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a dataset loader and converter for object detection segmentation and classification

Project description

polimòrfo

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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:

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

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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