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 bug in maskutils.mask_to_polygons
- add class 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
0.8.1 (2020-10-23)
- fixed bug for tqdm when removing a category and its annotations from the dataset
0.8.2 (2020-10-23)
- removed the prefix jpg when saving masks
- update draw instance to draw only bounding boxes
0.8.3 (2020-10-24)
- fixed bug in enum for draw instances
0.8.4 (2020-10-24)
- add show bounding boxes
0.8.5 (2020-10-24)
- changed representation for masks from [width, height, labels] to [labels, width, height]
0.8.6 (2020-10-24)
- added method to crop images
- added method to move annotations with respect a bounding box
0.8.7 (2020-10-24)
- support fully creation o a new dataset
0.8.8-11 (2020-10-26)
- fixed vairous bugs
0.8.12 (2020-10-26)
- fixed bug when the size of the segments is equal to 4
0.8.13 (2020-10-26)
- fixed bug in json dump to serialize numpy array
0.8.14 (2020-10-26)
- fixed bug in json dump to serialize numpy types
0.9.1 (2020-10-28)
- fixed various bugs
- add index for speedup lookup operations
0.9.2 (2020-10-28)
- add new feature to compute mean average precision and recall per class and global
0.9.3 (2020-10-28)
- add computation of mean average precision and mean average recall per image
0.9.4 (2020-10-28)
- fixed bug in score computation
0.9.36
- fixed bug in mask generation
- feature that allows us to add a single mask per component when saving segmentation results
0.9.38
- add min confidence when displaying prediction from a segmentation mask model
- now semantic coco accepts only logits to create annotations
0.9.39
- add new method to remap category idxs
0.9.48
- add new feature to save images and masks to a folder and filter out images and mask with less than k annotations
0.9.52
- the method get_segmentation_mask return also the avg score of the image annotations
- the method save_mask_images save also a weight files with the avg score for the image
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