yolov8Predictor.h

#pragma once
#include <iostream>
#include <opencv2/opencv.hpp>
#include <onnxruntime_cxx_api.h>
#include <utility>
#include "utils.h"


class YOLOPredictor
{
public:
    explicit YOLOPredictor(std::nullptr_t){};
    YOLOPredictor(const std::string &modelPath,
                  const bool &isGPU,
                  float confThreshold,
                  float iouThreshold,
                  float maskThreshold);
    // ~YOLOPredictor();
    std::vector<std::vector<Yolov8Result>> predict(std::vector<cv::Mat> &images);
    int classNums = 80;

private:
    Ort::Env env{nullptr};
    Ort::SessionOptions sessionOptions{nullptr};
    Ort::Session session{nullptr};

    void preprocessing(std::vector<cv::Mat> &image, float *&blob, std::vector<int64_t> &inputTensorShape);
    std::vector<std::vector<Yolov8Result>> postprocessing(const cv::Size &resizedImageShape,
                                             const std::vector<cv::Size> &originalImageShapes,
                                             std::vector<Ort::Value> &outputTensors);

    static void getBestClassInfo(std::vector<float>::iterator it,
                                 float &bestConf,
                                 int &bestClassId,
                                 const int _classNums);
    cv::Mat getMask(const cv::Mat &maskProposals, const cv::Mat &maskProtos);
    bool isDynamicInputShape{};

    std::vector<const char *> inputNames;
    std::vector<Ort::AllocatedStringPtr> input_names_ptr;

    std::vector<const char *> outputNames;
    std::vector<Ort::AllocatedStringPtr> output_names_ptr;

    std::vector<std::vector<int64_t>> inputShapes;
    std::vector<std::vector<int64_t>> outputShapes;
    float confThreshold = 0.3f;
    float iouThreshold = 0.4f;

    bool hasMask = false;
    float maskThreshold = 0.5f;
};