detect_manager.cpp 11 KB

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  1. //
  2. // Created by zx on 2023/11/24.
  3. //
  4. #include "detect_manager.h"
  5. Error_manager DetectManager::Init() {
  6. #ifdef ENABLE_TENSORRT_DETECT
  7. detector = new TensorrtWheelDetector(ETC_PATH PROJECT_NAME "/model.engine", ETC_PATH PROJECT_NAME "/class.txt");
  8. #else
  9. detector = new TensorrtWheelDetector(ETC_PATH PROJECT_NAME "/model.onnx", ETC_PATH PROJECT_NAME "/class.txt");
  10. #endif
  11. m_thread = new std::thread(&DetectManager::run, this);
  12. m_condit.notify_all(true);
  13. return Error_manager();
  14. }
  15. void DetectManager::run() {
  16. LOG(INFO) << "detect thread running in " << std::this_thread::get_id();
  17. DetectResult m_detect_result_record;
  18. std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> vct_wheel_cloud;
  19. for (int i = 0; i < DeviceAzimuth::DEVICE_AZIMUTH_MAX; i++) {
  20. vct_wheel_cloud.emplace_back(new pcl::PointCloud<pcl::PointXYZ>);
  21. }
  22. auto t_start_time = std::chrono::steady_clock::now();
  23. std::chrono::duration<double> cost = std::chrono::steady_clock::now() - t_start_time;
  24. while (m_condit.is_alive()) {
  25. m_condit.wait();
  26. // 参数初始化
  27. t_start_time = std::chrono::steady_clock::now();
  28. if (m_condit.is_alive()) {
  29. DetectResult detect_result;
  30. cv::Mat merge_mat = cv::Mat::zeros(480 * 2, 640 * 2, CV_8UC1);
  31. DeviceTof3D::DeviceTof3DSaveInfo t_device_mat[DeviceAzimuth::DEVICE_AZIMUTH_MAX];
  32. for (int device_index = 0; device_index < DeviceAzimuth::DEVICE_AZIMUTH_MAX; device_index++) {
  33. t_device_mat[device_index] = DeviceTof3D::iter()->getDeviceSaveInfo((DeviceAzimuth)device_index);
  34. t_device_mat[device_index].irMat.copyTo(merge_mat(cv::Rect((device_index & 0x1) * 640,
  35. ((device_index & 0x2) >> 1) * 480, 640, 480)));
  36. }
  37. cost = std::chrono::steady_clock::now() - t_start_time;
  38. DLOG(INFO) << "cost time is " << cost.count() * 1000 << " ms";
  39. // 模型识别
  40. std::vector<Object> objs;
  41. cv::cvtColor(merge_mat, merge_mat, cv::COLOR_GRAY2RGB);
  42. detector->detect(merge_mat, objs, merge_mat);
  43. cv::imshow("merge_mat", merge_mat);
  44. cv::waitKey(1);
  45. if (objs.empty()) {
  46. detect_statu = MeasureStatu::Measure_Empty_Clouds;
  47. m_detect_result_record.reset();
  48. continue;
  49. }
  50. cost = std::chrono::steady_clock::now() - t_start_time;
  51. DLOG(INFO) << "cost time is " << cost.count() * 1000 << " ms";
  52. // 标记识别点云
  53. for (auto &obj: objs) {
  54. auto seg_points = detector->getPointsFromObj(obj);
  55. int device_index = (int(seg_points[0].x / 640) * 0x01) | (int(seg_points[0].y / 480) << 1);
  56. detect_result.wheel[device_index].confidence = obj.prob;
  57. if (obj.prob > 0.9) {
  58. detect_result.wheel[device_index].detectOk = true;
  59. vct_wheel_cloud[device_index]->clear();
  60. float d = MAX(obj.rect.height, obj.rect.width) / 2;
  61. pcl::PointXYZ point;
  62. int x_alpha = (device_index & 0x1);
  63. int y_alpha = ((device_index & 0x2) >> 1);
  64. for (auto &pt: seg_points) { // use 7.5~9ms
  65. pt.x -= x_alpha * t_device_mat[device_index].irMat.cols;
  66. pt.y -= y_alpha * t_device_mat[device_index].irMat.rows;
  67. t_device_mat[device_index].pointMat.at<cv::Vec4f>(pt)[3] = obj.prob;
  68. point.x = t_device_mat[device_index].pointMat.at<cv::Vec4f>(pt)[0];
  69. point.y = t_device_mat[device_index].pointMat.at<cv::Vec4f>(pt)[1];
  70. point.z = t_device_mat[device_index].pointMat.at<cv::Vec4f>(pt)[2];
  71. vct_wheel_cloud[device_index]->push_back(point);
  72. }
  73. TransitData::get_instance_pointer()->setMask(seg_points, device_index);
  74. }
  75. }
  76. cost = std::chrono::steady_clock::now() - t_start_time;
  77. DLOG(INFO) << "cost time is " << cost.count() * 1000 << " ms";
  78. // 点云处理
  79. if (!objs.empty()) {
  80. WheelCloudOptimize(vct_wheel_cloud, detect_result);
  81. cost = std::chrono::steady_clock::now() - t_start_time;
  82. DLOG(INFO) << "cost time is " << cost.count() * 1000 << " ms";
  83. bool ceres_detect_ret = false;
  84. detect_wheel_ceres wheel_detect;
  85. // 采用上次测量结果的数据
  86. if (!m_detect_result_time_lock_data.timeout(0.5)) {
  87. detect_result = m_detect_result_time_lock_data();
  88. detect_result.car.wheel_width = MIN(detect_result.car.wheel_width + 0.4, 2.5);
  89. } else {
  90. detect_result.car.wheel_width = 3;
  91. detect_result.car.wheel_base = 2.7;
  92. }
  93. // 根据车轮数采用不同的优化方式
  94. if (m_detect_result_record.car.wheel_width < 1.0 || m_detect_result_record.car.wheel_base < 2.0 ||
  95. objs.size() > 2) {
  96. ceres_detect_ret = wheel_detect.detect_2Step(vct_wheel_cloud[DeviceAzimuth::DEVICE_AZIMUTH_LF], vct_wheel_cloud[DeviceAzimuth::DEVICE_AZIMUTH_RF],
  97. vct_wheel_cloud[DeviceAzimuth::DEVICE_AZIMUTH_LR], vct_wheel_cloud[DeviceAzimuth::DEVICE_AZIMUTH_RR],
  98. detect_result.car.wheels_center_x, detect_result.car.wheels_center_y,
  99. detect_result.car.theta, detect_result.car.wheel_base,
  100. detect_result.car.wheel_width,detect_result.car.front_wheels_theta, detect_result.car.loss);
  101. if ((m_detect_result_record.car.wheel_width < 1.0 && m_detect_result_record.car.wheel_base < 1.0 && objs.size() == 2) || objs.size() < 2) {
  102. detect_result.car.wheel_width = m_detect_result_record.car.wheel_width;
  103. detect_result.car.wheel_base = m_detect_result_record.car.wheel_base;
  104. } else if (objs.size() == 2) {
  105. std::vector<int> have_cloud;
  106. for (int device_index = 0; device_index < DeviceAzimuth::DEVICE_AZIMUTH_MAX; device_index++) {
  107. if (!vct_wheel_cloud[device_index]->empty()) {
  108. have_cloud.push_back(device_index);
  109. }
  110. }
  111. if (have_cloud[0] % 2 == have_cloud[1] %2 ) {
  112. detect_result.car.wheel_width = m_detect_result_record.car.wheel_width;
  113. } else {
  114. detect_result.car.wheel_base = m_detect_result_record.car.wheel_base;
  115. }
  116. }
  117. } else {
  118. ceres_detect_ret = wheel_detect.detect_dynP(vct_wheel_cloud[DeviceAzimuth::DEVICE_AZIMUTH_LF], vct_wheel_cloud[DeviceAzimuth::DEVICE_AZIMUTH_RF],
  119. vct_wheel_cloud[DeviceAzimuth::DEVICE_AZIMUTH_LR], vct_wheel_cloud[DeviceAzimuth::DEVICE_AZIMUTH_RR],
  120. detect_result.car.wheels_center_x, detect_result.car.wheels_center_y,
  121. detect_result.car.theta, m_detect_result_record.car.wheel_base,
  122. m_detect_result_record.car.wheel_width,detect_result.car.front_wheels_theta, detect_result.car.loss);
  123. detect_result.car.wheel_base = m_detect_result_record.car.wheel_base;
  124. detect_result.car.wheel_width = m_detect_result_record.car.wheel_width;
  125. }
  126. // 如果车辆运动,不断刷新运动时间,如果由运动转为静止则刷新运动状态到静止
  127. float move_distance = detect_result.car.wheels_center_y - m_detect_result_record.car.wheels_center_y;
  128. if (fabs(move_distance) > 0.1) {
  129. move_statu = true;
  130. } else if (move_statu()) {
  131. move_statu = false;
  132. }
  133. if (ceres_detect_ret) {
  134. // 记录下测量结果
  135. m_detect_result_record = detect_result;
  136. detect_statu = Measure_OK;
  137. m_detect_result_time_lock_data = m_detect_result_record;
  138. } else {
  139. detect_statu = Measure_Failture;
  140. }
  141. }
  142. cost = std::chrono::steady_clock::now() - t_start_time;
  143. DLOG(INFO) << "cost time is " << cost.count() * 1000 << " ms";
  144. // 算法结果处理
  145. // static float min_wheel_width = 1e8, max_wheel_width = -1e8, min_wheel_base = 1e8, max_wheel_base = -1e8;
  146. // if (m_detect_result_record.car.wheel_width > 1.0) {
  147. // min_wheel_width = std::min(min_wheel_width, m_detect_result_record.car.wheel_width);
  148. // max_wheel_width = std::max(max_wheel_width, m_detect_result_record.car.wheel_width);
  149. // }
  150. // if (m_detect_result_record.car.wheel_base > 1.0) {
  151. // min_wheel_base = std::min(min_wheel_base, m_detect_result_record.car.wheel_base);
  152. // max_wheel_base = std::max(max_wheel_base, m_detect_result_record.car.wheel_base);
  153. // }
  154. // LOG(INFO) << "\n" << m_detect_result_record.info();
  155. usleep(1000 * 100);
  156. }
  157. }
  158. LOG(INFO) << "detect thread " << std::this_thread::get_id() << " closed.";
  159. }
  160. Error_manager DetectManager::WheelCloudOptimize(std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> &vct_wheel_cloud, DetectResult &result) {
  161. //下采样
  162. pcl::VoxelGrid<pcl::PointXYZ> vox; //创建滤波对象
  163. vox.setLeafSize(0.03f, 0.03f, 0.03f); //设置滤波时创建的体素体积为1cm的立方体
  164. pcl::PointCloud<pcl::PointXYZ>::Ptr filtered_cloud(new pcl::PointCloud<pcl::PointXYZ>);
  165. for (int device_index = 0; device_index < DeviceAzimuth::DEVICE_AZIMUTH_MAX; device_index++) {
  166. if (vct_wheel_cloud[device_index]->empty()) { continue;}
  167. vox.setInputCloud(vct_wheel_cloud[device_index]); //设置需要过滤的点云给滤波对象
  168. vox.filter(*vct_wheel_cloud[device_index]); //执行滤波处理,存储输出
  169. }
  170. return {};
  171. }
  172. MeasureStatu DetectManager::getMeasureResult(DetectManager::DetectResult &result) {
  173. if (detect_statu() != MeasureStatu::Measure_OK) {
  174. return detect_statu();
  175. }
  176. if (m_detect_result_time_lock_data.timeout()) {
  177. return MeasureStatu::Measure_Failture;
  178. }
  179. result = m_detect_result_time_lock_data();
  180. return MeasureStatu::Measure_OK;
  181. }
  182. Error_manager DetectManager::carMoveStatus() {
  183. if (move_statu.timeout(3) && !move_statu()) {
  184. return {FAILED, NORMAL};
  185. }
  186. return {};
  187. }