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fastlabel-0.9.9
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The official Python SDK for FastLabel API, the Data Platform for AI
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fastlabel-0.9.9 |
| نام | fastlabel |
| نسخه کتابخانه | 0.9.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | eisuke-ueta |
| ایمیل نویسنده | eisuke.ueta@fastlabel.ai |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/fastlabel/ |
| مجوز | - |
# FastLabel Python SDK
_If you are using FastLabel prototype, please install version 0.2.2._
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Limitation](#limitation)
- [Task](#task)
- [Image](#image)
- [Image Classification](#image-classification)
- [Multi Image](#multi-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)
- [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)
- [API Docs](#api-docs)
## Installation
```bash
pip install --upgrade fastlabel
```
> Python version 3.7 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",
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.
#### 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",
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
]
}
],
)
```
#### 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",
"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",
"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",
attributes=[
{
"key": "attribute-key",
"value": "attribute-value"
}
],
)
```
##### Limitation
- You can upload up to a size of 20 MB.
#### 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",
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",
"tags": [],
"assignee": "ASSIGNEE_NAME",
"reviewer": "REVIEWER_NAME",
"externalAssignee": "EXTERNAL_ASSIGNEE_NAME",
"externalReviewer": "EXTERNAL_REVIEWER_NAME",
"attributes": [
{
"key": "kind",
"name": "Kind",
"type": "text",
"value": "Scottish field"
}
],
"createdAt": "2021-02-22T11:25:27.158Z",
"updatedAt": "2021-02-22T11:25:27.158Z"
}
```
### Multi Image
Supported following project types:
- Multi Image - Bounding Box
- Multi Image - Polygon
- Multi Image - Keypoint
- Multi Image - Line
- Multi Image - Segmentation
#### Create Task
Create a new task.
```python
task = client.create_multi_image_task(
project="YOUR_PROJECT_SLUG",
name="sample",
folder_path="./sample",
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_multi_image_task(task_id="YOUR_TASK_ID")
```
Find a single task by name.
```python
tasks = client.find_multi_image_task_by_name(project="YOUR_PROJECT_SLUG", task_name="YOUR_TASK_NAME")
```
#### Get Tasks
Get tasks.
```python
tasks = client.get_multi_image_tasks(project="YOUR_PROJECT_SLUG")
```
#### Update Task
Update a single task.
```python
task_id = client.update_multi_image_task(
task_id="YOUR_TASK_ID",
status="approved",
assignee="USER_SLUG",
tags=["tag1", "tag2"],
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",
"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",
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.
#### 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",
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",
"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",
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",
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",
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",
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",
"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",
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",
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",
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",
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",
"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",
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",
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",
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",
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",
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",
"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/",
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",
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",
"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",
}
```
### Common
APIs for update and delete 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",
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")
```
#### 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",
)
```
## 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")
```
## Dataset
### Create Dataset
Create a new dataset.
```python
dataset = client.create_dataset(
name="Japanese Dogs",
slug="japanese-dogs",
type="image"
)
```
#### Response Dataset
See API docs for details.
```python
{
'id': 'YOUR_DATASET_ID',
'name': 'Japanese Dogs',
'slug': 'japanese-dogs',
'type': 'image',
'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 type and keywords.
```python
datasets = client.get_datasets(
type="image", # 'image', 'video', 'audio'
keyword="dog"
)
```
If you wish to retrieve more than 1000 data sets, 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="World dogs"
)
```
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.
```python
dataset_object = client.create_image_dataset_object(
dataset_id="YOUR_DATASET_ID",
name="brushwood_dog.jpg",
file_path="./brushwood_dog.jpg",
)
```
#### Response Dataset Object
See API docs for details.
```python
{
'id': 'YOUR_DATASET_OBJECT_ID',
'name': 'brushwood_dog.jpg',
'size': 6717,
'height': 225,
'width': 225,
'groupId': None,
'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_object_id="YOUR_DATASET_OBJECT_ID"
)
```
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_id="YOUR_DATASET_ID")
```
The success response is the same as when created, but it is an array.
You can filter by keywords.
```python
dataset_objects = client.get_dataset_objects(
dataset_id="YOUR_DATASET_ID", keyword="dog"
)
```
If you wish to retrieve more than 1000 data sets, please refer to the Task [sample code](#get-tasks).
### Delete Dataset Object
Delete a multi dataset objects.
**⚠️ Related tasks will also be deleted, so please check them carefully before execution.**
```python
client.delete_dataset_objects(
dataset_id="YOUR_DATASET_ID",
dataset_object_ids=[
"YOUR_DATASET_OBJECT_ID_1",
"YOUR_DATASET_OBJECT_ID_2",
],
)
```
### Get Import Histories For Dataset Object
Get all import histories in the dataset. (Up to 1000 tasks)
```python
datasets = client.get_dataset_object_import_histories(
dataset_id="YOUR_DATASET_ID"
)
```
#### Response Dataset Object Import Histories
See API docs for details.
```python
[
{
'id': 'YOUR_DATASET_OBJECT_IMPORT_HISTORY_ID',
'type': 'local',
'status': 'completed',
'msgCode': 'none',
'msgLevel': 'none',
'userName': 'admin',
'count': 1,
'createdAt': '2022-10-30T08:31:31.588Z',
'updatedAt': '2022-11-02T07:36:07.636Z'
}
]
```
## 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.
## 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)
```
## API Docs
Check [this](https://api.fastlabel.ai/docs/) for further information.
نیازمندی
| مقدار | نام |
|---|---|
| ==2.25.1 | requests |
| >=1.21.2 | numpy |
| ==2.5.0 | geojson |
| ==0.12.0 | xmltodict |
| ==9.0.1 | Pillow |
| ==4.7.0.72 | opencv-python |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl fastlabel-0.9.9:
pip install fastlabel-0.9.9.whl
نصب پکیج tar.gz fastlabel-0.9.9:
pip install fastlabel-0.9.9.tar.gz