![](images/houghlines2.jpg)
-Hough Tranform in OpenCV
+Hough Transform in OpenCV
=========================
Everything explained above is encapsulated in the OpenCV function, \*\*cv2.HoughLines()\*\*. It simply returns an array of :math:(rho,
edges = cv2.Canny(gray,50,150,apertureSize = 3)
lines = cv2.HoughLines(edges,1,np.pi/180,200)
-for rho,theta in lines[0]:
+for line in lines:
+ rho,theta = line[0]
a = np.cos(theta)
b = np.sin(theta)
x0 = a*rho
img = cv2.imread('dave.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,50,150,apertureSize = 3)
-minLineLength = 100
-maxLineGap = 10
-lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength,maxLineGap)
-for x1,y1,x2,y2 in lines[0]:
+lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength=100,maxLineGap=10)
+for line in lines:
+ x1,y1,x2,y2 = line[0]
cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)
cv2.imwrite('houghlines5.jpg',img)
dst = cv2.Canny(src, 50, 200)
cdst = cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)
-# HoughLines()
-# lines = cv2.HoughLines(dst, 1, math.pi/180.0, 50, np.array([]), 0, 0)
-# a,b,c = lines.shape
-# for i in range(b):
-# rho = lines[0][i][0]
-# theta = lines[0][i][1]
-# a = math.cos(theta)
-# b = math.sin(theta)
-# x0, y0 = a*rho, b*rho
-# pt1 = ( int(x0+1000*(-b)), int(y0+1000*(a)) )
-# pt2 = ( int(x0-1000*(-b)), int(y0-1000*(a)) )
-# cv2.line(cdst, pt1, pt2, (0, 0, 255), 3, cv2.LINE_AA)
+if True: # HoughLinesP
+ lines = cv2.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10)
+ a,b,c = lines.shape
+ for i in range(a):
+ 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)
+
+else: # HoughLines
+ lines = cv2.HoughLines(dst, 1, math.pi/180.0, 50, np.array([]), 0, 0)
+ a,b,c = lines.shape
+ for i in range(a):
+ rho = lines[i][0][0]
+ theta = lines[i][0][1]
+ a = math.cos(theta)
+ b = math.sin(theta)
+ x0, y0 = a*rho, b*rho
+ pt1 = ( int(x0+1000*(-b)), int(y0+1000*(a)) )
+ pt2 = ( int(x0-1000*(-b)), int(y0-1000*(a)) )
+ cv2.line(cdst, pt1, pt2, (0, 0, 255), 3, cv2.LINE_AA)
-lines = cv2.HoughLinesP(dst, 1, math.pi/180.0, 50, np.array([]), 50, 10)
-a,b,c = lines.shape
-for i in range(b):
- 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)
cv2.imshow("source", src)
cv2.imshow("detected lines", cdst)