drop C-API sample code

doc/tutorials/core/file_input_output_with_xml_yml/file_input_output_with_xml_yml.markdown

@@ -2,7 +2,7 @@ File Input and Output using XML and YAML files {#tutorial_file_input_output_with
 ==============================================
 
 @prev_tutorial{tutorial_discrete_fourier_transform}
-@next_tutorial{tutorial_interoperability_with_OpenCV_1}
+@next_tutorial{tutorial_how_to_use_OpenCV_parallel_for_}
 
 Goal
 ----

doc/tutorials/core/how_to_use_OpenCV_parallel_for_/how_to_use_OpenCV_parallel_for_.markdown

@@ -1,7 +1,7 @@
 How to use the OpenCV parallel_for_ to parallelize your code {#tutorial_how_to_use_OpenCV_parallel_for_}
 ==================================================================
 
-@prev_tutorial{tutorial_interoperability_with_OpenCV_1}
+@prev_tutorial{tutorial_file_input_output_with_xml_yml}
 
 Goal
 ----

doc/tutorials/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.markdown

@@ -1,143 +0,0 @@
-Interoperability with OpenCV 1 {#tutorial_interoperability_with_OpenCV_1}
-==============================
-
-@prev_tutorial{tutorial_file_input_output_with_xml_yml}
-@next_tutorial{tutorial_how_to_use_OpenCV_parallel_for_}
-
-Goal
-----
-
-For the OpenCV developer team it's important to constantly improve the library. We are constantly
-thinking about methods that will ease your work process, while still maintain the libraries
-flexibility. The new C++ interface is a development of us that serves this goal. Nevertheless,
-backward compatibility remains important. We do not want to break your code written for earlier
-version of the OpenCV library. Therefore, we made sure that we add some functions that deal with
-this. In the following you'll learn:
-
--   What changed with the version 2 of OpenCV in the way you use the library compared to its first
-    version
--   How to add some Gaussian noise to an image
--   What are lookup tables and why use them?
-
-General
--------
-
-When making the switch you first need to learn some about the new data structure for images:
-@ref tutorial_mat_the_basic_image_container, this replaces the old *CvMat* and *IplImage* ones. Switching to the new
-functions is easier. You just need to remember a couple of new things.
-
-OpenCV 2 received reorganization. No longer are all the functions crammed into a single library. We
-have many modules, each of them containing data structures and functions relevant to certain tasks.
-This way you do not need to ship a large library if you use just a subset of OpenCV. This means that
-you should also include only those headers you will use. For example:
-@code{.cpp}
-#include <opencv2/core.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/highgui.hpp>
-@endcode
-All the OpenCV related stuff is put into the *cv* namespace to avoid name conflicts with other
-libraries data structures and functions. Therefore, either you need to prepend the *cv::* keyword
-before everything that comes from OpenCV or after the includes, you just add a directive to use
-this:
-@code{.cpp}
-using namespace cv;  // The new C++ interface API is inside this namespace. Import it.
-@endcode
-Because the functions are already in a namespace there is no need for them to contain the *cv*
-prefix in their name. As such all the new C++ compatible functions don't have this and they follow
-the camel case naming rule. This means the first letter is small (unless it's a name, like Canny)
-and the subsequent words start with a capital letter (like *copyMakeBorder*).
-
-Now, remember that you need to link to your application all the modules you use, and in case you are
-on Windows using the *DLL* system you will need to add, again, to the path all the binaries. For
-more in-depth information if you're on Windows read @ref tutorial_windows_visual_studio_opencv and for
-Linux an example usage is explained in @ref tutorial_linux_eclipse.
-
-Now for converting the *Mat* object you can use either the *IplImage* or the *CvMat* operators.
-While in the C interface you used to work with pointers here it's no longer the case. In the C++
-interface we have mostly *Mat* objects. These objects may be freely converted to both *IplImage* and
-*CvMat* with simple assignment. For example:
-@code{.cpp}
-Mat I;
-IplImage pI = I;
-CvMat    mI = I;
-@endcode
-Now if you want pointers the conversion gets just a little more complicated. The compilers can no
-longer automatically determinate what you want and as you need to explicitly specify your goal. This
-is to call the *IplImage* and *CvMat* operators and then get their pointers. For getting the pointer
-we use the & sign:
-@code{.cpp}
-Mat I;
-IplImage* pI     = &I.operator IplImage();
-CvMat* mI        =  &I.operator CvMat();
-@endcode
-One of the biggest complaints of the C interface is that it leaves all the memory management to you.
-You need to figure out when it is safe to release your unused objects and make sure you do so before
-the program finishes or you could have troublesome memory leaks. To work around this issue in OpenCV
-there is introduced a sort of smart pointer. This will automatically release the object when it's no
-longer in use. To use this declare the pointers as a specialization of the *Ptr* :
-@code{.cpp}
-Ptr<IplImage> piI = &I.operator IplImage();
-@endcode
-Converting from the C data structures to the *Mat* is done by passing these inside its constructor.
-For example:
-@code{.cpp}
-Mat K(piL), L;
-L = Mat(pI);
-@endcode
-
-A case study
-------------
-
-Now that you have the basics done [here's](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
-an example that mixes the usage of the C interface with the C++ one. You will also find it in the
-sample directory of the OpenCV source code library at the
-`samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp` .
-To further help on seeing the difference the programs supports two modes: one mixed C and C++ and
-one pure C++. If you define the *DEMO_MIXED_API_USE* you'll end up using the first. The program
-separates the color planes, does some modifications on them and in the end merge them back together.
-
-@snippet interoperability_with_OpenCV_1.cpp head
-@snippet interoperability_with_OpenCV_1.cpp start
-
-Here you can observe that with the new structure we have no pointer problems, although it is
-possible to use the old functions and in the end just transform the result to a *Mat* object.
-
-@snippet interoperability_with_OpenCV_1.cpp new
-
-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
-image some Gaussian noise and then mix together the channels according to some formula.
-
-The scanning version looks like:
-
-@snippet interoperability_with_OpenCV_1.cpp scanning
-
-Here you can observe that we may go through all the pixels of an image in three fashions: an
-iterator, a C pointer and an individual element access style. You can read a more in-depth
-description of these in the @ref tutorial_how_to_scan_images tutorial. Converting from the old function
-names is easy. Just remove the cv prefix and use the new *Mat* data structure. Here's an example of
-this by using the weighted addition function:
-
-@snippet interoperability_with_OpenCV_1.cpp noisy
-
-As you may observe the *planes* variable is of type *Mat*. However, converting from *Mat* to
-*IplImage* is easy and made automatically with a simple assignment operator.
-
-@snippet interoperability_with_OpenCV_1.cpp end
-
-The new *imshow* highgui function accepts both the *Mat* and *IplImage* data structures. Compile and
-run the program and if the first image below is your input you may get either the first or second as
-output:
-
-![](images/outputInteropOpenCV1.jpg)
-
-You may observe a runtime instance of this on the [YouTube
-here](https://www.youtube.com/watch?v=qckm-zvo31w) and you can [download the source code from here
-](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
-or find it in the
-`samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp`
-of the OpenCV source code library.
-
-@youtube{qckm-zvo31w}

doc/tutorials/core/mat_operations.markdown

@@ -150,11 +150,12 @@ If we need to copy the data, this is done using, for example, cv::Mat::copyTo or
 @snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Reference counting 2
 @end_toggle
 
-To the contrary with C API where an output image had to be created by the developer, an empty output Mat
-can be supplied to each function. Each implementation calls Mat::create for a destination matrix.
-This method allocates data for a matrix if it is empty. If it is not empty and has the correct size
-and type, the method does nothing. If however, size or type are different from the input arguments, the
-data is deallocated (and lost) and a new data is allocated. For example:
+An empty output Mat can be supplied to each function.
+Each implementation calls Mat::create for a destination matrix.
+This method allocates data for a matrix if it is empty.
+If it is not empty and has the correct size and type, the method does nothing.
+If however, size or type are different from the input arguments, the data is deallocated (and lost) and a new data is allocated.
+For example:
 
 @add_toggle_cpp
 @snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Reference counting 3
@@ -199,12 +200,6 @@ Selecting a region of interest:
 @snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Select ROI
 @end_toggle
 
-A conversion from Mat to C API data structures (**C++ only**):
-
-@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp C-API conversion
-
-Note that there is no data copying here.
-
 Conversion from color to greyscale:
 
 @add_toggle_cpp

doc/tutorials/core/table_of_content_core.markdown

@@ -82,17 +82,6 @@ understanding how to manipulate the images on a pixel level.
     You will see how to use the @ref cv::FileStorage data structure of OpenCV to write and read
     data to XML or YAML file format.
 
--   @subpage tutorial_interoperability_with_OpenCV_1
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Bernát Gábor
-
-    Did you used OpenCV before its 2.0 version? Do you wanna know what happened with your library
-    with 2.0? Don't you know how to convert your old OpenCV programs to the new C++ interface?
-    Look here to shed light on all this questions.
-
-
 -   @subpage tutorial_how_to_use_OpenCV_parallel_for_
 
     *Compatibility:* \>= OpenCV 2.4.3

doc/tutorials/introduction/transition_guide/transition_guide.markdown

@@ -62,8 +62,6 @@ Changes intended to ease the migration have been made in OpenCV 3.0, thus the fo
 #include "opencv2/<module>.hpp"
 @endcode
 
-2. If your code is using C API (`cv*` functions, `Cv*` structures or `CV_*` enumerations), include corresponding `*_c.h` headers. Although it is recommended to use C++ API, most of C-functions are still accessible in separate header files (opencv2/core/core_c.h, opencv2/core/types_c.h, opencv2/imgproc/imgproc_c.h, etc.).
-
 Modern way to use algorithm {#tutorial_transition_algorithm}
 ---------------------------
 1.  Algorithm instances must be created with cv::makePtr function or corresponding static factory method if available: