85 lines
4.2 KiB
C
85 lines
4.2 KiB
C
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#ifndef ANSONNXSEG_H
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#define ANSONNXSEG_H
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#pragma once
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#include "ANSEngineCommon.h"
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#include "engine.h"
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namespace ANSCENTER {
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class ANSENGINE_API ANSONNXSEG :public ANSODBase {
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public:
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virtual bool Initialize(std::string licenseKey, ModelConfig modelConfig, const std::string& modelZipFilePath, const std::string& modelZipPassword, std::string& labelMap) override;
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virtual bool LoadModel(const std::string& modelZipFilePath, const std::string& modelZipPassword)override;
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virtual bool LoadModelFromFolder(std::string licenseKey, ModelConfig modelConfig, std::string modelName, std::string className, const std::string& modelFolder, std::string& labelMap)override;
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virtual bool OptimizeModel(bool fp16, std::string& optimizedModelFolder);
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std::vector<Object> RunInference(const cv::Mat& input);
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std::vector<Object> RunInference(const cv::Mat& input, const std::string& camera_id);
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bool Destroy();
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~ANSONNXSEG();
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int getInstanceId() const { return instanceId_; }
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private:
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std::string _modelFilePath;
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bool _modelLoadValid;
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bool _fp16{ false };
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size_t vectorProduct(const std::vector<int64_t>& vector);
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void letterBox(const cv::Mat& image,
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cv::Mat& outImage,
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const cv::Size& newShape,
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const cv::Scalar& color = cv::Scalar(114, 114, 114),
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bool auto_ = true,
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bool scaleFill = false,
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bool scaleUp = true,
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int stride = 32);
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BoundingBox scaleCoords(const cv::Size& letterboxShape,
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const BoundingBox& coords,
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const cv::Size& originalShape,
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bool p_Clip = true);
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std::vector<cv::Scalar> generateColors(const std::vector<std::string>& classNames, int seed = 42);
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cv::Mat sigmoid(const cv::Mat& src);
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void NMSBoxes(const std::vector<BoundingBox>& boxes,
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const std::vector<float>& scores,
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float scoreThreshold,
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float nmsThreshold,
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std::vector<int>& indices);
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void drawSegmentations(cv::Mat& image,
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const std::vector<Object>& results,
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float maskAlpha = 0.5f) const;
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void drawSegmentationsAndBoxes(cv::Mat& image,
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const std::vector<Object>& results,
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float maskAlpha) const;
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const std::vector<cv::Scalar>& getClassColors() const { return classColors; }
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void warmupModel();
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bool Init(const std::string& modelPath, bool useGPU=true, int deviceId = 0);
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cv::Mat preprocess(const cv::Mat& image,float*& blobPtr,std::vector<int64_t>& inputTensorShape);
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std::vector<Object> postprocess(const cv::Size& origSize,const cv::Size& letterboxSize,
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const std::vector<Ort::Value>& outputs, const std::string& camera_id);
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std::vector<Object> segment(const cv::Mat& image, const std::string& camera_id);
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std::vector<cv::Point2f> maskToPolygon(const cv::Mat& binaryMask,
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const cv::Rect& boundingBox,
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float simplificationEpsilon = 2.0f,
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int minContourArea = 10);
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private:
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static std::atomic<int> instanceCounter_; // Thread-safe counter
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int instanceId_;
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int deviceId_ = 0;
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Ort::Env env{ nullptr }; // ONNX Runtime environment
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Ort::SessionOptions sessionOptions{ nullptr }; // Session options for ONNX Runtime
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Ort::Session session{ nullptr }; // ONNX Runtime session for running inference
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bool isDynamicInputShape{}; // Flag indicating if input shape is dynamic
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cv::Size inputImageShape; // Expected input image shape for the model
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// Vectors to hold allocated input and output node names
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std::vector<Ort::AllocatedStringPtr> inputNameAllocs;
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std::vector<const char*> inputNames;
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std::vector<Ort::AllocatedStringPtr> outputNameAllocs;
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std::vector<const char*> outputNames;
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size_t numInputNodes, numOutputNodes; // Number of input and output nodes in the model
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std::vector<cv::Scalar> classColors;
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float m_imgWidth = 0;
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float m_imgHeight = 0;
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};
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}
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#endif
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#pragma once
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