目标跟踪算法（bytetrack)-tensorrt部署教程

一、本机安装python环境

conda create -n bytetrace_env python=3.8
activate bytetrace_env
conda install pytorch torchvision cudatoolkit=10.1 -c

检测GPU是否可用，不可用不行

import torch
print(torch.cuda.is_available())

安装bytetrack

git clone https://github.com/ifzhang/ByteTrack.git
cd ByteTrack
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
python setup.py develop

上述即安装成功。
安装pycocotools

pip install cython
pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

或者（Linux）

pip install git+https://gitee.com/pursuit_zhangyu/cocoapi.git#subdirectory=PythonAPI

windows下

pip install pycocotools-windows

二、安装tensorrt环境

下载tensorrt包
TensorRT-8.4.3.1.Windows10.x86_64.cuda-10.2.cudnn8.4.zip

将所有的dll复制到
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin
并设置环境变量。

虚拟环境中python版本为python3.8

pip install tensorrt-8.4.3.1-cp38-none-win_amd64.whl

三、转换模型

https://pan.baidu.com/s/1PiP1kQfgxAIrnGUbFP6Wfg
qflm
获取bytetrack_s_mot17.pth.tar并创建pretrained进行存放

python tools/trt.py -f exps/example/mot/yolox_s_mix_det.py -c pretrained/bytetrack_s_mot17.pth.tar

最后在D:\git_clone\ByteTrack-main\YOLOX_outputs\yolox_s_mix_det目录下生成tensorrt模型与pth模型：

四、cmake生成eigen库并使用VS2015编译

https://pan.baidu.com/s/15kEfCxpy-T7tz60msxxExg
ueq4

五、下载opencv450

https://nchc.dl.sourceforge.net/project/opencvlibrary/4.5.0/opencv-4.5.0-vc14_vc15.exe?viasf=1
安装D:\opencv450

六、cmake生成bytetrack并使用VS2015编译

修改CMakeLists.txt

cmake_minimum_required(VERSION 2.6)

project(bytetrack)

add_definitions(-std=c++11)

option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)

find_package(CUDA REQUIRED)

include_directories(${
   PROJECT_SOURCE_DIR}/include)
include_directories(D:\VS2015_CUDA\TensorRT\eigen-3.3.9\build\install\include\eigen3)
link_directories(${
   PROJECT_SOURCE_DIR}/include)
# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
# cuda
include_directories(C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\include)
link_directories(C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\lib\x64)
# cudnn
include_directories(C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\include)
link_directories(C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\lib\x64)
# tensorrt
include_directories(D:\VS2015_CUDA\TensorRT\TensorRT-8.4.3.1\include)
link_directories(D:\VS2015_CUDA\TensorRT\TensorRT-8.4.3.1\lib)

set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -D_MWAITXINTRIN_H_INCLUDED")

set(OpenCV_INCLUDE_DIRS_DIRS D:\opencv450\build\include)
set(OpenCV_LIBS D:\opencv450\build\x64\vc14\lib)
include_directories(${
   OpenCV_INCLUDE_DIRS})

file(GLOB My_Source_Files ${
   PROJECT_SOURCE_DIR}/src/*.cpp)
add_executable(bytetrack ${
   My_Source_Files})
target_link_libraries(bytetrack nvinfer)
target_link_libraries(bytetrack cudart)
target_link_libraries(bytetrack ${
   OpenCV_LIBS})

add_definitions(-O2 -pthread)

使用CMake进行配置生成。
打开VS2015工程进行配置



opencv可以自己加进去编译。

修改bytetrack.cpp

int img_w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
int img_h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);

这里有个bug，高的参数不对，会影响后面的demo.mp4。
编译生成

七、使用Dependency Walker查看exe依赖

D:\VS2015_CUDA\TensorRT\cpp\model_trt.engine -i D:\VS2015_CUDA\TensorRT\cpp\palace.mp4

八、windows源码

bytetrack.cpp

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "logging.h"
#include "BYTETracker.h"

#define CHECK(status) \
    do\
    {
     \
        auto ret = (status);\
        if (ret != 0)\
        {
     \
            cerr << "Cuda failure: " << ret << endl;\
            abort();\
        }\
    } while (0)

#define DEVICE 0  // GPU id
#define NMS_THRESH 0.7
#define BBOX_CONF_THRESH 0.1

using namespace nvinfer1;

// stuff we know about the network and the input/output blobs
static const int INPUT_W = 1088;
static const int INPUT_H = 608;
const char* INPUT_BLOB_NAME = "input_0";
const char* OUTPUT_BLOB_NAME = "output_0";
static Logger gLogger;

Mat static_resize(Mat& img) {
   
    float r = min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));
    // r = std::min(r, 1.0f);
    int unpad_w = r * img.cols;
    int unpad_h = r * img.rows;
    Mat re(unpad_h, unpad_w, CV_8UC3);
    resize(img, re, re.size());
    Mat out(INPUT_H, INPUT_W, CV_8UC3, Scalar(114, 114, 114));
    re.copyTo(out(Rect(0, 0, re.cols, re.rows)));
    return out;
}

struct GridAndStride
{
   
    int grid0;
    int grid1;
    int stride;
};

static void generate_grids_and_stride(const int target_w, const int target_h, vector<int>& strides, vector<GridAndStride>& grid_strides)
{
   
    for (auto stride : strides)
    {
   
		GridAndStride GS;
        int num_grid_w = target_w / stride;
        int num_grid_h = target_h / stride;
        for (int g1 = 0; g1 < num_grid_h; g1++)
        {
   
            for (int g0 = 0; g0 < num_grid_w; g0++)
            {
   
				GS.grid0 = g0;
				GS.grid1 = g1;
				GS.stride = stride;
                grid_strides.push_back(GS);
            }
        }
    }
}

static inline float intersection_area(const Object& a, const Object& b)
{
   
    Rect_<float> inter = a.rect & b.rect;
    return inter.area();
}

static void qsort_descent_inplace(vector<Object>& faceobjects, int left, int right)
{
   
    int i = left;
    int j = right;
    float p = faceobjects[(left + right) / 2].prob;

    while (i <= j)
    {
   
        while (faceobjects[i].prob > p)
            i++;

        while (faceobjects[j].prob < p)
            j--;

        if (i <= j)
        {
   
            // swap
            swap(faceobjects[i], faceobjects[j]);

            i++;
            j--;
        }
    }

    #pragma omp parallel sections
    {
   
        #pragma omp section
        {
   
            if (left < j) qsort_descent_inplace(faceobjects, left, j);
        }
        #pragma omp section
        {
   
            if (i < right) qsort_descent_inplace(faceobjects, i, right);
        }
    }
}

static void qsort_descent_inplace(vector<Object>& objects)
{
   
    if (objects.empty())
        return;

    qsort_descent_inplace(objects, 0, objects.size() - 1);
}

static void nms_sorted_bboxes(const vector<Object>& faceobjects, vector<int>& picked, float nms_threshold)
{
   
    picked.clear();

    const int n = faceobjects.size();

    vector<float> areas(n);
    for (int i = 0; i < n; i++)
    {
   
        areas[i] = faceobjects[i].rect.area();
    }

    for (int i = 0; i < n; i++)
    {
   
        const Object& a = faceobjects[i];

        int keep = 1;
        for (int j = 0; j < (int)picked.size(); j++)
        {
   
            const Object& b = faceobjects[picked[j]];

            // intersection over union
            float inter_area = intersection_area(a, b);
            float union_area = areas[i] + areas[picked[j]] - inter_area;
            // float IoU = inter_area / union_area
            if (inter_area / union_area > nms_threshold)
                keep = 0;
        }

        if (keep)
            picked.push_back(i);
    }
}


static void generate_yolox_proposals(vector<GridAndStride> grid_strides, float* feat_blob, float prob_threshold, vector<Object>& objects)
{
   
    const int num_class = 1;

    const int num_anchors = grid_strides.size();

    for (int anchor_idx = 0; anchor_idx < num_anchors; anchor_idx++)
    {
   
        const int grid0 = grid_strides[anchor_idx].grid0;
        const int grid1 = grid_strides[anchor_idx].grid1;
        const int stride = grid_strides[anchor_idx].stride;

        const int basic_pos = anchor_idx * (num_class + 5);

        // yolox/models/yolo_head.py decode logic
        float x_center = (feat_blob[basic_pos+0] + grid0) * stride;
        float y_center = (feat_blob[basic_pos+1] + grid1) * stride;
        float w = exp(feat_blob[basic_pos+2]) * stride;
        float h = exp(feat_blob[basic_pos+3]) * stride;
        float x0 = x_center - w * 0.5f;
        float y0 = y_center - h * 0.5f;

        float box_objectness = feat_blob[basic_pos+4];
        for (int class_idx = 0; class_idx < num_class; class_idx++)
        {
   
            float box_cls_score = feat_blob[basic_pos + 5 + class_idx];
            float box_prob = box_objectness * box_cls_score;
            if (box_prob > prob_threshold)
            {
   
                Object obj;
                obj.rect.x = x0;
                obj.rect.y = y0;
                obj.rect.width = w;
                obj.rect.height = h;
                obj.label = class_idx;
                obj.prob = box_prob;

                objects.push_back(obj);
            }

        } // class loop

    } // point anchor loop
}

float* blobFromImage(Mat& img){
   
    cvtColor(img, img, COLOR_BGR2RGB);

    float* blob = new float[img.total()*3];
    int channels = 3;
    int img_h = img.rows;
    int img_w = img.cols;
    vector<float> mean = {
   0.485f, 0.456f, 0.406f};
    vector<float> std = {
   0.229f, 0.224f, 0.225f};
    for (size_t c = 0; c < channels; c++) 
    {
   
        for (size_t  h = 0; h < img_h; h++) 
        {
   
            for (size_t w = 0; w < img_w; w++) 
            {
   
                blob[c * img_w * img_h + h * img_w + w] =
                    (((float)img.at<Vec3b>(h, w)[c]) / 255.0f - mean[c]) / std[c];
            }
        }
    }
    return blob;
}


static void decode_outputs(float* prob, vector<Object>& objects, float scale, const int img_w, const int img_h) {
   
        vector<Object> proposals;
        vector<int> strides = {
   8, 16, 32};
        vector<GridAndStride> grid_strides;
        generate_grids_and_stride(INPUT_W, INPUT_H, strides, grid_strides);
        generate_yolox_proposals(grid_strides, prob,  BBOX_CONF_THRESH, proposals);
        //std::cout << "num of boxes before nms: " << proposals.size() << std::endl;

        qsort_descent_inplace(proposals);

        vector<int> picked;
        nms_sorted_bboxes(proposals, picked, NMS_THRESH);


        int count = picked.size();

        //std::cout << "num of boxes: " << count << std::endl;

        objects.resize(count);
        for (int i = 0; i < count; i++)
        {
   
            objects[i] = proposals[picked[i]];

            // adjust offset to original unpadded
            float x0 = (objects[i].rect.x) / scale;
            float y0 = (objects[i].rect.y) / scale;
            float x1 = (objects[i].rect.x + objects[i].rect.width) / scale;
            float y1 = (objects[i].rect.y + objects[i].rect.height) / scale;

            // clip
            // x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);
            // y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);
            // x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);
            // y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);

            objects[i].rect.x = x0;
            objects[i].rect.y = y0;
            objects[i].rect.width = x1 - x0;
            objects[i].rect.height = y1 - y0;
        }
}

const float color_list[80][3] =
{
   
    {
   0.000, 0.447, 0.741},
    {
   0.850, 0.325, 0.098},
    {
   0.929, 0.694, 0.125},
    {
   0.494, 0.184, 0.556},
    {
   0.466, 0.674, 0.188},
    {
   0.301, 0.745, 0.933},
    {
   0.635, 0.078, 0.184},
    {
   0.300, 0.300, 0.300},
    {
   0.600, 0.600, 0.600},
    {
   1.000, 0.000, 0.000},
    {
   1.000, 0.500, 0.000},
    {
   0.749, 0.749, 0.000},
    {
   0.000, 1.000, 0.000},
    {
   0.000, 0.000, 1.000},
    {
   0.667, 0.000, 1.000},
    {
   0.333, 0.333, 0.000},
    {
   0.333, 0.667, 0.000},
    {
   0.333, 1.000, 0.000},
    {
   0.667, 0.333, 0.000},
    {
   0.667, 0.667, 0.000},
    {
   0.667, 1.000, 0.000},
    {
   1.000, 0.333, 0.000},
    {
   1.000, 0.667, 0.000},
    {
   1.000, 1.000, 0.000},
    {
   0.000, 0.333, 0.500},
    {
   0.000, 0.667, 0.500},
    {
   0.000, 1.000, 0.500},
    {
   0.333, 0.000, 0.500},
    {
   0.333, 0.333, 0.500},
    {
   0.333, 0.667, 0.500},
    {
   0.333, 1.000, 0.500},
    {
   0.667, 0.000, 0.500},
    {
   0.667, 0.333, 0.500},
    {
   0.667, 0.667, 0.500},
    {
   0.667, 1.000, 0.500},
    {
   1.000, 0.000, 0.500},
    {
   1.000, 0.333, 0.500},
    {
   1.000, 0.667, 0.500},
    {
   1.000, 1.000, 0.500},
    {
   0.000, 0.333, 1.000},
    {
   0.000, 0.667, 1.000},
    {
   0.000, 1.000, 1.000},
    {
   0.333, 0.000, 1.000},
    {
   0.333, 0.333, 1.000},
    {
   0.333, 0.667, 1.000},
    {
   0.333, 1.000, 1.000},
    {
   0.667, 0.000, 1.000},
    {
   0.667, 0.333, 1.000},
    {
   0.667, 0.667, 1.000},
    {
   0.667, 1.000, 1.000},
    {
   1.000, 0.000, 1.000},
    {
   1.000, 0.333, 1.000},
    {
   1.000, 0.667, 1.000},
    {
   0.333, 0.000, 0.000},
    {
   0.500, 0.000, 0.000},
    {
   0.667, 0.000, 0.000},
    {
   0.833, 0.000, 0.000},
    {
   1.000, 0.000, 0.000},
    {
   0.000, 0.167, 0.000},
    {
   0.000, 0.333, 0.000},
    {
   0.000, 0.500, 0.000},
    {
   0.000, 0.667, 0.000},
    {
   0.000, 0.833, 0.000},
    {
   0.000, 1.000, 0.000},
    {
   0.000, 0.000, 0.167},
    {
   0.000, 0.000, 0.333},
    {
   0.000, 0.000, 0.500},
    {
   0.000, 0.000, 0.667},
    {
   0.000, 0.000, 0.833},
    {
   0.000, 0.000, 1.000},
    {
   0.000, 0.000, 0.000},
    {
   0.143, 0.143, 0.143},
    {
   0.286, 0.286, 0.286},
    {
   0.429, 0.429, 0.429},
    {
   0.571, 0.571, 0.571},
    {
   0.714, 0.714, 0.714},
    {
   0.857, 0.857, 0.857},
    {
   0.000, 0.447, 0.741},
    {
   0.314, 0.717, 0.741},
    {
   0.50, 0.5, 0}
};

void doInference(IExecutionContext& context, float* input, float* output, const int output_size, Size input_shape) {
   
    const ICudaEngine& engine = context.getEngine();

    // Pointers to input and output device buffers to pass to engine.
    // Engine requires exactly IEngine::getNbBindings() number of buffers.
    assert(engine.getNbBindings() == 2);
    void* buffers[2];

    // In order to bind the buffers, we need to know the names of the input and output tensors.
    // Note that indices are guaranteed to be less than IEngine::getNbBindings()
    const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);

    assert(engine.getBindingDataType(inputIndex) == nvinfer1::DataType::kFLOAT);
    const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
    assert(engine.getBindingDataType(outputIndex) == nvinfer1::DataType::kFLOAT);
    int mBatchSize = engine.getMaxBatchSize();

    // Create GPU buffers on device
    CHECK(cudaMalloc(&buffers[inputIndex], 3 * input_shape.height * input_shape.width * sizeof(float)));
    CHECK(cudaMalloc(&buffers[outputIndex], output_size*sizeof(float)));

    // Create stream
    cudaStream_t stream;
    CHECK(cudaStreamCreate(&stream));

    // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
    CHECK(cudaMemcpyAsync(buffers[inputIndex], input, 3 * input_shape.height * input_shape.width * sizeof(float), cudaMemcpyHostToDevice, stream));
    context.enqueue(1, buffers, stream, nullptr);
    CHECK(cudaMemcpyAsync(output, buffers[outputIndex], output_size * sizeof(float), cudaMemcpyDeviceToHost, stream));
    cudaStreamSynchronize(stream);

    // Release stream and buffers
    cudaStreamDestroy(stream);
    CHECK(cudaFree(buffers[inputIndex]));
    CHECK(cudaFree(buffers[outputIndex]));
}

int main(int argc, char** argv) {
   
    cudaSetDevice(DEVICE);
    
    // create a model using the API directly and serialize it to a stream
    char *trtModelStream{
   nullptr};
    size_t size{
   0};

    if (argc == 4 && string(argv[2]) == "-i") {
   
        const string engine_file_path {
   argv[1]};
        ifstream file(engine_file_path, ios::binary);
        if (file.good()) {
   
            file.seekg(0, file.end);
            size = file.tellg();
            file.seekg(0, file.beg);
            trtModelStream = new char[size];
            assert(trtModelStream);
            file.read(trtModelStream, size);
            file.close();
        }
    } else {
   
        cerr << "arguments not right!" &