The official Python SDK for FastLabel API, the Data Platform for AI
Project description
FastLabel Python SDK
Table of Contents
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
]
}]
)
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"
}
Multi Image Classification
Supported following project types:
- Multi Image - Classification
Create Task
Create a new task.
task = client.create_multi_image_classification_task(
project="YOUR_PROJECT_SLUG",
name="sample",
folder_path="./sample",
priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30
attributes=[
{
"type": "text",
"key": "attribute-key",
"value": "attribute-value"
}
]
)
Limitation
- You can upload up to a size of 20 MB.
- You can upload up to a total size of 2 GB.
- You can upload up to 6 files in total.
Find Task
Find a single task.
task = client.find_multi_image_classification_task(task_id="YOUR_TASK_ID")
Find a single task by name.
tasks = client.find_multi_image_classification_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")
Get Tasks
Get tasks.
tasks = client.get_multi_image_classification_tasks(project="YOUR_PROJECT_SLUG")
Update Task
Update a single task.
task_id = client.update_multi_image_classification_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
attributes=[
{
"type": "text",
"key": "attribute-key",
"value": "attribute-value"
}
]
)
Response
Example of a single task object
{
"id": "YOUR_TASK_ID",
"name": "sample",
"contents": [
{
"name": "content-name-1",
"url": "content-url-1",
"width": 100,
"height": 100,
},
{
"name": "content-name-2",
"url": "content-url-2",
"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",
"attributes": [
{
"type": "text",
"key": "attribute-key-1",
"value": "attribute-value-1"
},
{
"type": "text",
"key": "attribute-key-2",
"value": "attribute-value-2"
}
],
"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"
}
Appendix
Processing of various types of appendix information is supported.
Import Camera Calibration
Import camera calibration and image appendix information for tasks in pcd and sequential pcd projects.
client.import_appendix_file(project="YOUR_PROJECT_SLUG", file_path="ZIP_FILE_PATH")
The folder structure inside the ZIP file is as follows
.
└── task_name
├── content_name_1.pcd
│ ├── 000001.png
│ └── 000001.yaml
└── content_name_2.pcd
├── 000002.png
├── 000002.yaml
├── 000003.png
└── 000003.yaml
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")
Tags
Get Tags
Get tags. (Up to 1000 tags)
keyword are search terms in the tag name (Optional). offset is the starting position number to fetch (Optional). limit is the max number to fetch (Optional).
If you need to fetch more than 1000 tags, please loop this method using offset and limit. In the sample code below, you can fetch 1000 tags starting from the 2001st position.
projects = client.get_tags(
project="YOUR_PROJECT_SLUG",
keyword="dog", # (Optional)
offset=2000, # (Optional)
limit=1000, # (Optional. Default is 100.)
)
Response
Example of tags object
[
{
"id": "YOUR_TAG_ID",
"name": "YOUR_TAG_NAME",
"order": 1,
"createdAt": "2023-08-14T11: 32: 36.462Z",
"updatedAt": "2023-08-14T11: 32: 36.462Z"
}
]
Delete Tags
Delete tags.
client.delete_tags(
tag_ids=[
"YOUR_TAG_ID_1",
"YOUR_TAG_ID_2",
],
)
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'
)
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.
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.
licenses=["MIT", "my-license"], # max 10 licenses per dataset object
annotations=[
{
"keypoints": [
{
"value": [
102.59,
23.04,
1
],
"key": "head"
}
],
"attributes": [
{
"type": "text",
"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"
}
)
If you would like to create a new dataset object with classification type annotations, please pass empty points and value of the annotation named 'classification'.
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.
licenses=["MIT", "my-license"], # max 10 licenses per dataset object
annotations=[
{
"type": "classification",
"value": "classification",
"points": [],
"attributes": [
{
"type": "text",
"value": "Scottish field",
"key": "kind"
}
]
}
]
)
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, licenses and tags.
dataset_objects = client.get_dataset_objects(
dataset="YOUR_DATASET_NAME",
version="latest", # default is "latest"
tags=["cat"],
licenses=["fastlabel"],
types=["train", "valid"], # choices are "train", "valid", "test" and "none" (Optional)
offset=0, # default is 0 (Optional)
limit=1000, # default is 1000, and must be less than 1000 (Optional)
)
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)
licenses=["fastlabel"],
offset=0, # default is 0 (Optional)
limit=1000, # default is 1000, and must be less than 1000 (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.
licenses=["MIT", "my-license"], # max 10 licenses 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
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.
Mask To FastLabel Segmentation Points
Convert mask image to FastLabel's segmentation coordinate format.
points = client.mask_to_fastlabel_segmentation_points(
mask_image = binary_image_path (or binary_image_array)
)
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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| MD5 | e166ecdd7ec75216d0b8f72fa5974401 |
|
| BLAKE2b-256 | e5d76c0c5dc678bb76c449b26f347b8cb0ae6a3a2182c5beb2f63ffad24bcd6a |
