qrdet 2.4

Robust QR Detector based on YOLOv8

QRDet

QRDet is a robust QR Detector based on YOLOv8.

QRDet will detect & segment QR codes even in difficult positions or tricky images. If you are looking for a complete QR Detection + Decoding pipeline, take a look at QReader.

Installation

To install QRDet, simply run:

pip install qrdet

Usage

There is only one function you'll need to call to use QRDet, detect:

from qrdet import QRDetector
import cv2

detector = QRDetector(model_size='s')
image = cv2.imread(filename='resources/qreader_test_image.jpeg')
detections = detector.detect(image=image, is_bgr=True)

# Draw the detections
for detection in detections:
    x1, y1, x2, y2 = detections['bbox_xyxy']
    confidence = detections['confidence']
    segmenation_xy = detections['quadrilateral_xy']
    cv2.rectangle(image, (x1, y1), (x2, y2), color=(0, 255, 0), thickness=2)
    cv2.putText(image, f'{confidence:.2f}', (x1, y1 - 10), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
                fontScale=1, color=(0, 255, 0), thickness=2)
# Save the results
cv2.imwrite(filename='resources/qreader_test_image_detections.jpeg', img=image)

API Reference

QReader.detect(image, is_bgr = False, **kwargs)

• image: np.ndarray|'PIL.Image'|'torch.Tensor'|str. np.ndarray of shape (H, W, 3), PIL.Image, Tensor of shape (1, 3, H, W), or path/url to the image to predict. 'screen' for grabbing a screenshot.

• is_bgr: bool. If True the image is expected to be in BGR. Otherwise, it will be expected to be RGB. Only used when image is np.ndarray or torch.tensor. Default: False

• legacy: bool. If sent as kwarg, will parse the output to make it identical to 1.x versions. Not Recommended. Default: False.

• 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.

Key	Value Desc.	Value Type	Value Form
confidence	Detection confidence	float	conf.
bbox_xyxy	Bounding box	np.ndarray (4)	[x1, y1, x2, y2]
cxcy	Center of bounding box	tuple[float, float]	(x, y)
wh	Bounding box width and height	tuple[float, float]	(w, h)
polygon_xy	Precise polygon that segments the QR	np.ndarray (N, 2)	[[x1, y1], [x2, y2], ...]
quad_xy	Four corners polygon that segments the QR	np.ndarray (4, 2)	[[x1, y1], ..., [x4, y4]]
padded_quad_xy	quad_xy padded to fully cover polygon_xy	np.ndarray (4, 2)	[[x1, y1], ..., [x4, y4]]
image_shape	Shape of the input image	tuple[float, float]	(h, w)

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

Acknowledgements

This library is based on the following projects:

• YoloV8 model for Object Segmentation.
• QuadrilateralFitter for fitting 4 corners polygons from noisy segmentation outputs.