/**
 * 车号属性第一步推理前预处理
 * */

#ifndef TRAINSTEPONEENGINE_H
#define TRAINSTEPONEENGINE_H

#include "AppCommon.h"
#include "MyYaml.h"
#include "EngineBase.h"
#include "EngineFactory.h"
#include "yolov5_clearity_inference.h"

class TrainStepOneEngine : public ai_matrix::EngineBase
{
public:
    TrainStepOneEngine();
    ~TrainStepOneEngine();

    APP_ERROR Init() override;
    APP_ERROR DeInit() override;
    APP_ERROR Process() override;

private:
	//初始化识别模型
	APP_ERROR InitModel();
	//获取模型配置
	APP_ERROR ReadModelInfo();
    //设置大框类型
    void SetTargetType(PostSubData &postSubData);
    //过滤无效信息
    void FilterInvalidInfo(std::vector<stDetection> &vecRet, std::shared_ptr<ProcessData> &pProcessData);
    //push数据到队列，队列满时则休眠一段时间再push
    void PushData(const std::string &strPort, const std::shared_ptr<ProcessData> &pProcessData);
	
    bool bUseEngine_;
    std::string strPort0_;
    ai_matrix::ModelConfig modelConfig_;
    std::string strResultPath_;
    bool bStepOneImgSaveFlag_;
    YoloV5ClearityInference yolov5model;
	
	const char*  INPUT_BLOB_NAME = "images";   //输入层名称
	const char*  OUTPUT_BLOB_NAME = "output";   //输出层名称
	unsigned int img_width = IMAGE_WIDTH;
	unsigned int img_height = IMAGE_HEIGHT;
	unsigned int model_width = STEP1_INPUT_W;
	unsigned int model_height = STEP1_INPUT_H;
	unsigned int clear_num = STEP1_CLEAR_NUM;
	unsigned int class_num = STEP1_CLASS_NUM;
	unsigned int input_size = STEP1_INPUT_SIZE;
	unsigned int output_size = STEP1_OUTPUT_SIZE;
	unsigned int det_size = STEP1_CLASS_NUM + STEP1_CLEAR_NUM + 5;
    unsigned int batch_size = STEP1_BATCH_SIZE; 
	float score_threshold = STEP1_SCORE_THRESH;
	float nms_threshold = STEP1_NMS_THRESH;

	YoloV5ClearityModelInfo modelinfo;

    std::map<int, ai_matrix::DataSourceConfig> mapDataSourceCfg_; //[key-数据源id, value-数据源参数配置信息]
    std::map<int, int> mapSameSideFlatcarSid_;                    //[key-数据源id, value-同侧识别平车数据源id]
    int iSpaceMinRBXPer_;  //间隔框最低点不应小于画面某个高度值(该值为画面百分比)
	
};

ENGINE_REGIST(TrainStepOneEngine)

#endif
