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@ -162,12 +162,12 @@ You can passes your own DNN layers into the DeepGaze1 object and retrain it with |
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class CV_EXPORTS_W DeepGaze1 : public StaticSaliency |
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{ |
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private: |
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/** @brief This is the field to store the input DNN
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* @sa dnn::Net |
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*/ |
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/** @brief This is the field to store the input DNN
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* @sa dnn::Net |
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*/ |
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dnn::Net net; |
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/** @brief This is the field to store the name of selected layer
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*/ |
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/** @brief This is the field to store the name of selected layer
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*/ |
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std::vector<std::string> layers_names; |
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/** @brief This is the field to store the weight of selected layer
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*/ |
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@ -297,11 +297,11 @@ This method also provides an optimization framework may help foreground based sa |
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class CV_EXPORTS_W BackgroundContrast : public StaticSaliency |
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{ |
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private: |
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/** @brief
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* limitOfSp is the maximum number of superpixel |
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* nOfLevel, usePrior, histBin is same as the seed superpixel method |
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* bgWei determine the weight of background when optimized with foreground method |
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*/ |
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/** @brief
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* limitOfSp is the maximum number of superpixel |
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* nOfLevel, usePrior, histBin is same as the seed superpixel method |
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* bgWei determine the weight of background when optimized with foreground method |
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*/ |
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int limitOfSP; |
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int nOfLevel; |
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int usePrior; |
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