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

doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown

@@ -16,15 +16,15 @@ another trainImage, found the features in that image too and we found the best m
 In short, we found locations of some parts of an object in another cluttered image. This information
 is sufficient to find the object exactly on the trainImage.
 
-For that, we can use a function from calib3d module, ie **cv2.findHomography()**. If we pass the set
+For that, we can use a function from calib3d module, ie **cv.findHomography()**. If we pass the set
 of points from both the images, it will find the perpective transformation of that object. Then we
-can use **cv2.perspectiveTransform()** to find the object. It needs atleast four correct points to
+can use **cv.perspectiveTransform()** to find the object. It needs atleast four correct points to
 find the transformation.
 
 We have seen that there can be some possible errors while matching which may affect the result. To
 solve this problem, algorithm uses RANSAC or LEAST_MEDIAN (which can be decided by the flags). So
 good matches which provide correct estimation are called inliers and remaining are called outliers.
-**cv2.findHomography()** returns a mask which specifies the inlier and outlier points.
+**cv.findHomography()** returns a mask which specifies the inlier and outlier points.
 
 So let's do it !!!
 
@@ -35,16 +35,16 @@ First, as usual, let's find SIFT features in images and apply the ratio test to
 matches.
 @code{.py}
 import numpy as np
-import cv2
+import cv2 as cv
 from matplotlib import pyplot as plt
 
 MIN_MATCH_COUNT = 10
 
-img1 = cv2.imread('box.png',0)          # queryImage
-img2 = cv2.imread('box_in_scene.png',0) # trainImage
+img1 = cv.imread('box.png',0)          # queryImage
+img2 = cv.imread('box_in_scene.png',0) # trainImage
 
 # Initiate SIFT detector
-sift = cv2.xfeatures2d.SIFT_create()
+sift = cv.xfeatures2d.SIFT_create()
 
 # find the keypoints and descriptors with SIFT
 kp1, des1 = sift.detectAndCompute(img1,None)
@@ -54,7 +54,7 @@ FLANN_INDEX_KDTREE = 1
 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
 search_params = dict(checks = 50)
 
-flann = cv2.FlannBasedMatcher(index_params, search_params)
+flann = cv.FlannBasedMatcher(index_params, search_params)
 
 matches = flann.knnMatch(des1,des2,k=2)
 
@@ -75,14 +75,14 @@ if len(good)>MIN_MATCH_COUNT:
     src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
     dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
 
-    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
+    M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0)
     matchesMask = mask.ravel().tolist()
 
     h,w,d = img1.shape
     pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
-    dst = cv2.perspectiveTransform(pts,M)
+    dst = cv.perspectiveTransform(pts,M)
 
-    img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)
+    img2 = cv.polylines(img2,[np.int32(dst)],True,255,3, cv.LINE_AA)
 
 else:
     print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
@@ -95,7 +95,7 @@ draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                    matchesMask = matchesMask, # draw only inliers
                    flags = 2)
 
-img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
+img3 = cv.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
 
 plt.imshow(img3, 'gray'),plt.show()
 @endcode