Metadata-Version: 2.4
Name: rastereasy
Version: 0.1.0
Summary: A library to ease the manipulation of geospatial images
Author-email: Thomas Corpetti <thomas.corpetti@gmail.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/pypa/sampleproject
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENCE
Requires-Dist: rasterio
Requires-Dist: geopandas
Requires-Dist: POT>=0.9.5
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: ipywidgets
Requires-Dist: ipympl
Dynamic: license-file





Introduction
============

**rastereasy** is a Python library for simple manipulation of georeferenced images (`*.tif`, `*.jp2`, `*.shp`, ...). The goal is to simplify geospatial workflows by offering tools for reading and processing raster and vector files, resampling, cropping, reprojecting, stacking, etc of raster images, easy visualizations such as color composites and spectral plots, use (train / apply) some classical Machine Learning algorithms on images, provide some tools for late fusion of classifications (Dempster-Shafer), ...

The main class, `Geoimage`, enables to process raster similarly than numpy arrays while keeping and adapting all meta data.

**Documentation**

A complete documentation can be found [here](https://rastereasy.github.io/)


**Example Usage**

Here's a quick example of what you can do with rastereasy:

```python
 import rastereasy

 # Load a georeferenced image
 image = rastereasy.Geoimage("example.tif")

 # Get image information
 image.info()

 # Print value of pixel [100,200]
 print(image[100,200])

 # Create a color composite
 image.colorcomp(['4', '3', '2'])

 # Resample and reproject
 image_resampled = image.resampling(2)
 image_reproject = image.reproject("EPSG:4326")

 # Save the processed image
 image.save("processed_image.tif")


```
# Installation

####  With pip

Install **rastereasy** via `pip` (the easiest method):

```shell
 $ pip install rastereasy
```

#### From source

To install rastereasy from source:

1. Clone the repository from GitHub:
```shell
$ git clone https://github.com/pythonraster/rastereasy.git
```
2. Navigate to the repository's root directory:
```shell
$ cd rastereasy
```
3. Install the package using pip:
```shell
$ pip install .
```

# Interactive Visualization Notes
 ![Spectra visualization](./illus/spectra.png "Spectra visualization")

As illustrated, **rastereasy** supports interactive plotting of spectral bands for individual pixels. To enable this functionality in Jupyter Notebook, rastereasy installs some Jupyter extensions in your environment. You then need to rebuild jupyter by the command:

```
jupyter lab build
```


 <!--
1. Install the required Jupyter extensions: --
 ```
pip install ipympl
```

2. Rebuild JupyterLab:
```
jupyter lab build
``` -->
------

# To do

Check conda installation

Authors
=======
- [Thomas Corpetti](https://tcorpetti.github.io/)
- Pierrick Matelot
- Augustin de la Brosse
- [Candide Lissak](https://clissak.github.io/)

Citation
=======
If you use rastereasy, please cite:

Thomas Corpetti, Pierrick Matelot, Augustin de la Brosse, Candide Lissak
Rastereasy: A Python package for an easy manipulation of remote sensing images
Journal of Open Source Software, submitted, 2025.


## License
This project is licensed under the MIT License – see the [LICENCE](https://github.com/pythonraster/rastereasy/blob/main/LICENCE) file for details.
