本文主要是介绍opencv C++ dnn模块调用yolov5以及Intel RealSense D435深度相机联合使用进行目标检测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
一、代码
#include <opencv2/opencv.hpp>
#include <opencv2/dnn/dnn.hpp>
#include <librealsense2/rs.hpp> // Include RealSense Cross Platform APIusing namespace cv;
using namespace dnn;
using namespace std;
using namespace rs2;// 类名数组，这里需要替换为实际YOLO模型所检测的对象的类名
const char* classNames[] = {"object1", "object2", "object3", "object4"};int main(int argc, char** argv)
{// 模型权重和配置文件路径，这些文件包含了训练好的YOLO模型参数和网络配置String model = "yolov8.onnx"; // 替换为实际模型文件路径// 加载预训练的模型和配置到DNN网络中Net net = readNetFromONNX(model);// 设置推理引擎后端为OpenCV，目标设备为CPUnet.setPreferableBackend(DNN_BACKEND_OPENCV);net.setPreferableTarget(DNN_TARGET_CPU);// Declare depth colorizer for pretty visualization of depth datacolorizer color_map;// Declare RealSense pipeline, encapsulating the actual device and sensorspipeline p;// Start streaming with default recommended configurationp.start();// 循环直到用户按下键盘上的任意键while (waitKey(1) < 0) {// Wait for the next set of frames from the cameraframeset frames = p.wait_for_frames();// Get a frame from the RGB cameraframe color = frames.get_color_frame();// Create OpenCV matrix of size (color_height, color_width)Mat frame(Size(640, 480), CV_8UC3, (void*)color.get_data(), Mat::AUTO_STEP);Mat blob; // 用于存储处理后的图像，以适应网络输入// 将帧图像转换为网络输入所需格式blobFromImage(frame, blob, 1/255.0, cv::Size(416, 416), Scalar(0,0,0), true, false);// 将blob设置为网络的输入net.setInput(blob);// 运行前向传递以获取网络的输出层vector<Mat> outs;net.forward(outs, net.getUnconnectedOutLayersNames());// 遍历网络输出的每一层结果for (size_t i = 0; i < outs.size(); ++i) {for (int j = 0; j < outs[i].rows; ++j) {Mat scores = outs[i].row(j).colRange(5, outs[i].cols);Point classIdPoint;double confidence;minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);if (confidence > 0.5) {int centerX = (int)(outs[i].at<float>(j, 0) * frame.cols);int centerY = (int)(outs[i].at<float>(j, 1) * frame.rows);int width = (int)(outs[i].at<float>(j, 2) * frame.cols);int height = (int)(outs[i].at<float>(j, 3) * frame.rows);int left = centerX - width / 2;int top = centerY - height / 2;rectangle(frame, Rect(left, top, width, height), Scalar(0, 255, 0), 2);int classIdx = static_cast<int>(classIdPoint.x);string classLabel = string(classNames[classIdx]);string label = classLabel + ":" + format("%.2f", confidence);int baseLine;Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);top = max(top, labelSize.height);rectangle(frame, Point(left, top - labelSize.height), Point(left + labelSize.width, top + baseLine), Scalar::all(255), FILLED);putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0));}}}// 展示处理后的帧imshow("YoloV8", frame);}return 0;
}
注意：由于手头上没有该摄像头，本人只是查询资料，以及文档之后写的代码，并没有实操
二、安装包
需要安装opencv、librealsense2库
链接：Intel.RealSense.SDK.zip资源-CSDN文库
这篇关于opencv C++ dnn模块调用yolov5以及Intel RealSense D435深度相机联合使用进行目标检测的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
