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Description:

qrdet 2.5

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 = detection['bbox_xyxy']
confidence = detection['confidence']
segmenation_xy = detection['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
QRDetector(model_size = 's', conf_th = 0.5, nms_iou = 0.3, weights_folder = '<qrdet_package>/.model')

model_size: "n"|"s"|"m"|"l". Size of the model to load. Smaller models will be faster, while larger models will be more capable for difficult situations. Default: 's'.
conf_th: float. Confidence threshold to consider that a detection is valid. Incresing this value will reduce false positives while decreasing will reduce false_negatives. Default: 0.5.
nms_iou: float. Intersection over Union (IoU) threshold for Non-Maximum Suppression (NMS). NMS is a technique used to eliminate redundant bounding boxes for the same object. Increase this number if you find problems with duplicated detections. Default: 0.3
weights_folder: str. Folder where detection model will be downloaded. By default, it points out to an internal folder within the package, 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 #11. Default: '<qrdet_package>/.model'.

QRDetector.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.