Replaced internal variational refinement implementation inside DeepFlow
with a new optimized one, which provides about the same quality while
being 4-6 times faster.

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.

* Sparse match interpolator interface and EdgeAwareInterpolator were
added to the ximgproc module
* New optical flow algorithm, based on PyrLK sparse OF and sparse match
interpolation, is added to the optflow module

Algorithm::info()-related code disabled-after moving to opencv_contrib caused compilation errors

Added usage examples

Added documentation for optflow_io

+bugfix: handling invalid algorithm choice

+ solving MSVC compilation errors; assuming that int and float are 4-bytes long - possibly needs fixing