怎么优化网站关键词排名,会员插件wordpress,商城网站jq楼层响应,公司网站建设行业怎么样目录 下载源码安装onnxruntime安装Eigen3编译opencv 下载源码
https://github.com/shaoshengsong/DeepSORT安装onnxruntime
安装方法参考博客
安装Eigen3
当谈及线性代数计算库时#xff0c;Eigen3是一个强大而受欢迎的选择。Eigen3是一个C模板库#xff0c;提供了许多用… 目录 下载源码安装onnxruntime安装Eigen3编译opencv 下载源码
https://github.com/shaoshengsong/DeepSORT安装onnxruntime
安装方法参考博客
安装Eigen3
当谈及线性代数计算库时Eigen3是一个强大而受欢迎的选择。Eigen3是一个C模板库提供了许多用于线性代数运算的功能如向量、矩阵、矩阵运算、线性方程组求解等。以下是Eigen3的一些主要特点和功能
高性能Eigen3使用了优化的算法和技术具有出色的运行速度和高效的内存使用。它通过使用表达式模板技术来在编译时生成高效的计算代码避免了一些运行时的开销。
简洁易用Eigen3提供了直观且易于使用的API使得进行线性代数计算变得简单和直观。它的API设计简洁明了提供了丰富的运算符重载和友好的语法使得代码编写更加简单和可读。
广泛的功能Eigen3支持多种线性代数运算包括向量和矩阵的基本运算、线性方程组的求解、特征值和特征向量的计算、矩阵分解如LU分解、QR分解等、广义特征值问题的求解等。它还提供了一些高级功能如支持稀疏矩阵和动态大小矩阵等。
跨平台支持Eigen3是一个跨平台的库可以在各种操作系统和编译器上使用。它使用标准的C语法和特性并且不依赖于任何特定的硬件或操作系统。
开源免费Eigen3是一个开源库遵循MPL2.0协议可以免费使用和修改。它的源代码可在GitHub上获得方便用户进行定制和扩展。 Eigen3已被广泛应用于科学计算、机器学习、计算机图形学等领域。无论是在学术研究还是实际应用中Eigen3都是一个强大而可靠的线性代数库。
sudo apt-get install libeigen3-dev编译opencv
编译命令下载opencv4的源码包后解压并下载contrib扩展包开启dnn 读取视频需要安装ffmpeg:
sudo apt-get install build-essential
sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev对于libjasper-dev安装失败
apt-get install software-properties-common
add-apt-repository deb http://security.ubuntu.com/ubuntu xenial-security main
apt update
apt-get install libjasper-devsudo apt-get install ffmpeg获取ffmpeg的相关安装目录
pkg-config --cflags libavformat编译
cmake -DCMAKE_BUILD_TYPERelease -DOPENCV_EXTRA_MODULES_PATH/home/opencv_contrib-4.7.0/opencv_contrib-4.7.0/modules -DWITH_OPENCLOFF -DBUILD_DOCSOFF -DBUILD_EXAMPLESOFF -DBUILD_WITH_DEBUG_INFOOFF -DBUILD_TESTSOFF -DWITH_1394OFF -DWITH_CUDAOFF -DWITH_CUBLASOFF -DWITH_CUFFTOFF -DWITH_OPENCLAMDBLASOFF -DWITH_OPENCLAMDFFTOFF -DINSTALL_C_EXAMPLESOFF -DINSTALL_PYTHON_EXAMPLESOFF -DINSTALL_TO_MANGLED_PATHSOFF -DBUILD_ANDROID_EXAMPLESOFF -DBUILD_opencv_pythonOFF -DBUILD_opencv_python_bindings_generatorOFF -DBUILD_opencv_appsOFF -DBUILD_opencv_calib3dOFF -DBUILD_opencv_features2dOFF -DBUILD_opencv_flannOFF -DBUILD_opencv_java_bindings_generatorOFF -DBUILD_opencv_jsOFF -DBUILD_opencv_mlOFF -DBUILD_opencv_objdetectOFF -DBUILD_opencv_photoOFF -DBUILD_opencv_python3OFF -DBUILD_opencv_python_testsOFF -DBUILD_opencv_shapeOFF -DBUILD_opencv_stitchingOFF -DBUILD_opencv_superresOFF -DBUILD_opencv_tsOFF -DBUILD_opencv_videostabOFF -DBUILD_opencv_worldON -DBUILD_opencv_dnnON -D WITH_FFMPEGON -D WITH_TIFFOFF -D BUILD_TIFFOFF -DWITH_FFMPEGON -DFFMPEG_LIBRARIES/usr/local/lib/ -D FFMPEG_INCLUDE_DIRS/usr/local/include/ ..make install更改deepsort对应的cpp文件
#include fstream
#include sstream
#include opencv2/imgproc.hpp
#include opencv2/opencv.hpp
#include opencv2/dnn.hpp
#include YOLOv5Detector.h#include FeatureTensor.h
#include BYTETracker.h //bytetrack
#include tracker.h//deepsort
//Deep SORT parameterconst int nn_budget100;
const float max_cosine_distance0.2;void get_detections(DETECTBOX box,float confidence,DETECTIONS d)
{DETECTION_ROW tmpRow;tmpRow.tlwh box;//DETECTBOX(x, y, w, h);tmpRow.confidence confidence;d.push_back(tmpRow);
}void test_deepsort(cv::Mat frame, std::vectordetect_result results,tracker mytracker)
{std::vectordetect_result objects;DETECTIONS detections;for (detect_result dr : results){//cv::putText(frame, classes[dr.classId], cv::Point(dr.box.tl().x10, dr.box.tl().y - 10), cv::FONT_HERSHEY_SIMPLEX, .8, cv::Scalar(0, 255, 0));if(dr.classId 0) //person{objects.push_back(dr);cv::rectangle(frame, dr.box, cv::Scalar(255, 0, 0), 2);get_detections(DETECTBOX(dr.box.x, dr.box.y,dr.box.width, dr.box.height),dr.confidence, detections);}}std::coutbegin trackstd::endl;if(FeatureTensor::getInstance()-getRectsFeature(frame, detections)){std::cout get feature succeed!std::endl;mytracker.predict();mytracker.update(detections);std::vectorRESULT_DATA result;for(Track track : mytracker.tracks) {if(!track.is_confirmed() || track.time_since_update 1) continue;result.push_back(std::make_pair(track.track_id, track.to_tlwh()));}for(unsigned int k 0; k detections.size(); k){DETECTBOX tmpbox detections[k].tlwh;cv::Rect rect(tmpbox(0), tmpbox(1), tmpbox(2), tmpbox(3));cv::rectangle(frame, rect, cv::Scalar(0,0,255), 4);// cvScalar的储存顺序是B-G-RCV_RGB的储存顺序是R-G-Bfor(unsigned int k 0; k result.size(); k){DETECTBOX tmp result[k].second;cv::Rect rect cv::Rect(tmp(0), tmp(1), tmp(2), tmp(3));rectangle(frame, rect, cv::Scalar(255, 255, 0), 2);std::string label cv::format(%d, result[k].first);cv::putText(frame, label, cv::Point(rect.x, rect.y), cv::FONT_HERSHEY_SIMPLEX, 0.8, cv::Scalar(255, 255, 0), 2);}}}std::coutend trackstd::endl;
}void test_bytetrack(cv::Mat frame, std::vectordetect_result results,BYTETracker tracker)
{std::vectordetect_result objects;for (detect_result dr : results){if(dr.classId 0) //person{objects.push_back(dr);}}std::vectorSTrack output_stracks tracker.update(objects);for (unsigned long i 0; i output_stracks.size(); i){std::vectorfloat tlwh output_stracks[i].tlwh;bool vertical tlwh[2] / tlwh[3] 1.6;if (tlwh[2] * tlwh[3] 20 !vertical){cv::Scalar s tracker.get_color(output_stracks[i].track_id);cv::putText(frame, cv::format(%d, output_stracks[i].track_id), cv::Point(tlwh[0], tlwh[1] - 5),0, 0.6, cv::Scalar(0, 0, 255), 2, cv::LINE_AA);cv::rectangle(frame, cv::Rect(tlwh[0], tlwh[1], tlwh[2], tlwh[3]), s, 2);}}}
int main(int argc, char *argv[])
{//deepsorttracker mytracker(max_cosine_distance, nn_budget);//bytetrackint fps20;BYTETracker bytetracker(fps, 30);//-----------------------------------------------------------------------// 加载类别名称std::vectorstd::string classes;std::string file./coco_80_labels_list.txt;std::ifstream ifs(file);if (!ifs.is_open())CV_Error(cv::Error::StsError, File file not found);std::string line;while (std::getline(ifs, line)){classes.push_back(line);}//-----------------------------------------------------------------------std::coutclasses:classes.size();std::shared_ptrYOLOv5Detector detector(new YOLOv5Detector());detector-init(/home/DeepSORT-master/DeepSORT-master/build/yolov5x.onnx);std::coutbegin read videostd::endl;const std::string source /home/DeepSORT-master/DeepSORT-master/build/test.mp4;cv::VideoCapture capture(source);if (!capture.isOpened()) {printf(could not read this video file...\n);return -1;}std::coutend read videostd::endl;std::vectordetect_result results;int num_frames 0;cv::VideoWriter video(out.avi,cv::VideoWriter::fourcc(M,J,P,G),10, cv::Size(1920,1080));while (true){cv::Mat frame;if (!capture.read(frame)) // if not success, break loop{std::cout\n Cannot read the video file. please check your video.\n;break;}num_frames ;//Second/Millisecond/Microsecond 秒s/毫秒ms/微秒usauto start std::chrono::system_clock::now();detector-detect(frame, results);auto end std::chrono::system_clock::now();auto detect_time std::chrono::duration_caststd::chrono::milliseconds(end - start).count();//msstd::coutclasses.size():results.size():num_framesstd::endl;//test_deepsort(frame, results,mytracker);test_bytetrack(frame, results,bytetracker);//cv::imshow(YOLOv5-6.x, frame);video.write(frame);if(cv::waitKey(30) 27) // Wait for esc key press to exit{break;}results.clear();}capture.release();video.release();cv::destroyAllWindows();}
编译并运行
