下载源码
https://github.com/shaoshengsong/DeepSORT
安装onnxruntime
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-dev
sudo apt-get install ffmpeg
获取ffmpeg的相关安装目录：
pkg-config --cflags libavformat
编译：
cmake -DCMAKE_BUILD_TYPE=Release -DOPENCV_EXTRA_MODULES_PATH=/home/opencv_contrib-4.7.0/opencv_contrib-4.7.0/modules -DWITH_OPENCL=OFF -DBUILD_DOCS=OFF -DBUILD_EXAMPLES=OFF -DBUILD_WITH_DEBUG_INFO=OFF -DBUILD_TESTS=OFF -DWITH_1394=OFF -DWITH_CUDA=OFF -DWITH_CUBLAS=OFF -DWITH_CUFFT=OFF -DWITH_OPENCLAMDBLAS=OFF -DWITH_OPENCLAMDFFT=OFF -DINSTALL_C_EXAMPLES=OFF -DINSTALL_PYTHON_EXAMPLES=OFF -DINSTALL_TO_MANGLED_PATHS=OFF -DBUILD_ANDROID_EXAMPLES=OFF -DBUILD_opencv_python=OFF -DBUILD_opencv_python_bindings_generator=OFF -DBUILD_opencv_apps=OFF -DBUILD_opencv_calib3d=OFF -DBUILD_opencv_features2d=OFF -DBUILD_opencv_flann=OFF -DBUILD_opencv_java_bindings_generator=OFF -DBUILD_opencv_js=OFF -DBUILD_opencv_ml=OFF -DBUILD_opencv_objdetect=OFF -DBUILD_opencv_photo=OFF -DBUILD_opencv_python3=OFF -DBUILD_opencv_python_tests=OFF -DBUILD_opencv_shape=OFF -DBUILD_opencv_stitching=OFF -DBUILD_opencv_superres=OFF -DBUILD_opencv_ts=OFF -DBUILD_opencv_videostab=OFF -DBUILD_opencv_world=ON -DBUILD_opencv_dnn=ON -D WITH_FFMPEG=ON -D WITH_TIFF=OFF -D BUILD_TIFF=OFF -DWITH_FFMPEG=ON -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 parameter
const int nn_budget=100;
const float max_cosine_distance=0.2;
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::vector<detect_result>& results,tracker& mytracker)
{
std::vector<detect_result> objects;
DETECTIONS detections;
for (detect_result dr : results)
{
//cv::putText(frame, classes[dr.classId], cv::Point(dr.box.tl().x+10, 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::cout<<"begin track"<<std::endl;
if(FeatureTensor::getInstance()->getRectsFeature(frame, detections))
{
std::cout << "get feature succeed!"<<std::endl;
mytracker.predict();
mytracker.update(detections);
std::vector<RESULT_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-R，CV_RGB的储存顺序是R-G-B
for(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::cout<<"end track"<<std::endl;
}
void test_bytetrack(cv::Mat& frame, std::vector<detect_result>& results,BYTETracker& tracker)
{
std::vector<detect_result> objects;
for (detect_result dr : results)
{
if(dr.classId == 0) //person
{
objects.push_back(dr);
}
}
std::vector<STrack> output_stracks = tracker.update(objects);
for (unsigned long i = 0; i < output_stracks.size(); i++)
{
std::vector<float> 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[])
{
//deepsort
tracker mytracker(max_cosine_distance, nn_budget);
//bytetrack
int fps=20;
BYTETracker bytetracker(fps, 30);
//-----------------------------------------------------------------------
// 加载类别名称
std::vector<std::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::cout<<"classes:"<<classes.size();
std::shared_ptr<YOLOv5Detector> detector(new YOLOv5Detector());
detector->init("/home/DeepSORT-master/DeepSORT-master/build/yolov5x.onnx");
std::cout<<"begin read video"<<std::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::cout<<"end read video"<<std::endl;
std::vector<detect_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us
auto start = std::chrono::system_clock::now();
detector->detect(frame, results);
auto end = std::chrono::system_clock::now();
auto detect_time =std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count();//ms
std::cout<<classes.size()<<":"<<results.size()<<":"<<num_frames<<std::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：