
463
        ll_angle(rho, N_BINS, list);
wester committed
464 465 466
    }

    LOG_NT = 5 * (log10(double(img_width)) + log10(double(img_height))) / 2 + log10(11.0);
a  
Kai Westerkamp committed
467
    const int min_reg_size = int(-LOG_NT/log10(p)); // minimal number of points in region that can give a meaningful event
wester committed
468 469 470 471

    // // Initialize region only when needed
    // Mat region = Mat::zeros(scaled_image.size(), CV_8UC1);
    used = Mat_<uchar>::zeros(scaled_image.size()); // zeros = NOTUSED
a  
Kai Westerkamp committed
472
    std::vector<RegionPoint> reg(img_width * img_height);
wester committed
473 474

    // Search for line segments
a  
Kai Westerkamp committed
475
    unsigned int ls_count = 0;
wester committed
476 477
    for(size_t i = 0, list_size = list.size(); i < list_size; ++i)
    {
a  
Kai Westerkamp committed
478 479
        unsigned int adx = list[i].p.x + list[i].p.y * img_width;
        if((used.ptr()[adx] == NOTUSED) && (angles_data[adx] != NOTDEF))
wester committed
480
        {
a  
Kai Westerkamp committed
481
            int reg_size;
wester committed
482
            double reg_angle;
a  
Kai Westerkamp committed
483
            region_grow(list[i].p, reg, reg_size, reg_angle, prec);
wester committed
484 485

            // Ignore small regions
a  
Kai Westerkamp committed
486
            if(reg_size < min_reg_size) { continue; }
wester committed
487 488 489

            // Construct rectangular approximation for the region
            rect rec;
a  
Kai Westerkamp committed
490
            region2rect(reg, reg_size, reg_angle, prec, p, rec);
wester committed
491 492 493 494 495

            double log_nfa = -1;
            if(doRefine > LSD_REFINE_NONE)
            {
                // At least REFINE_STANDARD lvl.
a  
Kai Westerkamp committed
496
                if(!refine(reg, reg_size, reg_angle, prec, p, rec, DENSITY_TH)) { continue; }
wester committed
497 498 499 500 501 502 503 504 505

                if(doRefine >= LSD_REFINE_ADV)
                {
                    // Compute NFA
                    log_nfa = rect_improve(rec);
                    if(log_nfa <= LOG_EPS) { continue; }
                }
            }
            // Found new line
a  
Kai Westerkamp committed
506
            ++ls_count;
wester committed
507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524

            // 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);
a  
Kai Westerkamp committed
525 526 527 528 529 530 531


            // //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;
            // }
wester committed
532 533 534 535 536
        }
    }
}

void LineSegmentDetectorImpl::ll_angle(const double& threshold,
a  
Kai Westerkamp committed
537 538
                                   const unsigned int& n_bins,
                                   std::vector<coorlist>& list)
wester committed
539 540 541 542 543
{
    //Initialize data
    angles = Mat_<double>(scaled_image.size());
    modgrad = Mat_<double>(scaled_image.size());

a  
Kai Westerkamp committed
544 545 546 547
    angles_data = angles.ptr<double>(0);
    modgrad_data = modgrad.ptr<double>(0);
    scaled_image_data = scaled_image.ptr<double>(0);

wester committed
548 549 550 551 552 553 554 555
    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
a  
Kai Westerkamp committed
556 557 558 559
    CV_Assert(scaled_image.isContinuous() &&
              modgrad.isContinuous() &&
              angles.isContinuous());   // Accessing image data linearly

wester committed
560 561 562
    double max_grad = -1;
    for(int y = 0; y < img_height - 1; ++y)
    {
a  
Kai Westerkamp committed
563
        for(int addr = y * img_width, addr_end = addr + img_width - 1; addr < addr_end; ++addr)
wester committed
564
        {
a  
Kai Westerkamp committed
565 566 567 568 569
            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
wester committed
570

a  
Kai Westerkamp committed
571
            modgrad_data[addr] = norm;    // store gradient
wester committed
572 573 574

            if (norm <= threshold)  // norm too small, gradient no defined
            {
a  
Kai Westerkamp committed
575
                angles_data[addr] = NOTDEF;
wester committed
576 577 578
            }
            else
            {
a  
Kai Westerkamp committed
579
                angles_data[addr] = fastAtan2(float(gx), float(-gy)) * DEG_TO_RADS;  // gradient angle computation
wester committed
580 581 582 583 584 585 586
                if (norm > max_grad) { max_grad = norm; }
            }

        }
    }

    // Compute histogram of gradient values
a  
Kai Westerkamp committed
587
    list = std::vector<coorlist>(img_width * img_height);
wester committed
588 589 590 591 592 593 594
    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)
    {
a  
Kai Westerkamp committed
595 596
        const double* norm = modgrad_data + y * img_width;
        for(int x = 0; x < img_width - 1; ++x, ++norm)
wester committed
597 598
        {
            // Store the point in the right bin according to its norm
a  
Kai Westerkamp committed
599
            int i = int((*norm) * bin_coef);
wester committed
600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635
            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,
a  
Kai Westerkamp committed
636
                                      int& reg_size, double& reg_angle, const double& prec)
wester committed
637 638
{
    // Point to this region
a  
Kai Westerkamp committed
639 640 641 642 643 644 645 646
    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.ptr() + addr;
    reg_angle = angles_data[addr];
    reg[0].angle = reg_angle;
    reg[0].modgrad = modgrad_data[addr];
wester committed
647 648 649

    float sumdx = float(std::cos(reg_angle));
    float sumdy = float(std::sin(reg_angle));
a  
Kai Westerkamp committed
650
    *reg[0].used = USED;
wester committed
651 652

    //Try neighboring regions
a  
Kai Westerkamp committed
653
    for(int i = 0; i < reg_size; ++i)
wester committed
654 655 656 657 658 659
    {
        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)
        {
a  
Kai Westerkamp committed
660 661
            int c_addr = xx_min + yy * img_width;
            for(int xx = xx_min; xx <= xx_max; ++xx, ++c_addr)
wester committed
662
            {
a  
Kai Westerkamp committed
663 664
                if((used.ptr()[c_addr] != USED) &&
                   (isAligned(c_addr, reg_angle, prec)))
wester committed
665 666
                {
                    // Add point
a  
Kai Westerkamp committed
667 668
                    used.ptr()[c_addr] = USED;
                    RegionPoint& region_point = reg[reg_size];
wester committed
669 670
                    region_point.x = xx;
                    region_point.y = yy;
a  
Kai Westerkamp committed
671 672 673
                    region_point.used = &(used.ptr()[c_addr]);
                    region_point.modgrad = modgrad_data[c_addr];
                    const double& angle = angles_data[c_addr];
wester committed
674
                    region_point.angle = angle;
a  
Kai Westerkamp committed
675
                    ++reg_size;
wester committed
676 677 678 679 680 681 682 683 684 685 686 687

                    // 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;
                }
            }
        }
    }
}

a  
Kai Westerkamp committed
688
void LineSegmentDetectorImpl::region2rect(const std::vector<RegionPoint>& reg, const int reg_size,
wester committed
689 690 691
                                      const double reg_angle, const double prec, const double p, rect& rec) const
{
    double x = 0, y = 0, sum = 0;
a  
Kai Westerkamp committed
692
    for(int i = 0; i < reg_size; ++i)
wester committed
693 694 695 696 697 698 699 700 701 702 703 704 705 706
    {
        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;

a  
Kai Westerkamp committed
707
    double theta = get_theta(reg, reg_size, x, y, reg_angle, prec);
wester committed
708 709 710 711 712 713

    // 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;

a  
Kai Westerkamp committed
714
    for(int i = 0; i < reg_size; ++i)
wester committed
715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745
    {
        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;
}

a  
Kai Westerkamp committed
746
double LineSegmentDetectorImpl::get_theta(const std::vector<RegionPoint>& reg, const int& reg_size, const double& x,
wester committed
747 748 749 750 751 752 753
                                      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
a  
Kai Westerkamp committed
754
    for(int i = 0; i < reg_size; ++i)
wester committed
755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783
    {
        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;
}

a  
Kai Westerkamp committed
784
bool LineSegmentDetectorImpl::refine(std::vector<RegionPoint>& reg, int& reg_size, double reg_angle,
wester committed
785 786
                                 const double prec, double p, rect& rec, const double& density_th)
{
a  
Kai Westerkamp committed
787
    double density = double(reg_size) / (dist(rec.x1, rec.y1, rec.x2, rec.y2) * rec.width);
wester committed
788 789 790 791 792 793 794 795 796 797

    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;

a  
Kai Westerkamp committed
798
    for (int i = 0; i < reg_size; ++i)
wester committed
799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814
    {
        *(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
a  
Kai Westerkamp committed
815
    region_grow(Point(reg[0].x, reg[0].y), reg, reg_size, reg_angle, tau);
wester committed
816

a  
Kai Westerkamp committed
817
    if (reg_size < 2) { return false; }
wester committed
818

a  
Kai Westerkamp committed
819 820
    region2rect(reg, reg_size, reg_angle, prec, p, rec);
    density = double(reg_size) / (dist(rec.x1, rec.y1, rec.x2, rec.y2) * rec.width);
wester committed
821 822 823

    if (density < density_th)
    {
a  
Kai Westerkamp committed
824
        return reduce_region_radius(reg, reg_size, reg_angle, prec, p, rec, density, density_th);
wester committed
825 826 827 828 829 830 831
    }
    else
    {
        return true;
    }
}

a  
Kai Westerkamp committed
832
bool LineSegmentDetectorImpl::reduce_region_radius(std::vector<RegionPoint>& reg, int& reg_size, double reg_angle,
wester committed
833 834 835 836 837 838 839 840 841 842 843 844 845
                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
a  
Kai Westerkamp committed
846
        for(int i = 0; i < reg_size; ++i)
wester committed
847 848 849 850 851
        {
            if(distSq(xc, yc, double(reg[i].x), double(reg[i].y)) > radSq)
            {
                // Remove point from the region
                *(reg[i].used) = NOTUSED;
a  
Kai Westerkamp committed
852 853
                std::swap(reg[i], reg[reg_size - 1]);
                --reg_size;
wester committed
854 855 856 857
                --i; // To avoid skipping one point
            }
        }

a  
Kai Westerkamp committed
858
        if(reg_size < 2) { return false; }
wester committed
859 860

        // Re-compute rectangle
a  
Kai Westerkamp committed
861
        region2rect(reg, reg_size ,reg_angle, prec, p, rec);
wester committed
862 863

        // Re-compute region points density
a  
Kai Westerkamp committed
864
        density = double(reg_size) /
wester committed
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981
                  (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;

a  
Kai Westerkamp committed
982
    std::vector<edge> ordered_x(4);
wester committed
983 984 985 986 987 988 989 990
    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;

a  
Kai Westerkamp committed
991
    std::sort(ordered_x.begin(), ordered_x.end(), AsmallerB_XoverY);
wester committed
992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075

    // 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 = 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;

a  
Kai Westerkamp committed
1076 1077
        int adx = y * img_width + int(left_x);
        for(int x = int(left_x); x <= int(right_x); ++x, ++adx)
wester committed
1078 1079 1080 1081
        {
            if (x < 0 || x >= img_width) continue;

            ++total_pts;
a  
Kai Westerkamp committed
1082
            if(isAligned(adx, rec.theta, rec.prec))
wester committed
1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135
            {
                ++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;
}

a  
Kai Westerkamp committed
1136
inline bool LineSegmentDetectorImpl::isAligned(const int& address, const double& theta, const double& prec) const
wester committed
1137
{
a  
Kai Westerkamp committed
1138 1139
    if(address < 0) { return false; }
    const double& a = angles_data[address];
wester committed
1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252
    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_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();
    int N = _lines.checkVector(4);

    // Draw segments
    for(int i = 0; i < N; ++i)
    {
        const Vec4f& v = _lines.at<Vec4f>(i);
        Point2f b(v[0], v[1]);
        Point2f e(v[2], v[3]);
        line(_image.getMatRef(), b, e, Scalar(0, 0, 255), 1);
    }
}


int LineSegmentDetectorImpl::compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image)
{
    Size sz = size;
    if (_image.needed() && _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();
    int N1 = _lines1.checkVector(4);
    int N2 = _lines2.checkVector(4);

    // Draw segments
    for(int i = 0; i < N1; ++i)
    {
        Point2f b(_lines1.at<Vec4f>(i)[0], _lines1.at<Vec4f>(i)[1]);
        Point2f e(_lines1.at<Vec4f>(i)[2], _lines1.at<Vec4f>(i)[3]);
        line(I1, b, e, Scalar::all(255), 1);
    }
    for(int i = 0; i < N2; ++i)
    {
        Point2f b(_lines2.at<Vec4f>(i)[0], _lines2.at<Vec4f>(i)[1]);
        Point2f e(_lines2.at<Vec4f>(i)[2], _lines2.at<Vec4f>(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.needed())
    {
        CV_Assert(_image.channels() == 3);
        Mat img = _image.getMatRef();
        CV_Assert(img.isContinuous() && I1.isContinuous() && I2.isContinuous());

        for (unsigned int i = 0; i < I1.total(); ++i)
        {
            uchar i1 = I1.ptr()[i];
            uchar i2 = I2.ptr()[i];
            if (i1 || i2)
            {
                unsigned int base_idx = i * 3;
                if (i1) img.ptr()[base_idx] = 255;
                else img.ptr()[base_idx] = 0;
                img.ptr()[base_idx + 1] = 0;
                if (i2) img.ptr()[base_idx + 2] = 255;
                else img.ptr()[base_idx + 2] = 0;
            }
        }
    }

    return N;
}

} // namespace cv