Python SDK (old v0.x)
The Python SDK is a convenient wrapper for the API.
First install the SDK.
pip install segments-ai --upgrade
Import the
segments package in your python file and and set up a client with an API key. An API key can be created on your user account page.from segments import SegmentsClient
api_key = "YOUR_API_KEY"
client = SegmentsClient(api_key)
datasets = client.get_datasets()
for dataset in datasets:
print(dataset["name"], dataset["description"])
client.get_datasets(user=None)
"""Get a list of datasets.
Args:
user (str, optional): The user for which to get the datasets. Leave empty to get datasets of current user. Defaults to None.
Returns:
list: a list of dictionaries representing the datasets.
"""
dataset_identifier = "jane/flowers"
dataset = client.get_dataset(dataset_identifier)
print(dataset)
client.get_dataset(dataset_identifier)
"""Get a dataset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
Returns:
dict: a dictionary representing the dataset.
"""
dataset_name = "flowers"
description = "A dataset containing flowers of all kinds."
task_type = "segmentation-bitmap"
dataset = client.add_dataset(dataset_name, description, task_type)
print(dataset)
client.add_dataset(self, name, description='', task_type='segmentation-bitmap', task_attributes=None, category='other', public=False, readme='', enable_skip_labeling=True, enable_skip_reviewing=False, enable_ratings=False)
"""Add a dataset.
Args:
name (str): The dataset name. Example: flowers.
description (str, optional): The dataset description. Defaults to ''.
task_type (str, optional): The dataset's task type. One of 'segmentation-bitmap', 'segmentation-bitmap-highres', 'vector', 'bboxes', 'keypoints'. Defaults to 'segmentation-bitmap', 'pointcloud-segmentation', 'pointcloud-detection'.
task_attributes (dict, optional): The dataset's task attributes. Defaults to None.
category (str, optional): The dataset category. Defaults to 'other'.
public (bool, optional): The dataset visibility. Defaults to False.
readme (str, optional): The dataset readme. Defaults to ''.
enable_skip_labeling (bool, optional): Enable the skip button in the labeling workflow. Defaults to True.
enable_skip_reviewing (bool, optional): Enable the skip button in the reviewing workflow. Defaults to False.
enable_ratings (bool, optional): Enable star-ratings for labeled images. Defaults to False.
organization (str, optional): The username of the organization for which this dataset should be created. None will create a dataset for the current user. Defaults to None.
Returns:
dict: a dictionary representing the newly created dataset.
"""
dataset_identifier = "jane/flowers"
description = "A dataset containing flowers of all kinds."
dataset = client.update_dataset(dataset_identifier, description)
print(dataset)
client.update_dataset(self, dataset_identifier, description=None, task_type=None, task_attributes=None, category=None, public=None, readme=None, enable_skip_labeling=None, enable_skip_reviewing=None, enable_ratings=None)
"""Update a dataset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
description (str, optional): The dataset description.
task_type (str, optional): The dataset's task type. One of 'segmentation-bitmap', 'segmentation-bitmap-highres', 'vector', 'bboxes', 'keypoints', 'pointcloud-segmentation', 'pointcloud-detection'.
task_attributes (dict, optional): The dataset's task attributes.
category (str, optional): The dataset category.
public (bool, optional): The dataset visibility.
readme (str, optional): The dataset readme.
enable_skip_labeling (bool, optional): Enable the skip button in the labeling workflow.
enable_skip_reviewing (bool, optional): Enable the skip button in the reviewing workflow.
enable_ratings: Enable star-ratings for labeled images.
Returns:
dict: a dictionary representing the updated dataset.
"""
dataset_identifier = "jane/flowers"
client.delete_dataset(dataset_identifier)
client.delete_dataset(dataset_identifier)
"""Delete a dataset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
"""
dataset_identifier = "jane/flowers"
username = "john"
role = "reviewer"
client.add_dataset_collaborator(dataset_identifier, username, role)
client.add_dataset_collaborator(dataset_identifier, username, role='labeler')
"""Add a collaborator to a dataset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
username (str): The username of the collaborator to be added.
role (str, optional): The role of the collaborator to be added. One of labeler, reviewer, admin. Defaults to labeler.
Returns:
dict: a dictionary containing the newly added collaborator with its role.
"""
dataset_identifier = "jane/flowers"
samples = client.get_samples(dataset_identifier)
for sample in samples:
print(sample["name"], sample["uuid"])
get_samples(self, dataset_identifier, name=None, label_status=None, metadata=None, sort='name', direction='asc', per_page=1000, page=1)
"""Get the samples in a dataset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
name (str, optional): Name to filter by. Defaults to None (no filtering).
label_status (list, optional): List of label statuses to filter by. Defaults to None (no filtering).
metadata (list, optional): List of 'key:value' metadata attributes to filter by. Defaults to None (no filtering).
sort (str, optional): What to sort results by. One of 'name', 'created', 'priority'. Defaults to 'name'.
direction (str, optional): Sorting direction. One of 'asc' (ascending) or 'desc' (descending). Defaults to 'asc'.
per_page (int, optional): Pagination parameter indicating the maximum number of samples to return. Defaults to 1000.
page (int, optional): Pagination parameter indicating the page to return. Defaults to 1.
Returns:
list: a list of dictionaries representing the samples.
"""
sample = client.get_sample(uuid="602a3eec-a61c-4a77-9fcc-3037ce5e9606")
print(sample)
client.get_sample(uuid, labelset=None)
"""Get a sample.
Args:
uuid (str): The sample uuid.
labelset (str, optional): If defined, this additionally returns the label for the given labelset. Defaults to None.
Returns:
dict: a dictionary representing the sample.
"""
If the image file is on your local computer, you should first upload it to our asset storage service (using
upload_asset()) or to another cloud storage service.If you create a sample with a URL from a public S3 bucket and you see an error on the platform, make sure to properly configure your bucket's CORS settings.
dataset_identifier = "jane/flowers"
name = "violet.jpg"
attributes = {
"image": {
"url": "https://example.com/violet.jpg"
}
}
# metadata and priority are optional fields
metadata = {
"city": "London",
"weather": "cloudy",
"robot_id": 3
}
priority = 10 # Samples with higher priority value will be labeled first. Default is 0.
sample = client.add_sample(dataset_identifier, name, attributes, metadata, priority)
print(sample)
client.add_sample(dataset_identifier, name, attributes, metadata=None, priority=None)
"""Add a sample to a dataset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
name (str): The name of the sample.
attributes (dict): The sample attributes. Please refer to the online documentation.
metadata (dict, optional): Any sample metadata. Example: {'weather': 'sunny', 'camera_id': 3}.
priority (float, optional): Priority in the labeling queue. Samples with higher values will be labeled first. Default is 0.
Returns:
dict: a dictionary representing the newly created sample.
"""
uuid = "602a3eec-a61c-4a77-9fcc-3037ce5e9606"
metadata = {
"city": "London",
"weather": "cloudy",
"robot_id": 3
}
priority = 10 # Samples with higher priority value will be labeled first. Default is 0.
sample = client.update_sample(uuid, metadata=metadata, priority=priority)
print(sample)
client.update_sample(uuid, name=None, attributes=None, metadata=None, priority=None)
"""Update a sample.
Args:
uuid (str): The sample uuid.
name (str, optional): The name of the sample.
attributes (dict, optional): The sample attributes. Please refer to the online documentation.
metadata (dict, optional): Any sample metadata. Example: {'weather': 'sunny', 'camera_id': 3}.
priority (float, optional): Priority in the labeling queue. Samples with higher values will be labeled first. Default is 0.
Returns:
dict: a dictionary representing the updated sample.
"""
client.delete_sample(uuid="602a3eec-a61c-4a77-9fcc-3037ce5e9606")
client.delete_sample(uuid)
"""Delete a sample.
Args:
uuid (str): The sample uuid.
"""
sample_uuid = "602a3eec-a61c-4a77-9fcc-3037ce5e9606"
label = client.get_label(sample_uuid)
print(label)
client.get_label(sample_uuid, labelset='ground-truth')
"""Get a label.
Args:
sample_uuid (str): The sample uuid.
labelset (str): The labelset this label belongs to. Defaults to 'ground-truth'.
Returns:
dict: a dictionary representing the label.
"""
A label is added to a sample in relation to a label set, such as the default ground-truth label set, or a newly created label set for uploading model predictions. You can create a new label set by clicking the "Add new label set" link on the Samples tab.
sample_uuid = "602a3eec-a61c-4a77-9fcc-3037ce5e9606"
labelset = "ground-truth"
attributes = {
"format_version": "0.1",
"annotations": [
{
"id": 1,
"category_id": 1,
"type": "bbox",
"points": [
[12.34, 56.78],
[90.12, 34.56]
]
},
]
}
client.add_label(sample_uuid, labelset, attributes)
client.add_label(sample_uuid, labelset, attributes, label_status='PRELABELED')
"""Add a label to a sample.
Args:
sample_uuid (str): The sample uuid.
labelset (str): The labelset this label belongs to.
attributes (dict): The label attributes. Please refer to the online documentation.
label_status (str, optional): The label status. Defaults to 'PRELABELED'.
score (float, optional): The label score. Defaults to None.
Returns:
dict: a dictionary representing the newly created label.
"""
sample_uuid = "602a3eec-a61c-4a77-9fcc-3037ce5e9606"
labelset = "ground-truth"
attributes = {
"format_version": "0.1",
"annotations": [
{
"id": 1,
"category_id": 1,
"type": "bbox",
"points": [
[12.34, 56.78],
[90.12, 34.56]
]
},
]
}
client.update_label(sample_uuid, labelset, attributes)
client.update_label(sample_uuid, labelset, attributes=None, label_status='PRELABELED', score=None)
"""Update a label.
Args:
sample_uuid (str): The sample uuid.
labelset (str): The labelset this label belongs to.
attributes (dict): The label attributes. Please refer to the online documentation.
label_status (str, optional): The label status. Defaults to 'PRELABELED'.
score (float, optional): The label score. Defaults to None.
Returns:
dict: a dictionary representing the updated label.
"""
sample_uuid = "602a3eec-a61c-4a77-9fcc-3037ce5e9606"
labelset = "ground-truth"
client.delete_label(sample_uuid, labelset)
client.delete_label(sample_uuid, labelset)
"""Delete a label.
Args:
sample_uuid (str): The sample uuid.
labelset (str): The labelset this label belongs to.
"""
A label is added to a sample in relation to a label set, such as the default ground-truth label set, or a newly created label set for uploading model predictions.
dataset_identifier = "jane/flowers"
labelsets = client.get_labelsets(dataset_identifier)
for labelset in labelsets:
print(labelset["name"], labelset["description"])
client.get_labelsets(dataset_identifier)
"""Get the labelsets in a dataset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
Returns:
list: a list of dictionaries representing the labelsets.
"""
dataset_identifier = "jane/flowers"
name = "model-predictions"
labelset = client.get_labelset(dataset_identifier, name)
print(labelset)
client.get_labelset(dataset_identifier, name)
"""Get a labelset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
name (str): The name of the labelset.
Returns:
dict: a dictionary representing the labelset.
"""
You can also create a new label set by clicking the "Add new label set" link on the Samples tab.
dataset_identifier = "jane/flowers"
name = "model-predictions-resnet50"
client.add_labelset(dataset_identifier, name)
client.add_labelset(dataset_identifier, name, description='')
"""Add a labelset to a dataset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
name (str): The name of the labelset.
description (str, optional): The labelset description.
Returns:
dict: a dictionary representing the labelset.
"""
dataset_identifier = "jane/flowers"
name = "model-predictions"
client.delete_labelset(dataset_identifier, name)
client.delete_labelset(dataset_identifier, name)
"""Delete a labelset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
name (str): The name of the labelset.
"""
dataset_identifier = "jane/flowers"
releases = client.get_releases(dataset_identifier)
for release in releases:
print(release["name"], release["description"], release["attributes"]["url"])
client.get_releases(dataset_identifier)
"""Get the releases in a dataset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
Returns:
list: a list of dictionaries representing the releases.
"""
dataset_identifier = "jane/flowers"
name = "v0.1"
release = client.get_release(dataset_identifier, name)
print(release)
client.get_release(dataset_identifier, name)
"""Get a release.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
name (str): The name of the release.
Returns:
dict: a dictionary representing the release.
"""
dataset_identifier = "jane/flowers"
name = "v0.1"
description = "My first release."
release = client.add_release(dataset_identifier, name, description)
print(release)
client.add_release(dataset_identifier, name, description='')
"""Add a release to a dataset.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
name (str): The name of the release.
description (str, optional): The release description.
Returns:
dict: a dictionary representing the newly created release.
"""
dataset_identifier = "jane/flowers"
name = "v0.1"
client.delete_release(dataset_identifier, name)
client.delete_release(dataset_identifier, name)
"""Delete a release.
Args:
dataset_identifier (str): The dataset identifier, consisting of the name of the dataset owner followed by the name of the dataset itself. Example: jane/flowers.
name (str): The name of the release.
"""
filename = "/home/jane/flowers/violet.jpg"
with open(filename, "rb") as f:
asset = client.upload_asset(f, filename="violet.jpg")
image_url = asset["url"]
client.upload_asset(file, filename='label.png')
"""Upload an asset.
Args:
file (object): A file object.
filename (str, optional): The file name. Defaults to label.png.
Returns:
dict: a dictionary representing the uploaded file.
"""
Last modified 9mo ago