dnn(eltwise): fix handling of different number of channels

* dnn(test): reproducer for Eltwise layer issue from PR16063

* dnn(eltwise): rework support for inputs with different channels

* dnn(eltwise): get rid of finalize(), variableChannels

* dnn(eltwise): update input sorting by number of channels

- do not swap inputs if number of channels are same after truncation

* dnn(test): skip "shortcut" with batch size 2 on MYRIAD targets

-c is required to avoid linking (and the associated missing "main" message)
when linker flags like "-Wl,-z,relro" are passed to GCC

* Add eps error checking for approxPolyDP to allow sensible values only
for epsilon value of Douglas-Peucker algorithm.

* Review changes for PR

* 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.

* imgproc(IPP): wrong result from threshold(THRESH_TOZERO)

* imgproc(IPP): disable IPP code to pass THRESH_TOZERO test

- don't override current application thread names
- set name for own threads only

Test create custom layer in python

* check is contiguos

* Add custom layer test

* Fix test

* Remove assert

* Move assert to pyopencv dnn

* remove assert

* Add unregister

* Fix python2

* proto to bytearray

* Fix data type