Finding the “fitLine ” of contours in OpenCV

Question

I\'m a program that find contours in a stream, for example :

I want to find \"set of points \

Answer 1

Interesting task :) Here is my solution:

Here is code:

#include 
#include 
#include 
#include 
#include 
#include "opencv2/opencv.hpp"
#include "fstream"
#include "iostream"
using namespace std;
using namespace cv;


int Thinning(unsigned char * ucBinedImg, unsigned char * ucThinnedImage, long lWidth, long lHeight, long lIterativeLimit)
{
    if(ucBinedImg == NULL)
        return -1;

    if(ucThinnedImage == NULL)
        return -2;

    if(lIterativeLimit == -1)
        lIterativeLimit = 60000;

    unsigned char x1, x2, x3, x4, x5, x6, x7, x8, xp;
    unsigned char g1, g2, g3, g4;
    unsigned char b1, b2, b3, b4;
    unsigned char np1, np2, npm;
    unsigned char *pUp, *pDown, *pImg;
    long    lDeletedPoints = 0;

    // set border 
    memcpy(ucThinnedImage, ucBinedImg, lWidth*lHeight);

    for(long it=0; itnp2?np2:np1;
                g2 = npm>=2 && npm<=3;

                g3 = (x1 && (x2 || x3 || !x8)) == 0;
                g4 = (x5 && (x6 || x7 || !x4)) == 0;

                // first part
                if(g1 && g2 && g3)
                {
                    // delete this point
                    ucThinnedImage[lWidth*i + j] = 0;
                    ++lDeletedPoints;
                }
            }

        }

        //syn
        memcpy(ucBinedImg, ucThinnedImage, lWidth*lHeight);

        for(long i=1; inp2?np2:np1;
                g2 = npm>=2 && npm<=3;

                g3 = (x1 && (x2 || x3 || !x8)) == 0;
                g4 = (x5 && (x6 || x7 || !x4)) == 0;

                // second part
                if(g1 && g2 && g4)
                {
                    // delete this point
                    ucThinnedImage[lWidth*i + j] = 0;
                    ++lDeletedPoints;
                }

            }

        }
        //syn
        memcpy(ucBinedImg, ucThinnedImage, lWidth*lHeight);

        // if no points to be deleted
        if(lDeletedPoints == 0)
            break;

    }

    // clear edge bar
    for(long i=0; i=lHeight-16)
                ucThinnedImage[i*lWidth+j] = 0;
            else if(j<16)
                ucThinnedImage[i*lWidth+j] = 0;
            else if(j>=lWidth-16)
                ucThinnedImage[i*lWidth+j] = 0;
        }
    }

    return 0;
}

void Thinning(Mat& src,Mat& dst,long IterativeLimit=-1)
{
    Mat bin_img=src&1;
    if(!dst.empty()){dst.release();}
    dst=Mat::zeros(src.size(),CV_8UC1);
    Thinning(bin_img.data,dst.data,bin_img.cols,bin_img.rows,IterativeLimit);
    dst*=255;
}

int main(int argc, char* argv[])
{
    namedWindow("source");
    namedWindow("result");

    Mat img=imread("raw_image.jpg",0);
    cv::threshold(img,img,128,255,cv::THRESH_BINARY);

    int erosion_size=5;
    Mat element = getStructuringElement( cv::MORPH_ELLIPSE,Size( 2*erosion_size + 1, 2*erosion_size+1 ),Point( erosion_size, erosion_size ) );

    cv::dilate(img,img,element);

    Mat thinned;
    Thinning(img,thinned);

    vector lines;
    HoughLines(thinned, lines, 0.5, CV_PI/360, 50, 0, 0 );

    float hist_theta[2]={0,0};
    float hist_rho[2]={0,0};
    float n[2]={0,0};
    for( size_t i = 0; i < lines.size(); i++ )
    {
        float rho = lines[i][0], theta = lines[i][1];
        if(fabs(theta-CV_PI/2)

Here is a hack in this source it uses two 1D histograms for postprocessing. In real life application it should use 2D histogram for close lines averaging.