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