{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/9"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/19"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"},{"label":"Education","url":"https://skillfed.io/packages/category/education"}],"enrichment":{"capability":"Converts between bounding box formats (COCO, YOLO, VOC, FiftyOne, Albumentations) and computes geometric operations like IoU, area, and intersection.","skillfed_tags":["computer-vision","object-detection","data-preprocessing"],"use_cases":["Convert bounding boxes between COCO and YOLO formats when switching between dataset sources or training frameworks","Compute IoU and intersection metrics for non-maximum suppression or model evaluation pipelines","Batch-convert annotation files across YOLO, COCO, and VOC formats during dataset preparation","Validate and filter out-of-bounds boxes with strict mode during data cleaning","Perform geometric operations (area, union, difference) on detected objects for post-processing"],"what_it_does":"PyBboxes is a lightweight toolkit for working with bounding boxes in computer vision workflows. It handles conversion between five common bounding box formats used across popular frameworks and datasets: COCO (top-left + width/height), YOLO (center + normalized width/height), VOC (top-left + bottom-right), FiftyOne (normalized COCO), and Albumentations (normalized VOC). The package also provides geometric operations like IoU computation, area calculation, and intersection/union calculations between boxes.\n\nThe library is built on numpy, pycocotools, and pyyaml and supports both strict and lenient modes for handling out-of-bounds boxes. It includes an annotation file converter for batch processing YOLO, COCO, and VOC format files. Typical use cases are converting between dataset formats during preprocessing, computing overlap metrics during model evaluation, and batch-converting annotation files across different computer vision frameworks.","worth_installing":"Yes. PyBboxes is a focused, low-friction utility with no known vulnerabilities, permissive MIT licensing, and stable production status. It solves a concrete problem in computer vision workflows\u2014format conversion and box geometry\u2014that most practitioners encounter. Dormant maintenance is acceptable for a mature, feature-complete library; the last commit (2024-10-07) is recent enough to indicate active monitoring. Install if you work with multiple bounding box formats or need reliable geometric operations."},"id":"pybboxes","links":{"html":"https://skillfed.io/packages/pybboxes","md":"https://skillfed.io/packages/pybboxes.md","pypi":"https://pypi.org/project/pybboxes/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-10-07","license_spdx":null,"license_treatment":"permissive","name":"pybboxes","python_support":"supports_current","summary":"Light Weight Toolkit for Bounding Boxes"},"popularity":{"monthly_downloads":118906,"position":12099,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.0"}