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
Added clapack
* bring a small subset of Lapack, automatically converted to C, into OpenCV
* added missing lsame_ prototype
* * small fix in make_clapack script
* trying to fix remaining CI problems
* fixed character arrays' initializers
* get rid of F2C_STR_MAX
* * added back single-precision versions for QR, LU and Cholesky decompositions. It adds very little extra overhead.
* added stub version of sdesdd.
* uncommented calls to all the single-precision Lapack functions from opencv/core/src/hal_internal.cpp.
* fixed warning from Visual Studio + cleaned f2c runtime a bit
* * regenerated Lapack w/o forward declarations of intrinsic functions (such as sqrt(), r_cnjg() etc.)
* at once, trailing whitespaces are removed from the generated sources, just in case
* since there is no declarations of intrinsic functions anymore, we could turn some of them into inline functions
* trying to eliminate the crash on ARM
* fixed API and semantics of s_copy
* * CLapack has been tested successfully. It's now time to restore the standard LAPACK detection procedure
* removed some more trailing whitespaces
* * retained only the essential stuff in CLapack
* added checks to lapack calls to gracefully return "not implemented" instead of returning invalid results with "ok" status
* disabled warning when building lapack
* cmake: update LAPACK detection
Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
- OpenCL kernel cleanup processing is asynchronous and can be called even after forced clFinish()
- buffers are released later in asynchronous mode
- silence these false positive cases for asynchronous cleanup
* 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()
trying to fix handling file storages with extremely long lines
* trying to fix handling of file storages with extremely long lines: https://github.com/opencv/opencv/issues/11061
* * fixed errorneous pointer access in JSON parser.
* it's now crash-test time! temporarily set the initial parser buffer size to just 40 bytes. let's run all the test and check if the buffer is always correctly resized and handled
* fixed pointer use in JSON parser; added the proper test to catch this case
* fixed the test to make it more challenging. generate test json with
*
**
***
etc. shape
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
