* Replace boost::python with pybind11

* Replace add_python_module with pybind11_add_module

* Fix clang error on type-dependent expression

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.