C/C++ TensorRT引擎多线程推理多个rtsp流并显示或保存

主要思路

每个thread负责一个rtsp流，主线程里定义WrapData类型的消息队列（目的是包含frame和对应的rtsp信息），传到每个线程里面去通过opencv获取frame，将frame和rtsp信息包装到WrapData后push到队列中，主函数中捕捉到队列长度大于1则进行.front()推理并在推理结束后.pop()

一、超参数和常量

#define NMS_THRESH 0.4
#define CONF_THRESH 0.5
#define BATCH_SIZE 1

// stuff we know about the network and the input/output blobs
static const int INPUT_H = Yolo::INPUT_H;
static const int INPUT_W = Yolo::INPUT_W;
static const int OUTPUT_SIZE = Yolo::MAX_OUTPUT_BBOX_COUNT * sizeof(Yolo::Detection) / sizeof(float) + 1;  // we assume the yololayer outputs no more than MAX_OUTPUT_BBOX_COUNT boxes that conf >= 0.1
const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
static Logger gLogger;

const char *class_name[4] = {
   
   "1","2","3","4"};

二、简易的WrapData类

class WrapData{
   
   
public:
    Mat img;
    string channel;
};

三、opencv获取rtsp流

int capture_show(string& rtsp_address, queue<WrapData>& Wrap_images){
   
   
    WrapData Wrap_img;
    Mat frame;
    VideoCapture cap;
    cap.open(rtsp_address);
    if (!cap.isOpened()) {
   
   
        cerr << "ERROR! Unable to open camera\n";
        return -1;
    }
    for (;;)
    {
   
   
        // wait for a new frame from camera and store it into 'frame'
        cap.read(frame);
        // check if we succeeded
        if (frame.empty()) {
   
   
            cerr << "ERROR! blank frame grabbed\n";
            break;
        }
        Wrap_img.channel = rtsp_address;
        Wrap_img.img = frame;
        Wrap_images.push(Wrap_img);
    }
    cap.release();
    return 1;
}

四、.engine 推理部分

void doInference(IExecutionContext& context, cudaStream_t& stream, void *