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/*
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* @Descripttion:
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* @version:
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* @Author: sueRimn
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* @Date: 2022-03-16 17:09:11
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* @LastEditors: xiewenji 527774126@qq.com
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* @LastEditTime: 2025-09-23 10:15:49
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*/
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/***********************************************/
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/************ ***************/
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/************金佰利检测算法参数定义**************/
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/************ **************/
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/**********************************************/
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#ifndef _ImgCheckConfig_HPP_
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#define _ImgCheckConfig_HPP_
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#include <string>
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#include <opencv2/opencv.hpp>
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#include <unordered_map> // unordered_map
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#define RESULT_VERSION 31
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#define MAX_BLOB_NUM 200
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// 输入模型图片尺寸
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#define SRCIMG_WIDTH 14200
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#define SRCIMG_HEIGHT 10640
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#define CHECKIMG_HEIGHT 2000
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#define CHECKIMG_WIDTH 4000
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#define MASK_IMG_STEP 16
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#define MASK_IMG_STARTVALUE 48
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// 检测日志 等级
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enum DET_LOG_LEVEL_
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{
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DET_LOG_LEVEL_0, // 极简信息
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DET_LOG_LEVEL_1, // 包含检测关键信息
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DET_LOG_LEVEL_2, // 关键信息+ 一般节点信息
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DET_LOG_LEVEL_3, // 详细信息
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};
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struct VERSION_INFO
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{
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int ConfigVersion = 0;
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int ResultVersion = RESULT_VERSION;
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int InterfaceVersion = 0;
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};
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// 检测错误代码（与缺陷列表一致；支架偏移/支架缺失/把手缺失为传统算法检出，无AI分类）
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enum ERROR_TYPE_
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{
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ERROR_TYPE_OK, // 0 好品
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ERROR_TYPE_ZJ_PianYi, // 1 支架偏移
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ERROR_TYPE_ZJ_QueShi, // 2 支架缺失
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ERROR_TYPE_CGMo_QueShi, // 3 把手缺失
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ERROR_TYPE_CGMark, // 4 把手标记
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ERROR_TYPE_BQBMark, // 5 补强板标记
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ERROR_TYPE_TBBQ, // 6 铜箔标签
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ERROR_TYPE_TBMark, // 7 铜箔标记
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ERROR_TYPE_COUNT,
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};
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extern std::vector<std::string> QX_Result_Names;
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extern std::vector<std::string> QX_Result_Code;
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// 检测检测参数类型
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enum CHECK_CONFIG_TYPE_
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{
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CHECK_CONFIG_Run, // 运行参数
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CHECK_CONFIG_Module, // 参数模块
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CHECK_CONFIG_COUNT,
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};
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// 输入检测图片的
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enum IMG_INPUT_
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{
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IMG_INPUT_SRC,
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IMG_INPUT_COUNT,
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};
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// 输入检测缺陷小图
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enum IMG_OUTPUT_
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{
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IMG_OUTPUT_RESIZE,
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IMG_OUTPUT_COUNT,
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};
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// 输入图片的状态
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enum IN_IMG_Status_
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{
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IN_IMG_Status_Start,
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IN_IMG_Status_Other,
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IN_IMG_Status_End,
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IN_IMG_Status_OneImg,
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};
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// 检测模式
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enum DET_MODE_
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{
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DET_MODE_NULL,
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DET_MODE_EDGE,
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DET_MODE_MergeImg,
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DET_MODE_YX,
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DET_MODE_UP,
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DET_MODE_Det,
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DET_MODE_MarkLine,
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DET_MODE_ReJson,
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};
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#define MAX_REGION_NUM 20
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struct ReadFlawCode
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{
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std::string flaw_name;
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std::string flaw_code;
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std::vector<std::string> config_flaw_name;
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ReadFlawCode()
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{
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config_flaw_name.clear();
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flaw_name = "";
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flaw_code = "";
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}
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};
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extern std::vector<ReadFlawCode> m_FlawCodeList;
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// 一个检测项基本信息，包括图片序号，图片、开始时间
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struct shareImage
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{
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int Det_Mode; // 检测模式
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int Status;
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bool bCam_AB; // 是AB两个相机
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int camera_ID; // 相机ID
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int img_id;
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cv::Mat img;
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cv::Mat img_B;
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cv::Mat other_channel_Result_mask;
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cv::Mat AI_maskImg; // 推理图片
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std::string strSnowID; // 相机发古来的雪花码
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long getImgTimeMs; // 获取图片的时间点
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long readImg_start;
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long readImg_end;
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long time_PushIn;
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long time_sendCheck;
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long time_startCheck;
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long time_EndCheck;
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int imgtype;
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cv::Rect cutRoi;
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int ninstruct; // 运行指令
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int nlogLevel; // 日志等级
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std::string strCameraName; // 相机名称
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std::string strImgProductID; // 产品名称
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std::string strChannel; // 通道名称
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std::string strImgName; // 图片名称
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std::string imgstr;
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int nImgBigIdx;
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int otherValue;
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int otherValue_1;
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std::string resultJson; // 检测结果 json 字符串
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bool bsaveProcessImg; // 保存处理图片
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std::unordered_map<std::string, int> runCommand; // 运行命令 map
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shareImage()
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{
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Init();
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}
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~shareImage()
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{
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}
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void Init()
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{
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if (!img.empty())
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{
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img.release();
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}
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if (!other_channel_Result_mask.empty())
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{
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other_channel_Result_mask.release();
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}
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if (!AI_maskImg.empty())
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{
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AI_maskImg.release();
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}
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Det_Mode = 0;
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Status = 0;
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nlogLevel = DET_LOG_LEVEL_0;
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img_id = -1;
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getImgTimeMs = 0;
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time_PushIn = 0;
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time_sendCheck = 0;
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time_startCheck = 0;
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time_EndCheck = 0;
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readImg_start = 0;
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readImg_end = 0;
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imgtype = 0;
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imgstr = "";
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strSnowID = "";
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camera_ID = 0;
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bCam_AB = false;
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strImgName = "";
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strCameraName = "";
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strImgProductID = "";
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strChannel = "";
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nImgBigIdx = 0;
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cutRoi = cv::Rect(0, 0, 0, 0);
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otherValue = 0;
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ninstruct = 0;
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resultJson = "";
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bsaveProcessImg = false;
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}
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void InitImg(int ImgW, int imgH, bool Isgray = true)
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{
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if (Isgray)
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{
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img = cv::Mat(imgH, ImgW, CV_8UC1);
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}
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else
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{
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img = cv::Mat(imgH, ImgW, CV_8UC3);
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}
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}
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};
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// 检测结果基本信息
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struct BasicResult
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{
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int img_id;
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long checkUseTimeMs; // 所有时间
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int64_t snowId; // 雪花ID
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int imgtype;
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std::string imgstr;
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std::string strChannel;
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void Init()
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{
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img_id = 0;
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checkUseTimeMs = 0;
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snowId = 0;
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imgtype = 0;
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imgstr = "";
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strChannel = "";
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}
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void copy(BasicResult tem)
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{
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this->img_id = tem.img_id;
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this->checkUseTimeMs = tem.checkUseTimeMs;
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this->snowId = tem.snowId;
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this->imgtype = tem.imgtype;
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this->imgstr = tem.imgstr;
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this->strChannel = tem.strChannel;
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}
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};
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struct DetectInfo
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{
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int nresult;
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std::string keyName;
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std::string keyCode;
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int num;
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DetectInfo()
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{
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Init();
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}
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void Init()
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{
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nresult = 0;
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num = 0;
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keyName = "";
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keyCode = "";
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}
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void copy(DetectInfo tem)
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{
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this->nresult = tem.nresult;
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this->num = tem.num;
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this->keyName = tem.keyName;
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this->keyCode = tem.keyCode;
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}
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};
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struct QXImageResult
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{
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int type; // 缺陷类型
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std::string qx_Code; // 缺陷code
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std::string strTypeName; // 缺陷名称
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std::string qx_level; // 缺陷等级
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cv::Mat srcImg; // 缺陷原始图片
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cv::Mat resizeImg; // 缺陷缩略图
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cv::Mat AI_in_Img;
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cv::Mat AI_out_img;
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cv::Rect srcImgroi; // 相对原图的 框坐标;
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cv::Rect CutImgroi; // 裁剪原图 框坐标;
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cv::Rect resizeImgroi; // 相对相对小图 框坐标;
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float x_pixel; // 缺陷坐标 像素
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float y_pixel; // 缺陷坐标 像素
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float x_mm; // 缺陷坐标 mm
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float y_mm; // 缺陷坐标 mm
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int idx;
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float area;
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float energy;
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float hj;
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float max_v;
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float len;
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float breadth;
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float fScore;
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int qx_type; // 缺陷的原因
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float minDis_mm;
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int qx_num;
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float density; // 密度
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QXImageResult()
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{
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Init();
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}
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void Init()
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{
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if (!srcImg.empty())
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{
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srcImg.release();
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}
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if (!resizeImg.empty())
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{
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resizeImg.release();
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}
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if (!AI_in_Img.empty())
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{
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AI_in_Img.release();
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/* code */
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}
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if (!AI_out_img.empty())
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{
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AI_out_img.release();
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/* code */
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}
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x_pixel = 0;
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y_pixel = 0;
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x_mm = 0;
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y_mm = 0;
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type = 0;
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area = 0;
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energy = 0;
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hj = 0;
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max_v = 0;
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strTypeName = "";
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qx_Code = "";
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qx_level = "";
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len = 0;
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breadth = 0;
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idx = 0;
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fScore = 0;
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qx_type = 0;
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minDis_mm = 0;
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qx_num = 0;
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density = 0;
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srcImgroi = cv::Rect(0, 0, 0, 0);
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CutImgroi = cv::Rect(0, 0, 0, 0);
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resizeImgroi = cv::Rect(0, 0, 0, 0);
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}
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};
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// 结果信息
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struct CheckResult
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{
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// 原始图片，输入检测的图片
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int checkStatus;
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int nDetStep; // 处理步骤
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int nresult;
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int nProductResult; // 产品结果
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int nYS_result;
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float productWidht_mm; // 产品宽度 毫米
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float productHeight_mm; // 产品 高度 毫米
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cv::Mat cutSrcimg;
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cv::Mat resultimg;
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cv::Mat SrcResultImg; // 结果
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cv::Mat resultMaskImg; // AI mask Result
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std::shared_ptr<shareImage> in_shareImage; // 输入图片信息
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DetectInfo defectResultList[ERROR_TYPE_COUNT]; // 缺陷检测结果list
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BasicResult basicResult; // 基本检测结果信息
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std::vector<QXImageResult> qxImageResult; // 缺陷小图结果
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std::vector<QXImageResult> YS_ImageResult; // 疑似小图结果
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std::vector<std::string> det_LogList; // 检测日志
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std::string strResultJson; // 缺陷结果 jason
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bool bSaveALL;
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int nflage1;
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int nflage2;
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CheckResult()
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{
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Init();
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}
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~CheckResult()
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{
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release();
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}
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void Init()
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{
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nProductResult = 0;
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checkStatus = 0;
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nDetStep = 0;
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nresult = ERROR_TYPE_OK;
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nYS_result = ERROR_TYPE_OK;
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bSaveALL = false;
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nflage1 = 0;
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nflage2 = 0;
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productWidht_mm = 0;
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productHeight_mm = 0;
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basicResult.Init();
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strResultJson = "";
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if (!resultimg.empty())
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{
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resultimg.release();
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}
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if (!cutSrcimg.empty())
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{
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cutSrcimg.release();
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}
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if (!SrcResultImg.empty())
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{
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SrcResultImg.release();
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}
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if (!resultMaskImg.empty())
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{
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resultMaskImg.release();
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}
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for (int i = 0; i < ERROR_TYPE_COUNT; i++)
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{
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defectResultList[i].Init();
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}
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std::vector<QXImageResult> tmp;
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qxImageResult.swap(tmp);
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YS_ImageResult.swap(tmp);
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qxImageResult.erase(qxImageResult.begin(), qxImageResult.end());
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YS_ImageResult.erase(YS_ImageResult.begin(), YS_ImageResult.end());
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det_LogList.erase(det_LogList.begin(), det_LogList.end());
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}
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void release()
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{
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if (!resultimg.empty())
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{
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resultimg.release();
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}
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if (!cutSrcimg.empty())
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{
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cutSrcimg.release();
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}
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if (!SrcResultImg.empty())
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{
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SrcResultImg.release();
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}
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if (!resultMaskImg.empty())
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{
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resultMaskImg.release();
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}
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strResultJson = "";
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std::vector<QXImageResult> tmp;
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qxImageResult.swap(tmp);
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YS_ImageResult.swap(tmp);
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det_LogList.erase(det_LogList.begin(), det_LogList.end());
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}
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};
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#endif //_CORELOGICFACTORY_HPP_
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