Applying perspective transform correct the degree of sheet of paper in a image

Question

I am working on a project which will help us to correct the degree of orientation of image.

Here in this code i am detecting a sheet of paper. Steps that i used 1.Apply houghLine transform 2.Detect corner. 3.Applied perspective transform. And with all this I am able to detect sheet of paper but it only works for only one or two images it does not work on all the images and I am not understanding why,

The problem that I think in this code is that it is not able to detect the corners correctly , because of which I am not able to correct the perspective of a image .

it works on this image

but when i used some other image instead of this then i am not able to do so

#include <cv.h>
#include <highgui.h>


using namespace std;
using namespace cv;


Point2f center(0,0);

Point2f computeIntersect(Vec4i a, Vec4i b)
{
    int x1 = a[0], y1 = a[1], x2 = a[2], y2 = a[3], x3 = b[0], y3 = b[1], x4 = b[2], y4 = b[3];
    float denom;

    if (float d = ((float)(x1 - x2) * (y3 - y4)) - ((y1 - y2) * (x3 - x4)))
    {
        Point2f pt;
        pt.x = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / d;
        pt.y = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / d;
        return pt;
    }
    else
        return Point2f(-1, -1);
}

void sortCorners(vector<Point2f>& corners, Point2f center)
{
    vector<Point2f> top, bot;

    for (int i = 0; i < corners.size(); i++)
    {
        if (corners[i].y < center.y)
            top.push_back(corners[i]);
        else
            bot.push_back(corners[i]);
    }
    corners.clear();

    if (top.size() == 2 && bot.size() == 2){
        Point2f tl = top[0].x > top[1].x ? top[1] : top[0];
        Point2f tr = top[0].x > top[1].x ? top[0] : top[1];
        Point2f bl = bot[0].x > bot[1].x ? bot[1] : bot[0];
        Point2f br = bot[0].x > bot[1].x ? bot[0] : bot[1];


        corners.push_back(tl);
        corners.push_back(tr);
        corners.push_back(br);
        corners.push_back(bl);
    }
}

int main()
{
    Mat src,cann,hsv;
    src = imread("C:\\im.jpg",WINDOW_AUTOSIZE);

    if (src.empty())
        return -1;

    imshow("original",src);

    blur(src, src, Size(3, 3));
    Canny(src, cann, 50, 200, 3);
    cvtColor(cann, hsv, CV_GRAY2BGR);

    vector<Vec4i> lines;
    HoughLinesP(cann, lines, 1, CV_PI/180, 70, 30, 10);
    for( size_t i = 0; i < lines.size(); i++ )
    {
        Vec4i l = lines[i];
        line( hsv, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0,0,255), 2, CV_AA);
    } 

    // Expand the lines
    for (int i = 0; i < lines.size(); i++)
    {
        Vec4i v = lines[i];
        lines[i][0] = 0;
        lines[i][1] = ((float)v[1] - v[3]) / (v[0] - v[2]) * -v[0] + v[1]; 
        lines[i][2] = src.cols; 
        lines[i][3] = ((float)v[1] - v[3]) / (v[0] - v[2]) * (src.cols - v[2]) + v[3];
    }

    vector<Point2f> corners;
    for (int i = 0; i < lines.size(); i++)
    {
        for (int j = i+1; j < lines.size(); j++)
        {
            Point2f pt = computeIntersect(lines[i], lines[j]);
            if (pt.x >= 0 && pt.y >= 0)
                corners.push_back(pt);
        }
    }

    vector<Point2f> approx;
    approxPolyDP(Mat(corners), approx, arcLength(Mat(corners), true) * 0.02, true);

    //if (approx.size() != 4)
//  {
    //  cout << "The object is not quadrilateral!" << endl;
        //return -1;
    //}



    // Get mass center
    for (int i = 0; i < corners.size(); i++)
        center += corners[i];

    center *= (1. / corners.size());

    sortCorners(corners, center);
    if (corners.size() == 0)
    {
        cout << "The corners were not sorted correctly!" << endl;
        return -1;
    }

    Mat dst = src.clone();

    // Draw lines
    for (int i = 0; i < lines.size(); i++)
    {
        Vec4i v = lines[i];
        line(dst, Point(v[0], v[1]), Point(v[2], v[3]), CV_RGB(0,255,0));
    }

    // Draw corner points
    circle(dst, corners[0], 3, CV_RGB(255,0,0), 2);
    circle(dst, corners[1], 3, CV_RGB(0,255,0), 2);
    circle(dst, corners[2], 3, CV_RGB(0,0,255), 2);
    circle(dst, corners[3], 3, CV_RGB(255,255,255), 2);
    // Draw mass center
    circle(dst, center, 3, CV_RGB(255,255,0), 2);

    Mat quad = Mat::zeros(300, 220, CV_8UC3);

    vector<Point2f> quad_pts;
    quad_pts.push_back(Point2f(0, 0));
    quad_pts.push_back(Point2f(quad.cols, 0));
    quad_pts.push_back(Point2f(quad.cols, quad.rows));
    quad_pts.push_back(Point2f(0, quad.rows));

    Mat transmtx = getPerspectiveTransform(corners, quad_pts);
    warpPerspective(src, quad, transmtx, quad.size());

    imshow("blurr",src);
    imshow("canney",cann);
    imshow("hough",hsv);
    imshow("image", dst);
    imshow("quadrilateral", quad);

    waitKey(0);
    return 0;
}

please please help me this i am really get stuck with this .

Answer 1

Your algorithm assumes that HoughLinesP function will always detect only 4 lines and that each one will lie on a different edge of the paper. However, this assumption is wrong. In your particular case, when you work with the second image, it returns 5 lines when you work on the second image. Click to see the detected lines (marked by non-gray colours).

Quick fix

I changed the value of 6th HoughLinesP argument (minLineThreshold parameter) to 70. After that, only four lines were detected in the image, but another bug surfaced; 5 corners were detected instead of 4. The reason? Two of the opposite edges were not parallel and they intersected far outside the image area. This condition was causing the problem:

if (pt.x >= 0 && pt.y >= 0)
            corners.push_back(pt);

It is not enough to check whether corners coordinates are non-negative. Instead, you have to make sure that the corners are within certain boundaries that make sense; in your case these could be boundaries of the image.

if (pt.x >= 0 && pt.y >= 0 && pt.x <src.cols && pt.y < src.rows)
            corners.push_back(pt);

After changing threshold and fixing the condition, I obtained this result: (Click to see an image)

Warning

As you can see, yet another problem surfaced - the corners are not detected as accurately as they could be. You can use information provided by canny edges to your advantage here. But I do not want to venture out of the scope of your question here, so I'll stop.

I named my solution as a "quick fix" because it only solves one particular case. If you want more general solution and if you want to keep using your algorithm, you will have to compute a reasonable threshold estimate every time before you use HoughLineP.