#!/usr/bin/python # # The full "Square Detector" program. # It loads several images subsequentally and tries to find squares in # each image # import urllib2 from math import sqrt import cv2.cv as cv 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 = (img.width & -2, img.height & -2) timg = cv.CloneImage(img); # make a copy of input image gray = cv.CreateImage(sz, 8, 1) pyr = cv.CreateImage((sz.width/2, sz.height/2), 8, 3) # create empty sequence that will contain points - # 4 points per square (the square's vertices) squares = cv.CreateSeq(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 = cv.GetSubRect(timg, cv.Rect(0, 0, sz.width, sz.height)) # down-scale and upscale the image to filter out the noise cv.PyrDown(subimage, pyr, 7) cv.PyrUp(pyr, subimage, 7) tgray = cv.CreateImage(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 cv.Split(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) cv.Canny(tgray, gray, 0, thresh, 5) # dilate canny output to remove potential # holes between edge segments cv.Dilate(gray, gray, None, 1) else: # apply threshold if l!=0: # tgray(x, y) = gray(x, y) < (l+1)*255/N ? 255 : 0 cv.Threshold(tgray, gray, (l+1)*255/N, 255, cv.CV_THRESH_BINARY) # find contours and store them all as a list count, contours = cv.FindContours(gray, storage, sizeof_CvContour, cv.CV_RETR_LIST, cv. CV_CHAIN_APPROX_SIMPLE, (0, 0)) if not contours: continue # test each contour for contour in contours.hrange(): # approximate contour with accuracy proportional # to the contour perimeter result = cv.ApproxPoly(contour, sizeof_CvContour, storage, cv.CV_POLY_APPROX_DP, cv.ContourPerimeter(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(cv.ContourArea(result)) > 1000 and cv.CheckContourConvexity(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 = cv.CloneImage(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 cv.PolyLine(cpy, [pt], 1, cv.CV_RGB(0, 255, 0), 3, cv. CV_AA, 0) i+=4 # show the resultant image cv.ShowImage(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 = cv.CreateMemStorage(0) for name in names: img0 = cv.LoadImage(name, 1) if not img0: print "Couldn't load %s" % name continue img = cv.CloneImage(img0) # create window and a trackbar (slider) with parent "image" and set callback # (the slider regulates upper threshold, passed to Canny edge detector) cv.NamedWindow(wndname, 1) cv.CreateTrackbar("canny thresh", wndname, thresh, 1000, on_trackbar) # force the image processing on_trackbar(0) # wait for key. # Also the function cv.WaitKey takes care of event processing c = cv.WaitKey(0) % 0x100 # clear memory storage - reset free space position cv.ClearMemStorage(storage) if(c == '\x1b'): break cv.DestroyWindow(wndname)