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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