Fixed some inconsistencies in interfaces of different white balance algorithms and improved
the documentation a bit. Also added an option of loading a model during runtime in the
learning-based white balance algorithm. Added a simple tutorial describing how to train a
white balance model.

Implemented a learning-based color balance algorithm, which typically provides
noticeably better quality than grayworld, while being several times slower.
Added a python script for training this algorithm, as well as the benchmarking script
for objective quality evaluation.

Also made some modifications to the grayworld algorithm implementation, including the
16 bit image support and overall acceleration by about 1.7 times (for 8 bit images).

Added variational refinement as a separate class (based on implementation
inside DeepFlow, but significantly accelerated, about 4-6 times faster),
accelerated the main dense inverse search algorithm. Added several new
features including patch mean normalization for increased robustness to
illumination changes and spatial propagation, which often helps to recover
from errors introduced by the coarse-to-fine scheme. Expanded the
documentation, added new accuracy and perf tests. Refactored some of
the already existing optical flow accuracy tests.

Basic interfaces and a partial implementation of the Dense Inverse
Search (DIS) optical flow algorithm without variational refinement. Also
added a python benchmarking script that can evaluate different optical
flow algorithms on the MPI Sintel and Middlebury datasets and build
overall comparative charts.

Update 2

ensure that the double pass through CMakeLists.txt files do not generate double output

see https://github.com/Itseez/opencv/issues/6370

  - remove double-check, make MSVC happy