added python wrapper macros in edge_filter.hpp
Merge branch 'edgefilter'
adas fix

- lowered memory usage (now the train use ~3x less memory)
- improved speed (removed some bottlenecks and added fast_log)
- added support to grayscale images
- the LUV features are not computed if the image is grayscale (leads to
an improved detection rate)
- added various parameters (limit_ps, limit_bg, alpha, slidingStep,
is_grayscale)
- added detect() function for a better control of the resize factor
warning fixes
buffer overrun fix
Adaptive manifold filter changes: Memory consuming was reduced and performance was improved (10% average).
Merge branch 'master' of github.com:Itseez/opencv_contrib
Adaptive manifold filter: downsample/upsample operations where paralleled.
adding more clear documentation
Java wrappers for bioinspired module
updated or_sun dataset loader
quickly corrected the previous refactoring of features2d/xfeatures2d: moved from set(SOME_PROP, val) to setSomeProp(val)
Merge pull request #118 from vpisarev/refactor_algorithms2

quickly corrected the previous refactoring of features2d/xfeatures2d
Bugfix: NNs not found due to precision error.
Corrected function call.
Merge pull request #120 from sanchom/master

Bugfix: NNs not found due to precision error.
Merge pull request #115 from mshabunin/java_automation

Java wrappers for bioinspired module
added fr_adience dataset loader
added tr_svt benchmark
logtopolar