This is an official implementation of "Correlated Siamese Change Detection Network (CSCDNet)" in "Weakly Supervised Silhouette-based Semantic Change Detection".
This code was developed and tested with Python 3.6.8 and PyTorch 1.0 and CUDA 9.2.
- GCC
# Build and install GCC (>= 7.4.0) if not installed
# Set path variables
export PATH=/home/$USER/local/gcc/bin:$PATH
export LD_LIBRARY_PATH=/home/$USER/local/gcc/lib64:$LD_LIBRARY_PATH
- Virtualenv for system setting
# Set CUDA path.
# In case of server, the following CUDA path setting with module load command might be necessary.
module load cuda/9.2/9.2.88.1
# Create a virtualenv environment
virtualenv -p python /path/to/env/pytorch1.0cuda9.2
#Activate the virtualenv environment
source /path/to/env/pytorch1.0cuda9.2/bin/activate
# Install dependencies
pip install -r requirements.txt
- Download the pretrained model of resnet18
sh download_resnet.sh
- Build correlation layer package from flownet2.
sh build_correlation_package.sh
TSUNAMI and GSV in Panoramic Change Detection dataset are available through an e-mail contact described here including the dataset used for five-fold cross validation in our paper, in which image cropping and data augumentation have been performed.
Training
pcd_5cv
├── set0/
│ ├── train/ # *.jpg
│ ├── test/ # *.jpg
│ ├── mask/ # *.png
| ├── train.txt
| ├── test.txt
├── set1/
...
├── set2/
...
├── set3/
...
├── set4/
├── train/ # *.jpg
├── test/ # *.jpg
├── mask/ # *.png
├── train.txt
├── test.txt
Testing
pcd
├── TSUNAMI/
│ ├── t0/ # *.jpg
│ ├── t1/ # *.jpg
│ ├── mask/ # *.png
├── GSV/
├── t0/ # *.jpg
├── t1/ # *.jpg
├── mask/ # *.png
Train change detection network with correlation layers (CSCDNet)
# i-th set of five-hold cross-validation (0 <= i < 5)
python train.py --cvset i --use-corr --datadir /path/to/pcd_5cv --checkpointdir /path/to/log --max-iteration 50000 --num-workers 16 --batch-size 32 --icount-plot 50 --icount-save 10000
Train change detection network without correlation layers (CDNet)
# i-th set of five-hold cross-validation (0 <= i < 5)
python train.py --cvset i --datadir /path/to/pcd_5cv --checkpointdir /path/to/log --max-iteration 50000 --num-workers 16 --batch-size 32 --icount-plot 50 --icount-save 10000
You can start a tensorboard session
tensorboard --logdir=/path/to/log
CSCDNet
python test.py --use-corr --dataset PCD --datadir /path/to/pcd --checkpointdir /path/to/log/cscdnet/checkpoint
CDNet
python test.py --dataset PCD --datadir /path/to/pcd --checkpointdir /path/to/log/cdnet/checkpoint
If you find this implementation useful in your work, please cite the paper. Here is a BibTeX entry:
@article{sakurada2018weakly,
title={Weakly Supervised Silhouette-based Semantic Change Detection},
author={Sakurada, Ken},
journal={arXiv preprint arXiv:1811.11985},
year={2018}
}
The preprint can be found here.
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