If the disparity maps have the depth CV_32F, they are wls-filtered
in `DisparityWLSFilterImpl::filter` but the result is not saved in the
OutputArray of the method. Now, the result is copied to that array.
Minor changes to the quality module to support Visual Studio 2013 (#2046)
* quality: revise syntax to support Visual C++ 2013
* quality: changes to standardize constness between Visual C++ 2013 and other compilers
* Fixed bug that made cuda::filter give corrupted output when different filters called in different threads. This was due to use of global texture variable shared by all filters.
* REV: fixed tab issue for opencv coding style...
original commit: 8158e5b7a0
This built correctly before because tests were run with the highgui module enabled.
The sfm module links against the features2d module, which brings in the highgui module as an optional target if enabled.
Through this path, imread was working correctly despite the file including the wrong header and not declaring its linking dependency.
Expose line descriptor python (#1994)
* Created wrappers for other line_descriptor functions/classes
* Changes to expose LSD and EDLines params in Python and make line descriptor available in Python
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: fb8e652c3f
* __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: 970293a229
- 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: 4366c8734f
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.
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
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