问题
I am trying to detect a circular shape from an image which appears to have very good definition. I do realize that part of the circle is missing but from what I've read about the Hough transform it doesn't seem like that should cause the problem I'm experiencing.
Input:
Output:
Code:
// Read the image
Mat src = Highgui.imread("input.png");
// Convert it to gray
Mat src_gray = new Mat();
Imgproc.cvtColor(src, src_gray, Imgproc.COLOR_BGR2GRAY);
// Reduce the noise so we avoid false circle detection
//Imgproc.GaussianBlur( src_gray, src_gray, new Size(9, 9), 2, 2 );
Mat circles = new Mat();
/// Apply the Hough Transform to find the circles
Imgproc.HoughCircles(src_gray, circles, Imgproc.CV_HOUGH_GRADIENT, 1, 1, 160, 25, 0, 0);
// Draw the circles detected
for( int i = 0; i < circles.cols(); i++ ) {
double[] vCircle = circles.get(0, i);
Point center = new Point(vCircle[0], vCircle[1]);
int radius = (int) Math.round(vCircle[2]);
// circle center
Core.circle(src, center, 3, new Scalar(0, 255, 0), -1, 8, 0);
// circle outline
Core.circle(src, center, radius, new Scalar(0, 0, 255), 3, 8, 0);
}
// Save the visualized detection.
String filename = "output.png";
System.out.println(String.format("Writing %s", filename));
Highgui.imwrite(filename, src);
I have Gaussian blur commented out because (counter intuitively) it was greatly increasing the number of equally inaccurate circles found.
Is there anything wrong with my input image that would cause Hough to not work as well as I expect? Are my parameters way off?
EDIT: first answer brought up a good point about the min/max radius hint for Hough. I resisted adding those parameters as the example image in this post is just one of thousands of images all with varying radii from ~20 to almost infinity.
回答1:
If you'd set minRadius and maxRadius paramaeters properly, it'd give you good results.
For your image, I tried following parameters.
method - CV_HOUGH_GRADIENT
minDist - 100
dp - 1
param1 - 80
param2 - 10
minRadius - 250
maxRadius - 300
I got the following output
- Note: I tried this in C++.
回答2:
I've adjusted my RANSAC algorithm from this answer: Detect semi-circle in opencv
Idea:
- choose randomly 3 points from your binary edge image
- create a circle from those 3 points
- test how "good" this circle is
if it is better than the previously best found circle in this image, remember
loop 1-4 until some number of iterations reached. then accept the best found circle.
remove that accepted circle from the image
repeat 1-6 until you have found all circles
problems:
- at the moment you must know how many circles you want to find in the image
- tested only for that one image.
- c++ code
result:
code:
inline void getCircle(cv::Point2f& p1,cv::Point2f& p2,cv::Point2f& p3, cv::Point2f& center, float& radius)
{
float x1 = p1.x;
float x2 = p2.x;
float x3 = p3.x;
float y1 = p1.y;
float y2 = p2.y;
float y3 = p3.y;
// PLEASE CHECK FOR TYPOS IN THE FORMULA :)
center.x = (x1*x1+y1*y1)*(y2-y3) + (x2*x2+y2*y2)*(y3-y1) + (x3*x3+y3*y3)*(y1-y2);
center.x /= ( 2*(x1*(y2-y3) - y1*(x2-x3) + x2*y3 - x3*y2) );
center.y = (x1*x1 + y1*y1)*(x3-x2) + (x2*x2+y2*y2)*(x1-x3) + (x3*x3 + y3*y3)*(x2-x1);
center.y /= ( 2*(x1*(y2-y3) - y1*(x2-x3) + x2*y3 - x3*y2) );
radius = sqrt((center.x-x1)*(center.x-x1) + (center.y-y1)*(center.y-y1));
}
std::vector<cv::Point2f> getPointPositions(cv::Mat binaryImage)
{
std::vector<cv::Point2f> pointPositions;
for(unsigned int y=0; y<binaryImage.rows; ++y)
{
//unsigned char* rowPtr = binaryImage.ptr<unsigned char>(y);
for(unsigned int x=0; x<binaryImage.cols; ++x)
{
//if(rowPtr[x] > 0) pointPositions.push_back(cv::Point2i(x,y));
if(binaryImage.at<unsigned char>(y,x) > 0) pointPositions.push_back(cv::Point2f(x,y));
}
}
return pointPositions;
}
float verifyCircle(cv::Mat dt, cv::Point2f center, float radius, std::vector<cv::Point2f> & inlierSet)
{
unsigned int counter = 0;
unsigned int inlier = 0;
float minInlierDist = 2.0f;
float maxInlierDistMax = 100.0f;
float maxInlierDist = radius/25.0f;
if(maxInlierDist<minInlierDist) maxInlierDist = minInlierDist;
if(maxInlierDist>maxInlierDistMax) maxInlierDist = maxInlierDistMax;
// choose samples along the circle and count inlier percentage
for(float t =0; t<2*3.14159265359f; t+= 0.05f)
{
counter++;
float cX = radius*cos(t) + center.x;
float cY = radius*sin(t) + center.y;
if(cX < dt.cols)
if(cX >= 0)
if(cY < dt.rows)
if(cY >= 0)
if(dt.at<float>(cY,cX) < maxInlierDist)
{
inlier++;
inlierSet.push_back(cv::Point2f(cX,cY));
}
}
return (float)inlier/float(counter);
}
float evaluateCircle(cv::Mat dt, cv::Point2f center, float radius)
{
float completeDistance = 0.0f;
int counter = 0;
float maxDist = 1.0f; //TODO: this might depend on the size of the circle!
float minStep = 0.001f;
// choose samples along the circle and count inlier percentage
//HERE IS THE TRICK that no minimum/maximum circle is used, the number of generated points along the circle depends on the radius.
// if this is too slow for you (e.g. too many points created for each circle), increase the step parameter, but only by factor so that it still depends on the radius
// the parameter step depends on the circle size, otherwise small circles will create more inlier on the circle
float step = 2*3.14159265359f / (6.0f * radius);
if(step < minStep) step = minStep; // TODO: find a good value here.
//for(float t =0; t<2*3.14159265359f; t+= 0.05f) // this one which doesnt depend on the radius, is much worse!
for(float t =0; t<2*3.14159265359f; t+= step)
{
float cX = radius*cos(t) + center.x;
float cY = radius*sin(t) + center.y;
if(cX < dt.cols)
if(cX >= 0)
if(cY < dt.rows)
if(cY >= 0)
if(dt.at<float>(cY,cX) <= maxDist)
{
completeDistance += dt.at<float>(cY,cX);
counter++;
}
}
return counter;
}
int main()
{
//RANSAC
cv::Mat color = cv::imread("HoughCirclesAccuracy.png");
// convert to grayscale
cv::Mat gray;
cv::cvtColor(color, gray, CV_RGB2GRAY);
// get binary image
cv::Mat mask = gray > 0;
unsigned int numberOfCirclesToDetect = 2; // TODO: if unknown, you'll have to find some nice criteria to stop finding more (semi-) circles
for(unsigned int j=0; j<numberOfCirclesToDetect; ++j)
{
std::vector<cv::Point2f> edgePositions;
edgePositions = getPointPositions(mask);
std::cout << "number of edge positions: " << edgePositions.size() << std::endl;
// create distance transform to efficiently evaluate distance to nearest edge
cv::Mat dt;
cv::distanceTransform(255-mask, dt,CV_DIST_L1, 3);
unsigned int nIterations = 0;
cv::Point2f bestCircleCenter;
float bestCircleRadius;
//float bestCVal = FLT_MAX;
float bestCVal = -1;
//float minCircleRadius = 20.0f; // TODO: if you have some knowledge about your image you might be able to adjust the minimum circle radius parameter.
float minCircleRadius = 0.0f;
//TODO: implement some more intelligent ransac without fixed number of iterations
for(unsigned int i=0; i<2000; ++i)
{
//RANSAC: randomly choose 3 point and create a circle:
//TODO: choose randomly but more intelligent,
//so that it is more likely to choose three points of a circle.
//For example if there are many small circles, it is unlikely to randomly choose 3 points of the same circle.
unsigned int idx1 = rand()%edgePositions.size();
unsigned int idx2 = rand()%edgePositions.size();
unsigned int idx3 = rand()%edgePositions.size();
// we need 3 different samples:
if(idx1 == idx2) continue;
if(idx1 == idx3) continue;
if(idx3 == idx2) continue;
// create circle from 3 points:
cv::Point2f center; float radius;
getCircle(edgePositions[idx1],edgePositions[idx2],edgePositions[idx3],center,radius);
if(radius < minCircleRadius)continue;
//verify or falsify the circle by inlier counting:
//float cPerc = verifyCircle(dt,center,radius, inlierSet);
float cVal = evaluateCircle(dt,center,radius);
if(cVal > bestCVal)
{
bestCVal = cVal;
bestCircleRadius = radius;
bestCircleCenter = center;
}
++nIterations;
}
std::cout << "current best circle: " << bestCircleCenter << " with radius: " << bestCircleRadius << " and nInlier " << bestCVal << std::endl;
cv::circle(color,bestCircleCenter,bestCircleRadius,cv::Scalar(0,0,255));
//TODO: hold and save the detected circle.
//TODO: instead of overwriting the mask with a drawn circle it might be better to hold and ignore detected circles and dont count new circles which are too close to the old one.
// in this current version the chosen radius to overwrite the mask is fixed and might remove parts of other circles too!
// update mask: remove the detected circle!
cv::circle(mask,bestCircleCenter, bestCircleRadius, 0, 10); // here the radius is fixed which isnt so nice.
}
cv::namedWindow("edges"); cv::imshow("edges", mask);
cv::namedWindow("color"); cv::imshow("color", color);
cv::imwrite("detectedCircles.png", color);
cv::waitKey(-1);
return 0;
}
来源：https://stackoverflow.com/questions/23976766/hough-circle-detection-accuracy-very-low