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174 lines
5.3 KiB
Mathematica
174 lines
5.3 KiB
Mathematica
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#! /usr/bin/env octave
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##
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## The full "Square Detector" program.
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## It loads several images subsequentally and tries to find squares in
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## each image
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##
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cv;
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highgui;
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global g;
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g.thresh = 50;
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g.img = [];
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g.img0 = [];
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g.storage = [];
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g.wndname = "Square Detection Demo";
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function ret = compute_angle( pt1, pt2, pt0 )
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dx1 = pt1.x - pt0.x;
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dy1 = pt1.y - pt0.y;
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dx2 = pt2.x - pt0.x;
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dy2 = pt2.y - pt0.y;
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ret = (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
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endfunction
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function squares = findSquares4( img, storage )
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global g;
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global cv;
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N = 11;
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sz = cvSize( img.width, img.height );
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timg = cvCloneImage( img ); # make a copy of input image
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gray = cvCreateImage( sz, 8, 1 );
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pyr = cvCreateImage( cvSize(int32(sz.width/2), int32(sz.height/2)), 8, 3 );
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## create empty sequence that will contain points -
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## 4 points per square (the square's vertices)
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squares = cvCreateSeq( 0, cv.sizeof_CvSeq, cv.sizeof_CvPoint, storage );
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squares = cv.CvSeq_CvPoint.cast( squares );
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## select the maximum ROI in the image
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## with the width and height divisible by 2
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subimage = cvGetSubRect( timg, cvRect( 0, 0, sz.width, sz.height ));
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## down-scale and upscale the image to filter out the noise
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cvPyrDown( subimage, pyr, 7 );
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cvPyrUp( pyr, subimage, 7 );
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tgray = cvCreateImage( sz, 8, 1 );
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## find squares in every color plane of the image
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for c=1:3,
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## extract the c-th color plane
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channels = {[], [], []};
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channels{c} = tgray;
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cvSplit( subimage, channels{1}, channels{2}, channels{3}, [] ) ;
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for l=1:N,
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## hack: use Canny instead of zero threshold level.
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## Canny helps to catch squares with gradient shading
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if( l == 1 )
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## apply Canny. Take the upper threshold from slider
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## and set the lower to 0 (which forces edges merging)
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cvCanny( tgray, gray, 0, g.thresh, 5 );
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## dilate canny output to remove potential
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## holes between edge segments
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cvDilate( gray, gray, [], 1 );
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else
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## apply threshold if l!=0
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## tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
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cvThreshold( tgray, gray, l*255/N, 255, cv.CV_THRESH_BINARY );
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endif
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## find contours and store them all as a list
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[count, contours] = cvFindContours( gray, storage, cv.sizeof_CvContour, cv.CV_RETR_LIST, cv.CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );
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if (!swig_this(contours))
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continue;
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endif
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## test each contour
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for contour = CvSeq_hrange(contours),
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## approximate contour with accuracy proportional
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## to the contour perimeter
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result = cvApproxPoly( contour, cv.sizeof_CvContour, storage, cv.CV_POLY_APPROX_DP, cvContourPerimeter(contours)*0.02, 0 );
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## square contours should have 4 vertices after approximation
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## relatively large area (to filter out noisy contours)
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## and be convex.
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## Note: absolute value of an area is used because
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## area may be positive or negative - in accordance with the
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## contour orientation
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if( result.total == 4 &&
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abs(cvContourArea(result)) > 1000 &&
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cvCheckContourConvexity(result) )
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s = 0;
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for i=1:5,
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## find minimum angle between joint
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## edges (maximum of cosine)
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if( i > 2 )
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t = abs(compute_angle( result{i}, result{i-2}, result{i-1}));
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if (s<t)
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s=t;
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endif
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endif
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endfor
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## if cosines of all angles are small
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## (all angles are ~90 degree) then write quandrange
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## vertices to resultant sequence
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if( s < 0.3 )
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for i=1:4,
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squares.append( result{i} )
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endfor
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endif
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endif
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endfor
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endfor
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endfor
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endfunction
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## the function draws all the squares in the image
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function drawSquares( img, squares )
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global g;
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global cv;
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cpy = cvCloneImage( img );
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## read 4 sequence elements at a time (all vertices of a square)
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i=0;
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while (i<squares.total)
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pt = { squares{i}, squares{i+1}, squares{i+2}, squares{i+3} };
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## draw the square as a closed polyline
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cvPolyLine( cpy, {pt}, 1, CV_RGB(0,255,0), 3, cv.CV_AA, 0 );
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i+=4;
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endwhile
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## show the resultant image
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cvShowImage( g.wndname, cpy );
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endfunction
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function on_trackbar( a )
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global g;
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if( swig_this(g.img) )
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drawSquares( g.img, findSquares4( g.img, g.storage ) );
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endif
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endfunction
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g.names = {"../c/pic1.png", "../c/pic2.png", "../c/pic3.png", \
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"../c/pic4.png", "../c/pic5.png", "../c/pic6.png" };
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## create memory storage that will contain all the dynamic data
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g.storage = cvCreateMemStorage(0);
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for name = g.names,
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g.img0 = cvLoadImage( name, 1 );
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if (!swig_this(g.img0))
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printf("Couldn't load %s\n",name);
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continue;
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endif
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g.img = cvCloneImage( g.img0 );
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## create window and a trackbar (slider) with parent "image" and set callback
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## (the slider regulates upper threshold, passed to Canny edge detector)
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cvNamedWindow( g.wndname, 1 );
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cvCreateTrackbar( "canny thresh", g.wndname, g.thresh, 1000, @on_trackbar );
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## force the image processing
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on_trackbar(0);
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## wait for key.
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## Also the function cvWaitKey takes care of event processing
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c = cvWaitKey(0);
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## clear memory storage - reset free space position
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cvClearMemStorage( g.storage );
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if( c == '\x1b' )
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break;
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endif
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endfor
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cvDestroyWindow( g.wndname );
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