Register OTC Data (SDK)

Using cloud storage data in Encord is a multi-step process:

Set up your cloud storage so Encord can access your data
Create a cloud storage integration on Encord to link to your cloud storage
Create a JSON or CSV file to import your data
Create a Dataset
Perform the registration using the JSON or CSV file

See our OTC integration documentation for a detailed guide to setting up an integration.

Step 1: Setup OTC Integration

Before you can do anything with the Encord platform and cloud storage, you need to configure your cloud storage to work with Encord. Once the integration between Encord and your cloud storage is complete, you can then use your data in Encord. In order to integrate with Open Telecom Cloud, you need to:

Create the account which accesses data in the Object Storage Service.
Give the account read access to the desired buckets by:
1. Creating a Custom Bucket Policy.
2. (Optional) If you have Cross-origin resource sharing (CORS) configured on your buckets, make sure that *.encord.com is given read access.
Create the integration by giving Encord access to that account’s credentials.

See our OTC integration documentation for a detailed guide to setting up an OTC integration.

Step 1: Create Account Step 2: Give Read access Step 3: Configure CORS

Step 2: Create Encord Integration

On the Encord platform enter the Access key ID and Secret access key, which should be located in the access key file, generated with the creation of the user. (if the access key has been misplaced, a new one can be created from the IAM User menu).

Optionally check the box to enable Strict client-only access, server-side media features will not be available if you would like Encord to sign URLs, but refrain from downloading any media files onto Encord servers. Read more about this feature here.

Finally, click the Create button at the bottom of the pop-up. The integration will now appear in the list of integrations in the ‘Integrations’ tab.

Step 3: Create JSON or CSV for Registration

All types of data (videos, images, image groups, image sequences, and DICOM) from a private cloud are added to a Dataset in the same way, by using a JSON or CSV file. The file includes links to all images, image groups, videos and DICOM files in your cloud storage.

For a list of supported file formats for each data type, go here

Encord supports file names up to 300 characters in length for any file or video for upload.

Encord enforces the following upload limits for each JSON file used for file registration:

Up to 1 million URLs
A maximum of 500,000 items (e.g. images, image groups, videos, DICOMs)
URLs can be up to 16 KB in size

Optimal upload chunking can vary depending on your data type and the amount of associated metadata. For tailored recommendations, contact Encord support. We recommend starting with smaller uploads and gradually increasing the size based on how quickly jobs are processed. Generally, smaller chunks result in faster data reflection within the platform.

BEST PRACTICE: If you want to use Index or Active with your video data, we STRONGLY RECOMMEND using custom metadata (clientMetadata) to specify key frames, custom metadata, and custom embeddings. For more information go here or here for information on using the SDK.

Create JSON file for Registration

For detailed information about the JSON file format used for import go here. The information provided about each of the following data types is designed to get you up and running as quickly as possible without going too deeply into the why or how. Look at the template for each data type, then the examples, and adjust the examples to suit your needs.

If skip_duplicate_urls is set to true, all object URLs that exactly match existing images/videos in the dataset are skipped.

Videos

Video MetadataWhen the videoMetadata flag is present in the JSON file, we directly use the supplied metadata without performing any additional validation, and do not store the file on our servers.

To guarantee accurate labels, it is crucial that the videoMetadata provided is accurate.

{
  "videos": [
    {
      "objectUrl": "cloud-path-to-your-video-1"
    },
    {
      "objectUrl": "cloud-path-to-your-video-2",
        "videoMetadata": {
          "fps": frames-per-second,
          "duration": duration-in-seconds,
          "width": frame-width,
          "height": frame-height,
          "file_size": file-size-in-bytes,
          "mime_type": "MIME-file-type-extension"
        }
      }
  ],
  "skip_duplicate_urls": true
}

Audio files

Audio Files

The following is an example JSON file for uploading two audio files to Encord.

Template: Imports audio files with an Encord title.
Audio Metadata: Imports one audio file with the audiometadata flag. When the audiometadata flag is present in the JSON file, we directly use the supplied metadata without performing any additional validation, and do not store the file on our servers. To guarantee accurate labels, it is crucial that the metadata you provide is accurate.

{
  "audio": [
    {
      "objectUrl": "<object url_1>"
    },
    {
      "objectUrl": "<object url_2>",
      "title": "my-custom-audio-file-title.mp3"
    }
  ],
  "skip_duplicate_urls": true
}

PDFs

The following is an example JSON file for uploading PDFs to Encord.

Template: Imports PDFs with an Encord data_title.
Data: Imports two PDFs with no title or custom metadata.

{
  "pdfs": [
    {
      "objectUrl": "<object url_1>"
    },
    {
      "objectUrl": "<object url_2>",
      "title": "my-file.html"
    }
  ],
  "skip_duplicate_urls": true
}

Text Files

The following is an example JSON file for uploading text files to Encord.

Template: Imports text files with an Encord title.
Data: Imports two text files with no title or custom metadata.

{
  "text": [
    {
      "objectUrl": "<object url_1>"
    },
    {
      "objectUrl": "<object url_2>",
      "title": "my-file.html"
    }
  ],
  "skip_duplicate_urls": true
}

Single images

Single Images

For detailed information about the JSON file format used for import go here.The JSON structure for single images parallels that of videos.Template: Provides the proper JSON format to import images into Encord.Examples:

Data Imports the images only.
Image Metadata: Imports images with image metadata. This improves the import speed for your images.

{
  "images": [
    {
      "objectUrl": "file/path/to/images/file-name-01.file-extension"
    },
    {
      "objectUrl": "file/path/to/images/file-name-02.file-extension"
    },
    {
      "objectUrl": "file/path/to/images/file-name-03.file-extension",
      "title": "image-title.file-extension"
    }
  ],
  "skip_duplicate_urls": true
}

Image groups

For detailed information about the JSON file format used for import go here.

Image groups are collections of images that are processed as one annotation task.
Images within image groups remain unaltered, meaning that images of different sizes and resolutions can form an image group without the loss of data.
Image groups do NOT require ‘write’ permissions to your cloud storage.
If skip_duplicate_urls is set to true, all URLs exactly matching existing image groups in the dataset are skipped.

The position of each image within the sequence needs to be specified in the key (objectUrl_{position_number}).

Template: Provides the proper JSON format to import image groups into Encord.Examples:

Data: Imports the image groups only.

{
  "image_groups": [
    {
      "title": "<title 1>",
      "createVideo": false,
      "objectUrl_0": "file/path/to/images/file-name-01.file-extension",
      "objectUrl_1": "file/path/to/images/file-name-02.file-extension",
      "objectUrl_2": "file/path/to/images/file-name-03.file-extension",
    },
    {
      "title": "<title 2>",
      "createVideo": false,
      "objectUrl_0": "file/path/to/images/file-name-01.file-extension",
      "objectUrl_1": "file/path/to/images/file-name-02.file-extension",
      "objectUrl_2": "file/path/to/images/file-name-03.file-extension",
      "clientMetadata": {"optional": "metadata"}
    }
  ],
  "skip_duplicate_urls": true
}

Image sequences

For detailed information about the JSON file format used for import go here.

Image sequences are collections of images that are processed as one annotation task and represented as a video.
Images within image sequences may be altered as images of varying sizes and resolutions are made to match that of the first image in the sequence.
Creating Image sequences from cloud storage requires ‘write’ permissions, as new files have to be created in order to be read as a video.
Each object in the image_groups array with the createVideo flag set to true represents a single image sequence.
If skip_duplicate_urls is set to true, all URLs exactly matching existing image sequences in the dataset are skipped.

The only difference between adding image groups and image sequences using a JSON file is that image sequences require the createVideo flag to be set to true. Both use the key image_groups.

The position of each image within the sequence needs to be specified in the key (objectUrl_{position_number}).

Encord supports up to 32,767 entries (21:50 minutes) for a single image sequence. We recommend up to 10,000 to 15,000 entries for a single image sequence for best performance. If you need a longer sequence, we recommend using video instead of an image sequence.

Template: Provides the proper JSON format to import image groups into Encord.Examples:

Data: Imports the images groups only.

{
  "image_groups": [
    {
      "title": "<title 1>",
      "createVideo": true,
      "objectUrl_0": "<object url>"
    },
    {
      "title": "<title 2>",
      "createVideo": true,
      "objectUrl_0": "<object url>",
      "objectUrl_1": "<object url>",
      "objectUrl_2": "<object url>"
    }
  ],
  "skip_duplicate_urls": true
}

DICOM

For detailed information about the JSON file format used for import go here.

Each dicom_series element can contain one or more DICOM series.
Each series requires a title and at least one object URL, as shown in the example below.
If skip_duplicate_urls is set to true, all object URLs exactly matching existing DICOM files in the dataset will be skipped.

Custom metadata is distinct from patient metadata, which is included in the .dcm file and does not have to be specific during the upload to Encord.

The following is an example JSON for uploading three DICOM series belonging to a study. Each title and object URL correspond to individual DICOM series.

The first series contains only a single object URL, as it is composed of a single file.
The second series contains 3 object URLs, as it is composed of three separate files.
The third series contains 2 object URLs, as it is composed of two separate files.

For each DICOM upload, an additional DicomSeries file is created. This file represents the series file-set. Only DicomSeries are displayed in the Encord application.

JSON for DICOM

{
  "dicom_series": [
    {
      "title": "Series-1",
      "objectUrl_0": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/study1-series1-file.dcm"
    },
    {
      "title": "Series-2",
      "objectUrl_0": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/study1-series2-file1.dcm",
      "objectUrl_1": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/study1-series2-file2.dcm",
      "objectUrl_2": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/study1-series2-file3.dcm",
    },
      {
      "title": "Series-3",
      "objectUrl_0": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/study1-series3-file1.dcm",
      "objectUrl_1": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/study1-series3-file2.dcm",
    }
  ],
  "skip_duplicate_urls": true
}

NIfTI

The following is an example JSON file for uploading two NIfTI files to Encord.

{
    "nifti": [
      {
        "title": "<file-1>",
        "objectUrl": "https://my-bucket/.../nifti-file1.nii"
      },
      {
        "title": "<file-2>",
        "objectUrl": "https://my-bucket/.../nifti-file2.nii.gz"
      }
    ],
    "skip_duplicate_urls": true
  }

Multiple file types

You can upload multiple file types using a single JSON file. The example below shows 1 image, 2 videos, 2 image sequences, and 1 image group.

Multiple file types


{
  "images": [
    {
      "objectUrl": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/Image1.png"
    }
  ],
  "videos": [
    {
      "objectUrl": "https://encord-bucket.obs.eu-de.otc.t-systems.com/videos/Cooking.mp4"
    },
    {
      "objectUrl": "https://encord-bucket.obs.eu-de.otc.t-systems.com/videos/Oranges.mp4"
    }
  ],
  "image_groups": [
    {
      "title": "apple-samsung-light",
      "createVideo": true,
      "objectUrl_0": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/1+(32).jpg",
      "objectUrl_1": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/1+(33).jpg",
      "objectUrl_2": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/1+(34).jpg",
      "objectUrl_3": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/1+(35).jpg"
    },
    {
      "title": "apple-samsung-dark",
      "createVideo": true,
      "objectUrl_0": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/2+(32).jpg",
      "objectUrl_1": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/2+(33).jpg",
      "objectUrl_2": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/2+(34).jpg",
      "objectUrl_3": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/2+(35).jpg"
    }
  ],
  "image_groups": [
    {
      "title": "apple-ios-light",
      "createVideo": false,
      "objectUrl_0": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/3+(32).jpg",
      "objectUrl_1": "https://encord-bucket.obs.eu-de.otc.t-systems.com/images/3+(33).jpg"
    }
  ],
  "skip_duplicate_urls": true
}

Create CSV File for Registration

In the CSV file format, the column headers specify which type of data is being uploaded. You can add and single file format at a time, or combine multiple data types in a single CSV file. Details for each data format are given in the sections below.

Encord supports up to 10,000 entries for upload in the CSV file.

Object URLs can’t contain whitespace.
For backwards compatibility reasons, a single column CSV is supported. A file with the single ObjectUrl column is interpreted as a request for video upload. If your objects are of a different type (for example, images), this error displays: “Expected a video, got a file of type XXX”.

Videos

A CSV file containing videos should contain two columns with the following mandatory column headings:
‘ObjectURL’ and ‘Video title’. All headings are case-insensitive.

The ‘ObjectURL’ column containing the objectUrl. This field is mandatory for each file, as it specifies the full URL of the video resource.
The ‘Video title’ column containing the video_title. If left blank, the original file name is used.

In the example below files 1, 2 and 4 will be assigned the names in the title column, while file 3 will keep its original file name.

ObjectUrl	Video title
path/to/storage-location/frame1.mp4	Video 1
path/to/storage-location/frame2.mp4	Video 2
path/to/storage-location/frame3.mp4
path/to/storage-location/frame4.mp4	Video 3

Single images

A CSV file containing single images should contain two columns with the following mandatory headings:
‘ObjectURL’ and ‘Image title’. All headings are case-insensitive.

The ‘ObjectURL’ column containing the objectUrl. This field is mandatory for each file, as it specifies the full URL of the image resource.
The ‘Image title’ column containing the image_title. If left blank, the original file name is used.

In the example below files 1, 2 and 4 will be assigned the names in the title column, while file 3 will keep its original file name.

ObjectUrl	Image title
path/to/storage-location/frame1.jpg	Image 1
path/to/storage-location/frame2.jpg	Image 2
path/to/storage-location/frame3.jpg
path/to/storage-location/frame4.jpg	Image 3

Image groups

A CSV file containing image groups should contain three columns with the following mandatory headings:
‘ObjectURL’, ‘Image group title’, and ‘Create video’. All three headings are case-insensitive.

The ‘ObjectURL’ column containing the objectUrl. This field is mandatory for each file, as it specifies the full URL of the resource.
The ‘Image group title’ column containing the image_group_title. This field is mandatory, as it determines which image group a file will be assigned to.

In the example below the first two URLs are grouped together into ‘Group 1’, while the following two files are grouped together into ‘Group 2’.

ObjectUrl	Image group title	Create video
path/to/storage-location/frame1.jpg	Group 1	false
path/to/storage-location/frame2.jpg	Group 1	false
path/to/storage-location/frame3.jpg	Group 2	false
path/to/storage-location/frame4.jpg	Group 2	false

Image groups do not require ‘write’ permissions.

Image sequences

A CSV file containing image sequences should contain three columns with the following mandatory headings: ‘ObjectURL’, ‘Image group title’, and ‘Create video’. All three headings are case-insensitive.

The ‘ObjectURL’ column containing the objectUrl. This field is mandatory for each file, as it specifies the full URL of the resource.
The ‘Image group title’ column containing the image_group_title. This field is mandatory, as it determines which image sequence a file will be assigned to. The dimensions of the image sequence are determined by the first file in the sequence.
The ‘Create video’ column. This can be left blank, as the default value is ‘true’.

In the example below the first two URLs are grouped together into ‘Sequence 1’, while the second two files are grouped together into ‘Sequence 2’.

ObjectUrl	Image group title	Create video
path/to/storage-location/frame1.jpg	Sequence 1	true
path/to/storage-location/frame2.jpg	Sequence 1	true
path/to/storage-location/frame3.jpg	Sequence 2	true
path/to/storage-location/frame4.jpg	Sequence 2	true

Image groups and image sequences are only distinguished by the presence of the ‘Create video’ column.

Image sequences require ‘write’ permissions against your storage bucket to save the compressed video.

DICOM

A CSV file containing DICOM files should contain two columns with the following mandatory headings: ‘ObjectURL’ and ‘Dicom title’. Both headings are case-insensitive.

The ‘ObjectURL’ column containing the objectUrl. This field is mandatory for each file, as it specifies the full URL of the resource.
The ‘Series title’ column containing the dicom_title. When two files are given the same title they are grouped into the same DICOM series. If left blank, the original file name is used.

In the example below the first two files are grouped into ‘dicom series 1’, the next two files are grouped into ‘dicom series 2’, while the final file will remain separated as ‘dicom series 3’.

ObjectUrl	Series title
path/to/storage-location/frame1.dcm	dicom series 1
path/to/storage-location/frame2.dcm	dicom series 1
path/to/storage-location/frame3.dcm	dicom series 2
path/to/storage-location/frame4.dcm	dicom series 2
path/to/storage-location/frame5.dcm	dicom series 3

Multiple file types

You can upload multiple file types with a single CSV file by using a new header each time there is a change of file type. Three headings will be required if image sequences are included.

Since the ‘Create video’ column defaults to true all files that are not image sequences must contain the value false

The example below shows a CSV file for the following:

Two image sequences composed of 2 files each.
One image group composed of 2 files.
One single image.
One video.

ObjectUrl	Image group title	Create video
path/to/storage-location/frame1.jpg	Sequence 1	true
path/to/storage-location/frame2.jpg	Sequence 1	true
path/to/storage-location/frame3.jpg	Sequence 2	true
path/to/storage-location/frame4.jpg	Sequence 2	true
path/to/storage-location/frame5.jpg	Group 1	false
path/to/storage-location/frame6.jpg	Group 1	false
ObjectUrl	Image title	Create video
path/to/storage-location/frame1.jpg	Image 1	false
ObjectUrl	Image title	Create video
full/storage/path/video.mp4	Video 1	false

Step 4: Register Data with Encord

To use your data in Encord, it must be uploaded to the Encord Files storage. Once uploaded, your data can be reused across multiple Projects and contain no labels or annotations themselves. Files stores your data, while Projects store your labels. The following script creates a folder in Files and uses your AWS integration to register data in that folder. The following script creates a new folder in Files and initiates uploads from AWS. It works for all file types.

If Upload is still in progress, try again later! is returned, use the script to check the upload status to see whether the upload has finished.

Ensure that you:

Replace <private_key_path> with the path to your private key.
Replace <integration_title> with the title of the integration you want to use.
Replace <folder_name> with the folder name. The scripts assume that the specified folder name is unique.
Replace path/to/json/file.json with the path to a JSON file specifying which cloud storage files should be uploaded.
Replace A folder to store my files with a meaningful description for your folder.
Replace "my": "folder_metadata" with any metadata you want to add to the folder.

The script has several possible outputs:

“Upload is still in progress, try again later!”: The registration has not finished. Run this script again later to check if the data registration has finished.
“Upload completed”: The registration completed. If any files failed to upload, the URLs are listed.
“Upload failed”: The entire registration failed, and not just individual files. Ensure your JSON file is formatted correctly.


# Import dependencies
from encord import EncordUserClient
from encord.orm.dataset import LongPollingStatus  # Ensure correct import path

# Instantiate user client. Replace <private_key_path> with the path to your private key
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path="<private_key_path>"
)

# Specify the integration you want to use
integrations = user_client.get_cloud_integrations()
integration_idx = [i.title for i in integrations].index("<integration_title>")
integration = integrations[integration_idx].id

# Create a storage folder
folder_name = "<folder_name>"
folder_description = "A folder to store my files"
folder_metadata = {"my": "folder_metadata"}
storage_folder = user_client.create_storage_folder(
    folder_name, folder_description, client_metadata=folder_metadata
)

# Initiate cloud data registration
upload_job_id = storage_folder.add_private_data_to_folder_start(
    integration_id=integration, private_files="path/to/json/file.json", ignore_errors=True
)

# Check upload status
res = storage_folder.add_private_data_to_folder_get_result(upload_job_id, timeout_seconds=5)
print(f"Execution result: {res}")

if res.status == LongPollingStatus.PENDING:
    print("Upload is still in progress, try again later!")
elif res.status == LongPollingStatus.DONE:
    print("Upload completed")
    if res.unit_errors:
        print("The following URLs failed to upload:")
        for e in res.unit_errors:
            print(e.object_urls)
else:
    print(f"Upload failed: {res.errors}")

Step 5: Check Registration Status

If Step 5 returns "Upload is still in progress, try again later!", run the following code to query the Encord server again. Ensure that you replace <upload_job_id> with the output by the previous code. In the example above upload_job_id=c4026edb-4fw2-40a0-8f05-a1af7f465727. The script has several possible outputs:

“Upload is still in progress, try again later!”: The registration has not finished. Run this script again later to check if the data registration has finished.
“Upload completed”: The registration completed. If any files failed to upload, the URLs are listed.
“Upload failed”: The entire registration failed, and not just individual files. Ensure your JSON file is formatted correctly.

# Import dependencies
from encord import EncordUserClient
from encord.orm.dataset import LongPollingStatus

upload_job_id = <upload_job_id>

# Authenticate with Encord using the path to your private key. 
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path="<private_key_path>"
    )

# Check upload status
res = dataset.add_private_data_to_dataset_get_result(upload_job_id, timeout_seconds=5)
print(f"Execution result: {res}")

if res.status == LongPollingStatus.PENDING:
    print("Upload is still in progress, try again later!")
elif res.status == LongPollingStatus.DONE:
    print("Upload completed")
    if res.unit_errors:
        print("The following URLs failed to upload:")
        for e in res.unit_errors:
            print(e.object_urls)
else:
    print(f"Upload failed: {res.errors}")

Omitting the timeout_seconds argument from the add_private_data_to_dataset_get_result() method performs status checks until the status upload has finished.

Step 6: Create a Dataset

Creating a Dataset and adding files to a Dataset are two distinct steps. Click here to learn how to add data to an existing Dataset.

Datasets cannot be deleted using the SDK or the API. Use the Encord platform to delete Datasets.

The following example creates a Dataset called “Houses” that expects data hosted on OTC.

Substitute <private_key_path> with the file path for your private key.
Replace “Houses” with the name you want your Dataset to have.

# Import dependencies
from encord import EncordUserClient
from encord.orm.dataset import StorageLocation

# Authenticate with Encord using the path to your private key
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path="<private_key_path>"
)

# Create a new dataset
dataset = user_client.create_dataset(
    dataset_title="Houses",
    dataset_type=StorageLocation.OTC,
    create_backing_folder=False,
)

# Prints a CreateDatasetResponse object. Verify the Dataset creation
print(dataset_response)

# Print the storage location
print(f"Using storage location: OTC")

Step 7: Add Your Data to a Dataset

Now that you registered your data and created a Dataset, it is time to add your files to the Dataset. The following scripts add all files in a specified folder to a Dataset.

Replace <private_key_path> with the path to your private key.
Replace <folder_name> with the name you want to give your Storage folder.
Replace <dataset_hash> with the hash of the Dataset you want to add the data units to.

Files added to the folder at a later time will not be automatically added to the Dataset.

All files

from encord import EncordUserClient

# Authentication
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path="<private_key_path>"
)

# Find the storage folder by name
folder_name = "<folder_name>"  # Replace with your folder's name
folders = list(user_client.find_storage_folders(search=folder_name, page_size=1))

dataset = user_client.get_dataset("<dataset_hash>")

# Ensure the folder was found
if folders:
    storage_folder = folders[0]

    # List all data units
    items = list(storage_folder.list_items())

    # Collect all item UUIDs
    item_uuids = [item.uuid for item in items]

    # Output the retrieved data units
    for item in items:
        print(f"UUID: {item.uuid}, Name: {item.name}, Type: {item.item_type}")

    # Link all items at once if there are any
    if item_uuids:
        dataset.link_items(item_uuids)
else:
    print("Folder not found.")

Step 8: Verify your files are in the Dataset

After adding your files to the Dataset, verify that all the files you expect to be there made it into the Dataset. The following script prints the URLs of all the files in a Dataset. Ensure that you:

Replace <private_key_path> with the path to your private key.
Replace <dataset_hash> with the hash of your Dataset.

Sample Code

#Import dependencies
from encord import EncordUserClient, Project,Dataset
from encord.objects.project import ProjectDataset
from encord.orm.dataset import DatasetAccessSettings

#Initiate client
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path="<private_key_path>"
)

#Files the file links to all files in the Dataset
dataset_level_file_links = []
dataset: Dataset = user_client.get_dataset("<dataset_hash>")
for data in dataset.list_data_rows():
    dataset_level_file_links.append(data.file_link)
print(dataset_level_file_links)

Step 9: Prepare Your Data for Label/Annotation Import

Prepare Data for Labels

Step 10: Import Labels/Annotations

Import Labels/Annotations

Get Started

General

Index

Ontologies

Projects

Labels

Datasets

DICOM

Active API & SDK

Register OTC Data (SDK)

Step 1: Setup OTC Integration

Step 2: Create Encord Integration

Step 3: Create JSON or CSV for Registration

Create JSON file for Registration

Videos

Audio Files

PDFs

Text Files

Single Images

Image groups

Image sequences

DICOM

NIfTI

Create CSV File for Registration

Videos

Image groups

Image sequences

Multiple file types

Step 4: Register Data with Encord

Step 5: Check Registration Status

Step 6: Create a Dataset

Step 7: Add Your Data to a Dataset

Step 8: Verify your files are in the Dataset

Step 9: Prepare Your Data for Label/Annotation Import

Step 10: Import Labels/Annotations

Get Started

General

Index

Ontologies

Projects

Labels

Datasets

DICOM

Active API & SDK

​Step 1: Setup OTC Integration

​Step 2: Create Encord Integration

​Step 3: Create JSON or CSV for Registration

​Create JSON file for Registration

​Create CSV File for Registration

​Videos

​Image groups

​Image sequences

​Multiple file types

​Step 4: Register Data with Encord

​Step 5: Check Registration Status

​Step 6: Create a Dataset

​Step 7: Add Your Data to a Dataset

​Step 8: Verify your files are in the Dataset

​Step 9: Prepare Your Data for Label/Annotation Import

​Step 10: Import Labels/Annotations

Step 1: Setup OTC Integration

Step 2: Create Encord Integration

Step 3: Create JSON or CSV for Registration

Create JSON file for Registration

Create CSV File for Registration

Videos

Image groups

Image sequences

Multiple file types

Step 4: Register Data with Encord

Step 5: Check Registration Status

Step 6: Create a Dataset

Step 7: Add Your Data to a Dataset

Step 8: Verify your files are in the Dataset

Step 9: Prepare Your Data for Label/Annotation Import

Step 10: Import Labels/Annotations