Image Quality Analysis (IQA) API (#1990)

* Implement Quality API

* Update README, fixes for gcc/tests

* Add license headers

* Update README

* Update README

* Case fixing, CMakeLists.txt update

* Update CV_EXPORTS_W

* Updates for compiler and doxygen warnings

* Updates for doxygen

* Updates based on feedback from @alalek

* Compliance updates

* case fixing

* case fixing

* OpenCL test fixes

* Case fixing

* Python fixes

* Python fixes

* Compliance updates

* Python binding fix

* Documentation updates

* Update README, fix cv::Scalar ops

* Updated precompiled headers for src cpp

* Removed internal a/b perf tests

Due to size limit of shared memory, histogram is built on
the global memory for CV_16UC1 case.

The amount of memory needed for building histogram is:

    65536 * 4byte = 256KB

and shared memory limit is 48KB typically.

Added test cases for CV_16UC1 and various clip limits.
Added perf tests for CV_16UC1 on both CPU and CUDA code.

There was also a bug in CV_8UC1 case when redistributing
"residual" clipped pixels. Adding the test case where clip
limit is 5.0 exposes this bug.

original commit: https://github.com/opencv/opencv/commit/fb8e652c3f20d377e9f935faee370ed28fb60122

* __shfl_up_sync with proper mask value for CUDA >= 9

* BlockScanInclusive for CUDA >= 9

* compatible_shfl_up for use in integral.hpp

* Use CLAHE in cudev

* Add tests for BlockScan

original commit: https://github.com/opencv/opencv/commit/970293a229ef314603ffaf77fc62495bf849aba8

* Fix spelling of perpectiveRemovePixelPerCell in struct docstring

* Fix spelling of perpectiveRemovePixelPerCell in detection tutorial

- Before this PR, following tests failed on some platform.
  CUDA_OptFlow/FarnebackOpticalFlow.Accuracy/19
  CUDA_OptFlow/FarnebackOpticalFlow.Accuracy/23

- The algorithm now recognizes the OPTFLOW_USE_INITIAL_FLOW flag.
  Previously, when the flag was set, it did not use the flow data
  passed as input, instead used some garbage data in memory.

- More strict test limit.

original commit: https://github.com/opencv/opencv/commit/4366c8734fa7cf69c4a9bdcea91f41d1af345e9c

Resolves #1963

Add robust local optical flow (RLOF) implementations (#1940)

* Add robust local optical flow (RLOF) implementations which is an improved pyramidal iterative Lucas-Kanade approach. This implementations contains interfaces for sparse optical flow for feature tracking and dense optical flow based on sparse-to-dense interpolation schemes.
Add performance and accuracy tests have been implementation as well as documentation with the related publications

* - exchange tabs with spaces
- fix optflow.bib indentation
- remove optflow_o.hpp
- change RLOFOpticalFlowParameter interfaces to Ptr<RLOFOpticalFlowParameter>
to remove error on building. Fix warnings

* introducing precompiler flag RLOD_SSE

* remove header that could not be found

* remove whitespaces
fix perf and accuracy tests

* remove x86intrin.h header

* fix ios and arm by removing last sse commands

* fix warnings for windows compilation

* fix documentation RLOFOpticalFlowParameter

* integrate cast to remove last warnings

* * add create method and function inferfaces to RLOFOpticalFlowParamter to enable python wrapper interfaces

* white space fixes / coding style

* fix perf test

* other changes: precomp.hpp / static

* use Matx44f and Vec4f instead of Mat

* normSigmas into constants

* replace ceil() calls

* maximum level is set to 5 so that it is similar value used in the papers

* implement paralellized horizontal cross segmentation as used in Geistert2016

* drop dead code

* Avoid using "data" and "step" calculations. Use .ptr<mat_type>(row, col) instead.

* Avoid using "data" and "step" calculations. Use .ptr<mat_type>(row, col) instead.

* bugfix on BEPLK with ica and adapt the accuracy tests

* more 'static' functions

* bugfix after changing ptr + step to .ptr(y,x) calls by adjusting ROI of
prevImage, currImage and derivI as well as changing the offset of the
points in the invoker classes.

* add some static_cast to avoid warning

* remove 50 grid size sample from perf test. This grid size is to sparse
for the epic interpolation

* remove notSameColor function since it is not used anymore

Implementation of Quasi Dense Stereo algorithm. (#1941)

* initial commit.

* Remove license header.

* Fix python wrap flags

* Change std::string to cv::String, in function declarations, to resolve compilation issues.

* Add python wrapper extending header

* Fix python wrapper conflicts

* Fix implicit type conversions

* Change C API types and enums to C++.

* Remove redundant included headers and move wanted headers to src/precomp.hpp

* Remove saturate header

* Remove unnecessary python wrapping flags

* Removed defaults parameter header

* Split declaration and implementation of the class using Pimpl.

* Fix to comply with new public API.

* Remove unnecessary modules

* Fix maybe-uninitialized warnings on linux

* Migration to stereo module

* Remove CV_PROP_RW flag.

* Remove CV_EXPORTS flags from class members.

* Fix: Removed misplaced flag

* Remove empty lines.

* Move queue to private headers.

* Fix default arguments of public methods.

* Add authors information and switch to the compact version of license header.

* Reorganize and fix markdown files. Create a table of content and move tutorials in new directories. Modify samples and tutorials to use snippet and include Doxygen commands.

* Change argument name dMatch->denseMatch, to avoid confusion with cv::DMatch build-in type.

* Remove duplicate snippet.

* Fix: change vector resize to reserve.

* Fix: replace extensive license header with the compact version.

nvidia video codec sdk need to be installed separately (https://developer.nvidia.com/nvidia-video-codec-sdk#Download)
only Samples/NvCodec/NvDecoder/nvcuvid.h and Samples/NvCodec/NvDecoder/cuviddec.h is necessary

so meshes in any orientation can be properly viewed

I think that that there was a typo in the variable names. fill2, texture2, and size2 were never used. 
Also, minor fix to reduce initialization costs of vector