Skip to main content

The next-generation stereo processing pipeline for sea waves 3D reconstruction.

Project description

WASSfast is the next-generation stereo processing pipeline for sea waves 3D reconstruction. It exploits the linear dispertion relation and sparse feature triangulation to resolve sea-surface elevation in quasi real-time. At present state, WASSfast can work in two operating modes:

  1. Predict-Update (PU) mode. See the paper

  2. Convolutional Neural Network (CNN) mode (suggested). See the paper

For the standard pipeline see http://www.dais.unive.it/wass

Note Due to TensorFlow version incompatibilities, WASSfast installed via Pypi supports CNN mode only.

How to use it

Install via pip:

python -m pip install wassfast

and run wassfast --help for a brief description of the command-line options.

Try it with test data

  1. Download the test data
  2. Extract the test data 7z x wassfast_testdata_256.7z. This will create a directory named wassfast_testdata_256
  3. Enter the newly extracted directory: cd wassfast_testdata_256
  4. Execute WASSfast:
wassfast ./input ./config256.mat ./config ./settings.cfg RLTB CNN --batchsize 16 -n 49 -r 15.0 -o output.nc 

After the processing, the NetCDF file output.nc is produced. Use Panoply to inspect the reconstructed surface.

Acknowledgements

The study was partially supported by the project of Construction of Ocean Research Stations and their Application Studies funded by the Ministry of Oceans and Fisheries, Republic of Korea.

License

Copyright (C) 2020-2023 Filippo Bergamasco 

WASSfast is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

WASSfast is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

wassfast-1.5.1.tar.gz (25.9 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

wassfast-1.5.1-py3-none-any.whl (21.8 MB view details)

Uploaded Python 3

File details

Details for the file wassfast-1.5.1.tar.gz.

File metadata

  • Download URL: wassfast-1.5.1.tar.gz
  • Upload date:
  • Size: 25.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for wassfast-1.5.1.tar.gz
Algorithm Hash digest
SHA256 cda401b7b811e186a278f31caac87ac0b7e49f68acd440658ee66766e62f4de5
MD5 dd2f322656043a8a54c44adf68e5ff93
BLAKE2b-256 5780f9e8444a93178ef15297eca400c8a798756e31ffce647474cf85a2c452a6

See more details on using hashes here.

File details

Details for the file wassfast-1.5.1-py3-none-any.whl.

File metadata

  • Download URL: wassfast-1.5.1-py3-none-any.whl
  • Upload date:
  • Size: 21.8 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for wassfast-1.5.1-py3-none-any.whl
Algorithm Hash digest
SHA256 b7b519787971830f335b35bf24becc9a1bec74b14a6dccaf5119844dc43e9d90
MD5 3da91e113f0bb45319d554abc786d95a
BLAKE2b-256 779cc418390797d8037996f8b909b589ce83808841d947787e1c0e1128bd453a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page