Before the fix, the computed AP score is slightly
lower than it should be. It doesn’t affect
training, but just shows slightly lower results AP
in the notebooks.

This is useful to run prediction on images of
variable sizes without resizing them to a fixed
size. While using mode=“none” is another option,
it requires that you pre-process your images to
ensure the width and height are multiples of 64.
This mode handles that automatically.

* 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.

Move code to separate functions and fix off-by-1
error.

skimage.transform.resize() crashes with a floating
point exception if the image has 1-pixel width or
height and the mode=“reflection”. Changing mode to
“constant” (the default) fixes the issue. And, 
it’s the correct mode to use anyway.

If the mask is a rectangle, its mini-mask pixels
become all 1s, and it has no zeros. Scipy imresize
fails on that and returns 0s instead. This update
fixes the problem.