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Add preprocessing warps for separate parameters
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@@ -999,9 +999,14 @@ CV__DNN_INLINE_NS_BEGIN
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* Model creates net from file with trained weights and config,
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* sets preprocessing input and runs forward pass.
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*/
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class CV_EXPORTS_W Model : public Net
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class CV_EXPORTS_W_SIMPLE Model : public Net
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{
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public:
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/**
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* @brief Default constructor.
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*/
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Model();
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/**
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* @brief Create model from deep learning network represented in one of the supported formats.
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* An order of @p model and @p config arguments does not matter.
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@@ -1020,7 +1025,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param[in] size New input size.
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* @note If shape of the new blob less than 0, then frame size not change.
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*/
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Model& setInputSize(const Size& size);
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CV_WRAP Model& setInputSize(const Size& size);
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/** @brief Set input size for frame.
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* @param[in] width New input width.
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@@ -1028,27 +1033,27 @@ CV__DNN_INLINE_NS_BEGIN
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* @note If shape of the new blob less than 0,
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* then frame size not change.
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*/
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Model& setInputSize(int width, int height);
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CV_WRAP Model& setInputSize(int width, int height);
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/** @brief Set mean value for frame.
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* @param[in] mean Scalar with mean values which are subtracted from channels.
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*/
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Model& setInputMean(const Scalar& mean);
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CV_WRAP Model& setInputMean(const Scalar& mean);
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/** @brief Set scalefactor value for frame.
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* @param[in] scale Multiplier for frame values.
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*/
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Model& setInputScale(double scale);
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CV_WRAP Model& setInputScale(double scale);
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/** @brief Set flag crop for frame.
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* @param[in] crop Flag which indicates whether image will be cropped after resize or not.
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*/
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Model& setInputCrop(bool crop);
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CV_WRAP Model& setInputCrop(bool crop);
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/** @brief Set flag swapRB for frame.
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* @param[in] swapRB Flag which indicates that swap first and last channels.
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*/
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Model& setInputSwapRB(bool swapRB);
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CV_WRAP Model& setInputSwapRB(bool swapRB);
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/** @brief Set preprocessing parameters for frame.
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* @param[in] size New input size.
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@@ -1078,7 +1083,7 @@ CV__DNN_INLINE_NS_BEGIN
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* ClassificationModel creates net from file with trained weights and config,
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* sets preprocessing input, runs forward pass and return top-1 prediction.
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*/
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class CV_EXPORTS_W ClassificationModel : public Model
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class CV_EXPORTS_W_SIMPLE ClassificationModel : public Model
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{
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public:
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/**
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@@ -1111,7 +1116,7 @@ CV__DNN_INLINE_NS_BEGIN
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* sets preprocessing input, runs forward pass and return result detections.
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* For DetectionModel SSD, Faster R-CNN, YOLO topologies are supported.
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*/
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class CV_EXPORTS_W DetectionModel : public Model
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class CV_EXPORTS_W_SIMPLE DetectionModel : public Model
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{
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public:
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/**
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