GitLab

Fixed pybind11 not doing the overload resolution correctly on the cnn_face_detector's operator() in Python.

* Added set_dnn_prefer_smallest_algorithms()

* Added description for set_dnn_prefer_smallest_algorithms();w

* Changed architectures to algorithms.

- Moved all uses of PYBIND11_MAKE_OPAQUE to a common header to avoid possilbe ODR violations.
- Added python binding for make_bounding_box_regression_training_data().

check for any exception.

* Replace boost::python with pybind11

* Replace add_python_module with pybind11_add_module

* Fix clang error on type-dependent expression

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.

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.