This repository has been archived and merged into the Label Studio SDK: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
Examples
JSON
CSV
CoNLL 2003
COCO
Pascal VOC XML
YOLO to Label Studio Converter
Usage
Tutorial: Importing YOLO Pre-Annotated Images to Label Studio using Local Storage
Contributing
License
Introduction
Label Studio Format Converter helps you to encode labels into the format of your favorite machine learning library.
from label_studio_converter import Converterc = 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"
}
]
from label_studio_converter import Converterc = 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
...
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 (or c:yolodatasetsoneimages 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:
set LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
set LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=C:yolodatasets
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 or c:yolodatasetsoneimages 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:
Replace one/images/.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/.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:
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`
