This is an implementation of the paper PointScatter: Point Set Representation for Tubular Structure Extraction (ECCV2022 Oral).
1.First install MMSegmentation following mmseg_github.
2.To install pointscatter, run python setup.py develop
The location of the datasets should be the same as the path indicated in the corresponding file in "./configs/_base_/datasets/".
For DRIVE and STARE, refer to mmsegmentation for preparation process.
Massachusetts Roads can be downloaded from massroad_kaggle. We select a subset of 694 images to train, which is indicated in "./configs/_base_/datasets/massroads_split/", and test the model on the 13 official test images.
DeepGlobe can be downloaded from deepglobe_challenge. We split it by "./configs/_base_/datasets/deepglobe_split/".
You may need to prepare the two road datasets according to the following structure after downloading them.
mass_roads_choose
│
└───training
│ └───input
│ └───output
│ └───output_cline
└───testing
│ └───...
Train pointscatter with a single GPU:
python train.py configs/segmentors/3_pointscatter.py --dataset drive --backbone unet --work-dir ../output/drive_log/unet_3_pointscatter
Train with multiple GPUs:
python -m torch.distributed.run --nproc_per_node=4 --master_port=8888 train.py configs/segmentors/3_pointscatter.py --dataset drive --backbone unet --work-dir ../output/drive_log/unet_3_pointscatter --launcher pytorch
Test a trained model:
python test.py configs/segmentors/3_pointscatter.py ../output/drive_log/unet_3_pointscatter/latest.pth --dataset drive --backbone unet --work-dir ../output/drive_log/unet_3_pointscatter --eval mDice
Pretrained Models can be found:
Google Drive
Baidu Cloud
You can customize your own model by:
1. Add a new backbone in "./pointscatter/modeling/backbones" or register a new dataset in "./pointscatter/datasets".
2. Configure the new model components in "./configs/".
3. Train the model.