py_tutorials: add print() braces for python3

doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown

@@ -30,18 +30,18 @@ You can access a pixel value by its row and column coordinates. For BGR image, i
 of Blue, Green, Red values. For grayscale image, just corresponding intensity is returned.
 @code{.py}
 >>> px = img[100,100]
->>> print px
+>>> print( px )
 [157 166 200]
 
 # accessing only blue pixel
 >>> blue = img[100,100,0]
->>> print blue
+>>> print( blue )
 157
 @endcode
 You can modify the pixel values the same way.
 @code{.py}
 >>> img[100,100] = [255,255,255]
->>> print img[100,100]
+>>> print( img[100,100] )
 [255 255 255]
 @endcode
 
@@ -76,7 +76,7 @@ etc.
 Shape of image is accessed by img.shape. It returns a tuple of number of rows, columns and channels
 (if image is color):
 @code{.py}
->>> print img.shape
+>>> print( img.shape )
 (342, 548, 3)
 @endcode
 
@@ -85,12 +85,12 @@ good method to check if loaded image is grayscale or color image.
 
 Total number of pixels is accessed by `img.size`:
 @code{.py}
->>> print img.size
+>>> print( img.size )
 562248
 @endcode
 Image datatype is obtained by \`img.dtype\`:
 @code{.py}
->>> print img.dtype
+>>> print( img.dtype )
 uint8
 @endcode
 

doc/py_tutorials/py_core/py_image_arithmetics/py_image_arithmetics.markdown

@@ -23,10 +23,10 @@ For example, consider below sample:
 >>> x = np.uint8([250])
 >>> y = np.uint8([10])
 
->>> print cv2.add(x,y) # 250+10 = 260 => 255
+>>> print( cv2.add(x,y) ) # 250+10 = 260 => 255
 [[255]]
 
->>> print x+y          # 250+10 = 260 % 256 = 4
+>>> print( x+y )          # 250+10 = 260 % 256 = 4
 [4]
 @endcode
 It will be more visible when you add two images. OpenCV function will provide a better result. So

doc/py_tutorials/py_core/py_optimization/py_optimization.markdown

@@ -44,7 +44,7 @@ for i in xrange(5,49,2):
     img1 = cv2.medianBlur(img1,i)
 e2 = cv2.getTickCount()
 t = (e2 - e1)/cv2.getTickFrequency()
-print t
+print( t )
 
 # Result I got is 0.521107655 seconds
 @endcode