Format converter add-on for Label Studio
Project description
Warning
This repository has been archived and merged into the Label Studio SDK: https://github.com/HumanSignal/label-studio-sdk/tree/master/src/label_studio_sdk/converter
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:
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
YOLO directory structure
Check the structure of YOLO folder first, keep in mind that the root is /yolo/datasets/one
.
/yolo/datasets/one
images
- 1.jpg
- 2.jpg
- ...
labels
- 1.txt
- 2.txt
classes.txt
classes.txt example
Airplane
Car
Usage
label-studio-converter import yolo -i /yolo/datasets/one -o ls-tasks.json --image-root-url "/data/local-files/?d=one/images"
Where the URL path from ?d=
is relative to the path you set in LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT
.
Note for Local Storages
- It's very important to set
LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/yolo/datasets
(not to/yolo/datasets/one
, but/yolo/datasets
) for Label Studio to run. - Add a new Local Storage in the project settings and set Absolute local path to
/yolo/datasets/one/images
(orc:\yolo\datasets\one\images
for Windows).
Note for Cloud Storages
- Use
--image-root-url
to make correct prefixes for task URLs, e.g.--image-root-url s3://my-bucket/yolo/datasets/one
. - Add a new Cloud Storage in the project settings with the corresponding bucket and prefix.
Help command
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"
--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
Tutorial: Importing YOLO Pre-Annotated Images to Label Studio using Local Storage
This tutorial will guide you through the process of importing a folder with YOLO annotations into Label Studio for further annotation. We'll cover setting up your environment, converting YOLO annotations to Label Studio's format, and importing them into your project.
Prerequisites
- Label Studio installed locally
- YOLO annotated images and corresponding .txt label files in the directory
/yolo/datasets/one
. - label-studio-converter installed (available via
pip install label-studio-converter
)
Step 1: Set Up Your Environment and Run Label Studio
Before starting Label Studio, set the following environment variables to enable Local Storage file serving:
Unix systems:
export LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
export LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/yolo/datasets
label-studio
Windows:
set LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
set LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=C:\\yolo\\datasets
label-studio
Replace /yolo/datasets
with the actual path to your YOLO datasets directory.
Step 2: Setup Local Storage
- Create a new project.
- Go to the project settings and select Cloud Storage.
- Click Add Source Storage and select Local files from the Storage Type options.
- Set the Absolute local path to
/yolo/datasets/one/images
orc:\yolo\datasets\one\images
on Windows. - Click
Add storage
.
Check more details about Local Storages in the documentation.
Step 3: Verify Image Access
Before importing the converted annotations from YOLO, verify that you can access an image from your Local storage via Label Studio. Open a new browser tab and enter the following URL:
http://localhost:8080/data/local-files/?d=one/images/<your_image>.jpg
Replace one/images/<your_image>.jpg
with the path to one of your images. The image should display in the new tab of the browser.
If you can't open an image, the Local Storage configuration is incorrect. The most likely reason is that you made a mistake when specifying your Path
in Local Storage settings or in LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT
.
Note: The URL path from ?d=
should be relative to LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/yolo/datasets
,
it means that the real path will be /yolo/datasets/one/images/<your_image>.jpg
and this image should exist on your hard drive.
Step 4: Convert YOLO Annotations
Use the label-studio-converter to convert your YOLO annotations to a format that Label Studio can understand:
label-studio-converter import yolo -i /yolo/datasets/one -o output.json --image-root-url "/data/local-files/?d=one/images"
Step 5: Import Converted Annotations
Now import the output.json
file into Label Studio:
- Go to your Label Studio project.
- From the Data Manager, click Import.
- Select the
output.json
file and import it.
Step 6: Verify Annotations
After importing, you should see your images with the pre-annotated bounding boxes in Label Studio. Verify that the annotations are correct and make any necessary adjustments.
Troubleshooting
If you encounter issues with paths or image access, ensure that:
- The LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT is set correctly.
- The
--image-root-url
in the conversion command matches the relative path:
`Absolute local path from Local Storage Settings` - `LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT` = `path for --image_root_url`
e.g.:
/yolo/datasets/one/images - /yolo/datasets/ = one/images
- The Local Storage in Label Studio is set up correctly with the Absolute local path to your images (
/yolo/datasets/one/images
) - For more details, refer to the documentation on importing pre-annotated data and setting up Cloud Storages.
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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