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

 by   superannotateai Python Version: v4.4.12 License: MIT

kandi X-RAY | superannotate-python-sdk Summary

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:.

            kandi-support 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.
              There were 10 major release(s) 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

            kandi-Quality Quality

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

            kandi-Security 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.

            kandi-License 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.

            kandi-Reuse 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
            Pythondot img1Lines of Code : 5dot img1License : Permissive (MIT)
            copy iconCopy
            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
            Pythondot img2Lines of Code : 1dot img2License : Permissive (MIT)
            copy iconCopy
            pip install superannotate

            Community Discussions


            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?



            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:



            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:



            Answered 2020-Nov-16 at 07:24


            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.



            Answered 2020-Oct-27 at 22:39

            Is the data behind virtual network by any chance?


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


            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.


            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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