update 弱线检测合并边缘模型结果

AlgorithmModule/include/AI_Edge_QX_Det.h

@@ -45,7 +45,13 @@ public:
 	AI_Edge_QX_Det(/* args */);
 	~AI_Edge_QX_Det();
 
-	int Detect(const cv::Mat &img, cv::Mat &detmask, DetConfig *pDetConfig,std::shared_ptr<DetLog> pdetlog);
+	/// @brief 边缘缺陷检测，检测结果根据配置参数合并到 mask
+	/// @param img 检测原图
+	/// @param detmask 合并目标 mask（主检测）
+	/// @param pDetConfig 检测参数
+	/// @param pdetlog 日志
+	/// @param pDetmask2 可选：第二个合并目标 mask（如弱线检测mask），按与主检测相同规则合并
+	int Detect(const cv::Mat &img, cv::Mat &detmask, DetConfig *pDetConfig, std::shared_ptr<DetLog> pdetlog, cv::Mat *pDetmask2 = nullptr);
 
 private:
 	int SaveProcessImg(const cv::Mat &inImg, const cv::Mat &outImg, bool isALLzeros, DetConfig *pDetConfig, int idx = 0, int nruntype = 0);

AlgorithmModule/include/ImgCheckAnalysisy.hpp

@@ -195,6 +195,8 @@ private:
     int GetBlob_127cell();
     // 获得 RLine_512 blob
     int GetBlob_RLine_512();
+    // 更新 RLine_512 mask 的 blob 行标记（合并边缘检测结果后刷新）
+    int UpdateBlobHFlag_RLine_512();
     // 获得 BQ标签 blob
     int GetBlob_BQ();
 

AlgorithmModule/src/AI_Edge_QX_Det.cpp

@@ -122,7 +122,7 @@ std::string AI_Edge_QX_Det::GetImgSaveName(int flag)
 
     return str_root;
 }
-int AI_Edge_QX_Det::Detect(const cv::Mat &img, cv::Mat &detmask, DetConfig *pDetConfig, std::shared_ptr<DetLog> pdetlog)
+int AI_Edge_QX_Det::Detect(const cv::Mat &img, cv::Mat &detmask, DetConfig *pDetConfig, std::shared_ptr<DetLog> pdetlog, cv::Mat *pDetmask2)
 {
     static int n_count = 0;
     int nRuntype = 0; // 0:检测 1:测试。。。。。
@@ -169,6 +169,14 @@ int AI_Edge_QX_Det::Detect(const cv::Mat &img, cv::Mat &detmask, DetConfig *pDet
 
     m_pdetlog->AddCheckstr(PrintLevel_2, DET_LOG_LEVEL_3, "AI_Edge_QX_Det ", "nruntype  %d  %d\n", pDetConfig->pdege_Det_config->bsave_all, pDetConfig->pdege_Det_config->bsave_qx);
 
+    // 第二个合并目标（如弱线检测mask）：尺寸与主检测mask一致时才参与合并
+    bool bMergeDetmask2 = false;
+    if (pDetmask2 != nullptr && !pDetmask2->empty() && pDetmask2->size() == detmask.size())
+    {
+        bMergeDetmask2 = true;
+        m_pdetlog->AddCheckstr(PrintLevel_2, DET_LOG_LEVEL_3, "AI_Edge_QX_Det ", "extra detmask merge on  %s", pDetConfig->strCamChannel.c_str());
+    }
+
     if (bdege || nRuntype != 0)
     {
         showImg = img.clone();
@@ -305,14 +313,27 @@ int AI_Edge_QX_Det::Detect(const cv::Mat &img, cv::Mat &detmask, DetConfig *pDet
             if (pDetConfig->pdege_Det_config->type == Function_Edge_Det::Edge_type_Add)
             {
                 outMask(smallImg_roi).copyTo(detmask(mask_roi), outMask(smallImg_roi));
+                // 第二个合并目标（如弱线检测mask）：与主检测mask相同规则合并
+                if (bMergeDetmask2)
+                {
+                    outMask(smallImg_roi).copyTo((*pDetmask2)(mask_roi), outMask(smallImg_roi));
+                }
             }
             else if (pDetConfig->pdege_Det_config->type == Function_Edge_Det::Edge_type_And)
             {
                 cv::bitwise_and(detmask(mask_roi), outMask(smallImg_roi), detmask(mask_roi));
+                if (bMergeDetmask2)
+                {
+                    cv::bitwise_and((*pDetmask2)(mask_roi), outMask(smallImg_roi), (*pDetmask2)(mask_roi));
+                }
             }
             else
             {
                 outMask(smallImg_roi).copyTo(detmask(mask_roi));
+                if (bMergeDetmask2)
+                {
+                    outMask(smallImg_roi).copyTo((*pDetmask2)(mask_roi));
+                }
             }
         }
     }

AlgorithmModule/src/ImgCheckAnalysisy.cpp

@@ -3889,9 +3889,26 @@ int ImgCheckAnalysisy::Edge_Det()
 	// cv::Mat temMask = cv::Mat::zeros(m_TemCheck.temImgList[TEM_IMG_IDX_AImask].size(),  CV_8UC1);
 	if (m_pFuntion->function.f_dege_Det.bOpen)
 	{
+		// 弱线检测(512模型)的mask：与主检测mask一样，根据 f_dege_Det 参数合并边缘检测结果
+		cv::Mat *pRLineDetMask = nullptr;
+		if (m_pFuntion->function.f_RLineDet.bOpen &&
+			m_pFuntion->function.f_RLineDet.bOpen_512 &&
+			m_pImageAllResult->RLine_512_DetAIResult &&
+			!m_pImageAllResult->RLine_512_DetAIResult->AI_MaskImg.empty())
+		{
+			pRLineDetMask = &m_pImageAllResult->RLine_512_DetAIResult->AI_MaskImg;
+			m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, strBaseLog, " RLine_512 mask merge enable");
+		}
+
 		if (m_pFuntion->function.f_dege_Det.bdetUse)
 		{
-			m_AIEdge_QX_Det.Detect(m_CheckResult_shareP->in_shareImage->img, m_pImageAllResult->qx_DetAIResult->AI_MaskImg, &detconfig, m_pdetlog);
+			m_AIEdge_QX_Det.Detect(m_CheckResult_shareP->in_shareImage->img, m_pImageAllResult->qx_DetAIResult->AI_MaskImg, &detconfig, m_pdetlog, pRLineDetMask);
+
+			// 合并后刷新弱线mask的blob行标记，保证合并进来的像素参与blob分析
+			if (pRLineDetMask != nullptr)
+			{
+				UpdateBlobHFlag_RLine_512();
+			}
 		}
 		else
 		{
@@ -3909,6 +3926,50 @@ int ImgCheckAnalysisy::Edge_Det()
 	return 0;
 }
 
+// 弱线检测mask合并边缘检测结果后，刷新blob行标记，保证合并进来的像素参与blob分析
+int ImgCheckAnalysisy::UpdateBlobHFlag_RLine_512()
+{
+	std::shared_ptr<ImageAllResult::Image_AI_Det_Result> pDetAIResult = m_pImageAllResult->RLine_512_DetAIResult;
+	if (!pDetAIResult || !pDetAIResult->blobDetParam)
+	{
+		return 1;
+	}
+	unsigned char *imgBlobHFlagData = pDetAIResult->blobDetParam->imgBlobHFlagData;
+	cv::Mat maskimg = pDetAIResult->AI_MaskImg;
+	if (imgBlobHFlagData == NULL || maskimg.empty())
+	{
+		return 1;
+	}
+
+	int nRows = pDetAIResult->blobDetParam->ndatalen;
+	if (nRows > maskimg.rows)
+	{
+		nRows = maskimg.rows;
+	}
+	int width = maskimg.cols;
+	int nSetNum = 0;
+	for (int y = 0; y < nRows; y++)
+	{
+		if (imgBlobHFlagData[y] != 0)
+		{
+			continue;
+		}
+		const unsigned char *p = maskimg.ptr<unsigned char>(y);
+		for (int x = 0; x < width; x++)
+		{
+			if (p[x] != 0)
+			{
+				imgBlobHFlagData[y] = 1;
+				nSetNum++;
+				break;
+			}
+		}
+	}
+	m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "UpdateBlobHFlag_RLine_512", " RLine_512 blob HFlag update rows %d", nSetNum);
+
+	return 0;
+}
+
 int ImgCheckAnalysisy::DrawResult_Step_1()
 {
 	m_pdetlog->AddCheckstr(PrintLevel_0, "DrawResult_Step_1", "=======start");