#ifndef _TCSCHECK_H
#define _TCSCHECK_H
#include <thread>
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <iostream>
#include <sys/stat.h>   // 必需：mkdir 函数声明
#include <sys/types.h>  // 可选：通常被 sys/stat.h 包含，但建议显式包含
#include "TcsConfig.h"
#include <opencv2/opencv.hpp>


struct CHECK_PARAM{
    int nAreaLowFilter;      // low filter of the product area
    int nDiscardTop;        //Discard top edge width of the effective area
    int nDiscardBottom;     //Discard bottom edge width of the effective area
    int nDiscardLeft;       //Discard left edge width of the effective area
    int nDiscardRight;      //Discard right edge width of the effective area

    float fZoomRatio;         // scaling ratio of the source image

    int nFilterLow;           // low threshold of the filter
    int nFilterHigh;          // high threshold of the filter

    int nBlockSize;           // the size of the block

    int nAreaFilter;           // blob filter for drawing, only draw blob with area >= nAreaFilter

    int nCountFilter;          // blob filter for counting, Only the first nCountFilters with areas arranged from largest to smallest
};
/*
长宽比 >= 3 ?
  ├─ 是 → SCRATCH (划伤)
  └─ 否（紧凑形状）:
        ├─ area < 大块阈值 → POINT (点型，不论黑白)
        └─ area >= 大块阈值:
              ├─ greyDiff < 0  → DIRTY      (黑色大块脏污)
              └─ greyDiff > 0  → FADING_SPOTS (白色大块淡斑)
*/
enum DEFECT_TYPE_DEFINE{
    DEFECT_TYPE_OK = 0,
    DEFECT_TYPE_POINT = 1,
    DEFECT_TYPE_SCRATCH = 2,                // white line
    DEFECT_TYPE_DIRTY = 3,
    DEFECT_TYPE_FADING_SPOTS = 4
};
inline const std::string DEFECT_TYPE_CODE[5] = {
    "P0000",
    "MA505",
    "MA506",
    "MA504",
    "P0003"
};
inline const std::string DEFECT_TYPE_DESC[5] = {
    "OK",
    "硬质颗粒",
    "划伤",
    "脏污",
    "淡斑"
};

struct DEFECT_INFO{
    int nDefectType;

    int nDefectArea;
    int nDefectX;
    int nDefectY;
    int nDefectWidth;
    int nDefectHeight;
    double dGreyDiff;            // 灰阶差：blob均值 - 所属局部块均值
    std::string strDefectCode;
    std::string strDefectDesc;
};
class CTcsCheck{
public:
    int m_nInitStart;

    int m_bSystemExit;

    CHECK_PARAM m_cpCfg;

    int m_nInitEnd;

    pthread_mutex_t  m_mutex;

    std::vector<cv::String> m_fileList;

    std::vector<DEFECT_INFO> m_vecDefectInfo;

    CTcsCheck();
    ~CTcsCheck();

    void SetCheckDir(std::string dirIn,std::string dirOut);
    void SetChecConfig(CHECK_PARAM* cp);
    void ProcessImages(bool bDrawResult);

    // ========== 独立检测/分类接口 ==========
    // 传统检测：对单张图做完整预处理+自适应二值化，输出残点二值图
    // 返回: 0=成功, -1=无产品/输入为空
    int TraditionalDetect(const cv::Mat& img, cv::Mat& blobImg);
    // 传统分类：对残点二值图做连通域分析+缺陷分类，结果写入 m_vecDefectInfo
    // 前提: 已调用 TraditionalDetect（内部 m_matBlur 已就绪）
    // 返回: 分类到的缺陷数量，<0 表示异常
    int TraditionalClassify(const cv::Mat& blobImg);

private:
    
    std::string m_strDirIn;
    std::string m_strDirOut;

    cv::Mat m_matLoad;
    cv::Mat m_matBlob;
    cv::Mat m_matBlur;     // 模糊图，供 TraditionalClassify 使用
    cv::Mat m_matDraw;

    cv::Size m_sizeImage;

    std::string m_strCurFile;

    bool CreateDirectories(std::string  path, mode_t mode  = 0755) ;
    void LoadImages();
    std::string GetFileName(const std::string& path) const;
    cv::Rect GetBoundingRect(cv::Mat matBinary);
    cv::Rect GetCropArea(cv::Rect rtValid);
    cv::Mat AdaptiveBinary(cv::Mat matBlur);

    void Process(bool bDraw = false);

    // 纯分类：对二值图做连通域分析+缺陷分类，结果写入 m_vecDefectInfo
    void ClassifyBlobs(const cv::Mat& blurCrop, const cv::Mat& imgBlob);
    // 纯绘制：基于 m_vecDefectInfo 绘制缺陷标注
    cv::Mat DrawBlobInfoImage(const cv::Mat& imgCrop, const cv::Mat& imgBlob);

    void DetectWithAdaptiveBinary(bool bDraw = false);
    void DetectWithAdaptiveBinaryOptimized(bool bDraw = false);
    void DetectWithDoH(bool bDraw = false, double dSigma = 1.0 ,int nThreshold = 50);
    void DetectWithLoG(bool bDraw = false, double dSigma = 1.0, int nThreshold = 50);
};

#endif