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SDK Documentation

How to install and use Ango Hub's Python SDK.
We provide a Python SDK to interface programmatically with Ango Hub.

How to Install the Ango SDK

To use the Ango SDK, you will need the ango Python package, which we publish on PyPI.
To download and add the package to your current Python environment, simply run
pip install ango

How to upgrade the Ango SDK

To upgrade the ango package to its latest version in your current Python environment run
pip install ango --upgrade

Obtaining your API Key

To use the ango package, you will need an API key. To obtain your API key, from your account page, enter the API tab. From here, you can create and copy your API key.
Creating a new API key will revoke the existing one.

Obtaining Project and Task IDs

Each project and task in Ango Hub is assigned its own unique ID. You may need to use such IDs in the SDK.

Project IDs

To obtain a project's ID, open the project and look at your browser's address bar. The URL will be in the format:
https://hub.ango.ai/projects/<project_id>
Copy the project ID from your address bar.

Task and Asset IDs

To obtain Task and Asset IDs, from the Task or the Assets tab, open the task you'd like to copy the ID of. Then, open the Task Info (1) panel on the right and copy the ID you need.
You may use one of the Copy to Clipboard buttons (2) to speed up the process.

SDK Examples

You can find examples for each method below, under each method's heading.
You can also find those examples in our Colab.

SDK Methods (v0.1.18)

ango.sdk.SDK.

assign_batches(project_id, asset_ids, batches)

Assign specific asset(s) to specific batches.
Parameters:
  • project_id: string
    • ID of the project where the attachment will be created. Project IDs can be obtained from the UI and from list_projects.
    • Example: '0000000aa0a00a0000aaaa0a'
  • asset_ids: List[str]
    • List of asset IDs to assign to batches. Asset IDs can be obtained from the UI, or from get_assets.
    • Example: ['0000000aa0a00a0000aaaa0a', '0000000aa0a00a0000aaaa0b']
  • batches: List[str]
    • List of batches to which assets will be assigned.
    • You can choose to pass either a list of batch names or a list of batch IDs. Batch names and batch IDs can be obtained with get_batches.
    • Example:
      • ['Batch-1', 'Batch-2'] or
      • ['0000000aa0a00a0000aaaa0a', '0000000aa0a00a0000aaaa0b']
Returns:
  • output: dict
Example:
from ango.sdk import SDK
​
api_key = '<YOUR API KEY>'
project_id = '<YOUR PROJECT ID>'
ango_sdk = SDK(api_key)
​
asset_ids = ['<YOUR ASSET ID 1>', '<YOUR ASSET ID 2>']
batch_ids = ['<YOUR BATCH ID 1>', '<YOUR BATCH ID 2>']
​
ango_sdk.assign_batches(project_id, asset_ids, batch_ids)
Outputs:
{
"status": "success",
"data": {
"assets": 2
}
}
Where "assets" is the number of assets successfully assigned to the batch(es).
See also
​create_batch, get_batches​

assign_task(task_id, user_id, email)

Assign a task to a specific user with either user_id or email.
Parameters:
  • task_id: string
    • ID of the task to be assigned. Task IDs can be obtained from the UI, and from get_tasks.
    • Example: '0000000aa0a00a0000aaaa0a'
  • user_id: string, Optional
    • ID of the user to assign the task to. User IDs can be obtained with get_project. Not required if email was provided.
    • Example: '0000000aa0a00a0000aaaa0a'
  • email: string, Optional
    • Mail address with which the user to be assigned the task to registered. Not required if user_id was provided.
    • Example: '[email protected]'
Returns:
  • output: dict
Example:
from ango.sdk import SDK
​
api_key = '<YOUR API KEY>'
ango_sdk = SDK(api_key)
​
task_id = '<YOUR TASK ID>'
email = '<[email protected]>'
ango_sdk.assign_task(task_id, email)
See also
​get_tasks​

create_attachment(project_id, attachments)

Add attachments to assets in a project. More on attachments and uploading them here.
Parameters:
  • project_id: string
    • ID of the project where the attachment will be created. Project IDs can be obtained from the UI and from list_projects.
    • Example: '0000000aa0a00a0000aaaa0a'
  • attachments: List[dict]
    • List of attachments to attach to existing assets. Attachments are dictionaries containing information about the asset the attachment will be attached to, the type of attachment, and the content of the attachment.
    • Example attachment parameter with 3 attachments being attached to 2 assets:
attachments = [
{"externalId": "sample_image_1.png",
"attachments": [
{"type": "IMAGE", "value": "https://sample-attachment-image.jpg"},
{"type": "TEXT", "value": "Some sample text."}
]
},
{"externalId": "sample_image_2.png",
"attachments": [
{"type": "VIDEO", "value": "https://sample-attachment-video.jpg"}
]
}
]
Attachments can have one of the types "IMAGE", "TEXT", or "VIDEO".
For IMAGE and VIDEO, you will need to provide a link to the resource. JPG, PNG, and MP4 are supported.
For text, you will need to provide the text that will be attached.
For image and video attachments, you may provide links to assets in private buckets, provided that you've connected them to Ango Hub. More information on how to do so can be found in the Attachments page.
Returns:
  • output: dict
Example:
from ango.sdk import SDK
​
api_key = '<YOUR API KEY>'
project_id = '<YOUR PROJECT ID>'
ango_sdk = SDK(api_key)
​
attachments = [{"externalId": "sample_image_1.png",
"attachments": [{"type": "IMAGE",
"value": "https://sample-attachment-image.jpg"},
{"type": "TEXT",
"value": "Some sample text."}]
},
{"externalId": "sample_image_2.png",
"attachments": [{"type": "VIDEO",
"value": "https://sample-attachment-video.jpg"}]
}]
​
ango_sdk.create_attachment(project_id, attachments)

create_batch(project_id, batch_name)

Create batches in a specific project.
Parameters:
  • project_id: str
    • ID of the project where the batch will be created. Project IDs can be obtained from the UI and from list_projects.
    • Example: '0000000aa0a00a0000aaaa0a'
  • batch_name: str
    • Name of the batch to be created.
    • Example: 'My Batch 1'
Returns:
  • output: dict
Example:
from ango.sdk import SDK
​
api_key = '<YOUR API KEY>'
project_id = '<YOUR PROJECT ID>'
ango_sdk = SDK(api_key)
​
ango_sdk.create_batch(project_id, "My Batch Name")

create_issue(task_id, content, position)

Opens an issue to the specified task, with given content on the given position.
Parameters:
  • task_id: string
    • ID of the task to be assigned. Task IDs can be obtained from the UI and from get_tasks.
    • Example: '0000000aa0a00a0000aaaa0a'
  • content: string
    • Text content of the issue.
    • Example: 'The bounding box here should reach the edges.'
  • position: List[integer]
    • Position, in pixel, of where the issue should be placed on the image asset.
    • Example: [25, 15]
Returns:
  • output: dict
Example:
from ango.sdk import SDK
​
api_key = '<YOUR API KEY>'
ango_sdk = SDK(api_key)
​
task_id = '<YOUR TASK ID>'
content = 'Hey! Check this annotation.'
position = [25, 15]
​
ango_sdk.create_issue(task_id, content, position)
See also
​get_tasks​

create_label_set(project_id, tools, classifications, relations)

Create and set the project's ontology.
As this method is more complex than others, we recommend also consulting the examples at the end of this section.
Parameters:
  • project_id: string
    • ID of the project where the label set will be created. Project IDs can be obtained from the UI and from list_projects.
    • Example: '0000000aa0a00a0000aaaa0a'
  • tools: List[ToolCategory], optional
    • List of tools that will be added to the label set.
    • Example: [ToolCategory(Tool.Segmentation, title="SegmentationTool")]
  • classifications: List[ClassificationCategory], optional, default None
    • List of classifications that will be added to the label set.
    • Example: [ClassificationCategory(Classification.Single_dropdown, title = "Choice", options=[LabelOption("First"), LabelOption("Second")])]
  • relations: List[RelationCategory], optional, default None
    • List of relations that will be added to the label set.
    • Example: [RelationCategory(Relation.Single, title="SingleRelationTool")]

Label Set Classes

ToolCategory: {Segmentation, Polyline, Polygon, Rotated_bounding_box, Ner, Point, Pdf}
ToolCategory Parameters:
  • tool: Tool
    • The tool type. ex.: Tool.Segmentation
  • title: string, default ""
    • The title of the tool.
  • required: bool, default None
    • Whether annotators are required to draw at least one instance of this tool.
  • schemaId: string, default None
    • Sets the tool's schemaId.
  • columnField: bool, default False
    • Whether this tool should be a table column.
  • color: string, default ""
    • The color assigned to this labeling tool, in the format "#FFFFFF"
  • shortcutKey: string, default ""
    • The shortcut to quickly select this tool, "0"-"9", "ctrl+0"-"ctrl+9", "a"-"k"
  • classifications: List[ClassificationCategory], default []
    • List of nested classifications, if any
  • options: List[LabelOption], default []
    • The tool's answers (options.)
ClassificationCategory: {Multi_dropdown, Single_dropdown Tree_dropdown, Radio, Checkbox, Text, Instance}
ClassificationCategory Parameters:
  • classification: Classification
    • The classification type. ex.:Classification.Tree_dropdown
  • title: string, default ""
    • The title of the classification.
  • required: bool, default None
    • Whether annotators have to answer this classification or not.
  • schemaId: string, default None
    • Sets the classification's Schema ID.
  • columnField: bool, default False
    • Whether this classification should be a table column.
  • color: string, default ""
    • The color assigned to this labeling tool, in the format "#FFFFFF"
  • shortcutKey: string, default ""
    • The shortcut to quickly select this tool, "0"-"9", "ctrl+0"-"ctrl+9", "a"-"k"
  • classifications: List, default [ClassificationCategory]
    • List of nested classifications, if any
  • options: List[LabelOption], default []
    • The classification's answers (options.)
  • treeOptions: List[TreeOption], default []
    • For trees, the tree's leaves/branches.
  • parentOptionId: string, default ""
    • The schema ID of the parent option. That is, the option that the labeler needs to select in order for this classification to appear. Enables conditional nesting.
  • richText: bool, default False
    • Set to True to enable the Rich Text editor for the selected text classification tool.
RelationCategory: {Single, Group}
RelationCategory Parameters:
  • relation: Relation
    • The classification type. ex.:Relation.Single
  • title: string, default ""
    • The title of the relation.
  • required: bool, default None
    • Whether annotators have to include at least one such relation in order to submit their annotation.
  • schemaId: string, default None
    • Sets the schemaId of the relation.
  • columnField: bool, default False
    • Whether this relation should be a table column.
  • color: string, default ""
    • The color assigned to this relation, in the format "#FFFFFF"
  • shortcutKey: string, default ""
    • The shortcut to quickly select this relation, "0"-"9", "ctrl+0"-"ctrl+9", "a"-"k"
  • classifications: List[ClassificationCategory], default []
    • List of nested classifications, if any
  • options: List[LabelOption], default []
    • The relation's answers (options.)
LabelOption parameters:
  • value: string
    • The text of the answer (option.)
  • schemaId: string, default None
    • The schema ID of the option. Necessary for conditional nesting.
Returns:
  • output: dict
Examples:
Creating an ontology with:
  • A Single Dropdown classification, with two choices named "First" and "Second":
from ango.sdk import SDK
from ango.models.label_category import LabelOption, ClassificationCategory, Classification
​
api_key = '<YOUR API KEY>'
project_id = '<YOUR PROJECT ID>'
​
ango_sdk = SDK(api_key)
​
category = ClassificationCategory(Classification.Single_dropdown,
title = "Choice",
options=[LabelOption("First"), LabelOption("Second")])
​
label_set = [category]
​
response = ango_sdk.create_label_set(project_id=project_id, classifications=label_set)
print(response)
Creating an ontology with:
from ango.sdk import SDK
from ango.models.label_category import ClassificationCategory, Classification, LabelOption, Tool, ToolCategory, RelationCategory, Relation
​
api_key = '<YOUR API KEY>'
project_id = '<YOUR PROJECT ID>'
​
ango_sdk = SDK(api_key)
​
dropdown = ClassificationCategory(Classification.Single_dropdown,
title = "SingleDropdown",
options = [LabelOption('First'), LabelOption('Second')])
classifications = [dropdown]
​
segmentation = ToolCategory(Tool.Segmentation, title="SegmentationTool")
tools = [segmentation]
​
relation = RelationCategory(Relation.Single, title="SingleRelationTool")
relations = [relation]
​
ango_sdk.create_label_set(project_id=project_id,
tools=tools,
classifications=classifications,
relations=relations)
Creating an ontology with:
  • A Single Dropdown classification called "Entity Type" with the choices "Vehicle" and "Person"
  • Another Single Dropdown classification nested inside the first unconditionally (that is, any choice in the first dropdown will open this second) named "Position" with the choices "On Road" and "Off Road".
from ango.sdk import SDK
from ango.models.label_category import ToolCategory, ClassificationCategory, RelationCategory, LabelOption, Classification, Tool, Relation
import json
​
API_KEY = '<YOUR API KEY>'
PROJECT_ID = '<YOUR PROJECT ID>'
ango_sdk = SDK(API_KEY)
​
nestedClassInDropdown = ClassificationCategory(Classification.Single_dropdown,
title="Position",
options=[LabelOption('On Road'),
LabelOption('Off Road')]
)
​
dropdown = ClassificationCategory(Classification.Single_dropdown,
title="Entity Type",
options=[LabelOption('Vehicle'),
LabelOption('Person')],
classifications=[nestedClassInDropdown],
required=True
)
​
classifications = [dropdown]
​
print(ango_sdk.create_label_set(
project_id=PROJECT_ID,
classifications=classifications)
)
Creating an ontology with:
  • A Tree Dropdown tool with:
    • A root
      • With a "tree0" branch
        • With a "subtree0" leaf
        • With a "subtree1" leaf
      • With a "tree1" leaf
from ango.sdk import SDK
from ango.models.label_category import ClassificationCategory, LabelOption, Classification, Tool, Relation, TreeOption
import json
​
API_KEY = '<YOUR API KEY>'
PROJECT_ID = '<YOUR PROJECT ID>'
ango_sdk = SDK(API_KEY)
​
subTree0 = TreeOption(title="subtree0")
subTree1 = TreeOption(title="subtree1")
tree0 = TreeOption(title="tree0", children=[subTree0, subTree1])
tree1 = TreeOption(title="tree1")
​
treeTool = ClassificationCategory(
classification=Classification.Tree_dropdown,
title="Tree One",
treeOptions=[tree0, tree1]
)
​
ango_sdk.create_label_set(
project_id=PROJECT_ID,
classifications=[treeTool]
)
Creating an ontology with:
  • A radio classification tool with two possible answers, "Radio Option 1" and "Radio Option 2"
  • A conditionally nested Text classification tool using the rich text editor, which only appears if the labeler clicks on "Radio Option 1" (here, parentOptionId links the text tool to the option which reveals it)
from ango.sdk import SDK
from ango.models.label_category import ClassificationCategory, LabelOption, Classification, Tool, Relation, TreeOption
import json
​
API_KEY = '<YOUR_API_KEY>'
PROJECT_ID = '<YOUR_PROJECT_ID>'
ango_sdk = SDK(API_KEY)
​
radio1 = LabelOption(
value="Radio Option 1",
schemaId="radioOption1SchemaId"
)
radio2 = LabelOption(
value="Radio Option 2"
)
​
conditionalText = ClassificationCategory(
classification=Classification.Text,
title="Text Tool",
parentOptionId="radioOption1SchemaId",
color="#333333",
richText=True
)
​
radio = ClassificationCategory(
classification=Classification.Radio,
title="Radio Classification",
options=[radio1, radio2],
classifications=[conditionalText]
)
​
res = ango_sdk.create_label_set(
project_id=CREATE_SCHEMA_PROJECT_ID,
classifications=[radio]
)
Creating an ontology with:
  • A radio classification tool with the possible answers "Radio Answer 1" and "Radio Answer 2"
  • A Tree Dropdown classification tool which only appears if the annotator clicks on "Radio Answer 1"
    • The Tree Dropdown has a main root
      • With a branch called "Branch 1"
        • With leaves called "Leaf 1" and "Leaf 2"
      • With a leaf called "Leaf 3"
leaf1 = TreeOption(
title="Leaf 1"
)
​
leaf2 = TreeOption(
title="Leaf 2"
)
branch1 = TreeOption(
title="Branch 1",
children=[leaf1, leaf2]
)
​
leaf3 = TreeOption(
title="Leaf 3"
)
​
treeTool = ClassificationCategory(
classification=Classification.Tree_dropdown,
title='tree',
treeOptions=[branch1, leaf3],
parentOptionId="radioOptionSchemaId"
)
​
radio1 = LabelOption(
value="Radio Answer 1",
schemaId="radioOptionSchemaId"
)
​
radio2 = LabelOption(
value="Radio Answer 2"
)
​
radio = ClassificationCategory(
classification=Classification.Radio,
title='radio',
classifications=[treeTool],
options=[radio1, radio2]
)
​
res = ango_sdk.create_label_set(
project_id="639879404e2aa4e134db0c48",
classifications=[radio]
)

create_project(name, description)

Creates a new project.
Parameters:
  • name: string
    • The name of the project to be created. This field cannot be empty.
    • Example: 'Project One'
  • description: string, optional, default ""
    • Example: 'Vehicle Classification Project'
Returns:
  • output: dict
Example:
from ango.sdk import SDK
​
api_key = '<YOUR API KEY>'
ango_sdk = SDK(api_key)
​
res = ango_sdk.create_project(
name="Created from SDK",
description="I created this from the SDK."
)
​
print(res)
Returns:
{
"status": "success",
"data": {
"project": {
"aiAssistance": {
"cocoRelations": []
},
"description": "I created this from the SDK.",
"categorySchema": {
"tools": [],
"classifications": [],
"relations": []
},
"consensusCount": 1,
"benchmark": false,
"deleted": false,
"reviewConf": {
"filters": []
},
"batches": [],
"_id": "PROJECT ID",
"name": "Created from SDK",
"user": "USER ID OF PROJECT CREATOR",
"organization": "ORGANIZATION ID",
"createdAt": "2022-12-13T13:28:34.479Z",
"assignedTo": [],
"tags": [],
"__v": 0
}
}
}
See also
​list_projects, get_project​

create_webhook(webhook_url, project_id, secret, types)

Adds a new webhook to a project of your choice. More on webhooks in Ango Hub here.
Parameters:
  • webhook_url: str
    • The URL where the webhook will be fired to.
    • Example: "https://3611-185-218-246-40.eu.ngrok.io/hook"
  • project_id: str
    • Example: 'Vehicle Classification Project'
  • secret: str
  • types: List[str]
    • The action triggers for your webhook. For example, adding "UPDATED", will fire a webhook anytime a label is updated.
    • Example: ['ACCEPTED', 'UPDATED', 'COMPLETED']
    • Accepted webhook types:
      • ACCEPTED: Fires any time a reviewer marks a labeling task as being accepted. (e.g. positive rewiew)
      • COMPLETED: Fires any time an annotator clicks on "Submit" and completes labeling.
      • UPDATED: Fires any time an user clicks on "Save" after updating an already-labeled labeling task.
Returns:
  • output: dict
Example:
from ango.sdk import SDK
​
api_key = '<YOUR API KEY>'
ango_sdk = SDK(api_key)
​
res = ango_sdk.create_webhook(
webhook_url='https://webhook.url/hook',
project_id='<YOUR_PROJECT_ID>',
secret='<YOUR_SECRET>',
types=['ACCEPTED']
)
​
print(res)
Returns:
{
"status": "success",
"data": { ... all integration data from the project }
}

export(project_id, assignees, completed_at, updated_at, batches)

Export annotated assets together with labels and metadata. Use assignee, completed_at, updated_at or batch filters to export specific parts of the dataset.
Parameters:
  • project_id: string
    • ID of the project which will be exported. Project IDs can be obtained from the UI and from list_projects.
    • Example: '0000000aa0a00a0000aaaa0a'
  • assignees: List[string], optional, default None
    • You may filter your export by only obtaining tasks completed by certain users. Enter your User IDs here. User IDs can be obtained with get_project.
    • Example: ['0000000aa0a00a0000aaaa0a', '0000000aa0a00a0000aaaa0b']
  • completed_at: List[datetime.datetime], optional, default None
    • You may obtain only tasks completed between two specific dates.
    • Example, to only export tasks completed from December 1st to today: [datetime.datetime.fromisoformat("2022-12-01"), datetime.datetime.now()]
  • updated_at: List[datetime.datetime], optional, default None
    • You may obtain only tasks updated between two specific dates.
    • Example, to only export tasks updated from December 1st to today: [datetime.datetime.fromisoformat("2022-12-01"), datetime.datetime.now()]
  • batches: List[string], optional, default None
    • You may choose to only export assets pertaining to one or more specific batches.
    • Example: ['0000000aa0a00a0000aaaa0a']
Returns:
  • output: dict
Example:
from ango.sdk import SDK
​
api_key = '<YOUR API KEY>'
project_id = '<YOUR PROJECT ID>'
ango_sdk = SDK(api_key)
​
ango_sdk.export(project_id)
See also
​import_labels​

get_assets(project_id, asset_id, external_id, page, limit)

Get details of assets from a project.