Merge pull request #13616 from atinfinity:fixed-py_matcher-tutorial

* fixed tutorial code of py_matcher

* fixed imread mode

doc/py_tutorials/py_feature2d/py_matcher/py_matcher.markdown

@@ -53,8 +53,8 @@ import numpy as np
 import cv2 as cv
 import matplotlib.pyplot as plt
 
-img1 = cv.imread('box.png',0)          # queryImage
-img2 = cv.imread('box_in_scene.png',0) # trainImage
+img1 = cv.imread('box.png',cv.IMREAD_GRAYSCALE)          # queryImage
+img2 = cv.imread('box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
 
 # Initiate ORB detector
 orb = cv.ORB_create()
@@ -79,7 +79,7 @@ matches = bf.match(des1,des2)
 matches = sorted(matches, key = lambda x:x.distance)
 
 # Draw first 10 matches.
-img3 = cv.drawMatches(img1,kp1,img2,kp2,matches[:10], flags=2)
+img3 = cv.drawMatches(img1,kp1,img2,kp2,matches[:10],None,flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
 
 plt.imshow(img3),plt.show()
 @endcode
@@ -104,13 +104,13 @@ so that we can apply ratio test explained by D.Lowe in his paper.
 @code{.py}
 import numpy as np
 import cv2 as cv
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
 
-img1 = cv.imread('box.png',0)          # queryImage
-img2 = cv.imread('box_in_scene.png',0) # trainImage
+img1 = cv.imread('box.png',cv.IMREAD_GRAYSCALE)          # queryImage
+img2 = cv.imread('box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
 
 # Initiate SIFT detector
-sift = cv.SIFT()
+sift = cv.xfeatures2d.SIFT_create()
 
 # find the keypoints and descriptors with SIFT
 kp1, des1 = sift.detectAndCompute(img1,None)
@@ -118,7 +118,7 @@ kp2, des2 = sift.detectAndCompute(img2,None)
 
 # BFMatcher with default params
 bf = cv.BFMatcher()
-matches = bf.knnMatch(des1,des2, k=2)
+matches = bf.knnMatch(des1,des2,k=2)
 
 # Apply ratio test
 good = []
@@ -127,7 +127,7 @@ for m,n in matches:
         good.append([m])
 
 # cv.drawMatchesKnn expects list of lists as matches.
-img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,good,flags=2)
+img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
 
 plt.imshow(img3),plt.show()
 @endcode
@@ -168,13 +168,13 @@ With this information, we are good to go.
 @code{.py}
 import numpy as np
 import cv2 as cv
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
 
-img1 = cv.imread('box.png',0)          # queryImage
-img2 = cv.imread('box_in_scene.png',0) # trainImage
+img1 = cv.imread('box.png',cv.IMREAD_GRAYSCALE)          # queryImage
+img2 = cv.imread('box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
 
 # Initiate SIFT detector
-sift = cv.SIFT()
+sift = cv.xfeatures2d.SIFT_create()
 
 # find the keypoints and descriptors with SIFT
 kp1, des1 = sift.detectAndCompute(img1,None)
@@ -190,7 +190,7 @@ flann = cv.FlannBasedMatcher(index_params,search_params)
 matches = flann.knnMatch(des1,des2,k=2)
 
 # Need to draw only good matches, so create a mask
-matchesMask = [[0,0] for i in xrange(len(matches))]
+matchesMask = [[0,0] for i in range(len(matches))]
 
 # ratio test as per Lowe's paper
 for i,(m,n) in enumerate(matches):
@@ -200,7 +200,7 @@ for i,(m,n) in enumerate(matches):
 draw_params = dict(matchColor = (0,255,0),
                    singlePointColor = (255,0,0),
                    matchesMask = matchesMask,
-                   flags = 0)
+                   flags = cv.DrawMatchesFlags_DEFAULT)
 
 img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)