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included in the edge graph.  If it isn't then the output labels from
chinese_whispers would be missing faces in this degenerate case.  So basically this fixes a bug
where chinese_whispers(), when called from python, would sometimes return a labels array
that doesn't include labels for all the inputs.

out of the random_cropper.

* remove unused variable

* modify variable type from int to size_t

* fix previous delete, we need to call chinese_whispers here but we don't need its result now

In particular, these new functions don't need to be inside the face
recognition class.  So I moved them out.  I also fixed many incorrect
copy/pasted comments and clarified parts of the example code.

landmarking models.

because it's a much less stressing hand motion to hit END that i in most cases.

* improvements to cnn face detection interface

* mmod rectangle object renaming. possibility to set batch size in multi image detection. Added check to make sure images are all the same size.

next using dlib::correlation_tracker.

histogram is equalized or unmodified.  This way, if you are looking at
particularly dark or badly contrasted images you can toggle this mode and maybe
get a better view of what you are labeling.

everything in it, it now makes a python file as before but an additional binary
file with all the weights in it.  This way, if you are working with a network
with a very large number of weights you won't end up with a crazy large python
script.

the behavior of dlib's add_prev layers.

when the converter is run.

So I just removed the error check that was preventing the conversion from
proceeding in that case.  I also added more useful output messages about
setting input tensor dimensions.

to caffe models.  Not all layers are supported yet, but it works on the net
from the first dlib dnn example program.  Will add the rest of the layers soon.