Metadata-Version: 2.1
Name: geode-ml
Version: 2.3.10
Summary: Classes and methods to help with the creation of geospatial training datasets and deep-learning models.
Author-email: Matt Reichenbach <matthew.reichenbach@gmail.com>
License: MIT License
        
        Copyright (c) 2022 mpreichenbach
        
        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.
Project-URL: Homepage, https://github.com/mpreichenbach/geode-ml
Keywords: deep-learning,training,dataset,geospatial
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

How to install **geode-ml**
====================

The **geode-ml** package depends on **GDAL** and **Tensorflow** for most of its functionality. It is easiest to install 
**GDAL** using the **conda** package manager:

```
conda create -n "geode_env" python>=3.7
conda activate geode_env
conda install gdal
```

However, installing **Tensorflow** with Conda is trickier; we recommend following official documentation for installing 
the cuDNN and CUDA Toolkit libraries with the **conda** package manager (if you have a compatible GPU), and then doing

```pip install tensorflow-gpu```

on a Windows machine, or

```pip install tensorflow```

on a Linux machine. After activating an environment which has both **GDAL** and **Tensorflow**, use **pip** to install 
**geode-ml**:

```
pip install geode-ml
```

The geode.datasets module
-------------------

The datasets module currently contains the classes:

1. SemanticSegmentation
	* creates and processes pairs of imagery and label rasters for scenes

The geode.losses module
--------------------

The losses module contains custom loss functions for model training; these may be removed in the future when
implementations are added to Keras.

The geode.metrics module
--------------------

The metrics module contains useful metrics for testing model performance.

The geode.models module
--------------------

The models module contains the classes:

1. SegmentationModel
	* A class which contains methods to compute metrics on test datasets,
2. Unet
	* a subclass of the SegmentationModel class which instantiates a Unet architecture.
3. VGG19UNet
	* a subclass of the SegmentationModel class which instantiates a Unet architecture, but which mirrors the VGG19
   	architecture for its downsampling and upsampling paths.

The geode.utilities module
--------------------

The utilities module currently contains functions to process, single examples of geospatial data. The datasets module
imports these functions to apply to batches of data; however, this module exists so that methods can be used by 
themselves, without instantiating a class object from another module.
