--- /dev/null
+#!/usr/bin/python
+#
+# The full "Square Detector" program.
+# It loads several images subsequentally and tries to find squares in
+# each image
+#
+
+from opencv.cv import *
+from opencv.highgui import *
+from math import sqrt
+
+thresh = 50;
+img = None;
+img0 = None;
+storage = None;
+wndname = "Square Detection Demo";
+
+def angle( pt1, pt2, pt0 ):
+ dx1 = pt1.x - pt0.x;
+ dy1 = pt1.y - pt0.y;
+ dx2 = pt2.x - pt0.x;
+ dy2 = pt2.y - pt0.y;
+ return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
+
+def findSquares4( img, storage ):
+ N = 11;
+ sz = cvSize( img.width & -2, img.height & -2 );
+ timg = cvCloneImage( img ); # make a copy of input image
+ gray = cvCreateImage( sz, 8, 1 );
+ pyr = cvCreateImage( cvSize(sz.width/2, sz.height/2), 8, 3 );
+ # create empty sequence that will contain points -
+ # 4 points per square (the square's vertices)
+ squares = cvCreateSeq( 0, sizeof_CvSeq, sizeof_CvPoint, storage );
+ squares = CvSeq_CvPoint.cast( squares )
+
+ # select the maximum ROI in the image
+ # with the width and height divisible by 2
+ subimage = cvGetSubRect( timg, cvRect( 0, 0, sz.width, sz.height ))
+
+ # down-scale and upscale the image to filter out the noise
+ cvPyrDown( subimage, pyr, 7 );
+ cvPyrUp( pyr, subimage, 7 );
+ tgray = cvCreateImage( sz, 8, 1 );
+ # find squares in every color plane of the image
+ for c in range(3):
+ # extract the c-th color plane
+ channels = [None, None, None]
+ channels[c] = tgray
+ cvSplit( subimage, channels[0], channels[1], channels[2], None )
+ for l in range(N):
+ # hack: use Canny instead of zero threshold level.
+ # Canny helps to catch squares with gradient shading
+ if( l == 0 ):
+ # apply Canny. Take the upper threshold from slider
+ # and set the lower to 0 (which forces edges merging)
+ cvCanny( tgray, gray, 0, thresh, 5 );
+ # dilate canny output to remove potential
+ # holes between edge segments
+ cvDilate( gray, gray, None, 1 );
+ else:
+ # apply threshold if l!=0:
+ # tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
+ cvThreshold( tgray, gray, (l+1)*255/N, 255, CV_THRESH_BINARY );
+
+ # find contours and store them all as a list
+ count, contours = cvFindContours( gray, storage, sizeof_CvContour,
+ CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );
+
+ if not contours:
+ continue
+
+ # test each contour
+ for contour in contours.hrange():
+ # approximate contour with accuracy proportional
+ # to the contour perimeter
+ result = cvApproxPoly( contour, sizeof_CvContour, storage,
+ CV_POLY_APPROX_DP, cvContourPerimeter(contours)*0.02, 0 );
+ # square contours should have 4 vertices after approximation
+ # relatively large area (to filter out noisy contours)
+ # and be convex.
+ # Note: absolute value of an area is used because
+ # area may be positive or negative - in accordance with the
+ # contour orientation
+ if( result.total == 4 and
+ abs(cvContourArea(result)) > 1000 and
+ cvCheckContourConvexity(result) ):
+ s = 0;
+ for i in range(5):
+ # find minimum angle between joint
+ # edges (maximum of cosine)
+ if( i >= 2 ):
+ t = abs(angle( result[i], result[i-2], result[i-1]))
+ if s<t:
+ s=t
+ # if cosines of all angles are small
+ # (all angles are ~90 degree) then write quandrange
+ # vertices to resultant sequence
+ if( s < 0.3 ):
+ for i in range(4):
+ squares.append( result[i] )
+
+ return squares;
+
+# the function draws all the squares in the image
+def drawSquares( img, squares ):
+ cpy = cvCloneImage( img );
+ # read 4 sequence elements at a time (all vertices of a square)
+ i=0
+ while i<squares.total:
+ pt = []
+ # read 4 vertices
+ pt.append( squares[i] )
+ pt.append( squares[i+1] )
+ pt.append( squares[i+2] )
+ pt.append( squares[i+3] )
+
+ # draw the square as a closed polyline
+ cvPolyLine( cpy, [pt], 1, CV_RGB(0,255,0), 3, CV_AA, 0 );
+ i+=4
+
+ # show the resultant image
+ cvShowImage( wndname, cpy );
+
+def on_trackbar( a ):
+ if( img ):
+ drawSquares( img, findSquares4( img, storage ) );
+
+names = ["../c/pic1.png", "../c/pic2.png", "../c/pic3.png",
+ "../c/pic4.png", "../c/pic5.png", "../c/pic6.png" ];
+
+if __name__ == "__main__":
+ # create memory storage that will contain all the dynamic data
+ storage = cvCreateMemStorage(0);
+ for name in names:
+ img0 = cvLoadImage( name, 1 );
+ if not img0:
+ print "Couldn't load %s" % name
+ continue;
+ img = cvCloneImage( img0 );
+ # create window and a trackbar (slider) with parent "image" and set callback
+ # (the slider regulates upper threshold, passed to Canny edge detector)
+ cvNamedWindow( wndname, 1 );
+ cvCreateTrackbar( "canny thresh", wndname, thresh, 1000, on_trackbar );
+ # force the image processing
+ on_trackbar(0);
+ # wait for key.
+ # Also the function cvWaitKey takes care of event processing
+ c = cvWaitKey(0);
+ # clear memory storage - reset free space position
+ cvClearMemStorage( storage );
+ if( c == '\x1b' ):
+ break;
+ cvDestroyWindow( wndname );
projects / opencv / blobdiff