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Add high level API (Merge pull request #14780)
* Add high level API * Fix Model * Add DetectionModel * Add ClassificationModel * Fix classify * Add python test * Fix pytest * Fix comments to review * Fix detect * Fix docs * Modify DetectionOutput postprocessing * Fix test * Extract ref boxes * Fix draw rect * fix test * Add rect wrap * Fix wrap * Fix detect * Fix Rect wrap * Fix OCL_FP16 * Fix MyriadX * Fix nms * Fix NMS * Fix coords
This commit is contained in:
Lubov Batanina
committed by
Maksim Shabunin
Maksim Shabunin
parent
f482050f9a
commit
778f42ad34
@@ -992,6 +992,155 @@ CV__DNN_INLINE_NS_BEGIN
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CV_OUT std::vector<int>& indices,
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const float eta = 1.f, const int top_k = 0);
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/** @brief This class is presented high-level API for neural networks.
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*
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* Model allows to set params for preprocessing input image.
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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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{
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public:
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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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* @param[in] model Binary file contains trained weights.
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP Model(const String& model, const String& config = "");
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/**
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* @brief Create model from deep learning network.
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* @param[in] network Net object.
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*/
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CV_WRAP Model(const Net& network);
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/** @brief Set input size for frame.
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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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/** @brief Set input size for frame.
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* @param[in] width New input width.
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* @param[in] height New input height.
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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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/** @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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/** @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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/** @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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/** @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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/** @brief Set preprocessing parameters for frame.
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* @param[in] size New input size.
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* @param[in] mean Scalar with mean values which are subtracted from channels.
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* @param[in] scale Multiplier for frame values.
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* @param[in] swapRB Flag which indicates that swap first and last channels.
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* @param[in] crop Flag which indicates whether image will be cropped after resize or not.
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* blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
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*/
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CV_WRAP void setInputParams(double scale = 1.0, const Size& size = Size(),
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const Scalar& mean = Scalar(), bool swapRB = false, bool crop = false);
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/** @brief Given the @p input frame, create input blob, run net and return the output @p blobs.
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* @param[in] frame The input image.
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* @param[out] outs Allocated output blobs, which will store results of the computation.
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*/
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CV_WRAP void predict(InputArray frame, OutputArrayOfArrays outs);
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protected:
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struct Impl;
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Ptr<Impl> impl;
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};
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/** @brief This class represents high-level API for classification models.
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*
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* ClassificationModel allows to set params for preprocessing input image.
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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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{
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public:
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/**
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* @brief Create classification model from 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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* @param[in] model Binary file contains trained weights.
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP ClassificationModel(const String& model, const String& config = "");
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/**
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* @brief Create model from deep learning network.
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* @param[in] network Net object.
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*/
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CV_WRAP ClassificationModel(const Net& network);
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/** @brief Given the @p input frame, create input blob, run net and return top-1 prediction.
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* @param[in] frame The input image.
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*/
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std::pair<int, float> classify(InputArray frame);
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/** @overload */
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CV_WRAP void classify(InputArray frame, CV_OUT int& classId, CV_OUT float& conf);
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};
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/** @brief This class represents high-level API for object detection networks.
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*
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* DetectionModel allows to set params for preprocessing input image.
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* DetectionModel creates net from file with trained weights and config,
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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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{
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public:
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/**
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* @brief Create detection model from 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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* @param[in] model Binary file contains trained weights.
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP DetectionModel(const String& model, const String& config = "");
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/**
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* @brief Create model from deep learning network.
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* @param[in] network Net object.
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*/
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CV_WRAP DetectionModel(const Net& network);
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/** @brief Given the @p input frame, create input blob, run net and return result detections.
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* @param[in] frame The input image.
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* @param[out] classIds Class indexes in result detection.
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* @param[out] confidences A set of corresponding confidences.
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* @param[out] boxes A set of bounding boxes.
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* @param[in] confThreshold A threshold used to filter boxes by confidences.
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* @param[in] nmsThreshold A threshold used in non maximum suppression.
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*/
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CV_WRAP void detect(InputArray frame, CV_OUT std::vector<int>& classIds,
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CV_OUT std::vector<float>& confidences, CV_OUT std::vector<Rect>& boxes,
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float confThreshold = 0.5f, float nmsThreshold = 0.0f);
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};
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//! @}
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CV__DNN_INLINE_NS_END
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}
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