to remove a semi-curved object from a binary image using opencv - c++

Question

I have a binary image:
I want to remove the bottom two crescent shapes(size and area may change with different images) from the image or at-least differentiate it from the rest.
I tried Hough circle transform to detect the curves as it resembles a portion of a circle, but that code was not working:
int main(int argc, char** argv)
{
Mat src, gray;
src = imread("446.bmp", 1);
namedWindow("src", 1);
imshow("src", src);
waitKey(0);
cvtColor(src, gray, CV_BGR2GRAY);
// Reduce the noise so we avoid false circle detection
GaussianBlur(gray, gray, Size(9, 9), 2, 2);
vector<Vec3f> circles;
// Apply the Hough Transform to find the circles
HoughCircles(gray, circles, CV_HOUGH_GRADIENT, 1, 30, 100, 100, 0, 0);
// Draw the circles detected
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]);
circle(src, center, 3, Scalar(0, 255, 0), -1, 8, 0);// circle center
circle(src, center, radius, Scalar(0, 0, 255), 3, 8, 0);// circle outline
cout << "center : " << center << "\nradius : " << radius << endl;
}
// Show your results
namedWindow("Hough Circle Transform Demo", CV_WINDOW_AUTOSIZE);
imshow("Hough Circle Transform Demo", src);
waitKey(0);
return 0;
}
But No circle is being drawn or the crescent moon shapes are not being detected at all. Any idea where I went wrong?
EDIT 1- I have added some other images too:
Edit-2 new image to try:-

Answer 1

i made some modification on the code posted for other question
you could try it
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
using namespace cv;
using namespace std;
//! Compute the distance between two points
/*! Compute the Euclidean distance between two points
*
* #param a Point a
* #param b Point b
*/
static double distanceBtwPoints(const cv::Point2f &a, const cv::Point2f &b)
{
double xDiff = a.x - b.x;
double yDiff = a.y - b.y;
return std::sqrt((xDiff * xDiff) + (yDiff * yDiff));
}
int main( int argc, char** argv )
{
Mat src,gray;
src = imread(argv[1]);
if(src.empty())
return -1;
cvtColor( src, gray, COLOR_BGR2GRAY );
gray = gray < 200;
vector<vector<Point> > contours;
findContours(gray.clone(), contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
RotatedRect _minAreaRect;
for (size_t i = 0; i < contours.size(); ++i)
{
double contour_area = contourArea(contours[i]);
_minAreaRect = minAreaRect( Mat(contours[i]) );
Point2f pts[4];
_minAreaRect.points(pts);
double dist0 = distanceBtwPoints(pts[0], pts[1]);
double dist1 = distanceBtwPoints(pts[1], pts[2]);
double angle = 0;
//if(dist0 > dist1 *1.2)
angle =atan2(pts[0].y - pts[1].y,pts[0].x - pts[1].x) * 180.0 / CV_PI;
//if(dist1 > dist0 *1.2)
angle =atan2(pts[1].y - pts[2].y,pts[1].x - pts[2].x) * 180.0 / CV_PI;
if( fabs(angle) > 91 ) // you can try different values
{
if( contour_area < dist0 * dist1 /2 ) // you can try different values
{
//drawContours(src,contours,i,Scalar(0,0,0),-1); // try to uncomment this line
for( int j = 0; j < 4; j++ )
line(src, pts[j], pts[(j+1)%4], Scalar(0, 0, 255), 1, LINE_AA);
}
}
}
imshow("result", src);
waitKey(0);
return 0;
}

Answer 2

I don't think there is an easy solution here unfortunately.
What you might want to try is to detect and label each image component. From here you need to detect which set of pixels looks like a crescent and which does not : as a crescent can be described by a polynomial equations you only need to describe each component (i.e a set of points) as a mathematical equation (using regression methods such as RANSAC) and see if that might be a crescent equation.