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

我认为问题 1 和 2 都指的是相同的解决方案：您需要将掩码转换为 json 注释。为此，我建议您阅读此链接，该链接发布在cocodataset存储库中。在那里您可以阅读有关此存储库的信息，您可以使用它来满足您的需要。您还可以直接使用 Coco PythonAPI ，调用此处定义的方法。对于问题 3，掩模已经是二值图像（因此，您可以将其显示为黑白像素）。