update 参数配置添加高低阈值，分块逻辑修改

AlgorithmModule/src/ImgCheckAnalysisy.cpp

@@ -1646,25 +1646,17 @@ int ImgCheckAnalysisy::Traditional_Detect_Thread(const cv::Mat &img, cv::Mat &Re
 		return -1;
 	}
 
-	Base_Function_TraditionDet traditionParam = m_pbaseCheckFunction->traditionDet;
+	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）
-	// 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      = traditionParam.nBlockSize;
-		cp.nDiscardTop     = 0;
-		cp.nDiscardBottom  = 0;
-		cp.nDiscardLeft    = 0;
-		cp.nDiscardRight   = 0;
 		cp.fZoomRatio      = traditionParam.fZoomRatio;
-		cp.nFilterLow      = 15;
+		cp.nFilterLow      = traditionParam.nFilterLow;
-		cp.nFilterHigh     = 15;
+		cp.nFilterHigh     = traditionParam.nFilterHigh;
 		cp.nAreaFilter     = traditionParam.nAreaFilter;
 		cp.nCountFilter    = traditionParam.nCountFilter;
 		m_tcsCheck.SetChecConfig(&cp);
-		bTcsInited = true;
 	}
 
 	cv::Rect detroi = cv::boundingRect(traditionParam.detArea);

ConfigModule/include/CheckConfigDefine.h

@@ -1788,6 +1788,8 @@ struct Base_Function_TraditionDet
 	float fZoomRatio; 			// 缩放比例
 	float nAreaFilter; 			// 面积过滤
 	int nCountFilter; 			// 数量过滤
+	int nFilterLow; 			// 低灰度过滤
+	int nFilterHigh; 			// 高灰度过滤
 	cv::Rect detArea_ROI;
 	std::vector<cv::Point> detArea;
 	bool bdetArea; 				// 是否使用区域
@@ -1804,6 +1806,8 @@ struct Base_Function_TraditionDet
 		fZoomRatio = 0;
 		nAreaFilter = 0;
 		nCountFilter = 0;
+		nFilterLow = 0;
+		nFilterHigh = 0;
 		bdetArea = false;
 		detArea_ROI = cv::Rect(0, 0, 0, 0);
 		detArea.clear();
@@ -1816,20 +1820,22 @@ struct Base_Function_TraditionDet
 		this->fZoomRatio = tem.fZoomRatio;
 		this->nAreaFilter = tem.nAreaFilter;
 		this->nCountFilter = tem.nCountFilter;
+		this->nFilterLow = tem.nFilterLow;
+		this->nFilterHigh = tem.nFilterHigh;
 		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(),
+		printf("%s>>bOpen %d nAreaLowFilter %f nBlockSize %d fZoomRatio %f nAreaFilter %f nCountFilter %d nFilterLow %d nFilterHigh %d \n", str.c_str(),
-			   bOpen, nAreaLowFilter, nBlockSize, fZoomRatio, nAreaFilter, nCountFilter);
+			   bOpen, nAreaLowFilter, nBlockSize, fZoomRatio, nAreaFilter, nCountFilter, nFilterLow, nFilterHigh);
 	}
 	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(),
+		sprintf(buffer, "%s>>bOpen %d nAreaLowFilter %f nBlockSize %d fZoomRatio %f nAreaFilter %f nCountFilter %d nFilterLow %d nFilterHigh %d \n", str.c_str(),
-				bOpen, nAreaLowFilter, nBlockSize, fZoomRatio, nAreaFilter, nCountFilter);
+				bOpen, nAreaLowFilter, nBlockSize, fZoomRatio, nAreaFilter, nCountFilter, nFilterLow, nFilterHigh);
 		std::string str123 = buffer;
 		return str123;
 	}

ConfigModule/src/JsonConfig.cpp

@@ -1333,6 +1333,14 @@ int BaseFuntonConfigJson::GetFunction(Json::Value value)
             {
                 _config.traditionDet.nCountFilter = value_f["form"]["Tradition_Param"]["nCountFilter"].asInt();
             }
+            if (value_f["form"]["Tradition_Param"]["nFilterLow"])
+            {
+                _config.traditionDet.nFilterLow = value_f["form"]["Tradition_Param"]["nFilterLow"].asInt();
+            }
+            if (value_f["form"]["Tradition_Param"]["nFilterHigh"])
+            {
+                _config.traditionDet.nFilterHigh = value_f["form"]["Tradition_Param"]["nFilterHigh"].asInt();
+            }
             // 2、读取区域点
             {
                 auto value_region = value_f["form"]["Tradition_Param"]["detArea"];

TcsCheckModule/src/TcsCheck.cpp

@@ -149,11 +149,25 @@ cv::Mat CTcsCheck::AdaptiveBinary(cv::Mat matBlur)
 	// 按 localBlockSize_ 分块，使用每个块的局部均值做动态阈值。
 	// 当前规则：落在 [avg-lower_, avg+upper_] 内置 0，超出置 255。
 	cv::Mat result = cv::Mat::zeros(matBlur.size(), CV_8UC1);
+	m_cpCfg.nBlockSize = std::min(m_cpCfg.nBlockSize, matBlur.cols);
+	m_cpCfg.nBlockSize = std::min(m_cpCfg.nBlockSize, matBlur.rows);
 	for (int y = 0; y < matBlur.rows; y += m_cpCfg.nBlockSize) {
 		for (int x = 0; x < matBlur.cols; x += m_cpCfg.nBlockSize) {
-			const int blockW = std::min(m_cpCfg.nBlockSize, matBlur.cols - x);
+			// const int blockW = std::min(m_cpCfg.nBlockSize, matBlur.cols - x);
-			const int blockH = std::min(m_cpCfg.nBlockSize, matBlur.rows - y);
+			// const int blockH = std::min(m_cpCfg.nBlockSize, matBlur.rows - y);
-			cv::Rect blockRect(x, y, blockW, blockH);
+			cv::Rect blockRect(x, y, m_cpCfg.nBlockSize, m_cpCfg.nBlockSize);
+			if(x + m_cpCfg.nBlockSize > matBlur.cols)
+			{
+				blockRect = cv::Rect(matBlur.cols - m_cpCfg.nBlockSize, y,  m_cpCfg.nBlockSize, m_cpCfg.nBlockSize);
+			}
+			if(y + m_cpCfg.nBlockSize > matBlur.rows)
+			{
+				blockRect = cv::Rect(x, matBlur.rows - m_cpCfg.nBlockSize,  m_cpCfg.nBlockSize, m_cpCfg.nBlockSize);
+			}
+			if(x + m_cpCfg.nBlockSize > matBlur.cols && y + m_cpCfg.nBlockSize > matBlur.rows)
+			{
+				blockRect = cv::Rect(matBlur.cols - m_cpCfg.nBlockSize, matBlur.rows - m_cpCfg.nBlockSize,  m_cpCfg.nBlockSize, m_cpCfg.nBlockSize);
+			}
 			cv::Mat block = matBlur(blockRect);
 
 			const double avg = cv::mean(block)[0];
@@ -324,6 +338,10 @@ int CTcsCheck::TraditionalDetect(const cv::Mat& img, cv::Rect detRoi, cv::Mat& b
 		return -1;
 	}
 
+	// getchar();
+	// cv::Mat showImg = img.clone();	
+	// cv::rectangle(showImg, detRoi, cv::Scalar(255), 2);
+	// cv::imwrite("detRoi.png", showImg);
 	m_matLoad = img;
 	// 确保 m_sizeImage 始终与 m_matLoad 同步
 	m_sizeImage = img.size();
@@ -368,7 +386,8 @@ int CTcsCheck::TraditionalDetect(const cv::Mat& img, cv::Rect detRoi, cv::Mat& b
 
 	// 5. 高斯模糊 — 缓存到 m_matBlur
 	cv::GaussianBlur(matResized, m_matBlur, cv::Size(5, 5), 0);
-
+	// cv::imwrite("matResized.png", matResized);
+	// cv::imwrite("m_matBlur.png", m_matBlur);
 	// 6. 自适应二值化检测
 	m_matBlob = AdaptiveBinary(m_matBlur);
 	blobImg = m_matBlob.clone();