{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/21"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/11"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"},{"label":"Graphics","url":"https://skillfed.io/packages/category/multimedia-graphics/2"},{"label":"Image Recognition","url":"https://skillfed.io/packages/category/scientific-engineering-image-recognition"}],"enrichment":{"capability":"Detects and segments QR codes in images using a YOLOv8-based model, returning bounding boxes, confidence scores, and precise polygon coordinates even in difficult positions.","skillfed_tags":["qr-code","object-detection","computer-vision"],"use_cases":["Locating QR codes in product photos or inventory images for automated scanning workflows.","Extracting precise QR code boundaries in rotated or skewed images before passing to a decoder.","Building a QR detection pipeline in computer vision applications where confidence scores and segmentation polygons are needed.","Processing batches of images to find all QR codes and their exact positions for downstream processing.","Detecting QR codes in challenging conditions where simpler detectors fail."],"what_it_does":"QRDet is a QR code detector built on YOLOv8 that finds and segments QR codes in images, even when they are rotated, partially obscured, or in challenging lighting. It returns detection results as dictionaries containing bounding boxes, confidence scores, and precise polygon coordinates that outline the QR code's shape. The package offers four model sizes (n, s, m, l) to trade off speed against detection capability, and allows tuning of confidence thresholds and non-maximum suppression parameters to control false positives and duplicate detections.\n\nThe core dependency chain includes ultralytics (for YOLOv8), numpy (for array operations), quadrilateral-fitter (for polygon refinement), requests (for model weight downloads), and tqdm (for progress feedback). It accepts images as numpy arrays, PIL Images, torch Tensors, file paths, URLs, or screenshots. The package is designed for detection only; if you need both detection and decoding, the description suggests looking at QReader instead.","worth_installing":"Yes, if you need robust QR detection with polygon-level precision. The package is stable (Production/Stable classifier), permissively licensed, has no known vulnerabilities, and offers flexible model sizing. The aging maintenance status (last release mid-2024) is a minor concern but not a blocker if the current version meets your needs. Install friction is low. Not suitable if you also need QR decoding in the same package."},"id":"qrdet","links":{"html":"https://skillfed.io/packages/qrdet","md":"https://skillfed.io/packages/qrdet.md","pypi":"https://pypi.org/project/qrdet/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2024-06-15","license_spdx":null,"license_treatment":"permissive","name":"qrdet","python_support":"unspecified","summary":"Robust QR Detector based on YOLOv8"},"popularity":{"monthly_downloads":81588,"position":14214,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.5"}