OpenCV C++/Obj-C: Detecting a sheet of paper / Square Detection

c++, computer-vision, image-processing, objective-c, opencv

Solution

This is a recurring subject in Stackoverflow and since I was unable to find a relevant implementation I decided to accept the challenge.

I made some modifications to the squares demo present in OpenCV and the resulting C++ code below is able to detect a sheet of paper in the image:

void find_squares(Mat& image, vector<vector<Point> >& squares)
{
    // blur will enhance edge detection
    Mat blurred(image);
    medianBlur(image, blurred, 9);

    Mat gray0(blurred.size(), CV_8U), gray;
    vector<vector<Point> > contours;

    // find squares in every color plane of the image
    for (int c = 0; c < 3; c++)
    {
        int ch[] = {c, 0};
        mixChannels(&blurred, 1, &gray0, 1, ch, 1);

        // try several threshold levels
        const int threshold_level = 2;
        for (int l = 0; l < threshold_level; l++)
        {
            // Use Canny instead of zero threshold level!
            // Canny helps to catch squares with gradient shading
            if (l == 0)
            {
                Canny(gray0, gray, 10, 20, 3); // 

                // Dilate helps to remove potential holes between edge segments
                dilate(gray, gray, Mat(), Point(-1,-1));
            }
            else
            {
                    gray = gray0 >= (l+1) * 255 / threshold_level;
            }

            // Find contours and store them in a list
            findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);

            // Test contours
            vector<Point> approx;
            for (size_t i = 0; i < contours.size(); i++)
            {
                    // approximate contour with accuracy proportional
                    // to the contour perimeter
                    approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);

                    // Note: absolute value of an area is used because
                    // area may be positive or negative - in accordance with the
                    // contour orientation
                    if (approx.size() == 4 &&
                            fabs(contourArea(Mat(approx))) > 1000 &&
                            isContourConvex(Mat(approx)))
                    {
                            double maxCosine = 0;

                            for (int j = 2; j < 5; j++)
                            {
                                    double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1]));
                                    maxCosine = MAX(maxCosine, cosine);
                            }

                            if (maxCosine < 0.3)
                                    squares.push_back(approx);
                    }
            }
        }
    }
}

After this procedure is executed, the sheet of paper will be the largest square in `vector<vector<Point> >`:

I'm letting you write the function to find the largest square. ;)

Problem

I successfully implemented the OpenCV square-detection example in my test application, but now need to filter the output, because it's quite messy - or is my code wrong? I'm interested in the four corner points of the paper for skew reduction (like that) and further processing … Input & Output: Original image: click Code: ``` double angle( cv::Point pt1, cv::Point pt2, cv::Point pt0 ) { double dx1 = pt1.x - pt0.x; double dy1 = pt1.y - pt0.y; double dx2 = pt2.x - pt0.x; double dy2 = pt2.y - pt0.y; return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10); } - (std::vector<std::vector<cv::Point> >)findSquaresInImage:(cv::Mat)_image { std::vector<std::vector<cv::Point> > squares; cv::Mat pyr, timg, gray0(_image.size(), CV_8U), gray; int thresh = 50, N = 11; cv::pyrDown(_image, pyr, cv::Size(_image.cols/2, _image.rows/2)); cv::pyrUp(pyr, timg, _image.size()); std::vector<std::vector<cv::Point> > contours; for( int c = 0; c < 3; c++ ) { int ch[] = {c, 0}; mixChannels(&timg, 1, &gray0, 1, ch, 1); for( int l = 0; l < N; l++ ) { if( l == 0 ) { cv::Canny(gray0, gray, 0, thresh, 5); cv::dilate(gray, gray, cv::Mat(), cv::Point(-1,-1)); } else { gray = gray0 >= (l+1)*255/N; } cv::findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE); std::vector<cv::Point> approx; for( size_t i = 0; i < contours.size(); i++ ) { cv::approxPolyDP(cv::Mat(contours[i]), approx, arcLength(cv::Mat(contours[i]), true)*0.02, true); if( approx.size() == 4 && fabs(contourArea(cv::Mat(approx))) > 1000 && cv::isContourConvex(cv::Mat(approx))) { double maxCosine = 0; for( int j = 2; j < 5; j++ ) { double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1])); maxCosine = MAX(maxCosine, cosine); } if( maxCosine < 0.3 ) { squares.push_back(approx); } } } } } return squares; } ``` EDIT 17/08/2012: To draw the detected squares on the image use this code: ``` cv::Mat debugSquares( std::vector<std::vector<cv::Point> > squares, cv::Mat image ) { for ( int i = 0; i< squares.size(); i++ ) { // draw contour cv::drawContours(image, squares, i, cv::Scalar(255,0,0), 1, 8, std::vector<cv::Vec4i>(), 0, cv::Point()); // draw bounding rect cv::Rect rect = boundingRect(cv::Mat(squares[i])); cv::rectangle(image, rect.tl(), rect.br(), cv::Scalar(0,255,0), 2, 8, 0); // draw rotated rect cv::RotatedRect minRect = minAreaRect(cv::Mat(squares[i])); cv::Point2f rect_points[4]; minRect.points( rect_points ); for ( int j = 0; j < 4; j++ ) { cv::line( image, rect_points[j], rect_points[(j+1)%4], cv::Scalar(0,0,255), 1, 8 ); // blue } } return image; } ```

Original source

Related problems