feat 初步添加BQ模型检测

AlgorithmModule/include/ImageAllResult.h

@@ -173,6 +173,7 @@ public:
 
     std::shared_ptr<Image_AI_Det_Result> qx_DetAIResult;      // BLob 检测要用到的一些 参数 。
     std::shared_ptr<Image_AI_Det_Result> cell127_DetAIResult; // 127 cell 检测要用到的一些 参数 。
+    std::shared_ptr<Image_AI_Det_Result> bq_DetAIResult;      // BQ标签检测要用到的一些 参数 。
 
     // 结果图片
     cv::Mat resultImg;

AlgorithmModule/include/ImageDetConfig.h

@@ -345,7 +345,10 @@ struct BQ_Result
 {
 	int nresult = 1;
 	bool bShield_BQ = true;			   // 是否屏蔽标签
-	std::vector<cv::Rect> pBQ_roiList;	   // 标签的区域
+	bool bBQ_AI_Det = false;		   // 是否启用标签独立AI检测
+	std::vector<cv::Rect> pBQ_roiList;	   // 标签的原始区域
+	std::vector<cv::Rect> pBQ_expandedRoiList; // 标签的扩展后区域（用于AI检测）
+	std::vector<cv::Mat> pBQ_cropImages;	   // 标签扩展区域图像
 	std::vector<cv::Point> BQ_centerPoint; // 标签的中心点位置
 };
 struct CameraBaseResult

AlgorithmModule/include/ImgCheckAnalysisy.hpp

@@ -194,6 +194,12 @@ private:
     // 获得 127cell blob
     int GetBlob_127cell();
 
+    // 获得 BQ标签 blob
+    int GetBlob_BQ();
+
+    // BQ标签 AI 检测
+    int AI_Detect_BQ();
+
     // 对AI mask图片进行 结果处理
     int AIMaskDet();
 

AlgorithmModule/src/CameraCheckAnalysisy.cpp

@@ -697,6 +697,7 @@ int CameraCheckAnalysisy::preDet_BQ(const cv::Mat &L255CutImg, std::shared_ptr<I
 	long ts = CheckUtil::getcurTime();
 	m_pCheck_Result->cameraBaseResult->pBQ_Result = std::make_shared<BQ_Result>();
 	m_pCheck_Result->cameraBaseResult->pBQ_Result->bShield_BQ = m_AnalysisyConfig.commonCheckConfig.baseConfig.bShield_BQ;
+	m_pCheck_Result->cameraBaseResult->pBQ_Result->bBQ_AI_Det = m_pbaseCheckFunction->Det_BQ.bBQ_AI_Det;
 
 	cv::Rect Det_CropRoi = m_pCheck_Result->cameraBaseResult->pEdgeDetResult->cutRoi;
 
@@ -718,7 +719,7 @@ int CameraCheckAnalysisy::preDet_BQ(const cv::Mat &L255CutImg, std::shared_ptr<I
 	}
 
 	// 如果要屏蔽标签
-	if (m_pCheck_Result->cameraBaseResult->pBQ_Result->bShield_BQ && m_pbaseCheckFunction->Det_BQ.bOpen)
+	if (m_pCheck_Result->cameraBaseResult->pBQ_Result->bShield_BQ)
 	{
 		m_pdetlog->AddCheckstr(PrintLevel_2, "preDet_BQ", "shieldBQ is Open");
 		cv::Mat detImg_mask = m_pCheck_Result->cameraBaseResult->pEdgeDetResult->shieldMask;
@@ -733,7 +734,16 @@ int CameraCheckAnalysisy::preDet_BQ(const cv::Mat &L255CutImg, std::shared_ptr<I
 			boundingRect = boundingRect & cv::Rect(0, 0, detImg_mask.cols, detImg_mask.rows);
 			if (boundingRect.width > 0 && boundingRect.height > 0)
 			{
+				// 同时屏蔽主AI，避免重复检测
 				detImg_mask(boundingRect).setTo(255);
+
+				// 保存裁剪图像供独立BQ AI检测
+				if (m_pbaseCheckFunction->Det_BQ.bBQ_AI_Det)
+				{
+					m_pCheck_Result->cameraBaseResult->pBQ_Result->pBQ_expandedRoiList.push_back(boundingRect);
+					cv::Mat bqCrop = L255CutImg(boundingRect).clone();
+					m_pCheck_Result->cameraBaseResult->pBQ_Result->pBQ_cropImages.push_back(bqCrop);
+				}
 			}
 		}
 		if (L255->result->in_shareImage->bDebugsaveImg)

AlgorithmModule/src/ImgCheckAnalysisy.cpp

@@ -518,6 +518,14 @@ int ImgCheckAnalysisy::CheckRun()
 
 		Edge_Det();
 
+		// BQ标签AI检测
+		{
+			long t_BQ_s = CheckUtil::getcurTime();
+			int recBQ = AI_Detect_BQ();
+			long t_BQ_e = CheckUtil::getcurTime();
+			m_pdetlog->AddCheckstr(PrintLevel_0, "4、BQ Detect", "-------------------------BQ AI Detect--------%ld ms-------\n", t_BQ_e - t_BQ_s);
+		}
+
 		m_CheckResult_shareP->resultMaskImg = m_pImageAllResult->qx_DetAIResult->AI_MaskImg;
 
 		// cv::imwrite("dddddddd.png", m_pImageAllResult->qx_DetAIResult->AI_MaskImg);
@@ -1695,6 +1703,8 @@ int ImgCheckAnalysisy::GetALLBlob()
 	re = GetBlob_QX();
 	long t4 = CheckUtil::getcurTime();
 	re = GetBlob_127cell();
+	long t5 = CheckUtil::getcurTime();
+	re = GetBlob_BQ();
 
 	if (blobs.blobCount > 100)
 	{
@@ -1704,8 +1714,8 @@ int ImgCheckAnalysisy::GetALLBlob()
 
 	long te = CheckUtil::getcurTime();
 	m_pdetlog->AddCheckstr(PrintLevel_0, strBaseLog,
-						   "---GetALLBlob  End ;use time %ld ms  yx %ld ms  lack %ld ms  qx %ld ms  127 %ld ms",
+						   "---GetALLBlob  End ;use time %ld ms  yx %ld ms  lack %ld ms  qx %ld ms  127 %ld ms  bq %ld ms",
-						   te - t1, t2 - t1, t3 - t2, t4 - t3, te - t4);
+						   te - t1, t2 - t1, t3 - t2, t4 - t3, t5 - t4, te - t5);
 
 	// getchar();
 	return 0;
@@ -2143,6 +2153,199 @@ int ImgCheckAnalysisy::GetBlob_127cell()
 	return 0;
 }
 
+// BQAI检测
+int ImgCheckAnalysisy::AI_Detect_BQ()
+{
+	std::string strBaseLog = "AI_Detect_BQ";
+
+	// 检查BQ标签AI检测是否启用
+	if (!m_pImageAllResult->cameraBaseResult->pBQ_Result)
+	{
+		return 0;
+	}
+	if (!m_pImageAllResult->cameraBaseResult->pBQ_Result->bBQ_AI_Det)
+	{
+		return 0;
+	}
+
+	int bqNum = static_cast<int>(m_pImageAllResult->cameraBaseResult->pBQ_Result->pBQ_cropImages.size());
+	if (bqNum <= 0)
+	{
+		return 0;
+	}
+
+	m_pdetlog->AddCheckstr(PrintLevel_1, strBaseLog, "============= Start, BQ num = %d", bqNum);
+	long ts = CheckUtil::getcurTime();
+
+	// 初始化BQ AI检测结果
+	m_pImageAllResult->bq_DetAIResult = std::make_shared<ImageAllResult::Image_AI_Det_Result>();
+	std::shared_ptr<ImageAllResult::Image_AI_Det_Result> pDetAIResult = m_pImageAllResult->bq_DetAIResult;
+
+	// 使用与主检测相同的AI模型（后续更改为BQ AI模型）
+	std::shared_ptr<AIModel_Base> pAI_Model = AI_Factory->AI_defect_NF;
+
+	int AIInputImg_width = pAI_Model->input_0.width;
+	int AIInputImg_height = pAI_Model->input_0.height;
+	cv::Size modelInputSize(AIInputImg_width, AIInputImg_height);
+
+	// 创建全图大小的AI mask（与主检测detImg同尺寸）
+	cv::Mat AI_detImage = m_pImageAllResult->AI_detImg;
+	pDetAIResult->AI_MaskImg = cv::Mat::zeros(AI_detImage.size(), CV_8UC1);
+
+	// 先提交所有BQ AI推理任务（仿照AI_Detect_QX的提交-收集模式）
+	int submitted = 0;
+	int completed = 0;
+	while (completed < bqNum)
+	{
+		// 提交任务（限制并发数不超过2，避免占用过多资源）
+		if (submitted < bqNum && runner->GetProcessingCount() < 2)
+		{
+			cv::Mat bqCrop = m_pImageAllResult->cameraBaseResult->pBQ_Result->pBQ_cropImages.at(submitted);
+			if (!bqCrop.empty())
+			{
+				cv::Mat resizedCrop;
+				cv::resize(bqCrop, resizedCrop, modelInputSize);
+
+				std::shared_ptr<AIMulThreadRunBase::AITask> task = std::make_shared<AIMulThreadRunBase::AITask>();
+				task->id = submitted;
+				task->roi = cv::Rect(0, 0, modelInputSize.width, modelInputSize.height);
+				task->input = resizedCrop;
+				task->output = std::make_shared<cv::Mat>();
+				task->engine = pAI_Model;
+
+				runner->SubmitTask(task);
+			}
+			submitted++;
+		}
+
+		// 收集已完成的结果
+		std::shared_ptr<AIMulThreadRunBase::AITask> result;
+		if (runner->PopResult(result))
+		{
+			int idx = result->id;
+			if (idx >= 0 && idx < bqNum)
+			{
+				cv::Rect bqRoi = m_pImageAllResult->cameraBaseResult->pBQ_Result->pBQ_expandedRoiList.at(idx);
+				cv::Rect validRoi = bqRoi & cv::Rect(0, 0, AI_detImage.cols, AI_detImage.rows);
+
+				if (validRoi.width > 0 && validRoi.height > 0)
+				{
+					cv::Mat &outMask = *(result->output);
+					if (!outMask.empty())
+					{
+						// 将AI输出mask resize回BQ裁剪图原始尺寸
+						cv::Mat resizedMask;
+						cv::resize(outMask, resizedMask, cv::Size(bqRoi.width, bqRoi.height));
+
+						// 将BQ的mask结果放到全图mask的对应位置
+						resizedMask.copyTo(pDetAIResult->AI_MaskImg(validRoi), resizedMask);
+
+						m_pdetlog->AddCheckstr(PrintLevel_2, strBaseLog, "BQ[%d] AI done, roi=[%d,%d,%d,%d]",
+											   idx, validRoi.x, validRoi.y, validRoi.width, validRoi.height);
+					}
+					else
+					{
+						m_pdetlog->AddCheckstr(PrintLevel_2, strBaseLog, "BQ[%d] AI output empty", idx);
+					}
+				}
+			}
+			completed++;
+		}
+		else
+		{
+			std::this_thread::sleep_for(std::chrono::milliseconds(1));
+		}
+	}
+
+	long te = CheckUtil::getcurTime();
+	m_pdetlog->AddCheckstr(PrintLevel_1, strBaseLog, "============= End, time=%ld ms", te - ts);
+
+	// 调试存图
+	if (DetImgInfo_shareP->bDebugsaveImg)
+	{
+		cv::imwrite(m_strCurDetCamChannel + "_AI_BQ_mask.png", pDetAIResult->AI_MaskImg);
+	}
+
+	return 0;
+}
+
+int ImgCheckAnalysisy::GetBlob_BQ()
+{
+	std::string strBaseLog = "GetBlob_BQ";
+
+	// 检查BQ标签AI检测是否启用
+	if (!m_pImageAllResult->cameraBaseResult->pBQ_Result)
+	{
+		return 0;
+	}
+	if (!m_pImageAllResult->cameraBaseResult->pBQ_Result->bBQ_AI_Det)
+	{
+		return 0;
+	}
+	if (!m_pImageAllResult->bq_DetAIResult)
+	{
+		m_pdetlog->AddCheckstr(PrintLevel_2, strBaseLog, "bq_DetAIResult is null, skip");
+		return 0;
+	}
+
+	std::shared_ptr<ImageAllResult::Image_AI_Det_Result> pDetAIResult = m_pImageAllResult->bq_DetAIResult;
+	cv::Mat maskimg = pDetAIResult->AI_MaskImg;
+	if (maskimg.empty())
+	{
+		m_pdetlog->AddCheckstr(PrintLevel_2, strBaseLog, "AI_MaskImg is empty, skip");
+		return 0;
+	}
+
+	long t1 = CheckUtil::getcurTime();
+
+	// 使用与主流程相同的blob提取方式：GetBlobs_V3
+	// BQ检测的mask中，缺陷像素值为非零值
+	ERROR_DOTS_BLOBS blobs_bq;
+	memset(&blobs_bq, 0x00, sizeof(ERROR_DOTS_BLOBS));
+
+	unsigned char *pGrayErrordata = (unsigned char *)maskimg.data;
+	int width = maskimg.cols;
+	int height = maskimg.rows;
+	int minArea = 10; // BQ标签区域最小缺陷面积阈值
+
+	// 使用逐像素扫描方式提取blob（GetBlobs_V3对mask中的非零值进行blob提取）
+	GetBlobs_V3(&blobs_bq, pGrayErrordata, width, height, minArea);
+
+	// 设置BQ blob的默认缺陷类型（后续分类会重新确定类型）
+	// 使用非cell类型，确保进入AI_Classify_New分类流程
+	for (int i = 0; i < blobs_bq.blobCount; i++)
+	{
+		blobs_bq.blobTab[i].ErrType = CONFIG_QX_NAME_LD; // 以亮点作为初始类型，后续分类修正
+	}
+
+	// 将BQ的blob汇入主blob列表
+	PushBlob(&blobs, &blobs_bq);
+
+	long te = CheckUtil::getcurTime();
+	m_pdetlog->AddCheckstr(PrintLevel_1, strBaseLog, 
+		"BQ blob num = %d, merged to main blobs (total=%d), time=%ld ms",
+		blobs_bq.blobCount, blobs.blobCount, te - t1);
+
+	// 调试存图
+	if (DetImgInfo_shareP->bDebugsaveImg && blobs_bq.blobCount > 0)
+	{
+		cv::Mat tm;
+		cv::cvtColor(maskimg, tm, cv::COLOR_GRAY2RGB);
+		for (int i = 0; i < blobs_bq.blobCount; i++)
+		{
+			cv::Rect roi;
+			roi.x = blobs_bq.blobTab[i].minx;
+			roi.y = blobs_bq.blobTab[i].miny;
+			roi.width = blobs_bq.blobTab[i].maxx - blobs_bq.blobTab[i].minx + 1;
+			roi.height = blobs_bq.blobTab[i].maxy - blobs_bq.blobTab[i].miny + 1;
+			cv::rectangle(tm, roi, cv::Scalar(0, 0, 255), 2);
+		}
+		cv::imwrite(m_strCurDetCamChannel + "_BQ_blob.png", tm);
+	}
+
+	return 0;
+}
+
 int ImgCheckAnalysisy::AIMaskDet()
 {
 	m_pdetlog->AddCheckstr(PrintLevel_0, "AIMaskDet", "=======start");

ConfigModule/include/CheckConfigDefine.h

@@ -2263,8 +2263,8 @@ struct Base_Function_AD_Check
 
 struct Base_Function_Det_BQ
 { 
-	bool bOpen;
 	int BQ_expand;
+	bool bBQ_AI_Det;		// 是否启用标签独立AI检测，汇入主流程分类
 
 	Base_Function_Det_BQ()
 	{
@@ -2272,24 +2272,24 @@ struct Base_Function_Det_BQ
 	}
 	void Init()
 	{
-		bOpen = false;
 		BQ_expand = 0;
+		bBQ_AI_Det = false;
 	}
 	void copy(Base_Function_Det_BQ tem)
 	{
-		this->bOpen = tem.bOpen;
 		this->BQ_expand = tem.BQ_expand;
+		this->bBQ_AI_Det = tem.bBQ_AI_Det;
 	}
 	void print(std::string str)
 	{
-		printf("%s>>bOpen %d BQ_expand %d\n", str.c_str(),
+		printf("%d BQ_expand %d bBQ_AI_Det %d\n", str.c_str(),
-			   bOpen, BQ_expand);
+			   BQ_expand, bBQ_AI_Det);
 	}
 	std::string GetInfo(std::string str)
 	{
 		char buffer[256];
-		sprintf(buffer, "%s>>bOpen %d BQ_expand %d\n", str.c_str(),
+		sprintf(buffer, "%d BQ_expand %d bBQ_AI_Det %d\n", str.c_str(),
-				bOpen, BQ_expand);
+				BQ_expand, bBQ_AI_Det);
 		std::string str123 = buffer;
 		return str123;
 	}

ConfigModule/src/JsonConfig.cpp

@@ -1477,18 +1477,13 @@ int BaseFuntonConfigJson::GetFunction(Json::Value value)
         auto value_f = value;
         // std::cout << value_f << std::endl;
         // getchar();
-        _config.Det_BQ.bOpen = value_f["isOpen"].asBool();
+        _config.Det_BQ.bBQ_AI_Det = value_f["isOpen"].asBool();
-        if (_config.Det_BQ.bOpen)
         {
             if (value_f["form"]["bShield_BQ"]["BQ_expand"])
             {
                 _config.Det_BQ.BQ_expand = value_f["form"]["bShield_BQ"]["BQ_expand"].asInt();
             }
         }
-        else
-        {
-            _config.Det_BQ.Init();
-        }
         //  _config.edgeDet.print("edgeDet");
         //  getchar();
     }