Clarify component statistics documentation
* Change ConnectedComponentsTypes documentation
Change from "algorithm output formats" to "statistics" because it specifies types of statistics, not formats.
* Documentation: clarify component statistics
Explain that ConnectedComponentTypes selects a statistic.
* improved fitEllipse and fitEllipseDirect accuracy in singular or close-to-singular cases (see issue #9923)
* scale points using double precision
* added normalization to fitEllipseAMS as well; fixed Java test case by raising the tolerance (it's unclear what is the correct result in this case).
* improved point perturbation a bit. make the code a little bit more clear
* trying to fix Java fitEllipseTest by slightly raising the tolerance threshold
* synchronized C++ version of Java's fitEllipse test
* removed trailing whitespaces
* Vectorize calculating integral for line for single and multiple channels
* Single vector processing for 4-channels - 25-30% faster
* Single vector processing for 4-channels - 25-30% faster
* Fixed AVX512 code for 4 channels
* Disable 3 channel 8UC1 to 32S for SSE2 and SSE3 (slower). Use new version of 8UC1 to 64F for AVX512.
fixed the ordering of contour convex hull points
* partially fixed the issue #4539
* fixed warnings and test failures
* fixed integer overflow (issue #14521)
* added comment to force buildbot to re-run
* extended the test for the issue 4539. Check the expected behaviour on the original contour as well
* added comment; fixed typo, renamed another variable for a little better clarity
* added yet another part to the test for issue #4539, where we run convexHull and convexityDetects on the original contour, without any manipulations. the rest of the test stays the same
* fixed several problems when running tests on Mac:
* OCL_pyrUp
* OCL_flip
* some basic UMat tests
* histogram badarg test (out of range access)
* retained the storepix fix in ocl_flip only for 16U/16S datatype, where the OpenCL compiler on Mac generates incorrect code
* moved deletion of ACCESS_FAST flag to non-SVM branch (where SVM is shared virtual memory (in OpenCL 2.x), not support vector machine)
* force OpenCL to use read/write for GPU<=>CPU memory transfers on machines with discrete video only on Macs. On Windows/Linux the drivers are seemingly smart enough to implement map/unmap properly (and maybe more efficiently than explicit read/write)
* Fix NN resize with dimentions > 4
* add test check for nn resize with channels > 4
* Change types from float to double
* Del unnecessary test file. Move nn test to test_imgwarp. Add 5 channels test only.
Actually, we can do this in constant time. xofs always
contains same or increasing offset values. We can instead
find the most extreme value used and never attempt to load it.
Similarly, we can note for all dx >= 0 and dx < (dwidth - cn)
where xofs[dx] + cn < xofs[dwidth-cn] implies dx < (dwidth - cn).
Thus, we can use this to control our loop termination optimally.
This fixes#16137 with little or no performance impact. I have
also added a debug check as a sanity check.
* Handle det == 0 in findCircle3pts.
Issue 16051 shows a case where findCircle3pts returns NaN for the
center coordinates and radius due to dividing by a determinant of 0. In
this case, the points are colinear, so the longest distance between any
2 points is the diameter of the minimum enclosing circle.
* imgproc(test): update test checks for minEnclosingCircle()
* imgproc: fix handling of special cases in minEnclosingCircle()
* imgproc: Prevent 1B overrun of 8C3 SIMD optimization
The fourth value read via v_load_q is essentially ignored,
but can cause trouble if it happens to cross page boundaries.
The final few iterations may attempt to read the most extreme
elements of S, which will read 1B beyond the array in most
aligment cases. Dynamically compute the stop. This could be
hoised from the loop, but will require a more extensive change.
Likewise, cleanup the iteration increment statements to make
it more obvious they do channel count (3) elements per pass.
This should resolve#16137
* imgproc(resize): extra check
* 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.
kadi soheib
Suleyman TURKMEN
Alexander Smorkalov
Alexander Alekhin
Ken Shirriff
Philippe FOUBERT
Vadim Pisarevsky
Michal W. Tarnowski
jsxyhelu
Ganesh Kathiresan
Tomoaki Teshima
Chip Kerchner
Alexander Alekhin
keeper121
Arnaud Brejeon
Paul E. Murphy
Brian Wignall
Vitaly Tuzov
mcellis33
Paul Murphy
shimat
Maksim Shabunin
RAJKIRAN NATARAJAN