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 () | 
|   | 
| 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  | 
          ( | 
          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 = "",  | 
        
        
           | 
           | 
          const std::string &  | 
          super_resolution_caffe_model_path = ""  | 
        
        
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          ) | 
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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
 - 
  
    | 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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inline   | 
  
 
 
◆ 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
 - 
  
    | 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  | 
          ( | 
          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: