@@ -11,32 +11,32 @@ namespace face{
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class CV_EXPORTS_W FacemarkKazemi : public Algorithm
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{
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public:
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- struct CV_EXPORTS Params
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+ struct CV_EXPORTS_W_SIMPLE Params
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{
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/* *
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* \brief Constructor
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*/
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- Params ();
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+ CV_PROP Params ();
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// / cascade_depth This stores the deapth of cascade used for training.
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- unsigned long cascade_depth;
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+ CV_PROP_RW unsigned long cascade_depth;
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// / tree_depth This stores the max height of the regression tree built.
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- unsigned long tree_depth;
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+ CV_PROP_RW unsigned long tree_depth;
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// / num_trees_per_cascade_level This stores number of trees fit per cascade level.
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- unsigned long num_trees_per_cascade_level;
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+ CV_PROP_RW unsigned long num_trees_per_cascade_level;
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// / learning_rate stores the learning rate in gradient boosting, also reffered as shrinkage.
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- float learning_rate;
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+ CV_PROP_RW float learning_rate;
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// / oversampling_amount stores number of initialisations used to create training samples.
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- unsigned long oversampling_amount;
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+ CV_PROP_RW unsigned long oversampling_amount;
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// / num_test_coordinates stores number of test coordinates.
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- unsigned long num_test_coordinates;
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+ CV_PROP_RW unsigned long num_test_coordinates;
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// / lambda stores a value to calculate probability of closeness of two coordinates.
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- float lambda;
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+ CV_PROP_RW float lambda;
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// / num_test_splits stores number of random test splits generated.
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- unsigned long num_test_splits;
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+ CV_PROP_RW unsigned long num_test_splits;
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// / configfile stores the name of the file containing the values of training parameters
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- String configfile;
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+ CV_PROP_RW String configfile;
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};
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- static Ptr<FacemarkKazemi> create (const FacemarkKazemi::Params ¶meters = FacemarkKazemi::Params());
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+ CV_WRAP static Ptr<FacemarkKazemi> create (const FacemarkKazemi::Params ¶meters = FacemarkKazemi::Params());
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virtual ~FacemarkKazemi ();
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// / @brief training the facemark model, input are the file names of image list and landmark annotation
@@ -54,13 +54,13 @@ class CV_EXPORTS_W FacemarkKazemi : public Algorithm
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/* * @brief This function is used to load the trained model..
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*@param filename A variable of type cv::String which stores the name of the file in which trained model is stored.
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*/
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- virtual void loadModel (String filename)=0;
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+ CV_WRAP virtual void loadModel (String filename)=0;
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/* * @brief This functions retrieves a centered and scaled face shape, according to the bounding rectangle.
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*@param image A variable of type cv::InputArray which stores the image whose landmarks have to be found
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*@param faces A variable of type cv::InputArray which stores the bounding boxes of faces found in a given image.
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*@param landmarks A variable of type cv::InputOutputArray which stores the landmarks of all the faces found in the image
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*/
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- virtual bool fit ( InputArray image, InputArray faces, InputOutputArray landmarks )=0;// !< from many ROIs
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+ CV_WRAP virtual bool fit ( InputArray image, InputArray faces, OutputArray landmarks )=0;// !< from many ROIs
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// / set the custom face detector
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virtual bool setFaceDetector (bool (*f)(InputArray , OutputArray, void *), void* userData)=0;
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// / get faces using the custom detector
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