Merge pull request #5548 from berak:patch-2

doc/py_tutorials/py_feature2d/py_brief/py_brief.markdown

@@ -48,6 +48,8 @@ BRIEF in OpenCV
 
 Below code shows the computation of BRIEF descriptors with the help of CenSurE detector. (CenSurE
 detector is called STAR detector in OpenCV)
+
+note, that you need [opencv contrib](https://github.com/Itseez/opencv_contrib)) to use this.
 @code{.py}
 import numpy as np
 import cv2
@@ -55,11 +57,11 @@ from matplotlib import pyplot as plt
 
 img = cv2.imread('simple.jpg',0)
 
-# Initiate STAR detector
+# Initiate FAST detector
-star = cv2.FeatureDetector_create("STAR")
+star = cv2.xfeatures2d.StarDetector_create()
 
 # Initiate BRIEF extractor
-brief = cv2.DescriptorExtractor_create("BRIEF")
+brief = cv2.BriefDescriptorExtractor_create()
 
 # find the keypoints with STAR
 kp = star.detect(img,None)

doc/py_tutorials/py_feature2d/py_fast/py_fast.markdown

@@ -101,7 +101,7 @@ from matplotlib import pyplot as plt
 img = cv2.imread('simple.jpg',0)
 
 # Initiate FAST object with default values
-fast = cv2.FastFeatureDetector()
+fast = cv2.FastFeatureDetector_create()
 
 # find and draw the keypoints
 kp = fast.detect(img,None)

doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown

@@ -44,7 +44,7 @@ img1 = cv2.imread('box.png',0)          # queryImage
 img2 = cv2.imread('box_in_scene.png',0) # trainImage
 
 # Initiate SIFT detector
-sift = cv2.SIFT()
+sift = cv2.xfeatures2d.SIFT_create()
 
 # find the keypoints and descriptors with SIFT
 kp1, des1 = sift.detectAndCompute(img1,None)
@@ -78,7 +78,7 @@ if len(good)>MIN_MATCH_COUNT:
     M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
     matchesMask = mask.ravel().tolist()
 
-    h,w = img1.shape
+    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)
 

doc/py_tutorials/py_feature2d/py_orb/py_orb.markdown

@@ -69,8 +69,8 @@ from matplotlib import pyplot as plt
 
 img = cv2.imread('simple.jpg',0)
 
-# Initiate STAR detector
+# Initiate ORB detector
-orb = cv2.ORB()
+orb = cv2.ORB_create()
 
 # find the keypoints with ORB
 kp = orb.detect(img,None)

doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown

@@ -104,7 +104,7 @@ greater than 0.8, they are rejected. It eliminaters around 90% of false matches
 
 So this is a summary of SIFT algorithm. For more details and understanding, reading the original
 paper is highly recommended. Remember one thing, this algorithm is patented. So this algorithm is
-included in Non-free module in OpenCV.
+included in [the opencv contrib repo](https://github.com/Itseez/opencv_contrib)
 
 SIFT in OpenCV
 --------------
@@ -119,7 +119,7 @@ import numpy as np
 img = cv2.imread('home.jpg')
 gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
 
-sift = cv2.SIFT()
+sift = cv2.xfeatures2d.SIFT_create()
 kp = sift.detect(gray,None)
 
 img=cv2.drawKeypoints(gray,kp)
@@ -151,7 +151,7 @@ Now to calculate the descriptor, OpenCV provides two methods.
 
 We will see the second method:
 @code{.py}
-sift = cv2.SIFT()
+sift = cv2.xfeatures2d.SIFT_create()
 kp, des = sift.detectAndCompute(gray,None)
 @endcode
 Here kp will be a list of keypoints and des is a numpy array of shape

doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.markdown

@@ -80,7 +80,7 @@ examples are shown in Python terminal since it is just same as SIFT only.
 
 # Create SURF object. You can specify params here or later.
 # Here I set Hessian Threshold to 400
->>> surf = cv2.SURF(400)
+>>> surf = cv2.xfeatures2d.SURF_create(400)
 
 # Find keypoints and descriptors directly
 >>> kp, des = surf.detectAndCompute(img,None)
@@ -92,12 +92,12 @@ examples are shown in Python terminal since it is just same as SIFT only.
 While matching, we may need all those features, but not now. So we increase the Hessian Threshold.
 @code{.py}
 # Check present Hessian threshold
->>> print surf.hessianThreshold
+>>> print surf.getHessianThreshold()
 400.0
 
 # We set it to some 50000. Remember, it is just for representing in picture.
 # In actual cases, it is better to have a value 300-500
->>> surf.hessianThreshold = 50000
+>>> surf.setHessianThreshold(50000)
 
 # Again compute keypoints and check its number.
 >>> kp, des = surf.detectAndCompute(img,None)
@@ -119,10 +119,10 @@ on wings of butterfly. You can test it with other images.
 Now I want to apply U-SURF, so that it won't find the orientation.
 @code{.py}
 # Check upright flag, if it False, set it to True
->>> print surf.upright
+>>> print surf.getUpright()
 False
 
->>> surf.upright = True
+>>> surf.setUpright(True)
 
 # Recompute the feature points and draw it
 >>> kp = surf.detect(img,None)
@@ -143,7 +143,7 @@ Finally we check the descriptor size and change it to 128 if it is only 64-dim.
 64
 
 # That means flag, "extended" is False.
->>> surf.extended
+>>> surf.getExtended()
  False
 
 # So we make it to True to get 128-dim descriptors.