TensorBay Python SDK
TensorBay Python SDK is a python library to access TensorBay and manage your datasets.
It provides:
- A pythonic way to access your TensorBay resources by TensorBay OpenAPI.
- An easy-to-use CLI tool
gas(Graviti AI service) to communicate with TensorBay. - A consistent dataset format to read and write your datasets.
Installation
pip3 install tensorbay
Documentation
More information can be found on the documentation site
Usage
An AccessKey is needed to communicate with TensorBay. Please visit this page to get an AccessKey first.
Authorize a client object
from tensorbay import GAS
gas = GAS("<YOUR_ACCESSKEY>")
Create a Dataset
gas.create_dataset("DatasetName")
List Dataset names
dataset_names = gas.list_dataset_names()
Upload images to the Dataset
from tensorbay.dataset import Data, Dataset
# Organize the local dataset by the "Dataset" class before uploading.
dataset = Dataset("DatasetName")
# TensorBay uses "segment" to separate different parts in a dataset.
segment = dataset.create_segment("SegmentName")
segment.append(Data("0000001.jpg"))
segment.append(Data("0000002.jpg"))
dataset_client = gas.upload_dataset(dataset, jobs=8)
# TensorBay provides dataset version control feature, commit the uploaded data before using it.
dataset_client.commit("Initial commit")
Read images from the Dataset
from PIL import Image
dataset = Dataset("DatasetName", gas)
segment = dataset[0]
for data in segment:
with data.open() as fp:
image = Image.open(fp)
width, height = image.size
image.show()
Delete the Dataset
gas.delete_dataset("DatasetName")
