previous dlib versions.

conform to dlib's generic image interface.  This makes dealing with
numpy images in pybind11 modules much nicer.

- Moved all uses of PYBIND11_MAKE_OPAQUE to a common header to avoid possilbe ODR violations.
- Added python binding for make_bounding_box_regression_training_data().

* Replace boost::python with pybind11

* Replace add_python_module with pybind11_add_module

* Fix clang error on type-dependent expression

Add a simple print and serialization scheme for
shape_predictor_training_options. This enables you to serialize
your training options.

Also just cleaned up a few minor details.

This is the biggest change so far. Now, there are two different
classes of interface. One where you pass ONLY file paths,
and one where you pass ONLY Python objects.

The file paths are maintained to keep a matching interface with
the C++ examples of dlib. So shape predicition and object
detection can be trained using the dlib XML file paths and then
serialize the detectors to disk.

Shape prediction and object detection can also be trained using
numpy arrays and in-memory objects. In this case, the predictor
and detector objects are returned from the training functions.
To facilitate serializing these objects, they now have a 'save'
method.

Tetsing follows a similar pattern, in that it can take either XML
files are or in-memory objects. I also added back the concept of
upsampling during testing to make amends for removing the
simple_object_detector_py struct.

Also, removed the saving of the upsample which I missed from
before (since I'm not using the struct now). I understand why
the upsample was being saved, but I don't necessarily agree it
is particularly useful as you should really be upsampling on
a case by case basis at test time.

Fix typo as well

This includes the full_object_detection, a new struct in the same
vein as the simple_object_detector_training_options and of
course, the shape predictor classes themselves.

All of training, fitting and testing are wrapped.