Log Message:
Update graphics/opencv to 3.0.0.

Major changes (besides bugfixes):
- opencv_contrib (http://github.com/itseez/opencv_contrib) repository
  has been added.
- a subset of Intel IPP (IPPCV) is given to us and our users free
  of charge, free of licensing fees, for commercial and non-commerical
  use.
- T-API (transparent API) has been introduced, this is transparent GPU
  acceleration layer using OpenCL. It does not add any compile-time or
  runtime dependency of OpenCL. When OpenCL is available, it's detected
  and used, but it can be disabled at compile time or at runtime.
- ~40 OpenCV functions have been accelerated using NEON intrinsics and
  because these are mostly basic functions, some higher-level functions
  got accelerated as well.
- There is also new OpenCV HAL layer that will simplifies creation
  of NEON-optimized code and that should form a base for the open-source
  and proprietary OpenCV accelerators.
- The documentation is now in Doxygen: http://docs.opencv.org/master/
- We cleaned up API of many high-level algorithms from features2d, calib3d,
  objdetect etc. They now follow the uniform "abstract interface - hidden
  implementation" pattern and make extensive use of smart pointers \ 
(Ptr<>).
- Greatly improved and extended Python & Java bindings (also, see below on
  the Python bindings), newly introduced Matlab bindings
- Improved Android support - now OpenCV Manager is in Java and supports
  both 2.4 and 3.0.
- Greatly improved WinRT support, including video capturing and
  multi-threading capabilities. Thanks for Microsoft team for this!
- Big thanks to Google who funded several successive GSoC programs and
  let OpenCV in. The results of many successful GSoC 2013 and 2014 projects
  have been integrated in opencv 3.0 and opencv_contrib (earlier results
  are also available in OpenCV 2.4.x). We can name:
  - text detection
  - many computational photography algorithms (HDR, inpainting, edge-aware
    filters, superpixels,...)
  - tracking and optical flow algorithms
  - new features, including line descriptors, KAZE/AKAZE
  - general use optimization (hill climbing, linear programming)
  - greatly improved Python support, including Python 3.0 support, many new
    tutorials & samples on how to use OpenCV with Python.
  - 2d shape matching module and 3d surface matching module
  - RGB-D module
  - VTK-based 3D visualization module

For full changelog see:

  http://code.opencv.org/projects/opencv/wiki/ChangeLog

For 2.4 to 3.0 transition, see the transition guide:

  http://docs.opencv.org/master/db/dfa/tutorial_transition_guide.html