Unified regex expression for both, Linux and Windows.

- Current implementation creates self.log_dir in set_log_dir() function,
  which creates an empty log directory if none exists. This causes
  find_last() to fail after creating a model because it finds this new
  empty directory instead of the previous training directory.
- New implementation moves log_dir creation to the train() function to
  ensure it is only created when it will be used.

Add the possibility to exclude some sources from augmentation by passing a list of sources. This is useful when you want to retrain a model having few images.

Add an optional parameter for calling a list of keras.callbacks to be add to the original list.

Improve previous commit to not break on older versions of Keras.

Raise clear error if last training weights are not foundIf using the --weights=last (or --model=last) to resume trainingbut the weights are not found now it raises a clear error message.

This allows one to set a callable in Config.BACKBONE to use a custom
backbone model.

Fix matterport/Mask_RCNN#170

Print a message when re-starting from saved epoch

fix typo

Reported here: https://github.com/matterport/Mask_RCNN/pull/500

TF replaced keep_dims with keepdims a while ago
and now shows a warning when using the old name.

Although a bug, it has no practical side-effect
because it works if the masks are square, which is
the case always during training and this code is
invoked during training.

And cleaner path split in nucleus sample.

The detect() method resizes and molds the input
images before detection. In case we already have
the images molded, which is common in debugging or
visualizations, when loading with load_image_gt()
then use this method.

config.IMAGE_RESIZE_MODE = "crop" can be used in
training only to pick random crops from training
images.

If you stop training at epoch 15 then continue
from where you left off, it resumes with 17 which
skips 16. Does cause any problems other that being
a bit annoying.

- Update ROOT_DIR depending on path of file
- Remove unneeded __init__.py files
- Remove setting matplotlib backend

* create package mrcnn
* move notebooks to samples

A step towards supporting detection on different
image sizes.

This also changes utils.resize_image() such that
it doesn’t convert images to 0-255 range, but
rather keep the input range. This would only
affect users who use input image of range 0-1
rather than 0-255.

This doesn’t change the functionality, other than
cleaning up the code to prepare for future changes
to support detection on different image sizes.

The image_meta tensor stores meta data about the
image. This update adds the scale by which the
image was resized and it’s final shape.