/home/laradmin/lar/perception/road/road_recognition/src/linefinder.h

Go to the documentation of this file.

/*------------------------------------------------------------------------------------------*\
   This file contains material supporting chapter 7 of the cookbook:  
   Computer Vision Programming using the OpenCV Library. 
   by Robert Laganiere, Packt Publishing, 2011.

   This program is free software; permission is hereby granted to use, copy, modify, 
   and distribute this source code, or portions thereof, for any purpose, without fee, 
   subject to the restriction that the copyright notice may not be removed 
   or altered from any source or altered source distribution. 
   The software is released on an as-is basis and without any warranties of any kind. 
   In particular, the software is not guaranteed to be fault-tolerant or free from failure. 
   The author disclaims all warranties with regard to this software, any use, 
   and any consequent failure, is purely the responsibility of the user.
 
   Copyright (C) 2010-2011 Robert Laganiere, www.laganiere.name
\*------------------------------------------------------------------------------------------*/

#if !defined LINEF
#define LINEF

#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#define PI 3.1415926

class LineFinder {

  private:

          // original image
          cv::Mat img;

          // vector containing the end points 
          // of the detected lines
          std::vector<cv::Vec4i> lines;

          // accumulator resolution parameters
          double deltaRho;
          double deltaTheta;

          // minimum number of votes that a line 
          // must receive before being considered
          int minVote;

          // min length for a line
          double minLength;

          // max allowed gap along the line
          double maxGap;

  public:
          
          // Default accumulator resolution is 1 pixel by 1 degree
          // no gap, no mimimum length
          
          LineFinder() : deltaRho(1), deltaTheta(PI/180), minVote(10), minLength(0.), maxGap(0.) {}

          // Set the resolution of the accumulator
          void setAccResolution(double dRho, double dTheta) {

                  deltaRho= dRho;
                  deltaTheta= dTheta;
          }

          // Set the minimum number of votes
          void setMinVote(int minv) {

                  minVote= minv;
          }

          // Set line length and gap
          void setLineLengthAndGap(double length, double gap) {

                  minLength= length;
                  maxGap= gap;
          }

          // Apply probabilistic Hough Transform
          std::vector<cv::Vec4i> findLines(cv::Mat& binary) {

                  lines.clear();
                  cv::HoughLinesP(binary,lines,deltaRho,deltaTheta,minVote, minLength, maxGap);

                  return lines;
          }

          // Draw the detected lines on an image
          void drawDetectedLines(cv::Mat &image, cv::Scalar color=cv::Scalar(255,255,255)) {
        
                  // Draw the lines
                  std::vector<cv::Vec4i>::const_iterator it2= lines.begin();
        
                  while (it2!=lines.end()) {
                
                          cv::Point pt1((*it2)[0],(*it2)[1]);        
                          cv::Point pt2((*it2)[2],(*it2)[3]);

                          cv::line( image, pt1, pt2, color);
                
                          ++it2;        
                  }
          }

          // Eliminates lines that do not have an orientation equals to
          // the ones specified in the input matrix of orientations
          // At least the given percentage of pixels on the line must 
          // be within plus or minus delta of the corresponding orientation
          std::vector<cv::Vec4i> removeLinesOfInconsistentOrientations(
                  const cv::Mat &orientations, double percentage, double delta) {

                          std::vector<cv::Vec4i>::iterator it= lines.begin();
        
                          // check all lines
                          while (it!=lines.end()) {

                                  // end points
                                  int x1= (*it)[0];
                                  int y1= (*it)[1];
                                  int x2= (*it)[2];
                                  int y2= (*it)[3];
                   
                                  // line orientation + 90o to get the parallel line
                                  double ori1= atan2(static_cast<double>(y1-y2),static_cast<double>(x1-x2))+PI/2;
                                  if (ori1>PI) ori1= ori1-2*PI;

                                  double ori2= atan2(static_cast<double>(y2-y1),static_cast<double>(x2-x1))+PI/2;
                                  if (ori2>PI) ori2= ori2-2*PI;
        
                                  // for all points on the line
                                  cv::LineIterator lit(orientations,cv::Point(x1,y1),cv::Point(x2,y2));
                                  int i,count=0;
                                  for(i = 0, count=0; i < lit.count; i++, ++lit) { 
                
                                          float ori= *(reinterpret_cast<float *>(*lit));

                                          // is line orientation similar to gradient orientation ?
                                          if (std::min(fabs(ori-ori1),fabs(ori-ori2))<delta)
                                                  count++;
                
                                  }

                                  double consistency= count/static_cast<double>(i);

                                  // set to zero lines of inconsistent orientation
                                  if (consistency < percentage) {
 
                                          (*it)[0]=(*it)[1]=(*it)[2]=(*it)[3]=0;

                                  }

                                  ++it;
                          }

                          return lines;
          }
};


std::vector<cv::Vec4i> convert2Matlab(const std::vector<cv::Vec4i>& ori)
{
        cv::Vec4i vi;
        std::vector<cv::Vec4i> vout;
        
        for(uint i=0;i<ori.size();i++)
        {
                vi[0] = ori[i][1];
                vi[1] = ori[i][0];
                vi[2] = ori[i][3];
                vi[3] = ori[i][2];
        
                vout.push_back(vi);
        }
        
        return vout;
}
          
#endif

---

road_recognition
Author(s): Morais
autogenerated on Wed Jul 23 04:35:03 2014