475 lines
14 KiB
Markdown
475 lines
14 KiB
Markdown
# 工件角点检测功能实现说明
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## 功能概述
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本文档说明如何在 WorkpieceApp 中集成工件角点提取功能，使用 SDK/workpieceCornerExtraction 算法库进行检测，并通过 JSON 格式将检测结果发送给客户端。
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## 实现步骤
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### 1. 配置结构更新 ✅
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#### 1.1 更新 IVrConfig.h
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在 `VrWorkpieceParam` 结构中添加了 `lineLen` 参数：
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```cpp
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struct VrWorkpieceParam
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{
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double lapHeight = 2.0; // 搭接厚度
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double weldMinLen = 2.0; // 最小焊缝长度
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int weldRefPoints = 2; // 输出参考点数量
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WeldScanMode scanMode = WeldScanMode::ScanMode_V;
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double lineLen = 100.0; // 工件角点提取：直线段长度阈值
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};
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```
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#### 1.2 更新配置文件 config.xml
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```xml
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<WorkpieceParam lapHeight="2.0" weldMinLen="2.0" weldRefPoints="2" lineLen="100.0" />
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```
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#### 1.3 更新 VrConfig.cpp
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- 加载配置时读取 `lineLen` 参数
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- 保存配置时写入 `lineLen` 参数
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### 2. DetectPresenter 算法集成 (待实现)
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#### 2.1 添加SDK头文件引用
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```cpp
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#include "BQ_workpieceCornerExtraction_Export.h"
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```
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#### 2.2 实现角点检测方法
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在 `DetectWorkpiece` 方法中添加工件角点提取调用：
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```cpp
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int DetectPresenter::DetectWorkpiece(
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int cameraIndex,
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std::vector<std::pair<EVzResultDataType, SVzLaserLineData>>& laserLines,
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const VrAlgorithmParams& algorithmParams,
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const VrDebugParam& debugParam,
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LaserDataLoader& dataLoader,
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const double clibMatrix[16],
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DetectionResult& detectionResult)
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{
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// 1. 转换激光线数据为算法需要的格式
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std::vector<std::vector<SVzNL3DPosition>> scanLines;
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// ... 数据转换代码 ...
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// 2. 准备算法参数
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SSX_BQworkpiecePara workpieceParam;
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workpieceParam.lineLen = algorithmParams.workpieceParam.lineLen;
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SSG_cornerParam cornerParam;
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cornerParam.cornerTh = algorithmParams.cornerParam.cornerTh;
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cornerParam.scale = algorithmParams.cornerParam.scale;
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cornerParam.minEndingGap = algorithmParams.cornerParam.minEndingGap;
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cornerParam.minEndingGap_z = algorithmParams.cornerParam.minEndingGap_z;
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cornerParam.jumpCornerTh_1 = algorithmParams.cornerParam.jumpCornerTh_1;
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cornerParam.jumpCornerTh_2 = algorithmParams.cornerParam.jumpCornerTh_2;
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SSG_outlierFilterParam filterParam;
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filterParam.continuityTh = algorithmParams.filterParam.continuityTh;
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filterParam.outlierTh = algorithmParams.filterParam.outlierTh;
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SSG_treeGrowParam growParam;
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growParam.maxLineSkipNum = algorithmParams.growParam.maxLineSkipNum;
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growParam.yDeviation_max = algorithmParams.growParam.yDeviation_max;
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growParam.maxSkipDistance = algorithmParams.growParam.maxSkipDistance;
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growParam.zDeviation_max = algorithmParams.growParam.zDeviation_max;
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growParam.minLTypeTreeLen = algorithmParams.growParam.minLTypeTreeLen;
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growParam.minVTypeTreeLen = algorithmParams.growParam.minVTypeTreeLen;
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// 3. 获取调平参数
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SSG_planeCalibPara groundCalibPara;
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const VrCameraPlaneCalibParam* cameraCalib =
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algorithmParams.planeCalibParam.GetCameraCalibParam(cameraIndex);
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if (cameraCalib && cameraCalib->isCalibrated) {
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memcpy(groundCalibPara.planeCalib, cameraCalib->planeCalib, sizeof(double) * 9);
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memcpy(groundCalibPara.invRMatrix, cameraCalib->invRMatrix, sizeof(double) * 9);
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groundCalibPara.planeHeight = cameraCalib->planeHeight;
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} else {
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// 使用单位矩阵
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double identity[9] = {1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0};
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memcpy(groundCalibPara.planeCalib, identity, sizeof(double) * 9);
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memcpy(groundCalibPara.invRMatrix, identity, sizeof(double) * 9);
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groundCalibPara.planeHeight = -1.0;
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}
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// 4. 调用算法
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SSX_debugInfo debugContours[100]; // 调试信息
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int errCode = 0;
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SSX_BQworkpieceResult result = sx_BQ_getWorkpieceCorners(
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scanLines,
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cornerParam,
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filterParam,
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growParam,
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groundCalibPara,
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workpieceParam,
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#if _OUTPUT_DEBUG_DATA
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debugContours,
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#endif
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&errCode
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);
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if (errCode != 0) {
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LOG_ERROR("工件角点检测失败，错误码: %d\n", errCode);
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return errCode;
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}
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// 5. 将结果转换为手眼坐标系
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// 手眼标定变换矩阵应用到检测结果
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// ... 坐标转换代码 ...
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// 6. 填充检测结果
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detectionResult.cameraIndex = cameraIndex;
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detectionResult.positions.clear();
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// 添加左侧角点
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for (int i = 0; i < 3; i++) {
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WorkpiecePosition pos;
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pos.x = result.corner_L[i].x;
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pos.y = result.corner_L[i].y;
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pos.z = result.corner_L[i].z;
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detectionResult.positions.push_back(pos);
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}
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// 添加右侧角点
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for (int i = 0; i < 3; i++) {
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WorkpiecePosition pos;
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pos.x = result.corner_R[i].x;
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pos.y = result.corner_R[i].y;
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pos.z = result.corner_R[i].z;
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detectionResult.positions.push_back(pos);
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}
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// 添加顶部角点
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for (int i = 0; i < 3; i++) {
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WorkpiecePosition pos;
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pos.x = result.corner_T[i].x;
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pos.y = result.corner_T[i].y;
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pos.z = result.corner_T[i].z;
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detectionResult.positions.push_back(pos);
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}
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// 添加底部角点
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for (int i = 0; i < 3; i++) {
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WorkpiecePosition pos;
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pos.x = result.corner_B[i].x;
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pos.y = result.corner_B[i].y;
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pos.z = result.corner_B[i].z;
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detectionResult.positions.push_back(pos);
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}
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// 7. 生成可视化图像（如果需要）
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if (debugParam.saveDebugImage) {
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// 创建点云可视化图像
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// ... 图像生成代码 ...
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}
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return SUCCESS;
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}
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```
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### 3. WorkpiecePresenter 业务逻辑集成 (待实现)
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#### 3.1 更新检测任务方法
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在 `_DetectTask()` 方法中调用 `DetectPresenter::DetectWorkpiece()`
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```cpp
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int WorkpiecePresenter::_DetectTask()
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{
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LOG_INFO("[Algo Thread] Start workpiece corner extraction detection\n");
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std::lock_guard<std::mutex> lock(m_detectionDataMutex);
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if (m_detectionDataCache.empty()) {
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LOG_WARNING("No cached detection data available\n");
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return ERR_CODE(DEV_DATA_INVALID);
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}
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// 执行检测
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DetectionResult detectionResult;
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int nRet = m_pDetectPresenter->DetectWorkpiece(
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m_currentCameraIndex,
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m_detectionDataCache,
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m_algorithmParams,
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m_debugParam,
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m_dataLoader,
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m_clibMatrixList[m_currentCameraIndex - 1].clibMatrix,
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detectionResult
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);
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if (nRet != SUCCESS) {
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LOG_ERROR("Detection failed with error: %d\n", nRet);
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if (m_pStatus) {
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m_pStatus->OnStatusUpdate("检测失败");
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}
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return nRet;
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}
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// 通知UI显示结果
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detectionResult.cameraIndex = m_currentCameraIndex;
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m_pStatus->OnDetectionResult(detectionResult);
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// 发送结果到TCP客户端
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_SendDetectionResultToTCP(detectionResult, m_currentCameraIndex);
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// 更新状态
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m_currentWorkStatus = WorkStatus::Completed;
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m_pStatus->OnWorkStatusChanged(WorkStatus::Completed);
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return SUCCESS;
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}
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```
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#### 3.2 实现TCP结果发送方法
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```cpp
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void WorkpiecePresenter::_SendDetectionResultToTCP(
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const DetectionResult& detectionResult,
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int cameraIndex)
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{
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if (!m_pTCPServer || !m_bTCPConnected) {
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LOG_WARNING("TCP not connected, skip sending detection result\n");
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return;
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}
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// 构建JSON格式的检测结果
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QJsonObject jsonResult;
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jsonResult["cameraIndex"] = cameraIndex;
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jsonResult["timestamp"] = QDateTime::currentMSecsSinceEpoch();
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jsonResult["cornerCount"] = static_cast<int>(detectionResult.positions.size());
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QJsonArray cornersArray;
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for (const auto& pos : detectionResult.positions) {
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QJsonObject cornerObj;
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cornerObj["x"] = pos.x;
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cornerObj["y"] = pos.y;
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cornerObj["z"] = pos.z;
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cornerObj["roll"] = pos.roll;
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cornerObj["pitch"] = pos.pitch;
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cornerObj["yaw"] = pos.yaw;
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cornersArray.append(cornerObj);
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}
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jsonResult["corners"] = cornersArray;
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QJsonDocument jsonDoc(jsonResult);
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QByteArray jsonData = jsonDoc.toJson(QJsonDocument::Compact);
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// 发送JSON数据
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m_pTCPServer->SendToAll(jsonData.data(), jsonData.size());
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LOG_INFO("Sent detection result to TCP client: %d corners\n",
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detectionResult.positions.size());
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}
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```
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### 4. 参数配置界面 (待实现)
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#### 4.1 更新 dialogalgoarg.ui
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在算法参数对话框中添加工件参数配置控件：
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- `lineEdit_lineLen`: 直线段长度阈值输入框
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- `label_lineLen`: 参数标签
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#### 4.2 更新 dialogalgoarg.cpp
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```cpp
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void DialogAlgoarg::LoadConfigToUI()
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{
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// ... 现有加载代码 ...
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// 加载工件参数
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ui->lineEdit_lineLen->setText(
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QString::number(m_configData.algorithmParams.workpieceParam.lineLen));
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}
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bool DialogAlgoarg::SaveConfigFromUI()
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{
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// ... 现有保存代码 ...
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// 保存工件参数
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m_configData.algorithmParams.workpieceParam.lineLen =
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ui->lineEdit_lineLen->text().toDouble();
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// 保存到配置文件
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QString configPath = PathManager::GetConfigFilePath();
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return m_vrConfig->SaveConfig(configPath.toStdString(), m_configData);
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}
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```
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### 5. 主窗口显示结果 (待实现)
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#### 5.1 更新 mainwindow.cpp
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实现 `OnDetectionResult` 回调方法：
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```cpp
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void MainWindow::OnDetectionResult(const DetectionResult& result)
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{
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// 1. 显示检测图像
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if (!result.image.isNull()) {
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ui->label_image->setPixmap(QPixmap::fromImage(result.image));
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}
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// 2. 更新结果列表
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ui->listWidget_results->clear();
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for (size_t i = 0; i < result.positions.size(); i++) {
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const auto& pos = result.positions[i];
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QString resultText = QString("角点 %1: X=%.2f, Y=%.2f, Z=%.2f")
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.arg(i + 1)
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.arg(pos.x)
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.arg(pos.y)
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.arg(pos.z);
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ui->listWidget_results->addItem(resultText);
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}
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// 3. 更新状态栏
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QString statusText = QString("检测完成，找到 %1 个角点")
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.arg(result.positions.size());
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ui->statusBar->showMessage(statusText);
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}
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```
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### 6. 项目配置更新 (待实现)
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#### 6.1 更新 WorkpieceApp.pro
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添加工件角点提取SDK依赖：
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```qmake
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# 工件角点提取算法SDK
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INCLUDEPATH += ../../../SDK/workpieceCornerExtraction/Inc
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win32:CONFIG(release, debug|release): {
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LIBS += -L$$PWD/../../../SDK/workpieceCornerExtraction/Windows/x64/Release
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LIBS += -lBQ_workpieceCornerExtraction -lbaseAlgorithm
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}
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else:win32:CONFIG(debug, debug|release): {
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LIBS += -L$$PWD/../../../SDK/workpieceCornerExtraction/Windows/x64/Debug
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LIBS += -lBQ_workpieceCornerExtraction -lbaseAlgorithm
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}
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else:unix:!macx: {
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LIBS += -L$$PWD/../../../SDK/workpieceCornerExtraction/Arm/aarch64
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LIBS += -lworkpieceCornerExtraction -lbaseAlgorithm
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}
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```
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### 7. TCP通信协议
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#### 7.1 客户端触发检测
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客户端发送触发命令：
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```
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@,1,1,Trig,$
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```
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格式：`@，视觉号，视觉模版号，启动信息，$`
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#### 7.2 服务端返回检测结果
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服务端以JSON格式返回角点检测结果：
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```json
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{
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"cameraIndex": 1,
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"timestamp": 1640000000000,
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"cornerCount": 12,
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"corners": [
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{
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"x": 100.5,
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"y": 200.3,
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"z": 50.2,
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"roll": 0.0,
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"pitch": 0.0,
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"yaw": 0.0
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},
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// ... 更多角点数据 ...
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]
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}
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```
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## 数据流程
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```
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客户端触发 → TCPServerProtocol → WorkpiecePresenter::StartDetection
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↓
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相机采集点云数据 → _DetectionCallback → m_detectionDataCache
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↓
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相机扫描完成 → _AlgoDetectThread → _DetectTask
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↓
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DetectPresenter::DetectWorkpiece → sx_BQ_getWorkpieceCorners (SDK算法)
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↓
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坐标转换（手眼标定）→ DetectionResult
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↓
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MainWindow::OnDetectionResult (UI显示) + _SendDetectionResultToTCP (发送给客户端)
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```
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## 关键技术点
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### 1. 点云数据转换
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需要将 `SVzLaserLineData` 格式转换为 `std::vector<std::vector<SVzNL3DPosition>>` 格式
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### 2. 调平参数应用
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使用相机的平面校准参数对点云数据进行调平处理
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### 3. 手眼标定
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将相机坐标系下的检测结果转换到机械臂坐标系
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### 4. JSON序列化
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使用Qt的QJsonObject/QJsonDocument进行JSON格式转换
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### 5. 多线程安全
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- 使用 `m_detectionDataMutex` 保护检测数据缓存
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- 使用 `m_algoDetectCondition` 进行线程同步
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## 编译和部署
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### Windows平台
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1. 确保SDK库文件在正确路径
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2. 使用Qt Creator打开项目
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3. 选择Release或Debug配置
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4. 构建项目
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### ARM/Linux平台
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1. 配置交叉编译环境
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2. 确保ARM版本的SDK库文件存在
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3. 使用qmake生成Makefile
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4. 执行make编译
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```bash
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cd App/Workpiece/WorkpieceApp
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qmake WorkpieceApp.pro
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make
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```
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## 测试验证
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### 1. 参数配置测试
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- 打开算法参数配置对话框
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- 修改lineLen参数
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- 保存并验证config.xml文件
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### 2. 算法检测测试
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- 加载调试点云数据
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- 触发检测
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- 验证检测结果
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### 3. TCP通信测试
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- 启动TCP服务器
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- 客户端连接并发送触发命令
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- 验证JSON结果返回
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### 4. UI显示测试
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- 验证检测图像显示
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- 验证角点结果列表
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- 验证状态信息更新
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## 注意事项
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1. **SDK版本兼容性**：确保使用的SDK版本与项目兼容
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2. **内存管理**：注意释放算法分配的内存资源
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3. **坐标系转换**：确保手眼标定矩阵正确应用
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4. **错误处理**：完善错误检测和日志输出
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5. **性能优化**：大点云数据处理时注意性能
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6. **线程安全**：确保多线程访问的数据安全
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## 后续优化建议
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1. 增加算法参数实时调整功能
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2. 支持多种工件类型的角点检测
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3. 增加检测结果的3D可视化
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4. 优化大数据量下的处理速度
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5. 增加离线调试和参数优化工具
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