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c++ opencv调用yolo onnx文件

news 2025/8/10 6:54:23

网上找了一段代码，测试c++ opencv调用yolo onnx文件

yolov8n.onnx opencv版本是4.12 ，另外测试了4.4和4.6版本的opencv运行有问题，可能对opencv版本有要求，有待研究，都在编译了contrib库的情况下测试的

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>
#include <chrono>int main()
{// 加载 ONNX 模型std::string modelPath = "yolov8n.onnx";cv::dnn::Net net = cv::dnn::readNetFromONNX(modelPath);net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);// 定义完整的COCO数据集类别名称std::vector<std::string> classes = {"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light","fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow","elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee","skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard","tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple","sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch","potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone","microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear","hair drier", "toothbrush"};// 打开视频文件或摄像头// 0 表示默认摄像头，也可以替换为视频文件路径如 "video.mp4"cv::VideoCapture cap("nfs.mp4");// 检查视频是否成功打开if (!cap.isOpened()) {std::cerr << "Error: Unable to open video source" << std::endl;return -1;}// 获取视频的帧率double fps = cap.get(cv::CAP_PROP_FPS);if (fps == 0) fps = 30.0; // 默认帧率// 用于计算FPS的变量auto lastTime = std::chrono::high_resolution_clock::now();int frameCount = 0;double currentFps = 0.0;cv::Mat frame;while (true) {// 读取帧cap >> frame;// 检查是否成功读取帧if (frame.empty()) {std::cout << "End of video or error reading frame" << std::endl;break;}// 计算FPSframeCount++;auto currentTime = std::chrono::high_resolution_clock::now();auto elapsedTime = std::chrono::duration_cast<std::chrono::milliseconds>(currentTime - lastTime).count();if (elapsedTime >= 1000) { // 每秒更新一次FPScurrentFps = frameCount / (elapsedTime / 1000.0);frameCount = 0;lastTime = currentTime;}// 将图像转换为blob格式cv::Mat blob = cv::dnn::blobFromImage(frame, 1 / 255.0, cv::Size(640, 640), cv::Scalar(0, 0, 0), true, false);net.setInput(blob);// 前向传播, 获取检测结果std::vector <cv::Mat> outputs;net.forward(outputs, net.getUnconnectedOutLayersNames());// output.size [ 1, 84, 8400]int rows = outputs[0].size[2];// 每个目标存储了多少个值(x,y,w,h+类别数)int length = outputs[0].size[1];// 转成单通道outputs[0] = outputs[0].reshape(1, length);// 按对角线翻转cv::transpose(outputs[0], outputs[0]);float* data = (float*)outputs[0].data;float xFactor = (float)frame.cols / 640;float yFactor = (float)frame.rows / 640;// 解析检测结果std::vector<int> classIds;std::vector<float> confidences;std::vector<cv::Rect> boxes;for (int i = 0; i < rows; i++){// 存储每个类别的置信度cv::Mat scores(1, classes.size(), CV_32FC1, data + 4);cv::Point classId;double maxClassScore;// 读取最大置信度并获得它的索引cv::minMaxLoc(scores, 0, &maxClassScore, 0, &classId);if (maxClassScore > 0.1){confidences.push_back(maxClassScore);classIds.push_back(classId.x);float x = data[0];float y = data[1];float w = data[2];float h = data[3];int left = int((x - 0.5 * w) * xFactor);int top = int((y - 0.5 * h) * yFactor);int width = int(w * xFactor);int height = int(h * yFactor);boxes.push_back(cv::Rect(left, top, width, height));}data += length;}// 执行非最大抑制,以消除具有较低置信度的冗余重叠框(NMS)std::vector<int> nmsResult;cv::dnn::NMSBoxes(boxes, confidences, 0.25, 0.7, nmsResult);for (int i = 0; i < nmsResult.size(); i++){int idx = nmsResult[i];int classId = classIds[idx];float confidence = confidences[idx];cv::Rect box = boxes[idx];// 绘制检测框并显示类别名称cv::rectangle(frame, box, cv::Scalar(0, 0, 255), 2);cv::putText(frame, classes[classId] + ": " + std::to_string(confidence).substr(0, 4),cv::Point(box.x, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 255));}// 在图像上显示FPSstd::string fpsText = "FPS: " + std::to_string(static_cast<int>(currentFps));cv::putText(frame, fpsText, cv::Point(10, 30), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 0), 2);// 显示结果cv::imshow("YOLO Detection", frame);// 按ESC键退出if (cv::waitKey(1) == 27) {break;}}// 释放资源cap.release();cv::destroyAllWindows();return 0;
}