Add bounding box creation schemes

[jabooth]

The code to generate bounding box initialisations should probably live inside menpobench. The two initialisations that interest us from the start are:

1. Initialise from object detection (to start with from dlib)
2. Initialise with noise, using the approach of menpofit (post refactor)

For the object detection, we will want to:

1. Detect bounding boxes using the object detector
2. Define an measure of 'acceptable performance', based on overlap of gt/detected bounding boxes
3. If the detection failed, or is below threshold for acceptable performance, a new bounding box will be synthesised
4. Synthesised bounding boxes are placed by using a learned multivariate gaussian on the acceptable bounding boxes.

@patricksnape @nontas @jalabort Do you agree with this approach? Some help with the following questions would be great:

1. What do we feel is a good overlap amount? I was thinking that 80% of ground truth is covered by detected?
2. If there are multiple detections, we just keep one right?
3. Sorry @jalabort, can you point me once again to the new style noise initialisations in menpofit/refactor?

If we get this sorted I can add lfpw, afw, ibug, and helen datasets to menpobench with detections today. Any other datasets we want for testing?

[jabooth]

26dc94e implements a first version of this. Usage:

> menpobench bbox dlib ./path/to/images/*.png --synthesize

This creates bounding boxes next to each image found (e.g. for img_001.png, creates img_001_dlib.ljson)

If --synthesize is passed, missing detections are generated from a PCA basis learned on the successful detections. See here for details. Would be great to have another pair of eyes check over that and make sure the approach seems sensible.

See here for where we use these new annotations in loading a dataset in menpobench. Also, note that I am moving to store the datasets on github, e.g menpo/lfpw-train. I think this could be a great way to iteratively fix issues with datasets in an open way.

Reopened as I'd like feedback on the design before we go too much further with it.