GSoC 2016 - Adding ALIASES for tutorial (#7041)

* GSoC 2016 - Adding toggle files to be used by tutorials.

Add a toggle option for tutorials.
* adds a button on the HTML tutorial pages to switch between blocks
* the default option is for languages: one can write a block
for C++ and another one for Python without re-writing the tutorial

Add aliases to the doxyfile.
* adding alises to make a link to previous and next tutorial.
* adding alias to specify the toggle options in the tutorials index.
* adding alias to add a youtube video directly from link.

Add a sample tutorial (mat_mask_opertaions) using the developed aliases:
* youtube alias
* previous and next tutorial alias
* buttons
* languages info for tutorial table of content
* code referances with snippets (and associated sample code files)

* Removing the automatic ordering.
Adding specific toggles for cpp, java and python.
Move all the code to the footer / header and Doxyfile.
Updating documentation.

samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py

@@ -0,0 +1,57 @@
+import time
+import numpy as np
+import cv2
+## [basic_method]
+def sharpen(my_image):
+    my_image = cv2.cvtColor(my_image, cv2.CV_8U)
+
+    height, width, n_channels = my_image.shape
+    result = np.zeros(my_image.shape, my_image.dtype)
+
+    ## [basic_method_loop]
+    for j in range  (1, height-1):
+        for i in range  (1, width-1):
+            for k in range  (0, n_channels):
+                sum = 5 * my_image[j, i, k] - my_image[j + 1, i, k] - my_image[j - 1, i, k]\
+                     - my_image[j, i + 1, k] - my_image[j, i - 1, k];
+
+                if sum > 255:
+                    sum = 255
+                if sum < 0:
+                    sum = 0
+
+                result[j, i, k] = sum
+    ## [basic_method_loop]
+
+    return result
+## [basic_method]
+
+
+I = cv2.imread("../data/lena.jpg")
+cv2.imshow('Input Image', I)
+
+t = round(time.time())
+J = sharpen(I)
+t = (time.time() - t)/1000
+print "Hand written function times passed in seconds: %s" % t
+
+cv2.imshow('Output Image', J)
+
+t = time.time()
+## [kern]
+kernel = np.array([ [0,-1,0],
+                    [-1,5,-1],
+                    [0,-1,0] ],np.float32) # kernel should be floating point type
+## [kern]
+
+## [filter2D]
+K = cv2.filter2D(I, -1, kernel) # ddepth = -1, means destination image has depth same as input image.
+## [filter2D]
+
+t = (time.time() - t)/1000
+print "Built-in filter2D time passed in seconds:      %s" % t
+
+cv2.imshow('filter2D Output Image', K)
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()

samples/python/tutorial_code/introduction/documentation/documentation.py

@@ -0,0 +1,5 @@
+print('Not showing this text because it is outside the snippet')
+
+## [hello_world]
+print('Hello world!')
+## [hello_world]