Merge pull request #3722 from berak:py_houghlines_sample

doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown

@@ -59,7 +59,7 @@ denotes they are the parameters of possible lines in the image. (Image courtesy:
 
 ![](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,
@@ -78,7 +78,8 @@ gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
 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
@@ -123,10 +124,9 @@ import numpy as np
 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)