GitLab

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.

* imglab: select next/previous image with 's' and 'w'

* imglab: make 'w' and 's' keys behave like UP and DOWN keys; add 'about' text

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.