Merge pull request #727 from alalek:migration

.github/ISSUE_TEMPLATE.md

 <!--
 If you have a question rather than reporting a bug please go to http://answers.opencv.org where you get much faster responses.
-If you need further assistance please read [How To Contribute](https://github.com/Itseez/opencv/wiki/How_to_contribute).
+If you need further assistance please read [How To Contribute](https://github.com/opencv/opencv/wiki/How_to_contribute).
 
 This is a template helping you to create an issue which can be processed as quickly as possible. This is the bug reporting section for the OpenCV library.
 -->

.travis.yml

@@ -4,7 +4,7 @@ compiler:
   - clang
 before_script:
   - cd ../
-  - git clone https://github.com/Itseez/opencv.git
+  - git clone https://github.com/opencv/opencv.git
   - mkdir build-opencv
   - cd build-opencv
   - cmake -DOPENCV_EXTRA_MODULES_PATH=../opencv_contrib/modules ../opencv

CONTRIBUTING.md

 ## Contributing guidelines
 
-All guidelines for contributing to the OpenCV repository can be found at [`How to contribute guideline`](https://github.com/Itseez/opencv/wiki/How_to_contribute).
+All guidelines for contributing to the OpenCV repository can be found at [`How to contribute guideline`](https://github.com/opencv/opencv/wiki/How_to_contribute).

modules/dnn/tutorials/tutorial_dnn_build.markdown

@@ -3,7 +3,7 @@ Build opencv_contrib with dnn module {#tutorial_dnn_build}
 
 Introduction
 ------------
-opencv_dnn module is placed in the secondary [opencv_contrib](https://github.com/Itseez/opencv_contrib) repository,
+opencv_dnn module is placed in the secondary [opencv_contrib](https://github.com/opencv/opencv_contrib) repository,
 which isn't distributed in binary form, therefore you need to build it manually.
 
 To do this you need to have installed: [CMake](http://www.cmake.org/download), git, and build system (*gcc* with *make* for Linux or *MS Visual Studio* for Windows)
@@ -12,12 +12,12 @@ Steps
 -----
 -# Make any directory, for example **opencv_root**
 
--# Clone [opencv](https://github.com/Itseez/opencv) and [opencv_contrib](https://github.com/Itseez/opencv_contrib) repos to the **opencv_root**.
+-# Clone [opencv](https://github.com/opencv/opencv) and [opencv_contrib](https://github.com/opencv/opencv_contrib) repos to the **opencv_root**.
    You can do it in terminal like here:
 @code
 cd opencv_root
-git clone https://github.com/Itseez/opencv
-git clone https://github.com/Itseez/opencv_contrib
+git clone https://github.com/opencv/opencv
+git clone https://github.com/opencv/opencv_contrib
 @endcode
 
 -# Run [CMake-gui] and set source and build directories:

modules/text/include/opencv2/text.hpp

@@ -92,7 +92,7 @@ grouping horizontally aligned text, and the method proposed by Lluis Gomez and D
 in [Gomez13][Gomez14] for grouping arbitrary oriented text (see erGrouping).
 
 To see the text detector at work, have a look at the textdetection demo:
-<https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/textdetection.cpp>
+<https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/textdetection.cpp>
 
     @defgroup text_recognize Scene Text Recognition
   @}

modules/text/include/opencv2/text/erfilter.hpp

@@ -345,7 +345,7 @@ single vector\<Point\>, the function separates them in two different vectors (th
 ERStats where extracted from two different channels).
 
 An example of MSERsToERStats in use can be found in the text detection webcam_demo:
-<https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp>
+<https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp>
  */
 CV_EXPORTS void MSERsToERStats(InputArray image, std::vector<std::vector<Point> > &contours,
                                std::vector<std::vector<ERStat> > &regions);

modules/text/include/opencv2/text/ocr.hpp

@@ -81,10 +81,10 @@ Notice that it is compiled only when tesseract-ocr is correctly installed.
 @note
    -   (C++) An example of OCRTesseract recognition combined with scene text detection can be found
         at the end_to_end_recognition demo:
-        <https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/end_to_end_recognition.cpp>
+        <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/end_to_end_recognition.cpp>
     -   (C++) Another example of OCRTesseract recognition combined with scene text detection can be
         found at the webcam_demo:
-        <https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp>
+        <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp>
  */
 class CV_EXPORTS_W OCRTesseract : public BaseOCR
 {
@@ -152,7 +152,7 @@ enum decoder_mode
 @note
    -   (C++) An example on using OCRHMMDecoder recognition combined with scene text detection can
         be found at the webcam_demo sample:
-        <https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp>
+        <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp>
  */
 class CV_EXPORTS_W OCRHMMDecoder : public BaseOCR
 {
@@ -165,7 +165,7 @@ public:
 
     The default character classifier and feature extractor can be loaded using the utility funtion
     loadOCRHMMClassifierNM and KNN model provided in
-    <https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/OCRHMM_knn_model_data.xml.gz>.
+    <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/OCRHMM_knn_model_data.xml.gz>.
      */
     class CV_EXPORTS_W ClassifierCallback
     {
@@ -321,7 +321,7 @@ CV_EXPORTS_W Ptr<OCRHMMDecoder::ClassifierCallback> loadOCRHMMClassifierCNN(cons
  * The function calculate frequency statistics of character pairs from the given lexicon and fills the output transition_probabilities_table with them. The transition_probabilities_table can be used as input in the OCRHMMDecoder::create() and OCRBeamSearchDecoder::create() methods.
  * @note
  *    -   (C++) An alternative would be to load the default generic language transition table provided in the text module samples folder (created from ispell 42869 english words list) :
- *            <https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/OCRHMM_transitions_table.xml>
+ *            <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/OCRHMM_transitions_table.xml>
  **/
 CV_EXPORTS void createOCRHMMTransitionsTable(std::string& vocabulary, std::vector<std::string>& lexicon, OutputArray transition_probabilities_table);
 
@@ -335,7 +335,7 @@ CV_EXPORTS_W Mat createOCRHMMTransitionsTable(const String& vocabulary, std::vec
 @note
    -   (C++) An example on using OCRBeamSearchDecoder recognition combined with scene text detection can
         be found at the demo sample:
-        <https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/word_recognition.cpp>
+        <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/word_recognition.cpp>
  */
 class CV_EXPORTS_W OCRBeamSearchDecoder : public BaseOCR
 {
@@ -348,7 +348,7 @@ public:
 
     The default character classifier and feature extractor can be loaded using the utility funtion
     loadOCRBeamSearchClassifierCNN with all its parameters provided in
-    <https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/OCRBeamSearch_CNN_model_data.xml.gz>.
+    <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/OCRBeamSearch_CNN_model_data.xml.gz>.
      */
     class CV_EXPORTS_W ClassifierCallback
     {

modules/text/src/ocr_hmm_decoder.cpp

@@ -1206,7 +1206,7 @@ the output transition_probabilities_table with them.
 The transition_probabilities_table can be used as input in the OCRHMMDecoder::create() and OCRBeamSearchDecoder::create() methods.
 @note
    -   (C++) An alternative would be to load the default generic language transition table provided in the text module samples folder (created from ispell 42869 english words list) :
-        <https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/OCRHMM_transitions_table.xml>
+        <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/OCRHMM_transitions_table.xml>
  */
 void createOCRHMMTransitionsTable(string& vocabulary, vector<string>& lexicon, OutputArray _transitions)
 {

modules/tracking/tutorials/tutorial_introduction_to_tracker.markdown

@@ -28,8 +28,8 @@ Explanation
     as shown in help. In the help, it means that the image files are numbered with 4 digits
     (e.g. the file naming will be 0001.jpg, 0002.jpg, and so on).
 
-    You can find video samples in Itseez/opencv_extra/testdata/cv/tracking
-    <https://github.com/Itseez/opencv_extra/tree/master/testdata/cv/tracking>
+    You can find video samples in opencv_extra/testdata/cv/tracking
+    <https://github.com/opencv/opencv_extra/tree/master/testdata/cv/tracking>
 
 -#  **Declares the required variables**
 

modules/ximgproc/samples/structured_edge_detection.cpp

 /**************************************************************************************
 The structered edge demo requires you to provide a model.
 This model can be found at the opencv_extra repository on Github on the following link:
-https://github.com/Itseez/opencv_extra/blob/master/testdata/cv/ximgproc/model.yml.gz
+https://github.com/opencv/opencv_extra/blob/master/testdata/cv/ximgproc/model.yml.gz
 ***************************************************************************************/
 
 #include <opencv2/ximgproc.hpp>

modules/ximgproc/tutorials/disparity_filtering.markdown

@@ -27,7 +27,7 @@ Source Stereoscopic Image
 Source Code
 -----------
 
-We will be using snippets from the example application, that can be downloaded [here ](https://github.com/Itseez/opencv_contrib/blob/master/modules/ximgproc/samples/disparity_filtering.cpp).
+We will be using snippets from the example application, that can be downloaded [here ](https://github.com/opencv/opencv_contrib/blob/master/modules/ximgproc/samples/disparity_filtering.cpp).
 
 Explanation
 -----------