The official Python SDK for FastLabel API, the Data Platform for AI

Project description

FastLabel Python SDK

Installation
Usage
- Limitation
Task
Annotation
Project
Dataset
Converter
Model
API Docs

Installation

pip install --upgrade fastlabel

Python version 3.8 or greater is required

Usage

Configure API Key in environment variable.

export FASTLABEL_ACCESS_TOKEN="YOUR_ACCESS_TOKEN"

Initialize fastlabel client.

import fastlabel
client = fastlabel.Client()

Limitation

API is allowed to call 10000 times per 10 minutes. If you create/delete a large size of tasks, please wait a second for every requests.

Task

Image

Supported following project types:

Image - Bounding Box
Image - Polygon
Image - Keypoint
Image - Line
Image - Segmentation
Image - Pose Estimation
Image - All

Create Task

Create a new task.

task_id = client.create_image_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.jpg",
    file_path="./sample.jpg"
)

Create a new task with pre-defined annotations. (Class should be configured on your project in advance)

task_id = client.create_image_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.jpg",
    file_path="./sample.jpg",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    annotations=[{
        "type": "bbox",
        "value": "annotation-value",
        "attributes": [
            {
                "key": "attribute-key",
                "value": "attribute-value"
            }
        ],
        "points": [
            100,  # top-left x
            100,  # top-left y
            200,  # bottom-right x
            200   # bottom-right y
        ]
    }]
)

Check examples/create_image_task.py.

Limitation

You can upload up to a size of 20 MB.

Create Integrated Image Task

Create a new task by integrated image. (Project storage setting should be configured in advance.)

task_id = client.create_integrated_image_task(
    project="YOUR_PROJECT_SLUG",
    file_path="<integrated-storage-dir>/sample.jpg",
    storage_type="gcp",
)

Create a new task with pre-defined annotations. (Class should be configured on your project in advance)

task_id = client.create_image_task(
    project="YOUR_PROJECT_SLUG",
    file_path="<integrated-storage-dir>/sample.jpg",
    storage_type="gcp",
    annotations=[{
        "type": "bbox",
        "value": "annotation-value",
        "attributes": [
            {
                "key": "attribute-key",
                "value": "attribute-value"
            }
        ],
        "points": [
            100,  # top-left x
            100,  # top-left y
            200,  # bottom-right x
            200   # bottom-right y
        ]
    }]
)

Limitation

You can upload up to a size of 20 MB.

Find Task

Find a single task.

task = client.find_image_task(task_id="YOUR_TASK_ID")

Find a single task by name.

tasks = client.find_image_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks. (Up to 1000 tasks)

tasks = client.get_image_tasks(project="YOUR_PROJECT_SLUG")

Filter and Get tasks. (Up to 1000 tasks)

tasks = client.get_image_tasks(
    project="YOUR_PROJECT_SLUG",
    status="approved", # status can be 'pending', 'registered', 'completed', 'skipped', 'reviewed' 'sent_back', 'approved', 'declined'
    tags=["tag1", "tag2"] # up to 10 tags
)

Get a large size of tasks. (Over 1000 tasks)

import time

# Iterate pages until new tasks are empty.
all_tasks = []
offset = None
while True:
    time.sleep(1)

    tasks = client.get_image_tasks(project="YOUR_PROJECT_SLUG", offset=offset)
    all_tasks.extend(tasks)

    if len(tasks) > 0:
        offset = len(all_tasks)  # Set the offset
    else:
        break

Please wait a second before sending another requests!

Update Tasks

Update a single task.

task_id = client.update_image_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    assignee="USER_SLUG",
    tags=["tag1", "tag2"],
    annotations=[
        {
            "type": "bbox",
            "value": "cat"
            "attributes": [
                { "key": "kind", "value": "Scottish field" }
            ],
            "points": [
                100,  # top-left x
                100,  # top-left y
                200,  # bottom-right x
                200   # bottom-right y
            ]
        }
    ],
    # pass annotation indexes to update
    relations=[
      {
        "startIndex": 1,
        "endIndex": 0,
      },
      {
        "startIndex": 2,
        "endIndex": 0
      }
    ]
)

Response

Example of a single image task object

{
    "id": "YOUR_TASK_ID",
    "name": "cat.jpg",
    "width": 100,   # image width
    "height": 100,  # image height
    "url": "YOUR_TASK_URL",
    "status": "registered",
    "externalStatus": "registered",
    "priority": 10,
    "tags": [],
    "assignee": "ASSIGNEE_NAME",
    "reviewer": "REVIEWER_NAME",
    "externalAssignee": "EXTERNAL_ASSIGNEE_NAME",
    "externalReviewer": "EXTERNAL_REVIEWER_NAME",
    "annotations": [
        {
            "attributes": [
                { "key": "kind", "name": "Kind", "type": "text", "value": "Scottish field" }
            ],
            "color": "#b36d18",
            "points": [
                100,  # top-left x
                100,  # top-left y
                200,  # bottom-right x
                200   # bottom-right y
            ],
            "rotation": 0,
            "title": "Cat",
            "type": "bbox",
            "value": "cat"
        }
    ],
    "createdAt": "2021-02-22T11:25:27.158Z",
    "updatedAt": "2021-02-22T11:25:27.158Z"
}

Example when the project type is Image - Pose Estimation

{
    "id": "YOUR_TASK_ID",
    "name": "person.jpg",
    "width": 255,   # image width
    "height": 255,  # image height
    "url": "YOUR_TASK_URL",
    "status": "registered",
    "externalStatus": "registered",
    "priority": 10,
    "tags": [],
    "assignee": "ASSIGNEE_NAME",
    "reviewer": "REVIEWER_NAME",
    "externalAssignee": "EXTERNAL_ASSIGNEE_NAME",
    "externalReviewer": "EXTERNAL_REVIEWER_NAME",
    "annotations":[
       {
          "type":"pose_estimation",
          "title":"jesture",
          "value":"jesture",
          "color":"#10c414",
          "attributes": [],
          "keypoints":[
             {
                "name":"頭",
                "key":"head",
                "value":[
                   102.59, # x
                   23.04,  # y
                   1       # 0:invisible, 1:visible
                ],
                "edges":[
                   "right_shoulder",
                   "left_shoulder"
                ]
             },
             {
                "name":"右肩",
                "key":"right_shoulder",
                "value":[
                   186.69,
                   114.11,
                   1
                ],
                "edges":[
                   "head"
                ]
             },
             {
                "name":"左肩",
                "key":"left_shoulder",
                "value":[
                   37.23,
                   109.29,
                   1
                ],
                "edges":[
                   "head"
                ]
             }
          ]
       }
    ],
    "createdAt": "2021-02-22T11:25:27.158Z",
    "updatedAt": "2021-02-22T11:25:27.158Z"
}

Export Image With Annotations

Get tasks and export images with annotations. Only support the following image extension.

jpeg
jpg
png
tif
tiff
bmp

tasks = client.get_image_tasks(project="YOUR_PROJECT_SLUG")
client.export_image_with_annotations(
    tasks=tasks, image_dir="IMAGE_DIR", output_dir="OUTPUT_DIR"
)

Integrate Task

This function is alpha version. It is subject to major changes in the future.

Integration is possible only when tasks are registered from the objects divided by the dataset. Only bbox and polygon annotation types are supported.

In the case of a task divided under the following conditions.

Dataset slug: image
Object name: cat.jpg
Split count: 3×3

Objects are registered in the data set in the following form.

image/cat/1.jpg
image/cat/2.jpg
image/cat/3.jpg
(omit)
image/cat/9.jpg

The annotations at the edges of the image are combined. However, annotations with a maximum length of 300px may not work.

In this case, SPLIT_IMAGE_TASK_NAME_PREFIX specifies image/cat.

task = client.find_integrated_image_task_by_prefix(
    project="YOUR_PROJECT_SLUG",
    prefix="SPLIT_IMAGE_TASK_NAME_PREFIX",
)

Response

Example of a integrated image task object

{
    'name': 'image/cat.jpg',
    "annotations": [
        {
            "attributes": [],
            "color": "#b36d18",
            "confidenceScore"; -1,
            "keypoints": [],
            "points": [200,200,300,400],
            "rotation": 0,
            "title": "Bird",
            "type": "polygon",
            "value": "bird"
        }
    ],
}

Image Classification

Supported following project types:

Image - Classification

Create Task

Create a new task.

task_id = client.create_image_classification_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.jpg",
    file_path="./sample.jpg",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    attributes=[
        {
            "key": "attribute-key",
            "value": "attribute-value"
        }
    ],
)

Limitation

You can upload up to a size of 20 MB.

Create Integrated Image Classification Task

Create a new classification task by integrated image. (Project storage setting should be configured in advance.)

task_id = client.create_integrated_image_classification_task(
    project="YOUR_PROJECT_SLUG",
    file_path="<integrated-storage-dir>/sample.jpg",
    storage_type="gcp",
)

Find Task

Find a single task.

task = client.find_image_classification_task(task_id="YOUR_TASK_ID")

Find a single task by name.

tasks = client.find_image_classification_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks. (Up to 1000 tasks)

tasks = client.get_image_classification_tasks(project="YOUR_PROJECT_SLUG")

Update Tasks

Update a single task.

task_id = client.update_image_classification_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    assignee="USER_SLUG",
    tags=["tag1", "tag2"],
    attributes=[
        {
            "key": "attribute-key",
            "value": "attribute-value"
        }
    ],
)

Response

Example of a single image classification task object

{
    "id": "YOUR_TASK_ID",
    "name": "cat.jpg",
    "width": 100,   # image width
    "height": 100,  # image height
    "url": "YOUR_TASK_URL",
    "status": "registered",
    "externalStatus": "registered",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    "tags": [],
    "assignee": "ASSIGNEE_NAME",
    "reviewer": "REVIEWER_NAME",
    "externalAssignee": "EXTERNAL_ASSIGNEE_NAME",
    "externalReviewer": "EXTERNAL_REVIEWER_NAME",
    "attributes": [
        {
            "key": "kind",
            "name": "Kind",
            "type": "text",
            "value": "Scottish field"
        }
    ],
    "createdAt": "2021-02-22T11:25:27.158Z",
    "updatedAt": "2021-02-22T11:25:27.158Z"
}

Sequential Image

Supported following project types:

Sequential Image - Bounding Box
Sequential Image - Polygon
Sequential Image - Keypoint
Sequential Image - Line
Sequential Image - Segmentation

Create Task

Create a new task.

task = client.create_sequential_image_task(
    project="YOUR_PROJECT_SLUG",
    name="sample",
    folder_path="./sample",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    annotations=[{
        "type": "segmentation",
        "value": "annotation-value",
        "attributes": [
            {
                "key": "attribute-key",
                "value": "attribute-value"
            }
        ],
        "content": "01.jpg",
        "points": [[[
            100,
            100,
            300,
            100,
            300,
            300,
            100,
            300,
            100,
            100
        ]]] # clockwise rotation
    }]
)

Limitation

You can upload up to a size of 20 MB.
You can upload up to a total size of 512 MB.
You can upload up to 250 files in total.

Find Task

Find a single task.

task = client.find_sequential_image_task(task_id="YOUR_TASK_ID")

Find a single task by name.

tasks = client.find_sequential_image_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks.

tasks = client.get_sequential_image_tasks(project="YOUR_PROJECT_SLUG")

Update Task

Update a single task.

task_id = client.update_sequential_image_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    assignee="USER_SLUG",
    tags=["tag1", "tag2"],
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    annotations=[
        {
            "type": "bbox",
            "value": "cat",
            "content": "cat1.jpg",
            "attributes": [
                { "key": "key", "value": "value1" }
            ],
            "points": [990, 560, 980, 550]
        }
    ]
)

Response

Example of a single task object

{
    "id": "YOUR_TASK_ID",
    "name": "cat.jpg",
    "contents": [
        {
            "name": "content-name",
            "url": "content-url",
            "width": 100,
            "height": 100,
        }
    ],
    "status": "registered",
    "externalStatus": "registered",
    "priority": 10,
    "tags": [],
    "assignee": "ASSIGNEE_NAME",
    "reviewer": "REVIEWER_NAME",
    "externalAssignee": "EXTERNAL_ASSIGNEE_NAME",
    "externalReviewer": "EXTERNAL_REVIEWER_NAME",
    "annotations": [
        {
            "content": "content-name"
            "attributes": [],
            "color": "#b36d18",
            "points": [[[
                100,
                100,
                300,
                100,
                300,
                300,
                100,
                300,
                100,
                100
            ]]]
            "title": "Cat",
            "type": "bbox",
            "value": "cat"
        }
    ],
    "createdAt": "2021-02-22T11:25:27.158Z",
    "updatedAt": "2021-02-22T11:25:27.158Z"
}

Video

Supported following project types:

Video - Bounding Box
Video - Keypoint
Video - Line

Create Task

Create a new task.

task_id = client.create_video_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.mp4",
    file_path="./sample.mp4"
)

Create a new task with pre-defined annotations. (Class should be configured on your project in advance)

task_id = client.create_video_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.mp4",
    file_path="./sample.mp4",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    annotations=[{
        "type": "bbox",
        "value": "person",
        "points": {
            "1": {  # number of frame
                "value": [
                    100,  # top-left x
                    100,  # top-left y
                    200,  # bottom-right x
                    200   # bottom-right y
                ],
                # Make sure to set `autogenerated` False for the first and last frame. "1" and "3" frames in this case.
                # Otherwise, annotation is auto-completed for rest of frames when you edit.
                "autogenerated": False
            },
            "2": {
                "value": [
                    110,
                    110,
                    220,
                    220
                ],
                "autogenerated": True
            },
            "3": {
                "value": [
                    120,
                    120,
                    240,
                    240
                ],
                "autogenerated": False
            }
        }
    }]
)

Limitation

You can upload up to a size of 250 MB.
You can upload only videos with H.264 encoding.
You can upload only MP4 file format.

Find Task

Find a single task.

task = client.find_video_task(task_id="YOUR_TASK_ID")

Find a single task by name.

tasks = client.find_video_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks. (Up to 10 tasks)

tasks = client.get_video_tasks(project="YOUR_PROJECT_SLUG")

Update Task

Update a single task.

task_id = client.update_video_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    assignee="USER_SLUG",
    tags=["tag1", "tag2"],
    annotations=[{
        "type": "bbox",
        "value": "bird",
        "points": {
            "1": {
                "value": [
                    100,
                    100,
                    200,
                    200
                ],
                "autogenerated": False
            },
            "2": {
                "value": [
                    110,
                    110,
                    220,
                    220
                ],
                "autogenerated": True
            },
            "3": {
                "value": [
                    120,
                    120,
                    240,
                    240
                ],
                "autogenerated": False
            }
        }
    }]
)

Integrate Video

This function is alpha version. It is subject to major changes in the future.

Integration is possible only when tasks are registered from the objects divided by the dataset.

In the case of a task divided under the following conditions.

Dataset slug: video
Object name: cat.mp4
Split count: 3

Objects are registered in the data set in the following form.

video/cat/1.mp4
video/cat/2.mp4
video/cat/3.mp4

In this case, SPLIT_VIDEO_TASK_NAME_PREFIX specifies video/cat.

task = client.find_integrated_video_task_by_prefix(
    project="YOUR_PROJECT_SLUG",
    prefix="SPLIT_VIDEO_TASK_NAME_PREFIX",
)

Response

Example of a single vide task object

{
    "id": "YOUR_TASK_ID",
    "name": "cat.jpg",
    "width": 100,   # image width
    "height": 100,  # image height
    "fps": 30.0,    # frame per seconds
    "frameCount": 480,  # total frame count of video
    "duration": 16.0,   # total duration of video
    "url": "YOUR_TASK_URL",
    "status": "registered",
    "externalStatus": "registered",
    "priority": 10,
    "tags": [],
    "assignee": "ASSIGNEE_NAME",
    "reviewer": "REVIEWER_NAME",
    "externalAssignee": "EXTERNAL_ASSIGNEE_NAME",
    "externalReviewer": "EXTERNAL_REVIEWER_NAME",
    "annotations": [
        {
            "attributes": [],
            "color": "#b36d18",
            "points": {
                "1": {  # number of frame
                    "value": [
                        100,  # top-left x
                        100,  # top-left y
                        200,  # bottom-right x
                        200   # bottom-right y
                    ],
                    "autogenerated": False  # False when annotated manually. True when auto-generated by system.
                },
                "2": {
                    "value": [
                        110,
                        110,
                        220,
                        220
                    ],
                    "autogenerated": True
                },
                "3": {
                    "value": [
                        120,
                        120,
                        240,
                        240
                    ],
                    "autogenerated": False
                }
            },
            "title": "Cat",
            "type": "bbox",
            "value": "cat"
        }
    ],
    "createdAt": "2021-02-22T11:25:27.158Z",
    "updatedAt": "2021-02-22T11:25:27.158Z"
}

Video Classification

Supported following project types:

Video - Classification (Single)

Create Task

Create a new task.

task_id = client.create_video_classification_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.mp4",
    file_path="./sample.mp4",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    attributes=[
        {
            "key": "attribute-key",
            "value": "attribute-value"
        }
    ],
)

Limitation

You can upload up to a size of 250 MB.

Find Task

Find a single task.

task = client.find_video_classification_task(task_id="YOUR_TASK_ID")

Find a single task by name.

tasks = client.find_video_classification_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks. (Up to 1000 tasks)

tasks = client.get_video_classification_tasks(project="YOUR_PROJECT_SLUG")

Update Tasks

Update a single task.

task_id = client.update_video_classification_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    assignee="USER_SLUG",
    tags=["tag1", "tag2"],
    attributes=[
        {
            "key": "attribute-key",
            "value": "attribute-value"
        }
    ],
)

Text

Supported following project types:

Text - NER

Create Task

Create a new task.

task_id = client.create_text_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.txt",
    file_path="./sample.txt"
)

Create a new task with pre-defined annotations. (Class should be configured on your project in advance)

task_id = client.create_text_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.txt",
    file_path="./sample.txt",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    annotations=[{
        "type": "ner",
        "value": "person",
        "start": 0,
        "end": 10,
        "text": "1234567890"
    }]
)

Limitation

You can upload up to a size of 2 MB.

Find Task

Find a single task.

task = client.find_text_task(task_id="YOUR_TASK_ID")

Find a single task by name.

tasks = client.find_text_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks. (Up to 10 tasks)

tasks = client.get_text_tasks(project="YOUR_PROJECT_SLUG")

Update Task

Update a single task.

task_id = client.update_text_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    assignee="USER_SLUG",
    tags=["tag1", "tag2"],
    annotations=[{
        "type": "bbox",
        "value": "bird",
        "start": 0,
        "end": 10,
        "text": "0123456789"
    }]
)

Response

Example of a single text task object

{
    "id": "YOUR_TASK_ID",
    "name": "cat.txt",
    "url": "YOUR_TASK_URL",
    "status": "registered",
    "externalStatus": "registered",
    "priority": 10,
    "tags": [],
    "assignee": "ASSIGNEE_NAME",
    "reviewer": "REVIEWER_NAME",
    "externalAssignee": "EXTERNAL_ASSIGNEE_NAME",
    "externalReviewer": "EXTERNAL_REVIEWER_NAME",
    "annotations": [
        {
            "attributes": [],
            "color": "#b36d18",
            "text": "0123456789",
            "start": 0,
            "end": 10,
            "title": "Cat",
            "type": "ner",
            "value": "cat"
        }
    ],
    "createdAt": "2021-02-22T11:25:27.158Z",
    "updatedAt": "2021-02-22T11:25:27.158Z"
}

Text Classification

Supported following project types:

Text - Classification (Single)

Create Task

Create a new task.

task_id = client.create_text_classification_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.txt",
    file_path="./sample.txt",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    attributes=[
        {
            "key": "attribute-key",
            "value": "attribute-value"
        }
    ],
)

Limitation

You can upload up to a size of 2 MB.

Find Task

Find a single task.

task = client.find_text_classification_task(task_id="YOUR_TASK_ID")

Find a single task by name.

tasks = client.find_text_classification_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks. (Up to 1000 tasks)

tasks = client.get_text_classification_tasks(project="YOUR_PROJECT_SLUG")

Update Tasks

Update a single task.

task_id = client.update_text_classification_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    assignee="USER_SLUG",
    tags=["tag1", "tag2"],
    attributes=[
        {
            "key": "attribute-key",
            "value": "attribute-value"
        }
    ],
)

Audio

Supported following project types:

Audio - Segmentation

Create Task

Create a new task.

task_id = client.create_audio_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.mp3",
    file_path="./sample.mp3"
)

Create a new task with pre-defined annotations. (Class should be configured on your project in advance)

task_id = client.create_audio_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.mp3",
    file_path="./sample.mp3",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    annotations=[{
        "type": "segmentation",
        "value": "person",
        "start": 0.4,
        "end": 0.5
    }]
)

Limitation

You can upload up to a size of 120 MB.

Find Task

Find a single task.

task = client.find_audio_task(task_id="YOUR_TASK_ID")

Find a single task by name.

tasks = client.find_audio_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks. (Up to 10 tasks)

tasks = client.get_audio_tasks(project="YOUR_PROJECT_SLUG")

Update Task

Update a single task.

task_id = client.update_audio_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    assignee="USER_SLUG",
    tags=["tag1", "tag2"],
    annotations=[{
        "type": "segmentation",
        "value": "bird",
        "start": 0.4,
        "end": 0.5
    }]
)

Response

Example of a single audio task object

{
    "id": "YOUR_TASK_ID",
    "name": "cat.mp3",
    "url": "YOUR_TASK_URL",
    "status": "registered",
    "externalStatus": "registered",
    "priority": 10,
    "tags": [],
    "assignee": "ASSIGNEE_NAME",
    "reviewer": "REVIEWER_NAME",
    "externalAssignee": "EXTERNAL_ASSIGNEE_NAME",
    "externalReviewer": "EXTERNAL_REVIEWER_NAME",
    "annotations": [
        {
            "attributes": [],
            "color": "#b36d18",
            "start": 0.4,
            "end": 0.5,
            "title": "Bird",
            "type": "segmentation",
            "value": "bird"
        }
    ],
    "createdAt": "2021-02-22T11:25:27.158Z",
    "updatedAt": "2021-02-22T11:25:27.158Z"
}

Integrate Task

This function is alpha version. It is subject to major changes in the future.

Integration is possible only when tasks are registered from the objects divided by the dataset.

In the case of a task divided under the following conditions.

Dataset slug: audio
Object name: voice.mp3
Split count: 3

Objects are registered in the data set in the following form.

audio/voice/1.mp3
audio/voice/2.mp3
audio/voice/3.mp3

Annotations are combined when the end point specified in the annotation is the end time of the task and the start point of the next task is 0 seconds.

In this case, SPLIT_AUDIO_TASK_NAME_PREFIX specifies audio/voice.

task = client.find_integrated_audio_task_by_prefix(
    project="YOUR_PROJECT_SLUG",
    prefix="SPLIT_AUDIO_TASK_NAME_PREFIX",
)

Response

Example of a integrated audio task object

{
    'name': 'audio/voice.mp3',
    "annotations": [
        {
            "attributes": [],
            "color": "#b36d18",
            "start": 0.4,
            "end": 0.5,
            "title": "Bird",
            "type": "segmentation",
            "value": "bird"
        }
    ],
}

Audio Classification

Supported following project types:

Audio - Classification (Single)

Create Task

Create a new task.

task_id = client.create_audio_classification_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.mp3",
    file_path="./sample.mp3",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    attributes=[
        {
            "key": "attribute-key",
            "value": "attribute-value"
        }
    ],
)

Limitation

You can upload up to a size of 120 MB.

Find Task

Find a single task.

task = client.find_audio_classification_task(task_id="YOUR_TASK_ID")

Find a single task by name.

tasks = client.find_audio_classification_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks. (Up to 1000 tasks)

tasks = client.get_audio_classification_tasks(project="YOUR_PROJECT_SLUG")

Update Tasks

Update a single task.

task_id = client.update_audio_classification_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    assignee="USER_SLUG",
    tags=["tag1", "tag2"],
    attributes=[
        {
            "key": "attribute-key",
            "value": "attribute-value"
        }
    ],
)

PCD

Supported following project types:

PCD - Cuboid
PCD - Segmentation

Create Task

Create a new task.

task_id = client.create_pcd_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.pcd",
    file_path="./sample.pcd"
)

Create a new task with pre-defined annotations. (Class should be configured on your project in advance)

Annotation Type: cuboid

task_id = client.create_pcd_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.pcd",
    file_path="./sample.pcd",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    annotations=[
        {
            "type": "cuboid",
            "value": "car",
            "points": [ # For cuboid, it is a 9-digit number.
                1, # Coordinate X
                2, # Coordinate Y
                3, # Coordinate Z
                1, # Rotation x
                1, # Rotation Y
                1, # Rotation Z
                2, # Length X
                2, # Length Y
                2  # Length Z
            ],
        }
    ],
)

Annotation Type: segmentation

task_id = client.create_pcd_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.pcd",
    file_path="./sample.pcd",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    annotations=[
        {
            "type": "segmentation",
            "value": "car",
            "points": [1, 2, 3, 4, 5], # For segmentation, it is an arbitrary numeric array.
        }
    ],
)

Limitation

You can upload up to a size of 30 MB.

Find Task

Find a single task.

task = client.find_pcd_task(task_id="YOUR_TASK_ID")

Find a single task by name.

tasks = client.find_pcd_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks. (Up to 1000 tasks)

tasks = client.get_pcd_tasks(project="YOUR_PROJECT_SLUG")

Update Task

Update a single task.

task_id = client.update_pcd_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    assignee="USER_SLUG",
    tags=["tag1", "tag2"],
    annotations=[
        {
            "type": "cuboid",
            "value": "car",
            "points": [ # For cuboid, it is a 9-digit number.
                1, # Coordinate X
                2, # Coordinate Y
                3, # Coordinate Z
                1, # Rotation x
                1, # Rotation Y
                1, # Rotation Z
                2, # Length X
                2, # Length Y
                2  # Length Z
            ],
        }
    ],
)

Response

Example of a single PCD task object

{
    "id": "YOUR_TASK_ID",
    "name": "sample.pcd",
    "url": "YOUR_TASK_URL",
    "status": "registered",
    "externalStatus": "registered",
    "priority": 10,
    "tags": ["tag1", "tag2"],
    "assignee": "ASSIGNEE_NAME",
    "reviewer": "REVIEWER_NAME",
    "approver": "APPROVER_NAME",
    "externalAssignee": "EXTERNAL_ASSIGNEE_NAME",
    "externalReviewer": "EXTERNAL_REVIEWER_NAME",
    "externalApprover": "EXTERNAL_APPROVER_NAME",
    "annotations": [
        {
            "attributes": [],
            "color": "#b36d18",
            "title": "Car",
            "type": "segmentation",
            "value": "car",
            "points": [1, 2, 3, 1, 1, 1, 2, 2, 2],
        }
    ],
    "createdAt": "2021-02-22T11:25:27.158Z",
    "updatedAt": "2021-02-22T11:25:27.158Z"
}

Sequential PCD

Supported following project types:

Sequential PCD - Cuboid

Create Tasks

Create a new task.

task_id = client.create_sequential_pcd_task(
    project="YOUR_PROJECT_SLUG",
    name="drive_record",
    folder_path="./drive_record/", # Path where sequence PCD files are directory
)

The order of frames is determined by the ascending order of PCD file names located in the specified directory. File names are optional, but we recommend naming them in a way that makes the order easy to understand.

./drive_record/
├── 0001.pcd => frame 1
├── 0002.pcd => frame 2
...
└── xxxx.pcd => frame n

Create a new task with pre-defined annotations. (Class should be configured on your project in advance)

task_id = client.create_sequential_pcd_task(
    project="YOUR_PROJECT_SLUG",
    name="drive_record",
    folder_path="./drive_record/",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    annotations=[
        {
            "type": "cuboid", # annotation class type
            "value": "human", # annotation class value
            "points": {
                "1": { # number of frame
                    "value": [ # For cuboid, it is a 9-digit number.
                        1, # Coordinate X
                        2, # Coordinate Y
                        3, # Coordinate Z
                        1, # Rotation x
                        1, # Rotation Y
                        1, # Rotation Z
                        2, # Length X
                        2, # Length Y
                        2  # Length Z
                    ],
                    # Make sure to set `autogenerated` False for the first and last frame. "1" and "3" frames in this case.
                    # Otherwise, annotation is auto-completed for rest of frames when you edit.
                    "autogenerated": False,
                },
                "2": {
                    "value": [
                        11,
                        12,
                        13,
                        11,
                        11,
                        11,
                        12,
                        12,
                        12
                    ],
                    "autogenerated": True,
                },
                "3": {
                    "value": [
                        21,
                        22,
                        23,
                        21,
                        21,
                        21,
                        22,
                        22,
                        22
                    ],
                    "autogenerated": False,
                },
            },
        },
    ]
)

Limitation

You can upload up to a size of 30 MB per file.

Find Tasks

Find a single task.

task = client.find_sequential_pcd_task(task_id="YOUR_TASK_ID")

Find a single task by name.

task = client.find_sequential_pcd_task(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks. (Up to 10 tasks)

tasks = client.get_sequential_pcd_tasks(project="YOUR_PROJECT_SLUG")

Update Tasks

Update a single task.

task_id = client.update_sequential_pcd_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    assignee="USER_SLUG",
    tags=["tag1", "tag2"],
    annotations=[
        {
            "type": "cuboid",
            "value": "human",
            "points": {
                "1": {
                    "value": [
                        1,
                        2,
                        3,
                        1,
                        1,
                        1,
                        2,
                        2,
                        2
                    ],
                    "autogenerated": False,
                },
                "2": {
                    "value": [
                        11,
                        12,
                        13,
                        11,
                        11,
                        11,
                        12,
                        12,
                        12
                    ],
                    "autogenerated": False,
                },
            },
        },
    ]
)

Response

Example of a single Sequential PCD task object

{
    "id": "YOUR_TASK_ID",
    "name": "YOUR_TASK_NAME",
    "status": "registered",
    "externalStatus": "registered",
    "priority": 10,
    "annotations": [
        {
            "id": "YOUR_TASK_ANNOTATION_ID",
            "type": "cuboid",
            "title": "human",
            "value": "human",
            "color": "#4bdd62",
            "attributes": [],
            "points": {
                "1": {
                    "value": [2.61, 5.07, 0, 0, 0, 0, 2, 2, 2],
                    "autogenerated": False,
                },
                "2": {
                    "value": [2.61, 5.07, 0, 0, 0, 0, 2, 2, 2],
                    "autogenerated": True,
                },
                "3": {
                    "value": [2.61, 5.07, 0, 0, 0, 0, 2, 2, 2],
                    "autogenerated": False,
                },
            },
        },
        {
            "id": "YOUR_TASK_ANNOTATION_ID",
            "type": "cuboid",
            "title": "building",
            "value": "building",
            "color": "#223543",
            "attributes": [],
            "points": {
                "1": {
                    "value": [2.8, -8.64, 0.15, 0, 0, 0, 4.45, 4.2, 2],
                    "autogenerated": False,
                },
                "2": {
                    "value": [2.8, -8.64, 0.15, 0, 0, 0, 4.45, 4.2, 2],
                    "autogenerated": True,
                },
                "3": {
                    "value": [2.8, -8.64, 0.15, 0, 0, 0, 4.45, 4.2, 2],
                    "autogenerated": True,
                },
                "4": {
                    "value": [2.8, -8.64, 0.15, 0, 0, 0, 4.45, 4.2, 2],
                    "autogenerated": True,
                },
                "5": {
                    "value": [2.8, -8.64, 0.15, 0, 0, 0, 4.45, 4.2, 2],
                    "autogenerated": True,
                },
            },
        },
    ],
    "tags": [],
    "assignee": None,
    "reviewer": None,
    "approver": None,
    "externalAssignee": None,
    "externalReviewer": None,
    "externalApprover": None,
    "createdAt": "2023-03-24T08:39:08.524Z",
    "updatedAt": "2023-03-24T08:39:08.524Z",
}

DICOM

Supported following project types:

DICOM -Bounding Box

Create Task

Create a new task. You should receive task import history status Find Task Import History. Once you receive the status completed, you can get the task.

history = client.create_dicom_task(
    project="YOUR_PROJECT_SLUG",
    file_path="./sample.zip"
)

Limitation

You can upload up to a size of 2 GB per file.

Find Task

Find a single task.

task = client.find_dicom_task(task_id="YOUR_TASK_ID")

Find a single task by name.

tasks = client.find_dicom_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")

Get Tasks

Get tasks. (Up to 1000 tasks)

tasks = client.get_dicom_tasks(project="YOUR_PROJECT_SLUG")

Update Tasks

Update a single task.

task_id = client.update_dicom_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    assignee="USER_SLUG",
    tags=["tag1", "tag2"]
)

Response

Example of a single dicom task object

{
    "id": "YOUR_TASK_ID",
    "name": "dicom.zip",
    "url": "YOUR_TASK_URL",
    'height': 512,
    'width': 512,
    "status": "registered",
    "externalStatus": "registered",
    "tags": [],
    "assignee": "ASSIGNEE_NAME",
    "reviewer": "REVIEWER_NAME",
    "externalAssignee": "EXTERNAL_ASSIGNEE_NAME",
    "externalReviewer": "EXTERNAL_REVIEWER_NAME",
    "annotations": [
        {
            "attributes": [],
            "color": "#b36d18",
            "contentId": "CONTENT_ID"
            "points": [100, 200, 100, 200],
            "title": "Heart",
            "type": "bbox",
            "value": "heart"
        }
    ],
    "createdAt": "2021-02-22T11:25:27.158Z",
    "updatedAt": "2021-02-22T11:25:27.158Z"
}

Common

APIs for update and delete and count are same over all tasks.

Update Task

Update a single task status, tags and assignee.

task_id = client.update_task(
    task_id="YOUR_TASK_ID",
    status="approved",
    priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
    tags=["tag1", "tag2"],
    assignee="USER_SLUG"
)

Delete Task

Delete a single task.

client.delete_task(task_id="YOUR_TASK_ID")

Delete Task Annotation

Delete annotations in a task.

client.delete_task_annotations(task_id="YOUR_TASK_ID")

Get Tasks Id and Name map

id_name_map = client.get_task_id_name_map(project="YOUR_PROJECT_SLUG")

Count Task

task_count = client.count_tasks(
    project="YOUR_PROJECT_SLUG",
    status="approved", # status can be 'pending', 'registered', 'completed', 'skipped', 'reviewed' 'sent_back', 'approved', 'declined'
    tags=["tag1", "tag2"] # up to 10 tags
)

Create Task from S3

Task creation from S3.

Support project
- Image
- Video
- Audio
- Text
To use it, you need to set the contents of the following link. https://docs.fastlabel.ai/docs/integrations-aws-s3
Setup AWS S3 properties

status = client.update_aws_s3_storage(
    project="YOUR_PROJECT_SLUG",
    bucket_name="S3_BUCKET_NAME",
    bucket_region="S3_REGIONS",
)

Run create task from AWS S3

history = client.create_task_from_aws_s3(
    project="YOUR_PROJECT_SLUG",
)

Get AWS S3 import status

history = client.get_aws_s3_import_status_by_project(
    project="YOUR_PROJECT_SLUG",
)

Find Task Import History

Find a single history.

history = client.find_history(history_id="YOUR_HISTORY_ID")

Get Task Import Histories

histories = client.get_histories(project="YOUR_PROJECT_SLUG")

Response

Example of a single history object

{
    "id": "YOUR_HISTORY_ID",
    "storageType": "zip",
    "status": "running",
    "createdAt": "2021-02-22T11:25:27.158Z",
    "updatedAt": "2021-02-22T11:25:27.158Z"
}

Annotation

Create Annotation

Create a new annotation.

annotation_id = client.create_annotation(
    project="YOUR_PROJECT_SLUG", type="bbox", value="cat", title="Cat")

Create a new annotation with color and attributes.

attributes = [
    {
        "type": "text",
        "name": "Kind",
        "key": "kind"
    },
    {
        "type": "select",
        "name": "Size",
        "key": "size",
        "options": [ # select, radio and checkbox type requires options
            {
                "title": "Large",
                "value": "large"
            },
            {
                "title": "Small",
                "value": "small"
            },
        ]
    },
]
annotation_id = client.create_annotation(
    project="YOUR_PROJECT_SLUG", type="bbox", value="cat", title="Cat", color="#FF0000", attributes=attributes)

Create a new classification annotation.

annotation_id = client.create_classification_annotation(
    project="YOUR_PROJECT_SLUG", attributes=attributes)

Find Annotation

Find an annotation.

annotation = client.find_annotation(annotation_id="YOUR_ANNOTATION_ID")

Find an annotation by value.

annotation = client.find_annotation_by_value(project="YOUR_PROJECT_SLUG", value="cat")

Find an annotation by value in classification project.

annotation = client.find_annotation_by_value(
    project="YOUR_PROJECT_SLUG", value="classification") # "classification" is fixed value

Get Annotations

Get annotations. (Up to 1000 annotations)

annotations = client.get_annotations(project="YOUR_PROJECT_SLUG")

Response

Example of an annotation object

{
    "id": "YOUR_ANNOTATION_ID",
    "type": "bbox",
    "value": "cat",
    "title": "Cat",
    "color": "#FF0000",
    "order": 1,
    "vertex": 0,
    "attributes": [
        {
            "id": "YOUR_ATTRIBUTE_ID",
            "key": "kind",
            "name": "Kind",
            "options": [],
            "order": 1,
            "type": "text",
            "value": ""
        },
        {
            "id": "YOUR_ATTRIBUTE_ID",
            "key": "size",
            "name": "Size",
            "options": [
                {"title": "Large", "value": "large"},
                {"title": "Small", "value": "small"}
            ],
            "order": 2,
            "type": "select",
            "value": ""
        }
    ],
    "createdAt": "2021-05-25T05:36:50.459Z",
    "updatedAt": "2021-05-25T05:36:50.459Z"
}

Example when the annotation type is Pose Estimation

{
   "id":"b12c81c3-ddec-4f98-b41b-cef7f77d26a4",
   "type":"pose_estimation",
   "title":"jesture",
   "value":"jesture",
   "color":"#10c414",
   "order":1,
   "attributes": [],
   "keypoints":[
      {
         "id":"b03ea998-a2f1-4733-b7e9-78cdf73bd38a",
         "name":"頭",
         "key":"head",
         "color":"#0033CC",
         "edges":[
            "195f5852-c516-498b-b392-24513ce3ea67",
            "06b5c968-1786-4d75-a719-951e915e5557"
         ],
         "value": []
      },
      {
         "id":"195f5852-c516-498b-b392-24513ce3ea67",
         "name":"右肩",
         "key":"right_shoulder",
         "color":"#0033CC",
         "edges":[
            "b03ea998-a2f1-4733-b7e9-78cdf73bd38a"
         ],
         "value": []
      },
      {
         "id":"06b5c968-1786-4d75-a719-951e915e5557",
         "name":"左肩",
         "key":"left_shoulder",
         "color":"#0033CC",
         "edges":[
            "b03ea998-a2f1-4733-b7e9-78cdf73bd38a"
         ],
         "value": []
      }
   ],
   "createdAt":"2021-11-21T09:59:46.714Z",
   "updatedAt":"2021-11-21T09:59:46.714Z"
}

Update Annotation

Update an annotation.

annotation_id = client.update_annotation(
    annotation_id="YOUR_ANNOTATION_ID", value="cat2", title="Cat2", color="#FF0000")

Update an annotation with attributes.

attributes = [
    {
        "id": "YOUR_ATTRIBUTE_ID",  # check by sdk get methods
        "type": "text",
        "name": "Kind2",
        "key": "kind2"
    },
    {
        "id": "YOUR_ATTRIBUTE_ID",
        "type": "select",
        "name": "Size2",
        "key": "size2",
        "options": [
            {
                "title": "Large2",
                "value": "large2"
            },
            {
                "title": "Small2",
                "value": "small2"
            },
        ]
    },
]
annotation_id = client.update_annotation(
    annotation_id="YOUR_ANNOTATION_ID", value="cat2", title="Cat2", color="#FF0000", attributes=attributes)

Update a classification annotation.

annotation_id = client.update_classification_annotation(
    project="YOUR_PROJECT_SLUG", attributes=attributes)

Delete Annotation

Delete an annotation.

client.delete_annotation(annotation_id="YOUR_ANNOTATION_ID")

Project

Create Project

Create a new project.

project_id = client.create_project(
    type="image_bbox", name="ImageNet", slug="image-net")

Find Project

Find a project.

project = client.find_project(project_id="YOUR_PROJECT_ID")

Find a project by slug.

project = client.find_project_by_slug(slug="YOUR_PROJECT_SLUG")

Get Projects

Get projects. (Up to 1000 projects)

projects = client.get_projects()

Response

Example of a project object

{
    "id": "YOUR_PROJECT_ID",
    "type": "image_bbox",
    "slug": "YOUR_PROJECT_SLUG",
    "name": "YOUR_PROJECT_NAME",
    "isPixel": False,
    "jobSize": 10,
    "status": "active",
    "createdAt": "2021-04-20T03:20:41.427Z",
    "updatedAt": "2021-04-20T03:20:41.427Z",
}

Update Project

Update a project.

project_id = client.update_project(
    project_id="YOUR_PROJECT_ID", name="NewImageNet", slug="new-image-net", job_size=20)

Delete Project

Delete a project.

client.delete_project(project_id="YOUR_PROJECT_ID")

Copy Project

Copy a project.

project_id = client.copy_project(project_id="YOUR_PROJECT_ID")

Dataset

Create Dataset

Create a new dataset.

dataset = client.create_dataset(
    name="object-detection", # Only lowercase alphanumeric characters + hyphen is available
    tags=["cat", "dog"], # max 5 tags per dataset.
    visibility="workspace", # visibility can be 'workspace' or 'public' or 'organization'
    license="The MIT License" # Optional
)

Response Dataset

See API docs for details.

{
    'id': 'YOUR_DATASET_ID',
    'name': 'object-detection',
    'tags': ['cat', 'dog'],
    'visibility': 'workspace',
    'license': 'The MIT License',
    'createdAt': '2022-10-31T02:20:00.248Z',
    'updatedAt': '2022-10-31T02:20:00.248Z'
}

Find Dataset

Find a single dataset.

dataset = client.find_dataset(dataset_id="YOUR_DATASET_ID")

Success response is the same as when created.

Get Dataset

Get all datasets in the workspace. (Up to 1000 tasks)

datasets = client.get_datasets()

The success response is the same as when created, but it is an array.

You can filter by keywords and visibility, tags.

datasets = client.get_datasets(
    keyword="dog",
    tags=["cat", "dog"], # max 5 tags per dataset.
    license="mit",
    visibility="workspace", # visibility can be 'workspace' or 'public' or 'organization'.
)

If you wish to retrieve more than 1000 datasets, please refer to the Task sample code.

Update Dataset

Update a single dataset.

dataset = client.update_dataset(
    dataset_id="YOUR_DATASET_ID", name="object-detection", tags=["cat", "dog"]
)

Success response is the same as when created.

Delete Dataset

Delete a single dataset.

⚠️ The dataset object and its associated tasks that dataset has will also be deleted, so check carefully before executing.

client.delete_dataset(dataset_id="YOUR_DATASET_ID")

Create Dataset Object

Create object in the dataset.

The types of objects that can be created are "image", "video", and "audio". There are type-specific methods. but they can be used in the same way.

Created object are automatically assigned to the "latest" dataset version.

dataset_object = client.create_dataset_object(
    dataset="YOUR_DATASET_NAME",
    name="brushwood_dog.jpg",
    file_path="./brushwood_dog.jpg",
    tags=["dog"], # max 5 tags per dataset object.
    annotations=[
        {
            "keypoints": [
                {
                    "value": [
                        102.59,
                        23.04,
                        1
                    ],
                    "key": "head"
                }
            ],
            "attributes": [
                {
                    "value": "Scottish field",
                    "key": "kind"
                }
            ],
            "confidenceScore": 0,
            "rotation": 0,
            "points": [
                0
            ],
            "value": "dog",
            "type": "bbox" # type can be 'bbox', 'segmentation'.
        }
    ],
    custom_metadata={
      "key": "value",
      "metadata": "metadata-value"
    }
)

Response Dataset Object

See API docs for details.

{
    'name': 'brushwood_dog.jpg',
    'size': 6717,
    'height': 225,
    'width': 225,
    'tags': [
        'dog'
    ],
    "annotations": [
        {
            "id": "YOUR_DATASET_OBJECT_ANNOTATION_ID",
            "type": "bbox",
            "title": "dog",
            "value": "dog",
            "points": [
                0
            ],
            "attributes": [
                {
                    "value": "Scottish field",
                    "key": "kind",
                    "name": "Kind",
                    "type": "text"
                }
            ],
            "keypoints": [
                {
                    "edges": [
                        "right_shoulder",
                        "left_shoulder"
                    ],
                    "value": [
                        102.59,
                        23.04,
                        1
                    ],
                    "key": "head",
                    "name": "頭"
                }
            ],
            "rotation": 0,
            "color": "#FF0000",
            "confidenceScore": -1
        }
    ],
  "customMetadata": {
    "key": "value",
    "metadata": "metadata-value"
  },
    'createdAt': '2022-10-30T08:32:20.748Z',
    'updatedAt': '2022-10-30T08:32:20.748Z'
}

Find Dataset Object

Find a single dataset object.

dataset_object = client.find_dataset_object(
    dataset_id="YOUR_DATASET_ID",
    object_name="brushwood_dog.jpg"
)

You can find a object of specified revision by version or revision_id.

dataset_object = client.find_dataset_object(
    dataset_id="YOUR_DATASET_ID",
    object_name="brushwood_dog.jpg",
    version="YOUR_VERSION_NAME" # default is "latest"
)

dataset_object = client.find_dataset_object(
    dataset_id="YOUR_DATASET_ID",
    object_name="brushwood_dog.jpg",
    revision_id="YOUR_REVISION_ID" # 8 characters or more
)

Success response is the same as when created.

Get Dataset Object

Get all dataset object in the dataset. (Up to 1000 tasks)

dataset_objects = client.get_dataset_objects(dataset="YOUR_DATASET_NAME")

The success response is the same as when created, but it is an array.

You can filter by version or revision_id and tags.

dataset_objects = client.get_dataset_objects(
    dataset="YOUR_DATASET_NAME",
    version="latest", # default is "latest"
    tags=["cat"],
)

dataset_objects = client.get_dataset_objects(
    dataset="YOUR_DATASET_NAME",
    revision_id="YOUR_REVISION_ID" # 8 characters or more
)

Download Dataset Objects

Download dataset objects in the dataset to specific directories.

You can filter by version, tags and types.

client.download_dataset_objects(
  dataset="YOUR_DATASET_NAME",
  path="YOUR_DOWNLOAD_PATH",
  version="latest", # default is "latest"
  tags=["cat"],
  types=["train", "valid"],  # choices are "train", "valid", "test" and "none" (Optional)
)

Update Dataset Object

dataset_object = client.update_dataset_object(
    dataset_id="YOUR_DATASET_ID",
    object_name="brushwood_dog.jpg",
    tags=["dog"], # max 5 tags per dataset object.
    annotations=[
        {
            "keypoints": [
                {
                    "value": [
                        102.59,
                        23.04,
                        1
                    ],
                    "key": "head"
                }
            ],
            "attributes": [
                {
                    "value": "Scottish field",
                    "key": "kind"
                }
            ],
            "confidenceScore": 0,
            "rotation": 0,
            "points": [
                0
            ],
            "value": "dog",
            "type": "bbox" # type can be 'bbox', 'segmentation'.
        }
    ],
    custom_metadata={
      "key": "value",
      "metadata": "metadata-value"
    }
)

Delete Dataset Object

Delete a single dataset object.

client.delete_dataset_object(
    dataset_id="YOUR_DATASET_ID",
    object_name="brushwood_dog.jpg"
)

Converter

FastLabel To COCO

Support the following annotation types.

bbox
polygon
pose estimation

Get tasks and export as a COCO format file.

project_slug = "YOUR_PROJECT_SLUG"
tasks = client.get_image_tasks(project=project_slug)
client.export_coco(project=project_slug, tasks=tasks)

Export with specifying output directory and file name.

client.export_coco(project="YOUR_PROJECT_SLUG", tasks=tasks, output_dir="YOUR_DIRECTROY", output_file_name="YOUR_FILE_NAME")

If you would like to export pose estimation type annotations, please pass annotations.

project_slug = "YOUR_PROJECT_SLUG"
tasks = client.get_image_tasks(project=project_slug)
annotations = client.get_annotations(project=project_slug)
client.export_coco(project=project_slug, tasks=tasks, annotations=annotations, output_dir="YOUR_DIRECTROY", output_file_name="YOUR_FILE_NAME")

FastLabel To YOLO

Support the following annotation types.

bbox
polygon

Get tasks and export as YOLO format files.

project_slug = "YOUR_PROJECT_SLUG"
tasks = client.get_image_tasks(project=project_slug)
client.export_yolo(project=project_slug, tasks=tasks, output_dir="YOUR_DIRECTROY")

Get tasks and export as YOLO format files with classes.txt You can use fixed classes.txt and arrange order of each annotaiton file's order

project_slug = "YOUR_PROJECT_SLUG"
tasks = client.get_image_tasks(project=project_slug)
annotations = client.get_annotations(project=project_slug)
classes = list(map(lambda annotation: annotation["value"], annotations))
client.export_yolo(project=project_slug, tasks=tasks, classes=classes, output_dir="YOUR_DIRECTROY")

FastLabel To Pascal VOC

Support the following annotation types.

bbox
polygon

Get tasks and export as Pascal VOC format files.

project_slug = "YOUR_PROJECT_SLUG"
tasks = client.get_image_tasks(project=project_slug)
client.export_pascalvoc(project=project_slug, tasks=tasks)

FastLabel To labelme

Support the following annotation types.

bbox
polygon
points
line

Get tasks and export as labelme format files.

tasks = client.get_image_tasks(project="YOUR_PROJECT_SLUG")
client.export_labelme(tasks)

FastLabel To Segmentation

Get tasks and export index color instance/semantic segmentation (PNG files). Only support the following annotation types.

bbox
polygon
segmentation (Hollowed points are not supported.)

tasks = client.get_image_tasks(project="YOUR_PROJECT_SLUG")
client.export_instance_segmentation(tasks)

tasks = client.get_image_tasks(project="YOUR_PROJECT_SLUG")
client.export_semantic_segmentation(tasks)

COCO To FastLabel

Supported bbox , polygon or pose_estimation annotation type.

Convert annotation file of COCO format as a Fastlabel format and create task.

file_path: COCO annotation json file path

annotations_map = client.convert_coco_to_fastlabel(file_path="./sample.json", annotation_type="bbox")
# annotation_type = "bbox", "polygon" or "pose_estimation

task_id = client.create_image_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.jpg",
    file_path="./sample.jpg",
    annotations=annotations_map.get("sample.jpg")
)

Example of converting annotations to create multiple tasks.

In the case of the following tree structure.

dataset
├── annotation.json
├── sample1.jpg
└── sample2.jpg

Example source code.

import fastlabel

project = "YOUR_PROJECT_SLUG"
input_file_path = "./dataset/annotation.json"
input_dataset_path = "./dataset/"

annotations_map = client.convert_coco_to_fastlabel(file_path=input_file_path)
for image_file_path in glob.iglob(os.path.join(input_dataset_path, "**/**.jpg"), recursive=True):
    time.sleep(1)
    name = image_file_path.replace(os.path.join(*[input_dataset_path, ""]), "")
    file_path = image_file_path
    annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []
    task_id = client.create_image_task(
        project=project,
        name=name,
        file_path=file_path,
        annotations=annotations
    )

YOLO To FastLabel

Supported bbox annotation type.

Convert annotation file of YOLO format as a Fastlabel format and create task.

classes_file_path: YOLO classes text file path dataset_folder_path: Folder path containing YOLO Images and annotation

annotations_map = client.convert_yolo_to_fastlabel(
    classes_file_path="./classes.txt",
    dataset_folder_path="./dataset/"
)
task_id = client.create_image_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.jpg",
    file_path="./dataset/sample.jpg",
    annotations=annotations_map.get("sample.jpg")
)

Example of converting annotations to create multiple tasks.

In the case of the following tree structure.

yolo
├── classes.txt
└── dataset
    ├── sample1.jpg
    ├── sample1.txt
    ├── sample2.jpg
    └── sample2.txt

Example source code.

import fastlabel

project = "YOUR_PROJECT_SLUG"
input_file_path = "./classes.txt"
input_dataset_path = "./dataset/"
annotations_map = client.convert_yolo_to_fastlabel(
    classes_file_path=input_file_path,
    dataset_folder_path=input_dataset_path
)
for image_file_path in glob.iglob(os.path.join(input_dataset_path, "**/**.jpg"), recursive=True):
    time.sleep(1)
    name = image_file_path.replace(os.path.join(*[input_dataset_path, ""]), "")
    file_path = image_file_path
    annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []
    task_id = client.create_image_task(
        project=project,
        name=name,
        file_path=file_path,
        annotations=annotations
    )

Pascal VOC To FastLabel

Supported bbox annotation type.

Convert annotation file of Pascal VOC format as a Fastlabel format and create task.

folder_path: Folder path including pascal VOC format annotation files

annotations_map = client.convert_pascalvoc_to_fastlabel(folder_path="./dataset/")
task_id = client.create_image_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.jpg",
    file_path="./dataset/sample.jpg",
    annotations=annotations_map.get("sample.jpg")
)

Example of converting annotations to create multiple tasks.

In the case of the following tree structure.

dataset
├── sample1.jpg
├── sample1.xml
├── sample2.jpg
└── sample2.xml

Example source code.

import fastlabel

project = "YOUR_PROJECT_SLUG"
input_dataset_path = "./dataset/"

annotations_map = client.convert_pascalvoc_to_fastlabel(folder_path=input_dataset_path)
for image_file_path in glob.iglob(os.path.join(input_dataset_path, "**/**.jpg"), recursive=True):
    time.sleep(1)
    name = image_file_path.replace(os.path.join(*[input_dataset_path, ""]), "")
    file_path = image_file_path
    annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []
    task_id = client.create_image_task(
        project=project,
        name=name,
        file_path=file_path,
        annotations=annotations
    )

labelme To FastLabel

Support the following annotation types.

bbox
polygon
points
line

Convert annotation file of labelme format as a Fastlabel format and create task.

folder_path: Folder path including labelme format annotation files

annotations_map = client.convert_labelme_to_fastlabel(folder_path="./dataset/")
task_id = client.create_image_task(
    project="YOUR_PROJECT_SLUG",
    name="sample.jpg",
    file_path="./sample.jpg",
    annotations=annotations_map.get("sample.jpg")
)

Example of converting annotations to create multiple tasks.

In the case of the following tree structure.

dataset
├── sample1.jpg
├── sample1.json
├── sample2.jpg
└── sample2.json

Example source code.

import fastlabel

project = "YOUR_PROJECT_SLUG"
input_dataset_path = "./dataset/"

annotations_map = client.convert_labelme_to_fastlabel(folder_path=input_dataset_path)
for image_file_path in glob.iglob(os.path.join(input_dataset_path, "**/**.jpg"), recursive=True):
    time.sleep(1)
    name = image_file_path.replace(os.path.join(*[input_dataset_path, ""]), "")
    file_path = image_file_path
    annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []
    task_id = client.create_image_task(
        project=project,
        name=name,
        file_path=file_path,
        annotations=annotations
    )

Please check const.COLOR_PALLETE for index colors.

Model

Get training jobs

Get training jobs.

def get_training_jobs() -> list[dict]:
    all_training_jobs = []
    offset = None
    while True:
        time.sleep(1)

        training_jobs = client.get_training_jobs(offset=offset)
        all_training_jobs.extend(training_jobs)

        if len(training_jobs) > 0:
            offset = len(all_training_jobs)
        else:
            break
    return all_training_jobs

Find Training job

Find a single training job.

task = client.find_training_job(id="YOUR_TRAINING_ID")

Response

Example of two training jobs.

[
    {
        "trainingJobId": "f40c5838-4c3a-482f-96b7-f77e16c96fed",
        "status": "in_progress",
        "baseModelName": "FastLabel Object Detection High Accuracy - 汎用",
        "instanceType": "ml.p3.2xlarge",
        "epoch": 300,
        "projects": [
            "image-bbox"
        ],
        "statuses": [],
        "tags": [],
        "contentCount": 23,
        "userName": "Admin",
        "createdAt": "2023-10-31T07:10:28.306Z",
        "completedAt": null,
        "customModel": {
            "modelId": "",
            "modelName": "",
            "modelURL": "",
            "classes": []
        }
    },
    {
        "trainingJobId": "1d2bc86a-c7f1-40a5-8e85-48246cc3c8d2",
        "status": "completed",
        "baseModelName": "custom-object-detection-image",
        "instanceType": "ml.p3.2xlarge",
        "epoch": 300,
        "projects": [
            "image-bbox"
        ],
        "statuses": [
            "approved"
        ],
        "tags": [
            "trainval"
        ],
        "contentCount": 20,
        "userName": "Admin",
        "createdAt": "2023-10-31T06:56:28.112Z",
        "completedAt": "2023-10-31T07:08:26.000Z",
        "customModel": {
            "modelId": "a6728876-2eb7-49b5-9fd8-7dee1b8a81b3",
            "modelName": "fastlabel_object_detection-2023-10-31-07-08-29",
            "modelURL": "URL for download model file",
            "classes": [
                "person"
            ]
        }
    }
]

Execute training job

Get training jobs.

training_job = client.execute_training_job(
    dataset_name="dataset_name",
    base_model_name="fastlabel_object_detection_light",  // "fastlabel_object_detection_light" or "fastlabel_object_detection_high_accuracy" or "fastlabel_u_net_general"
    epoch=300,
    use_dataset_train_val=True,
    resize_option="fixed",  // optional, "fixed" or "none"
    resize_dimension=1024, // optional, 512 or 1024
    annotation_value="person", // Annotation value is required if choose "fastlabel_keypoint_rcnn"
    config_file_path="config.yaml", // optional, YAML file path for training config file.
)

Get evaluation jobs

Get evaluation jobs.

def get_evaluation_jobs() -> list[dict]:
    all_evaluation_jobs = []
    offset = None
    while True:
        time.sleep(1)

        evaluation_jobs = client.get_evaluation_jobs(offset=offset)
        all_evaluation_jobs.extend(evaluation_jobs)

        if len(evaluation_jobs) > 0:
            offset = len(all_evaluation_jobs)
        else:
            break
    return all_evaluation_jobs

Find Evaluation job

Find a single evaluation job.

evaluation_job = client.find_evaluation_job(id="YOUR_EVALUATION_ID")

Response

Example of two evaluation jobs.

{
  id: "50873ea1-e008-48db-a368-241ca88d6f67",
  version: 59,
  status: "in_progress",
  modelType: "builtin",
  modelName: "FastLabel Object Detection Light - 汎用",
  customModelId: None,
  iouThreshold: 0.8,
  confidenceThreshold: 0.4,
  contentCount: 0,
  gtCount: 0,
  predCount: 0,
  mAP: 0,
  recall: 0,
  precision: 0,
  f1: 0,
  confusionMatrix: None,
  duration: 0,
  evaluationSource: "dataset",
  projects: [],
  statuses: [],
  tags: [],
  datasetId: "deacbe6d-406f-4086-bd87-80ffb1c1a393",
  dataset: {
    id: "deacbe6d-406f-4086-bd87-80ffb1c1a393",
    workspaceId: "df201d3c-af00-423a-aa7f-827376fd96de",
    name: "sample-dataset",
    createdAt: "2023-12-20T10:44:12.198Z",
    updatedAt: "2023-12-20T10:44:12.198Z",
  },
  datasetRevisionId: "2d26ab64-dfc0-482d-9211-ce8feb3d480b",
  useDatasetTest: True,
  userName: "",
  completedAt: None,
  createdAt: "2023-12-21T09:08:16.111Z",
  updatedAt: "2023-12-21T09:08:18.414Z",
};

Execute evaluation job

Execute evaluation jobs.

training_job = client.execute_evaluation_job(
    dataset_name="DATASET_NAME",
    model_name="fastlabel_object_detection_light",
    // If you want to use the built-in model, select the following.
    - "fastlabel_object_detection_light"
    - "fastlabel_object_detection_high_accuracy"
    - "fastlabel_fcn_resnet"
    // If you want to use the custom model, please fill　out model name.
    use_dataset_test=True,
)

Execute endpoint

Create the endpoint from the screen at first.

Currently, the feature to create endpoints is in alpha and is not available to users. If you would like to try it out, please contact a FastLabel representative.

import fastlabel
import numpy as np
import cv2
import base64
client = fastlabel.Client()

ENDPOINT_NAME = "YOUR ENDPOINT NAME"
IMAGE_FILE_PATH = "YOUR IMAGE FILE PATH"
RESULT_IMAGE_FILE_PATH = "YOUR RESULT IMAGE FILE PATH"

def base64_to_cv(img_str):
  if "base64," in img_str:
      img_str = img_str.split(",")[1]
  img_raw = np.frombuffer(base64.b64decode(img_str), np.uint8)
  img = cv2.imdecode(img_raw, cv2.IMREAD_UNCHANGED)
  return img

if __name__ == '__main__':
  # Execute endpoint
  response = client.execute_endpoint(
      endpoint_name=ENDPOINT_NAME, file_path=IMAGE_PATH)

  # Show  result
  print(response["json"])

  # Save result
  img = base64_to_cv(response["file"])
  cv2.imwrite(RESULT_IMAGE_FILE_PATH, img)

Create Request Results for Monitoring

You can integrate the results of model endpoint calls, which are targeted for aggregation in model monitoring, from an external source.

from datetime import datetime
import pytz
import fastlabel
client = fastlabel.Client()

jst = pytz.timezone("Asia/Tokyo")
dt_jst = datetime(2023, 5, 8, 12, 10, 53, tzinfo=jst)

client.create_model_monitoring_request_results(
    name="model-monitoring-name",  # The name of your model monitoring name
    results=[
        {
            "status": "success",  # success or failed
            "result": [
                {
                    "value": "person",  # The value of the inference class returned by your model
                    "confidenceScore": 0.92,  # 0 ~ 1
                }
            ],
            "requestAt": dt_jst.isoformat(),  # The time when your endpoint accepted the request
        }
    ],
)

API Docs

Check this for further information.

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0.11.11

Dec 9, 2021

0.11.10

Dec 2, 2021

0.11.9

Nov 26, 2021

0.11.8

Nov 25, 2021

0.11.7

Nov 15, 2021

0.11.6

Nov 2, 2021

0.11.5

Oct 31, 2021

0.11.4

Oct 18, 2021

0.11.3

Sep 11, 2021

0.11.2

Sep 6, 2021

0.11.1

Aug 27, 2021

0.11.0

Aug 13, 2021

0.10.0

Aug 10, 2021

0.9.10

Aug 4, 2021

0.9.9

Jul 30, 2021

0.9.8 yanked

Jul 30, 2021

0.9.7

Jul 27, 2021

0.9.6

Jul 23, 2021

0.9.5

Jul 19, 2021

0.9.4

Jul 3, 2021

0.9.3

Jun 30, 2021

0.9.2

Jun 20, 2021

0.9.1

Jun 13, 2021

0.9.0

Jun 2, 2021

0.8.1

May 26, 2021

0.8.0

May 26, 2021

0.7.1

May 25, 2021

0.7.0

May 23, 2021

0.6.0

May 21, 2021

0.5.0

May 14, 2021

0.4.2

May 13, 2021

0.4.1

May 13, 2021

0.4.0

May 2, 2021

0.3.1

Feb 26, 2021

0.3.0

Feb 24, 2021

0.2.2

Feb 1, 2021

0.2.1

Jan 24, 2021

0.2.0

Dec 26, 2020

0.1.1

Dec 19, 2020

0.1.0

Dec 18, 2020

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fastlabel 0.15.0

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FastLabel Python SDK

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