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‎.gitattributes‎

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weights.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

‎.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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model_js
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node_modules

‎Dockerfile‎

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FROM tensorflow/tensorflow:latest
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WORKDIR /usr/src/app
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RUN python3 -m pip install -U pip
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RUN pip3 install pillow tqdm
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RUN apt-get update
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RUN apt-get install git git-lfs -y
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RUN git clone https://github.com/code2k13/starrem2k13
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WORKDIR /usr/src/app/starrem2k13
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RUN git-lfs pull
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CMD ["bash"]

‎LICENSE‎

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MIT License
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Copyright (c) 2021 code2k13
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md‎

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# Star reduction in deep sky images
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Starrem2k13 is a simple and easy tool to remove stars from astronomical images. Starrem2k13 uses a GAN trained on augmented data. Code was inspired from a [sample at Tensorflow's website](https://www.tensorflow.org/tutorials/generative/pix2pix). The training data consists of only two images. One image of the Helix nebula and another is a starmap that was created from a star cluster image. Here is how the results look like:
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![images/example2.png](images/example2.jpg)
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![images/example.png](images/example.jpg)
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# Installing
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Currently binaries for Windows, Linux and MacOS are available in the releases section. These were created using PyInstaller. Using pre-complied binaries is the recommended way of running Starrem2k13. Once you have downloaded and installed the program you can run it simply by typing the following in command prompt/terminal
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Run inference on image.
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```shell
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starrem2k13 image_with_stars.jpg image_without_stars.jpg
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```
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## Running using Docker (Recommended)
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```
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docker run -v $PWD:/usr/src/app/starrem2k13/data \
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-it code2k13/starrem2k13 \
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/bin/bash -c "./starrem2k13.py ./data/example.jpg ./data/example_starless.jpg"
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```
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Note that *$PWD* refers to your current working directory. In the above example it is assumed that the file *example.jpg* resides in your current working directory. This directory is mounted as a volume with the path */usr/src/app/starrem2k13/data* inside the docker container. The output image *example_starless.jpg* will also be written to same directory.
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## Running with local python installation
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Clone the repository and navigate to the 'starrem2k13' folder. Install required packages :
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```shell
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pip install -r requirements.txt
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```
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Additionally you may also have to install lfs support for git
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```
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git clone https://github.com/code2k13/starrem2k13.git
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cd starrem2k13
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sudo apt-get install git-lfs
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git lfs pull
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```
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Run inference on image.
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```shell
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python starrem2k13.py image_with_stars.jpg image_without_stars.jpg
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```
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> Supprots greyscale and RGB images. Alpha channel (if any) in the source image is removed during processing. Gives issues on some types of TIFF files.
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## Trying out the model in a web browser
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Here is link to a online demo of star reduction created using a trained model, TFJS and ReactJS. Please use a **desktop browser** to access the demo (for memory and performance reasons). The demo runs locally inside your browser, no data outside of your computer. Here is the link to the demo : https://ashishware.com/static/star_removal/index.html
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![](https://ashishware.com/images/star_removal_demo1.jpg)
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## Training model on your images
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The [notebook](train/star-removal-from-astronomical-images-with-pix2pix.ipynb) is available in the train folder.
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You can also view/run it on Kaggle:
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[https://www.kaggle.com/finalepoch/star-removal-from-astronomical-images-with-pix2pix](https://www.kaggle.com/finalepoch/star-removal-from-astronomical-images-with-pix2pix)
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## Attribution
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The training images used in this code were sourced from Wikimedia Commons and processed using GIMP.
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### Antennae Galaxy Image
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This image was downloaded from Wikimedia Commons and converted to grayscale using GIMP by me for purpose of training the model.
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Link to processed image: [training_data/Antennae_galaxies_xl.png](training_data/Antennae_galaxies_xl.png)
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>[NASA, ESA, and the Hubble Heritage Team (STScI/AURA)-ESA/Hubble Collaboration](https://commons.wikimedia.org/wiki/File:Antennae_galaxies_xl.jpg), Public domain, via Wikimedia Commons
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Url : [https://commons.wikimedia.org/wiki/File:Antennae_galaxies_xl.jpg](https://commons.wikimedia.org/wiki/File:Antennae_galaxies_xl.jpg)
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Direct Link: [https://upload.wikimedia.org/wikipedia/commons/f/f6/Antennae_galaxies_xl.jpg](https://upload.wikimedia.org/wikipedia/commons/f/f6/Antennae_galaxies_xl.jpg)
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### Star cluster NGC 3572 and its surroundings
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This image was downloaded from Wikimedia Commons and star mask was created by me using GIMP
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Link to the processed image: [training_data/star_map_base.png](training_data/star_map_base.png)
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> [ESO/G. Beccari, CC BY 4.0](https://creativecommons.org/licenses/by/4.0), via Wikimedia Commons
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Url: [https://commons.wikimedia.org/wiki/File:The_star_cluster_NGC_3572_and_its_dramatic_surroundings.jpg](https://commons.wikimedia.org/wiki/File:The_star_cluster_NGC_3572_and_its_dramatic_surroundings.jpg)
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Direct Link: [https://upload.wikimedia.org/wikipedia/commons/9/95/The_star_cluster_NGC_3572_and_its_dramatic_surroundings.jpg](https://upload.wikimedia.org/wikipedia/commons/9/95/The_star_cluster_NGC_3572_and_its_dramatic_surroundings.jpg)
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‎batch_process.sh‎

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for file in samples/*.jpg
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do
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python starrem2k13.py "$file" "samples_starless/${file##*/}"
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done

‎convert_model.py‎

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import tensorflow as tf
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import model
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import tensorflowjs as tfjs
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G2 = model.Generator()
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G2.trainable = False
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G2.load_weights("weights/weights")
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# Convert the model
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converter = tf.lite.TFLiteConverter.from_keras_model(G2) # path to the SavedModel directory
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#converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_LATENCY]
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converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
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tflite_model = converter.convert()
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# Save the model.
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with open('model.tflite', 'wb') as f:
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f.write(tflite_model)
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tfjs.converters.save_keras_model(G2, "model_js")

‎docs/Docker-Logo-White-RGB_Moby.png‎

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