Cityscapes Dataset proprocessing code for CASENet
The preprocessing code is released under the MIT License (refer to the LICENSE file for details).
The repository contains the preprocessing code of the Cityscapes dataset for CASENet. CASENet is a recently proposed deep network with state of the art performance on category-aware semantic edge detection. For more information about CASENet, please refer to the arXiv paper and the paper published in CVPR 2017.
If you find CASENet useful in your research, please consider to cite:
@inproceedings{yu2017casenet,
author = {Yu, Zhiding and Feng, Chen and Liu, Ming-Yu and Ramalingam, Srikumar},
title = {CASENet: Deep Category-Aware Semantic Edge Detection},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
Year = {2017}
}
@inproceedings{yu2018seal,
author = {Yu, Zhiding and Liu, Weiyang and Zou, Yang and Feng, Chen and Ramalingam, Srikumar and Kumar, B. V. K. Vijaya and Kautz, Jan},
title = {Simultaneous Edge Alignment and Learning},
booktitle = {Proceedings of the European Conference on Computer Vision},
Year = {2018}
}
Note: In this part, we assume you are in the directory $CITYSCAPES_PREPROCESS_ROOT/
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Download the files "gtFine_trainvaltest.zip" and "leftImg8bit_trainvaltest.zip" from the Cityscapes website to
data_orig/, and unzip them.unzip data_orig/gtFine_trainvaltest.zip && rm data_orig/gtFine_trainvaltest.zip unzip data_orig/leftImg8bit_trainvaltest.zip && rm data_orig/leftImg8bit_trainvaltest.zip
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Run the matlab code to preprocess the data.
# In Matlab Command Window run code/demo_preproc.m
This will generate the .bin edge labels and the corresponding file lists that could be read by CASENet in
data_proc/.
The repository of the SBD preprocessing code can be found here.