#1672

updated links in cheatsheet
renamed directory for Mat tutorial 
changed links from willow docs to opencv.itseez.com, from Trac to current Redmine

modules/highgui/doc/reading_and_writing_images_and_video.rst

@@ -249,7 +249,7 @@ 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://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/kinect_maps.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 http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/kinect_maps.cpp
 
 
 VideoCapture::retrieve

modules/highgui/doc/user_interface.rst

@@ -184,7 +184,7 @@ Sets mouse handler for the specified window
 
     :param name: Window name
     
-    :param onMouse: Mouse callback. See OpenCV samples, such as  https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
+    :param onMouse: Mouse callback. See OpenCV samples, such as  http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
     
     :param param: The optional parameter passed to the callback.
 

modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst

@@ -258,7 +258,7 @@ 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 http://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/contours.cpp for the function usage model.
+See http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/contours.cpp for the function usage model.
 
 
 ApproxChains

modules/java/android_test/src/org/opencv/test/core/CoreTest.java

@@ -742,7 +742,7 @@ public class CoreTest extends OpenCVTestCase {
 
         Core.fillConvexPoly(gray0, polyline2, colorBlack, Core.LINE_8, 1);
 
-        assertEquals("see https://code.ros.org/trac/opencv/ticket/1284", 0, Core.countNonZero(gray0));
+        assertEquals("see http://code.opencv.org/issues/1284", 0, Core.countNonZero(gray0));
     }
 
     public void testFillPolyMatListOfListOfPointScalar() {

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://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/facedetect.cpp
+http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/facedetect.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.py

@@ -69,7 +69,7 @@ class OpenCVTests(unittest.TestCase):
 
     def get_sample(self, filename, iscolor = cv.CV_LOAD_IMAGE_COLOR):
         if not filename in self.image_cache:
-            filedata = urllib.urlopen("https://code.ros.org/svn/opencv/trunk/opencv/" + filename).read()
+            filedata = urllib.urlopen("http://code.opencv.org/svn/opencv/trunk/opencv/" + filename).read()
             imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
             cv.SetData(imagefiledata, filedata, len(filedata))
             self.image_cache[filename] = cv.DecodeImageM(imagefiledata, iscolor)