Bump tensorflow from 0.10.0rc0 to 1.15.2

.gitattributes

@@ -0,0 +1 @@
+*pyd binary

.github/workflows/publish-to-pypi.yml

@@ -0,0 +1,31 @@
+name: Publish Python 🐍 distributions 📦 to PyPI
+
+on:
+  - push
+
+jobs:
+  build-n-publish:
+    name: Build and publish Python 🐍 distributions 📦 to PyPI and TestPyPI
+    runs-on: ubuntu-18.04
+
+    strategy:
+      matrix:
+        python-version: [3.8]
+
+    steps:
+      - uses: actions/checkout@master
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v1
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip setuptools wheel twine apu
+          python -m pip install -r requirements.txt
+      - name: Publish distribution 📦 to PyPI
+        env:
+          TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
+          TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+        run: |
+          python setup.py sdist bdist_wheel
+          twine upload dist/*

.gitignore

@@ -1,9 +1,9 @@
-# Compiled files
-*.py[cod]
-*.a
-*.o
-*.so
-__pycache__
+*None
+*__py**__
+[Bb]uild
+*.json
+
+.vscode*
 
 # ipynb stuff
 .ipynb_checkpoints/
@@ -16,3 +16,5 @@ docs/.ipynb_checkpoints/
 
 # Mac OSX
 .DS_Store
+[Dd]ist/
+*egg*

License

@@ -0,0 +1,20 @@
+Copyright (c) 2021 Anton Feldmann
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -1,130 +1 @@
-# DeepMoon - Lunar Crater Counting Through Deep Learning
-Center for Planetary Sciences / Department of Astronomy & Astrophysics / Canadian Institute for Theoretical Astrophysics
-University of Toronto
-
-DeepMoon is a TensorFlow-based pipeline for training a convolutional neural
-network (CNN) to recognize craters on the Moon, and determine their positions and
-radii.  It is the companion repo to the paper 
-[Lunar Crater Identification via Deep Learning](https://arxiv.org/abs/1803.02192), which
-describes the motivation and development of the code, as well as results.
-
-## Getting Started
-
-### Overview
-
-The DeepMoon pipeline trains a neural net using data derived from a global
-digital elevation map (DEM) and catalogue of craters.  The code is divided into
-three parts.  The first generates a set images of the Moon randomly cropped
-from the DEM, with corresponding crater positions and radii.  The second
-trains a convnet using this data.  The third validates the convnet's
-predictions.
-
-To first order, our CNN activates regions with high negative gradients, i.e.
-pixels that decrease in value as you move across the image. Below illustrates 
-two examples of this, the first is a genuine DEM Lunar image from our dataset, 
-the second is a sample image taken from the web.
-![CNN1](docs/CNN1.png?raw=true)
-![CNN2](docs/CNN2.png?raw=true)
-
-### Dependences
-
-DeepMoon requires the following packages to function:
-
-- [Python](https://www.python.org/) version 2.7 or 3.5+
-- [Cartopy](http://scitools.org.uk/cartopy/) >= 0.14.2.  Cartopy itself has a
-number of [dependencies](http://scitools.org.uk/cartopy/docs/latest/installing.html#installing),
-including the GEOS and Proj.4.x libraries.  (For Ubuntu systems, these can be
-installed through the `libgeos++-dev` and `libproj-dev` packages,
-respectively.)
-- [h5py](http://www.h5py.org/) >= 2.6.0
-- [Keras](https://keras.io/) 1.2.2 [(documentation)](https://faroit.github.io/keras-docs/1.2.2/);
-  also tested with Keras >= 2.0.2
-- [Numpy](http://www.numpy.org/) >= 1.12
-- [OpenCV](https://pypi.python.org/pypi/opencv-python) >= 3.2.0.6
-- [*pandas*](https://pandas.pydata.org/) >= 0.19.1
-- [Pillow](https://python-pillow.org/) >= 3.1.2
-- [PyTables](http://www.pytables.org/) >=3.4.2
-- [TensorFlow](https://www.tensorflow.org/) 0.10.0rc0, also tested with
-  TensorFlow >= 1.0
-
-This list can also be found in the `requirements.txt`.
-
-### Data Sources
-Our train, validation and test datasets, global DEM, post-processed
-crater distribution on the test set, best model, and sample output
-images can be found [on Zenodo](https://doi.org/10.5281/zenodo.1133969).
-
-Examples of how to read these data can be found in the
-`docs/Using Zenodo Data.ipynb` IPython notebook.
-
-#### Digital Elevation Maps
-
-We use the [LRO-Kaguya merged 59 m/pixel DEM][lola dem].  The DEM was
-downsampled to 118 m/pixel and converted to 16-bit GeoTiff with the USGS
-Astrogeology Cloud Processing service, and then rescaled to 8-bit PNG using
-the [GDAL](http://www.gdal.org/) library:
-
-```
-gdal_translate -of PNG -scale -21138 21138 -co worldfile=no 
-    LunarLROLrocKaguya_118mperpix_int16.tif LunarLROLrocKaguya_118mperpix.png
-```
-
-#### Crater Catalogues
-
-For the ground truth longitude / latitude locations and sizes of craters, we
-combine the [LROC Craters 5 to 20 km diameter][lroc cat] dataset with the
-[Head et al. 2010 >= 20 km diameter][head cat] one ([alternate download
-link][head cat2]).  The LROC dataset was converted from ESRI shapefile to .csv.
-They can be found under the `catalogues` folder of the repo, and have had their
-formatting slightly modified to be read into *pandas*.
-
-During initial testing, we also used the [Salamunićcar LU78287GT
-catalogue][sala cat].
-
-### Running DeepMoon
-
-Each stage of DeepMoon has a corresponding script: `run_input_data_gen.py` for
-generating input data, `run_model_training.py` to build and train the convnet, 
-and `run_get_unique_craters.py` to validate predictions and generate a crater
-atlas.  User-defined parameters, and instructions on on how to use each script,
-can be found in the scripts themselves.
-
-We recommend copying these scripts into a new working directory (and appending
-this repo to your Python path) instead of modifying them in the repo.
-
-Our model with default parameters was trained on a 16GB Tesla P100 GPU, however
-12GB GPUs are more standard. Therefore, our default model may not run on many 
-systems without reducing the batch size, number of filters, etc., which can 
-affect final model convergence. 
-
-### Quick Usage
-
-See `docs/Using Zenodo Data.ipynb` for basic examples on generating sample
-datasets, loading a pre-trained CNN and using it to make predictions on
-samples.
-
-## Authors
-
-* **Ari Silburt** - convnet architecture, crater extraction and post-processing
-  [silburt](https://github.com/silburt)
-* **Charles Zhu** - input image generation, data ingestion and post-processing
-  [cczhu](https://github.com/cczhu)
-
-### Contributors
-
-* Mohamad Ali-Dib - [malidib](https://github.com/malidib/)
-* Kristen Menou - [kmenou](https://www.kaggle.com/kmenou)
-* Alan Jackson
-
-## License
-
-Copyright 2018 Ari Silburt, Charles Zhu and contributors.
-
-DeepMoon is free software made available under the MIT License. For details see
-the LICENSE.md file.
-
-[lola dem]: https://astrogeology.usgs.gov/search/map/Moon/LRO/LOLA/Lunar_LRO_LrocKaguya_DEMmerge_60N60S_512ppd
-[lroc cat]: http://wms.lroc.asu.edu/lroc/rdr_product_select?filter%5Btext%5D=&filter%5Blat%5D=&filter%5Blon%5D=&filter%5Brad%5D=&filter%5Bwest%5D=&filter%5Beast%5D=&filter%5Bsouth%5D=&filter%5Bnorth%5D=&filter%5Btopographic%5D=either&filter%5Bprefix%5D%5B%5DSHAPEFILE&show_thumbs=0&per_page=100&commit=Search
-[head cat]: http://science.sciencemag.org/content/329/5998/1504/tab-figures-data
-[head cat2]: http://www.planetary.brown.edu/html_pages/LOLAcraters.html
-[sala cat]: https://astrogeology.usgs.gov/search/map/Moon/Research/Craters/GoranSalamuniccar_MoonCraters
+https://github.com/silburt/DeepMoon/