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Format converter add-on for Label Studio

Project description

Label Studio Converter

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Table of Contents

Introduction

Label Studio Format Converter helps you to encode labels into the format of your favorite machine learning library.

Examples

JSON

Running from the command line:

python backend/converter/cli.py --input examples/sentiment_analysis/completions/ --config examples/sentiment_analysis/config.xml --output tmp/output.json

Running from python:

from label_studio_converter import Converter

c = Converter('examples/sentiment_analysis/config.xml')
c.convert_to_json('examples/sentiment_analysis/completions/', 'tmp/output.json')

Getting output file: tmp/output.json

[
  {
    "reviewText": "Good case, Excellent value.",
    "sentiment": "Positive"
  },
  {
    "reviewText": "What a waste of money and time!",
    "sentiment": "Negative"
  },
  {
    "reviewText": "The goose neck needs a little coaxing",
    "sentiment": "Neutral"
  }
]

Use cases: any tasks

CSV

Running from the command line:

python backend/converter/cli.py --input examples/sentiment_analysis/completions/ --config examples/sentiment_analysis/config.xml --output output_dir --format CSV --csv-separator $'\t'

Running from python:

from label_studio_converter import Converter

c = Converter('examples/sentiment_analysis/config.xml')
c.convert_to_csv('examples/sentiment_analysis/completions/', 'output_dir', sep='\t', header=True)

Getting output file tmp/output.tsv:

reviewText	sentiment
Good case, Excellent value.	Positive
What a waste of money and time!	Negative
The goose neck needs a little coaxing	Neutral

Use cases: any tasks

CoNLL 2003

Running from the command line:

python backend/converter/cli.py --input examples/named_entity/completions/ --config examples/named_entity/config.xml --output tmp/output.conll --format CONLL2003

Running from python:

from label_studio_converter import Converter

c = Converter('examples/named_entity/config.xml')
c.convert_to_conll2003('examples/named_entity/completions/', 'tmp/output.conll')

Getting output file tmp/output.conll

-DOCSTART- -X- O
Showers -X- _ O
continued -X- _ O
throughout -X- _ O
the -X- _ O
week -X- _ O
in -X- _ O
the -X- _ O
Bahia -X- _ B-Location
cocoa -X- _ O
zone, -X- _ O
...

Use cases: text tagging

COCO

Running from the command line:

python backend/converter/cli.py --input examples/image_bbox/completions/ --config examples/image_bbox/config.xml --output tmp/output.json --format COCO --image-dir tmp/images

Running from python:

from label_studio_converter import Converter

c = Converter('examples/image_bbox/config.xml')
c.convert_to_coco('examples/image_bbox/completions/', 'tmp/output.conll', output_image_dir='tmp/images')

Output images could be found in tmp/images

Getting output file tmp/output.json

{
  "images": [
    {
      "width": 800,
      "height": 501,
      "id": 0,
      "file_name": "tmp/images/62a623a0d3cef27a51d3689865e7b08a"
    }
  ],
  "categories": [
    {
      "id": 0,
      "name": "Planet"
    },
    {
      "id": 1,
      "name": "Moonwalker"
    }
  ],
  "annotations": [
    {
      "id": 0,
      "image_id": 0,
      "category_id": 0,
      "segmentation": [],
      "bbox": [
        299,
        6,
        377,
        260
      ],
      "ignore": 0,
      "iscrowd": 0,
      "area": 98020
    },
    {
      "id": 1,
      "image_id": 0,
      "category_id": 1,
      "segmentation": [],
      "bbox": [
        288,
        300,
        132,
        90
      ],
      "ignore": 0,
      "iscrowd": 0,
      "area": 11880
    }
  ],
  "info": {
    "year": 2019,
    "version": "1.0",
    "contributor": "Label Studio"
  }
}

Use cases: image object detection

Pascal VOC XML

Running from the command line:

python backend/converter/cli.py --input examples/image_bbox/completions/ --config examples/image_bbox/config.xml --output tmp/voc-annotations --format VOC --image-dir tmp/images

Running from python:

from label_studio_converter import Converter

c = Converter('examples/image_bbox/config.xml')
c.convert_to_voc('examples/image_bbox/completions/', 'tmp/output.conll', output_image_dir='tmp/images')

Output images can be found in tmp/images

Corresponding annotations could be found in tmp/voc-annotations/*.xml:

<?xml version="1.0" encoding="utf-8"?>
<annotation>
<folder>tmp/images</folder>
<filename>62a623a0d3cef27a51d3689865e7b08a</filename>
<source>
<database>MyDatabase</database>
<annotation>COCO2017</annotation>
<image>flickr</image>
<flickrid>NULL</flickrid>
</source>
<owner>
<flickrid>NULL</flickrid>
<name>Label Studio</name>
</owner>
<size>
<width>800</width>
<height>501</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>Planet</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>299</xmin>
<ymin>6</ymin>
<xmax>676</xmax>
<ymax>266</ymax>
</bndbox>
</object>
<object>
<name>Moonwalker</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>288</xmin>
<ymin>300</ymin>
<xmax>420</xmax>
<ymax>390</ymax>
</bndbox>
</object>
</annotation>

Use cases: image object detection

Contributing

We would love to get your help for creating converters to other models. Please feel free to create pull requests.

License

This software is licensed under the Apache 2.0 LICENSE © Heartex. 2020

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