Python and C++ are giving different results for Hough Circle detection

asked 2016-10-26 01:09:34 -0500

Roy2511 gravatar image

Hi all,

So I have implemented the same code in both C++ and Python. The code does nothing much, just reads a grayscale image, implements the hough circle detection algorithm and gives the output.

Code in C++:

#include <iostream>
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/opencv.hpp"

#define IMAGE_ROW 1882

int t_rows, t_cols;
using namespace cv;

int main()
{
    Mat img = imread("<path>\\org5_6.bmp", 0);
    int circle[3];

    std::cout << "OpenCV Version = " << CV_VERSION << '\n';
    if (!img.data)
    {
        std::cout << "Could not load image." << '\n';
    }
    else
    {
        std::cout << "Start HCircles.. " << '\n';

        t_rows = img.rows;
        t_cols = img.cols;

        std::cout << "t_cols : " << img.cols << std::endl;
        std::cout << "t_rows : " << img.rows << std::endl;

        std::vector<cv::Vec3f> circles;
        int circlesExist = 1;
        Mat GrayImg = img;
        Mat ResizedImg;
        Mat BlurredImg;
        int black_circle_radius_low;
        int black_circle_radius_upper;

        //cvtColor(RGBimg, GrayImg, CV_BGR2GRAY);
        //GrayImg = imread("/Volumes/Seagate Backup Plus Drive/CppTest/org5_6.bmp", 0);
        Size size(t_cols * IMAGE_ROW / t_rows, IMAGE_ROW);
        resize(GrayImg, ResizedImg, size);

        std::cout << "After resizing" << '\n';
        std::cout << "t_cols : " << ResizedImg.cols << std::endl;
        std::cout << "t_rows : " << ResizedImg.rows << std::endl;
        t_rows = ResizedImg.rows;
        t_cols = ResizedImg.cols;

        medianBlur(ResizedImg, BlurredImg, 5);
        Mat BlurredImgCopy = BlurredImg;
        black_circle_radius_low = (int)(0.4 * std::min(t_rows, t_cols));
        black_circle_radius_upper = (int)(0.45 * std::min(t_rows, t_cols));

        std::cout << "Radius min = " << black_circle_radius_low << '\n';
        std::cout << "Radius max = " << black_circle_radius_upper << '\n';

        HoughCircles(BlurredImg, circles, CV_HOUGH_GRADIENT, 1, 300, 30, 30, black_circle_radius_low, black_circle_radius_upper);

        std::cout << "Total number of circles detected = " << circles.size() << std::endl;

        if (circles.size() == 0)
        {
            std::cout << "No circles were detected" << '\n';
            circlesExist = 0;
            circle[0] = 0;
            circle[1] = 0;
            circle[2] = 0;
        }
        else
        {
            for (size_t i = 0; i < circles.size(); i++)
            {
                //Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
                //int radius = cvRound(circles[i][2]);

                std::cout << "Circle Center - (x: " << circles[i][0] << "  y: " << circles[i][1] << ") and Radius: " << circles[i][2] << '\n';
                /*
                // circle center
                circle( BlurredImgCopy, center, 3, Scalar(0,255,0), -1, 8, 0 );
                // circle outline
                circle( BlurredImgCopy, center, radius, Scalar(0,0,255), 100, 8, 0 ); */
            }
        }
    }
}

And for Python:

import cv2
import cv2.cv as cv
import numpy
IMAGE_ROW = 1882

print "OpenCV version = ",cv2.__version__
FILENAME = '<path>/org5_6.bmp'

img = cv2.imread(FILENAME, 0)
print "Read image"
#cv2.imshow('img', img)
#cv2.waitKey(0) 

circle_exsit=1
[t_rows,t_cols]= img.shape
print 't_rows = ', t_rows
print 't_cols = ', t_cols
img=cv2.resize(img, (t_cols*IMAGE_ROW/t_rows,IMAGE_ROW))
img = cv2.medianBlur(img, 5)

[t_rows,t_cols]=img.shape
print 't_rows = ', t_rows
print 't_cols = ', t_cols

limit=min(t_rows,t_cols)
black_circle_radius_low=int(0.4*limit)
black_circle_radius_upper=int(0.45*limit)
print 'r_min = ',black_circle_radius_low
print 'rmax = ',black_circle_radius_upper

circles = cv2.HoughCircles(img,cv.CV_HOUGH_GRADIENT,1,300,param1=30,param2=30,minRadius=black_circle_radius_low,maxRadius=black_circle_radius_upper)
print circles

These codes are quite similar, but the results are different for C++ and Python -

Output from C++:

OpenCV Version = 2.4.11
Start HCircles..
t_cols : 3664
t_rows : 2748
After resizing
t_cols : 2509
t_rows : 1882
Radius min = 752
Radius max = 846
Total number of circles detected = 3
Circle Center - (x: 1325.5  y: 1094.5) and Radius: 759 ...
(more)
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