change links from samples/python2 to samples/python

doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/py_2d_histogram.markdown

@@ -16,8 +16,7 @@ intensity value of the pixel. But in two-dimensional histograms, you consider tw
 it is used for finding color histograms where two features are Hue & Saturation values of every
 pixel.
 
-There is a [python sample in the official
-samples](https://github.com/Itseez/opencv/blob/master/samples/python2/color_histogram.py) already
+There is a python sample (samples/python/color_histogram.py) already
 for finding color histograms. We will try to understand how to create such a color histogram, and it
 will be useful in understanding further topics like Histogram Back-Projection.
 
@@ -106,10 +105,11 @@ You can verify it with any image editing tools like GIMP.
 
 ### Method 3 : OpenCV sample style !!
 
-There is a [sample code for color-histogram in OpenCV-Python2
-samples](https://github.com/Itseez/opencv/blob/master/samples/python2/color_histogram.py). If you
-run the code, you can see the histogram shows the corresponding color also. Or simply it outputs a
-color coded histogram. Its result is very good (although you need to add extra bunch of lines).
+There is a sample code for color-histogram in OpenCV-Python2 samples
+(samples/python/color_histogram.py).
+If you run the code, you can see the histogram shows the corresponding color also.
+Or simply it outputs a color coded histogram.
+Its result is very good (although you need to add extra bunch of lines).
 
 In that code, the author created a color map in HSV. Then converted it into BGR. The resulting
 histogram image is multiplied with this color map. He also uses some preprocessing steps to remove

doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown

@@ -155,8 +155,8 @@ should be due to the sky)
 
 Well, here you adjust the values of histograms along with its bin values to look like x,y
 coordinates so that you can draw it using cv2.line() or cv2.polyline() function to generate same
-image as above. This is already available with OpenCV-Python2 official samples. [Check the
-Code](https://github.com/Itseez/opencv/raw/master/samples/python2/hist.py)
+image as above. This is already available with OpenCV-Python2 official samples. Check the
+code at samples/python/hist.py.
 
 Application of Mask
 -------------------

doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown

@@ -13,7 +13,7 @@ OCR of Hand-written Digits
 
 Our goal is to build an application which can read the handwritten digits. For this we need some
 train_data and test_data. OpenCV comes with an image digits.png (in the folder
-opencv/samples/python2/data/) which has 5000 handwritten digits (500 for each digit). Each digit is
+opencv/samples/data/) which has 5000 handwritten digits (500 for each digit). Each digit is
 a 20x20 image. So our first step is to split this image into 5000 different digits. For each digit,
 we flatten it into a single row with 400 pixels. That is our feature set, ie intensity values of all
 pixels. It is the simplest feature set we can create. We use first 250 samples of each digit as

doc/py_tutorials/py_photo/py_inpainting/py_inpainting.markdown

@@ -81,7 +81,7 @@ Additional Resources
 Exercises
 ---------
 
--#  OpenCV comes with an interactive sample on inpainting, samples/python2/inpaint.py, try it.
+-#  OpenCV comes with an interactive sample on inpainting, samples/python/inpaint.py, try it.
 2.  A few months ago, I watched a video on [Content-Aware
     Fill](http://www.youtube.com/watch?v=ZtoUiplKa2A), an advanced inpainting technique used in
     Adobe Photoshop. On further search, I was able to find that same technique is already there in

doc/py_tutorials/py_video/py_lucas_kanade/py_lucas_kanade.markdown

@@ -156,7 +156,7 @@ in image, there is a chance that optical flow finds the next point which may loo
 actually for a robust tracking, corner points should be detected in particular intervals. OpenCV
 samples comes up with such a sample which finds the feature points at every 5 frames. It also run a
 backward-check of the optical flow points got to select only good ones. Check
-samples/python2/lk_track.py).
+samples/python/lk_track.py).
 
 See the results we got:
 
@@ -213,7 +213,7 @@ See the result below:
 ![image](images/opticalfb.jpg)
 
 OpenCV comes with a more advanced sample on dense optical flow, please see
-samples/python2/opt_flow.py.
+samples/python/opt_flow.py.
 
 Additional Resources
 --------------------
@@ -221,5 +221,5 @@ Additional Resources
 Exercises
 ---------
 
--#  Check the code in samples/python2/lk_track.py. Try to understand the code.
-2.  Check the code in samples/python2/opt_flow.py. Try to understand the code.
+-#  Check the code in samples/python/lk_track.py. Try to understand the code.
+2.  Check the code in samples/python/opt_flow.py. Try to understand the code.