how to find blur corner position with opencv?

Question

I want to find the corner position of an blurred image with a corner inside it. like the following example:

I can make sure that only one corner is inside the image, and I assume that

the corner is part of a black and white chessboard.

How can I detect the cross position with openCV? Thanks!

Answer 1

Usually you can determine the corner using the gradient:

Gx = im[i][j+1] - im[i][j-1]; Gy = im[i+1][j] – im[i-1][j];

G^2 = Gx^2 + Gy^2;

teta = atan2 (Gy, Gx);

As your image is blurred, you should compute the gradient at a larger scale:

Gx = im[i][j+delta] - im[i][j-delta]; Gy = im[i+ delta][j] – im[i- delta][j];

Here is the result that I obtained for delta = 50:

The gradient norm (multiplied by 20)

gradient norm http://imageshack.us/scaled/thumb/822/xdpp.jpg

The gradient direction:

gradient direction http://imageshack.us/scaled/thumb/844/h6zp.jpg

Answer 2

another solution

#include <opencv2/opencv.hpp>

using namespace cv;
int main()
{

    Mat img=imread("c:/data/corner.jpg");
    Mat gray;
    cvtColor(img,gray,CV_BGR2GRAY);
    threshold(gray,gray,100,255,CV_THRESH_BINARY);
    int step=15;
    std::vector<Point> points;
    for(int i=0;i<gray.rows;i+=step)
        for(int j=0;j<gray.cols;j+=step)
            if(gray.at<uchar>(i,j)==255)            
                points.push_back(Point(j,i));

    //fit a rotated rectangle

    RotatedRect box = minAreaRect(Mat(points));
    //circle(img,box.center,2,Scalar(255,0,0),-1);

    //invert it,fit again and get average of centers(may not be needed if a 'good' threshold is found)
    Point p1=Point(box.center.x,box.center.y);
    points.clear();
    gray=255-gray;
    for(int i=0;i<gray.rows;i+=step)
        for(int j=0;j<gray.cols;j+=step)
            if(gray.at<uchar>(i,j)==255)
                points.push_back(Point(j,i));
    box = minAreaRect(Mat(points));
    Point p2=Point(box.center.x,box.center.y);

    //circle(img,p2,2,Scalar(0,255,0),-1);
    circle(img,Point((p1.x+p2.x)/2,(p1.y+p2.y)/2),3,Scalar(0,0,255),-1);
    imshow("img",img);
    waitKey();

    return 0;
}

Answer 3

Rather than work right away at a ridiculously large scale, as suggested by others, I recommend downsizing first (which has the effect of deblurring), do one pass of Harris to find the corner, then upscale its position and do a pass of findCornerSubpix at full resolution with a large window (large enough to encompass the obvious saddle point of the intensity).

In this way you get the best of both worlds: fast detection to initialize the refinement, and accurate refinement given the original imagery.

See also this other relevant answer