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154 lines
5.7 KiB
Python
Executable File
154 lines
5.7 KiB
Python
Executable File
#!/usr/bin/python
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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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import urllib2
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from math import sqrt
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import cv
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thresh = 50
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img = None
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img0 = None
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storage = None
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wndname = "Square Detection Demo"
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def 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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return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10)
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def findSquares4(img, storage):
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N = 11
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sz = (img.width & -2, img.height & -2)
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timg = cv.CloneImage(img); # make a copy of input image
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gray = cv.CreateImage(sz, 8, 1)
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pyr = cv.CreateImage((sz.width/2, 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 = cv.CreateSeq(0, sizeof_CvSeq, sizeof_CvPoint, storage)
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squares = 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 = cv.GetSubRect(timg, cv.Rect(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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cv.PyrDown(subimage, pyr, 7)
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cv.PyrUp(pyr, subimage, 7)
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tgray = cv.CreateImage(sz, 8, 1)
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# find squares in every color plane of the image
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for c in range(3):
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# extract the c-th color plane
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channels = [None, None, None]
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channels[c] = tgray
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cv.Split(subimage, channels[0], channels[1], channels[2], None)
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for l in range(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 == 0):
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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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cv.Canny(tgray, gray, 0, thresh, 5)
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# dilate canny output to remove potential
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# holes between edge segments
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cv.Dilate(gray, gray, None, 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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cv.Threshold(tgray, gray, (l+1)*255/N, 255, cv.CV_THRESH_BINARY)
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# find contours and store them all as a list
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count, contours = cv.FindContours(gray, storage, sizeof_CvContour,
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cv.CV_RETR_LIST, cv. CV_CHAIN_APPROX_SIMPLE, (0, 0))
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if not contours:
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continue
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# test each contour
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for contour in contours.hrange():
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# approximate contour with accuracy proportional
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# to the contour perimeter
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result = cv.ApproxPoly(contour, sizeof_CvContour, storage,
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cv.CV_POLY_APPROX_DP, cv.ContourPerimeter(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 and
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abs(cv.ContourArea(result)) > 1000 and
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cv.CheckContourConvexity(result)):
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s = 0
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for i in range(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(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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# 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 in range(4):
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squares.append(result[i])
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return squares
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# the function draws all the squares in the image
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def drawSquares(img, squares):
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cpy = cv.CloneImage(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 = []
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# read 4 vertices
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pt.append(squares[i])
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pt.append(squares[i+1])
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pt.append(squares[i+2])
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pt.append(squares[i+3])
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# draw the square as a closed polyline
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cv.PolyLine(cpy, [pt], 1, cv.CV_RGB(0, 255, 0), 3, cv. CV_AA, 0)
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i+=4
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# show the resultant image
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cv.ShowImage(wndname, cpy)
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def on_trackbar(a):
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if(img):
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drawSquares(img, findSquares4(img, storage))
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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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if __name__ == "__main__":
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# create memory storage that will contain all the dynamic data
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storage = cv.CreateMemStorage(0)
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for name in names:
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img0 = cv.LoadImage(name, 1)
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if not img0:
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print "Couldn't load %s" % name
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continue
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img = cv.CloneImage(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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cv.NamedWindow(wndname, 1)
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cv.CreateTrackbar("canny thresh", wndname, 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 cv.WaitKey takes care of event processing
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c = cv.WaitKey(0) % 0x100
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# clear memory storage - reset free space position
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cv.ClearMemStorage(storage)
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if(c == '\x1b'):
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break
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cv.DestroyWindow(wndname)
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