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

The word detector didn't make much sense!

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.

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.