diff --git a/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown b/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown
index 0bfdcacb61..881cf2a29e 100644
--- a/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown
+++ b/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)
diff --git a/samples/python2/houghlines.py b/samples/python2/houghlines.py
index 5836bee547..abf25dd059 100755
--- a/samples/python2/houghlines.py
+++ b/samples/python2/houghlines.py
@@ -18,23 +18,25 @@ src = cv2.imread(fn)
 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)