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POOpenCV C++/Obj-C: Detecting a sheet of paper / Square Detection
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  1. POOpenCV C++/Obj-C: Detecting a sheet of paper / Square Detection
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    copied!<p>I successfully implemented the OpenCV square-detection example in my test application, but now need to filter the output, because it's quiet messy - or is my code wrong?</p> <p>I'm interested in the four corner points of the paper for skew reduction (like <a href="https://stackoverflow.com/questions/7838487/executing-cvwarpperspective-for-a-fake-deskewing-on-a-set-of-cvpoint">that</a>) and further processing …</p> <p><strong>Input &amp; Output:</strong> <a href="https://i.stack.imgur.com/5e2Mv.jpg" rel="noreferrer"><img src="https://i.stack.imgur.com/5e2Mv.jpg" alt="Input &amp; Output"></a></p> <p><strong>Original image:</strong></p> <p><a href="https://i.stack.imgur.com/MIm24.jpg" rel="noreferrer">click</a></p> <p><strong>Code:</strong></p> <pre><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&lt;std::vector&lt;cv::Point&gt; &gt;)findSquaresInImage:(cv::Mat)_image { std::vector&lt;std::vector&lt;cv::Point&gt; &gt; 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&lt;std::vector&lt;cv::Point&gt; &gt; contours; for( int c = 0; c &lt; 3; c++ ) { int ch[] = {c, 0}; mixChannels(&amp;timg, 1, &amp;gray0, 1, ch, 1); for( int l = 0; l &lt; 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 &gt;= (l+1)*255/N; } cv::findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE); std::vector&lt;cv::Point&gt; approx; for( size_t i = 0; i &lt; contours.size(); i++ ) { cv::approxPolyDP(cv::Mat(contours[i]), approx, arcLength(cv::Mat(contours[i]), true)*0.02, true); if( approx.size() == 4 &amp;&amp; fabs(contourArea(cv::Mat(approx))) &gt; 1000 &amp;&amp; cv::isContourConvex(cv::Mat(approx))) { double maxCosine = 0; for( int j = 2; j &lt; 5; j++ ) { double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1])); maxCosine = MAX(maxCosine, cosine); } if( maxCosine &lt; 0.3 ) { squares.push_back(approx); } } } } } return squares; } </code></pre> <p><strong><em>EDIT 17/08/2012:</em></strong></p> <p>To draw the detected squares on the image use this code:</p> <pre><code>cv::Mat debugSquares( std::vector&lt;std::vector&lt;cv::Point&gt; &gt; squares, cv::Mat image ) { for ( int i = 0; i&lt; squares.size(); i++ ) { // draw contour cv::drawContours(image, squares, i, cv::Scalar(255,0,0), 1, 8, std::vector&lt;cv::Vec4i&gt;(), 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 &lt; 4; j++ ) { cv::line( image, rect_points[j], rect_points[(j+1)%4], cv::Scalar(0,0,255), 1, 8 ); // blue } } return image; } </code></pre>
 

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