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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