a dataset loader and converter for object detection segmentation and classification
Project description
polimòrfo
Polimòrfo (πολύμορϕος, comp. di πολυ- «poli-» e μορϕή «forma») is a dataset loader and converter library for object detection segmentation and classification. The goal of the project is to create a library able to process dataset in format:
COCO: Common Objects in Context
Pascal VOC: Visual Object Classes Challenge
Google Open Images: Object Detection and Segmentation dataset released by Google
and transform these dataset into a common format (COCO).
Moreover, the library offers utilies to handle (load, convert, store and transform) the various type of annotations. This is important when you need to: - convert mask to polygons - store mask in a efficient format - convert mask/poygons into bounding boxes
Free software: Apache Software License 2.0
Documentation: https://polimorfo.readthedocs.io.
Features
TODO
[X] Coco dataset
[X] download coco datasets for train and val
[X] add annotations loader and converter
[X] add the ability to create dataet from scratch
[ ] add voc dataset format
[ ]
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
History
0.2.0 (2020-02-18)
Add support to process coco dataset
0.2.1 (2020-02-28)
add support to download files and archives from the web and google drive
0.3.0 (2020-10-04)
addedd support for removing categories and other utilities
0.4.0 (2020-10-05)
addedd support to create a dataset from scratch
0.5.0 (2020-10-06)
added support to visualize images and annotations
make image removing optional during annotations and categories deletion
0.6.0 (2020-10-12)
added copy dataset
added split dataset
0.6.1 (2020-10-12)
fixed a bug in colors generation for show images
0.6.2 (2020-10-12)
update signature for function def update_images_path(self, func):
0.7.0 (2020-10-19)
add method to dump dataset in format segmentation map
0.8.0 (2020-10-23)
fixed bud in maskutils.mask_to_polygons
add classes to transform the predictions from instance and semantic segmentation in coco format
fixed bug in add_image, add_annotation, add_category
make load_image and load_images load random images sampled from the dataset
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