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#pragma once
#define RET_OK nullptr
#ifdef _WIN32
#include <Windows.h>
#include <direct.h>
#include <io.h>
#endif
#include <string>
#include <vector>
#include <cstdio>
#include <opencv2/opencv.hpp>
#include "onnxruntime_cxx_api.h"
#ifdef USE_CUDA
#include <cuda_fp16.h>
#endif
enum MODEL_TYPE
{
//FLOAT32 MODEL
YOLO_DETECT_V8 = 1,
YOLO_POSE = 2,
YOLO_CLS = 3,
//FLOAT16 MODEL
YOLO_DETECT_V8_HALF = 4,
YOLO_POSE_V8_HALF = 5,
};
typedef struct _DL_INIT_PARAM
{
std::string modelPath;
MODEL_TYPE modelType = YOLO_DETECT_V8;
std::vector<int> imgSize = { 640, 640 };
float rectConfidenceThreshold = 0.6;
float iouThreshold = 0.5;
int keyPointsNum = 2;//Note:kpt number for pose
bool cudaEnable = false;
int logSeverityLevel = 3;
int intraOpNumThreads = 1;
} DL_INIT_PARAM;
typedef struct _DL_RESULT
{
int classId;
float confidence;
cv::Rect box;
std::vector<cv::Point2f> keyPoints;
} DL_RESULT;
class YOLO_V8
{
public:
YOLO_V8();
~YOLO_V8();
public:
char* CreateSession(DL_INIT_PARAM& iParams);
char* RunSession(cv::Mat& iImg, std::vector<DL_RESULT>& oResult);
char* WarmUpSession();
template<typename N>
char* TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims,
std::vector<DL_RESULT>& oResult);
char* PreProcess(cv::Mat& iImg, std::vector<int> iImgSize, cv::Mat& oImg);
std::vector<std::string> classes{};
private:
Ort::Env env;
Ort::Session* session;
bool cudaEnable;
Ort::RunOptions options;
std::vector<const char*> inputNodeNames;
std::vector<const char*> outputNodeNames;
MODEL_TYPE modelType;
std::vector<int> imgSize;
float rectConfidenceThreshold;
float iouThreshold;
float resizeScales;//letterbox scale
};