A QR code can store up to 4,296 alphanumeric characters, although the commonly used ones tend to contain fewer characters and so allow for easy decoding by a smartphone’s camera. Short for ‘ Quick Response’, a QR code is a type of machine-scannable barcode that, as implied by its name, is designed to be read and interpreted instantly by a digital device. In this article, we’ll look at how fraudsters use the codes for illicit profit and what to watch out for when scanning a QR code. This trend has even prompted an alert from the United States’ Federal Bureau of Investigation (FBI). Much like any other popular technology, however, the widespread use of QR codes has also caught the attention of scammers, who have co-opted them for nefarious purposes. These days, you can spot them all over the place, with the codes put to use for everything from displaying restaurant menus to facilitating contactless transactions to being built into contact tracing apps. The humble squares may have been around since 1994, but it wasn’t until the COVID-19 era that they became a truly household name. The improved detection-decoding rate that QReader achieves comes from the combination of different image pre-processing techniques and the YOLOv8 based QR detector that is able to detect QR codes in harder conditions than classical Computer Vision methods.QR codes are having a moment. Note that QReader internally uses pyzbar as decoder. The output of the previous code is: Image: test_mobile.jpeg -> QReader: (''). If return_detections is True it will look like: (('Decoded QR 1'. If return_detections is False, the output will look like: ('Decoded QR 1', 'Decoded QR 2', None, 'Decoded QR 4'. Returns: tuple | tuple]], str | None]]: A tuple with all detected QR codes decodified. If True, the received image is expected to be BGR instead of RGB. If False, it will return only the decoded content of the QR codes. If True, it will return the full detection results together with the decoded QRs. It is expected to be RGB or BGR ( uint8). This method will decode the QR codes in the given image and return the decoded strings (or None, if any of them was detected but not decoded). tect_and_decode(image, return_detections = False) 'cp65001' for Asian languages (Thanks to for the suggestion).Recommendations that have been found useful: If you find some characters being decoded incorrectly, try to set a Code Page that matches your specific charset. The encoding to reencode the utf-8 decoded QR string. Values closer to 0.0 can get more False Positives, while values closer to 1.0 can lose difficult QRs. The minimum confidence of the QR detection to be considered valid. Larger models are more accurate but slower. It can be 'n' (nano), 's' (small), 'm' (medium) or 'l' (large). Please, try to instantiate it just once to avoid loading the model every time you need to detect a QR code. API Reference QReader(model_size = 's', min_confidence = 0.5, reencode_to = 'shift-jis') NOTE: Some entries can be None, it will happen when a QR have been detected but couldn't be decoded. detect_and_decode ( image = image )ĭetect_and_decode will return a tuple containing the decoded string of every QR found in the image. COLOR_BGR2RGB ) # Use the detect_and_decode function to get the decoded QR data decoded_text = qreader. For most use cases, you'll only need to call detect_and_decode: from qreader import QReader import cv2 # Create a QReader instance qreader = QReader () # Get the image that contains the QR code image = cv2. QReader is a very simple and straight-forward library. To do so, run: pip install torch -no-cache-dir (Thanks to for his advice). NOTE: If you're running QReader in a server with very limited resources, you may want to install the CPU version of PyTorch, before installing QReader. If it happens, install the vcredist_圆4.exe from the Visual C++ Redistributable Packages for Visual Studio 2013 Rarely, you can see an ugly ImportError related with lizbar-64.dll. You may need to install some additional pyzbar dependencies: To install QReader, simply run: pip install qreader Using the information extracted from this QR Detector, QReader transparently applies, on top of Pyzbar, different image preprocessing techniques that maximize the decoding rate on difficult images. Powered by a YOLOv8 model.īehind the scenes, the library is composed by two main building blocks: A YOLOv8 QR Detector model trained to detect and segment QR codes (also offered as stand-alone), and the Pyzbar QR Decoder. QReader is a Robust and Straight-Forward solution for reading difficult and tricky QR codes within images in Python.
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