Automatically test for NEON instructions when building python extensions and enable them if available.

Forgot about --yes USE_NEON_INSTRUCTIONS when removing --yes a moment
ago.  It's on by default now so now --yes really is unneeded.  The dlib
policy on this kind of thing going forward is to always have a cmake
script that tests if a feature like this is available and to enable it
automatically when building python extensions.

Removed --yes option from setup.py since it has long been a noop and its presence just confuses users.

* Add python api that generates desciptor(s) from the aligned image(s)

* Remove asserts from face_recognition.py example/tutorial

* In batch_compute_face_descriptors_from_aligned_images, use for-in loop to simplify the code

Improvde the document on binding methods and the error message if the aligned image is not of size 150x150

Fixed pytest not working with python 2.7.  So turning travis tests back on for that version of python.

incompatibility between python 2.7 and pytest, but only on travis.

Removing the install_requires cmake from setup.py because the pip cmake package is hopelessly busted at the moment.

* Exposing chinese_whispers directly

* Addressing comments

* Changed description
* Added support for distance weights
* Added tests

* Improving test to check returned results

fixed check for excessive detections in loss_mmod_

Ran into the problem where dets.size() was equal to max_num_initial_dets which then throws a subscript out of range error when accesing: dets[max_num_initial_dets].detection_confidence.  This fixes that issue.

Remove Python 3.5 tests on AppVeyor because something is wrong with python 3.5 on appveyor and the other tests are sufficient for python coverage.

Apply the #1514 fix even on non-Windows platforms, and change all remaining cudaStreamSynchronize calls (#1596)

* Apply the #1514 fix even on non-Windows platforms

* Try to fix #1513 even more by circumventing the remaining cudaStreamSynchronize calls

* Make the fix apply only if CUDA_VERSION == 10000

* Make the fix apply on CUDA 9.2 also

* CHECK_CUDA(cudaStreamSynchronize(stream));

ECX must be set to 0 to read this flag, the easiest way to achieve this
is to use __cpuid_count with 0 as the second argument.

* Add concat_prev layer, and U-net example for semantic segmentation

* Allow to supply mini-batch size as command-line parameter

* Decrease default mini-batch size from 30 to 24

* Resize t1, if needed

* Use DenseNet-style blocks instead of residual learning

* Increase default mini-batch size to 50

* Increase default mini-batch size from 50 to 60

* Resize even during the backward step, if needed

* Use resize_bilinear_gradient for the backward step

* Fix function call ambiguity problem

* Clear destination before adding gradient

* Works OK-ish

* Add more U-tags

* Tweak default mini-batch size

* Define a simpler network when using Microsoft Visual C++ compiler; clean up the DenseNet stuff (leaving it for a later PR)

* Decrease default mini-batch size from 24 to 23

* Define separate dnn filename for MSVC++ and not

* Add documentation for the resize_to_prev layer; move the implementation so that it comes after mult_prev

* Fix previous typo

* Minor formatting changes

* Reverse the ordering of levels

* Increase the learning-rate stopping criterion back to 1e-4 (was 1e-8)

* Use more U-tags even on Windows

* Minor formatting

* Latest MSVC 2017 builds fast, so there's no need to limit the depth any longer

* Tweak default mini-batch size again

* Even though latest MSVC can now build the extra layers, it does not mean we should add them!

* Fix naming

the register storage specifier was deprecated in C++11 and removed in C++17

* Add pointwise_divide operator

* Add tests for pointwise_divide function

* Replace in affine layer pointwise_multiply for division by its equivalent pointwise_divide.

* Add DLIB_USE_MKL_WITH_TBB opcion to CMake for Windows.

* Add to CMake MKL with TBB support for Linux

* Remove unnecessary tbb includes from CMake.

* Add back white spaces.