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.
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#include <opencv2/wechat_qrcode.hpp>
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| 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...
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| ~WeChatQRCode () |
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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...
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float | getScaleFactor () |
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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 = scaleFactor*width height = scaleFactor*width More...
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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.
◆ WeChatQRCode()
cv::wechat_qrcode::WeChatQRCode::WeChatQRCode |
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const std::string & |
detector_prototxt_path = "" , |
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const std::string & |
detector_caffe_model_path = "" , |
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const std::string & |
super_resolution_prototxt_path = "" , |
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const std::string & |
super_resolution_caffe_model_path = "" |
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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
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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 |
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◆ detectAndDecode()
Python: |
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| 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
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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 |
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Python: |
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| cv.wechat_qrcode.WeChatQRCode.getScaleFactor( | | ) -> | retval |
◆ setScaleFactor()
void cv::wechat_qrcode::WeChatQRCode::setScaleFactor |
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float |
_scalingFactor | ) |
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Python: |
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| 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.
Ptr<Impl> cv::wechat_qrcode::WeChatQRCode::p |
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protected |
The documentation for this class was generated from the following file: