Skip to content
/ dwt Public

Deep Watershed Transform for Instance Segmentation

License

Notifications You must be signed in to change notification settings

min2209/dwt

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

10 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Deep Watershed Transform

Performs instance level segmentation detailed in the following paper:

Min Bai and Raquel Urtasun, Deep Watershed Transformation for Instance Segmentation, in CVPR 2017. Accessible at https://arxiv.org/abs/1611.08303.

This page is still under construction.

Dependencies

Developed and tested on Ubuntu 14.04 and 16.04.

  1. TensorFlow www.tensorflow.org
  2. Numpy, Scipy, and Skimage (sudo apt-get install python-numpy python-scipy python-skimage)

Inputs

  1. Cityscapes images (www.cityscapes-dataset.com).
  2. Semantic Segmentation for input images. In our case, we used the output from PSPNet (by H. Zhao et al. https://github.com/hszhao/PSPNet). These are uint8 images with pixel-wise semantic labels encoded with 'trainIDs' defined by Cityscapes. For more information, visit https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py

Outputs

The model produces pixel-wise instance labels as a uint16 image with the same formatting as the Cityscapes instance segmentation challenge ground truth. In particular, each pixel is labeled as 'id' * 1000 + instance_id, where 'id' is as defined by Cityscapes (for more information, consult labels.py in the above link), and instance_id is an integer indexing the object instance.

Testing the Model

  1. Clone repository into dwt/.
  2. Download the model from www.cs.toronto.edu/~mbai/dwt_cityscapes_pspnet.mat and place into the "dwt/model" directory.
  3. run "cd E2E"
  4. run "python main.py"
  5. The results will be available in "dwt/example/output".

Training the Model

  1. Will be available soon.

About

Deep Watershed Transform for Instance Segmentation

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published