superannotate-python-sdk | SuperAnnotate Python SDK | Data Labeling library

by superannotateai Python Version: v4.4.12 License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | superannotate-python-sdk Summary

superannotate-python-sdk is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Data Labeling applications. superannotate-python-sdk has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install superannotate-python-sdk' or download it from GitHub, PyPI.

SuperAnnotate Python SDK allows access to the platform without web browser:.

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Quality

Security

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Support

superannotate-python-sdk has a low active ecosystem.

It has 30 star(s) with 10 fork(s). There are 3 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 9 have been closed. On average issues are closed in 22 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of superannotate-python-sdk is v4.4.12

Quality

superannotate-python-sdk has 0 bugs and 0 code smells.

Security

superannotate-python-sdk has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

superannotate-python-sdk code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

superannotate-python-sdk is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

superannotate-python-sdk releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

It has 29045 lines of code, 1706 functions and 217 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed superannotate-python-sdk and discovered the below as its top functions. This is intended to give you an instant insight into superannotate-python-sdk implemented functionality, and help decide if they suit your requirements.

Execute the image
Generate a random color
Draws a bounding box
Draw an ellipse
Convert image to SuperAnnotate format
Create vector instance
Create an SSA object
Convert from soco_vector to keypoint
The error message
Executes the query
Executes the action
Convert a coco instance to S2 pixel segmentation
Add items to a project
Convert labelbox to SuperAnnotate JSON
Execute this task
Compiles the image
Convert JSON files to SuperAnnotate
Load image annotation
Convert image segmentation to SuperAnnotate format
Convert dataloop to SuperAnnotate format
Generate a series of hypergraphsectors from a json file
Upload a big annotation
Convert a labelbox to SuperAnnotate JSON format
Convert keypoint detection to SuperAnnotate format
Executes the project
Execute this plugin

Get all kandi verified functions for this library.

superannotate-python-sdk Key Features

No Key Features are available at this moment for superannotate-python-sdk.

superannotate-python-sdk Examples and Code Snippets

SuperAnnotate Python SDK

Python

Lines of Code : 5

License : Permissive (MIT)

Copy

import superannotate as sa

sa.create_project("Example Project 1", "example", "Vector")

sa.upload_images_from_folder_to_project("Example Project 1", "")

SuperAnnotate Python SDK,Installation

Python

Lines of Code : 1

License : Permissive (MIT)

Copy

pip install superannotate

Community Discussions

Trending Discussions on Data Labeling

How can I do this split process in Python?

Replacing a character with a space and dividing the string into two words in R

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

...

ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

Source https://stackoverflow.com/questions/70532286

QUESTION

Replacing a character with a space and dividing the string into two words in R

Asked 2020-Nov-18 at 07:32

I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

i have this:

...

ANSWER

Answered 2020-Nov-16 at 07:24

Try something like:

Source https://stackoverflow.com/questions/64853962

QUESTION

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

Asked 2020-Oct-28 at 20:31

Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

...

ANSWER

Answered 2020-Oct-27 at 22:39

Is the data behind virtual network by any chance?

Source https://stackoverflow.com/questions/64546521

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install superannotate-python-sdk

SDK is available on PyPI:. The package officially supports Python 3.6+ and was tested under Linux and Windows (Anaconda) platforms. For more detailed installation steps and package usage please have a look at the tutorial.

Support

Search projectsCreate/delete a projectUpload images to a project from a local or AWS S3 folderUpload videos to a project from a local folderUpload annotations/pre-annotations to a project from local or AWS S3 folderSet the annotation status of the images being uploadedExport annotations from a project to a local or AWS S3 folderShare and unshare a project with a team contributorInvite a team contributorSearch images in a projectDownload a single imageCopy/move image between projectsGet image bytes (e.g., for numpy array creation)Set image annotation statusDownload image annotations/pre-annotationsCreate/download project annotation classesConvert annotation format from/to COCOConvert annotation format from VOC, SuperVisely, LabelBox, DataLoop, VGG, VoTT, SageMaker, GoogleCloud, YOLOAdd annotations to images on platformAdd annotations to local SuperAnnotate format JSONsCLI commands for simple tasks

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