python tutorials: removed remainders of former Algorithm interface

doc/py_tutorials/py_feature2d/py_brief/py_brief.markdown

@@ -61,7 +61,7 @@ img = cv2.imread('simple.jpg',0)
 star = cv2.xfeatures2d.StarDetector_create()
 
 # Initiate BRIEF extractor
-brief = cv2.BriefDescriptorExtractor_create()
+brief = cv2.xfeatures2d.BriefDescriptorExtractor_create()
 
 # find the keypoints with STAR
 kp = star.detect(img,None)
@@ -69,10 +69,10 @@ kp = star.detect(img,None)
 # compute the descriptors with BRIEF
 kp, des = brief.compute(img, kp)
 
-print brief.getInt('bytes')
+print brief.descriptorSize()
 print des.shape
 @endcode
-The function brief.getInt('bytes') gives the \f$n_d\f$ size used in bytes. By default it is 32. Next one
+The function brief.getDescriptorSize() gives the \f$n_d\f$ size used in bytes. By default it is 32. Next one
 is matching, which will be done in another chapter.
 
 Additional Resources

doc/py_tutorials/py_feature2d/py_fast/py_fast.markdown

@@ -108,15 +108,15 @@ kp = fast.detect(img,None)
 img2 = cv2.drawKeypoints(img, kp, None, color=(255,0,0))
 
 # Print all default params
-print "Threshold: ", fast.getInt('threshold')
-print "nonmaxSuppression: ", fast.getBool('nonmaxSuppression')
-print "neighborhood: ", fast.getInt('type')
+print "Threshold: ", fast.getThreshold()
+print "nonmaxSuppression: ", fast.getNonmaxSuppression()
+print "neighborhood: ", fast.getType()
 print "Total Keypoints with nonmaxSuppression: ", len(kp)
 
 cv2.imwrite('fast_true.png',img2)
 
 # Disable nonmaxSuppression
-fast.setBool('nonmaxSuppression',0)
+fast.setNonmaxSuppression(0)
 kp = fast.detect(img,None)
 
 print "Total Keypoints without nonmaxSuppression: ", len(kp)