This repository has been archived and merged into the Label Studio SDK: https://github.com/HumanSignal/label-studio-sdk/tree/master/src/label_studio_sdk/converter

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• 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

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

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

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

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

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

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

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

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 (or c:\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:https://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.:
                        https://example.com/images or s3:https://my-bucket
  --image-ext IMAGE_EXT
                        image extension to search: .jpg, .png

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.

• 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)

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.

1. Create a new project.
2. Go to the project settings and select Cloud Storage.
3. Click Add Source Storage and select Local files from the Storage Type options.
4. Set the Absolute local path to /yolo/datasets/one/images or c:\yolo\datasets\one\images on Windows.
5. Click Add storage.

Check more details about Local Storages in the documentation.

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:

https://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.

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"

Now import the output.json file into Label Studio:

1. Go to your Label Studio project.
2. From the Data Manager, click Import.
3. Select the output.json file and import it.

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.

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.

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

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