Label Studio Converter

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For AppMAIS purposes, reference the wiki for how this repository is used!

Link to the wiki

Table of Contents

• Introduction
• Examples
  • JSON
  • CSV
  • CoNLL 2003
  • COCO
  • Pascal VOC XML
• Contributing
• License

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:

pip install -U label-studio-converter
python label-studio-converter export -i exported_tasks.json -c examples/sentiment_analysis/config.xml -o output_dir -f CSV

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 label_studio_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 label_studio_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 label_studio_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 label_studio_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

YOLO to Label Studio converter

Usage:

label-studio-converter import yolo -i /yolo/root/directory -o ls-tasks.json

Help:

label-studio-converter import yolo -h

usage: label-studio-converter import yolo [-h] -i INPUT [-o OUTPUT]
                                          [--to-name TO_NAME]
                                          [--from-name FROM_NAME]
                                          [--out-type OUT_TYPE]
                                          [--image-root-url IMAGE_ROOT_URL]
                                          [--image-ext IMAGE_EXT]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        directory with YOLO where images, labels, notes.json
                        are located
  -o OUTPUT, --output OUTPUT
                        output file with Label Studio JSON tasks
  --to-name TO_NAME     object name from Label Studio labeling config
  --from-name FROM_NAME
                        control tag name from Label Studio labeling config
  --out-type OUT_TYPE   annotation type - "annotations" or "predictions"
  --yolo-type YOLO_TYPE label type - "rectanglelabels" or "polygonlabels"
  --image-root-url IMAGE_ROOT_URL
                        root URL path where images will be hosted, e.g.:
                        http://example.com/images or s3://my-bucket
  --image-ext IMAGE_EXT
                        image extension to search: .jpg, .png

YOLO export folder example:

yolo-folder
  images
   - 1.jpg
   - 2.jpg
   - ...
  labels
   - 1.txt
   - 2.txt

  classes.txt

classes.txt example

Airplane
Car

Contributing

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

• Contributing Guideline
• Code Of Conduct

License

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