on some newer OS X installs.

are supposed to since, on some systems, these libraries aren't installed
correctly and will cause linker errors if used.

incorrect outputs when the requested chip stretched the image unevenly
vertically or horizontally.  This is because we used the best similarity
transform rather than affine transform between the image and the output chip.

certain compiler errors you get when accidentally trying to mutate
a const image a little easier to understand.

Also just cleaned up a few minor details.

I changed the spec to reflect this.

like cv_image as input.

A little bit hacky, but should be fine. Supports both fhog
detectors and the "cached" simple_object_detector. Also, maintains
the upscale parameter for testing

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.

has valid outputs.

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.