Python SDK

The Python SDK is a convenient wrapper for the API.
Setup
Please refer to this blog post for a full example of working with the Python SDK.
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 = "eabdde840de8c8853329c086bc4165591cb3d74c"
client = SegmentsClient(api_key)
Datasets
List datasets
user = "jane"
datasets = client.get_datasets(user)
​
for dataset in datasets:
    print(dataset["name"], dataset["description"])
Get a dataset
dataset_identifier = "jane/flowers"
dataset = client.get_dataset(dataset_identifier)
print(dataset)
Create a dataset
dataset_name = "flowers"
description = "A dataset containing flowers of all kinds."
category = "garden"
​
dataset = client.add_dataset(dataset_name, description, category)
print(sample)
Delete a dataset
dataset_identifier = "jane/flowers"
client.delete_dataset(dataset_identifier)
Samples
List samples
dataset = "jane/flowers"
samples = client.get_samples(dataset)
​
for sample in samples:
    print(sample["name"], sample["uuid"])
Get a sample
sample = client.get_sample(uuid="602a3eec-a61c-4a77-9fcc-3037ce5e9606")
print(sample)
Create a sample
dataset = "jane/flowers"
name = "violet.jpg"
attributes = {
    "image": {
        "url": "https://example.com/violet.jpg"
    }
}
​
sample = client.add_sample(dataset, name, attributes)
print(sample)
If the image is on your local computer, you should first upload it to a cloud storage service like Amazon S3, Google Cloud Storage, Imgur, or our asset 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.
Delete a sample
client.delete_sample(uuid="602a3eec-a61c-4a77-9fcc-3037ce5e9606")
Labels
Get a label
sample_uuid = "602a3eec-a61c-4a77-9fcc-3037ce5e9606"
task = "segmentation"
​
label = client.get_label(sample_uuid, task)
print(label)
Create or update a label
A label can be added to a sample in relation to a task defined on the dataset, such as an image classification task or an image segmentation task.
The task specifies the format that is required for the label: for a classification task, the attributesfield should contain different fields than for a segmentation task.
sample_uuid = "602a3eec-a61c-4a77-9fcc-3037ce5e9606"
task_name = "segmentation"
attributes = {
    "segmentation_bitmap": {
        "url": "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/bert/49f6aa10-8967-4305-985c-cdc1e8f89b93.png"
    },
    "annotations": [
        {
            "id": 1,
            "category_id": 2
        },
        {
            "id": 2,
            "category_id": 3
        }
    ]
}
​
client.add_label(sample_uuid, task_name, attributes)
Thesegmentation_bitmap_urlrefers to a 32-bit RGBA png image. The alpha channel is set to 255, and the remaining 24-bit values in the RGB channels correspond toinstance_ids.
The easiest way to transform a segmentation bitmap into this format and upload it is by using the util functionbitmap2file:
from segments.utils import bitmap2file
​
# segmentation_bitmap is a numpy array of type np.uint32, with values corresponding to instance_ids
file = bitmap2file(segmentation_bitmap)
asset = client.upload_asset(file, "label.png")
segmentation_bitmap_url = asset["url"]
For a full example of uploading model-generated labels to Segments.ai, please refer to this blogpost.
Delete a label
sample_uuid = "602a3eec-a61c-4a77-9fcc-3037ce5e9606"
task_name = "segmentation"
client.delete_label(sample_uuid, task)
Upload a file as an asset
Example with an image:
filename = "/home/jane/flowers/violet.jpg"
with open(filename, "rb") as f:
    asset = client.upload_asset(f, filename="violet.jpg")
    
image_url = asset["url"]
API
Exporting data
Last updated 2 weeks ago