| Name | fastlabel JSON |
| Version |
0.14.13
JSON |
| download |
| home_page | None |
| Summary | The official Python SDK for FastLabel API, the Data Platform for AI |
| upload_time | 2024-04-17 11:49:16 |
| maintainer | None |
| docs_url | None |
| author | eisuke-ueta |
| requires_python | >=3.8 |
| license | None |
| keywords |
|
| VCS |
|
| bugtrack_url |
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| requirements |
No requirements were recorded.
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No Travis.
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| coveralls test coverage |
No coveralls.
|
# FastLabel Python SDK
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Limitation](#limitation)
- [Task](#task)
- [Image](#image)
- [Image Classification](#image-classification)
- [Sequential Image](#sequential-image)
- [Video](#video)
- [Video Classification](#video-classification)
- [Text](#text)
- [Text Classification](#text-classification)
- [Audio](#audio)
- [Audio Classification](#audio-classification)
- [PCD](#pcd)
- [Sequential PCD](#sequential-pcd)
- [DICOM](#dicom)
- [Common](#common)
- [Annotation](#annotation)
- [Project](#project)
- [Dataset](#dataset)
- [Converter](#converter)
- [FastLabel To COCO](#fastlabel-to-coco)
- [FastLabel To YOLO](#fastlabel-to-yolo)
- [FastLabel To Pascal VOC](#fastlabel-to-pascal-voc)
- [FastLabel To labelme](#fastlabel-to-labelme)
- [FastLabel To Segmentation](#fastlabel-to-segmentation)
- [COCO To FastLabel](#coco-to-fastlabel)
- [YOLO To FastLabel](#yolo-to-fastlabel)
- [Pascal VOC To FastLabel](#pascal-voc-to-fastlabel)
- [labelme To FastLabel](#labelme-to-fastlabel)
- [Model](#model)
- [API Docs](#api-docs)
## Installation
```bash
pip install --upgrade fastlabel
```
> Python version 3.8 or greater is required
## Usage
Configure API Key in environment variable.
```bash
export FASTLABEL_ACCESS_TOKEN="YOUR_ACCESS_TOKEN"
```
Initialize fastlabel client.
```python
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.
```python
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)
```python
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](/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.)
```python
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)
```python
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.
```python
task = client.find_image_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
tasks = client.find_image_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")
```
#### Get Tasks
Get tasks. (Up to 1000 tasks)
```python
tasks = client.get_image_tasks(project="YOUR_PROJECT_SLUG")
```
- Filter and Get tasks. (Up to 1000 tasks)
```python
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)
```python
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.
```python
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
```python
{
"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
```python
{
"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
```python
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`.
```python
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
```python
{
'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.
```python
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.)
```python
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.
```python
task = client.find_image_classification_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
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)
```python
tasks = client.get_image_classification_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Tasks
Update a single task.
```python
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
```python
{
"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.
```python
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.
```python
task = client.find_sequential_image_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
tasks = client.find_sequential_image_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")
```
#### Get Tasks
Get tasks.
```python
tasks = client.get_sequential_image_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Task
Update a single task.
```python
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
```python
{
"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.
```python
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)
```python
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.
```python
task = client.find_video_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
tasks = client.find_video_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")
```
#### Get Tasks
Get tasks. (Up to 10 tasks)
```python
tasks = client.get_video_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Task
Update a single task.
```python
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`.
```python
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
```python
{
"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.
```python
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.
```python
task = client.find_video_classification_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
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)
```python
tasks = client.get_video_classification_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Tasks
Update a single task.
```python
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.
```python
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)
```python
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.
```python
task = client.find_text_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
tasks = client.find_text_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")
```
#### Get Tasks
Get tasks. (Up to 10 tasks)
```python
tasks = client.get_text_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Task
Update a single task.
```python
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
```python
{
"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.
```python
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.
```python
task = client.find_text_classification_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
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)
```python
tasks = client.get_text_classification_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Tasks
Update a single task.
```python
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.
```python
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)
```python
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.
```python
task = client.find_audio_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
tasks = client.find_audio_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")
```
#### Get Tasks
Get tasks. (Up to 10 tasks)
```python
tasks = client.get_audio_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Task
Update a single task.
```python
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
```python
{
"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`.
```python
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
```python
{
'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.
```python
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.
```python
task = client.find_audio_classification_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
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)
```python
tasks = client.get_audio_classification_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Tasks
Update a single task.
```python
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.
```python
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
```python
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
```python
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.
```python
task = client.find_pcd_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
tasks = client.find_pcd_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")
```
#### Get Tasks
Get tasks. (Up to 1000 tasks)
```python
tasks = client.get_pcd_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Task
Update a single task.
```python
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
```python
{
"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.
```python
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)
```python
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.
```python
task = client.find_sequential_pcd_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
task = client.find_sequential_pcd_task(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")
```
#### Get Tasks
Get tasks. (Up to 10 tasks)
```python
tasks = client.get_sequential_pcd_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Tasks
Update a single task.
```python
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
```python
{
"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](#find-task-import-history).
Once you receive the status completed, you can get the task.
```python
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.
```python
task = client.find_dicom_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
tasks = client.find_dicom_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")
```
#### Get Tasks
Get tasks. (Up to 1000 tasks)
```python
tasks = client.get_dicom_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Tasks
Update a single task.
```python
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
```python
{
"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.
```python
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.
```python
client.delete_task(task_id="YOUR_TASK_ID")
```
#### Delete Task Annotation
Delete annotations in a task.
```python
client.delete_task_annotations(task_id="YOUR_TASK_ID")
```
#### Get Tasks Id and Name map
```python
id_name_map = client.get_task_id_name_map(project="YOUR_PROJECT_SLUG")
```
#### Count Task
```python
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
```python
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
```python
history = client.create_task_from_aws_s3(
project="YOUR_PROJECT_SLUG",
)
```
- Get AWS S3 import status
```python
history = client.get_aws_s3_import_status_by_project(
project="YOUR_PROJECT_SLUG",
)
```
#### Find Task Import History
Find a single history.
```python
history = client.find_history(history_id="YOUR_HISTORY_ID")
```
#### Get Task Import Histories
```python
histories = client.get_histories(project="YOUR_PROJECT_SLUG")
```
#### Response
Example of a single history object
```python
{
"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.
```python
annotation_id = client.create_annotation(
project="YOUR_PROJECT_SLUG", type="bbox", value="cat", title="Cat")
```
Create a new annotation with color and attributes.
```python
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.
```python
annotation_id = client.create_classification_annotation(
project="YOUR_PROJECT_SLUG", attributes=attributes)
```
### Find Annotation
Find an annotation.
```python
annotation = client.find_annotation(annotation_id="YOUR_ANNOTATION_ID")
```
Find an annotation by value.
```python
annotation = client.find_annotation_by_value(project="YOUR_PROJECT_SLUG", value="cat")
```
Find an annotation by value in classification project.
```python
annotation = client.find_annotation_by_value(
project="YOUR_PROJECT_SLUG", value="classification") # "classification" is fixed value
```
### Get Annotations
Get annotations. (Up to 1000 annotations)
```python
annotations = client.get_annotations(project="YOUR_PROJECT_SLUG")
```
### Response
Example of an annotation object
```python
{
"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
```python
{
"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.
```python
annotation_id = client.update_annotation(
annotation_id="YOUR_ANNOTATION_ID", value="cat2", title="Cat2", color="#FF0000")
```
Update an annotation with attributes.
```python
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.
```python
annotation_id = client.update_classification_annotation(
project="YOUR_PROJECT_SLUG", attributes=attributes)
```
### Delete Annotation
Delete an annotation.
```python
client.delete_annotation(annotation_id="YOUR_ANNOTATION_ID")
```
## Project
### Create Project
Create a new project.
```python
project_id = client.create_project(
type="image_bbox", name="ImageNet", slug="image-net")
```
### Find Project
Find a project.
```python
project = client.find_project(project_id="YOUR_PROJECT_ID")
```
Find a project by slug.
```python
project = client.find_project_by_slug(slug="YOUR_PROJECT_SLUG")
```
### Get Projects
Get projects. (Up to 1000 projects)
```python
projects = client.get_projects()
```
### Response
Example of a project object
```python
{
"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.
```python
project_id = client.update_project(
project_id="YOUR_PROJECT_ID", name="NewImageNet", slug="new-image-net", job_size=20)
```
### Delete Project
Delete a project.
```python
client.delete_project(project_id="YOUR_PROJECT_ID")
```
### Copy Project
Copy a project.
```python
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.
```python
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
```python
[
{
"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.
```python
client.delete_tags(
tag_ids=[
"YOUR_TAG_ID_1",
"YOUR_TAG_ID_2",
],
)
```
## Dataset
### Create Dataset
Create a new dataset.
```python
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.
```python
{
'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.
```python
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)
```python
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.
```python
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](#get-tasks).
### Update Dataset
Update a single dataset.
```python
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.**
```python
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.
```python
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.
```python
{
'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.
```python
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.
```python
dataset_object = client.find_dataset_object(
dataset_id="YOUR_DATASET_ID",
object_name="brushwood_dog.jpg",
version="YOUR_VERSION_NAME" # default is "latest"
)
```
```python
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)
```python
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.
```python
dataset_objects = client.get_dataset_objects(
dataset="YOUR_DATASET_NAME",
version="latest", # default is "latest"
tags=["cat"],
)
```
```python
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.
```python
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
```python
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.
```python
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](https://cocodataset.org/#format-data) file.
```python
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.
```python
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.
```python
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.
```python
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
```python
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.
```python
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.
```python
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.)
```python
tasks = client.get_image_tasks(project="YOUR_PROJECT_SLUG")
client.export_instance_segmentation(tasks)
```
```python
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](https://cocodataset.org/#format-data) as a Fastlabel format and create task.
file_path: COCO annotation json file path
```python
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.
```python
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
```python
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.
```python
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
```python
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.
```python
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
```python
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.
```python
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.
```python
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.
```python
task = client.find_training_job(id="YOUR_TRAINING_ID")
```
#### Response
Example of two training jobs.
```python
[
{
"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.
```python
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.
```python
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.
```python
evaluation_job = client.find_evaluation_job(id="YOUR_EVALUATION_ID")
```
#### Response
Example of two evaluation jobs.
```python
{
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.
```python
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.
```python
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.
```python
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](https://api.fastlabel.ai/docs/) for further information.
Raw data
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"name": "fastlabel",
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"author": "eisuke-ueta",
"author_email": "eisuke.ueta@fastlabel.ai",
"download_url": "https://files.pythonhosted.org/packages/11/fa/16d1dbdd4819409f7b64102ccd4d44c62e4385781949ae1b4532646e2884/fastlabel-0.14.13.tar.gz",
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"description": "# FastLabel Python SDK\n\n## Table of Contents\n\n- [Installation](#installation)\n- [Usage](#usage)\n - [Limitation](#limitation)\n- [Task](#task)\n - [Image](#image)\n - [Image Classification](#image-classification)\n - [Sequential Image](#sequential-image)\n - [Video](#video)\n - [Video Classification](#video-classification)\n - [Text](#text)\n - [Text Classification](#text-classification)\n - [Audio](#audio)\n - [Audio Classification](#audio-classification)\n - [PCD](#pcd)\n - [Sequential PCD](#sequential-pcd)\n - [DICOM](#dicom)\n - [Common](#common)\n- [Annotation](#annotation)\n- [Project](#project)\n- [Dataset](#dataset)\n- [Converter](#converter)\n - [FastLabel To COCO](#fastlabel-to-coco)\n - [FastLabel To YOLO](#fastlabel-to-yolo)\n - [FastLabel To Pascal VOC](#fastlabel-to-pascal-voc)\n - [FastLabel To labelme](#fastlabel-to-labelme)\n - [FastLabel To Segmentation](#fastlabel-to-segmentation)\n - [COCO To FastLabel](#coco-to-fastlabel)\n - [YOLO To FastLabel](#yolo-to-fastlabel)\n - [Pascal VOC To FastLabel](#pascal-voc-to-fastlabel)\n - [labelme To FastLabel](#labelme-to-fastlabel)\n- [Model](#model)\n- [API Docs](#api-docs)\n\n## Installation\n\n```bash\npip install --upgrade fastlabel\n```\n\n> Python version 3.8 or greater is required\n\n## Usage\n\nConfigure API Key in environment variable.\n\n```bash\nexport FASTLABEL_ACCESS_TOKEN=\"YOUR_ACCESS_TOKEN\"\n```\n\nInitialize fastlabel client.\n\n```python\nimport fastlabel\nclient = fastlabel.Client()\n```\n\n### Limitation\n\nAPI 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.\n\n## Task\n\n### Image\n\nSupported following project types:\n\n- Image - Bounding Box\n- Image - Polygon\n- Image - Keypoint\n- Image - Line\n- Image - Segmentation\n- Image - Pose Estimation\n- Image - All\n\n#### Create Task\n\nCreate a new task.\n\n```python\ntask_id = client.create_image_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.jpg\",\n file_path=\"./sample.jpg\"\n)\n```\n\nCreate a new task with pre-defined annotations. (Class should be configured on your project in advance)\n\n```python\ntask_id = client.create_image_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.jpg\",\n file_path=\"./sample.jpg\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n annotations=[{\n \"type\": \"bbox\",\n \"value\": \"annotation-value\",\n \"attributes\": [\n {\n \"key\": \"attribute-key\",\n \"value\": \"attribute-value\"\n }\n ],\n \"points\": [\n 100, # top-left x\n 100, # top-left y\n 200, # bottom-right x\n 200 # bottom-right y\n ]\n }]\n)\n```\n\n> Check [examples/create_image_task.py](/examples/create_image_task.py).\n\n##### Limitation\n\n- You can upload up to a size of 20 MB.\n\n#### Create Integrated Image Task\n\nCreate a new task by integrated image.\n(Project storage setting should be configured in advance.)\n\n```python\ntask_id = client.create_integrated_image_task(\n project=\"YOUR_PROJECT_SLUG\",\n file_path=\"<integrated-storage-dir>/sample.jpg\",\n storage_type=\"gcp\",\n)\n```\n\nCreate a new task with pre-defined annotations. (Class should be configured on your project in advance)\n\n```python\ntask_id = client.create_image_task(\n project=\"YOUR_PROJECT_SLUG\",\n file_path=\"<integrated-storage-dir>/sample.jpg\",\n storage_type=\"gcp\",\n annotations=[{\n \"type\": \"bbox\",\n \"value\": \"annotation-value\",\n \"attributes\": [\n {\n \"key\": \"attribute-key\",\n \"value\": \"attribute-value\"\n }\n ],\n \"points\": [\n 100, # top-left x\n 100, # top-left y\n 200, # bottom-right x\n 200 # bottom-right y\n ]\n }]\n)\n```\n\n##### Limitation\n\n- You can upload up to a size of 20 MB.\n\n#### Find Task\n\nFind a single task.\n\n```python\ntask = client.find_image_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntasks = client.find_image_task_by_name(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks. (Up to 1000 tasks)\n\n```python\ntasks = client.get_image_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n- Filter and Get tasks. (Up to 1000 tasks)\n\n```python\ntasks = client.get_image_tasks(\n project=\"YOUR_PROJECT_SLUG\",\n status=\"approved\", # status can be 'pending', 'registered', 'completed', 'skipped', 'reviewed' 'sent_back', 'approved', 'declined'\n tags=[\"tag1\", \"tag2\"] # up to 10 tags\n)\n```\n\nGet a large size of tasks. (Over 1000 tasks)\n\n```python\nimport time\n\n# Iterate pages until new tasks are empty.\nall_tasks = []\noffset = None\nwhile True:\n time.sleep(1)\n\n tasks = client.get_image_tasks(project=\"YOUR_PROJECT_SLUG\", offset=offset)\n all_tasks.extend(tasks)\n\n if len(tasks) > 0:\n offset = len(all_tasks) # Set the offset\n else:\n break\n```\n\n> Please wait a second before sending another requests!\n\n#### Update Tasks\n\nUpdate a single task.\n\n```python\ntask_id = client.update_image_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"],\n annotations=[\n {\n \"type\": \"bbox\",\n \"value\": \"cat\"\n \"attributes\": [\n { \"key\": \"kind\", \"value\": \"Scottish field\" }\n ],\n \"points\": [\n 100, # top-left x\n 100, # top-left y\n 200, # bottom-right x\n 200 # bottom-right y\n ]\n }\n ],\n # pass annotation indexes to update\n relations=[\n {\n \"startIndex\": 1,\n \"endIndex\": 0,\n },\n {\n \"startIndex\": 2,\n \"endIndex\": 0\n }\n ]\n)\n```\n\n#### Response\n\nExample of a single image task object\n\n```python\n{\n \"id\": \"YOUR_TASK_ID\",\n \"name\": \"cat.jpg\",\n \"width\": 100, # image width\n \"height\": 100, # image height\n \"url\": \"YOUR_TASK_URL\",\n \"status\": \"registered\",\n \"externalStatus\": \"registered\",\n \"priority\": 10,\n \"tags\": [],\n \"assignee\": \"ASSIGNEE_NAME\",\n \"reviewer\": \"REVIEWER_NAME\",\n \"externalAssignee\": \"EXTERNAL_ASSIGNEE_NAME\",\n \"externalReviewer\": \"EXTERNAL_REVIEWER_NAME\",\n \"annotations\": [\n {\n \"attributes\": [\n { \"key\": \"kind\", \"name\": \"Kind\", \"type\": \"text\", \"value\": \"Scottish field\" }\n ],\n \"color\": \"#b36d18\",\n \"points\": [\n 100, # top-left x\n 100, # top-left y\n 200, # bottom-right x\n 200 # bottom-right y\n ],\n \"rotation\": 0,\n \"title\": \"Cat\",\n \"type\": \"bbox\",\n \"value\": \"cat\"\n }\n ],\n \"createdAt\": \"2021-02-22T11:25:27.158Z\",\n \"updatedAt\": \"2021-02-22T11:25:27.158Z\"\n}\n```\n\nExample when the project type is Image - Pose Estimation\n\n```python\n{\n \"id\": \"YOUR_TASK_ID\",\n \"name\": \"person.jpg\",\n \"width\": 255, # image width\n \"height\": 255, # image height\n \"url\": \"YOUR_TASK_URL\",\n \"status\": \"registered\",\n \"externalStatus\": \"registered\",\n \"priority\": 10,\n \"tags\": [],\n \"assignee\": \"ASSIGNEE_NAME\",\n \"reviewer\": \"REVIEWER_NAME\",\n \"externalAssignee\": \"EXTERNAL_ASSIGNEE_NAME\",\n \"externalReviewer\": \"EXTERNAL_REVIEWER_NAME\",\n \"annotations\":[\n {\n \"type\":\"pose_estimation\",\n \"title\":\"jesture\",\n \"value\":\"jesture\",\n \"color\":\"#10c414\",\n \"attributes\": [],\n \"keypoints\":[\n {\n \"name\":\"\u982d\",\n \"key\":\"head\",\n \"value\":[\n 102.59, # x\n 23.04, # y\n 1 # 0:invisible, 1:visible\n ],\n \"edges\":[\n \"right_shoulder\",\n \"left_shoulder\"\n ]\n },\n {\n \"name\":\"\u53f3\u80a9\",\n \"key\":\"right_shoulder\",\n \"value\":[\n 186.69,\n 114.11,\n 1\n ],\n \"edges\":[\n \"head\"\n ]\n },\n {\n \"name\":\"\u5de6\u80a9\",\n \"key\":\"left_shoulder\",\n \"value\":[\n 37.23,\n 109.29,\n 1\n ],\n \"edges\":[\n \"head\"\n ]\n }\n ]\n }\n ],\n \"createdAt\": \"2021-02-22T11:25:27.158Z\",\n \"updatedAt\": \"2021-02-22T11:25:27.158Z\"\n}\n```\n\n#### Export Image With Annotations\n\nGet tasks and export images with annotations.\nOnly support the following image extension.\n\n- jpeg\n- jpg\n- png\n- tif\n- tiff\n- bmp\n\n```python\ntasks = client.get_image_tasks(project=\"YOUR_PROJECT_SLUG\")\nclient.export_image_with_annotations(\n tasks=tasks, image_dir=\"IMAGE_DIR\", output_dir=\"OUTPUT_DIR\"\n)\n```\n\n#### Integrate Task\n\nThis function is alpha version. It is subject to major changes in the future.\n\nIntegration is possible only when tasks are registered from the objects divided by the dataset.\nOnly bbox and polygon annotation types are supported.\n\nIn the case of a task divided under the following conditions.\n\n- Dataset slug: `image`\n- Object name: `cat.jpg`\n- Split count: `3\u00d73`\n\nObjects are registered in the data set in the following form.\n\n- image/cat/1.jpg\n- image/cat/2.jpg\n- image/cat/3.jpg\n- (omit)\n- image/cat/9.jpg\n\nThe annotations at the edges of the image are combined. However, annotations with a maximum length of 300px may not work.\n\nIn this case, SPLIT_IMAGE_TASK_NAME_PREFIX specifies `image/cat`.\n\n```python\ntask = client.find_integrated_image_task_by_prefix(\n project=\"YOUR_PROJECT_SLUG\",\n prefix=\"SPLIT_IMAGE_TASK_NAME_PREFIX\",\n)\n```\n\n##### Response\n\nExample of a integrated image task object\n\n```python\n{\n 'name': 'image/cat.jpg',\n \"annotations\": [\n {\n \"attributes\": [],\n \"color\": \"#b36d18\",\n \"confidenceScore\"; -1,\n \"keypoints\": [],\n \"points\": [200,200,300,400],\n \"rotation\": 0,\n \"title\": \"Bird\",\n \"type\": \"polygon\",\n \"value\": \"bird\"\n }\n ],\n}\n```\n\n### Image Classification\n\nSupported following project types:\n\n- Image - Classification\n\n#### Create Task\n\nCreate a new task.\n\n```python\ntask_id = client.create_image_classification_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.jpg\",\n file_path=\"./sample.jpg\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n attributes=[\n {\n \"key\": \"attribute-key\",\n \"value\": \"attribute-value\"\n }\n ],\n)\n```\n\n##### Limitation\n\n- You can upload up to a size of 20 MB.\n\n#### Create Integrated Image Classification Task\n\nCreate a new classification task by integrated image.\n(Project storage setting should be configured in advance.)\n\n```python\ntask_id = client.create_integrated_image_classification_task(\n project=\"YOUR_PROJECT_SLUG\",\n file_path=\"<integrated-storage-dir>/sample.jpg\",\n storage_type=\"gcp\",\n)\n```\n\n#### Find Task\n\nFind a single task.\n\n```python\ntask = client.find_image_classification_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntasks = client.find_image_classification_task_by_name(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks. (Up to 1000 tasks)\n\n```python\ntasks = client.get_image_classification_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Update Tasks\n\nUpdate a single task.\n\n```python\ntask_id = client.update_image_classification_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"],\n attributes=[\n {\n \"key\": \"attribute-key\",\n \"value\": \"attribute-value\"\n }\n ],\n)\n```\n\n#### Response\n\nExample of a single image classification task object\n\n```python\n{\n \"id\": \"YOUR_TASK_ID\",\n \"name\": \"cat.jpg\",\n \"width\": 100, # image width\n \"height\": 100, # image height\n \"url\": \"YOUR_TASK_URL\",\n \"status\": \"registered\",\n \"externalStatus\": \"registered\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n \"tags\": [],\n \"assignee\": \"ASSIGNEE_NAME\",\n \"reviewer\": \"REVIEWER_NAME\",\n \"externalAssignee\": \"EXTERNAL_ASSIGNEE_NAME\",\n \"externalReviewer\": \"EXTERNAL_REVIEWER_NAME\",\n \"attributes\": [\n {\n \"key\": \"kind\",\n \"name\": \"Kind\",\n \"type\": \"text\",\n \"value\": \"Scottish field\"\n }\n ],\n \"createdAt\": \"2021-02-22T11:25:27.158Z\",\n \"updatedAt\": \"2021-02-22T11:25:27.158Z\"\n}\n```\n\n### Sequential Image\n\nSupported following project types:\n\n- Sequential Image - Bounding Box\n- Sequential Image - Polygon\n- Sequential Image - Keypoint\n- Sequential Image - Line\n- Sequential Image - Segmentation\n\n#### Create Task\n\nCreate a new task.\n\n```python\ntask = client.create_sequential_image_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample\",\n folder_path=\"./sample\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n annotations=[{\n \"type\": \"segmentation\",\n \"value\": \"annotation-value\",\n \"attributes\": [\n {\n \"key\": \"attribute-key\",\n \"value\": \"attribute-value\"\n }\n ],\n \"content\": \"01.jpg\",\n \"points\": [[[\n 100,\n 100,\n 300,\n 100,\n 300,\n 300,\n 100,\n 300,\n 100,\n 100\n ]]] # clockwise rotation\n }]\n)\n```\n\n##### Limitation\n\n- You can upload up to a size of 20 MB.\n- You can upload up to a total size of 512 MB.\n- You can upload up to 250 files in total.\n\n#### Find Task\n\nFind a single task.\n\n```python\ntask = client.find_sequential_image_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntasks = client.find_sequential_image_task_by_name(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks.\n\n```python\ntasks = client.get_sequential_image_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Update Task\n\nUpdate a single task.\n\n```python\ntask_id = client.update_sequential_image_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"],\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n annotations=[\n {\n \"type\": \"bbox\",\n \"value\": \"cat\",\n \"content\": \"cat1.jpg\",\n \"attributes\": [\n { \"key\": \"key\", \"value\": \"value1\" }\n ],\n \"points\": [990, 560, 980, 550]\n }\n ]\n)\n```\n\n#### Response\n\nExample of a single task object\n\n```python\n{\n \"id\": \"YOUR_TASK_ID\",\n \"name\": \"cat.jpg\",\n \"contents\": [\n {\n \"name\": \"content-name\",\n \"url\": \"content-url\",\n \"width\": 100,\n \"height\": 100,\n }\n ],\n \"status\": \"registered\",\n \"externalStatus\": \"registered\",\n \"priority\": 10,\n \"tags\": [],\n \"assignee\": \"ASSIGNEE_NAME\",\n \"reviewer\": \"REVIEWER_NAME\",\n \"externalAssignee\": \"EXTERNAL_ASSIGNEE_NAME\",\n \"externalReviewer\": \"EXTERNAL_REVIEWER_NAME\",\n \"annotations\": [\n {\n \"content\": \"content-name\"\n \"attributes\": [],\n \"color\": \"#b36d18\",\n \"points\": [[[\n 100,\n 100,\n 300,\n 100,\n 300,\n 300,\n 100,\n 300,\n 100,\n 100\n ]]]\n \"title\": \"Cat\",\n \"type\": \"bbox\",\n \"value\": \"cat\"\n }\n ],\n \"createdAt\": \"2021-02-22T11:25:27.158Z\",\n \"updatedAt\": \"2021-02-22T11:25:27.158Z\"\n}\n```\n\n### Video\n\nSupported following project types:\n\n- Video - Bounding Box\n- Video - Keypoint\n- Video - Line\n\n#### Create Task\n\nCreate a new task.\n\n```python\ntask_id = client.create_video_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.mp4\",\n file_path=\"./sample.mp4\"\n)\n```\n\nCreate a new task with pre-defined annotations. (Class should be configured on your project in advance)\n\n```python\ntask_id = client.create_video_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.mp4\",\n file_path=\"./sample.mp4\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n annotations=[{\n \"type\": \"bbox\",\n \"value\": \"person\",\n \"points\": {\n \"1\": { # number of frame\n \"value\": [\n 100, # top-left x\n 100, # top-left y\n 200, # bottom-right x\n 200 # bottom-right y\n ],\n # Make sure to set `autogenerated` False for the first and last frame. \"1\" and \"3\" frames in this case.\n # Otherwise, annotation is auto-completed for rest of frames when you edit.\n \"autogenerated\": False\n },\n \"2\": {\n \"value\": [\n 110,\n 110,\n 220,\n 220\n ],\n \"autogenerated\": True\n },\n \"3\": {\n \"value\": [\n 120,\n 120,\n 240,\n 240\n ],\n \"autogenerated\": False\n }\n }\n }]\n)\n```\n\n##### Limitation\n\n- You can upload up to a size of 250 MB.\n- You can upload only videos with H.264 encoding.\n- You can upload only MP4 file format.\n\n#### Find Task\n\nFind a single task.\n\n```python\ntask = client.find_video_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntasks = client.find_video_task_by_name(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks. (Up to 10 tasks)\n\n```python\ntasks = client.get_video_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Update Task\n\nUpdate a single task.\n\n```python\ntask_id = client.update_video_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"],\n annotations=[{\n \"type\": \"bbox\",\n \"value\": \"bird\",\n \"points\": {\n \"1\": {\n \"value\": [\n 100,\n 100,\n 200,\n 200\n ],\n \"autogenerated\": False\n },\n \"2\": {\n \"value\": [\n 110,\n 110,\n 220,\n 220\n ],\n \"autogenerated\": True\n },\n \"3\": {\n \"value\": [\n 120,\n 120,\n 240,\n 240\n ],\n \"autogenerated\": False\n }\n }\n }]\n)\n```\n\n#### Integrate Video\n\nThis function is alpha version. It is subject to major changes in the future.\n\nIntegration is possible only when tasks are registered from the objects divided by the dataset.\n\nIn the case of a task divided under the following conditions.\n\n- Dataset slug: `video`\n- Object name: `cat.mp4`\n- Split count: `3`\n\nObjects are registered in the data set in the following form.\n\n- video/cat/1.mp4\n- video/cat/2.mp4\n- video/cat/3.mp4\n\nIn this case, SPLIT_VIDEO_TASK_NAME_PREFIX specifies `video/cat`.\n\n```python\ntask = client.find_integrated_video_task_by_prefix(\n project=\"YOUR_PROJECT_SLUG\",\n prefix=\"SPLIT_VIDEO_TASK_NAME_PREFIX\",\n)\n```\n\n#### Response\n\nExample of a single vide task object\n\n```python\n{\n \"id\": \"YOUR_TASK_ID\",\n \"name\": \"cat.jpg\",\n \"width\": 100, # image width\n \"height\": 100, # image height\n \"fps\": 30.0, # frame per seconds\n \"frameCount\": 480, # total frame count of video\n \"duration\": 16.0, # total duration of video\n \"url\": \"YOUR_TASK_URL\",\n \"status\": \"registered\",\n \"externalStatus\": \"registered\",\n \"priority\": 10,\n \"tags\": [],\n \"assignee\": \"ASSIGNEE_NAME\",\n \"reviewer\": \"REVIEWER_NAME\",\n \"externalAssignee\": \"EXTERNAL_ASSIGNEE_NAME\",\n \"externalReviewer\": \"EXTERNAL_REVIEWER_NAME\",\n \"annotations\": [\n {\n \"attributes\": [],\n \"color\": \"#b36d18\",\n \"points\": {\n \"1\": { # number of frame\n \"value\": [\n 100, # top-left x\n 100, # top-left y\n 200, # bottom-right x\n 200 # bottom-right y\n ],\n \"autogenerated\": False # False when annotated manually. True when auto-generated by system.\n },\n \"2\": {\n \"value\": [\n 110,\n 110,\n 220,\n 220\n ],\n \"autogenerated\": True\n },\n \"3\": {\n \"value\": [\n 120,\n 120,\n 240,\n 240\n ],\n \"autogenerated\": False\n }\n },\n \"title\": \"Cat\",\n \"type\": \"bbox\",\n \"value\": \"cat\"\n }\n ],\n \"createdAt\": \"2021-02-22T11:25:27.158Z\",\n \"updatedAt\": \"2021-02-22T11:25:27.158Z\"\n}\n```\n\n### Video Classification\n\nSupported following project types:\n\n- Video - Classification (Single)\n\n#### Create Task\n\nCreate a new task.\n\n```python\ntask_id = client.create_video_classification_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.mp4\",\n file_path=\"./sample.mp4\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n attributes=[\n {\n \"key\": \"attribute-key\",\n \"value\": \"attribute-value\"\n }\n ],\n)\n```\n\n##### Limitation\n\n- You can upload up to a size of 250 MB.\n\n#### Find Task\n\nFind a single task.\n\n```python\ntask = client.find_video_classification_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntasks = client.find_video_classification_task_by_name(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks. (Up to 1000 tasks)\n\n```python\ntasks = client.get_video_classification_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Update Tasks\n\nUpdate a single task.\n\n```python\ntask_id = client.update_video_classification_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"],\n attributes=[\n {\n \"key\": \"attribute-key\",\n \"value\": \"attribute-value\"\n }\n ],\n)\n```\n\n### Text\n\nSupported following project types:\n\n- Text - NER\n\n#### Create Task\n\nCreate a new task.\n\n```python\ntask_id = client.create_text_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.txt\",\n file_path=\"./sample.txt\"\n)\n```\n\nCreate a new task with pre-defined annotations. (Class should be configured on your project in advance)\n\n```python\ntask_id = client.create_text_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.txt\",\n file_path=\"./sample.txt\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n annotations=[{\n \"type\": \"ner\",\n \"value\": \"person\",\n \"start\": 0,\n \"end\": 10,\n \"text\": \"1234567890\"\n }]\n)\n```\n\n##### Limitation\n\n- You can upload up to a size of 2 MB.\n\n#### Find Task\n\nFind a single task.\n\n```python\ntask = client.find_text_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntasks = client.find_text_task_by_name(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks. (Up to 10 tasks)\n\n```python\ntasks = client.get_text_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Update Task\n\nUpdate a single task.\n\n```python\ntask_id = client.update_text_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"],\n annotations=[{\n \"type\": \"bbox\",\n \"value\": \"bird\",\n \"start\": 0,\n \"end\": 10,\n \"text\": \"0123456789\"\n }]\n)\n```\n\n#### Response\n\nExample of a single text task object\n\n```python\n{\n \"id\": \"YOUR_TASK_ID\",\n \"name\": \"cat.txt\",\n \"url\": \"YOUR_TASK_URL\",\n \"status\": \"registered\",\n \"externalStatus\": \"registered\",\n \"priority\": 10,\n \"tags\": [],\n \"assignee\": \"ASSIGNEE_NAME\",\n \"reviewer\": \"REVIEWER_NAME\",\n \"externalAssignee\": \"EXTERNAL_ASSIGNEE_NAME\",\n \"externalReviewer\": \"EXTERNAL_REVIEWER_NAME\",\n \"annotations\": [\n {\n \"attributes\": [],\n \"color\": \"#b36d18\",\n \"text\": \"0123456789\",\n \"start\": 0,\n \"end\": 10,\n \"title\": \"Cat\",\n \"type\": \"ner\",\n \"value\": \"cat\"\n }\n ],\n \"createdAt\": \"2021-02-22T11:25:27.158Z\",\n \"updatedAt\": \"2021-02-22T11:25:27.158Z\"\n}\n```\n\n### Text Classification\n\nSupported following project types:\n\n- Text - Classification (Single)\n\n#### Create Task\n\nCreate a new task.\n\n```python\ntask_id = client.create_text_classification_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.txt\",\n file_path=\"./sample.txt\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n attributes=[\n {\n \"key\": \"attribute-key\",\n \"value\": \"attribute-value\"\n }\n ],\n)\n```\n\n##### Limitation\n\n- You can upload up to a size of 2 MB.\n\n#### Find Task\n\nFind a single task.\n\n```python\ntask = client.find_text_classification_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntasks = client.find_text_classification_task_by_name(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks. (Up to 1000 tasks)\n\n```python\ntasks = client.get_text_classification_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Update Tasks\n\nUpdate a single task.\n\n```python\ntask_id = client.update_text_classification_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"],\n attributes=[\n {\n \"key\": \"attribute-key\",\n \"value\": \"attribute-value\"\n }\n ],\n)\n```\n\n### Audio\n\nSupported following project types:\n\n- Audio - Segmentation\n\n#### Create Task\n\nCreate a new task.\n\n```python\ntask_id = client.create_audio_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.mp3\",\n file_path=\"./sample.mp3\"\n)\n```\n\nCreate a new task with pre-defined annotations. (Class should be configured on your project in advance)\n\n```python\ntask_id = client.create_audio_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.mp3\",\n file_path=\"./sample.mp3\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n annotations=[{\n \"type\": \"segmentation\",\n \"value\": \"person\",\n \"start\": 0.4,\n \"end\": 0.5\n }]\n)\n```\n\n##### Limitation\n\n- You can upload up to a size of 120 MB.\n\n#### Find Task\n\nFind a single task.\n\n```python\ntask = client.find_audio_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntasks = client.find_audio_task_by_name(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks. (Up to 10 tasks)\n\n```python\ntasks = client.get_audio_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Update Task\n\nUpdate a single task.\n\n```python\ntask_id = client.update_audio_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"],\n annotations=[{\n \"type\": \"segmentation\",\n \"value\": \"bird\",\n \"start\": 0.4,\n \"end\": 0.5\n }]\n)\n```\n\n#### Response\n\nExample of a single audio task object\n\n```python\n{\n \"id\": \"YOUR_TASK_ID\",\n \"name\": \"cat.mp3\",\n \"url\": \"YOUR_TASK_URL\",\n \"status\": \"registered\",\n \"externalStatus\": \"registered\",\n \"priority\": 10,\n \"tags\": [],\n \"assignee\": \"ASSIGNEE_NAME\",\n \"reviewer\": \"REVIEWER_NAME\",\n \"externalAssignee\": \"EXTERNAL_ASSIGNEE_NAME\",\n \"externalReviewer\": \"EXTERNAL_REVIEWER_NAME\",\n \"annotations\": [\n {\n \"attributes\": [],\n \"color\": \"#b36d18\",\n \"start\": 0.4,\n \"end\": 0.5,\n \"title\": \"Bird\",\n \"type\": \"segmentation\",\n \"value\": \"bird\"\n }\n ],\n \"createdAt\": \"2021-02-22T11:25:27.158Z\",\n \"updatedAt\": \"2021-02-22T11:25:27.158Z\"\n}\n```\n\n#### Integrate Task\n\nThis function is alpha version. It is subject to major changes in the future.\n\nIntegration is possible only when tasks are registered from the objects divided by the dataset.\n\nIn the case of a task divided under the following conditions.\n\n- Dataset slug: `audio`\n- Object name: `voice.mp3`\n- Split count: `3`\n\nObjects are registered in the data set in the following form.\n\n- audio/voice/1.mp3\n- audio/voice/2.mp3\n- audio/voice/3.mp3\n\nAnnotations 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.\n\nIn this case, SPLIT_AUDIO_TASK_NAME_PREFIX specifies `audio/voice`.\n\n```python\ntask = client.find_integrated_audio_task_by_prefix(\n project=\"YOUR_PROJECT_SLUG\",\n prefix=\"SPLIT_AUDIO_TASK_NAME_PREFIX\",\n)\n```\n\n##### Response\n\nExample of a integrated audio task object\n\n```python\n{\n 'name': 'audio/voice.mp3',\n \"annotations\": [\n {\n \"attributes\": [],\n \"color\": \"#b36d18\",\n \"start\": 0.4,\n \"end\": 0.5,\n \"title\": \"Bird\",\n \"type\": \"segmentation\",\n \"value\": \"bird\"\n }\n ],\n}\n```\n\n### Audio Classification\n\nSupported following project types:\n\n- Audio - Classification (Single)\n\n#### Create Task\n\nCreate a new task.\n\n```python\ntask_id = client.create_audio_classification_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.mp3\",\n file_path=\"./sample.mp3\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n attributes=[\n {\n \"key\": \"attribute-key\",\n \"value\": \"attribute-value\"\n }\n ],\n)\n```\n\n##### Limitation\n\n- You can upload up to a size of 120 MB.\n\n#### Find Task\n\nFind a single task.\n\n```python\ntask = client.find_audio_classification_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntasks = client.find_audio_classification_task_by_name(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks. (Up to 1000 tasks)\n\n```python\ntasks = client.get_audio_classification_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Update Tasks\n\nUpdate a single task.\n\n```python\ntask_id = client.update_audio_classification_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"],\n attributes=[\n {\n \"key\": \"attribute-key\",\n \"value\": \"attribute-value\"\n }\n ],\n)\n```\n\n### PCD\n\nSupported following project types:\n\n- PCD - Cuboid\n- PCD - Segmentation\n\n#### Create Task\n\nCreate a new task.\n\n```python\ntask_id = client.create_pcd_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.pcd\",\n file_path=\"./sample.pcd\"\n)\n```\n\nCreate a new task with pre-defined annotations. (Class should be configured on your project in advance)\n\nAnnotation Type: cuboid\n\n```python\ntask_id = client.create_pcd_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.pcd\",\n file_path=\"./sample.pcd\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n annotations=[\n {\n \"type\": \"cuboid\",\n \"value\": \"car\",\n \"points\": [ # For cuboid, it is a 9-digit number.\n 1, # Coordinate X\n 2, # Coordinate Y\n 3, # Coordinate Z\n 1, # Rotation x\n 1, # Rotation Y\n 1, # Rotation Z\n 2, # Length X\n 2, # Length Y\n 2 # Length Z\n ],\n }\n ],\n)\n```\n\nAnnotation Type: segmentation\n\n```python\ntask_id = client.create_pcd_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.pcd\",\n file_path=\"./sample.pcd\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n annotations=[\n {\n \"type\": \"segmentation\",\n \"value\": \"car\",\n \"points\": [1, 2, 3, 4, 5], # For segmentation, it is an arbitrary numeric array.\n }\n ],\n)\n```\n\n##### Limitation\n\n- You can upload up to a size of 30 MB.\n\n#### Find Task\n\nFind a single task.\n\n```python\ntask = client.find_pcd_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntasks = client.find_pcd_task_by_name(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks. (Up to 1000 tasks)\n\n```python\ntasks = client.get_pcd_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Update Task\n\nUpdate a single task.\n\n```python\ntask_id = client.update_pcd_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"],\n annotations=[\n {\n \"type\": \"cuboid\",\n \"value\": \"car\",\n \"points\": [ # For cuboid, it is a 9-digit number.\n 1, # Coordinate X\n 2, # Coordinate Y\n 3, # Coordinate Z\n 1, # Rotation x\n 1, # Rotation Y\n 1, # Rotation Z\n 2, # Length X\n 2, # Length Y\n 2 # Length Z\n ],\n }\n ],\n)\n```\n\n#### Response\n\nExample of a single PCD task object\n\n```python\n{\n \"id\": \"YOUR_TASK_ID\",\n \"name\": \"sample.pcd\",\n \"url\": \"YOUR_TASK_URL\",\n \"status\": \"registered\",\n \"externalStatus\": \"registered\",\n \"priority\": 10,\n \"tags\": [\"tag1\", \"tag2\"],\n \"assignee\": \"ASSIGNEE_NAME\",\n \"reviewer\": \"REVIEWER_NAME\",\n \"approver\": \"APPROVER_NAME\",\n \"externalAssignee\": \"EXTERNAL_ASSIGNEE_NAME\",\n \"externalReviewer\": \"EXTERNAL_REVIEWER_NAME\",\n \"externalApprover\": \"EXTERNAL_APPROVER_NAME\",\n \"annotations\": [\n {\n \"attributes\": [],\n \"color\": \"#b36d18\",\n \"title\": \"Car\",\n \"type\": \"segmentation\",\n \"value\": \"car\",\n \"points\": [1, 2, 3, 1, 1, 1, 2, 2, 2],\n }\n ],\n \"createdAt\": \"2021-02-22T11:25:27.158Z\",\n \"updatedAt\": \"2021-02-22T11:25:27.158Z\"\n}\n```\n\n### Sequential PCD\n\nSupported following project types:\n\n- Sequential PCD - Cuboid\n\n#### Create Tasks\n\nCreate a new task.\n\n```python\ntask_id = client.create_sequential_pcd_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"drive_record\",\n folder_path=\"./drive_record/\", # Path where sequence PCD files are directory\n)\n```\n\nThe order of frames is determined by the ascending order of PCD file names located in the specified directory.\nFile names are optional, but we recommend naming them in a way that makes the order easy to understand.\n\n```\n./drive_record/\n\u251c\u2500\u2500 0001.pcd => frame 1\n\u251c\u2500\u2500 0002.pcd => frame 2\n...\n\u2514\u2500\u2500 xxxx.pcd => frame n\n```\n\nCreate a new task with pre-defined annotations. (Class should be configured on your project in advance)\n\n```python\ntask_id = client.create_sequential_pcd_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"drive_record\",\n folder_path=\"./drive_record/\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n annotations=[\n {\n \"type\": \"cuboid\", # annotation class type\n \"value\": \"human\", # annotation class value\n \"points\": {\n \"1\": { # number of frame\n \"value\": [ # For cuboid, it is a 9-digit number.\n 1, # Coordinate X\n 2, # Coordinate Y\n 3, # Coordinate Z\n 1, # Rotation x\n 1, # Rotation Y\n 1, # Rotation Z\n 2, # Length X\n 2, # Length Y\n 2 # Length Z\n ],\n # Make sure to set `autogenerated` False for the first and last frame. \"1\" and \"3\" frames in this case.\n # Otherwise, annotation is auto-completed for rest of frames when you edit.\n \"autogenerated\": False,\n },\n \"2\": {\n \"value\": [\n 11,\n 12,\n 13,\n 11,\n 11,\n 11,\n 12,\n 12,\n 12\n ],\n \"autogenerated\": True,\n },\n \"3\": {\n \"value\": [\n 21,\n 22,\n 23,\n 21,\n 21,\n 21,\n 22,\n 22,\n 22\n ],\n \"autogenerated\": False,\n },\n },\n },\n ]\n)\n```\n\n##### Limitation\n\nYou can upload up to a size of 30 MB per file.\n\n#### Find Tasks\n\nFind a single task.\n\n```python\ntask = client.find_sequential_pcd_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntask = client.find_sequential_pcd_task(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks. (Up to 10 tasks)\n\n```python\ntasks = client.get_sequential_pcd_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Update Tasks\n\nUpdate a single task.\n\n```python\ntask_id = client.update_sequential_pcd_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"],\n annotations=[\n {\n \"type\": \"cuboid\",\n \"value\": \"human\",\n \"points\": {\n \"1\": {\n \"value\": [\n 1,\n 2,\n 3,\n 1,\n 1,\n 1,\n 2,\n 2,\n 2\n ],\n \"autogenerated\": False,\n },\n \"2\": {\n \"value\": [\n 11,\n 12,\n 13,\n 11,\n 11,\n 11,\n 12,\n 12,\n 12\n ],\n \"autogenerated\": False,\n },\n },\n },\n ]\n)\n```\n\n#### Response\n\nExample of a single Sequential PCD task object\n\n```python\n{\n \"id\": \"YOUR_TASK_ID\",\n \"name\": \"YOUR_TASK_NAME\",\n \"status\": \"registered\",\n \"externalStatus\": \"registered\",\n \"priority\": 10,\n \"annotations\": [\n {\n \"id\": \"YOUR_TASK_ANNOTATION_ID\",\n \"type\": \"cuboid\",\n \"title\": \"human\",\n \"value\": \"human\",\n \"color\": \"#4bdd62\",\n \"attributes\": [],\n \"points\": {\n \"1\": {\n \"value\": [2.61, 5.07, 0, 0, 0, 0, 2, 2, 2],\n \"autogenerated\": False,\n },\n \"2\": {\n \"value\": [2.61, 5.07, 0, 0, 0, 0, 2, 2, 2],\n \"autogenerated\": True,\n },\n \"3\": {\n \"value\": [2.61, 5.07, 0, 0, 0, 0, 2, 2, 2],\n \"autogenerated\": False,\n },\n },\n },\n {\n \"id\": \"YOUR_TASK_ANNOTATION_ID\",\n \"type\": \"cuboid\",\n \"title\": \"building\",\n \"value\": \"building\",\n \"color\": \"#223543\",\n \"attributes\": [],\n \"points\": {\n \"1\": {\n \"value\": [2.8, -8.64, 0.15, 0, 0, 0, 4.45, 4.2, 2],\n \"autogenerated\": False,\n },\n \"2\": {\n \"value\": [2.8, -8.64, 0.15, 0, 0, 0, 4.45, 4.2, 2],\n \"autogenerated\": True,\n },\n \"3\": {\n \"value\": [2.8, -8.64, 0.15, 0, 0, 0, 4.45, 4.2, 2],\n \"autogenerated\": True,\n },\n \"4\": {\n \"value\": [2.8, -8.64, 0.15, 0, 0, 0, 4.45, 4.2, 2],\n \"autogenerated\": True,\n },\n \"5\": {\n \"value\": [2.8, -8.64, 0.15, 0, 0, 0, 4.45, 4.2, 2],\n \"autogenerated\": True,\n },\n },\n },\n ],\n \"tags\": [],\n \"assignee\": None,\n \"reviewer\": None,\n \"approver\": None,\n \"externalAssignee\": None,\n \"externalReviewer\": None,\n \"externalApprover\": None,\n \"createdAt\": \"2023-03-24T08:39:08.524Z\",\n \"updatedAt\": \"2023-03-24T08:39:08.524Z\",\n}\n```\n\n### DICOM\n\nSupported following project types:\n\n- DICOM -Bounding Box\n\n#### Create Task\n\nCreate a new task.\nYou should receive task import history status [Find Task Import History](#find-task-import-history).\nOnce you receive the status completed, you can get the task.\n\n```python\nhistory = client.create_dicom_task(\n project=\"YOUR_PROJECT_SLUG\",\n file_path=\"./sample.zip\"\n)\n```\n\n#### Limitation\n\n- You can upload up to a size of 2 GB per file.\n\n#### Find Task\n\nFind a single task.\n\n```python\ntask = client.find_dicom_task(task_id=\"YOUR_TASK_ID\")\n```\n\nFind a single task by name.\n\n```python\ntasks = client.find_dicom_task_by_name(project=\"YOUR_PROJECT_SLUG\", task_name=\"YOUR_TASK_NAME\")\n```\n\n#### Get Tasks\n\nGet tasks. (Up to 1000 tasks)\n\n```python\ntasks = client.get_dicom_tasks(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Update Tasks\n\nUpdate a single task.\n\n```python\ntask_id = client.update_dicom_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n assignee=\"USER_SLUG\",\n tags=[\"tag1\", \"tag2\"]\n)\n```\n\n#### Response\n\nExample of a single dicom task object\n\n```python\n{\n \"id\": \"YOUR_TASK_ID\",\n \"name\": \"dicom.zip\",\n \"url\": \"YOUR_TASK_URL\",\n 'height': 512,\n 'width': 512,\n \"status\": \"registered\",\n \"externalStatus\": \"registered\",\n \"tags\": [],\n \"assignee\": \"ASSIGNEE_NAME\",\n \"reviewer\": \"REVIEWER_NAME\",\n \"externalAssignee\": \"EXTERNAL_ASSIGNEE_NAME\",\n \"externalReviewer\": \"EXTERNAL_REVIEWER_NAME\",\n \"annotations\": [\n {\n \"attributes\": [],\n \"color\": \"#b36d18\",\n \"contentId\": \"CONTENT_ID\"\n \"points\": [100, 200, 100, 200],\n \"title\": \"Heart\",\n \"type\": \"bbox\",\n \"value\": \"heart\"\n }\n ],\n \"createdAt\": \"2021-02-22T11:25:27.158Z\",\n \"updatedAt\": \"2021-02-22T11:25:27.158Z\"\n}\n```\n\n### Common\n\nAPIs for update and delete and count are same over all tasks.\n\n#### Update Task\n\nUpdate a single task status, tags and assignee.\n\n```python\ntask_id = client.update_task(\n task_id=\"YOUR_TASK_ID\",\n status=\"approved\",\n priority=10, # (optional) none: 0, low: 10, medium: 20, high: 30\n tags=[\"tag1\", \"tag2\"],\n assignee=\"USER_SLUG\"\n)\n```\n\n#### Delete Task\n\nDelete a single task.\n\n```python\nclient.delete_task(task_id=\"YOUR_TASK_ID\")\n```\n\n#### Delete Task Annotation\n\nDelete annotations in a task.\n\n```python\nclient.delete_task_annotations(task_id=\"YOUR_TASK_ID\")\n```\n\n#### Get Tasks Id and Name map\n\n```python\nid_name_map = client.get_task_id_name_map(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Count Task\n\n```python\ntask_count = client.count_tasks(\n project=\"YOUR_PROJECT_SLUG\",\n status=\"approved\", # status can be 'pending', 'registered', 'completed', 'skipped', 'reviewed' 'sent_back', 'approved', 'declined'\n tags=[\"tag1\", \"tag2\"] # up to 10 tags\n)\n```\n\n#### Create Task from S3\n\nTask creation from S3.\n\n- Support project\n\n - Image\n - Video\n - Audio\n - Text\n\n- To use it, you need to set the contents of the following link.\n <https://docs.fastlabel.ai/docs/integrations-aws-s3>\n\n- Setup AWS S3 properties\n\n```python\nstatus = client.update_aws_s3_storage(\n project=\"YOUR_PROJECT_SLUG\",\n bucket_name=\"S3_BUCKET_NAME\",\n bucket_region=\"S3_REGIONS\",\n)\n```\n\n- Run create task from AWS S3\n\n```python\nhistory = client.create_task_from_aws_s3(\n project=\"YOUR_PROJECT_SLUG\",\n)\n```\n\n- Get AWS S3 import status\n\n```python\nhistory = client.get_aws_s3_import_status_by_project(\n project=\"YOUR_PROJECT_SLUG\",\n)\n```\n\n#### Find Task Import History\n\nFind a single history.\n\n```python\nhistory = client.find_history(history_id=\"YOUR_HISTORY_ID\")\n```\n\n#### Get Task Import Histories\n\n```python\nhistories = client.get_histories(project=\"YOUR_PROJECT_SLUG\")\n```\n\n#### Response\n\nExample of a single history object\n\n```python\n{\n \"id\": \"YOUR_HISTORY_ID\",\n \"storageType\": \"zip\",\n \"status\": \"running\",\n \"createdAt\": \"2021-02-22T11:25:27.158Z\",\n \"updatedAt\": \"2021-02-22T11:25:27.158Z\"\n}\n```\n\n## Annotation\n\n### Create Annotation\n\nCreate a new annotation.\n\n```python\nannotation_id = client.create_annotation(\n project=\"YOUR_PROJECT_SLUG\", type=\"bbox\", value=\"cat\", title=\"Cat\")\n```\n\nCreate a new annotation with color and attributes.\n\n```python\nattributes = [\n {\n \"type\": \"text\",\n \"name\": \"Kind\",\n \"key\": \"kind\"\n },\n {\n \"type\": \"select\",\n \"name\": \"Size\",\n \"key\": \"size\",\n \"options\": [ # select, radio and checkbox type requires options\n {\n \"title\": \"Large\",\n \"value\": \"large\"\n },\n {\n \"title\": \"Small\",\n \"value\": \"small\"\n },\n ]\n },\n]\nannotation_id = client.create_annotation(\n project=\"YOUR_PROJECT_SLUG\", type=\"bbox\", value=\"cat\", title=\"Cat\", color=\"#FF0000\", attributes=attributes)\n```\n\nCreate a new classification annotation.\n\n```python\nannotation_id = client.create_classification_annotation(\n project=\"YOUR_PROJECT_SLUG\", attributes=attributes)\n```\n\n### Find Annotation\n\nFind an annotation.\n\n```python\nannotation = client.find_annotation(annotation_id=\"YOUR_ANNOTATION_ID\")\n```\n\nFind an annotation by value.\n\n```python\nannotation = client.find_annotation_by_value(project=\"YOUR_PROJECT_SLUG\", value=\"cat\")\n```\n\nFind an annotation by value in classification project.\n\n```python\nannotation = client.find_annotation_by_value(\n project=\"YOUR_PROJECT_SLUG\", value=\"classification\") # \"classification\" is fixed value\n```\n\n### Get Annotations\n\nGet annotations. (Up to 1000 annotations)\n\n```python\nannotations = client.get_annotations(project=\"YOUR_PROJECT_SLUG\")\n```\n\n### Response\n\nExample of an annotation object\n\n```python\n{\n \"id\": \"YOUR_ANNOTATION_ID\",\n \"type\": \"bbox\",\n \"value\": \"cat\",\n \"title\": \"Cat\",\n \"color\": \"#FF0000\",\n \"order\": 1,\n \"vertex\": 0,\n \"attributes\": [\n {\n \"id\": \"YOUR_ATTRIBUTE_ID\",\n \"key\": \"kind\",\n \"name\": \"Kind\",\n \"options\": [],\n \"order\": 1,\n \"type\": \"text\",\n \"value\": \"\"\n },\n {\n \"id\": \"YOUR_ATTRIBUTE_ID\",\n \"key\": \"size\",\n \"name\": \"Size\",\n \"options\": [\n {\"title\": \"Large\", \"value\": \"large\"},\n {\"title\": \"Small\", \"value\": \"small\"}\n ],\n \"order\": 2,\n \"type\": \"select\",\n \"value\": \"\"\n }\n ],\n \"createdAt\": \"2021-05-25T05:36:50.459Z\",\n \"updatedAt\": \"2021-05-25T05:36:50.459Z\"\n}\n```\n\nExample when the annotation type is Pose Estimation\n\n```python\n{\n \"id\":\"b12c81c3-ddec-4f98-b41b-cef7f77d26a4\",\n \"type\":\"pose_estimation\",\n \"title\":\"jesture\",\n \"value\":\"jesture\",\n \"color\":\"#10c414\",\n \"order\":1,\n \"attributes\": [],\n \"keypoints\":[\n {\n \"id\":\"b03ea998-a2f1-4733-b7e9-78cdf73bd38a\",\n \"name\":\"\u982d\",\n \"key\":\"head\",\n \"color\":\"#0033CC\",\n \"edges\":[\n \"195f5852-c516-498b-b392-24513ce3ea67\",\n \"06b5c968-1786-4d75-a719-951e915e5557\"\n ],\n \"value\": []\n },\n {\n \"id\":\"195f5852-c516-498b-b392-24513ce3ea67\",\n \"name\":\"\u53f3\u80a9\",\n \"key\":\"right_shoulder\",\n \"color\":\"#0033CC\",\n \"edges\":[\n \"b03ea998-a2f1-4733-b7e9-78cdf73bd38a\"\n ],\n \"value\": []\n },\n {\n \"id\":\"06b5c968-1786-4d75-a719-951e915e5557\",\n \"name\":\"\u5de6\u80a9\",\n \"key\":\"left_shoulder\",\n \"color\":\"#0033CC\",\n \"edges\":[\n \"b03ea998-a2f1-4733-b7e9-78cdf73bd38a\"\n ],\n \"value\": []\n }\n ],\n \"createdAt\":\"2021-11-21T09:59:46.714Z\",\n \"updatedAt\":\"2021-11-21T09:59:46.714Z\"\n}\n```\n\n### Update Annotation\n\nUpdate an annotation.\n\n```python\nannotation_id = client.update_annotation(\n annotation_id=\"YOUR_ANNOTATION_ID\", value=\"cat2\", title=\"Cat2\", color=\"#FF0000\")\n```\n\nUpdate an annotation with attributes.\n\n```python\nattributes = [\n {\n \"id\": \"YOUR_ATTRIBUTE_ID\", # check by sdk get methods\n \"type\": \"text\",\n \"name\": \"Kind2\",\n \"key\": \"kind2\"\n },\n {\n \"id\": \"YOUR_ATTRIBUTE_ID\",\n \"type\": \"select\",\n \"name\": \"Size2\",\n \"key\": \"size2\",\n \"options\": [\n {\n \"title\": \"Large2\",\n \"value\": \"large2\"\n },\n {\n \"title\": \"Small2\",\n \"value\": \"small2\"\n },\n ]\n },\n]\nannotation_id = client.update_annotation(\n annotation_id=\"YOUR_ANNOTATION_ID\", value=\"cat2\", title=\"Cat2\", color=\"#FF0000\", attributes=attributes)\n```\n\nUpdate a classification annotation.\n\n```python\nannotation_id = client.update_classification_annotation(\n project=\"YOUR_PROJECT_SLUG\", attributes=attributes)\n```\n\n### Delete Annotation\n\nDelete an annotation.\n\n```python\nclient.delete_annotation(annotation_id=\"YOUR_ANNOTATION_ID\")\n```\n\n## Project\n\n### Create Project\n\nCreate a new project.\n\n```python\nproject_id = client.create_project(\n type=\"image_bbox\", name=\"ImageNet\", slug=\"image-net\")\n```\n\n### Find Project\n\nFind a project.\n\n```python\nproject = client.find_project(project_id=\"YOUR_PROJECT_ID\")\n```\n\nFind a project by slug.\n\n```python\nproject = client.find_project_by_slug(slug=\"YOUR_PROJECT_SLUG\")\n```\n\n### Get Projects\n\nGet projects. (Up to 1000 projects)\n\n```python\nprojects = client.get_projects()\n```\n\n### Response\n\nExample of a project object\n\n```python\n{\n \"id\": \"YOUR_PROJECT_ID\",\n \"type\": \"image_bbox\",\n \"slug\": \"YOUR_PROJECT_SLUG\",\n \"name\": \"YOUR_PROJECT_NAME\",\n \"isPixel\": False,\n \"jobSize\": 10,\n \"status\": \"active\",\n \"createdAt\": \"2021-04-20T03:20:41.427Z\",\n \"updatedAt\": \"2021-04-20T03:20:41.427Z\",\n}\n```\n\n### Update Project\n\nUpdate a project.\n\n```python\nproject_id = client.update_project(\n project_id=\"YOUR_PROJECT_ID\", name=\"NewImageNet\", slug=\"new-image-net\", job_size=20)\n```\n\n### Delete Project\n\nDelete a project.\n\n```python\nclient.delete_project(project_id=\"YOUR_PROJECT_ID\")\n```\n\n### Copy Project\n\nCopy a project.\n\n```python\nproject_id = client.copy_project(project_id=\"YOUR_PROJECT_ID\")\n```\n\n## Tags\n\n### Get Tags\n\nGet tags. (Up to 1000 tags)\n\nkeyword are search terms in the tag name (Optional).\noffset is the starting position number to fetch (Optional).\nlimit is the max number to fetch (Optional).\n\nIf you need to fetch more than 1000 tags, please loop this method using offset and limit.\nIn the sample code below, you can fetch 1000 tags starting from the 2001st position.\n\n```python\nprojects = client.get_tags(\n project=\"YOUR_PROJECT_SLUG\",\n keyword=\"dog\", # (Optional)\n offset=2000, # (Optional)\n limit=1000, # (Optional. Default is 100.)\n)\n```\n\n### Response\n\nExample of tags object\n\n```python\n[\n {\n \"id\": \"YOUR_TAG_ID\",\n \"name\": \"YOUR_TAG_NAME\",\n \"order\": 1,\n \"createdAt\": \"2023-08-14T11: 32: 36.462Z\",\n \"updatedAt\": \"2023-08-14T11: 32: 36.462Z\"\n }\n]\n```\n\n### Delete Tags\n\nDelete tags.\n\n```python\nclient.delete_tags(\n tag_ids=[\n \"YOUR_TAG_ID_1\",\n \"YOUR_TAG_ID_2\",\n ],\n)\n```\n\n## Dataset\n\n### Create Dataset\n\nCreate a new dataset.\n\n```python\ndataset = client.create_dataset(\n name=\"object-detection\", # Only lowercase alphanumeric characters + hyphen is available\n tags=[\"cat\", \"dog\"], # max 5 tags per dataset.\n visibility=\"workspace\", # visibility can be 'workspace' or 'public' or 'organization'\n license=\"The MIT License\" # Optional\n)\n```\n\n#### Response Dataset\n\nSee API docs for details.\n\n```python\n{\n 'id': 'YOUR_DATASET_ID',\n 'name': 'object-detection',\n 'tags': ['cat', 'dog'],\n 'visibility': 'workspace',\n 'license': 'The MIT License',\n 'createdAt': '2022-10-31T02:20:00.248Z',\n 'updatedAt': '2022-10-31T02:20:00.248Z'\n}\n```\n\n### Find Dataset\n\nFind a single dataset.\n\n```python\ndataset = client.find_dataset(dataset_id=\"YOUR_DATASET_ID\")\n```\n\nSuccess response is the same as when created.\n\n### Get Dataset\n\nGet all datasets in the workspace. (Up to 1000 tasks)\n\n```python\ndatasets = client.get_datasets()\n```\n\nThe success response is the same as when created, but it is an array.\n\nYou can filter by keywords and visibility, tags.\n\n```python\ndatasets = client.get_datasets(\n keyword=\"dog\",\n tags=[\"cat\", \"dog\"], # max 5 tags per dataset.\n license=\"mit\",\n visibility=\"workspace\", # visibility can be 'workspace' or 'public' or 'organization'.\n)\n```\n\nIf you wish to retrieve more than 1000 datasets, please refer to the Task [sample code](#get-tasks).\n\n### Update Dataset\n\nUpdate a single dataset.\n\n```python\ndataset = client.update_dataset(\n dataset_id=\"YOUR_DATASET_ID\", name=\"object-detection\", tags=[\"cat\", \"dog\"]\n)\n```\n\nSuccess response is the same as when created.\n\n### Delete Dataset\n\nDelete a single dataset.\n\n**\u26a0\ufe0f The dataset object and its associated tasks that dataset has will also be deleted, so check carefully before executing.**\n\n```python\nclient.delete_dataset(dataset_id=\"YOUR_DATASET_ID\")\n```\n\n### Create Dataset Object\n\nCreate object in the dataset.\n\nThe types of objects that can be created are \"image\", \"video\", and \"audio\".\nThere are type-specific methods. but they can be used in the same way.\n\nCreated object are automatically assigned to the \"latest\" dataset version.\n\n```python\ndataset_object = client.create_dataset_object(\n dataset=\"YOUR_DATASET_NAME\",\n name=\"brushwood_dog.jpg\",\n file_path=\"./brushwood_dog.jpg\",\n tags=[\"dog\"], # max 5 tags per dataset object.\n annotations=[\n {\n \"keypoints\": [\n {\n \"value\": [\n 102.59,\n 23.04,\n 1\n ],\n \"key\": \"head\"\n }\n ],\n \"attributes\": [\n {\n \"value\": \"Scottish field\",\n \"key\": \"kind\"\n }\n ],\n \"confidenceScore\": 0,\n \"rotation\": 0,\n \"points\": [\n 0\n ],\n \"value\": \"dog\",\n \"type\": \"bbox\" # type can be 'bbox', 'segmentation'.\n }\n ],\n custom_metadata={\n \"key\": \"value\",\n \"metadata\": \"metadata-value\"\n }\n)\n```\n\n#### Response Dataset Object\n\nSee API docs for details.\n\n```python\n{\n 'name': 'brushwood_dog.jpg',\n 'size': 6717,\n 'height': 225,\n 'width': 225,\n 'tags': [\n 'dog'\n ],\n \"annotations\": [\n {\n \"id\": \"YOUR_DATASET_OBJECT_ANNOTATION_ID\",\n \"type\": \"bbox\",\n \"title\": \"dog\",\n \"value\": \"dog\",\n \"points\": [\n 0\n ],\n \"attributes\": [\n {\n \"value\": \"Scottish field\",\n \"key\": \"kind\",\n \"name\": \"Kind\",\n \"type\": \"text\"\n }\n ],\n \"keypoints\": [\n {\n \"edges\": [\n \"right_shoulder\",\n \"left_shoulder\"\n ],\n \"value\": [\n 102.59,\n 23.04,\n 1\n ],\n \"key\": \"head\",\n \"name\": \"\u982d\"\n }\n ],\n \"rotation\": 0,\n \"color\": \"#FF0000\",\n \"confidenceScore\": -1\n }\n ],\n \"customMetadata\": {\n \"key\": \"value\",\n \"metadata\": \"metadata-value\"\n },\n 'createdAt': '2022-10-30T08:32:20.748Z',\n 'updatedAt': '2022-10-30T08:32:20.748Z'\n}\n```\n\n### Find Dataset Object\n\nFind a single dataset object.\n\n```python\ndataset_object = client.find_dataset_object(\n dataset_id=\"YOUR_DATASET_ID\",\n object_name=\"brushwood_dog.jpg\"\n)\n```\n\nYou can find a object of specified revision by version or revision_id.\n\n```python\ndataset_object = client.find_dataset_object(\n dataset_id=\"YOUR_DATASET_ID\",\n object_name=\"brushwood_dog.jpg\",\n version=\"YOUR_VERSION_NAME\" # default is \"latest\"\n)\n```\n\n```python\ndataset_object = client.find_dataset_object(\n dataset_id=\"YOUR_DATASET_ID\",\n object_name=\"brushwood_dog.jpg\",\n revision_id=\"YOUR_REVISION_ID\" # 8 characters or more\n)\n```\n\nSuccess response is the same as when created.\n\n### Get Dataset Object\n\nGet all dataset object in the dataset. (Up to 1000 tasks)\n\n```python\ndataset_objects = client.get_dataset_objects(dataset=\"YOUR_DATASET_NAME\")\n```\n\nThe success response is the same as when created, but it is an array.\n\nYou can filter by version or revision_id and tags.\n\n```python\ndataset_objects = client.get_dataset_objects(\n dataset=\"YOUR_DATASET_NAME\",\n version=\"latest\", # default is \"latest\"\n tags=[\"cat\"],\n)\n```\n\n```python\ndataset_objects = client.get_dataset_objects(\n dataset=\"YOUR_DATASET_NAME\",\n revision_id=\"YOUR_REVISION_ID\" # 8 characters or more\n)\n```\n\n### Download Dataset Objects\n\nDownload dataset objects in the dataset to specific directories.\n\nYou can filter by version, tags and types.\n\n```python\nclient.download_dataset_objects(\n dataset=\"YOUR_DATASET_NAME\",\n path=\"YOUR_DOWNLOAD_PATH\",\n version=\"latest\", # default is \"latest\"\n tags=[\"cat\"],\n types=[\"train\", \"valid\"], # choices are \"train\", \"valid\", \"test\" and \"none\" (Optional)\n)\n```\n\n### Update Dataset Object\n```python\ndataset_object = client.update_dataset_object(\n dataset_id=\"YOUR_DATASET_ID\",\n object_name=\"brushwood_dog.jpg\",\n tags=[\"dog\"], # max 5 tags per dataset object.\n annotations=[\n {\n \"keypoints\": [\n {\n \"value\": [\n 102.59,\n 23.04,\n 1\n ],\n \"key\": \"head\"\n }\n ],\n \"attributes\": [\n {\n \"value\": \"Scottish field\",\n \"key\": \"kind\"\n }\n ],\n \"confidenceScore\": 0,\n \"rotation\": 0,\n \"points\": [\n 0\n ],\n \"value\": \"dog\",\n \"type\": \"bbox\" # type can be 'bbox', 'segmentation'.\n }\n ],\n custom_metadata={\n \"key\": \"value\",\n \"metadata\": \"metadata-value\"\n }\n)\n```\n\n### Delete Dataset Object\n\nDelete a single dataset object.\n\n```python\nclient.delete_dataset_object(\n dataset_id=\"YOUR_DATASET_ID\",\n object_name=\"brushwood_dog.jpg\"\n)\n```\n\n## Converter\n\n### FastLabel To COCO\n\nSupport the following annotation types.\n\n- bbox\n- polygon\n- pose estimation\n\nGet tasks and export as a [COCO format](https://cocodataset.org/#format-data) file.\n\n```python\nproject_slug = \"YOUR_PROJECT_SLUG\"\ntasks = client.get_image_tasks(project=project_slug)\nclient.export_coco(project=project_slug, tasks=tasks)\n```\n\nExport with specifying output directory and file name.\n\n```python\nclient.export_coco(project=\"YOUR_PROJECT_SLUG\", tasks=tasks, output_dir=\"YOUR_DIRECTROY\", output_file_name=\"YOUR_FILE_NAME\")\n```\n\nIf you would like to export pose estimation type annotations, please pass annotations.\n\n```python\nproject_slug = \"YOUR_PROJECT_SLUG\"\ntasks = client.get_image_tasks(project=project_slug)\nannotations = client.get_annotations(project=project_slug)\nclient.export_coco(project=project_slug, tasks=tasks, annotations=annotations, output_dir=\"YOUR_DIRECTROY\", output_file_name=\"YOUR_FILE_NAME\")\n```\n\n### FastLabel To YOLO\n\nSupport the following annotation types.\n\n- bbox\n- polygon\n\nGet tasks and export as YOLO format files.\n\n```python\nproject_slug = \"YOUR_PROJECT_SLUG\"\ntasks = client.get_image_tasks(project=project_slug)\nclient.export_yolo(project=project_slug, tasks=tasks, output_dir=\"YOUR_DIRECTROY\")\n```\n\nGet tasks and export as YOLO format files with classes.txt\nYou can use fixed classes.txt and arrange order of each annotaiton file's order\n\n```python\nproject_slug = \"YOUR_PROJECT_SLUG\"\ntasks = client.get_image_tasks(project=project_slug)\nannotations = client.get_annotations(project=project_slug)\nclasses = list(map(lambda annotation: annotation[\"value\"], annotations))\nclient.export_yolo(project=project_slug, tasks=tasks, classes=classes, output_dir=\"YOUR_DIRECTROY\")\n```\n\n### FastLabel To Pascal VOC\n\nSupport the following annotation types.\n\n- bbox\n- polygon\n\nGet tasks and export as Pascal VOC format files.\n\n```python\nproject_slug = \"YOUR_PROJECT_SLUG\"\ntasks = client.get_image_tasks(project=project_slug)\nclient.export_pascalvoc(project=project_slug, tasks=tasks)\n```\n\n### FastLabel To labelme\n\nSupport the following annotation types.\n\n- bbox\n- polygon\n- points\n- line\n\nGet tasks and export as labelme format files.\n\n```python\ntasks = client.get_image_tasks(project=\"YOUR_PROJECT_SLUG\")\nclient.export_labelme(tasks)\n```\n\n### FastLabel To Segmentation\n\nGet tasks and export index color instance/semantic segmentation (PNG files).\nOnly support the following annotation types.\n\n- bbox\n- polygon\n- segmentation (Hollowed points are not supported.)\n\n```python\ntasks = client.get_image_tasks(project=\"YOUR_PROJECT_SLUG\")\nclient.export_instance_segmentation(tasks)\n```\n\n```python\ntasks = client.get_image_tasks(project=\"YOUR_PROJECT_SLUG\")\nclient.export_semantic_segmentation(tasks)\n```\n\n### COCO To FastLabel\n\nSupported bbox , polygon or pose_estimation annotation type.\n\nConvert annotation file of [COCO format](https://cocodataset.org/#format-data) as a Fastlabel format and create task.\n\nfile_path: COCO annotation json file path\n\n```python\nannotations_map = client.convert_coco_to_fastlabel(file_path=\"./sample.json\", annotation_type=\"bbox\")\n# annotation_type = \"bbox\", \"polygon\" or \"pose_estimation\n\ntask_id = client.create_image_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.jpg\",\n file_path=\"./sample.jpg\",\n annotations=annotations_map.get(\"sample.jpg\")\n)\n```\n\nExample of converting annotations to create multiple tasks.\n\nIn the case of the following tree structure.\n\n```\ndataset\n\u251c\u2500\u2500 annotation.json\n\u251c\u2500\u2500 sample1.jpg\n\u2514\u2500\u2500 sample2.jpg\n```\n\nExample source code.\n\n```python\nimport fastlabel\n\nproject = \"YOUR_PROJECT_SLUG\"\ninput_file_path = \"./dataset/annotation.json\"\ninput_dataset_path = \"./dataset/\"\n\nannotations_map = client.convert_coco_to_fastlabel(file_path=input_file_path)\nfor image_file_path in glob.iglob(os.path.join(input_dataset_path, \"**/**.jpg\"), recursive=True):\n time.sleep(1)\n name = image_file_path.replace(os.path.join(*[input_dataset_path, \"\"]), \"\")\n file_path = image_file_path\n annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []\n task_id = client.create_image_task(\n project=project,\n name=name,\n file_path=file_path,\n annotations=annotations\n )\n```\n\n### YOLO To FastLabel\n\nSupported bbox annotation type.\n\nConvert annotation file of YOLO format as a Fastlabel format and create task.\n\nclasses_file_path: YOLO classes text file path\ndataset_folder_path: Folder path containing YOLO Images and annotation\n\n```python\nannotations_map = client.convert_yolo_to_fastlabel(\n classes_file_path=\"./classes.txt\",\n dataset_folder_path=\"./dataset/\"\n)\ntask_id = client.create_image_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.jpg\",\n file_path=\"./dataset/sample.jpg\",\n annotations=annotations_map.get(\"sample.jpg\")\n)\n```\n\nExample of converting annotations to create multiple tasks.\n\nIn the case of the following tree structure.\n\n```\nyolo\n\u251c\u2500\u2500 classes.txt\n\u2514\u2500\u2500 dataset\n \u251c\u2500\u2500 sample1.jpg\n \u251c\u2500\u2500 sample1.txt\n \u251c\u2500\u2500 sample2.jpg\n \u2514\u2500\u2500 sample2.txt\n```\n\nExample source code.\n\n```python\nimport fastlabel\n\nproject = \"YOUR_PROJECT_SLUG\"\ninput_file_path = \"./classes.txt\"\ninput_dataset_path = \"./dataset/\"\nannotations_map = client.convert_yolo_to_fastlabel(\n classes_file_path=input_file_path,\n dataset_folder_path=input_dataset_path\n)\nfor image_file_path in glob.iglob(os.path.join(input_dataset_path, \"**/**.jpg\"), recursive=True):\n time.sleep(1)\n name = image_file_path.replace(os.path.join(*[input_dataset_path, \"\"]), \"\")\n file_path = image_file_path\n annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []\n task_id = client.create_image_task(\n project=project,\n name=name,\n file_path=file_path,\n annotations=annotations\n )\n```\n\n### Pascal VOC To FastLabel\n\nSupported bbox annotation type.\n\nConvert annotation file of Pascal VOC format as a Fastlabel format and create task.\n\nfolder_path: Folder path including pascal VOC format annotation files\n\n```python\nannotations_map = client.convert_pascalvoc_to_fastlabel(folder_path=\"./dataset/\")\ntask_id = client.create_image_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.jpg\",\n file_path=\"./dataset/sample.jpg\",\n annotations=annotations_map.get(\"sample.jpg\")\n)\n```\n\nExample of converting annotations to create multiple tasks.\n\nIn the case of the following tree structure.\n\n```\ndataset\n\u251c\u2500\u2500 sample1.jpg\n\u251c\u2500\u2500 sample1.xml\n\u251c\u2500\u2500 sample2.jpg\n\u2514\u2500\u2500 sample2.xml\n```\n\nExample source code.\n\n```python\nimport fastlabel\n\nproject = \"YOUR_PROJECT_SLUG\"\ninput_dataset_path = \"./dataset/\"\n\nannotations_map = client.convert_pascalvoc_to_fastlabel(folder_path=input_dataset_path)\nfor image_file_path in glob.iglob(os.path.join(input_dataset_path, \"**/**.jpg\"), recursive=True):\n time.sleep(1)\n name = image_file_path.replace(os.path.join(*[input_dataset_path, \"\"]), \"\")\n file_path = image_file_path\n annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []\n task_id = client.create_image_task(\n project=project,\n name=name,\n file_path=file_path,\n annotations=annotations\n )\n```\n\n### labelme To FastLabel\n\nSupport the following annotation types.\n\n- bbox\n- polygon\n- points\n- line\n\nConvert annotation file of labelme format as a Fastlabel format and create task.\n\nfolder_path: Folder path including labelme format annotation files\n\n```python\nannotations_map = client.convert_labelme_to_fastlabel(folder_path=\"./dataset/\")\ntask_id = client.create_image_task(\n project=\"YOUR_PROJECT_SLUG\",\n name=\"sample.jpg\",\n file_path=\"./sample.jpg\",\n annotations=annotations_map.get(\"sample.jpg\")\n)\n```\n\nExample of converting annotations to create multiple tasks.\n\nIn the case of the following tree structure.\n\n```\ndataset\n\u251c\u2500\u2500 sample1.jpg\n\u251c\u2500\u2500 sample1.json\n\u251c\u2500\u2500 sample2.jpg\n\u2514\u2500\u2500 sample2.json\n```\n\nExample source code.\n\n```python\nimport fastlabel\n\nproject = \"YOUR_PROJECT_SLUG\"\ninput_dataset_path = \"./dataset/\"\n\nannotations_map = client.convert_labelme_to_fastlabel(folder_path=input_dataset_path)\nfor image_file_path in glob.iglob(os.path.join(input_dataset_path, \"**/**.jpg\"), recursive=True):\n time.sleep(1)\n name = image_file_path.replace(os.path.join(*[input_dataset_path, \"\"]), \"\")\n file_path = image_file_path\n annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []\n task_id = client.create_image_task(\n project=project,\n name=name,\n file_path=file_path,\n annotations=annotations\n )\n```\n\n> Please check const.COLOR_PALLETE for index colors.\n\n## Model\n\n### Get training jobs\n\nGet training jobs.\n\n```python\ndef get_training_jobs() -> list[dict]:\n all_training_jobs = []\n offset = None\n while True:\n time.sleep(1)\n\n training_jobs = client.get_training_jobs(offset=offset)\n all_training_jobs.extend(training_jobs)\n\n if len(training_jobs) > 0:\n offset = len(all_training_jobs)\n else:\n break\n return all_training_jobs\n\n```\n\n#### Find Training job\n\nFind a single training job.\n\n```python\ntask = client.find_training_job(id=\"YOUR_TRAINING_ID\")\n```\n\n#### Response\n\nExample of two training jobs.\n\n```python\n[\n {\n \"trainingJobId\": \"f40c5838-4c3a-482f-96b7-f77e16c96fed\",\n \"status\": \"in_progress\",\n \"baseModelName\": \"FastLabel Object Detection High Accuracy - \u6c4e\u7528\",\n \"instanceType\": \"ml.p3.2xlarge\",\n \"epoch\": 300,\n \"projects\": [\n \"image-bbox\"\n ],\n \"statuses\": [],\n \"tags\": [],\n \"contentCount\": 23,\n \"userName\": \"Admin\",\n \"createdAt\": \"2023-10-31T07:10:28.306Z\",\n \"completedAt\": null,\n \"customModel\": {\n \"modelId\": \"\",\n \"modelName\": \"\",\n \"modelURL\": \"\",\n \"classes\": []\n }\n },\n {\n \"trainingJobId\": \"1d2bc86a-c7f1-40a5-8e85-48246cc3c8d2\",\n \"status\": \"completed\",\n \"baseModelName\": \"custom-object-detection-image\",\n \"instanceType\": \"ml.p3.2xlarge\",\n \"epoch\": 300,\n \"projects\": [\n \"image-bbox\"\n ],\n \"statuses\": [\n \"approved\"\n ],\n \"tags\": [\n \"trainval\"\n ],\n \"contentCount\": 20,\n \"userName\": \"Admin\",\n \"createdAt\": \"2023-10-31T06:56:28.112Z\",\n \"completedAt\": \"2023-10-31T07:08:26.000Z\",\n \"customModel\": {\n \"modelId\": \"a6728876-2eb7-49b5-9fd8-7dee1b8a81b3\",\n \"modelName\": \"fastlabel_object_detection-2023-10-31-07-08-29\",\n \"modelURL\": \"URL for download model file\",\n \"classes\": [\n \"person\"\n ]\n }\n }\n]\n```\n\n### Execute training job\n\nGet training jobs.\n\n```python\ntraining_job = client.execute_training_job(\n dataset_name=\"dataset_name\",\n base_model_name=\"fastlabel_object_detection_light\", // \"fastlabel_object_detection_light\" or \"fastlabel_object_detection_high_accuracy\" or \"fastlabel_u_net_general\"\n epoch=300,\n use_dataset_train_val=True,\n resize_option=\"fixed\", // optional, \"fixed\" or \"none\"\n resize_dimension=1024, // optional, 512 or 1024\n annotation_value=\"person\", // Annotation value is required if choose \"fastlabel_keypoint_rcnn\"\n config_file_path=\"config.yaml\", // optional, YAML file path for training config file.\n)\n\n```\n\n### Get evaluation jobs\n\nGet evaluation jobs.\n\n```python\ndef get_evaluation_jobs() -> list[dict]:\n all_evaluation_jobs = []\n offset = None\n while True:\n time.sleep(1)\n\n evaluation_jobs = client.get_evaluation_jobs(offset=offset)\n all_evaluation_jobs.extend(evaluation_jobs)\n\n if len(evaluation_jobs) > 0:\n offset = len(all_evaluation_jobs)\n else:\n break\n return all_evaluation_jobs\n\n```\n\n#### Find Evaluation job\n\nFind a single evaluation job.\n\n```python\nevaluation_job = client.find_evaluation_job(id=\"YOUR_EVALUATION_ID\")\n```\n\n#### Response\n\nExample of two evaluation jobs.\n\n```python\n\n{\n id: \"50873ea1-e008-48db-a368-241ca88d6f67\",\n version: 59,\n status: \"in_progress\",\n modelType: \"builtin\",\n modelName: \"FastLabel Object Detection Light - \u6c4e\u7528\",\n customModelId: None,\n iouThreshold: 0.8,\n confidenceThreshold: 0.4,\n contentCount: 0,\n gtCount: 0,\n predCount: 0,\n mAP: 0,\n recall: 0,\n precision: 0,\n f1: 0,\n confusionMatrix: None,\n duration: 0,\n evaluationSource: \"dataset\",\n projects: [],\n statuses: [],\n tags: [],\n datasetId: \"deacbe6d-406f-4086-bd87-80ffb1c1a393\",\n dataset: {\n id: \"deacbe6d-406f-4086-bd87-80ffb1c1a393\",\n workspaceId: \"df201d3c-af00-423a-aa7f-827376fd96de\",\n name: \"sample-dataset\",\n createdAt: \"2023-12-20T10:44:12.198Z\",\n updatedAt: \"2023-12-20T10:44:12.198Z\",\n },\n datasetRevisionId: \"2d26ab64-dfc0-482d-9211-ce8feb3d480b\",\n useDatasetTest: True,\n userName: \"\",\n completedAt: None,\n createdAt: \"2023-12-21T09:08:16.111Z\",\n updatedAt: \"2023-12-21T09:08:18.414Z\",\n};\n\n```\n\n### Execute evaluation job\n\nExecute evaluation jobs.\n\n```python\ntraining_job = client.execute_evaluation_job(\n dataset_name=\"DATASET_NAME\",\n model_name=\"fastlabel_object_detection_light\",\n // If you want to use the built-in model, select the following.\n - \"fastlabel_object_detection_light\"\n - \"fastlabel_object_detection_high_accuracy\"\n - \"fastlabel_fcn_resnet\"\n // If you want to use the custom model, please fill\u3000out model name.\n use_dataset_test=True,\n)\n\n```\n\n### Execute endpoint\n\nCreate the endpoint from the screen at first.\n\nCurrently, the feature to create endpoints is in alpha and is not available to users.\nIf you would like to try it out, please contact a FastLabel representative.\n\n```python\nimport fastlabel\nimport numpy as np\nimport cv2\nimport base64\nclient = fastlabel.Client()\n\nENDPOINT_NAME = \"YOUR ENDPOINT NAME\"\nIMAGE_FILE_PATH = \"YOUR IMAGE FILE PATH\"\nRESULT_IMAGE_FILE_PATH = \"YOUR RESULT IMAGE FILE PATH\"\n\ndef base64_to_cv(img_str):\n if \"base64,\" in img_str:\n img_str = img_str.split(\",\")[1]\n img_raw = np.frombuffer(base64.b64decode(img_str), np.uint8)\n img = cv2.imdecode(img_raw, cv2.IMREAD_UNCHANGED)\n return img\n\nif __name__ == '__main__':\n # Execute endpoint\n response = client.execute_endpoint(\n endpoint_name=ENDPOINT_NAME, file_path=IMAGE_PATH)\n\n # Show result\n print(response[\"json\"])\n\n # Save result\n img = base64_to_cv(response[\"file\"])\n cv2.imwrite(RESULT_IMAGE_FILE_PATH, img)\n```\n\n### Create Request Results for Monitoring\n\nYou can integrate the results of model endpoint calls,\nwhich are targeted for aggregation in model monitoring, from an external source.\n\n```python\nfrom datetime import datetime\nimport pytz\nimport fastlabel\nclient = fastlabel.Client()\n\njst = pytz.timezone(\"Asia/Tokyo\")\ndt_jst = datetime(2023, 5, 8, 12, 10, 53, tzinfo=jst)\n\nclient.create_model_monitoring_request_results(\n name=\"model-monitoring-name\", # The name of your model monitoring name\n results=[\n {\n \"status\": \"success\", # success or failed\n \"result\": [\n {\n \"value\": \"person\", # The value of the inference class returned by your model\n \"confidenceScore\": 0.92, # 0 ~ 1\n }\n ],\n \"requestAt\": dt_jst.isoformat(), # The time when your endpoint accepted the request\n }\n ],\n)\n```\n\n## API Docs\n\nCheck [this](https://api.fastlabel.ai/docs/) for further information.\n\n\n",
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