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. More...

#include <opencv2/wechat_qrcode.hpp>

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

	~WeChatQRCode ()

std::vector< std::string >	detectAndDecode (InputArray img, OutputArrayOfArrays points=noArray())
	Both detects and decodes QR code. To simplify the usage, there is a only API: detectAndDecode. More...

float	getScaleFactor ()

void	setScaleFactor (float _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 More...

Protected Attributes
Ptr< Impl >	p

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 ( )

inline

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.

◆ 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	) ->	None

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 = scaleFactor*width height = scaleFactor*width

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

protected

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

opencv2/wechat_qrcode.hpp

Public Member Functions

Protected Attributes