QReader is a Robust and Straight-Forward solution for reading difficult and tricky QR codes within images in Python. Powered by a YOLOv8 model.

Behind 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. 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.

To install QReader, simply run:

pip install qreader

You may need to install some additional pyzbar dependencies:

On Windows:

Rarely, you can see an ugly ImportError related with lizbar-64.dll. If it happens, install the vcredist_x64.exe from the Visual C Redistributable Packages for Visual Studio 2013

On Linux:

sudo apt-get install libzbar0

On Mac OS X:

brew install zbar

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. To do so, run: pip install torch --no-cache-dir (Thanks to @cjwalther for his advice).

QReader is a very simple and straight-forward library. 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.cvtColor(cv2.imread("path/to/image.png"), cv2.COLOR_BGR2RGB)

# Use the detect_and_decode function to get the decoded QR data
decoded_text = qreader.detect_and_decode(image=image)

detect_and_decode will return a tuple containing the decoded string of every QR found in the image.

NOTE: Some entries can be None, it will happen when a QR have been detected but couldn't be decoded.

This is the main class of the library. Please, try to instantiate it just once to avoid loading the model every time you need to detect a QR code.

• model_size: str. The size of the model to use. It can be 'n' (nano), 's' (small), 'm' (medium) or 'l' (large). Larger models are more accurate but slower. Default: 's'.
• min_confidence: float. The minimum confidence of the QR detection to be considered valid. Values closer to 0.0 can get more False Positives, while values closer to 1.0 can lose difficult QRs. Default (and recommended): 0.5.
• reencode_to: str | None. The encoding to reencode the utf-8 decoded QR string. If None, it won't re-encode. If you find some characters being decoded incorrectly, try to set a Code Page that matches your specific charset. Recommendations that have been found useful:
  • 'shift-jis' for Germanic languages
  • 'cp65001' for Asian languages (Thanks to @nguyen-viet-hung for the suggestion)
• weights_folder: str|None. Folder where the detection model will be downloaded. If None, it will be downloaded to the default qrdet package internal folder, making sure that it gets correctly removed when uninstalling. You could need to change it when working in environments like AWS Lambda where only /tmp folder is writable, as issued in #21. Default: None (<qrdet_package>/.model).

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).

• image: np.ndarray. The image to be read. It is expected to be RGB or BGR (uint8). Format (HxWx3).

• return_detections: bool. If True, it will return the full detection results together with the decoded QRs. If False, it will return only the decoded content of the QR codes.

• is_bgr: boolean. If True, the received image is expected to be BGR instead of RGB.

• Returns: tuple[str | None] | tuple[tuple[dict[str, np.ndarray | float | tuple[float | int, float | int]]], str | None]]: A tuple with all detected QR codes decodified. If return_detections is False, the output will look like: ('Decoded QR 1', 'Decoded QR 2', None, 'Decoded QR 4', ...). If return_detections is True it will look like: (('Decoded QR 1', {'bbox_xyxy': (x1_1, y1_1, x2_1, y2_1), 'confidence': conf_1}), ('Decoded QR 2', {'bbox_xyxy': (x1_2, y1_2, x2_2, y2_2), 'confidence': conf_2, ...}), ...). Look QReader.detect() for more information about detections format.

This method detects the QR codes in the image and returns a tuple of dictionaries with all the detection information.

• image: np.ndarray. The image to be read. It is expected to be RGB or BGR (uint8). Format (HxWx3).

• is_bgr: boolean. If True, the received image is expected to be BGR instead of RGB.

• Returns: tuple[dict[str, np.ndarray|float|tuple[float|int, float|int]]]. A tuple of dictionaries containing all the information of every detection. Contains the following keys.

NOTE:

• All np.ndarray values are of type np.float32
• All keys (except confidence and image_shape) have a normalized ('n') version. For example,bbox_xyxy represents the bbox of the QR in image coordinates [[0., im_w], [0., im_h]], while bbox_xyxyn contains the same bounding box in normalized coordinates [0., 1.].
• bbox_xyxy[n] and polygon_xy[n] are clipped to image_shape. You can use them for indexing without further management

NOTE: Is this the only method you will need? Take a look at QRDet.

This method decodes a single QR code on the given image, described by a detection result.

Internally, this method will run the pyzbar decoder, using the information of the detection_result, to apply different image preprocessing techniques that heavily increase the detecoding rate.

• image: np.ndarray. NumPy Array with the image that contains the QR to decode. The image is expected to be in uint8 format [HxWxC], RGB.

• detection_result: dict[str, np.ndarray|float|tuple[float|int, float|int]]. One of the detection dicts returned by the detect method. Note that QReader.detect() returns a tuple of these dict. This method expects just one of them.

• Returns: str | None. The decoded content of the QR code or None if it couldn't be read.

The following code will try to decode these images containing QRs with QReader, pyzbar and OpenCV.

from qreader import QReader
from cv2 import QRCodeDetector, imread
from pyzbar.pyzbar import decode

# Initialize the three tested readers (QRReader, OpenCV and pyzbar)
qreader_reader, cv2_reader, pyzbar_reader = QReader(), QRCodeDetector(), decode

for img_path in ('test_mobile.jpeg', 'test_draw_64x64.jpeg'):
    # Read the image
    img = imread(img_path)

    # Try to decode the QR code with the three readers
    qreader_out = qreader_reader.detect_and_decode(image=img)
    cv2_out = cv2_reader.detectAndDecode(img=img)[0]
    pyzbar_out = pyzbar_reader(image=img)
    # Read the content of the pyzbar output (double decoding will save you from a lot of wrongly decoded characters)
    pyzbar_out = tuple(out.data.data.decode('utf-8').encode('shift-jis').decode('utf-8') for out in pyzbar_out)

    # Print the results
    print(f"Image: {img_path} -> QReader: {qreader_out}. OpenCV: {cv2_out}. pyzbar: {pyzbar_out}.")

The output of the previous code is:

Image: test_mobile.jpeg -> QReader: ('https://github.com/Eric-Canas/QReader'). OpenCV: . pyzbar: ().
Image: test_draw_64x64.jpeg -> QReader: ('https://github.com/Eric-Canas/QReader'). OpenCV: . pyzbar: ().

Note that QReader internally uses pyzbar as decoder. 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.

                             

Method	Max Rotation Degrees
Pyzbar	17º
OpenCV	46º
QReader	79º