From ef436b2569703676bcc8117933f35861b29856e6 Mon Sep 17 00:00:00 2001
From: arrichter <arrichter@users.noreply.github.com>
Date: Wed, 30 Sep 2020 15:38:29 +0200
Subject: [PATCH] Update py_matcher.rst

---
 .../py_feature2d/py_matcher/py_matcher.rst    | 52 ++++++++++---------
 1 file changed, 28 insertions(+), 24 deletions(-)

diff --git a/source/py_tutorials/py_feature2d/py_matcher/py_matcher.rst b/source/py_tutorials/py_feature2d/py_matcher/py_matcher.rst
index b81130b..5b99297 100644
--- a/source/py_tutorials/py_feature2d/py_matcher/py_matcher.rst
+++ b/source/py_tutorials/py_feature2d/py_matcher/py_matcher.rst
@@ -151,46 +151,50 @@ Second dictionary is the SearchParams. It specifies the number of times the tree
 With these informations, we are good to go. 
 ::
 
-    import numpy as np
-    import cv2
-    from matplotlib import pyplot as plt
+        import cv2 as cv
+    import matplotlib.pyplot as plt
 
-    img1 = cv2.imread('box.png',0)          # queryImage
-    img2 = cv2.imread('box_in_scene.png',0) # trainImage
+    img1 = cv.imread('L.png', 0)  # queryImage
+    img2 = cv.imread('R.png', 0)  # trainImage
 
     # Initiate SIFT detector
-    sift = cv2.SIFT()
+    sift = cv.SIFT_create()
 
-    # find the keypoints and descriptors with SIFT
-    kp1, des1 = sift.detectAndCompute(img1,None)
-    kp2, des2 = sift.detectAndCompute(img2,None)
+    # Find the keypoints and descriptors with SIFT
+    kp1, des1 = sift.detectAndCompute(img1, None)
+    kp2, des2 = sift.detectAndCompute(img2, None)
 
     # FLANN parameters
-    FLANN_INDEX_KDTREE = 0
-    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
+    FLANN_INDEX_KDTREE = 1
+    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
     search_params = dict(checks=50)   # or pass empty dictionary
 
-    flann = cv2.FlannBasedMatcher(index_params,search_params)
+    # Create Flann object
+    flann = cv.FlannBasedMatcher(index_params, search_params)
 
-    matches = flann.knnMatch(des1,des2,k=2)
+    # Match descriptors
+    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):
+    # Ratio test as per Lowe's paper
+    for i, (m, n) in enumerate(matches):
         if m.distance < 0.7*n.distance:
-            matchesMask[i]=[1,0]
+            matchesMask[i] = [1, 0]
 
-    draw_params = dict(matchColor = (0,255,0),
-                       singlePointColor = (255,0,0),
-                       matchesMask = matchesMask,
-                       flags = 0)       
-                        
-    img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)
+    # Draw only the matches that fit our mask
+    draw_params = dict(matchColor=(0, 255, 0),
+                    singlePointColor=(255, 0, 0),
+                    matchesMask=matchesMask,
+                    flags=cv.DrawMatchesFlags_DEFAULT)
 
-    plt.imshow(img3,),plt.show()  
+    # Draw the good matches
+    img3 = cv.drawMatchesKnn(img1, kp1, img2, kp2, matches, None, **draw_params)
 
+    plt.imshow(img3)
+    plt.axis('off')
+    plt.show() 
 
 See the result below: