This repository has been archived. Please checkout my new shiny tool (Starrem2k13) for star removal : https://code2k13.github.io/starrem2k13/ |
---|
A GAN model trained to remove stars from astronomical images. Code was inspired from a sample at Tensorflow's website. 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:
docker run -v $PWD:/usr/src/app/starreduction/data \
-it code2k13/starreduction \
/bin/bash -c "./removestars.py ./data/example.jpg ./data/example_starless.jpg"
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/starreduction/data inside the docker container. The output image example_starless.jpg will also be written to same directory.
Clone the repository and navigate to the 'starreduction' folder. Install required packages :
pip install -r requirements.txt
Additionally you may also have to install lfs support for git
git clone https://github.com/code2k13/starreduction.git
cd starreduction
sudo apt-get install git-lfs
git lfs pull
Run inference on image.
python removestars.py image_with_stars.jpg image_without_stars.jpg
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.
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
The notebook is available in the train folder.
You can also view/run it on Kaggle: https://www.kaggle.com/finalepoch/star-removal-from-astronomical-images-with-pix2pix
The training images used in this code were sourced from Wikimedia Commons and processed using GIMP.
This image was downloaded from Wikimedia Commons and converted to grayscale using GIMP by me for purpose of training the model.
Link to processed image: training_data/Antennae_galaxies_xl.png
NASA, ESA, and the Hubble Heritage Team (STScI/AURA)-ESA/Hubble Collaboration, Public domain, via Wikimedia Commons
Url : https://commons.wikimedia.org/wiki/File:Antennae_galaxies_xl.jpg
Direct Link: https://upload.wikimedia.org/wikipedia/commons/f/f6/Antennae_galaxies_xl.jpg
This image was downloaded from Wikimedia Commons and star mask was created by me using GIMP
Link to the processed image: training_data/star_map_base.png
ESO/G. Beccari, CC BY 4.0, via Wikimedia Commons
Url: https://commons.wikimedia.org/wiki/File:The_star_cluster_NGC_3572_and_its_dramatic_surroundings.jpg
Direct Link: https://upload.wikimedia.org/wikipedia/commons/9/95/The_star_cluster_NGC_3572_and_its_dramatic_surroundings.jpg