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网站开发预算编制,用手机怎么制作动漫视频,建设谷歌公司网站费用,网站做分布式部署一年多了#xff0c;一直忘了补充c版本测量物体尺寸的代码文章#xff0c;被微信公众号的同学催更#xff0c;#xff0c;#xff0c;#xff0c;#xff0c;#xff0c;本文适合有一定基础的同学#xff0c;环境搭建#xff0c;模型转换#xff0c;opencv4.6.0引用…一年多了一直忘了补充c版本测量物体尺寸的代码文章被微信公众号的同学催更本文适合有一定基础的同学环境搭建模型转换opencv4.6.0引用目录库目录什么的因为比较懒就不写那么详细啦。全部工程源码看主页我们使用d435i相机会有c软件包去官网下载librealsense-master.zip解压我们在examples文件夹内先测试一下相机的效果我改写了一下代码int main() { // 相机参数配置 rs2::pipeline pipe; rs2::config cfg; cfg.enable_stream(RS2_STREAM_DEPTH, 1280, 720, RS2_FORMAT_Z16, 30); cfg.enable_stream(RS2_STREAM_COLOR, 1280, 720, RS2_FORMAT_BGR8, 30); rs2::pipeline_profile profile pipe.start(cfg); rs2::align align_to_color(RS2_STREAM_COLOR); // 创建深度流和彩色流对齐的实例化对象 int topk 100; float score_thres 0.25f; float iou_thres 0.65f; std::vectorObject objs; while (true) { rs2::frameset frameset pipe.wait_for_frames(); auto aligned_frameset align_to_color.process(frameset); // 实际进行流对齐 // 基于对齐的混合流获取深度流和彩色流,进而获取流对齐后的深度内参 rs2::video_frame color_stream aligned_frameset.get_color_frame(); rs2::depth_frame aligned_depth_stream aligned_frameset.get_depth_frame(); rs2::video_stream_profile depth_stream_profile aligned_depth_stream.get_profile().asrs2::video_stream_profile(); const auto depth_intrinsics depth_stream_profile.get_intrinsics(); //获取对齐后的深度内参 // 获取彩色图像宽 const int w color_stream.asrs2::video_frame().get_width(); const int h color_stream.asrs2::video_frame().get_height(); // 获取图像中心像素点 float u 0.5; float v 0.5; int c w * u; int r h * v; float pixe_center[2]; pixe_center[0] c; pixe_center[1] r; // 将像素坐标投影到3D坐标 float point_in_color_coordinates[3]; // 像素坐标系转换到相机坐标系 float pixed_center_depth_value aligned_depth_stream.get_distance(pixe_center[0], pixe_center[1]); rs2_deproject_pixel_to_point(point_in_color_coordinates, depth_intrinsics, pixe_center, pixed_center_depth_value); std::cout 像素中心在在彩色相机坐标系下的X坐标 point_in_color_coordinates[0] Y坐标系 point_in_color_coordinates[1] Z坐标 point_in_color_coordinates[2] endl; // Create OpenCV matrix of size (w,h) from the colorized depth data Mat image(Size(w, h), CV_8UC3, (void*)color_stream.get_data(), Mat::AUTO_STEP); circle(image, Point(c, r), 5, Scalar(0, 0, 255), -1); // Draw red circle at center // Display 3D coordinates stringstream ss; ss 3D Coordinates: ( point_in_color_coordinates[0] X point_in_color_coordinates[1] Y point_in_color_coordinates[2] Z; putText(image, ss.str(), Point(10, 30), FONT_HERSHEY_SIMPLEX, 0.7, Scalar(0, 255, 0), 2); // Update the window with new data imshow(d453i, image); waitKey(1); } //cv::destroyAllWindows(); return 0; }c编译一下就能看到深度相机的显示和深度图显示我的d435i相机找不着了就不放效果图了。。。。。。。然后就是yolov8关键点算法的tensorrt框架的c版本部署可以先做图片或视频上的部署看看部署效果对不对或者用rknn框架也可以开启摄像头不是d435i相机看看关键点算法效果代码#include opencv2/opencv.hpp #include yolov8-pose.hpp #include chrono const std::vectorstd::vectorunsigned int KPS_COLORS {{0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {51, 153, 255}, {51, 153, 255}, {51, 153, 255}, {51, 153, 255}, {51, 153, 255}, {51, 153, 255}}; const std::vectorstd::vectorunsigned int SKELETON {{16, 14}, {14, 12}, {17, 15}, {15, 13}, {12, 13}, {6, 12}, {7, 13}, {6, 7}, {6, 8}, {7, 9}, {8, 10}, {9, 11}, {2, 3}, {1, 2}, {1, 3}, {2, 4}, {3, 5}, {4, 6}, {5, 7}}; const std::vectorstd::vectorunsigned int LIMB_COLORS {{51, 153, 255}, {51, 153, 255}, {51, 153, 255}, {51, 153, 255}, {255, 51, 255}, {255, 51, 255}, {255, 51, 255}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}}; int main(int argc, char** argv) { // cuda:0 cudaSetDevice(0); const std::string engine_file_path{argv[1]}; const std::string path{argv[2]}; std::vectorstd::string imagePathList; bool isVideo{false}; assert(argc 3); auto yolov8_pose new YOLOv8_pose(engine_file_path); yolov8_pose-make_pipe(true); if (IsFile(path)) { std::string suffix path.substr(path.find_last_of(.) 1); if (suffix jpg || suffix jpeg || suffix png) { imagePathList.push_back(path); } else if (suffix mp4 || suffix avi || suffix m4v || suffix mpeg || suffix mov || suffix mkv) { isVideo true; } else { printf(suffix %s is wrong !!!\n, suffix.c_str()); std::abort(); } } else if (IsFolder(path)) { cv::glob(path /*.jpg, imagePathList); } cv::Mat res, image; cv::Size size cv::Size{640, 640}; int topk 100; float score_thres 0.25f; float iou_thres 0.65f; std::vectorObject objs; cv::namedWindow(result, cv::WINDOW_AUTOSIZE); if (isVideo) { cv::VideoCapture cap(path); if (!cap.isOpened()) { printf(can not open %s\n, path.c_str()); return -1; } while (cap.read(image)) { objs.clear(); yolov8_pose-copy_from_Mat(image, size); auto start std::chrono::system_clock::now(); yolov8_pose-infer(); auto end std::chrono::system_clock::now(); yolov8_pose-postprocess(objs, score_thres, iou_thres, topk); yolov8_pose-draw_objects(image, res, objs, SKELETON, KPS_COLORS, LIMB_COLORS); auto tc (double)std::chrono::duration_caststd::chrono::microseconds(end - start).count() / 1000.; printf(cost %2.4lf ms\n, tc); cv::imshow(result, res); if (cv::waitKey(10) q) { break; } } } else { for (auto path : imagePathList) { objs.clear(); image cv::imread(path); yolov8_pose-copy_from_Mat(image, size); auto start std::chrono::system_clock::now(); yolov8_pose-infer(); auto end std::chrono::system_clock::now(); yolov8_pose-postprocess(objs, score_thres, iou_thres, topk); yolov8_pose-draw_objects(image, res, objs, SKELETON, KPS_COLORS, LIMB_COLORS); auto tc (double)std::chrono::duration_caststd::chrono::microseconds(end - start).count() / 1000.; printf(cost %2.4lf ms\n, tc); cv::imshow(result, res); cv::waitKey(0); } } cv::destroyAllWindows(); delete yolov8_pose; return 0; }具体怎么玩这里就不赘述了主要时间太长了我懒得再复现一次了。。。。然后大家把基于tensoort框架或者rknn框架的yolov8关键点算法部署成功之后就可以结合d435i相机做物体的测量啦下面是基于tensorrt框架的核心代码while (true) { rs2::frameset frameset pipe.wait_for_frames(); auto aligned_frameset align_to_color.process(frameset); // 实际进行流对齐 // 基于对齐的混合流获取深度流和彩色流,进而获取流对齐后的深度内参 rs2::video_frame color_stream aligned_frameset.get_color_frame(); rs2::depth_frame aligned_depth_stream aligned_frameset.get_depth_frame(); rs2::video_stream_profile depth_stream_profile aligned_depth_stream.get_profile().asrs2::video_stream_profile(); const auto depth_intrinsics depth_stream_profile.get_intrinsics(); //获取对齐后的深度内参 // 获取彩色图像宽 const int w color_stream.asrs2::video_frame().get_width(); const int h color_stream.asrs2::video_frame().get_height(); // 获取图像中心像素点 //float u 0.5; //float v 0.5; //int c w * u; //int r h * v; //float pixe_center[2]; //pixe_center[0] c; // pixe_center[1] r; // 将像素坐标投影到3D坐标 //float point_in_color_coordinates[3]; // 像素坐标系转换到相机坐标系 //float pixed_center_depth_value aligned_depth_stream.get_distance(pixe_center[0], pixe_center[1]); //rs2_deproject_pixel_to_point(point_in_color_coordinates, depth_intrinsics, pixe_center, pixed_center_depth_value); // std::cout 像素中心在在彩色相机坐标系下的X坐标 point_in_color_coordinates[0] Y坐标系 point_in_color_coordinates[1] Z坐标 point_in_color_coordinates[2] endl; // Create OpenCV matrix of size (w,h) from the colorized depth data Mat image(Size(w, h), CV_8UC3, (void*)color_stream.get_data(), Mat::AUTO_STEP); objs.clear(); yolov8_pose-copy_from_Mat(image, size); //auto start std::chrono::system_clock::now(); yolov8_pose-infer(); //auto end std::chrono::system_clock::now(); yolov8_pose-postprocess(objs, score_thres, iou_thres, topk); //yolov8_pose-draw_objects(image, res, objs, SKELETON, KPS_COLORS, LIMB_COLORS); //auto tc (double)std::chrono::duration_caststd::chrono::microseconds(end - start).count() / 1000.; //printf(cost %2.4lf ms\n, tc); for (const auto obj : objs) { std::cout Bounding Box: obj.rect , Score: obj.prob std::endl; for (int k 0; k 2; k) { int kps_x std::round(obj.kps[k * 3]); int kps_y std::round(obj.kps[k * 3 1]); float kps_s obj.kps[k * 3 2]; //std::cout Keypoint k 1 : (x kps_x , y kps_y , score kps_s ) std::endl; // // 绘制关键点 circle(image, Point(kps_x, kps_y), 5, Scalar(KPS_COLORS[k][0], KPS_COLORS[k][1], KPS_COLORS[k][2]), -1); //// 在图像上打印关键点坐标 //std::stringstream ss; //ss ( kps_x , kps_y ); //putText(image, ss.str(), Point(kps_x, kps_y), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255), 1, LINE_AA); } //if (objs.size() 2) { Point p0 Point(obj.kps[0 * 3], obj.kps[0 * 3 1]); Point p1 Point(obj.kps[1 * 3], obj.kps[1 * 3 1]); // 创建两个浮点数数组来保存像素坐标 float pixel_coords0[2] { static_castfloat(p0.x), static_castfloat(p0.y) }; float pixel_coords1[2] { static_castfloat(p1.x), static_castfloat(p1.y) }; // 绘制从尾部到头部之间的连线 line(image, p0, p1, Scalar(255, 0, 255), 4); // 将像素坐标投影到3D坐标 float point_in_color_coordinates0[3]; float point_in_color_coordinates1[3]; // 检查关键点是否在有效范围内 if (p0.x 0 p0.x 1280 p0.y 0 p0.y 720) { float point0_dis aligned_depth_stream.get_distance(p0.x, p0.y); // 获取第一个点的深度值 cout Point 0 Depth: point0_dis meters endl; rs2_deproject_pixel_to_point(point_in_color_coordinates0, depth_intrinsics, pixel_coords0, point0_dis); cout Point 0 3D coordinates: ( point_in_color_coordinates0[0] , point_in_color_coordinates0[1] , point_in_color_coordinates0[2] ) endl; } if (p1.x 0 p1.x 1280 p1.y 0 p1.y 720) { float point1_dis aligned_depth_stream.get_distance(p1.x, p1.y); // 获取第二个点的深度值 cout Point 1 Depth: point1_dis meters endl; rs2_deproject_pixel_to_point(point_in_color_coordinates1, depth_intrinsics, pixel_coords1, point1_dis); cout Point 1 3D coordinates: ( point_in_color_coordinates1[0] , point_in_color_coordinates1[1] , point_in_color_coordinates1[2] ) endl; } // 计算两点间的真实距离 if (p0.x 0 p0.x 1280 p0.y 0 p0.y 720 p1.x 0 p1.x 1280 p1.y 0 p1.y 720) { float distance std::sqrt( std::pow(point_in_color_coordinates0[0] - point_in_color_coordinates1[0], 2) std::pow(point_in_color_coordinates0[1] - point_in_color_coordinates1[1], 2) std::pow(point_in_color_coordinates0[2] - point_in_color_coordinates1[2], 2) ); std::cout Distance between points: distance meters std::endl; // 显示两点之间的实际距离 stringstream ss_dist; ss_dist Distance: distance meters; putText(image, ss_dist.str(), Point(10, 60), FONT_HERSHEY_SIMPLEX, 0.7, Scalar(0, 255, 0), 2, LINE_AA); } // Display 3D coordinates stringstream ss0, ss1; ss0 3D Coordinates: ( point_in_color_coordinates0[0] X0 point_in_color_coordinates0[1] Y0 point_in_color_coordinates0[2] Z0 ; ss1 3D Coordinates: ( point_in_color_coordinates1[0] X1 point_in_color_coordinates1[1] Y1 point_in_color_coordinates1[2] Z1 ; putText(image, ss0.str(), Point(p0.x, p0.y - 20), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 1, LINE_AA); putText(image, ss1.str(), Point(p1.x, p1.y - 20), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 1, LINE_AA); //} std::cout std::endl; } // Update the window with new data imshow(d453i, image); waitKey(1); }去年的效果全部工程源码在主页