v1.3.1

AlgorithmModule/include/ImageAllResult.h

@@ -64,6 +64,8 @@ public:
     cv::Mat detImg;
     // 裁剪后的彩色检测图(保留原图通道,供裁剪图 _RE_CutImg 输出;灰度源图时为空)
     cv::Mat detColorImg;
+    // 只做"裁剪"、不叠加任何 mask 的检测图(同 detImg 尺寸/坐标系)
+    cv::Mat detImgNoMask;
     // AI 推理的mask图片
     cv::Mat AIMaskImg;
     // 结果图片

AlgorithmModule/src/AI_Edge_Algin.cpp

@@ -273,12 +273,24 @@ int AI_Edge_Algin::Detect(const cv::Mat &img, DetConfig *pDetConfig, std::shared
     }
     if (re1 != 0)
     {
-        m_pdetlog->AddCheckstr(PrintLevel_2, DET_LOG_LEVEL_3, "AI_Edge_Algin ", "AICheck_Edge_Small----error  %d ", re1);
+        // Align_Inner(支架)未检出有效轮廓(4=无轮廓 / 6=轮廓点太少)：
-        if (m_pDetConfig->IsSaveProcessImg())
+        // 支架检测开启时不视为定位失败，继续带 smallmask(可能为空/全零) 返回，
+        // 由后续"支架缺失"判定(support_min_area)上报 ZJ_QueShi NG
+        bool bSupportDetOn = (pDetConfig->pBaseCheckFunction != NULL && pDetConfig->pBaseCheckFunction->supportDet.bDetSupport);
+        if (bSupportDetOn && (re1 == 4 || re1 == 6))
         {
-            cv::imwrite(str_error, img);
+            m_pdetlog->AddCheckstr(PrintLevel_2, DET_LOG_LEVEL_3, "AI_Edge_Algin ", "AICheck_Edge_Small no contour(%d), treat as support missing ", re1);
+            re1 = 0;
+        }
+        else
+        {
+            m_pdetlog->AddCheckstr(PrintLevel_2, DET_LOG_LEVEL_3, "AI_Edge_Algin ", "AICheck_Edge_Small----error  %d ", re1);
+            if (m_pDetConfig->IsSaveProcessImg())
+            {
+                cv::imwrite(str_error, img);
+            }
+            return re1;
         }
-        return re1;
     }
     if (re2 != 0)
     {

ConfigModule/include/CheckConfigDefine.h

@@ -924,12 +924,25 @@ struct Function_Support_Det
 	// 支架参数（Support_Detect）
 	bool bDetSupport; // 支架偏位检测开关（Support_Detect.isOpen）
 	bool bDrawSupport; // 支架绘制开关（Support_Detect.form.support_param.bDrawSupport）：是否绘制支架中心十字/距离线
+	bool bUseTraditionEdge; // 传统算法搜边开关（Support_Detect.form.support_param.bUseTraditionEdge）：
+	                        // true=产品边缘用裁剪图(detImg)转灰度、二值化后搜边(不依赖定位模型 bigmask)；false=用定位模型 bigmask
 	float x_standard; // 水平方向标准距离（left/right 用）
 	float y_standard; // 垂直方向标准距离（up/down 用）
 	std::vector<std::string> edge_select; // 参与计算的方向：up/down/left/right
 	float x_offset;
 	float y_offset;
-	float r_offset;
+	float r_offset; // 支架旋转偏移阈值（度）：支架旋转矩形长轴(对应左/右边缘)与产品左/右边缘夹角超过该值判偏移；<=0 不判定
+	float support_min_area; // 支架缺失判定：支架面积阈值(单位 mm²)。支架由 Align_Inner 模型(smallmask)检出，支架面积 < 该值判缺失；<=0 不判定
+	// 支架边缘原图精细化（Support_Detect.form.support_param，均可选；仅对 edge_select 选中的边缘生效）：
+	// 沿边缘按步长采样，从边缘向产品内侧逐像素找第一个灰度 >= 阈值的像素作为新边缘点，再重新拟合边缘线
+	float edge_refine_search; // edge_refine_search：最多向内搜索的像素数；<=0=未配置(默认 80)
+	float edge_refine_step;   // edge_refine_step：沿边采样步长(像素，不需要每个点都找)；<=0=未配置(默认 40)
+	float edge_refine_gray;   // edge_refine_gray：边缘点灰度阈值(0~255，灰度 >= 该值视为产品边缘)；<=0=未配置(默认 240)
+	// 支架方向精细化（Support_Detect.form.support_param，均可选）：模型 smallmask 精度低
+	// (1696×800 上采样回原图)，minAreaRect 求支架方向角误差大；改为在原图(detImg)上拟合支架长边求方向角
+	float support_refine_search;   // support_refine_search：取模型支架外接矩形最外侧多少列作为边缘搜索带；<=0=未配置(默认 30)
+	float support_refine_step;     // support_refine_step：沿支架长边采样步长(像素)；<=0=未配置(默认 20)
+	float support_refine_segments; // support_refine_segments：采样点分段数(每段稳健拟合一条线取角度中位数)；<=0=未配置(默认 6)
 	// 把手参数（Handle_Detect）
 	bool bDetHandle; // 把手缺失检测开关（Handle_Detect.isOpen）
 	// 把手区域（最多 HANDLE_REGION_MAX_NUM 个，只保存非空的；区域宽高用于把手卡控）
@@ -960,12 +973,20 @@ struct Function_Support_Det
 	{
 		bDetSupport = false;
 		bDrawSupport = false;
+		bUseTraditionEdge = false;
 		x_standard = 0;
 		y_standard = 0;
 		edge_select.clear();
 		x_offset = 0;
 		y_offset = 0;
 		r_offset = 0;
+		support_min_area = 0;
+		edge_refine_search = 0;
+		edge_refine_step = 0;
+		edge_refine_gray = 0;
+		support_refine_search = 0;
+		support_refine_step = 0;
+		support_refine_segments = 0;
 		bDetHandle = false;
 		handleRegions.clear();
 		handleBoxes.clear();
@@ -987,12 +1008,20 @@ struct Function_Support_Det
 	{
 		this->bDetSupport = tem.bDetSupport;
 		this->bDrawSupport = tem.bDrawSupport;
+		this->bUseTraditionEdge = tem.bUseTraditionEdge;
 		this->x_standard = tem.x_standard;
 		this->y_standard = tem.y_standard;
 		this->edge_select.assign(tem.edge_select.begin(), tem.edge_select.end());
 		this->x_offset = tem.x_offset;
 		this->y_offset = tem.y_offset;
 		this->r_offset = tem.r_offset;
+		this->support_min_area = tem.support_min_area;
+		this->edge_refine_search = tem.edge_refine_search;
+		this->edge_refine_step = tem.edge_refine_step;
+		this->edge_refine_gray = tem.edge_refine_gray;
+		this->support_refine_search = tem.support_refine_search;
+		this->support_refine_step = tem.support_refine_step;
+		this->support_refine_segments = tem.support_refine_segments;
 		this->bDetHandle = tem.bDetHandle;
 		this->handleRegions.assign(tem.handleRegions.begin(), tem.handleRegions.end());
 		this->handleBoxes.assign(tem.handleBoxes.begin(), tem.handleBoxes.end());
@@ -1011,16 +1040,16 @@ struct Function_Support_Det
 	}
 	void print(std::string str)
 	{
-		printf("%s>>bDetSupport %d bDrawSupport %d bDetHandle %d bDetTag %d  x_standard %f y_standard %f  x_offset %f y_offset %f r_offset %f  edge num %ld  handleRegion num %ld  handle_diff %f  handleRange[size %.2f~%.2f search %.1f depth %.1f area %.1f margin %.1f(0=自动,<0=默认)]  BQ[area %f energy %f len %f hj %f]\n",
+		printf("%s>>bDetSupport %d bDrawSupport %d bUseTraditionEdge %d bDetHandle %d bDetTag %d  x_standard %f y_standard %f  x_offset %f y_offset %f r_offset %f support_min_area %f  edgeRefine[search %.0f step %.0f gray %.0f]  supportRefine[search %.0f step %.0f seg %.0f]  edge num %ld  handleRegion num %ld  handle_diff %f  handleRange[size %.2f~%.2f search %.1f depth %.1f area %.1f margin %.1f(0=自动,<0=默认)]  BQ[area %f energy %f len %f hj %f]\n",
-			   str.c_str(), bDetSupport, bDrawSupport, bDetHandle, bDetTag, x_standard, y_standard, x_offset, y_offset, r_offset, edge_select.size(), handleRegions.size(), handle_diff,
+			   str.c_str(), bDetSupport, bDrawSupport, bUseTraditionEdge, bDetHandle, bDetTag, x_standard, y_standard, x_offset, y_offset, r_offset, support_min_area, edge_refine_search, edge_refine_step, edge_refine_gray, support_refine_search, support_refine_step, support_refine_segments, edge_select.size(), handleRegions.size(), handle_diff,
 			   handle_size_min_ratio, handle_size_max_ratio, handle_search_ratio, handle_min_depth_coef, handle_min_area_coef, handle_edge_margin_coef,
 			   BQ_area, BQ_energy, BQ_len, BQ_hj);
 	}
 	std::string GetInfo(std::string str)
 	{
 		char buffer[1024];
-		sprintf(buffer, "%s>>bDetSupport %d bDrawSupport %d bDetHandle %d bDetTag %d  x_standard %f y_standard %f  x_offset %f y_offset %f r_offset %f  edge num %ld  handleRegion num %ld  handle_diff %f  handleRange[size %.2f~%.2f search %.1f depth %.1f area %.1f margin %.1f(0=自动,<0=默认)]  BQ[area %f energy %f len %f hj %f]\n",
+		sprintf(buffer, "%s>>bDetSupport %d bDrawSupport %d bUseTraditionEdge %d bDetHandle %d bDetTag %d  x_standard %f y_standard %f  x_offset %f y_offset %f r_offset %f support_min_area %f  edgeRefine[search %.0f step %.0f gray %.0f]  supportRefine[search %.0f step %.0f seg %.0f]  edge num %ld  handleRegion num %ld  handle_diff %f  handleRange[size %.2f~%.2f search %.1f depth %.1f area %.1f margin %.1f(0=自动,<0=默认)]  BQ[area %f energy %f len %f hj %f]\n",
-				str.c_str(), bDetSupport, bDrawSupport, bDetHandle, bDetTag, x_standard, y_standard, x_offset, y_offset, r_offset, edge_select.size(), handleRegions.size(), handle_diff,
+				str.c_str(), bDetSupport, bDrawSupport, bUseTraditionEdge, bDetHandle, bDetTag, x_standard, y_standard, x_offset, y_offset, r_offset, support_min_area, edge_refine_search, edge_refine_step, edge_refine_gray, support_refine_search, support_refine_step, support_refine_segments, edge_select.size(), handleRegions.size(), handle_diff,
 				handle_size_min_ratio, handle_size_max_ratio, handle_search_ratio, handle_min_depth_coef, handle_min_area_coef, handle_edge_margin_coef,
 				BQ_area, BQ_energy, BQ_len, BQ_hj);
 		std::string str123 = buffer;

ConfigModule/src/JsonConfig.cpp

@@ -930,6 +930,11 @@ int BaseFuntonConfigJson::GetFunction(Json::Value value)
             {
                 _config.supportDet.bDrawSupport = value_param["bDrawSupport"].asBool();
             }
+            // 传统算法搜边开关
+            if (value_param["bUseTraditionEdge"].isBool())
+            {
+                _config.supportDet.bUseTraditionEdge = value_param["bUseTraditionEdge"].asBool();
+            }
 
             if (value_param["x_standard"])
             {
@@ -952,6 +957,46 @@ int BaseFuntonConfigJson::GetFunction(Json::Value value)
                 _config.supportDet.r_offset = value_param["r_offset"].asFloat();
             }
 
+            if (value_param["support_min_area"])
+            {
+                _config.supportDet.support_min_area = value_param["support_min_area"].asFloat();
+            }
+
+            // 支架边缘原图精细化（可选）：沿边缘采样，从边缘向内找第一个灰度 >= 阈值的像素作为新边缘点，
+            //   edge_refine_search：最多向内搜索像素数（未配置默认 80）
+            //   edge_refine_step  ：沿边采样步长(像素)（未配置默认 40，不需要每个点都找）
+            //   edge_refine_gray  ：边缘点灰度阈值(0~255，灰度 >= 该值视为产品边缘)（未配置默认 240）
+            if (value_param["edge_refine_search"])
+            {
+                _config.supportDet.edge_refine_search = value_param["edge_refine_search"].asFloat();
+            }
+            if (value_param["edge_refine_step"])
+            {
+                _config.supportDet.edge_refine_step = value_param["edge_refine_step"].asFloat();
+            }
+            if (value_param["edge_refine_gray"])
+            {
+                _config.supportDet.edge_refine_gray = value_param["edge_refine_gray"].asFloat();
+            }
+
+            // 支架方向精细化（可选）：模型 smallmask 精度低(1696×800 上采样回原图)，
+            //   minAreaRect 求支架方向角误差大，改为在 detImg 灰度原图上拟合支架长边求方向角。
+            //   support_refine_search  ：取模型支架外接矩形最外侧多少列作为边缘搜索带（未配置默认 30）
+            //   support_refine_step    ：沿支架长边采样步长(像素)（未配置默认 20）
+            //   support_refine_segments：采样点分段数（每段稳健拟合一条线取角度中位数，未配置默认 6）
+            if (value_param["support_refine_search"])
+            {
+                _config.supportDet.support_refine_search = value_param["support_refine_search"].asFloat();
+            }
+            if (value_param["support_refine_step"])
+            {
+                _config.supportDet.support_refine_step = value_param["support_refine_step"].asFloat();
+            }
+            if (value_param["support_refine_segments"])
+            {
+                _config.supportDet.support_refine_segments = value_param["support_refine_segments"].asFloat();
+            }
+
             auto value_qx = value_param["edge_select"];
             if (value_qx.isArray())
             {

example/deal.h

@@ -391,19 +391,33 @@ struct Det_File_Result_Info
 			}
 			for (size_t i = 0; i < qx_result_list.size(); i++)
 			{
-				float fr = qx_result_list.at(i).num * 1.0 / qx_all_num * 100;
+				// 除零保护：没有任何缺陷统计时百分比按 0 处理
+				float fr = (qx_all_num > 0) ? (qx_result_list.at(i).num * 100.0f / qx_all_num) : 0.0f;
 				std::string str_qx = "";
-				// printf("len %d \n", qx_result_list.at(i).qx_name.length());
+				// 名称补空格对齐到 20 字节。
-				for (int k = 0; k < (20 - qx_result_list.at(i).qx_name.length()); k++)
+				// 注意：qx_name.length() 返回 size_t（无符号），若写成 20 - length()，
+				// 当名称超过 20 字节时（中文缺陷名如"铜箔面不良标签"为 21 字节）会下溢成
+				// 巨大无符号值，导致循环近似死循环（-f 批量卡住的根因）。
+				size_t nNameLen = qx_result_list.at(i).qx_name.length();
+				if (nNameLen < 20)
 				{
-					str_qx += " ";
+					str_qx.append(20 - nNameLen, ' ');
 				}
 				str_qx += qx_result_list.at(i).qx_name;
 				char buffer123[64];
 				sprintf(buffer123, ":%d %.1f%%	", qx_result_list.at(i).num, fr);
 				std::string st1 = buffer123;
 				str_qx += st1;
-				int nlen = fr;
+				// 百分比条长度限制在 [0,100]，避免异常值造成超长字符串
+				int nlen = static_cast<int>(fr);
+				if (nlen < 0)
+				{
+					nlen = 0;
+				}
+				if (nlen > 100)
+				{
+					nlen = 100;
+				}
 
 				for (int j = 0; j < nlen; j++)
 				{