migration: github.com/opencv/opencv_contrib

2025-10-14 18:58:22 +08:00 · 2016-06-16 18:21:26 +03:00
parent a996bcff3f
commit 823dea726f
11 changed files with 21 additions and 21 deletions
--- a/.github/ISSUE_TEMPLATE.md
+++ b/.github/ISSUE_TEMPLATE.md
@@ -1,6 +1,6 @@
 <!--
 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.
 -->
--- a/.travis.yml
+++ b/.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
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,3 +1,3 @@
 ## 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).
--- a/modules/dnn/tutorials/tutorial_dnn_build.markdown
+++ b/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:
--- a/modules/text/include/opencv2/text.hpp
+++ b/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
  @}
--- a/modules/text/include/opencv2/text/erfilter.hpp
+++ b/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);
--- a/modules/text/include/opencv2/text/ocr.hpp
+++ b/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
    {
--- a/modules/text/src/ocr_hmm_decoder.cpp
+++ b/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)
 {
--- a/modules/tracking/tutorials/tutorial_introduction_to_tracker.markdown
+++ b/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**

--- a/modules/ximgproc/samples/structured_edge_detection.cpp
+++ b/modules/ximgproc/samples/structured_edge_detection.cpp
@@ -1,7 +1,7 @@
 /**************************************************************************************
 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>
--- a/modules/ximgproc/tutorials/disparity_filtering.markdown
+++ b/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
 -----------