Mask RCNN: How to add region annotation based on manually segmented image?

Question

There is a implementation of Mask RCNN on Github by Matterport. I'm trying to train my data for it. I'm adding polygons on images with this tool. I'm drawing polygons on images manually, but I already have manually segmented image below (black and white one)

My questions are:

1) When adding json annotation for region data, is there a way to use that pre-segmented image below?

2) Is there a way to train my data for this algorithm, without adding json annotation and use manually segmented images? The tutorials and posts I've seen uses json annotations to train.

3) This algorithm's output is image with masks obviously, is there a way get black and white output for segmentations?

Here's the code that I'm working on google colab.

Original Repo

My Fork

Manually segmented image

Answer 1

I think both questions 1 and 2 refer to the same solution: you need to convert your masks to json annotations. For that, I would suggest you to read this link, posted in the repository of the cocodataset. There you can read about this repository that you could use for what you need. You could also use directly the Coco PythonAPI, calling the methods here defined. For question 3, a mask is already binary image (therefore, you can show it as black and white pixels).