Merge pull request #6885 from alalek:migration2.4

modules/core/doc/basic_structures.rst

@@ -2898,5 +2898,5 @@ The above methods are usually enough for users. If you want to make your own alg
  * Make a class and specify ``Algorithm`` as its base class.
  * The algorithm parameters should be the class members. See ``Algorithm::get()`` for the list of possible types of the parameters.
  * Add public virtual method ``AlgorithmInfo* info() const;`` to your class.
- * Add constructor function, ``AlgorithmInfo`` instance and implement the ``info()`` method. The simplest way is to take https://github.com/Itseez/opencv/tree/master/modules/ml/src/ml_init.cpp as the reference and modify it according to the list of your parameters.
+ * Add constructor function, ``AlgorithmInfo`` instance and implement the ``info()`` method. The simplest way is to take https://github.com/opencv/opencv/tree/master/modules/ml/src/ml_init.cpp as the reference and modify it according to the list of your parameters.
  * Add some public function (e.g. ``initModule_<mymodule>()``) that calls info() of your algorithm and put it into the same source file as ``info()`` implementation. This is to force C++ linker to include this object file into the target application. See ``Algorithm::create()`` for details.

modules/features2d/test/test_lshindex_flannbased_matcher.cpp

@@ -31,7 +31,7 @@
  For OpenCV2.4/OpenCV3.0
 
  Test for Pull Request # 3829
- https://github.com/Itseez/opencv/pull/3829
+ https://github.com/opencv/opencv/pull/3829
 
  This test code creates brute force matcher for accuracy of reference, and the test target matcher.
  Then, add() and train() transformed query image descriptors, and some outlier images descriptors to both matchers.

modules/gpu/doc/introduction.rst

@@ -59,4 +59,4 @@ While developing algorithms for multiple GPUs, note a data passing overhead. For
 With this algorithm, a dual GPU gave a 180
 %
 performance increase comparing to the single Fermi GPU. For a source code example, see
-https://github.com/Itseez/opencv/tree/master/samples/gpu/.
+https://github.com/opencv/opencv/tree/master/samples/gpu/.

modules/highgui/doc/reading_and_writing_images_and_video.rst

@@ -319,7 +319,7 @@ The methods/functions grab the next frame from video file or camera and return t
 
 The primary use of the function is in multi-camera environments, especially when the cameras do not have hardware synchronization. That is, you call ``VideoCapture::grab()`` for each camera and after that call the slower method ``VideoCapture::retrieve()`` to decode and get frame from each camera. This way the overhead on demosaicing or motion jpeg decompression etc. is eliminated and the retrieved frames from different cameras will be closer in time.
 
-Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the correct way of retrieving data from it is to call `VideoCapture::grab` first and then call :ocv:func:`VideoCapture::retrieve` one or more times with different values of the ``channel`` parameter. See https://github.com/Itseez/opencv/tree/master/samples/cpp/openni_capture.cpp
+Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the correct way of retrieving data from it is to call `VideoCapture::grab` first and then call :ocv:func:`VideoCapture::retrieve` one or more times with different values of the ``channel`` parameter. See https://github.com/opencv/opencv/tree/master/samples/cpp/openni_capture.cpp
 
 
 VideoCapture::retrieve

modules/highgui/doc/user_interface.rst

@@ -224,7 +224,7 @@ Sets mouse handler for the specified window
 
     :param winname: Window name
 
-    :param onMouse: Mouse callback. See OpenCV samples, such as  https://github.com/Itseez/opencv/tree/master/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
+    :param onMouse: Mouse callback. See OpenCV samples, such as  https://github.com/opencv/opencv/tree/master/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
 
     :param userdata: The optional parameter passed to the callback.
 

modules/highgui/src/cap_libv4l.cpp

@@ -16,7 +16,7 @@ For Release:  OpenCV-Linux Beta4  opencv-0.9.6
 Tested On:    LMLBT44 with 8 video inputs
 Problems?     Post your questions at answers.opencv.org,
               Report bugs at code.opencv.org,
-              Submit your fixes at https://github.com/Itseez/opencv/
+              Submit your fixes at https://github.com/opencv/opencv/
 Patched Comments:
 
 TW: The cv cam utils that came with the initial release of OpenCV for LINUX Beta4

modules/highgui/src/cap_v4l.cpp

@@ -16,7 +16,7 @@ For Release:  OpenCV-Linux Beta4  opencv-0.9.6
 Tested On:    LMLBT44 with 8 video inputs
 Problems?     Post your questions at answers.opencv.org,
               Report bugs at code.opencv.org,
-              Submit your fixes at https://github.com/Itseez/opencv/
+              Submit your fixes at https://github.com/opencv/opencv/
 Patched Comments:
 
 TW: The cv cam utils that came with the initial release of OpenCV for LINUX Beta4

modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst

@@ -296,7 +296,7 @@ Approximates a polygonal curve(s) with the specified precision.
 The functions ``approxPolyDP`` approximate a curve or a polygon with another curve/polygon with less vertices so that the distance between them is less or equal to the specified precision. It uses the Douglas-Peucker algorithm
 http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm
 
-See https://github.com/Itseez/opencv/tree/master/samples/cpp/contours2.cpp for the function usage model.
+See https://github.com/opencv/opencv/tree/master/samples/cpp/contours2.cpp for the function usage model.
 
 
 ApproxChains

modules/objdetect/doc/cascade_classification.rst

@@ -21,7 +21,7 @@ The word "cascade" in the classifier name means that the resultant classifier co
 The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within the region of interest and the scale (this scale is not the same as the scale used at the detection stage, though these two scales are multiplied). For example, in the case of the third line feature (2c) the response is calculated as the difference between the sum of image pixels under the rectangle covering the whole feature (including the two white stripes and the black stripe in the middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to compensate for the differences in the size of areas. The sums of pixel values over a rectangular regions are calculated rapidly using integral images (see below and the :ocv:func:`integral` description).
 
 To see the object detector at work, have a look at the facedetect demo:
-https://github.com/Itseez/opencv/tree/master/samples/cpp/dbt_face_detection.cpp
+https://github.com/opencv/opencv/tree/master/samples/cpp/dbt_face_detection.cpp
 
 The following reference is for the detection part only. There is a separate application called  ``opencv_traincascade`` that can train a cascade of boosted classifiers from a set of samples.
 

modules/python/test/test_digits.py

@@ -58,7 +58,7 @@ def deskew(img):
 
 class StatModel(object):
     def load(self, fn):
-        self.model.load(fn)  # Known bug: https://github.com/Itseez/opencv/issues/4969
+        self.model.load(fn)  # Known bug: https://github.com/opencv/opencv/issues/4969
     def save(self, fn):
         self.model.save(fn)
 

modules/python/test/test_gaussian_mix.py

@@ -41,7 +41,7 @@ class gaussian_mix_test(NewOpenCVTests):
         em = cv2.EM(cluster_n,cv2.EM_COV_MAT_GENERIC)
         em.train(points)
         means = em.getMat("means")
-        covs = em.getMatVector("covs")  # Known bug: https://github.com/Itseez/opencv/pull/4232
+        covs = em.getMatVector("covs")  # Known bug: https://github.com/opencv/opencv/pull/4232
         found_distrs = zip(means, covs)
 
         matches_count = 0

modules/python/test/tests_common.py

@@ -21,7 +21,7 @@ class OpenCVTests(unittest.TestCase):
     # path to local repository folder containing 'samples' folder
     repoPath = None
     # github repository url
-    repoUrl = 'https://raw.github.com/Itseez/opencv/2.4'
+    repoUrl = 'https://raw.github.com/opencv/opencv/2.4'
     # path to local folder containing 'camera_calibration.tar.gz'
     dataPath = None
     # data url
@@ -126,7 +126,7 @@ class NewOpenCVTests(unittest.TestCase):
     repoPath = None
     extraTestDataPath = None
     # github repository url
-    repoUrl = 'https://raw.github.com/Itseez/opencv/master'
+    repoUrl = 'https://raw.github.com/opencv/opencv/master'
 
     def get_sample(self, filename, iscolor = cv2.IMREAD_COLOR):
         if not filename in self.image_cache: