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Merge pull request #3722 from berak:py_houghlines_sample
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@ -59,7 +59,7 @@ denotes they are the parameters of possible lines in the image. (Image courtesy:
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![](images/houghlines2.jpg)
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Hough Tranform in OpenCV
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Hough Transform in OpenCV
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=========================
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Everything explained above is encapsulated in the OpenCV function, \*\*cv2.HoughLines()\*\*. It simply returns an array of :math:(rho,
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@ -78,7 +78,8 @@ gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
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edges = cv2.Canny(gray,50,150,apertureSize = 3)
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lines = cv2.HoughLines(edges,1,np.pi/180,200)
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for rho,theta in lines[0]:
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for line in lines:
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rho,theta = line[0]
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a = np.cos(theta)
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b = np.sin(theta)
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x0 = a*rho
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@ -123,10 +124,9 @@ import numpy as np
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img = cv2.imread('dave.jpg')
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gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
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edges = cv2.Canny(gray,50,150,apertureSize = 3)
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minLineLength = 100
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maxLineGap = 10
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lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength,maxLineGap)
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for x1,y1,x2,y2 in lines[0]:
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lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength=100,maxLineGap=10)
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for line in lines:
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x1,y1,x2,y2 = line[0]
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cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)
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cv2.imwrite('houghlines5.jpg',img)
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@ -18,23 +18,25 @@ src = cv2.imread(fn)
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dst = cv2.Canny(src, 50, 200)
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cdst = cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)
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# HoughLines()
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# lines = cv2.HoughLines(dst, 1, math.pi/180.0, 50, np.array([]), 0, 0)
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# a,b,c = lines.shape
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# for i in range(b):
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# rho = lines[0][i][0]
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# theta = lines[0][i][1]
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# a = math.cos(theta)
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# b = math.sin(theta)
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# x0, y0 = a*rho, b*rho
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# pt1 = ( int(x0+1000*(-b)), int(y0+1000*(a)) )
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# pt2 = ( int(x0-1000*(-b)), int(y0-1000*(a)) )
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# cv2.line(cdst, pt1, pt2, (0, 0, 255), 3, cv2.LINE_AA)
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if True: # HoughLinesP
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lines = cv2.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10)
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a,b,c = lines.shape
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for i in range(a):
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cv2.line(cdst, (lines[i][0][0], lines[i][0][1]), (lines[i][0][2], lines[i][0][3]), (0, 0, 255), 3, cv2.LINE_AA)
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else: # HoughLines
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lines = cv2.HoughLines(dst, 1, math.pi/180.0, 50, np.array([]), 0, 0)
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a,b,c = lines.shape
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for i in range(a):
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rho = lines[i][0][0]
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theta = lines[i][0][1]
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a = math.cos(theta)
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b = math.sin(theta)
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x0, y0 = a*rho, b*rho
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pt1 = ( int(x0+1000*(-b)), int(y0+1000*(a)) )
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pt2 = ( int(x0-1000*(-b)), int(y0-1000*(a)) )
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cv2.line(cdst, pt1, pt2, (0, 0, 255), 3, cv2.LINE_AA)
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lines = cv2.HoughLinesP(dst, 1, math.pi/180.0, 50, np.array([]), 50, 10)
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a,b,c = lines.shape
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for i in range(b):
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cv2.line(cdst, (lines[0][i][0], lines[0][i][1]), (lines[0][i][2], lines[0][i][3]), (0, 0, 255), 3, cv2.LINE_AA)
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cv2.imshow("source", src)
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cv2.imshow("detected lines", cdst)
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