diff --git a/doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.markdown b/doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.markdown
index d192288721..285124d17c 100644
--- a/doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.markdown
+++ b/doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.markdown
@@ -188,7 +188,7 @@ blur = cv.GaussianBlur(img,(5,5),0)
 
 # find normalized_histogram, and its cumulative distribution function
 hist = cv.calcHist([blur],[0],None,[256],[0,256])
-hist_norm = hist.ravel()/hist.max()
+hist_norm = hist.ravel()/hist.sum()
 Q = hist_norm.cumsum()
 
 bins = np.arange(256)
@@ -199,6 +199,8 @@ thresh = -1
 for i in xrange(1,256):
     p1,p2 = np.hsplit(hist_norm,[i]) # probabilities
     q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes
+    if q1 < 1.e-6 or q2 < 1.e-6:
+        continue
     b1,b2 = np.hsplit(bins,[i]) # weights
 
     # finding means and variances