WeChat QR code detector for detecting and parsing QR code.#

Classes#

Name	Description
`class cv::wechat_qrcode::WeChatQRCode`	WeChat QRCode includes two CNN-based models: A object detection model and a super resolution model. Object detection model is applied to detect QRCode with the bounding box. super resolution model is applied to zoom in QRCode when it is small.

Class cv::wechat_qrcode::WeChatQRCode#

WeChat QRCode includes two CNN-based models: A object detection model and a super resolution model. Object detection model is applied to detect QRCode with the bounding box. super resolution model is applied to zoom in QRCode when it is small.

#include <opencv2/wechat_qrcode.hpp>

Collaboration diagram for cv::wechat_qrcode::WeChatQRCode:

Detailed Description#

WeChat QRCode includes two CNN-based models: A object detection model and a super resolution model. Object detection model is applied to detect QRCode with the bounding box. super resolution model is applied to zoom in QRCode when it is small.

Constructor & Destructor Documentation#

WeChatQRCode()#

cv::wechat_qrcode::WeChatQRCode::WeChatQRCode(
const std::string & detector_prototxt_path = “”,
const std::string & detector_caffe_model_path = “”,
const std::string & super_resolution_prototxt_path = “”,
const std::string & super_resolution_caffe_model_path = “” )

Python:

cv.wechat_qrcode.WeChatQRCode([, detector_prototxt_path[, detector_caffe_model_path[, super_resolution_prototxt_path[, super_resolution_caffe_model_path]]]]) -> <wechat_qrcode_WeChatQRCode object>

Initialize the WeChatQRCode. It includes two models, which are packaged with caffe format. Therefore, there are prototxt and caffe models (In total, four paramenters).

Parameters

detector_prototxt_path — prototxt file path for the detector
detector_caffe_model_path — caffe model file path for the detector
super_resolution_prototxt_path — prototxt file path for the super resolution model
super_resolution_caffe_model_path — caffe file path for the super resolution model

~WeChatQRCode()#

cv::wechat_qrcode::WeChatQRCode::~WeChatQRCode()

Member Function Documentation#

detectAndDecode()#

std::vector< std::string > cv::wechat_qrcode::WeChatQRCode::detectAndDecode(
InputArray img,
OutputArrayOfArrays points = noArray() )

Python:

cv.wechat_qrcode.WeChatQRCode.detectAndDecode(img[, points]) -> retval, points

Both detects and decodes QR code. To simplify the usage, there is a only API: detectAndDecode.

Parameters

img — supports grayscale or color (BGR) image.
points — optional output array of vertices of the found QR code quadrangle. Will be empty if not found.

Returns

list of decoded string.

Here is the call graph for this function:

getScaleFactor()#

float cv::wechat_qrcode::WeChatQRCode::getScaleFactor()

Python:

cv.wechat_qrcode.WeChatQRCode.getScaleFactor() -> retval

setScaleFactor()#

void cv::wechat_qrcode::WeChatQRCode::setScaleFactor(float _scalingFactor)

Python:

cv.wechat_qrcode.WeChatQRCode.setScaleFactor(_scalingFactor)

set scale factor QR code detector use neural network to detect QR. Before running the neural network, the input image is pre-processed by scaling. By default, the input image is scaled to an image with an area of 160000 pixels. The scale factor allows to use custom scale the input image: width = scaleFactorwidth height = scaleFactorwidth

scaleFactor valuse must be > 0 and <= 1, otherwise the scaleFactor value is set to -1 and use default scaled to an image with an area of 160000 pixels.

Member Data Documentation#

p#

Ptr< Impl > cv::wechat_qrcode::WeChatQRCode::p

Source file#

The documentation for this class was generated from the following file:

opencv2/wechat_qrcode.hpp