darwin-py 0.5.16

Library and command line interface for darwin.v7labs.com

Darwin

Official library to manage datasets along with V7 Darwin annotation platform.

Darwin-py can both be used from the command line and as a python library.

Main functions are (but not limited to):

• Client authentication
• Listing local and remote datasets
• Create/remove datasets
• Upload/download data to/from remote datasets
• Direct integration with PyTorch dataloaders

Support tested for python 3.8.

Installation

pip install darwin-py

You can now type darwin in your terminal and access the command line interface.

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Usage as a Command Line Interface (CLI)

Once installed, darwin is accessible as a command line tool. A useful way to navigate the CLI usage is through the help command -h/--help which will provide additional information for each command available.

Client Authentication

To perform remote operations on Darwin you first need to authenticate. This requires a team-specific API-key. If you do not already have a Darwin account, you can contact us and we can set one up for you.

To start the authentication process:

$ darwin authenticate
API key:
Make example-team the default team? [y/N] y
Datasets directory [~/.darwin/datasets]:
Authentication succeeded.

You will be then prompted to enter your API-key, whether you want to set the corresponding team as default and finally the desired location on the local file system for the datasets of that team. This process will create a configuration file at ~/.darwin/config.yaml. This file will be updated with future authentications for different teams.

Listing local and remote datasets

Lists a summary of local existing datasets

$ darwin dataset local
NAME            IMAGES     SYNC_DATE         SIZE
mydataset       112025     yesterday     159.2 GB

Lists a summary of remote datasets accessible by the current user.

$ darwin dataset remote
NAME                       IMAGES     PROGRESS
example-team/mydataset     112025        73.0%

Create/remove a dataset

To create an empty dataset remotely:

$ darwin dataset create test
Dataset 'test' (example-team/test) has been created.
Access at https://darwin.v7labs.com/datasets/579

The dataset will be created in the team you're authenticated for.

To delete the project on the server:

$ darwin dataset remove test
About to delete example-team/test on darwin.
Do you want to continue? [y/N] y

Upload/download data to/from a remote dataset

Uploads data to an existing remote project. It takes the dataset name and a single image (or directory) with images/videos to upload as parameters.

The -e/--exclude argument allows to indicate file extension/s to be ignored from the data_dir. e.g.: -e .jpg

For videos, the frame rate extraction rate can be specified by adding --fps <frame_rate>

Supported extensions:

• Video files: [.mp4, .bpm, .mov formats].
• Image files [.jpg, .jpeg, .png formats].

$ darwin dataset push test /path/to/folder/with/images
100%|████████████████████████| 2/2 [00:01<00:00,  1.27it/s]

Before a dataset can be downloaded, a release needs to be generated:

$ darwin dataset export test 0.1
Dataset test successfully exported to example-team/test:0.1

This version is immutable, if new images / annotations have been added you will have to create a new release to included them.

To list all available releases

$ darwin dataset releases test
NAME                           IMAGES     CLASSES                   EXPORT_DATE
example-team/test:0.1               4           0     2019-12-07 11:37:35+00:00

And to finally download a release.

$ darwin dataset pull test:0.1
Dataset example-team/test:0.1 downloaded at /directory/choosen/at/authentication/time.

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Usage as a Python library

The framework is designed to be usable as a standalone python library. Usage can be inferred from looking at the operations performed in darwin/cli_functions.py. A minimal example to download a dataset is provided below and a more extensive one can be found in darwin_demo.py.

from darwin.client import Client

client = Client.local() # use the configuration in ~/.darwin/config.yaml
dataset = client.get_remote_dataset("example-team/test")
dataset.pull() # downloads annotations and images for the latest exported version

Follow this guide for how to integrate darwin datasets directly in PyTorch.