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{{TOCright}} __TOC__
= Preparing a Dataset with Label Studio =


{{Note|If you're interested in use LogicalDOC API we suggest to '''take a look at ours [[Bindings_And_Samples|Bindings and Samples]] and take advantage of already usable examples which connect to webservices API.}}
This guide explains how to create an annotated dataset for YOLO training using Label Studio.


LogicalDOC has a [http://docs.logicaldoc.com/en/web-services-api complete API exposed via REST]. This means you can call any of these API methods from any programming language, like Java, PHP or Python among others. This feature makes it possible to create a custom client, or integrate with third-party applications like a CRM or a CMS.
=== Install Label Studio ===


{{Advice|Examples in this page refer to LogicalDOC 7.7; the [http://docs.logicaldoc.com/en/web-services-api REST API] is currently in development, so expect changes and additions.}}
Refer to the official installation guide:
https://labelstud.io/guide/install


If you point your browser to http://localhost:8080/services, you can see the SOAP API at first place but at the bottom you will see a '''Available RESTful services''' section. These URLs are protected by BASIC authentication so you need to provide an user and password to access them.
=== Enable Local File Storage ===


== Sample usage ==
For large projects it is not recommended to upload images directly through the Label Studio interface.
To try these API methods you can use an HTTP Client library or any REST client which ease this process. Or simply you can use the '''curl''' command-line application. For example, you can list the children folders:


  $ curl -u admin:admin -H "Accept: application/json" \
To enable local file access, configure the following environment variables:
    http://localhost:8080/services/rest/folder/listChildren?folderId=4


The result is:
<pre>
LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/path/to/images
</pre>


<source lang="text">
Launch Label Studio:
[
  {
    "id": 3440640,
    "name": "alfa",
    "parentId": 4,
    "description": "",
    "lastModified": "2016-06-15 15:49:40 +0200",
    "type": 0,
    "templateId": null,
    "templateLocked": 0,
    "creation": "2016-06-15 15:49:40 +0200",
    "creator": "Admin Admin",
    "position": 1,
    "hidden": 0,
    "foldRef": null,
    "attributes": [
     
    ]
  },
  {
    "id": 3440643,
    "name": "beta",
    "parentId": 4,
    "description": "",
    "lastModified": "2016-06-16 10:16:25 +0200",
    "type": 0,
    "templateId": null,
    "templateLocked": 0,
    "creation": "2016-06-16 09:49:27 +0200",
    "creator": "Admin Admin",
    "position": 1,
    "hidden": 0,
    "foldRef": null,
    "attributes": [
     
    ]
  }
]
</source>


In this case you can see the result in JSON format.
<pre>
Some endpoints can also provide the results in XML format but you have to check them, if that is supported we can make a call sending the appropriate '''Accept''' header:
label-studio start
 
</pre>
  $ curl -u admin:admin -H "Accept: application/xml" \
    http://localhost:8080/services/rest/folder/listChildren?folderId=4
 
The result in XML is:
 
<source lang="xml">
<?xml version="1.0" encoding="UTF-8"?>
<folders>
  <folder>
    <creation>2016-06-15 15:49:40 +0200</creation>
    <creator>Admin Admin</creator>
    <description></description>
    <hidden>0</hidden>
    <id>3440640</id>
    <lastModified>2016-06-15 15:49:40 +0200</lastModified>
    <name>alfa</name>
    <parentId>4</parentId>
    <position>1</position>
    <templateLocked>0</templateLocked>
    <type>0</type>
  </folder>
  <folder>
    <creation>2016-06-16 09:49:27 +0200</creation>
    <creator>Admin Admin</creator>
    <description></description>
    <hidden>0</hidden>
    <id>3440643</id>
    <lastModified>2016-06-16 10:16:25 +0200</lastModified>
    <name>beta</name>
    <parentId>4</parentId>
    <position>1</position>
    <templateLocked>0</templateLocked>
    <type>0</type>
  </folder>
</folders>
</source>


This is a Java client for the same call:
=== Create a Project ===


<source lang="java">
# Login to Label Studio
import java.io.BufferedReader;
# Click '''Create Project'''
import java.io.IOException;
# Enter a project name
import java.io.InputStreamReader;
# Configure the labeling interface
import java.net.Authenticator;
# Save the project
import java.net.HttpURLConnection;
import java.net.MalformedURLException;
import java.net.PasswordAuthentication;
import java.net.URL;


public class JavaRestClient {
=== Import Images ===
    public static void main(String[] args) throws Exception {
        try {
            long folderID = 4L;
            URL url = new URL("http://localhost:8080/services/rest/folder/listChildren?folderId=" + folderID);
            HttpURLConnection conn = (HttpURLConnection) url.openConnection();
            conn.setRequestMethod("GET");
            conn.setRequestProperty("Accept", "application/json");
           
            Authenticator.setDefault(new Authenticator() {
                protected PasswordAuthentication getPasswordAuthentication() {
                    return new PasswordAuthentication("admin", "admin".toCharArray());
                }
            });
           
            if (conn.getResponseCode() == 200) {
                BufferedReader br = new BufferedReader(new InputStreamReader((conn.getInputStream())));
                System.out.println("Output from Server .... \n");
                String output;
               
                while ((output = br.readLine()) != null) {
                    System.out.println(output);
                }
            } else {
                System.err.println("Failed : HTTP error code : " + conn.getResponseCode());
            }
           
            conn.disconnect();
        } catch (MalformedURLException e) {
            e.printStackTrace();
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}
</source>


== Folder ==
# Open the project
Let's create a new folder:
# Click '''Import'''
# Select '''Local Storage'''


  $ curl -u admin:admin -H "Accept: application/json" \
When importing images, choose '''Files''' as the import method.
    -X POST -H "Content-Type: text/plain" -d "/Default/Curl/newfolder" \
    http://localhost:8080/services/rest/folder/createSimple


Creates a path of folders starting from the folder with ID 4 (Default folder)
Unlike CVAT, Label Studio creates one task for each imported document image.


  $ curl -u admin:admin -H "Accept: application/json" \
[[File:LabelStudio-import-method.png|thumb|600px|center|Selecting the Files import method]]
    -X POST -H "Content-Type: application/x-www-form-urlencoded" -d parentId=4 -d path=How/to/POST/JSON/data/with/Curl \
    http://localhost:8080/services/rest/folder/createPath


== Document ==
=== Annotate Documents ===
Now we are going to create a document. For this, we need to provide the document binary data:


  $ curl -u admin:admin -H "Accept: application/json" \
# Open a task
    -X POST -F folderId=4 -F filename=CHANGELOG.txt -F filedata=@CHANGELOG.txt \
# Select a label
    http://localhost:8080/services/rest/document/upload
# Draw a bounding box around the target area
# Save the annotation


In this case the document will be added to the respository using the default language (english). Of course it is possible to specify the additional parameter 'language' to tell the system that the document we are storing is in german (ISO 639-2 code)
Example labels:


  $ curl -u admin:admin -H "Accept: application/json" \
* Invoice Number
    -X POST -F folderId=4 -F filename=pub_arbeitsplatz_straße.pdf -F language=de -F filedata=@pub_arbeitsplatz_straße.pdf \
* Date
    http://localhost:8080/services/rest/document/upload
* Seller Name
* Buyer Name
* Total Amount


[[File:LabelStudio-annotation-example.png|thumb|600px|center|Example annotation]]


Or also from a HTML form:
=== Export the Dataset ===


<source lang="html4strict">
# Open the project
<html>
# Click '''Export'''
  <body>
# Select the desired format
    <form method="POST" enctype="multipart/form-data"
          action="http://localhost:8080/services/rest/document/upload">
      Select folder: <input type="text" name="folderId" value="4"/><br/>
      Select filename: <input type="text" name="filename" /><br/>
      Select file: <input type="file" name="filedata" size="45"/><br/>
      <input type="submit" value="Upload" />
    </form>
  </body>
</html>
</source>


And now download it:
Supported formats include:


  $ curl -u admin:admin \
* YOLO
    http://localhost:8080/services/rest/document/getContent?docId=456456456
* COCO
* Pascal VOC
* CSV


If the document is a binary file you can redirect the output to a file adding '> filename' to the end of the command
For YOLO training, export the dataset in YOLO format.


  $ curl -u admin:admin \
=== Dataset Formats ===
    http://localhost:8080/services/rest/document/getContent?docId=456456456 > myFile.pdf


Delete a specific version of a given document (since v7.6.4)
==== COCO ====


  $ curl -u admin:admin \
COCO is a JSON-based dataset format commonly used for object detection datasets.
    -G -d docId=1803 -d version=1.3 -X DELETE http://localhost:8080/services/rest/document/deleteVersion


Update the document metadata. Specifically, we can see how to update an extended attribute field of type date (type = 3) using the property dateValue
More information:
 
https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html
  $ curl -v -u admin:admin -H "Content-Type: application/json" -H "Accept: application/json" -X PUT \
    -d "{ \"id\": 47, \"folderId\": 4, \"fileName\":\"Egzai_u002.doc\", \"templateId\":92241920, \"attributes\":[{\"name\":\"ack\",\"stringValue\":\"ack\",\"type\":0},{\"name\":\"Tar\",\"dateValue\":\"2017-03-18 19:10:00 +0100\",\"type\":3}] }" \
    http://localhost:8080/services/rest/document/update
 
Creates a document with the create method on Windows 10<br/>
Windows 10, curl 7.55.1 (Windows) libcurl/7.55.1 WinSSL
 
<pre>
curl -v -u admin:admin -X POST "http://localhost:8080/services/rest/document/create" -H "accept: application/json" -H "Content-Type: multipart/form-data" -F "document={ \"language\":\"en\",\"fileName\":\"ScreenHunter949.png\",\"folderId\":4 };type=application/json" -F "content=@C:\Users\shatz\Desktop\ScreenHunter949.png;type=application/octet-stream"
</pre>
 
Convert a document to PDF
Note: This will only perform the conversion, if completed successfully it returns HTTP code 204 and empty content
<pre>
curl -v -X PUT "http://localhost:8080/services/rest/document/createPdf?docId=125" -H  "accept: application/json" -H  "Authorization: Basic YWRtaW46YWRtaW4="
</pre>
 
Download the converted .PDF file (latest version of the document)
<pre>
curl -v -X GET "http://localhost:8080/services/rest/document/getResource?docId=125&suffix=conversion.pdf" -H  "accept: application/octet-stream" -H  "Authorization: Basic YWRtaW46YWRtaW4=" --output 125-conversion.pdf
</pre>
 
=== Versioning ===
Checkout an existing document<br/>
  $ curl -k -u admin:admin -X POST https://[url]/services/rest/document/checkout \
    -H  "accept: application/json" -H  "Content-Type: application/x-www-form-urlencoded" -d "docId=2686"
 
Checkin a new version of document (the document must be in checked-out state) [Windows 11]<br/>
  $ curl -v -u admin:admin -X POST "http://localhost:8080/services/rest/document/checkin" \
    -H  "accept: */*" -H  "Content-Type: multipart/form-data" -F "docId=721" -F "comment=" -F "release=false" -F "filename=Checkmarx.txt" -F "filedata=@Checkmarx.txt;type=text/plain"
 
 
Upload: Creates a new version of an existing document (in one step) [Windows 11]<br/>
 
  $ curl -u admin:admin -X POST "http://localhost:8080/services/rest/document/upload" \
    -H  "Accept: application/json" -H  "Content-Type: multipart/form-data" -F "docId=721" -F "folderId=" -F "release=false" \
    -F "filename=logicaldoc_community - Checkmarx AST.pdf" -F "language=en" -F "filedata=@logicaldoc_community - Checkmarx AST.pdf;type=application/pdf"
 
Upload: updates the content of an existing document generating a new version
  $ curl -v -u admin:admin -H "Accept: application/json" -X POST -F docId=118 -F filename=google.png -F filedata=@C:\tmp\google.png http://localhost:8080/services/rest/document/upload
 
== Search ==
Standard Full-text search on content, title and tags using english as language of the query (expressionLanguage) and on english documents (language):
 
  $ curl -u admin:admin -H "Content-Type: application/json" -H "Accept: application/json" -X POST \
    -d "{\"maxHits\":50,\"expression\":\"document management system\",\"expressionLanguage\":\"en\",\"language\":\"en\", \"dateFrom\": \"2023-01-01\"}"
    http://localhost:8080/services/rest/search/find
 
Date fields you can use:
<pre>
"dateFrom": "string",
"dateTo": "string",
"creationFrom": "string",
"creationTo": "string",
</pre>


dates must be expressed in ISO format: yyyy-mm-dd or yyyy-MM-dd HH:mm:ss (24 hours)
==== YOLO ====


YOLO datasets contain images and annotation files organized according to a predefined directory structure.


More info at:
More information:
https://docs.cvat.ai/docs/dataset_management/formats/format-yolo/


* [http://www.yilmazhuseyin.com/blog/dev/curl-tutorial-examples-usage/ curl tutorial with examples of usage]
==== YOLOv8 OBB ====
* [http://docs.logicaldoc.com/resources/wsdoc/rest/index.html?version=7.6.4 LogicalDOC REST API reference v7.6.4]
* [https://docs.logicaldoc.com/resources/wsdoc/rest/index.html?version=7.7.6 LogicalDOC REST API reference v7.7.6]
* [https://docs.logicaldoc.com/resources/wsdoc/rest/index.html?version=8.1.0 LogicalDOC REST API reference v8.1]
* [https://app.swaggerhub.com/apis/swatzniak/logicaldoc_rest_api/8.8.1 LogicalDOC REST API | 8.8.1 | SwaggerHub]
* [[Bindings_And_Samples|Webservices - Binding and Examples]]
* [https://docs.logicaldoc.com/en/web-services-api LogicalDOC Web Services API]


[[Category: RESTful Guide]]
YOLOv8 OBB (Oriented Bounding Boxes) extends the standard YOLO format by supporting rotated bounding boxes using eight normalized coordinates.

Revision as of 12:23, 23 June 2026

Preparing a Dataset with Label Studio

This guide explains how to create an annotated dataset for YOLO training using Label Studio.

Install Label Studio

Refer to the official installation guide: https://labelstud.io/guide/install

Enable Local File Storage

For large projects it is not recommended to upload images directly through the Label Studio interface.

To enable local file access, configure the following environment variables: 

LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/path/to/images

Launch Label Studio: 

label-studio start

Create a Project

  1. Login to Label Studio
  2. Click Create Project
  3. Enter a project name
  4. Configure the labeling interface
  5. Save the project

Import Images

  1. Open the project
  2. Click Import
  3. Select Local Storage

When importing images, choose Files as the import method.

Unlike CVAT, Label Studio creates one task for each imported document image.

File:LabelStudio-import-method.png
Selecting the Files import method

Annotate Documents

  1. Open a task
  2. Select a label
  3. Draw a bounding box around the target area
  4. Save the annotation

Example labels:

  • Invoice Number
  • Date
  • Seller Name
  • Buyer Name
  • Total Amount
File:LabelStudio-annotation-example.png
Example annotation

Export the Dataset

  1. Open the project
  2. Click Export
  3. Select the desired format

Supported formats include:

  • YOLO
  • COCO
  • Pascal VOC
  • CSV

For YOLO training, export the dataset in YOLO format.

Dataset Formats

COCO

COCO is a JSON-based dataset format commonly used for object detection datasets.

More information: https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html

YOLO

YOLO datasets contain images and annotation files organized according to a predefined directory structure.

More information: https://docs.cvat.ai/docs/dataset_management/formats/format-yolo/

YOLOv8 OBB

YOLOv8 OBB (Oriented Bounding Boxes) extends the standard YOLO format by supporting rotated bounding boxes using eight normalized coordinates.

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