feat 添加传统检测相关参数配置

AlgorithmModule/src/ImgCheckAnalysisy.cpp

@@ -513,7 +513,8 @@ int ImgCheckAnalysisy::Adapt_Config(Mat img, Rect cur_roi, bool b_update){
 	if(!b_update){
 		return 1;
 	}
-	if(cur_roi.width <= 0 || cur_roi.height <= 0){
+	int get_edge_roi = GetEdgeRoi(img, cur_roi, 20, 20);
+	if(get_edge_roi != 0){
 		return 2;
 	}
 	m_pdetlog->AddCheckstr(PrintLevel_0, "Adapt_Config", "-------------------start--------------");
@@ -639,6 +640,9 @@ int ImgCheckAnalysisy::CheckRun()
 	m_CheckResult_shareP->basicResult.strChannel = m_CheckResult_shareP->in_shareImage->strChannel;
 	m_strCurDetChannel = m_CheckResult_shareP->basicResult.strChannel;
 
+	/*自适应更新参数*/
+	Adapt_Config(m_CheckResult_shareP->in_shareImage->img, m_CutRoi, m_pbaseCheckFunction->markLine.badapt_region);
+
 	// 2、参数检查
 	int rec = ConfigCheck(DetImgInfo_shareP->img);
 	if (rec != CHECK_OK)
@@ -665,9 +669,6 @@ int ImgCheckAnalysisy::CheckRun()
 	m_pImageAllResult->pDetResult->CutRoi = m_CutRoi;
 	m_pImageAllResult->pDetResult->Param_CropRoi = m_Crop_Roi_paramImg;
 
-	/*自适应更新参数 — 基于AI边缘定位的精准ROI*/
-	Adapt_Config(m_CheckResult_shareP->in_shareImage->img, m_CutRoi, m_pbaseCheckFunction->markLine.badapt_region);
-
 	// 生成 检测的图片
 	cv::Mat image;
 	image = m_CheckResult_shareP->in_shareImage->img;
@@ -1308,7 +1309,7 @@ int ImgCheckAnalysisy::AIMaskDet()
 	memset(m_ImgBlobHFlagData, 0, sizeof(unsigned char) * m_pImageAllResult->detImg.rows);
 
 	// 传统检测路径：等待异步任务完成后，计算HFlag
-	if(m_pBasicConfig->bTraditionalDetect)
+	if(m_pbaseCheckFunction->traditionDet.bOpen)
 	{
 		m_AItask->waitComplate();
 		int rec = m_AItask->nresult;
@@ -1630,28 +1631,34 @@ int ImgCheckAnalysisy::Traditional_Detect_Thread(const cv::Mat &img, cv::Mat &Re
 	std::string strBaseLog = "Traditional_Detect";
 	m_pdetlog->AddCheckstr(PrintLevel_0, DET_LOG_LEVEL_3, strBaseLog, "Traditional_Detect  Start");
 
+	Base_Function_TraditionDet traditionParam = m_pbaseCheckFunction->traditionDet;
 	// 首次调用时初始化传统检测参数（从 m_AnalysisyConfig 映射）
 	static bool bTcsInited = false;
 	if (!bTcsInited)
 	{
 		CHECK_PARAM cp;
 		cp.nAreaLowFilter  = 80;
-		cp.nBlockSize      = 100;
-		cp.nDiscardTop     = 170;
-		cp.nDiscardBottom  = 170;
-		cp.nDiscardLeft    = 180;
-		cp.nDiscardRight   = 460;
-		cp.fZoomRatio      = 0.25f;
+		cp.nBlockSize      = traditionParam.nBlockSize;
+		cp.nDiscardTop     = 0;
+		cp.nDiscardBottom  = 0;
+		cp.nDiscardLeft    = 0;
+		cp.nDiscardRight   = 0;
+		cp.fZoomRatio      = traditionParam.fZoomRatio;
 		cp.nFilterLow      = 15;
 		cp.nFilterHigh     = 15;
-		cp.nAreaFilter     = 10;
-		cp.nCountFilter    = 50;
+		cp.nAreaFilter     = traditionParam.nAreaFilter;
+		cp.nCountFilter    = traditionParam.nCountFilter;
 		m_tcsCheck.SetChecConfig(&cp);
 		bTcsInited = true;
 	}
 
-	// 调用传统检测：输出残点二值图（已内部完成 crop+resize 反向映射，与 img 同尺寸）
-	int ret = m_tcsCheck.TraditionalDetect(img, ResultImg);
+	cv::Rect detroi = traditionParam.detArea_ROI;
+	if(!traditionParam.bdetArea)
+	{
+		detroi = cv::Rect(0, 0, img.cols, img.rows);
+	}
+	// 调用传统检测：输出残点二值图
+	int ret = m_tcsCheck.TraditionalDetect(img, detroi, ResultImg);
 	if (ret != 0)
 	{
 		m_pdetlog->AddCheckstr(PrintLevel_0, DET_LOG_LEVEL_3, strBaseLog, "Traditional_Detect  FAILED (no product)");
@@ -1832,7 +1839,7 @@ int ImgCheckAnalysisy::Traditional_QX_Class_Thread()
 {
 	m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Traditional_Class", " Start");
 
-	// 1. 调用传统分类：基于上次 TraditionalDetect 缓存的模糊图 + 当前 mask
+	// 1. 调用传统分类
 	if (m_pImageAllResult == nullptr || m_pImageAllResult->AIMaskImg.empty())
 	{
 		m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Traditional_Class", " No mask image");
@@ -1960,7 +1967,7 @@ void ImgCheckAnalysisy::TaskFun_AIDet(std::shared_ptr<TaskInfo> task)
 		t1 = CheckUtil::getcurTime();
 
 		int rec;
-		if(m_pBasicConfig->bTraditionalDetect)
+		if(m_pbaseCheckFunction->traditionDet.bOpen)
 		{
 			rec = Traditional_Detect_Thread(m_pImageAllResult->detImg, m_pImageAllResult->AIMaskImg);
 		}
@@ -1984,7 +1991,7 @@ void ImgCheckAnalysisy::TaskFun_QxClass(std::shared_ptr<TaskInfo> task)
 		t1 = CheckUtil::getcurTime();
 
 		int rec;
-		if(m_pBasicConfig->bTraditionalDetect)
+		if(m_pbaseCheckFunction->traditionDet.bOpen)
 		{
 			rec = Traditional_QX_Class_Thread();
 		}

ConfigModule/include/CheckConfigDefine.h

@@ -407,7 +407,6 @@ struct BasicConfig
 	float Product_Size_Height_mm; // 产品尺寸 高度 mm
 	float fImage_Scale_x;		  // 成像精度
 	float fImage_Scale_y;		  // 成像精度
-	bool bTraditionalDetect;	  // 使用传统算法检测
 	std::string strCamName;		  //
 
 	float density_R_mm; // 密度计算半径 像素
@@ -428,7 +427,6 @@ struct BasicConfig
 		Product_Size_Height_mm = 1000;
 		fImage_Scale_x = 0.03;
 		fImage_Scale_y = 0.03;
-		bTraditionalDetect = false;
 		density_R_mm = 5;
 		strCamName = "";
 		strCamearName = EMPTY_CONFIG_NAME;
@@ -452,7 +450,6 @@ struct BasicConfig
 		this->Product_Size_Height_mm = tem.Product_Size_Height_mm;
 		this->fImage_Scale_x = tem.fImage_Scale_x;
 		this->fImage_Scale_y = tem.fImage_Scale_y;
-		this->bTraditionalDetect = tem.bTraditionalDetect;
 		this->density_R_mm = tem.density_R_mm;
 		this->strCamearName = tem.strCamearName;
 	}
@@ -461,7 +458,7 @@ struct BasicConfig
 		printf("============================↓↓↓↓↓↓%s↓↓ %s ↓↓↓↓↓=========================\n", str.c_str(), strCamearName.c_str());
 		printf("bCal_ImageScale %d  Product_Size_Width =%f Product_Size_Height =%f \n", bCal_ImageScale, Product_Size_Width_mm, Product_Size_Height_mm);
 		printf("fImage_Scale_x =%f fImage_Scale_y=%f \n", fImage_Scale_x, fImage_Scale_y);
-		printf("bTraditionalDetect %d  density_R_mm=%f \n", bTraditionalDetect, density_R_mm);
+		printf("density_R_mm=%f \n", density_R_mm);
 		// printf("height_min =%d height_max=%d \n", height_min, height_max);
 		printf("bDrawShieldRoi  %d  bShield_ZF %d DrawPreRoi %d fUP_IOU %f  density_R_mm %f\n", bDrawShieldRoi, bShield_ZF, bDrawPreRoi, fUP_IOU, density_R_mm);
 		printf("============================↑↑↑↑↑↑%s↑↑↑↑↑↑=========================\n", str.c_str());
@@ -1782,6 +1779,61 @@ struct Base_Function_SaveImg
 	}
 };
 
+//传统检测
+struct Base_Function_TraditionDet
+{
+	bool bOpen; 				// 是否开启
+	float nAreaLowFilter; 		// 定位阈值
+	int nBlockSize; 			// 分块大小
+	float fZoomRatio; 			// 缩放比例
+	float nAreaFilter; 			// 面积过滤
+	int nCountFilter; 			// 数量过滤
+	cv::Rect detArea_ROI;
+	std::vector<cv::Point> detArea;
+	bool bdetArea; 				// 是否使用区域
+
+	Base_Function_TraditionDet()
+	{
+		Init();
+	}
+	void Init()
+	{
+		bOpen = false;
+		nAreaLowFilter = 0;
+		nBlockSize = 0;
+		fZoomRatio = 0;
+		nAreaFilter = 0;
+		nCountFilter = 0;
+		bdetArea = false;
+		detArea_ROI = cv::Rect(0, 0, 0, 0);
+		detArea.clear();
+	}
+	void copy(Base_Function_TraditionDet tem)
+	{
+		this->bOpen = tem.bOpen;
+		this->nAreaLowFilter = tem.nAreaLowFilter;
+		this->nBlockSize = tem.nBlockSize;
+		this->fZoomRatio = tem.fZoomRatio;
+		this->nAreaFilter = tem.nAreaFilter;
+		this->nCountFilter = tem.nCountFilter;
+		this->bdetArea = tem.bdetArea;
+		this->detArea_ROI = tem.detArea_ROI;
+		this->detArea.assign(tem.detArea.begin(), tem.detArea.end());
+	}
+	void print(std::string str)
+	{
+		printf("%s>>bOpen %d nAreaLowFilter %f nBlockSize %d fZoomRatio %f nAreaFilter %f nCountFilter %d \n", str.c_str(),
+			   bOpen, nAreaLowFilter, nBlockSize, fZoomRatio, nAreaFilter, nCountFilter);
+	}
+	std::string GetInfo(std::string str)
+	{
+		char buffer[256];
+		sprintf(buffer, "%s>>bOpen %d nAreaLowFilter %f nBlockSize %d fZoomRatio %f nAreaFilter %f nCountFilter %d \n", str.c_str(),
+				bOpen, nAreaLowFilter, nBlockSize, fZoomRatio, nAreaFilter, nCountFilter);
+		std::string str123 = buffer;
+		return str123;
+	}
+};
 //  大缺陷 NG
 struct Big_NG
 {
@@ -1970,6 +2022,7 @@ struct BaseCheckFunction
 	Base_Function_MarkLine markLine;
 	Base_Function_Edge_Det edgeDet;
 	Base_Function_SaveImg saveImg;
+	Base_Function_TraditionDet traditionDet;
 	Base_Function_BigNG bigNG;
 	BaseCheckFunction()
 	{
@@ -1980,6 +2033,7 @@ struct BaseCheckFunction
 		markLine.Init();
 		edgeDet.Init();
 		saveImg.Init();
+		traditionDet.Init();
 		bigNG.Init();
 	}
 	void copy(BaseCheckFunction tem)
@@ -1987,6 +2041,7 @@ struct BaseCheckFunction
 		this->markLine.copy(tem.markLine);
 		this->edgeDet.copy(tem.edgeDet);
 		this->saveImg.copy(tem.saveImg);
+		this->traditionDet.copy(tem.traditionDet);
 		this->bigNG.copy(tem.bigNG);
 	}
 	void print(std::string str)
@@ -1995,6 +2050,7 @@ struct BaseCheckFunction
 		markLine.print("markLine");
 		edgeDet.print("edgeDet");
 		saveImg.print("saveImg");
+		traditionDet.print("traditionDet");
 		bigNG.print("bigNG");
 	}
 	std::string GetInfo(std::string str)
@@ -2003,6 +2059,7 @@ struct BaseCheckFunction
 		str123 += markLine.GetInfo("markLine");
 		str123 += edgeDet.GetInfo("edgeDet");
 		str123 += saveImg.GetInfo("saveImg");
+		str123 += traditionDet.GetInfo("traditionDet");
 		str123 += bigNG.GetInfo("bigNG");
 		// str123 += "\n";
 		return str123;

ConfigModule/src/JsonConfig.cpp

@@ -57,7 +57,6 @@ void CommonParamToCheckConfigJson::toObjectFromValue(Json::Value root)
                 _config.baseConfig.Product_Size_Height_mm = value["Product_Size_H"].asFloat();
                 _config.baseConfig.fImage_Scale_x = value["Image_Scale_X"].asFloat();
                 _config.baseConfig.fImage_Scale_y = value["Image_Scale_Y"].asFloat();
-                _config.baseConfig.bTraditionalDetect = value["bTraditionalDetect"].asFloat();
                 if (value["Density_R"])
                 {
                     _config.baseConfig.density_R_mm = value["Density_R"].asFloat();
@@ -1309,6 +1308,62 @@ int BaseFuntonConfigJson::GetFunction(Json::Value value)
         //  _config.edgeDet.print("edgeDet");
         //  getchar();
     }
+    if ("Tradition_Detect" == strCode)
+    {
+        auto value_f = value;
+        // std::cout << value_f << std::endl;
+        // getchar();
+        _config.traditionDet.bOpen = value_f["isOpen"].asBool();
+        if (_config.traditionDet.bOpen)
+        {
+
+            if (value_f["form"]["Tradition_Param"]["nBlockSize"])
+            {
+                _config.traditionDet.nBlockSize = value_f["form"]["Tradition_Param"]["nBlockSize"].asInt();
+            }
+            if (value_f["form"]["Tradition_Param"]["fZoomRatio"])
+            {
+                _config.traditionDet.fZoomRatio = value_f["form"]["Tradition_Param"]["fZoomRatio"].asFloat();
+            }
+            if (value_f["form"]["Tradition_Param"]["nAreaFilter"])
+            {
+                _config.traditionDet.nAreaFilter = value_f["form"]["Tradition_Param"]["nAreaFilter"].asFloat();
+            }
+            if (value_f["form"]["Tradition_Param"]["nCountFilter"])
+            {
+                _config.traditionDet.nCountFilter = value_f["form"]["Tradition_Param"]["nCountFilter"].asInt();
+            }
+            // 2、读取区域点
+            {
+                auto value_region = value_f["form"]["Tradition_Param"]["detArea"];
+                if (value_region.isArray())
+                {
+                    for (int idx = 0; idx < value_region.size(); idx++)
+                    {
+                        cv::Point p;
+                        p.x = value_region[idx][0].asInt();
+                        p.y = value_region[idx][1].asInt();
+
+                        _config.traditionDet.detArea.emplace_back(p);
+                    }
+                    if (_config.traditionDet.detArea.size() > 0)
+                    {
+                        _config.traditionDet.detArea_ROI = boundingRect(_config.traditionDet.detArea);
+                    }
+                }
+            }
+            if (value_f["form"]["Tradition_Param"]["bdetArea"])
+            {
+                _config.traditionDet.bdetArea = value_f["form"]["Tradition_Param"]["bdetArea"].asFloat();
+            }
+        }
+        else
+        {
+            _config.traditionDet.Init();
+        }
+        //  _config.traditionDet.print("traditionDet");
+        //  getchar();
+    }
     if ("Big_NG" == strCode)
     {
         auto value_f = value;

TcsCheckModule/include/TcsCheck.h

@@ -96,12 +96,9 @@ public:
     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
+    // 传统检测,输出残点二值图 0=成功, -1=无产品/输入为空
+    int TraditionalDetect(const cv::Mat& img, cv::Rect detRoi, cv::Mat& blobImg);
+    // 传统分类,结果写入 m_vecDefectInfo
     int TraditionalClassify(const cv::Mat& blobImg);
 
 private:

TcsCheckModule/src/TcsCheck.cpp

@@ -15,9 +15,10 @@ protected:
 };
 
 CTcsCheck::CTcsCheck()
+    : m_nInitStart(0)
+    , m_bSystemExit(0)
+    , m_nInitEnd(0)
 {
-    memset(&m_nInitStart, 0, offsetof(CTcsCheck, m_nInitEnd) - offsetof(CTcsCheck, m_nInitStart) + sizeof(m_nInitEnd));
-
     m_cpCfg.nAreaLowFilter = 80;
     m_cpCfg.nBlockSize = 100;
     m_cpCfg.nDiscardTop = 170;
@@ -310,32 +311,37 @@ cv::Mat CTcsCheck::DrawBlobInfoImage(const cv::Mat& imgCrop, const cv::Mat& imgB
 	return cropColor;
 }
 // ============================================================
-// TraditionalDetect — 独立检测接口
+// 独立检测接口
 // 对单张图做：阈值定位→裁剪→缩放→高斯模糊→自适应二值化
 // 输出残点二值图 blobImg，内部缓存 m_matBlur 供后续分类
 // 返回: 0=成功, -1=无产品或输入异常
 // ============================================================
-int CTcsCheck::TraditionalDetect(const cv::Mat& img, cv::Mat& blobImg)
+int CTcsCheck::TraditionalDetect(const cv::Mat& img, cv::Rect detRoi, cv::Mat& blobImg)
 {
-	if (img.empty()) return -1;
+	// if (img.empty()) return -1;
 
 	m_matLoad = img;
-	m_sizeImage = img.size();
+	cv::Rect rtCrop = detRoi;
+	if(detRoi.size() == img.size())
+	{
+		m_sizeImage = img.size();
 
-	// 1. 全局阈值 → 产品区域定位
-	cv::Mat matBinary;
-	cv::threshold(m_matLoad, matBinary, m_cpCfg.nAreaLowFilter, 255, cv::THRESH_BINARY);
+		// 1. 全局阈值 → 产品区域定位
+		cv::Mat matBinary;
+		cv::threshold(m_matLoad, matBinary, m_cpCfg.nAreaLowFilter, 255, cv::THRESH_BINARY);
 
-	// 2. 获取最大连通域外接矩形
-	cv::Rect rtValid = GetBoundingRect(matBinary);
-	if (rtValid == cv::Rect(0, 0, 0, 0))
-	{
-		blobImg = cv::Mat();
-		return -1;
-	}
+		// 2. 获取最大连通域外接矩形
+		cv::Rect rtValid = GetBoundingRect(matBinary);
+		if (rtValid == cv::Rect(0, 0, 0, 0))
+		{
+			blobImg = cv::Mat();
+			return -1;
+		}
 
-	// 3. 裁剪边缘
-	cv::Rect rtCrop = GetCropArea(rtValid);
+		// 3. 裁剪边缘
+		rtCrop = GetCropArea(rtValid);
+	}
+	
 	cv::Mat matCrop = m_matLoad(rtCrop).clone();
 
 	// 4. 缩放

example/deal.cpp

@@ -5,6 +5,7 @@
 #include <sys/stat.h>
 #include <unistd.h>
 #include <string>
+#include <filesystem>
 #include "CheckUtil.hpp"
 std::string ExtractFileNameWithoutExtension(const std::string &strImgPath)
 {
@@ -1083,10 +1084,6 @@ bool deal::ReadSystemConfig(const std::string &strPath)
     }
     m_system_param.Use_CPU_StartIdx = root["Use_CPU_StartIdx"].asInt();
     // path
-    m_system_param.Analysis_Config_path = root["Analysis_Config_path"].asString();
-    m_system_param.Analysis_Config_path_Cam2 = root["Analysis_Config_path_Cam2"].asString();
-    m_system_param.Analysis_Config_path_Cam3 = root["Analysis_Config_path_Cam3"].asString();
-    m_system_param.Analysis_Config_path_Cam4 = root["Analysis_Config_path_Cam4"].asString();
     m_system_param.config_Root_Path = root["Config_Root_Path"].asString();
     m_system_param.Check_Config_path = root["Check_Config_path"].asString();
 
@@ -1095,8 +1092,83 @@ bool deal::ReadSystemConfig(const std::string &strPath)
     m_system_param.preCHeck_defect = root["preCHeck_defect"].asInt();
     m_nCurUseCPUIDX = m_system_param.Use_CPU_StartIdx;
 
+    // 从 Config_Root_Path 目录下自动扫描匹配 param_*.json 文件，映射到各相机
+    ScanConfigPaths();
+
     return m_system_param.valid();
 }
+int deal::ScanConfigPaths()
+{
+    std::string configRoot = m_system_param.config_Root_Path;
+    if (configRoot.empty())
+    {
+        printf("ScanConfigPaths: Config_Root_Path is empty\n");
+        return -1;
+    }
+
+    // 确保路径以 / 结尾
+    if (configRoot.back() != '/')
+    {
+        configRoot += '/';
+    }
+
+    // 相机名称与对应存储指针的映射: Cam1=BCA, Cam2=BTA, Cam3=DCA, Cam4=DTA
+    struct CamPathMapping
+    {
+        std::string camName;
+        std::string *targetPath;
+    };
+    CamPathMapping mappings[] = {
+        {"BCA", &m_system_param.Analysis_Config_path},
+        {"BTA", &m_system_param.Analysis_Config_path_Cam2},
+        {"DCA", &m_system_param.Analysis_Config_path_Cam3},
+        {"DTA", &m_system_param.Analysis_Config_path_Cam4},
+    };
+
+    // 先清空所有路径
+    for (auto &m : mappings)
+    {
+        *m.targetPath = "";
+    }
+
+    if (!std::filesystem::exists(configRoot))
+    {
+        printf("ScanConfigPaths: directory does not exist: %s\n", configRoot.c_str());
+        return -1;
+    }
+
+    int foundCount = 0;
+    for (const auto &entry : std::filesystem::directory_iterator(configRoot))
+    {
+        if (!entry.is_regular_file())
+            continue;
+
+        std::string filename = entry.path().filename().string();
+        // 匹配 param_XXX.json 格式
+        if (filename.size() < 11) // "param_X.json" 最少 11 字符
+            continue;
+        if (filename.substr(0, 6) != "param_" || filename.substr(filename.size() - 5) != ".json")
+            continue;
+
+        // 提取相机名称: param_BCA.json → BCA
+        std::string camName = filename.substr(6, filename.size() - 11);
+
+        for (auto &m : mappings)
+        {
+            if (camName == m.camName)
+            {
+                *m.targetPath = entry.path().string();
+                printf("ScanConfigPaths: auto-discovered %s config → %s\n",
+                       m.camName.c_str(), m.targetPath->c_str());
+                foundCount++;
+                break;
+            }
+        }
+    }
+
+    printf("ScanConfigPaths: found %d param config file(s) in %s\n", foundCount, configRoot.c_str());
+    return foundCount > 0 ? 0 : -1;
+}
 int deal::GetJcImageInfo(std::string strpath, std::vector<JC_IMAGE_INFO_> &jcImageInfoList)
 {
     LoadOfflineCheckImg(strpath);

example/deal.h

@@ -578,6 +578,8 @@ private:
 	void GetDealResultToQueu();
 	// 加载系统配置文件
 	bool ReadSystemConfig(const std::string &strPath);
+	// 从 Config_Root_Path 自动扫描匹配 param_*.json 文件
+	int ScanConfigPaths();
 	int GetJcImageInfo(std::string strpath, std::vector<JC_IMAGE_INFO_> &jcImageInfoList);
 
 	int ReadTestImgaData();