Hi all - firstly, I’m sorry if this is the wrong place to post this, but I’m honestly not sure how to tackle this problem. It may be worth prefacing this with the fact I’m still very new to TensorFlow and learning every day, so any help is really valuable.
I have a dataset structured as such:
Each image is named the same as the corresponding annotation XML, so image_1.jpg and image_1.xml. This is fine, and I’ve done a bunch with this such as overlaying the annotations and the images with different class colours to verify they’re correct.
Where I struggle now is that all of the resources I see online for dealing with XML files are for bounding boxes. These XML files all use polygons, structured like: (obviously the points aren’t actually all 1s)
There are several classes with several polygons per image.
How would I go about preparing this dataset for use in a semantic segmentation scenario?
Thanks in advance, I really appreciate any help I can get.
<polygon> <point> <x>1</x> <y>1</y> </point> <point> <x>1</x> <y>1</y> </point> <point> <x>1</x> <y>1/y> </point> <point> <x>1</x> <y>1</y> </point> <point> <x>1</x> <y>1</y> </point> </polygon>