Metadata-Version: 2.1
Name: shapedata
Version: 0.2.0
Summary: UNKNOWN
Home-page: https://github.com/justussschock/shapedata
Author: Justus Schock
Author-email: justus.schock@rwth-aachen.de
License: BSD 2-Clause License
Requires-Dist: delira (>=0.4.1)

Copyright (c) 2019, Justus Schock
All rights reserved.

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Description: # ShapeData
        
        [![PyPI version](https://badge.fury.io/py/shapedata.svg)](https://badge.fury.io/py/shapedata) [![Build Status](https://travis-ci.com/justusschock/shapedata.svg?token=GsT2RFaJJMxpqLAN3xuh&branch=master)](https://travis-ci.com/justusschock/shapedata) [![Documentation Status](https://readthedocs.org/projects/shapedata/badge/?version=master)](https://shapedata.readthedocs.io/en/master/?badge=master) [![codecov](https://codecov.io/gh/justusschock/shapedata/branch/master/graph/badge.svg?token=PeQndVRdEQ)](https://codecov.io/gh/justusschock/shapedata) ![LICENSE](https://img.shields.io/github/license/justusschock/shapedata.svg)
        
        This repository contains image classes to perform transformations on images with landmarks (similar to [menpo](https://github.com/menpo/menpo) but with much less dependencies). It also provides some basic Datasets for [delira](https://github.com/justusschock/delira)
        
        ## Installation
        This package can be installed via `pip install shapedata`
        
        ## Basic Usage
        To load a single image with landmarks you can simply do
        
        ```python
        import shapedata
        img = shapedata.SingleShapeSingleImage2D.from_files("./example_files/lenna.png")
        ```
        
        and to view this image do
        ```python
        from matplotlib import pyplot as plt
        img.view(view_landmarks=True)
        plt.show()
        ```
        
        To augment the image you can use `img.transform()` to transform the image with the origin as transformation base or `img.transform_about_centre()` to use the images's center as transformation base.
        Transformations as `img.translate()`, `img.rotate()`, `img.rescale()` or `img.resize()` are also implemented and will fall back on `img.transform()` or `img.transform_about_centre()`
        
        To access the image itself you can do `img.img` and accessing the landmarks works with `img.lmk` for instances of `SingleShapeSingleImage`
        For further usage have a look at the datasets and docstrings.
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
