This is an official PyTorch implementation for paper "V-FloodNet: A Video Segmentation System for Urban Flood Detection and Quantification".

We developed and tested the source code under Ubuntu 18.04 and PyTorch framework. The following packages are required to run the code.

First, a python virtual environment is recommended. I use pip to create a virtual environment named env and activate it. Then, recursively pull the submodules code.

python3 -m venv env
source env/bin/activate
git submodule update --init --recursive

In the virtual environment, install the following required packages from their official instructions.

• torch, torchvision, from PyTorch. We used v1.8.2+cu111 is used in our code.
• PyTorch Scatter for scatter operations. We used the version for torch 1.8.1+cu111.
• Detectron2 for reference objects segmentation.
• MeshTransformer for human detection and 3D mesh alignment.

We provide the corresponding installation command here

pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio==0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
pip install torch-scatter==2.0.8 -f https://data.pyg.org/whl/torch-1.8.1+cu111.html -v
pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.8/index.html
cd MeshTransformer
python setup.py build develop
pip install ./manopth/.

Then install the rest packages indicated in requirements.txt

pip install -r requirements.txt

Download and extract the pretrained weights, and put them in the folder ./records/. Weights and groundtruths are stored in Google Drive.

Put the testing images in image_folder, then

python test_image_seg.py \
    --test_path=/path/to/image_folder --test_name=<test_name>

The default output folder is output/segs/

If your input is a video, we provide a script scripts/cvt_video_to_imgs.py to extract frames of the video. Put the extracted frames in frame_folder, then

python test_video_seg.py \
    --test-path=/path/to/frame_folder --test-name=<test_name>

We provide three options stopsign, people, and ref for --opt to specify three types reference objects.

python est_waterlevel.py \
  --opt=<opt> --test-name=<test_name> --img-dir=/path/to/img_folder

For input video, to compare the estimated water level with the groundtruths in records/groundtruth/, you can use

python cmp_hydrograph.py --test-name=<test_name>

This paper is submitted to Elsevier Journal Computers, Environment and Urban Systems under review. The corresponding author is Xin Li (Xin Li [email protected]). All rights are reserved.