Merge pull request #3974 from StevenPuttemans:fix_RGB_naming_master

doc/tutorials/core/basic_geometric_drawing/basic_geometric_drawing.markdown

@@ -35,13 +35,13 @@ Point pt =  Point(10, 8);
 
 -   Represents a 4-element vector. The type Scalar is widely used in OpenCV for passing pixel
     values.
--   In this tutorial, we will use it extensively to represent RGB color values (3 parameters). It is
+-   In this tutorial, we will use it extensively to represent BGR color values (3 parameters). It is
     not necessary to define the last argument if it is not going to be used.
 -   Let's see an example, if we are asked for a color argument and we give:
     @code{.cpp}
     Scalar( a, b, c )
     @endcode
-    We would be defining a RGB color such as: *Red = c*, *Green = b* and *Blue = a*
+    We would be defining a BGR color such as: *Blue = a*, *Green = b* and *Red = c*
 
 Code
 ----

doc/tutorials/core/basic_linear_transform/basic_linear_transform.markdown

@@ -122,8 +122,8 @@ Explanation
     *image.size()* and *image.type()*
 
 -#  Now, to perform the operation \f$g(i,j) = \alpha \cdot f(i,j) + \beta\f$ we will access to each
-    pixel in image. Since we are operating with RGB images, we will have three values per pixel (R,
-    G and B), so we will also access them separately. Here is the piece of code:
+    pixel in image. Since we are operating with BGR images, we will have three values per pixel (B,
+    G and R), so we will also access them separately. Here is the piece of code:
     @code{.cpp}
     for( int y = 0; y < image.rows; y++ ) {
         for( int x = 0; x < image.cols; x++ ) {

doc/tutorials/core/how_to_scan_images/how_to_scan_images.markdown

@@ -57,7 +57,7 @@ the samples directory of OpenCV at the cpp tutorial code for the core section. I
 how_to_scan_images imageName.jpg intValueToReduce [G]
 @endcode
 The final argument is optional. If given the image will be loaded in gray scale format, otherwise
-the RGB color way is used. The first thing is to calculate the lookup table.
+the BGR color space is used. The first thing is to calculate the lookup table.
 
 @snippet how_to_scan_images.cpp dividewith
 
@@ -88,7 +88,7 @@ case of a gray scale image we have something like:
 ![](tutorial_how_matrix_stored_1.png)
 
 For multichannel images the columns contain as many sub columns as the number of channels. For
-example in case of an RGB color system:
+example in case of an BGR color system:
 
 ![](tutorial_how_matrix_stored_2.png)
 

doc/tutorials/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.markdown

@@ -101,7 +101,7 @@ possible to use the old functions and in the end just transform the result to a
 
 @snippet interoperability_with_OpenCV_1.cpp new
 
-Because, we want to mess around with the images luma component we first convert from the default RGB
+Because, we want to mess around with the images luma component we first convert from the default BGR
 to the YUV color space and then split the result up into separate planes. Here the program splits:
 in the first example it processes each plane using one of the three major image scanning algorithms
 in OpenCV (C [] operator, iterator, individual element access). In a second variant we add to the

doc/tutorials/core/mat_the_basic_image_container/mat_the_basic_image_container.markdown

@@ -118,8 +118,8 @@ added.
 
 There are, however, many other color systems each with their own advantages:
 
--   RGB is the most common as our eyes use something similar, our display systems also compose
-    colors using these.
+-   RGB is the most common as our eyes use something similar, however keep in mind that OpenCV standard display
+    system composes colors using the BGR color space (a switch of the red and blue channel).
 -   The HSV and HLS decompose colors into their hue, saturation and value/luminance components,
     which is a more natural way for us to describe colors. You might, for example, dismiss the last
     component, making your algorithm less sensible to the light conditions of the input image.