v1.2.0

AlgorithmModule/include/ImgCheckAnalysisy.hpp

@@ -200,7 +200,7 @@ private:
     int BLobToDetResult();
 
     //自适应参数区域更新
-    int Adapt_Config(Mat img, Rect roi, bool b_update);
+    int Adapt_Config(Mat img, Rect cur_roi, cv::RotatedRect& rot_cur_roi, bool b_update);
 
     std::shared_ptr<AIFactory> AI_Factory;
 

AlgorithmModule/src/ImgCheckAnalysisy.cpp

@@ -181,7 +181,7 @@ int ImgCheckAnalysisy::GetStatus()
 
 std::string ImgCheckAnalysisy::GetVersion()
 {
-	return std::string("BOE_1.1.0");
+	return std::string("BOE_1.2.0");
 }
 
 std::string ImgCheckAnalysisy::GetErrorInfo()
@@ -602,7 +602,7 @@ std::vector<cv::Point2f> sort_vertices(cv::RotatedRect rrect) {
     return vertices;
 }
 
-int ImgCheckAnalysisy::Adapt_Config(Mat img, Rect cur_roi, bool b_update){
+int ImgCheckAnalysisy::Adapt_Config(Mat img, Rect cur_roi, cv::RotatedRect& rot_cur_roi, bool b_update){
 	if (img.empty()) {
     	std::cerr << "Error: Input image 'img' is empty!" << std::endl;
     	return -1;
@@ -610,7 +610,7 @@ int ImgCheckAnalysisy::Adapt_Config(Mat img, Rect cur_roi, bool b_update){
 	if(!b_update){
 		return 1;
 	}
-	cv::RotatedRect rot_cur_roi;
+	// cv::RotatedRect rot_cur_roi;
 	int get_edge_roi = GetEdgeRoi(img, cur_roi, rot_cur_roi, 20, 20);
 	if(get_edge_roi != 0){
 		return 2;
@@ -802,7 +802,7 @@ int ImgCheckAnalysisy::CheckRun()
 	/*自适应更新参数*/
 	long time_adapt_s = CheckUtil::getcurTime();
 	cv::RotatedRect rot_cur_roi;
-	Adapt_Config(m_CheckResult_shareP->in_shareImage->img, m_CutRoi, m_pbaseCheckFunction->markLine.badapt_region);
+	Adapt_Config(m_CheckResult_shareP->in_shareImage->img, m_CutRoi, rot_cur_roi, m_pbaseCheckFunction->markLine.badapt_region);
 	long time_adapt_e = CheckUtil::getcurTime();
 	m_pdetlog->AddCheckstr(PrintLevel_0, "Adapt_Config", "time = %ld", time_adapt_e - time_adapt_s);
 
@@ -822,6 +822,8 @@ int ImgCheckAnalysisy::CheckRun()
 		m_pdetlog->AddCheckstr(PrintLevel_0, "Edge", "Not Use AI_Edge %s", m_CheckResult_shareP->basicResult.strChannel.c_str());
 		m_CutRoi = cv::Rect(rot_cur_roi.boundingRect().x - 50 , rot_cur_roi.boundingRect().y - 50, rot_cur_roi.boundingRect().width + 100, rot_cur_roi.boundingRect().height + 100)
 		& cv::Rect(0, 0, m_CheckResult_shareP->in_shareImage->img.cols, m_CheckResult_shareP->in_shareImage->img.rows);
+		m_pdetlog->AddCheckstr(PrintLevel_0, "Edge", "rot_cur_roi = [%d, %d, %d, %d]", rot_cur_roi.boundingRect().x, rot_cur_roi.boundingRect().y, rot_cur_roi.boundingRect().width, rot_cur_roi.boundingRect().height);
+		m_pdetlog->AddCheckstr(PrintLevel_0, "Edge", "CutRoi = [%d, %d, %d, %d]", m_CutRoi.x, m_CutRoi.y, m_CutRoi.width, m_CutRoi.height);
 	}
 	else
 	{
@@ -1068,7 +1070,48 @@ int ImgCheckAnalysisy::CalBlob_Other()
 		roi.width = blobs.blobTab[i].maxx - blobs.blobTab[i].minx + 1;
 		roi.height = blobs.blobTab[i].maxy - blobs.blobTab[i].miny + 1;
 
+		// 先计算不耗时的粗略量：面积、外接框长度/宽度（物理单位）
+		float JudgArea = blobs.blobTab[i].area * fs_x * fs_y;
+		blobs.blobTab[i].JudgArea = JudgArea;
+		float flen = roi.width * fs_x;
+		float fwid = roi.height * fs_x;
+		if (roi.height * fs_y > flen)
+		{
+			flen = roi.height * fs_y;
+			fwid = roi.width * fs_y;
+		}
+		blobs.blobTab[i].len = flen;
+		blobs.blobTab[i].breadth = fwid;
+
+		// 长度 / 宽度 / 面积足够大的残点：跳过 calc_blob_info_withstats 与
+		// Cal_QXLen 的昂贵精确计算，直接赋一个较大的值，避免大 ROI 耗时陡增。
+		const int BIG_BLOB_LEN_PIXEL = 10000;         // 长度或宽度阈值（像素）
+		const int BIG_BLOB_AREA_PIXEL = 10000 * 10000;  // 面积阈值（像素）
+		const bool bBigBlob = (roi.width >= BIG_BLOB_LEN_PIXEL ||
+							   roi.height >= BIG_BLOB_LEN_PIXEL ||
+							   (long long)roi.width * roi.height >= BIG_BLOB_AREA_PIXEL);
+
+		if (bBigBlob)
+		{
+			// 直接给较大的值，不再做精确计算
+			blobs.blobTab[i].energy = 99999999;  // 能量给较大值
+			blobs.blobTab[i].grayDis = 255.0f;   // 灰阶给较大值
+			// 白/黑不参与精确计算，保持默认黑色；ERR_TYPE_2 强制黑色
+			int QX_whiteBLACK = CONFIG_QX_BLACK;
+			if (blobs.blobTab[i].ErrType == ERR_TYPE_2)
+			{
+				QX_whiteBLACK = CONFIG_QX_BLACK; // 强制为 黑色
+			}
+			blobs.blobTab[i].whiteOrblack = QX_whiteBLACK;
+
+			printf("[CalBlob_Other] skip big blob %d roi %dx%d area %d\n",
+				i, roi.width, roi.height, blobs.blobTab[i].area);
+			continue;
+		}
+
+		long t_blob_s = CheckUtil::getcurTime();
 		cv::Scalar result = calc_blob_info_withstats(m_pImageAllResult->detImg, m_pImageAllResult->AIMaskImg, roi);
+		long t_stats_e = CheckUtil::getcurTime();
 
 		int QX_whiteBLACK = CONFIG_QX_BLACK;
 		if (result[0] > 0)
@@ -1081,18 +1124,6 @@ int ImgCheckAnalysisy::CalBlob_Other()
 		blobs.blobTab[i].energy = energe;
 		blobs.blobTab[i].grayDis = hj;
 
-		float JudgArea = blobs.blobTab[i].area * fs_x * fs_y;
-		blobs.blobTab[i].JudgArea = JudgArea;
-		float flen = roi.width * fs_x;
-		float fwid = roi.height * fs_x;
-		if (roi.height * fs_y > flen)
-		{
-			flen = roi.height * fs_y;
-			fwid = roi.width * fs_y;
-		}
-		blobs.blobTab[i].len = flen;
-		blobs.blobTab[i].breadth = fwid;
-
 		int nerrortype = 0;
 		int checkFlage = 0;
 
@@ -1107,6 +1138,7 @@ int ImgCheckAnalysisy::CalBlob_Other()
 
 		//  精确计算长度
 		vector<float> re_len = Cal_QXLen(m_pImageAllResult->detImg(roi), config_qx_type, fs_x, fs_y);
+		long t_len_e = CheckUtil::getcurTime();
 
 		if (re_len.size() > 2)
 		{
@@ -1120,6 +1152,14 @@ int ImgCheckAnalysisy::CalBlob_Other()
 			}
 
 		}
+
+		// 大 blob 耗时打印，便于定位大面积残点的性能瓶颈
+		if (roi.width * roi.height > 100000)
+		{
+			printf("[CalBlob_Other] blob %d roi %dx%d area %d | stats %ldms  QXLen %ldms\n",
+				i, roi.width, roi.height, blobs.blobTab[i].area,
+				t_stats_e - t_blob_s, t_len_e - t_stats_e);
+		}
 	}
 	return 0;
 }
@@ -1854,7 +1894,7 @@ int ImgCheckAnalysisy::Traditional_Detect_Thread(const cv::Mat &img, cv::Mat &Re
 		return -1;
 	}
 
-	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）
+	Base_Function_TraditionDet traditionParam = m_pbaseCheckFunction->traditionDet;
 	{
 		CHECK_PARAM cp;
 		cp.nAreaLowFilter  = 80;