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

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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Description: # ShapeNet-Data

[![Build Status](https://travis-ci.com/justusschock/shapedata.svg?token=GsT2RFaJJMxpqLAN3xuh&branch=master)](https://travis-ci.com/justusschock/shapedata) [![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/delira.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 git+https://github.com/justusschock/shapenet_data.git

## 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.

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