numpy's import_array() in a more robust way.

void*.  But the previous code didn't so it wouldn't compile.  Fixed that
problem.

* Exposed jitter_image in Python and added an example

* Return Numpy array directly

* Require numpy during setup

* Added install of Numpy before builds

* Changed pip install for user only due to security issues.

* Removed malloc

* Made presence of Numpy during compile optional.

* Conflict

* Refactored get_face_chip/get_face_chips to use Numpy as well.

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.

* 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.

* Expose python binding for running multiple detectors at once

* Remove unwanted typedef

* Add cmake option to use external libjpeg on Mac OS

* Add adjust_threshold to python object detector

* Add cmake option to use external libjpeg on Mac OS

* Add adjust_threshold to python object detector

* Revert "Add cmake option to use external libjpeg on Mac OS"

This reverts commit 01f7fd13ea3f2b519312900333f9b68847fd1633.

* Update detector example to set adjust_threshold

CMake.

Only issue is that the strong is hardcoded - otherwise
this is actually technically required by PEP 396

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

Easy change to allow comparisons on the Python side for
both rectangle and drectangle

non-square matrix.

This is useful for visualizing the tracking results.

Added the missing documentation for the correlation tracker.
Didn't realise that the tracker returned the side lobe ratio,
so fixed the API to return it properly.

This aids an API for the correlation based tracker that Davis
recently added to Dlib. I've made sure to allow overrides for
passing normal rectangles rather than drectangles (which isn't
currently supported in the Dlib C++ API). This is mostly
because I imagine people might initialize the tracking using
something like a bounding box from a detector (e.g.
load_frontal_face_detector).

The correlation tracker deals with the drectangle
(double rectangle) class which currently isn't wrapped. Therefore,
I add the drectangle class and refactor rectangles into their
own file. I also added a load of methods on rectangle that might
be useful such as intersection/contains/area etc.

The word detector didn't make much sense!