Hyperspectral Denoising algorithm toolbox in Python
General User Installation¶
This project requires the PyTorch-wavelets package. However, this package does not have a PyPi release. Therefore, the way to install this package as a pip package is as follows. Developers should use the Development Installation section further down this page.
pip install git+https://github.com/fbcotter/pytorch_wavelets pip install hyde-images
Image denoising is the task of recovering the true unknown image from a degraded observed image. It plays an important role in a variety of applications, for example in remote sensing imaging systems in lithological mapping. Hyperspectral Denoising is a Python toolbox aiming to provide, as the name suggests, denoising algorithms for hyperspectral image data. In particular, we provide:
A wide variety of hyperspectral denoising algorithms (see Features for details)
GPU acceleration for all algorithms
An inuitive pythonic API design
High Level Methods¶
Neural Methods (see src/hyde/nn)¶
MemNet + trainable HyRes step
Pretrained models are available on the github repository but NOT in the pip release.
High Level Function Usage¶
The high level functions (see Features above) are created with torch.nn.Modules. This means that they are classes which must be initialized before they can be used. An example of the using HyRes with the default parameters is shown below.
import hyde import torch input_tens = torch.tensor(loaded_image, dtype=torch.float32, device="gpu or cpu") hyres = hyde.HyRes() output = hyres(input_tens)
In order to set up the necessary environment:
review and uncomment what you need in
environment.ymland create an environment
hydewith the help of conda:
python -m venv hyde_venv
activate the new environment with:
pip install -r requirements.txt -e .
Optional and needed only once after
install several pre-commit git hooks with:
pre-commit install # You might also want to run `pre-commit autoupdate`
and checkout the configuration under
-n, --no-verifyflag of
git commitcan be used to deactivate pre-commit hooks temporarily.
install nbstripout git hooks to remove the output cells of committed notebooks with:
nbstripout --install --attributes notebooks/.gitattributes
This is useful to avoid large diffs due to plots in your notebooks. A simple
nbstripout --uninstallwill revert these changes.
Then take a look into the
├── AUTHORS.md <- List of developers and maintainers. ├── CHANGELOG.md <- Changelog to keep track of new features and fixes. ├── LICENSE.txt <- License as chosen on the command-line. ├── README.md <- The top-level README for developers. ├── configs <- Directory for configurations of model & application. ├── docs <- Directory for Sphinx documentation in rst or md. ├── environment.yml <- The conda environment file for reproducibility. ├── notebooks <- Jupyter notebooks. Naming convention is a number (for │ ordering), the creator's initials and a description, │ e.g. `1.0-fw-initial-data-exploration`. ├── pyproject.toml <- Build system configuration. Do not change! ├── references <- Data dictionaries, manuals, and all other materials. ├── scripts <- Analysis and production scripts which import the │ actual Python package, e.g. train_model.py. ├── setup.cfg <- Declarative configuration of your project. ├── setup.py <- Use `pip install -e .` to install for development or | or create a distribution with `tox -e build`. ├── src │ └── hyde <- Actual Python package where the main functionality goes. ├── tests <- Unit tests which can be run with `py.test`. ├── .coveragerc <- Configuration for coverage reports of unit tests. ├── .isort.cfg <- Configuration for git hook that sorts imports. └── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.
Hyperspectral Denoising is distributed under the BSD-3 license, see our LICENSE file.
This work is supported by the Helmholtz Association Initiative and Networking Fund under the Helmholtz AI platform grant.