python: 'cv2.' -> 'cv.' via 'import cv2 as cv'

doc/py_tutorials/py_imgproc/py_pyramids/py_pyramids.markdown

@@ -7,7 +7,7 @@ Goal
 In this chapter,
     -   We will learn about Image Pyramids
     -   We will use Image pyramids to create a new fruit, "Orapple"
-    -   We will see these functions: **cv2.pyrUp()**, **cv2.pyrDown()**
+    -   We will see these functions: **cv.pyrUp()**, **cv.pyrDown()**
 
 Theory
 ------
@@ -28,18 +28,18 @@ contribution from 5 pixels in underlying level with gaussian weights. By doing s
 image becomes \f$M/2 \times N/2\f$ image. So area reduces to one-fourth of original area. It is called
 an Octave. The same pattern continues as we go upper in pyramid (ie, resolution decreases).
 Similarly while expanding, area becomes 4 times in each level. We can find Gaussian pyramids using
-**cv2.pyrDown()** and **cv2.pyrUp()** functions.
+**cv.pyrDown()** and **cv.pyrUp()** functions.
 @code{.py}
-img = cv2.imread('messi5.jpg')
-lower_reso = cv2.pyrDown(higher_reso)
+img = cv.imread('messi5.jpg')
+lower_reso = cv.pyrDown(higher_reso)
 @endcode
 Below is the 4 levels in an image pyramid.
 
 ![image](images/messipyr.jpg)
 
-Now you can go down the image pyramid with **cv2.pyrUp()** function.
+Now you can go down the image pyramid with **cv.pyrUp()** function.
 @code{.py}
-higher_reso2 = cv2.pyrUp(lower_reso)
+higher_reso2 = cv.pyrUp(lower_reso)
 @endcode
 Remember, higher_reso2 is not equal to higher_reso, because once you decrease the resolution, you
 loose the information. Below image is 3 level down the pyramid created from smallest image in
@@ -79,38 +79,38 @@ blending, Laplacian Pyramids etc. Simply it is done as follows:
 Below is the full code. (For sake of simplicity, each step is done separately which may take more
 memory. You can optimize it if you want so).
 @code{.py}
-import cv2
+import cv2 as cv
 import numpy as np,sys
 
-A = cv2.imread('apple.jpg')
-B = cv2.imread('orange.jpg')
+A = cv.imread('apple.jpg')
+B = cv.imread('orange.jpg')
 
 # generate Gaussian pyramid for A
 G = A.copy()
 gpA = [G]
 for i in xrange(6):
-    G = cv2.pyrDown(G)
+    G = cv.pyrDown(G)
     gpA.append(G)
 
 # generate Gaussian pyramid for B
 G = B.copy()
 gpB = [G]
 for i in xrange(6):
-    G = cv2.pyrDown(G)
+    G = cv.pyrDown(G)
     gpB.append(G)
 
 # generate Laplacian Pyramid for A
 lpA = [gpA[5]]
 for i in xrange(5,0,-1):
-    GE = cv2.pyrUp(gpA[i])
-    L = cv2.subtract(gpA[i-1],GE)
+    GE = cv.pyrUp(gpA[i])
+    L = cv.subtract(gpA[i-1],GE)
     lpA.append(L)
 
 # generate Laplacian Pyramid for B
 lpB = [gpB[5]]
 for i in xrange(5,0,-1):
-    GE = cv2.pyrUp(gpB[i])
-    L = cv2.subtract(gpB[i-1],GE)
+    GE = cv.pyrUp(gpB[i])
+    L = cv.subtract(gpB[i-1],GE)
     lpB.append(L)
 
 # Now add left and right halves of images in each level
@@ -123,14 +123,14 @@ for la,lb in zip(lpA,lpB):
 # now reconstruct
 ls_ = LS[0]
 for i in xrange(1,6):
-    ls_ = cv2.pyrUp(ls_)
-    ls_ = cv2.add(ls_, LS[i])
+    ls_ = cv.pyrUp(ls_)
+    ls_ = cv.add(ls_, LS[i])
 
 # image with direct connecting each half
 real = np.hstack((A[:,:cols/2],B[:,cols/2:]))
 
-cv2.imwrite('Pyramid_blending2.jpg',ls_)
-cv2.imwrite('Direct_blending.jpg',real)
+cv.imwrite('Pyramid_blending2.jpg',ls_)
+cv.imwrite('Direct_blending.jpg',real)
 @endcode
 Additional Resources
 --------------------