A class to post process model predictions.  
 More...
#include <opencv2/core_detect.hpp>
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| void  | assert_predictions (std::vector< std::vector< double > > *min_max_boxes) | 
|   | Ensures that the bounding box values are within image boundaries.  More...
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|   | 
| void  | filter_top_n (std::vector< std::vector< double > > *probs, std::vector< std::vector< double > > *boxes, std::vector< std::vector< double > > &top_n_boxes, std::vector< size_t > &top_n_idxs, std::vector< double > &top_n_probs) | 
|   | Filter top n predictions.  More...
  | 
|   | 
| void  | final_probability_dist (std::vector< std::vector< double > > *final_probs) | 
|   | Computes final probability values of each bounding box.  More...
  | 
|   | 
| void  | intersection_over_union (std::vector< std::vector< double > > *boxes, std::vector< double > *base_box, std::vector< double > *iou) | 
|   | Computes intersection over union of bounding boxes.  More...
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|   | 
| void  | nms_wrapper (std::vector< std::vector< double > > &top_n_boxes, std::vector< size_t > &top_n_idxs, std::vector< double > &top_n_probs) | 
|   | Wrapper to apply Non-Maximal Supression.  More...
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|   | 
| std::vector< bool >  | non_maximal_suppression (std::vector< std::vector< double > > *boxes, std::vector< double > *probs) | 
|   | Applies Non-Maximal Supression.  More...
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|   | 
| void  | transform_bboxes (std::vector< std::vector< double > > *bboxes) | 
|   | Transform relative coordinates from ConvDet to bounding box coordinates.  More...
  | 
|   | 
| void  | transform_bboxes_inv (std::vector< std::vector< double > > *pre, std::vector< std::vector< double > > *post) | 
|   | Transform bounding boxes from [x, y, h, w] to [xmin, ymin, xmax, ymax].  More...
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|   | 
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| static bool  | comparator (std::pair< double, size_t > l1, std::pair< double, size_t > l2) | 
|   | 
A class to post process model predictions. 
 
◆ InferBbox()
      
        
          | cv::dnn_objdetect::InferBbox::InferBbox  | 
          ( | 
          Mat  | 
          _delta_bbox,  | 
        
        
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          Mat  | 
          _class_scores,  | 
        
        
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          Mat  | 
          _conf_scores  | 
        
        
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          ) | 
           |  | 
        
      
 
Default constructer. 
- Parameters
 - 
  
    | _delta_bbox | Blob containing relative coordinates of bounding boxes  | 
    | _class_scores | Blob containing the probability values of each class  | 
    | _conf_scores | Blob containing the confidence scores  | 
  
   
 
 
◆ assert_predictions()
  
  
      
        
          | void cv::dnn_objdetect::InferBbox::assert_predictions  | 
          ( | 
          std::vector< std::vector< double > > *  | 
          min_max_boxes | ) | 
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protected   | 
  
 
Ensures that the bounding box values are within image boundaries. 
- Parameters
 - 
  
    | min_max_boxes | Vector containing bounding boxes of the form [xmin, ymin, xmax, ymax]  | 
  
   
 
 
◆ comparator()
  
  
      
        
          | static bool cv::dnn_objdetect::InferBbox::comparator  | 
          ( | 
          std::pair< double, size_t >  | 
          l1,  | 
         
        
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          std::pair< double, size_t >  | 
          l2  | 
         
        
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          ) | 
           |  | 
         
       
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inlinestaticprotected   | 
  
 
 
◆ filter()
      
        
          | void cv::dnn_objdetect::InferBbox::filter  | 
          ( | 
          double  | 
          thresh = 0.8 | ) | 
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Filters the bounding boxes. 
 
 
◆ filter_top_n()
  
  
      
        
          | void cv::dnn_objdetect::InferBbox::filter_top_n  | 
          ( | 
          std::vector< std::vector< double > > *  | 
          probs,  | 
         
        
           | 
           | 
          std::vector< std::vector< double > > *  | 
          boxes,  | 
         
        
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           | 
          std::vector< std::vector< double > > &  | 
          top_n_boxes,  | 
         
        
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          std::vector< size_t > &  | 
          top_n_idxs,  | 
         
        
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          std::vector< double > &  | 
          top_n_probs  | 
         
        
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          ) | 
           |  | 
         
       
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protected   | 
  
 
Filter top n predictions. 
- Parameters
 - 
  
    | probs | Final probability values of bounding boxes  | 
    | boxes | Predicted bounding box co-ordinates  | 
    | top_n_boxes | Contains bounding box co-ordinates of top n boxes  | 
    | top_n_idxs | Containes class indices of top n bounding boxes  | 
    | top_n_probs | Contains probability values of top n bounding boxes  | 
  
   
 
 
◆ final_probability_dist()
  
  
      
        
          | void cv::dnn_objdetect::InferBbox::final_probability_dist  | 
          ( | 
          std::vector< std::vector< double > > *  | 
          final_probs | ) | 
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protected   | 
  
 
Computes final probability values of each bounding box. 
- Parameters
 - 
  
    | final_probs | Vector to hold the probability values  | 
  
   
 
 
◆ intersection_over_union()
  
  
      
        
          | void cv::dnn_objdetect::InferBbox::intersection_over_union  | 
          ( | 
          std::vector< std::vector< double > > *  | 
          boxes,  | 
         
        
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           | 
          std::vector< double > *  | 
          base_box,  | 
         
        
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          std::vector< double > *  | 
          iou  | 
         
        
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          ) | 
           |  | 
         
       
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protected   | 
  
 
Computes intersection over union of bounding boxes. 
- Parameters
 - 
  
    | boxes | Vector of bounding box co-ordinates  | 
    | base_box | Base box wrt which IOU is calculated  | 
    | iou | Vector to store IOU values  | 
  
   
 
 
◆ nms_wrapper()
  
  
      
        
          | void cv::dnn_objdetect::InferBbox::nms_wrapper  | 
          ( | 
          std::vector< std::vector< double > > &  | 
          top_n_boxes,  | 
         
        
           | 
           | 
          std::vector< size_t > &  | 
          top_n_idxs,  | 
         
        
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          std::vector< double > &  | 
          top_n_probs  | 
         
        
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          ) | 
           |  | 
         
       
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protected   | 
  
 
Wrapper to apply Non-Maximal Supression. 
- Parameters
 - 
  
    | top_n_boxes | Contains bounding box co-ordinates of top n boxes  | 
    | top_n_idxs | Containes class indices of top n bounding boxes  | 
    | top_n_probs | Contains probability values of top n bounding boxes  | 
  
   
 
 
◆ non_maximal_suppression()
  
  
      
        
          | std::vector<bool> cv::dnn_objdetect::InferBbox::non_maximal_suppression  | 
          ( | 
          std::vector< std::vector< double > > *  | 
          boxes,  | 
         
        
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           | 
          std::vector< double > *  | 
          probs  | 
         
        
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          ) | 
           |  | 
         
       
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protected   | 
  
 
Applies Non-Maximal Supression. 
- Parameters
 - 
  
    | boxes | Bounding box co-ordinates belonging to one class  | 
    | probs | Probability values of boxes belonging to one class  | 
  
   
 
 
◆ transform_bboxes()
  
  
      
        
          | void cv::dnn_objdetect::InferBbox::transform_bboxes  | 
          ( | 
          std::vector< std::vector< double > > *  | 
          bboxes | ) | 
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protected   | 
  
 
Transform relative coordinates from ConvDet to bounding box coordinates. 
- Parameters
 - 
  
    | bboxes | Vector to hold the predicted bounding boxes  | 
  
   
 
 
◆ transform_bboxes_inv()
  
  
      
        
          | void cv::dnn_objdetect::InferBbox::transform_bboxes_inv  | 
          ( | 
          std::vector< std::vector< double > > *  | 
          pre,  | 
         
        
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           | 
          std::vector< std::vector< double > > *  | 
          post  | 
         
        
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          ) | 
           |  | 
         
       
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protected   | 
  
 
Transform bounding boxes from [x, y, h, w] to [xmin, ymin, xmax, ymax]. 
- Parameters
 - 
  
    | pre | Vector conatining initial co-ordinates  | 
    | post | Vector containing the transformed co-ordinates  | 
  
   
 
 
◆ detections
      
        
          | std::vector<object> cv::dnn_objdetect::InferBbox::detections | 
        
      
 
Vector which holds the final detections of the model. 
 
 
The documentation for this class was generated from the following file: