labelme2Datasets | python scripts to convert labelme

by veraposeidon Python Version: 0.0.3 License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | labelme2Datasets Summary

labelme2Datasets is a Python library. labelme2Datasets 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 labelme2Datasets' or download it from GitHub, PyPI.

用于将LabelMe标注好的数据转换为VOC格式和COCO格式的数据集。

Support

Quality

Security

License

Reuse

Support

labelme2Datasets has a low active ecosystem.

It has 165 star(s) with 43 fork(s). There are 2 watchers for this library.

There were 1 major release(s) in the last 12 months.

There are 0 open issues and 7 have been closed. On average issues are closed in 63 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of labelme2Datasets is 0.0.3

Quality

labelme2Datasets has 0 bugs and 0 code smells.

Security

labelme2Datasets has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

labelme2Datasets code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

labelme2Datasets 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

labelme2Datasets 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 are not available. Examples and code snippets are available.

labelme2Datasets saves you 1402 person hours of effort in developing the same functionality from scratch.

It has 3136 lines of code, 16 functions and 91 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed labelme2Datasets and discovered the below as its top functions. This is intended to give you an instant insight into labelme2Datasets implemented functionality, and help decide if they suit your requirements.

Process an annotation file
Append bounding box to xml
Create a basic maker and xml
Save an image to an image file
Get base name from filename
Get bounding box boundaries
Convert a label file to an XML file
Generate coco annotation
Parse an annotation file
Saves the attr dict to an annotation file
Read an image from an annotation file
Get the category from a file
Get a list of xml annotations
Process a label file
Get the label names for the image
Save image and label
Get data and image from json file
Returns a dictionary mapping label names to strings

Get all kandi verified functions for this library.

labelme2Datasets Key Features

No Key Features are available at this moment for labelme2Datasets.

labelme2Datasets Examples and Code Snippets

No Code Snippets are available at this moment for labelme2Datasets.

Community Discussions

No Community Discussions are available at this moment for labelme2Datasets.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install labelme2Datasets

You can install using 'pip install labelme2Datasets' or download it from GitHub, PyPI.
You can use labelme2Datasets like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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