Ordinary quaternion
* version 1.0
* add assumeUnit;
add UnitTest;
check boundary value;
fix the func using method: func(obj);
fix 4x4;
add rodrigues vector transformation;
fix mat to quat;
* fix blank and tab
* fix blank and tab
modify test;cpp to hpp
* mainly improve comment;
add rvec2Quat;fix toRodrigues;
fix throw to CV_Error
* fix bug of quatd * int;
combine hpp and cpp;
fix << overload error in win system;
modify include in test file;
* move implementation to quaternion.ini.hpp;
change some constructor to createFrom* function;
change Rodrigues vector to rotation vector;
change the matexpr to mat of 3x3 return type;
improve comments;
* try fix log function error in win
* add enums for assumeUnit;
improve docs;
add using std::cos funcs
* remove using std::* from header;
add std::* in affine.hpp,warpers_inl.hpp;
* quat: coding style
* quat: AssumeType => QuatAssumeType
[GSoC] OpenCV.js: WASM SIMD optimization 2.0
* gsoc_2020_simd Add perf test for filter2d
* add perf test for kernel scharr and kernel gaussianBlur
* add perf test for blur, medianBlur, erode, dilate
* fix the errors for the opencv PR robot
fix the trailing whitespace.
* add perf tests for kernel remap, warpAffine, warpPersepective, pyrDown
* fix a bug in modules/js/perf/perf_imgproc/perf_remap.js
* add function smoothBorder in helpfun.js and remove replicated function in perf test of warpAffine and warpPrespective
* fix the trailing white space issues
* add OpenCV.js loader
* Implement the Loader with help of WebAssembly Feature Detection, remove trailing whitespaces
* modify the explantion for loader in js_setup.markdown and fix bug in loader.js
- Added cross compile cmake file for target riscv64-clang
- Extended cmake for RISC-V and added instruction checks
- Created intrin_rvv.hpp with C++ version universal intrinsics
* Add documentation about usage of cv2eigen functions in eigen.hpp
* Fixed Doxygen syntax.
Co-authored-by: Alexander Smorkalov <smorkalov.a.m@gmail.com>
* fixed#17044
1. fixed Python part of the tutorial about using OpenCV XML-YAML-JSON I/O functionality from C++ and Python.
2. added startWriteStruct() and endWriteStruct() methods to FileStorage
3. modifed FileStorage::write() methods to make them work well inside sequences, not only mappings.
* try to fix the doc builder
* added Python regression test for FileStorage I/O API ([TODO] iterating through long sequences can be very slow)
* fixed yaml testing
* add eigen tensor conversion functions
* add eigen tensor conversion tests
* add support for column major order
* update eigen tensor tests
* fix coding style and add conditional compilation
* fix conditional compilation checks
* remove whitespace
* rearrange functions for easier reading
* reformat function documentation and add tensormap unit test
* cleanup documentation of unit test
* remove condition duplication
* check Eigen major version, not minor version
* restrict to Eigen v3.3.0+
* add documentation note and add type checking to cv2eigen_tensormap()
Vectorize minMaxIdx functions
* Updated documentation and intrinsic tests for v_reduce
* Add other files back in from the forced push
* Prevent an constant overflow with v_reduce for int8 type
* Another alternative to fix constant overflow warning.
* Fix another compiler warning.
* Update comments and change comparison form to be consistent with other vectorized loops.
* Change return type of v_reduce_min & max for v_uint8 and v_uint16 to be same as lane type.
* Cast v_reduce functions to int to avoid overflow. Reduce number of parameters in MINMAXIDX_REDUCE macro.
* Restore cast type for v_reduce_min & max to LaneType
* resize: HResizeLinear reduce duplicate work
There appears to be a 2x unroll of the HResizeLinear against k,
however the k value is only incremented by 1 during the unroll. This
results in k - 1 duplicate passes when k > 1.
Likewise, the final pass may not respect the work done by the vector
loop. Start it with the offset returned by the vector op if
implemented. Note, no vector ops are implemented today.
The performance is most noticable on a linear downscale. A set of
performance tests are added to characterize this. The performance
improvement is 10-50% depending on the scaling.
* imgproc: vectorize HResizeLinear
Performance is mostly gated by the gather operations
for x inputs.
Likewise, provide a 2x unroll against k, this reduces the
number of alpha gathers by 1/2 for larger k.
While not a 4x improvement, it still performs substantially
better under P9 for a 1.4x improvement. P8 baseline is
1.05-1.10x due to reduced VSX instruction set.
For float types, this results in a more modest
1.2x improvement.
* Update U8 processing for non-bitexact linear resize
* core: hal: vsx: improve v_load_expand_q
With a little help, we can do this quickly without gprs on
all VSX enabled targets.
* resize: Fix cn == 3 step per feedback
Per feedback, ensure we don't overrun. This was caught via the
failure observed in Test_TensorFlow.inception_accuracy.
* core: disable invalid constructors in C API by default
- C API objects will lose their default initializers through constructors
* samples: stop using of C API
Tests for argument conversion of Python bindings generator
* Tests for parsing elemental types from Python bindings
- Add positive and negative tests for int, float, double, size_t,
const char*, bool.
- Tests with wrong conversion behavior are skipped.
* Move implicit conversion of bool to integer/floating types to wrong
conversion behavior.
Improving VSX performance of integral function
* Adding support for vector get function on VSX datatypes so the
integral function gains a bit of performance.
* Removing get as a datatype member function and implementing a new HAL
instruction v_extract_n to get the n-th element of a vector register.
* Adding SSE/NEON/AVX intrinsics.
* Implement new HAL instruction v_broadcast_element on VSX/AVX/NEON/SSE.
* core(simd): add tests for v_extract_n/v_broadcast_element
- updated docs
- commented out code to repair compilation
- added WASM and MSA default implementations
* core(simd): fix compilation
- x86: avoid _mm256_extract_epi64/32/16/8 with MSVS 2015
- x86: _mm_extract_epi64 is 64-bit only
* cleanup
- move TLS & instrumentation code out of core/utility.hpp
- (*) TLSData lost .gather() method (to dispose thread data on thread termination)
- use TLSDataAccumulator for reliable collecting of thread data
- prefer using of .detachData() + .cleanupDetachedData() instead of .gather() method
(*) API is broken: replace TLSData => TLSDataAccumulator if gather required
(objects disposal on threads termination is not available in accumulator mode)
Fixing bug with comparison of v_int64x2 or v_uint64x2
* Casting v_uint64x2 to v_float64x2 and comparing does NOT work in all cases. Rewrite using epi64 instructions - faster too.
* Fix bad merge.
* Fix equal comparsion for non-SSE4.1. Add test cases for v_int64x2 comparisons.
* Try to fix merge conflict.
* Only test v_int64x2 comparisons if CV_SIMD_64F
* Fix compiler warning.
* New v_reverse HAL intrinsic for reversing the ordering of a vector
* Fix conflict.
* Try to resolve conflict again.
* Try one more time.
* Add _MM_SHUFFLE. Remove non-vectorize code in SSE2. Fix copy and paste issue with NEON.
* Change v_uint16x8 SSE2 version to use shuffles
* core: rework and optimize SIMD implementation of dotProd
- add new universal intrinsics v_dotprod[int32], v_dotprod_expand[u&int8, u&int16, int32], v_cvt_f64(int64)
- add a boolean param for all v_dotprod&_expand intrinsics that change the behavior of addition order between
pairs in some platforms in order to reach the maximum optimization when the sum among all lanes is what only matters
- fix clang build on ppc64le
- support wide universal intrinsics for dotProd_32s
- remove raw SIMD and activate universal intrinsics for dotProd_8
- implement SIMD optimization for dotProd_s16&u16
- extend performance test data types of dotprod
- fix GCC VSX workaround of vec_mule and vec_mulo (in little-endian it must be swapped)
- optimize v_mul_expand(int32) on VSX
* core: remove boolean param from v_dotprod&_expand and implement v_dotprod_fast&v_dotprod_expand_fast
this changes made depend on "terfendail" review
- renamed Cascade Lake AVX512_CEL => AVX512_CLX (align with Intel SDE tool)
- fixed CLX instruction sets (no IFMA/VBMI)
- added flag to bypass CPU baseline check: OPENCV_SKIP_CPU_BASELINE_CHECK
[GSoC 2019] Improve the performance of JavaScript version of OpenCV (OpenCV.js)
* [GSoC 2019]
Improve the performance of JavaScript version of OpenCV (OpenCV.js):
1. Create the base of OpenCV.js performance test:
This perf test is based on benchmark.js(https://benchmarkjs.com). And first add `cvtColor`, `Resize`, `Threshold` into it.
2. Optimize the OpenCV.js performance by WASM threads:
This optimization is based on Web Worker API and SharedArrayBuffer, so it can be only used in browser.
3. Optimize the OpenCV.js performance by WASM SIMD:
Add WASM SIMD backend for OpenCV Universal Intrinsics. It's experimental as WASM SIMD is still in development.
* [GSoC2019]
1. use short license header
2. fix documentation node issue
3. remove the unused `hasSIMD128()` api
* [GSoC2019]
1. fix emscripten define
2. use fallback function for f16
* [GSoC2019]
Fix rebase issue
