change links from samples/python2 to samples/python

modules/calib3d/include/opencv2/calib3d.hpp

@@ -175,7 +175,7 @@ pattern (every view is described by several 3D-2D point correspondences).
     -   A calibration example on stereo matching can be found at
         opencv_source_code/samples/cpp/stereo_match.cpp
     -   (Python) A camera calibration sample can be found at
-        opencv_source_code/samples/python2/calibrate.py
+        opencv_source_code/samples/python/calibrate.py
 
   @{
     @defgroup calib3d_fisheye Fisheye camera model
@@ -553,7 +553,7 @@ projections, as well as the camera matrix and the distortion coefficients.
 
 @note
    -   An example of how to use solvePnP for planar augmented reality can be found at
-        opencv_source_code/samples/python2/plane_ar.py
+        opencv_source_code/samples/python/plane_ar.py
    -   If you are using Python:
         - Numpy array slices won't work as input because solvePnP requires contiguous
         arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
@@ -1674,7 +1674,7 @@ check, quadratic interpolation and speckle filtering).
 
 @note
    -   (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found
-        at opencv_source_code/samples/python2/stereo_match.py
+        at opencv_source_code/samples/python/stereo_match.py
  */
 class CV_EXPORTS_W StereoSGBM : public StereoMatcher
 {

modules/core/include/opencv2/core.hpp

@@ -2035,9 +2035,9 @@ so you need to "flip" the second convolution operand B vertically and horizontal
 -   An example using the discrete fourier transform can be found at
     opencv_source_code/samples/cpp/dft.cpp
 -   (Python) An example using the dft functionality to perform Wiener deconvolution can be found
-    at opencv_source/samples/python2/deconvolution.py
+    at opencv_source/samples/python/deconvolution.py
 -   (Python) An example rearranging the quadrants of a Fourier image can be found at
-    opencv_source/samples/python2/dft.py
+    opencv_source/samples/python/dft.py
 @param src input array that could be real or complex.
 @param dst output array whose size and type depends on the flags .
 @param flags transformation flags, representing a combination of the cv::DftFlags
@@ -2848,7 +2848,7 @@ and groups the input samples around the clusters. As an output, \f$\texttt{label
 
 @note
 -   (Python) An example on K-means clustering can be found at
-    opencv_source_code/samples/python2/kmeans.py
+    opencv_source_code/samples/python/kmeans.py
 @param data Data for clustering. An array of N-Dimensional points with float coordinates is needed.
 Examples of this array can be:
 -   Mat points(count, 2, CV_32F);

modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp

@@ -73,7 +73,7 @@ namespace cv { namespace cuda {
     -   A CUDA example applying the HOG descriptor for people detection can be found at
         opencv_source_code/samples/gpu/hog.cpp
     -   (Python) An example applying the HOG descriptor for people detection can be found at
-        opencv_source_code/samples/python2/peopledetect.py
+        opencv_source_code/samples/python/peopledetect.py
  */
 class CV_EXPORTS HOG : public Algorithm
 {

modules/features2d/include/opencv2/features2d.hpp

@@ -74,7 +74,7 @@ This section describes approaches based on local 2D features and used to categor
    -   A complete Bag-Of-Words sample can be found at
         opencv_source_code/samples/cpp/bagofwords_classification.cpp
     -   (Python) An example using the features2D framework to perform object categorization can be
-        found at opencv_source_code/samples/python2/find_obj.py
+        found at opencv_source_code/samples/python/find_obj.py
 
   @}
  */
@@ -331,7 +331,7 @@ than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz lap
 than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source
 code which is distributed under GPL.
 
-- (Python) A complete example showing the use of the %MSER detector can be found at samples/python2/mser.py
+- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py
 */
 class CV_EXPORTS_W MSER : public Feature2D
 {

modules/objdetect/include/opencv2/objdetect.hpp

@@ -261,7 +261,7 @@ public:
 
     @note
        -   (Python) A face detection example using cascade classifiers can be found at
-            opencv_source_code/samples/python2/facedetect.py
+            opencv_source_code/samples/python/facedetect.py
     */
     CV_WRAP void detectMultiScale( InputArray image,
                           CV_OUT std::vector<Rect>& objects,

modules/photo/include/opencv2/photo.hpp

@@ -107,7 +107,7 @@ objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting
    -   An example using the inpainting technique can be found at
         opencv_source_code/samples/cpp/inpaint.cpp
     -   (Python) An example using the inpainting technique can be found at
-        opencv_source_code/samples/python2/inpaint.py
+        opencv_source_code/samples/python/inpaint.py
  */
 CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
         OutputArray dst, double inpaintRadius, int flags );

modules/video/include/opencv2/video/tracking.hpp

@@ -74,7 +74,7 @@ See the OpenCV sample camshiftdemo.c that tracks colored objects.
 
 @note
 -   (Python) A sample explaining the camshift tracking algorithm can be found at
-    opencv_source_code/samples/python2/camshift.py
+    opencv_source_code/samples/python/camshift.py
  */
 CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_IN_OUT Rect& window,
                                    TermCriteria criteria );
@@ -166,9 +166,9 @@ The function implements a sparse iterative version of the Lucas-Kanade optical f
 -   An example using the Lucas-Kanade optical flow algorithm can be found at
     opencv_source_code/samples/cpp/lkdemo.cpp
 -   (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
-    opencv_source_code/samples/python2/lk_track.py
+    opencv_source_code/samples/python/lk_track.py
 -   (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
-    opencv_source_code/samples/python2/lk_homography.py
+    opencv_source_code/samples/python/lk_homography.py
  */
 CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg,
                                         InputArray prevPts, InputOutputArray nextPts,
@@ -213,7 +213,7 @@ The function finds an optical flow for each prev pixel using the @cite Farneback
 -   An example using the optical flow algorithm described by Gunnar Farneback can be found at
     opencv_source_code/samples/cpp/fback.cpp
 -   (Python) An example using the optical flow algorithm described by Gunnar Farneback can be
-    found at opencv_source_code/samples/python2/opt_flow.py
+    found at opencv_source_code/samples/python/opt_flow.py
  */
 CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next, InputOutputArray flow,
                                             double pyr_scale, int levels, int winsize,

modules/videoio/include/opencv2/videoio.hpp

@@ -380,11 +380,11 @@ class can be used: :
 -   Another basic video processing sample can be found at
     opencv_source_code/samples/cpp/video_dmtx.cpp
 -   (Python) A basic sample on using the VideoCapture interface can be found at
-    opencv_source_code/samples/python2/video.py
+    opencv_source_code/samples/python/video.py
 -   (Python) Another basic video processing sample can be found at
-    opencv_source_code/samples/python2/video_dmtx.py
+    opencv_source_code/samples/python/video_dmtx.py
 -   (Python) A multi threaded video processing sample can be found at
-    opencv_source_code/samples/python2/video_threaded.py
+    opencv_source_code/samples/python/video_threaded.py
  */
 class CV_EXPORTS_W VideoCapture
 {