any object part labels so that the flipped dataset has the same average part
layout as the source dataset. I added a --flip-basic that behaves like the old
--flip. However, most people flipping a dataset with part annotations will
want to use --flip.
* 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.
* 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.
* 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.
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.
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.
