Like mentioned by Andrei Pavlenko after merging pullrequest #1206, a wrong

ReST directive was used. Also fixed some other ReST directives that were
not correct and removed some warnings during buildbot checks.

modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst

@@ -105,16 +105,16 @@ The functions below use the above model to do the following:
 
  * Estimate the relative position and orientation of the stereo camera "heads" and compute the *rectification* transformation that makes the camera optical axes parallel.
 
-.. Sample code::
+.. note::
 
-   * : A calibration sample for 3 cameras in horizontal position can be found at opencv_source_code/samples/cpp/3calibration.cpp
-   * : A calibration sample based on a sequence of images can be found at opencv_source_code/samples/cpp/calibration.cpp
-   * : A calibration sample in order to do 3D reconstruction can be found at opencv_source_code/samples/cpp/build3dmodel.cpp
-   * : A calibration sample of an artificially generated camera and chessboard patterns can be found at opencv_source_code/samples/cpp/calibration_artificial.cpp
-   * : A calibration example on stereo calibration can be found at opencv_source_code/samples/cpp/stereo_calib.cpp
-   * : A calibration example on stereo matching can be found at opencv_source_code/samples/cpp/stereo_match.cpp
+   * A calibration sample for 3 cameras in horizontal position can be found at opencv_source_code/samples/cpp/3calibration.cpp
+   * A calibration sample based on a sequence of images can be found at opencv_source_code/samples/cpp/calibration.cpp
+   * A calibration sample in order to do 3D reconstruction can be found at opencv_source_code/samples/cpp/build3dmodel.cpp
+   * A calibration sample of an artificially generated camera and chessboard patterns can be found at opencv_source_code/samples/cpp/calibration_artificial.cpp
+   * A calibration example on stereo calibration can be found at opencv_source_code/samples/cpp/stereo_calib.cpp
+   * 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
+   * (Python) A camera calibration sample can be found at opencv_source_code/samples/python2/calibrate.py
 
 calibrateCamera
 ---------------
@@ -588,9 +588,9 @@ Finds an object pose from 3D-2D point correspondences.
 
 The function estimates the object pose given a set of object points, their corresponding image projections, as well as the camera matrix and the distortion coefficients.
 
-.. Sample code::
+.. note::
 
-   * : An example of how to use solvePNP for planar augmented reality can be found at opencv_source_code/samples/python2/plane_ar.py
+   * An example of how to use solvePNP for planar augmented reality can be found at opencv_source_code/samples/python2/plane_ar.py
 
 solvePnPRansac
 ------------------
@@ -777,9 +777,9 @@ Homography matrix is determined up to a scale. Thus, it is normalized so that
     :ocv:func:`warpPerspective`,
     :ocv:func:`perspectiveTransform`
 
-.. Sample code::
+.. note::
 
-   * : A example on calculating a homography for image matching can be found at opencv_source_code/samples/cpp/video_homography.cpp
+   * A example on calculating a homography for image matching can be found at opencv_source_code/samples/cpp/video_homography.cpp
 
 estimateAffine3D
 --------------------
@@ -1088,7 +1088,7 @@ The class is a C++ wrapper for the associated functions. In particular, :ocv:fun
 
 .. Sample code:
 
-   * : OCL : An example for using the stereoBM matching algorithm can be found at opencv_source_code/samples/ocl/stereo_match.cpp   
+   (Ocl) An example for using the stereoBM matching algorithm can be found at opencv_source_code/samples/ocl/stereo_match.cpp
 
 StereoBM::StereoBM
 ------------------
@@ -1188,9 +1188,9 @@ The class implements the modified H. Hirschmuller algorithm [HH08]_ that differs
 
  * Some pre- and post- processing steps from K. Konolige algorithm :ocv:funcx:`StereoBM::operator()`  are included, for example: pre-filtering (``CV_STEREO_BM_XSOBEL`` type) and post-filtering (uniqueness check, quadratic interpolation and speckle filtering).
 
-.. Sample code::
+.. note::
 
-   * : PYTHON : An example illustrating the use of the StereoSGBM matching algorithm can be found at opencv_source_code/samples/python2/stereo_match.py
+   * (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found at opencv_source_code/samples/python2/stereo_match.py
 
 StereoSGBM::StereoSGBM
 --------------------------

modules/contrib/doc/facerec/facerec_api.rst

@@ -5,9 +5,9 @@ FaceRecognizer
 
 .. Sample code::
 
-   * : An example using the FaceRecognizer class can be found at opencv_source_code/samples/cpp/facerec_demo.cpp
+   * An example using the FaceRecognizer class can be found at opencv_source_code/samples/cpp/facerec_demo.cpp
 
-   * : PYTHON :  An example using the FaceRecognizer class can be found at opencv_source_code/samples/python2/facerec_demo.py
+   * (Python)  An example using the FaceRecognizer class can be found at opencv_source_code/samples/python2/facerec_demo.py
 
 FaceRecognizer
 --------------

modules/contrib/doc/openfabmap.rst

@@ -9,9 +9,9 @@ FAB-MAP is an approach to appearance-based place recognition. FAB-MAP compares i
 
 openFABMAP requires training data (e.g. a collection of images from a similar but not identical environment) to construct a visual vocabulary for the visual bag-of-words model, along with a Chow-Liu tree representation of feature likelihood and for use in the Sampled new place method (see below).
 
-.. Sample code::
+.. note::
 
-   * : An example using the openFABMAP package can be found at opencv_source_code/samples/cpp/fabmap_sample.cpp
+   * An example using the openFABMAP package can be found at opencv_source_code/samples/cpp/fabmap_sample.cpp
 
 of2::FabMap
 --------------------

modules/contrib/doc/retina/index.rst

@@ -65,9 +65,9 @@ The retina can be settled up with various parameters, by default, the retina can
 
 .. Sample code::
 
-   * : An example on retina tone mapping can be found at opencv_source_code/samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping.cpp
-   * : An example on retina tone mapping on video input can be found at opencv_source_code/samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping.cpp
-   * : A complete example illustrating the retina interface can be found at opencv_source_code/samples/cpp/retinaDemo.cpp
+   * An example on retina tone mapping can be found at opencv_source_code/samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping.cpp
+   * An example on retina tone mapping on video input can be found at opencv_source_code/samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping.cpp
+   * A complete example illustrating the retina interface can be found at opencv_source_code/samples/cpp/retinaDemo.cpp
 
 Description
 +++++++++++

modules/core/doc/basic_structures.rst

@@ -803,9 +803,9 @@ Finally, there are STL-style iterators that are smart enough to skip gaps betwee
 
 The matrix iterators are random-access iterators, so they can be passed to any STL algorithm, including ``std::sort()`` .
 
-.. Sample code::
+.. note::
 
-   * : An example demonstrating the serial out capabilities of cv::Mat can be found at opencv_source_code/samples/cpp/cout_mat.cpp
+   * An example demonstrating the serial out capabilities of cv::Mat can be found at opencv_source_code/samples/cpp/cout_mat.cpp
 
 .. _MatrixExpressions:
 

modules/core/doc/clustering.rst

@@ -66,11 +66,11 @@ Basically, you can use only the core of the function, set the number of
 attempts to 1, initialize labels each time using a custom algorithm, pass them with the
 ( ``flags`` = ``KMEANS_USE_INITIAL_LABELS`` ) flag, and then choose the best (most-compact) clustering.
 
-.. Sample code::
+.. note::
 
-   * : An example on K-means clustering can be found at opencv_source_code/samples/cpp/kmeans.cpp
+   * An example on K-means clustering can be found at opencv_source_code/samples/cpp/kmeans.cpp
 
-   * : PYTHON : An example on K-means clustering can be found at opencv_source_code/samples/python2/kmeans.py
+   * (Python) An example on K-means clustering can be found at opencv_source_code/samples/python2/kmeans.py
 
 partition
 -------------

modules/core/doc/drawing_functions.rst

@@ -26,9 +26,9 @@ If a drawn figure is partially or completely outside the image, the drawing func
 
 .. note:: The functions do not support alpha-transparency when the target image is 4-channel. In this case, the ``color[3]`` is simply copied to the repainted pixels. Thus, if you want to paint semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main image.
 
-.. Sample code::
+.. note::
 
-   * : An example on using variate drawing functions like line, rectangle, ... can be found at opencv_source_code/samples/cpp/drawing.cpp
+   * An example on using variate drawing functions like line, rectangle, ... can be found at opencv_source_code/samples/cpp/drawing.cpp
 
 circle
 ----------

modules/core/doc/operations_on_arrays.rst

@@ -997,12 +997,12 @@ All of the above improvements have been implemented in :ocv:func:`matchTemplate`
 
 .. seealso:: :ocv:func:`dct` , :ocv:func:`getOptimalDFTSize` , :ocv:func:`mulSpectrums`, :ocv:func:`filter2D` , :ocv:func:`matchTemplate` , :ocv:func:`flip` , :ocv:func:`cartToPolar` , :ocv:func:`magnitude` , :ocv:func:`phase`
 
-.. Sample code::
+.. note::
 
-   * : An example using the discrete fourier transform can be found at opencv_source_code/samples/cpp/dft.cpp
+   * 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
-   * : PYTHON : An example rearranging the quadrants of a Fourier image can be found at opencv_source/samples/python2/dft.py
+   * (Python) An example using the dft functionality to perform Wiener deconvolution can be found at opencv_source/samples/python2/deconvolution.py
+   * (Python) An example rearranging the quadrants of a Fourier image can be found at opencv_source/samples/python2/dft.py
 
 
 divide
@@ -2268,9 +2268,9 @@ The sample below is the function that takes two matrices. The first function sto
     :ocv:func:`dft`,
     :ocv:func:`dct`
 
-.. Sample code::
+.. note::
 
-   * : An example using PCA for dimensionality reduction while maintaining an amount of variance can be found at opencv_source_code/samples/cpp/pca.cpp
+   * An example using PCA for dimensionality reduction while maintaining an amount of variance can be found at opencv_source_code/samples/cpp/pca.cpp
 
 PCA::PCA
 --------

modules/core/doc/xml_yaml_persistence.rst

@@ -91,9 +91,9 @@ Several things can be noted by looking at the sample code and the output:
  *
    In YAML (but not XML), mappings and sequences can be written in a compact Python-like inline form. In the sample above matrix elements, as well as each feature, including its lbp value, is stored in such inline form. To store a mapping/sequence in a compact form, put ":" after the opening character, e.g. use **"{:"** instead of **"{"** and **"[:"** instead of **"["**. When the data is written to XML, those extra ":" are ignored.
 
-.. Sample code::
+.. note::
 
-   * : A complete example using the FileStorage interface can be found at opencv_source_code/samples/cpp/filestorage.cpp
+   * A complete example using the FileStorage interface can be found at opencv_source_code/samples/cpp/filestorage.cpp
 
 
 Reading data from a file storage.

modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst

@@ -9,10 +9,10 @@ represented as vectors in a multidimensional space. All objects that implement t
 descriptor extractors inherit the
 :ocv:class:`DescriptorExtractor` interface.
 
-.. Sample code::
+.. note::
 
-   * : An example explaining keypoint extraction can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
-   * : An example on descriptor evaluation can be found at opencv_source_code/samples/cpp/detector_descriptor_evaluation.cpp
+   * An example explaining keypoint extraction can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
+   * An example on descriptor evaluation can be found at opencv_source_code/samples/cpp/detector_descriptor_evaluation.cpp
 
 DescriptorExtractor
 -------------------
@@ -141,6 +141,6 @@ Strecha C., Fua P. *BRIEF: Binary Robust Independent Elementary Features* ,
         ...
     };
 
-.. Sample code:: 
+.. note::
 
-   * : A complete BRIEF extractor sample can be found at opencv_source_code/samples/cpp/brief_match_test.cpp
+   * A complete BRIEF extractor sample can be found at opencv_source_code/samples/cpp/brief_match_test.cpp

modules/features2d/doc/common_interfaces_of_descriptor_matchers.rst

@@ -9,11 +9,11 @@ that are represented as vectors in a multidimensional space. All objects that im
 descriptor matchers inherit the
 :ocv:class:`DescriptorMatcher` interface.
 
-.. Sample code::
+.. note::
 
-   * : An example explaining keypoint matching can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
-   * : An example on descriptor matching evaluation can be found at opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp
-   * : An example on one to many image matching can be found at opencv_source_code/samples/cpp/matching_to_many_images.cpp
+   * An example explaining keypoint matching can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
+   * An example on descriptor matching evaluation can be found at opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp
+   * An example on one to many image matching can be found at opencv_source_code/samples/cpp/matching_to_many_images.cpp
 
 DMatch
 ------

modules/features2d/doc/common_interfaces_of_feature_detectors.rst

@@ -8,9 +8,9 @@ between different algorithms solving the same problem. All objects that implemen
 inherit the
 :ocv:class:`FeatureDetector` interface.
 
-.. Sample code::
+.. note::
 
-   * : An example explaining keypoint detection can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
+   * An example explaining keypoint detection can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
 
 
 KeyPoint

modules/features2d/doc/common_interfaces_of_generic_descriptor_matchers.rst

@@ -11,11 +11,11 @@ Every descriptor with the
 :ocv:class:`VectorDescriptorMatcher` ).
 There are descriptors such as the One-way descriptor and Ferns that have the ``GenericDescriptorMatcher`` interface implemented but do not support ``DescriptorExtractor``.
 
-.. Sample code::
+.. note::
 
-   * : An example explaining keypoint description can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
-   * : An example on descriptor matching evaluation can be found at opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp
-   * : An example on one to many image matching can be found at opencv_source_code/samples/cpp/matching_to_many_images.cpp
+   * An example explaining keypoint description can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
+   * An example on descriptor matching evaluation can be found at opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp
+   * An example on one to many image matching can be found at opencv_source_code/samples/cpp/matching_to_many_images.cpp
 
 GenericDescriptorMatcher
 ------------------------

modules/features2d/doc/feature_detection_and_description.rst

@@ -3,9 +3,9 @@ Feature Detection and Description
 
 .. highlight:: cpp
 
-.. Sample code::
+.. note::
 
-   * : An example explaining keypoint detection and description can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
+   * An example explaining keypoint detection and description can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
 
 FAST
 ----
@@ -55,9 +55,9 @@ Maximally stable extremal region extractor. ::
 The class encapsulates all the parameters of the MSER extraction algorithm (see
 http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http://code.opencv.org/projects/opencv/wiki/MSER for useful comments and parameters description.
 
-.. Sample code::
+.. note::
 
-   * : PYTHON : A complete example showing the use of the MSER detector can be found at opencv_source_code/samples/python2/mser.py
+   * (Python) A complete example showing the use of the MSER detector can be found at opencv_source_code/samples/python2/mser.py
 
 
 ORB
@@ -166,9 +166,9 @@ Class implementing the FREAK (*Fast Retina Keypoint*) keypoint descriptor, descr
 
 .. [AOV12] A. Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. CVPR 2012 Open Source Award Winner.
 
-.. Sample code::
+.. note::
 
-   * : An example on how to use the FREAK descriptor can be found at opencv_source_code/samples/cpp/freak_demo.cpp
+   * An example on how to use the FREAK descriptor can be found at opencv_source_code/samples/cpp/freak_demo.cpp
 
 FREAK::FREAK
 ------------

modules/features2d/doc/object_categorization.rst

@@ -5,11 +5,11 @@ Object Categorization
 
 This section describes approaches based on local 2D features and used to categorize objects.
 
-.. Sample code:: 
+.. note::
 
-   * : A complete Bag-Of-Words sample can be found at opencv_source_code/samples/cpp/bagofwords_classification.cpp
+   * 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
+   * (Python) An example using the features2D framework to perform object categorization can be found at opencv_source_code/samples/python2/find_obj.py
 
 BOWTrainer
 ----------

modules/gpu/doc/camera_calibration_and_3d_reconstruction.rst

@@ -44,11 +44,11 @@ The class also performs pre- and post-filtering steps: Sobel pre-filtering (if `
 
 This means that the input left image is low textured.
 
-.. Sample code::
+.. note::
 
-   * : A basic stereo matching example can be found at opencv_source_code/samples/gpu/stereo_match.cpp
-   * : A stereo matching example using several GPU's can be found at opencv_source_code/samples/gpu/stereo_multi.cpp
-   * : A stereo matching example using several GPU's and driver API can be found at opencv_source_code/samples/gpu/driver_api_stereo_multi.cpp
+   * A basic stereo matching example can be found at opencv_source_code/samples/gpu/stereo_match.cpp
+   * A stereo matching example using several GPU's can be found at opencv_source_code/samples/gpu/stereo_multi.cpp
+   * A stereo matching example using several GPU's and driver API can be found at opencv_source_code/samples/gpu/driver_api_stereo_multi.cpp
    
 gpu::StereoBM_GPU::StereoBM_GPU
 -----------------------------------

modules/gpu/doc/image_filtering.rst

@@ -5,9 +5,9 @@ Image Filtering
 
 Functions and classes described in this section are used to perform various linear or non-linear filtering operations on 2D images.
 
-.. Sample code::
+.. note::
 
-   * : An example containing all basic morphology operators like erode and dilate can be found at opencv_source_code/samples/gpu/morphology.cpp
+   * An example containing all basic morphology operators like erode and dilate can be found at opencv_source_code/samples/gpu/morphology.cpp
 
 gpu::BaseRowFilter_GPU
 ----------------------

modules/gpu/doc/image_processing.rst

@@ -966,9 +966,9 @@ Composites two images using alpha opacity values contained in each image.
 
     :param stream: Stream for the asynchronous version.
 
-.. Sample code::
+.. note::
 
-   * : An example demonstrating the use of alphaComp can be found at opencv_source_code/samples/gpu/alpha_comp.cpp
+   * An example demonstrating the use of alphaComp can be found at opencv_source_code/samples/gpu/alpha_comp.cpp
 
 gpu::Canny
 -------------------
@@ -1030,9 +1030,9 @@ Finds lines in a binary image using the classical Hough transform.
 
 .. seealso:: :ocv:func:`HoughLines`
 
-.. Sample code::
+.. note::
 
-   * : An example using the Hough lines detector can be found at opencv_source_code/samples/gpu/houghlines.cpp
+   * An example using the Hough lines detector can be found at opencv_source_code/samples/gpu/houghlines.cpp
 
 gpu::HoughLinesDownload
 -----------------------

modules/gpu/doc/object_detection.rst

@@ -62,12 +62,12 @@ The class implements Histogram of Oriented Gradients ([Dalal2005]_) object detec
 
 Interfaces of all methods are kept similar to the ``CPU HOG`` descriptor and detector analogues as much as possible.
 
-.. Sample code::
+.. note::
 
-   * : An example applying the HOG descriptor for people detection can be found at opencv_source_code/samples/cpp/peopledetect.cpp
-   * : A GPU example applying the HOG descriptor for people detection can be found at opencv_source_code/samples/gpu/hog.cpp
+   * An example applying the HOG descriptor for people detection can be found at opencv_source_code/samples/cpp/peopledetect.cpp
+   * A GPU 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
+   * (Python) An example applying the HOG descriptor for people detection can be found at opencv_source_code/samples/python2/peopledetect.py
 
 gpu::HOGDescriptor::HOGDescriptor
 -------------------------------------
@@ -235,10 +235,10 @@ Cascade classifier class used for object detection. Supports HAAR and LBP cascad
             Size getClassifierSize() const;
     };
 
-.. Sample code::
+.. note::
 
-   * : A cascade classifier example can be found at opencv_source_code/samples/gpu/cascadeclassifier.cpp
-   * : A Nvidea API specific cascade classifier example can be found at opencv_source_code/samples/gpu/cascadeclassifier_nvidia_api.cpp
+   * A cascade classifier example can be found at opencv_source_code/samples/gpu/cascadeclassifier.cpp
+   * A Nvidea API specific cascade classifier example can be found at opencv_source_code/samples/gpu/cascadeclassifier_nvidia_api.cpp
 
 gpu::CascadeClassifier_GPU::CascadeClassifier_GPU
 -----------------------------------------------------

modules/gpu/doc/video.rst

@@ -3,10 +3,10 @@ Video Analysis
 
 .. highlight:: cpp
 
-.. Sample code::
+.. note::
 
-   * : A general optical flow example can be found at opencv_source_code/samples/gpu/optical_flow.cpp
-   * : A feneral optical flow example using the nvidia API can be found at opencv_source_code/samples/gpu/opticalflow_nvidia_api.cpp
+   * A general optical flow example can be found at opencv_source_code/samples/gpu/optical_flow.cpp
+   * A general optical flow example using the Nvidia API can be found at opencv_source_code/samples/gpu/opticalflow_nvidia_api.cpp
    
 gpu::BroxOpticalFlow
 --------------------
@@ -47,9 +47,9 @@ Class computing the optical flow for two images using Brox et al Optical Flow al
         GpuMat buf;
     };
 
-.. Sample code::
+.. note::
 
-   * : An example illustrating the Brox et al optical flow algorithm can be found at opencv_source_code/samples/gpu/brox_optical_flow.cpp
+   * An example illustrating the Brox et al optical flow algorithm can be found at opencv_source_code/samples/gpu/brox_optical_flow.cpp
 
 gpu::GoodFeaturesToTrackDetector_GPU
 ------------------------------------
@@ -218,9 +218,9 @@ The class can calculate an optical flow for a sparse feature set or dense optica
 
 .. seealso:: :ocv:func:`calcOpticalFlowPyrLK`
 
-.. Sample code::
+.. note::
 
-   * : An example of the Lucas Kanade optical flow algorithm can be found at opencv_source_code/samples/gpu/pyrlk_optical_flow.cpp
+   * An example of the Lucas Kanade optical flow algorithm can be found at opencv_source_code/samples/gpu/pyrlk_optical_flow.cpp
 
 gpu::PyrLKOpticalFlow::sparse
 -----------------------------
@@ -425,9 +425,9 @@ The class discriminates between foreground and background pixels by building and
 
 .. seealso:: :ocv:class:`BackgroundSubtractorMOG`
 
-.. Sample code::
+.. note::
 
-   * : An example on gaussian mixture based background/foreground segmantation can be found at opencv_source_code/samples/gpu/bgfg_segm.cpp
+   * An example on gaussian mixture based background/foreground segmantation can be found at opencv_source_code/samples/gpu/bgfg_segm.cpp
 
 gpu::MOG_GPU::MOG_GPU
 ---------------------
@@ -706,9 +706,9 @@ The class uses H264 video codec.
 
 .. note:: Currently only Windows platform is supported.
 
-.. Sample code::
+.. note::
 
-   * : An example on how to use the videoWriter class can be found at opencv_source_code/samples/gpu/video_writer.cpp
+   * An example on how to use the videoWriter class can be found at opencv_source_code/samples/gpu/video_writer.cpp
 
 gpu::VideoWriter_GPU::VideoWriter_GPU
 -------------------------------------
@@ -921,9 +921,9 @@ Class for reading video from files.
 
 .. note:: Currently only Windows and Linux platforms are supported.
 
-.. Sample code::
+.. note::
 
-   * : An example on how to use the videoReader class can be found at opencv_source_code/samples/gpu/video_reader.cpp
+   * An example on how to use the videoReader class can be found at opencv_source_code/samples/gpu/video_reader.cpp
 
 gpu::VideoReader_GPU::Codec
 ---------------------------

modules/highgui/doc/reading_and_writing_images_and_video.rst

@@ -224,14 +224,14 @@ The class provides C++ API for capturing video from cameras or for reading video
 
 .. note:: In C API the black-box structure ``CvCapture`` is used instead of ``VideoCapture``.
 
-.. Sample code::
+.. note::
 
-   * : A basic sample on using the VideoCapture interface can be found at opencv_source_code/samples/cpp/starter_video.cpp
-   * : Another basic video processing sample can be found at opencv_source_code/samples/cpp/video_dmtx.cpp
+   * A basic sample on using the VideoCapture interface can be found at opencv_source_code/samples/cpp/starter_video.cpp
+   * 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
-   * : PYTHON : basic video processing sample can be found at opencv_source_code/samples/python2/video_dmtx.py
-   * : PYTHON : A multi threaded video processing sample can be found at opencv_source_code/samples/python2/video_threaded.py
+   * (Python) A basic sample on using the VideoCapture interface can be found at opencv_source_code/samples/python2/video.py
+   * (Python) Another basic video processing sample can be found at opencv_source_code/samples/python2/video_dmtx.py
+   * (Python) A multi threaded video processing sample can be found at opencv_source_code/samples/python2/video_threaded.py
 
 
 VideoCapture::VideoCapture

modules/highgui/doc/user_interface.rst

@@ -33,9 +33,9 @@ The function ``createTrackbar`` creates a trackbar (a slider or range control) w
 
 Clicking the label of each trackbar enables editing the trackbar values manually.
 
-.. Sample code::
+.. note::
 
-   * : An example of using the trackbar functionality can be found at opencv_source_code/samples/cpp/connected_components.cpp
+   * An example of using the trackbar functionality can be found at opencv_source_code/samples/cpp/connected_components.cpp
 
 getTrackbarPos
 ------------------

modules/imgproc/doc/feature_detection.rst

@@ -32,11 +32,11 @@ Finds edges in an image using the [Canny86]_ algorithm.
 The function finds edges in the input image ``image`` and marks them in the output map ``edges`` using the Canny algorithm. The smallest value between ``threshold1`` and ``threshold2`` is used for edge linking. The largest value is used to find initial segments of strong edges. See
 http://en.wikipedia.org/wiki/Canny_edge_detector
 
-.. Sample code::
+.. note::
 
-   * : An example on using the canny edge detector can be found at opencv_source_code/samples/cpp/edge.cpp
+   * An example on using the canny edge detector can be found at opencv_source_code/samples/cpp/edge.cpp
 
-   * : PYTHON : An example on using the canny edge detector can be found at opencv_source_code/samples/cpp/edge.py
+   * (Python) An example on using the canny edge detector can be found at opencv_source_code/samples/cpp/edge.py
 
 cornerEigenValsAndVecs
 ----------------------
@@ -89,9 +89,9 @@ The output of the function can be used for robust edge or corner detection.
     :ocv:func:`cornerHarris`,
     :ocv:func:`preCornerDetect`
 
-.. Sample code::
+.. note::
 
-   * : PYTHON : An example on how to use eigenvectors and eigenvalues to estimate image texture flow direction can be found at opencv_source_code/samples/python2/texture_flow.py
+   * (Python) An example on how to use eigenvectors and eigenvalues to estimate image texture flow direction can be found at opencv_source_code/samples/python2/texture_flow.py
 
 cornerHarris
 ------------
@@ -362,9 +362,9 @@ Example: ::
     :ocv:func:`fitEllipse`,
     :ocv:func:`minEnclosingCircle`
 
-.. Sample code::
+.. note::
 
-   * : An example using the Hough circle detector can be found at opencv_source_code/samples/cpp/houghcircles.cpp
+   * An example using the Hough circle detector can be found at opencv_source_code/samples/cpp/houghcircles.cpp
 
 HoughLines
 ----------
@@ -421,9 +421,9 @@ Finds lines in a binary image using the standard Hough transform.
 The function implements the standard or standard multi-scale Hough transform algorithm for line detection.  See http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm for a good explanation of Hough transform.
 See also the example in :ocv:func:`HoughLinesP` description.
 
-.. Sample code::
+.. note::
 
-   * : An example using the Hough line detector can be found at opencv_source_code/samples/cpp/houghlines.cpp
+   * An example using the Hough line detector can be found at opencv_source_code/samples/cpp/houghlines.cpp
 
 HoughLinesP
 -----------

modules/imgproc/doc/filtering.rst

@@ -22,9 +22,9 @@ OpenCV enables you to specify the extrapolation method. For details, see the fun
     * BORDER_CONSTANT:      iiiiii|abcdefgh|iiiiiii  with some specified 'i'
     */
 
-.. Sample code::
+.. note::
 
-   * : PYTHON : A complete example illustrating different morphological operations like erode/dilate, open/close, blackhat/tophat ... can be found at opencv_source_code/samples/python2/morphology.py
+   * (Python) A complete example illustrating different morphological operations like erode/dilate, open/close, blackhat/tophat ... can be found at opencv_source_code/samples/python2/morphology.py
 
 BaseColumnFilter
 ----------------
@@ -872,9 +872,9 @@ The function supports the in-place mode. Dilation can be applied several ( ``ite
     :ocv:func:`morphologyEx`,
     :ocv:func:`createMorphologyFilter`
 
-.. Sample code::
+.. note::
 
-   * : An example using the morphological dilate operation can be found at opencv_source_code/samples/cpp/morphology2.cpp
+   * An example using the morphological dilate operation can be found at opencv_source_code/samples/cpp/morphology2.cpp
 
 erode
 -----
@@ -915,9 +915,9 @@ The function supports the in-place mode. Erosion can be applied several ( ``iter
     :ocv:func:`morphologyEx`,
     :ocv:func:`createMorphologyFilter`
 
-.. Sample code::
+.. note::
 
-   * : An example using the morphological erode operation can be found at opencv_source_code/samples/cpp/morphology2.cpp
+   * An example using the morphological erode operation can be found at opencv_source_code/samples/cpp/morphology2.cpp
 
 filter2D
 --------
@@ -1254,9 +1254,9 @@ Any of the operations can be done in-place. In case of multi-channel images, eac
     :ocv:func:`erode`,
     :ocv:func:`createMorphologyFilter`
 
-.. Sample code::
+.. note::
 
-   * : An example using the morphologyEx function for the morphological opening and closing operations can be found at opencv_source_code/samples/cpp/morphology2.cpp
+   * An example using the morphologyEx function for the morphological opening and closing operations can be found at opencv_source_code/samples/cpp/morphology2.cpp
 
 Laplacian
 ---------
@@ -1302,9 +1302,9 @@ This is done when ``ksize > 1`` . When ``ksize == 1`` , the Laplacian is compute
     :ocv:func:`Sobel`,
     :ocv:func:`Scharr`
 
-.. Sample code::
+.. note::
 
-   * : An example using the Laplace transformation for edge detection can be found at opencv_source_code/samples/cpp/laplace.cpp
+   * An example using the Laplace transformation for edge detection can be found at opencv_source_code/samples/cpp/laplace.cpp
 
 pyrDown
 -------
@@ -1365,9 +1365,9 @@ Upsamples an image and then blurs it.
 The function performs the upsampling step of the Gaussian pyramid construction, though it can actually be used to construct the Laplacian pyramid. First, it upsamples the source image by injecting even zero rows and columns and then convolves the result with the same kernel as in
 :ocv:func:`pyrDown`  multiplied by 4.
 
-.. Sample code::
+.. note::
 
-   * : PYTHON : An example of Laplacian Pyramid construction and merging can be found at opencv_source_code/samples/python2/lappyr.py
+   * (Python) An example of Laplacian Pyramid construction and merging can be found at opencv_source_code/samples/python2/lappyr.py
 
 
 pyrMeanShiftFiltering
@@ -1418,9 +1418,9 @@ After the iterations over, the color components of the initial pixel (that is, t
 
 When ``maxLevel > 0``, the gaussian pyramid of ``maxLevel+1`` levels is built, and the above procedure is run on the smallest layer first. After that, the results are propagated to the larger layer and the iterations are run again only on those pixels where the layer colors differ by more than ``sr`` from the lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the results will be actually different from the ones obtained by running the meanshift procedure on the whole original image (i.e. when ``maxLevel==0``).
 
-.. Sample code::
+.. note::
 
-   * : An example using mean-shift image segmentation can be found at opencv_source_code/samples/cpp/meanshift_segmentation.cpp
+   * An example using mean-shift image segmentation can be found at opencv_source_code/samples/cpp/meanshift_segmentation.cpp
 
 sepFilter2D
 -----------

modules/imgproc/doc/geometric_transformations.rst

@@ -307,9 +307,9 @@ where
 
 The function emulates the human "foveal" vision and can be used for fast scale and rotation-invariant template matching, for object tracking and so forth. The function can not operate in-place.
 
-.. Sample code::
+.. note::
 
-   * : An example using the geometric logpolar operation in 4 applications can be found at opencv_source_code/samples/cpp/logpolar_bsm.cpp
+   * An example using the geometric logpolar operation in 4 applications can be found at opencv_source_code/samples/cpp/logpolar_bsm.cpp
 
 remap
 -----

modules/imgproc/doc/histograms.rst

@@ -99,12 +99,12 @@ input arrays at the same location. The sample below shows how to compute a 2D Hu
         waitKey();
     }
 
-.. Sample code::
+.. note::
 
-   * : An example for creating histograms of an image can be found at opencv_source_code/samples/cpp/demhist.cpp
+   * An example for creating histograms of an image can be found at opencv_source_code/samples/cpp/demhist.cpp
 
-   * : PYTHON : An example for creating color histograms can be found at opencv_source/samples/python2/color_histogram.py
-   * : PYTHON : An example illustrating RGB and grayscale histogram plotting can be found at opencv_source/samples/python2/hist.py
+   * (Python) An example for creating color histograms can be found at opencv_source/samples/python2/color_histogram.py
+   * (Python) An example illustrating RGB and grayscale histogram plotting can be found at opencv_source/samples/python2/hist.py
 
 
 calcBackProject

modules/imgproc/doc/miscellaneous_transformations.rst

@@ -481,11 +481,11 @@ In this mode, the complexity is still linear.
 That is, the function provides a very fast way to compute the Voronoi diagram for a binary image.
 Currently, the second variant can use only the approximate distance transform algorithm, i.e. ``maskSize=CV_DIST_MASK_PRECISE`` is not supported yet.
 
-.. Sample code::
+.. note::
 
-   * : An example on using the distance transform can be found at opencv_source_code/samples/cpp/distrans.cpp
+   * An example on using the distance transform can be found at opencv_source_code/samples/cpp/distrans.cpp
 
-   * : PYTHON : An example on using the distance transform can be found at opencv_source/samples/python2/distrans.py
+   * (Python) An example on using the distance transform can be found at opencv_source/samples/python2/distrans.py
 
 floodFill
 ---------
@@ -590,11 +590,11 @@ Use these functions to either mark a connected component with the specified colo
 
 .. seealso:: :ocv:func:`findContours`
 
-.. Sample code::
+.. note::
 
-   * : An example using the FloodFill technique can be found at opencv_source_code/samples/cpp/ffilldemo.cpp
+   * An example using the FloodFill technique can be found at opencv_source_code/samples/cpp/ffilldemo.cpp
 
-   * : PYTHON : An example using the FloodFill technique can be found at opencv_source_code/samples/python2/floodfill.cpp
+   * (Python) An example using the FloodFill technique can be found at opencv_source_code/samples/python2/floodfill.cpp
 
 integral
 --------
@@ -758,11 +758,11 @@ Visual demonstration and usage example of the function can be found in the OpenC
 
 .. seealso:: :ocv:func:`findContours`
 
-.. Sample code::
+.. note::
 
-   * : An example using the watershed algorithm can be found at opencv_source_code/samples/cpp/watershed.cpp
+   * An example using the watershed algorithm can be found at opencv_source_code/samples/cpp/watershed.cpp
 
-   * : PYTHON : An example using the watershed algorithm can be found at opencv_source_code/samples/python2/watershed.py
+   * (Python) An example using the watershed algorithm can be found at opencv_source_code/samples/python2/watershed.py
 
 grabCut
 -------
@@ -811,8 +811,8 @@ See the sample ``grabcut.cpp`` to learn how to use the function.
 
 .. [Telea04] Alexandru Telea, *An Image Inpainting Technique Based on the Fast Marching Method*. Journal of Graphics, GPU, and Game Tools 9 1, pp 23-34 (2004)
 
-.. Sample code::
+.. note::
 
-   * : An example using the GrabCut algorithm can be found at opencv_source_code/samples/cpp/grabcut.cpp
+   * An example using the GrabCut algorithm can be found at opencv_source_code/samples/cpp/grabcut.cpp
 
-   * : PYTHON : An example using the GrabCut algorithm can be found at opencv_source_code/samples/python2/grabcut.py
+   * (Python) An example using the GrabCut algorithm can be found at opencv_source_code/samples/python2/grabcut.py

modules/imgproc/doc/object_detection.rst

@@ -74,6 +74,6 @@ image patch:
 After the function finishes the comparison, the best matches can be found as global minimums (when ``CV_TM_SQDIFF`` was used) or maximums (when ``CV_TM_CCORR`` or ``CV_TM_CCOEFF`` was used) using the
 :ocv:func:`minMaxLoc` function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.
 
-.. Sample code::
+.. note::
 
-   * : PYTHON : An example on how to match mouse selected regions in an image can be found at opencv_source_code/samples/python2/mouse_and_match.py
+   * (Python) An example on how to match mouse selected regions in an image can be found at opencv_source_code/samples/python2/mouse_and_match.py

modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst

@@ -166,13 +166,13 @@ The function retrieves contours from the binary image using the algorithm
 
 .. note:: If you use the new Python interface then the ``CV_`` prefix has to be omitted in contour retrieval mode and contour approximation method parameters (for example, use ``cv2.RETR_LIST`` and ``cv2.CHAIN_APPROX_NONE`` parameters). If you use the old Python interface then these parameters have the ``CV_`` prefix (for example, use ``cv.CV_RETR_LIST`` and ``cv.CV_CHAIN_APPROX_NONE``).
 
-.. Sample code::
+.. note::
 
-   * : An example using the findContour functionality can be found at opencv_source_code/samples/cpp/contours2.cpp
-   * : An example using findContours to clean up a background segmentation result at opencv_source_code/samples/cpp/segment_objects.cpp
+   * An example using the findContour functionality can be found at opencv_source_code/samples/cpp/contours2.cpp
+   * An example using findContours to clean up a background segmentation result at opencv_source_code/samples/cpp/segment_objects.cpp
 
-   * : PYTHON : An example using the findContour functionality can be found at opencv_source/samples/python2/contours.py
-   * : PYTHON : An example of detecting squares in an image can be found at opencv_source/samples/python2/squares.py
+   * (Python) An example using the findContour functionality can be found at opencv_source/samples/python2/contours.py
+   * (Python) An example of detecting squares in an image can be found at opencv_source/samples/python2/squares.py
 
 drawContours
 ----------------
@@ -254,12 +254,12 @@ The function draws contour outlines in the image if
         waitKey(0);
     }
 
-.. Sample code::
+.. note::
 
-   * : An example using the drawContour functionality can be found at opencv_source_code/samples/cpp/contours2.cpp
-   * : An example using drawContours to clean up a background segmentation result at opencv_source_code/samples/cpp/segment_objects.cpp
+   * An example using the drawContour functionality can be found at opencv_source_code/samples/cpp/contours2.cpp
+   * An example using drawContours to clean up a background segmentation result at opencv_source_code/samples/cpp/segment_objects.cpp
 
-   * : PYTHON : An example using the drawContour functionality can be found at opencv_source/samples/python2/contours.py
+   * (Python) An example using the drawContour functionality can be found at opencv_source/samples/python2/contours.py
 
 approxPolyDP
 ----------------
@@ -430,9 +430,9 @@ The functions find the convex hull of a 2D point set using the Sklansky's algori
 that has
 *O(N logN)* complexity in the current implementation. See the OpenCV sample ``convexhull.cpp`` that demonstrates the usage of different function variants.
 
-.. Sample code::
+.. note::
 
-   * : An example using the convexHull functionality can be found at opencv_source_code/samples/cpp/convexhull.cpp
+   * An example using the convexHull functionality can be found at opencv_source_code/samples/cpp/convexhull.cpp
 
 
 convexityDefects
@@ -490,9 +490,9 @@ Fits an ellipse around a set of 2D points.
 
 The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed. The algorithm [Fitzgibbon95]_ is used.
 
-.. Sample code::
+.. note::
 
-   * : An example using the fitEllipse technique can be found at opencv_source_code/samples/cpp/fitellipse.cpp
+   * An example using the fitEllipse technique can be found at opencv_source_code/samples/cpp/fitellipse.cpp
 
 fitLine
 -----------
@@ -568,7 +568,7 @@ http://en.wikipedia.org/wiki/M-estimator
 
 .. Sample code:
 
-   * : PYTHON : An example of robust line fitting can be found at opencv_source_code/samples/python2/fitline.py
+   * (Python) An example of robust line fitting can be found at opencv_source_code/samples/python2/fitline.py
 
 isContourConvex
 -------------------

modules/legacy/doc/expectation_maximization.rst

@@ -5,11 +5,11 @@ This section describes obsolete ``C`` interface of EM algorithm. Details of the
 
 .. highlight:: cpp
 
-.. Sample code::
+.. note::
 
-   * : An example on using the Expectation Maximalization algorithm can be found at opencv_source_code/samples/cpp/em.cpp
+   * An example on using the Expectation Maximalization algorithm can be found at opencv_source_code/samples/cpp/em.cpp
 
-   * : PYTHON : An example using Expectation Maximalization for Gaussian Mixing can be found at opencv_source_code/samples/python2/gaussian_mix.py
+   * (Python) An example using Expectation Maximalization for Gaussian Mixing can be found at opencv_source_code/samples/python2/gaussian_mix.py
 
 
 CvEMParams

modules/legacy/doc/feature_detection_and_description.rst

@@ -75,7 +75,7 @@ Class containing a base structure for ``RTreeClassifier``. ::
             void estimateQuantPercForPosteriors(float perc[2]);
     };
 
-.. Sample code::
+.. note::
 
    * : PYTHON : An example using Randomized Tree training for letter recognition can be found at opencv_source_code/samples/python2/letter_recog.py
 
@@ -101,7 +101,7 @@ Trains a randomized tree using an input set of keypoints.
 
     :param num_quant_bits: Number of bits used for quantization.
 
-.. Sample code::
+.. note::
 
    * : An example on training a Random Tree Classifier for letter recognition can be found at opencv_source_code\samples\cpp\letter_recog.cpp
 

modules/ml/doc/k_nearest_neighbors.rst

@@ -11,11 +11,11 @@ CvKNearest
 
 The class implements K-Nearest Neighbors model as described in the beginning of this section.
 
-.. Sample code::
+.. note::
 
-   * : PYTHON : An example of digit recognition using KNearest can be found at opencv_source/samples/python2/digits.py
-   * : PYTHON : An example of grid search digit recognition using KNearest can be found at opencv_source/samples/python2/digits_adjust.py
-   * : PYTHON : An example of video digit recognition using KNearest can be found at opencv_source/samples/python2/digits_video.py   
+   * (Python) An example of digit recognition using KNearest can be found at opencv_source/samples/python2/digits.py
+   * (Python) An example of grid search digit recognition using KNearest can be found at opencv_source/samples/python2/digits_adjust.py
+   * (Python) An example of video digit recognition using KNearest can be found at opencv_source/samples/python2/digits_video.py
 
 CvKNearest::CvKNearest
 ----------------------

modules/ml/doc/support_vector_machines.rst

@@ -150,11 +150,11 @@ CvSVM
 
 Support Vector Machines.
 
-.. Sample code::
+.. note::
 
-   * : PYTHON : An example of digit recognition using SVM can be found at opencv_source/samples/python2/digits.py
-   * : PYTHON : An example of grid search digit recognition using SVM can be found at opencv_source/samples/python2/digits_adjust.py
-   * : PYTHON : An example of video digit recognition using SVM can be found at opencv_source/samples/python2/digits_video.py
+   * (Python) An example of digit recognition using SVM can be found at opencv_source/samples/python2/digits.py
+   * (Python) An example of grid search digit recognition using SVM can be found at opencv_source/samples/python2/digits_adjust.py
+   * (Python) An example of video digit recognition using SVM can be found at opencv_source/samples/python2/digits_video.py
 
 CvSVM::CvSVM
 ------------

modules/nonfree/doc/feature_detection.rst

@@ -75,10 +75,10 @@ SURF
 
 .. [Bay06] Bay, H. and Tuytelaars, T. and Van Gool, L. "SURF: Speeded Up Robust Features", 9th European Conference on Computer Vision, 2006
 
-.. Sample code::
+.. note::
 
-   * : An example using the SURF feature detector can be found at opencv_source_code/samples/cpp/generic_descriptor_match.cpp
-   * : Another example using the SURF feature detector, extractor and matcher can be found at opencv_source_code/samples/cpp/matcher_simple.cpp
+   * An example using the SURF feature detector can be found at opencv_source_code/samples/cpp/generic_descriptor_match.cpp
+   * Another example using the SURF feature detector, extractor and matcher can be found at opencv_source_code/samples/cpp/matcher_simple.cpp
 
 SURF::SURF
 ----------
@@ -234,9 +234,9 @@ The class ``SURF_GPU`` uses some buffers and provides access to it. All buffers
 
 .. seealso:: :ocv:class:`SURF`
 
-.. Sample code::
+.. note::
 
-   * : An example for using the SURF keypoint matcher on GPU can be found at opencv_source_code/samples/gpu/surf_keypoint_matcher.cpp
+   * An example for using the SURF keypoint matcher on GPU can be found at opencv_source_code/samples/gpu/surf_keypoint_matcher.cpp
 
 ocl::SURF_OCL
 -------------
@@ -336,6 +336,6 @@ The class ``SURF_OCL`` uses some buffers and provides access to it. All buffers
 
 .. seealso:: :ocv:class:`SURF`
 
-.. Sample code::
+.. note::
 
-   * : OCL : An example of the SURF detector can be found at opencv_source_code/samples/ocl/surf_matcher.cpp
+   * OCL : An example of the SURF detector can be found at opencv_source_code/samples/ocl/surf_matcher.cpp

modules/objdetect/doc/cascade_classification.rst

@@ -215,9 +215,9 @@ Detects objects of different sizes in the input image. The detected objects are
 
 The function is parallelized with the TBB library.
 
-.. Sample code::
+.. note::
 
-   * : PYTHON : A face detection example using cascade classifiers can be found at opencv_source_code/samples/python2/facedetect.py
+   * (Python) A face detection example using cascade classifiers can be found at opencv_source_code/samples/python2/facedetect.py
 
 
 CascadeClassifier::setImage

modules/ocl/doc/feature_detection_and_description.rst

@@ -363,9 +363,9 @@ The class implements Histogram of Oriented Gradients ([Dalal2005]_) object detec
 
 Interfaces of all methods are kept similar to the ``CPU HOG`` descriptor and detector analogues as much as possible.
 
-.. Sample code::
+.. note::
 
-   * : OCL : An example using the HOG descriptor can be found at opencv_source_code/samples/ocl/hog.cpp
+   (Ocl) An example using the HOG descriptor can be found at opencv_source_code/samples/ocl/hog.cpp
 
 ocl::HOGDescriptor::HOGDescriptor
 -------------------------------------

modules/ocl/doc/image_processing.rst

@@ -257,10 +257,10 @@ The class can calculate an optical flow for a sparse feature set or dense optica
 
 .. seealso:: :ocv:func:`calcOpticalFlowPyrLK`
 
-.. Sample code::
+.. note::
 
-   * : OCL : An example the Lucas Kanade optical flow pyramid method can be found at opencv_source_code/samples/ocl/pyrlk_optical_flow.cpp
-   * : OCL : An example for square detection can be found at opencv_source_code/samples/ocl/squares.cpp
+   (Ocl) An example the Lucas Kanade optical flow pyramid method can be found at opencv_source_code/samples/ocl/pyrlk_optical_flow.cpp
+   (Ocl) An example for square detection can be found at opencv_source_code/samples/ocl/squares.cpp
 
 ocl::PyrLKOpticalFlow::sparse
 -----------------------------

modules/ocl/doc/object_detection.rst

@@ -20,9 +20,9 @@ Cascade classifier class used for object detection. Supports HAAR cascade classi
                                       CvSize maxSize = cvSize(0, 0));
     };
 
-.. Sample code::
+.. note::
 
-   * : OCL : A face detection example using cascade classifiers can be found at opencv_source_code/samples/ocl/facedetect.cpp
+   (Ocl) A face detection example using cascade classifiers can be found at opencv_source_code/samples/ocl/facedetect.cpp
 
 ocl::OclCascadeClassifier::oclHaarDetectObjects
 ------------------------------------------------------

modules/photo/doc/inpainting.rst

@@ -32,8 +32,8 @@ The function reconstructs the selected image area from the pixel near the area b
 http://en.wikipedia.org/wiki/Inpainting
 for more details.
 
-.. Sample code::
+.. note::
 
-   * : An example using the inpainting technique can be found at opencv_source_code/samples/cpp/inpaint.cpp
+   * 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
+   * (Python) An example using the inpainting technique can be found at opencv_source_code/samples/python2/inpaint.py

modules/stitching/doc/high_level.rst

@@ -88,10 +88,10 @@ High level image stitcher. It's possible to use this class without being aware o
         /* hidden */
     };
 
-.. Sample code::
+.. note::
 
-   * : A basic example on image stitching can be found at opencv_source_code/samples/cpp/stitching.cpp
-   * : A detailed example on image stitching can be found at opencv_source_code/samples/cpp/stitching_detailed.cpp
+   * A basic example on image stitching can be found at opencv_source_code/samples/cpp/stitching.cpp
+   * A detailed example on image stitching can be found at opencv_source_code/samples/cpp/stitching_detailed.cpp
 
 Stitcher::createDefault
 -----------------------

modules/video/doc/motion_analysis_and_object_tracking.rst

@@ -42,12 +42,12 @@ Calculates an optical flow for a sparse feature set using the iterative Lucas-Ka
 
 The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00]_. The function is parallelized with the TBB library.
 
-.. Sample code::
+.. note::
 
-   * : An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
+   * 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
-   * : PYTHON : An example using the Lucas-Kanade tracker for homography matching can be found at opencv_source_code/samples/python2/lk_homography.py
+   * (Python) An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/python2/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
 
 buildOpticalFlowPyramid
 -----------------------
@@ -116,11 +116,11 @@ The function finds an optical flow for each ``prev`` pixel using the [Farneback2
 
     \texttt{prev} (y,x)  \sim \texttt{next} ( y + \texttt{flow} (y,x)[1],  x + \texttt{flow} (y,x)[0])
 
-.. Sample code::
+.. note::
 
-   * : An example using the optical flow algorithm described by Gunnar Farneback can be found at opencv_source_code/samples/cpp/fback.cpp
+   * 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
+   * (Python) An example using the optical flow algorithm described by Gunnar Farneback can be found at opencv_source_code/samples/python2/opt_flow.py
 
 estimateRigidTransform
 --------------------------
@@ -239,9 +239,9 @@ In fact,
 :ocv:func:`fastAtan2` and
 :ocv:func:`phase` are used so that the computed angle is measured in degrees and covers the full range 0..360. Also, the ``mask`` is filled to indicate pixels where the computed angle is valid.
 
-.. Sample code::
+.. note::
 
-   * : PYTHON : An example on how to perform a motion template technique can be found at opencv_source_code/samples/python2/motempl.py
+   * (Python) An example on how to perform a motion template technique can be found at opencv_source_code/samples/python2/motempl.py
 
 calcGlobalOrientation
 -------------------------
@@ -327,9 +327,9 @@ First, it finds an object center using
 
 See the OpenCV sample ``camshiftdemo.c`` that tracks colored objects.
 
-.. Sample code::
+.. note::
 
-   * : PYTHON : A sample explaining the camshift tracking algorithm can be found at opencv_source_code/samples/python2/camshift.py
+   * (Python) A sample explaining the camshift tracking algorithm can be found at opencv_source_code/samples/python2/camshift.py
 
 meanShift
 ---------
@@ -358,9 +358,9 @@ The function implements the iterative object search algorithm. It takes the inpu
 :ocv:func:`contourArea` ), and rendering the  remaining contours with
 :ocv:func:`drawContours` .
 
-.. Sample code::
+.. note::
 
-   * : A mean-shift tracking sample can be found at opencv_source_code/samples/cpp/camshiftdemo.cpp
+   * A mean-shift tracking sample can be found at opencv_source_code/samples/cpp/camshiftdemo.cpp
 
 KalmanFilter
 ------------
@@ -371,9 +371,9 @@ KalmanFilter
 The class implements a standard Kalman filter
 http://en.wikipedia.org/wiki/Kalman_filter, [Welch95]_. However, you can modify ``transitionMatrix``, ``controlMatrix``, and ``measurementMatrix`` to get an extended Kalman filter functionality. See the OpenCV sample ``kalman.cpp`` .
 
-.. Sample code::
+.. note::
 
-   * : An example using the standard Kalman filter can be found at opencv_source_code/samples/cpp/kalman.cpp
+   * An example using the standard Kalman filter can be found at opencv_source_code/samples/cpp/kalman.cpp
 
 
 KalmanFilter::KalmanFilter
@@ -661,9 +661,9 @@ Calculate an optical flow using "SimpleFlow" algorithm.
 
 See [Tao2012]_. And site of project - http://graphics.berkeley.edu/papers/Tao-SAN-2012-05/.
 
-.. Sample code::
+.. note::
 
-   * : An example using the simpleFlow algorithm can be found at opencv_source_code/samples/cpp/simpleflow_demo.cpp
+   * An example using the simpleFlow algorithm can be found at opencv_source_code/samples/cpp/simpleflow_demo.cpp
 
 createOptFlow_DualTVL1
 ----------------------