* Improve Canny by using _mm_movemask_epi8 to find next pixel magnitude greater than lower threshold. Added parallelized finalPass to Canny with variable gradients. Little changes in finalPass.
* Some things fixed
* use universal intrinsic for accumulate series using float/double
* accumulate, accumulateSquare, accumulateProduct and accumulateWeighted
* add v_cvt_f64_high in both SSE/NEON
* add test for conversion v_cvt_f64_high in test_intrin.cpp
* improve some existing universal intrinsic by using new instructions in Aarch64
* add workaround for Android build in intrin_neon.hpp
* Add Grana's connected components algorithm for 8-way connectivity. That algorithm is faster than Wu's one (currently implemented in opencv). For more details see https://github.com/prittt/YACCLAB.
* New functions signature and distance transform compatibility
* Add tests to imgproc/test/test_connectedcomponents.cpp
* Change of test_connectedcomponents.cpp for c++98 support
There is an issue with processing of abs(short) function for
negative argument.
Affected OpenCL devices:
- iGPU: Intel(R) HD Graphics 520 (OpenCL 2.0 )
- CPU: Intel(R) Core(TM) i5-6300U CPU @ 2.40GHz (OpenCL 2.0 (Build 10094))
* Common Canny parallelization added. TBB and single thread code removed. Final pass vectorized with SSE2 intrinsics.
* wrong #ifdef replaced with #if
* Merged to actual Canny version
* Merged common parallelized Canny with actual Canny implementation
* Remove 'Mutex *mutex' and pass 'Mutex mutex' from outside to parallelCanny
* Replaced extern Mutex with intern mutable Mutex.
When using OCL, the results of goodFeaturesToTrack() vary slightly from
run to run. This appears to be because the order of the results from
the findCorners kernel depends on thread execution and the sorting
function that is used at the end to rank the features only enforces are
partial sort order.
This does not materially impact the quality of the results, but it
makes it hard to build regression tests and generally introduces noise
into the system that should be avoided.
An easy fix is to change the sort function to enforce a total sort on
the features, even in cases where the match quality is exactly the same
for two features.
