Doxygen tutorials: python final edits

doc/py_tutorials/py_feature2d/py_brief/py_brief.markdown

@@ -5,7 +5,7 @@ Goal
 ----
 
 In this chapter
-   -   We will see the basics of BRIEF algorithm
+    -   We will see the basics of BRIEF algorithm
 
 Theory
 ------

doc/py_tutorials/py_feature2d/py_fast/py_fast.markdown

@@ -5,7 +5,7 @@ Goal
 ----
 
 In this chapter,
-   -   We will understand the basics of FAST algorithm
+    -   We will understand the basics of FAST algorithm
     -   We will find corners using OpenCV functionalities for FAST algorithm.
 
 Theory

doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown

@@ -5,7 +5,7 @@ Goal
 ----
 
 In this chapter,
-   -   We will mix up the feature matching and findHomography from calib3d module to find known
+    -   We will mix up the feature matching and findHomography from calib3d module to find known
         objects in a complex image.
 
 Basics

doc/py_tutorials/py_feature2d/py_features_harris/py_features_harris.markdown

@@ -43,7 +43,7 @@ determine if a window can contain a corner or not.
 \f[R = det(M) - k(trace(M))^2\f]
 
 where
-   -   \f$det(M) = \lambda_1 \lambda_2\f$
+    -   \f$det(M) = \lambda_1 \lambda_2\f$
     -   \f$trace(M) = \lambda_1 + \lambda_2\f$
     -   \f$\lambda_1\f$ and \f$\lambda_2\f$ are the eigen values of M
 

doc/py_tutorials/py_feature2d/py_matcher/py_matcher.markdown

@@ -5,7 +5,7 @@ Goal
 ----
 
 In this chapter
-   -   We will see how to match features in one image with others.
+    -   We will see how to match features in one image with others.
     -   We will use the Brute-Force matcher and FLANN Matcher in OpenCV
 
 Basics of Brute-Force Matcher

doc/py_tutorials/py_feature2d/py_orb/py_orb.markdown

@@ -5,7 +5,7 @@ Goal
 ----
 
 In this chapter,
-   -   We will see the basics of ORB
+    -   We will see the basics of ORB
 
 Theory
 ------

doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown

@@ -5,7 +5,7 @@ Goal
 ----
 
 In this chapter,
-   -   We will learn about the concepts of SIFT algorithm
+    -   We will learn about the concepts of SIFT algorithm
     -   We will learn to find SIFT Keypoints and Descriptors.
 
 Theory
@@ -155,7 +155,7 @@ sift = cv2.SIFT()
 kp, des = sift.detectAndCompute(gray,None)
 @endcode
 Here kp will be a list of keypoints and des is a numpy array of shape
-\f$Number_of_Keypoints \times 128\f$.
+\f$Number\_of\_Keypoints \times 128\f$.
 
 So we got keypoints, descriptors etc. Now we want to see how to match keypoints in different images.
 That we will learn in coming chapters.

doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.markdown

@@ -5,7 +5,7 @@ Goal
 ----
 
 In this chapter,
-   -   We will see the basics of SURF
+    -   We will see the basics of SURF
     -   We will see SURF functionalities in OpenCV
 
 Theory
@@ -76,40 +76,40 @@ and descriptors.
 First we will see a simple demo on how to find SURF keypoints and descriptors and draw it. All
 examples are shown in Python terminal since it is just same as SIFT only.
 @code{.py}
-img = cv2.imread('fly.png',0)
+>>> img = cv2.imread('fly.png',0)
 
 # Create SURF object. You can specify params here or later.
 # Here I set Hessian Threshold to 400
-surf = cv2.SURF(400)
+>>> surf = cv2.SURF(400)
 
 # Find keypoints and descriptors directly
-kp, des = surf.detectAndCompute(img,None)
+>>> kp, des = surf.detectAndCompute(img,None)
 
-len(kp)
+>>> len(kp)
  699
 @endcode
 1199 keypoints is too much to show in a picture. We reduce it to some 50 to draw it on an image.
 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.hessianThreshold
 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.hessianThreshold = 50000
 
 # Again compute keypoints and check its number.
-kp, des = surf.detectAndCompute(img,None)
+>>> kp, des = surf.detectAndCompute(img,None)
 
-print len(kp)
+>>> print len(kp)
 47
 @endcode
 It is less than 50. Let's draw it on the image.
 @code{.py}
-img2 = cv2.drawKeypoints(img,kp,None,(255,0,0),4)
+>>> img2 = cv2.drawKeypoints(img,kp,None,(255,0,0),4)
 
-plt.imshow(img2),plt.show()
+>>> plt.imshow(img2),plt.show()
 @endcode
 See the result below. You can see that SURF is more like a blob detector. It detects the white blobs
 on wings of butterfly. You can test it with other images.
@@ -119,16 +119,16 @@ 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.upright
 False
 
-surf.upright = True
+>>> surf.upright = True
 
 # Recompute the feature points and draw it
-kp = surf.detect(img,None)
-img2 = cv2.drawKeypoints(img,kp,None,(255,0,0),4)
+>>> kp = surf.detect(img,None)
+>>> img2 = cv2.drawKeypoints(img,kp,None,(255,0,0),4)
 
-plt.imshow(img2),plt.show()
+>>> plt.imshow(img2),plt.show()
 @endcode
 See the results below. All the orientations are shown in same direction. It is more faster than
 previous. If you are working on cases where orientation is not a problem (like panorama stitching)
@@ -139,19 +139,19 @@ etc, this is better.
 Finally we check the descriptor size and change it to 128 if it is only 64-dim.
 @code{.py}
 # Find size of descriptor
-print surf.descriptorSize()
+>>> print surf.descriptorSize()
 64
 
 # That means flag, "extended" is False.
-surf.extended
+>>> surf.extended
  False
 
 # So we make it to True to get 128-dim descriptors.
-surf.extended = True
-kp, des = surf.detectAndCompute(img,None)
-print surf.descriptorSize()
+>>> surf.extended = True
+>>> kp, des = surf.detectAndCompute(img,None)
+>>> print surf.descriptorSize()
 128
-print des.shape
+>>> print des.shape
 (47, 128)
 @endcode
 Remaining part is matching which we will do in another chapter.