yolo26已经正式发布了，因此使用C++代码实现YOLO26-obb旋转框检测的onnx模型部署，首先看yolo11n-obb网络结构，发现输出shape是1x20x21504
再来看看yolo26n-obb网络结构输出，输出shape是1x300x7
安装好yolo26环境，要求ultralytics==8.4.0，转换命令
yolo export model=yolo26n-obb.pt format=onnx opset=12
测试环境：
vs2019
cmake==3.30.1
onnxruntime-win-x64-gpu-1.20.1
opencv==4.9.0
运行步骤：
先删除build文件夹
然后打开CMakeLists.txt里面opencv和onnxruntime路径
重新cmake后会生成exe
测试命令：切换到exe路径后执行
测试图片：
YOLO26_ort --input=test.jpg
测试摄像头：
YOLO26_ort --input=0 [--gpu] 注意运行gpu需要安装onnxruntime-win-x64-gpu-1.20.1对应cuda这个官方可以查询到，测试cuda12.4+cudnn9.4.1可以其他版本应该也可以看要求
测试视频：
YOLO26_ort --input=test_video.mp4 --output=result.mp4 --conf=0.3
调用代码：
#include <iostream> #include <opencv2/opencv.hpp> #include <chrono> #include <algorithm> #include "YOLO26Obb.hpp" //注意如果onnx上推理显示为空，很可能是导出onnx问题，需要设置opset=12 struct Args { std::string model_path = "./yolo26n-obb.onnx"; std::string classes_path = "./labels.txt"; std::string input_path = "./P0032.png"; std::string output_path = "./output.jpg"; bool use_gpu = false; float conf_threshold = 0.25f; float iou_threshold = 0.25f; bool help = false; }; void print_help() { std::cout << "YOLOv26 C++ OBB\n\n"; std::cout << "Usage: ./yolo26_ort [options]\n\n"; std::cout << "Options:\n"; std::cout << " --model <path> Path to ONNX model file (default: ./yolo26n-obb.onnx)\n"; std::cout << " --classes <path> Path to class names file (default: ./labels.txt)\n"; std::cout << " --input <path> Path to input video/image file or camera device index (default: ./P0032.png)\n"; std::cout << " --output <path> Path to output video/image file (default: ./output.jpg)\n"; std::cout << " --gpu Use GPU acceleration if available (default: false)\n"; std::cout << " --conf <value> Confidence threshold (default: 0.25)\n"; std::cout << " --iou <value> IoU threshold for NMS (default: 0.25)\n"; std::cout << " --help Show this help message\n\n"; std::cout << "Examples:\n"; std::cout << " yolo26_ort --input=test_video.mp4 --output=result.mp4 --conf=0.3\n"; std::cout << " yolo26_ort --input=0 --gpu # Use webcam with GPU and obb\n"; std::cout << " yolo26_ort --input=test.jpg # Image obb\n"; } Args parse_args(int argc, char *argv[]) { Args args; for (int i = 1; i < argc; ++i) { std::string arg(argv[i]); if (arg == "--help" || arg == "-h") { args.help = true; } else if (arg.find("--model=") == 0) { args.model_path = arg.substr(8); } else if (arg.find("--classes=") == 0) { args.classes_path = arg.substr(10); } else if (arg.find("--input=") == 0) { args.input_path = arg.substr(8); } else if (arg.find("--output=") == 0) { args.output_path = arg.substr(9); } else if (arg == "--gpu") { args.use_gpu = true; } else if (arg.find("--conf=") == 0) { args.conf_threshold = std::stof(arg.substr(7)); } else if (arg.find("--iou=") == 0) { args.iou_threshold = std::stof(arg.substr(6)); } else { std::cerr << "Unknown argument: " << arg << std::endl; } } return args; } bool is_camera_input(const std::string &input) { try { std::stoi(input); return true; } catch (const std::exception &) { return false; } } bool is_image_file(const std::string &input) { std::string lower = input; std::transform(lower.begin(), lower.end(), lower.begin(), ::tolower); return lower.find(".jpg") != std::string::npos || lower.find(".jpeg") != std::string::npos || lower.find(".png") != std::string::npos || lower.find(".bmp") != std::string::npos; } void draw_fps(cv::Mat &frame, double fps) { std::string fps_text = "FPS: " + std::to_string(static_cast<int>(fps)); cv::putText(frame, fps_text, cv::Point(10, 30), cv::FONT_HERSHEY_SIMPLEX, 1.0, cv::Scalar(0, 255, 0), 2); } int main(int argc, char *argv[]) { Args args = parse_args(argc, argv); if (args.help) { print_help(); return 0; } std::cout << "YOLOv26 C++ OBB\n"; std::cout << "============================================\n"; std::cout << "Model: " << args.model_path << "\n"; std::cout << "Classes: " << args.classes_path << "\n"; std::cout << "Input: " << args.input_path << "\n"; std::cout << "Output: " << args.output_path << "\n"; std::cout << "GPU: " << (args.use_gpu ? "enabled" : "disabled") << "\n"; std::cout << "Confidence threshold: " << args.conf_threshold << "\n"; std::cout << "IoU threshold: " << args.iou_threshold << "\n"; std::cout << "\n"; YOLO26OBBDetector detector(args.model_path, args.classes_path, args.use_gpu); if (is_image_file(args.input_path)) { std::cout << "Processing single image: " << args.input_path << std::endl; cv::Mat image = cv::imread(args.input_path); if (image.empty()) { std::cerr << "Error: Cannot read image file: " << args.input_path << std::endl; return -1; } cv::Mat result = image.clone(); auto start_time = std::chrono::high_resolution_clock::now(); auto detections = detector.detect(image, args.conf_threshold, args.iou_threshold); auto end_time = std::chrono::high_resolution_clock::now(); auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time); std::cout << "Detection completed in " << duration.count() << "ms" << std::endl; std::cout << "Detected " << detections.size() << " objects" << std::endl; detector.drawBoundingBox(result, detections); std::string output_path = args.output_path; if (output_path == "./output.jpg") { size_t dot_pos = args.input_path.find_last_of('.'); std::string base_name = args.input_path.substr(0, dot_pos); output_path = base_name + "_result.jpg"; } cv::imwrite(output_path, result); std::cout << "Result saved to: " << output_path << std::endl; cv::Mat display_result; double scale = std::min(1280.0 / result.cols, 720.0 / result.rows); cv::Size display_size(result.cols * scale, result.rows * scale); cv::resize(result, display_result, display_size); cv::imshow("YOLOv26 Result", display_result); cv::waitKey(0); return 0; } cv::VideoCapture cap; if (is_camera_input(args.input_path)) { int camera_id = std::stoi(args.input_path); cap.open(camera_id); std::cout << "Opening camera " << camera_id << std::endl; } else { cap.open(args.input_path); std::cout << "Opening video file: " << args.input_path << std::endl; } if (!cap.isOpened()) { std::cerr << "Error: Cannot open input source: " << args.input_path << std::endl; return -1; } int frame_width = static_cast<int>(cap.get(cv::CAP_PROP_FRAME_WIDTH)); int frame_height = static_cast<int>(cap.get(cv::CAP_PROP_FRAME_HEIGHT)); double fps = cap.get(cv::CAP_PROP_FPS); if (fps <= 0) fps = 30.0; std::cout << "Video properties: " << frame_width << "x" << frame_height << " @ " << fps << " FPS\n\n"; cv::VideoWriter writer; if (!is_camera_input(args.input_path)) { int fourcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v'); writer.open(args.output_path, fourcc, fps, cv::Size(frame_width, frame_height)); if (!writer.isOpened()) { std::cerr << "Error: Cannot open output video file: " << args.output_path << std::endl; return -1; } std::cout << "Output will be saved to: " << args.output_path << std::endl; } auto start_time = std::chrono::high_resolution_clock::now(); int frame_count = 0; double avg_fps = 0.0; cv::Mat frame; std::cout << "\nProcessing... Press 'q' to quit.\n\n"; while (true) { auto frame_start = std::chrono::high_resolution_clock::now(); if (!cap.read(frame)) { if (is_camera_input(args.input_path)) { std::cerr << "Error reading from camera" << std::endl; break; } else { std::cout << "End of video file reached" << std::endl; break; } } cv::Mat result = frame.clone(); auto detections = detector.detect(frame, args.conf_threshold, args.iou_threshold); detector.drawBoundingBox(result, detections); auto frame_end = std::chrono::high_resolution_clock::now(); auto frame_duration = std::chrono::duration_cast<std::chrono::milliseconds>(frame_end - frame_start); double current_fps = 1000.0 / frame_duration.count(); frame_count++; avg_fps = (avg_fps * (frame_count - 1) + current_fps) / frame_count; draw_fps(result, current_fps); if (is_camera_input(args.input_path)) { cv::imshow("YOLOv26 OBB", result); char key = cv::waitKey(1) & 0xFF; if (key == 'q' || key == 27) { break; } } if (writer.isOpened()) { writer.write(result); } if (!is_camera_input(args.input_path) && frame_count % 30 == 0) { std::cout << "Processed " << frame_count << " frames, Average FPS: " << static_cast<int>(avg_fps) << std::endl; } } cap.release(); if (writer.isOpened()) { writer.release(); } cv::destroyAllWindows(); auto end_time = std::chrono::high_resolution_clock::now(); auto total_duration = std::chrono::duration_cast<std::chrono::seconds>(end_time - start_time); std::cout << "\nProcessing completed!\n"; std::cout << "Total frames processed: " << frame_count << std::endl; std::cout << "Total time: " << total_duration.count() << " seconds\n"; std::cout << "Average FPS: " << static_cast<int>(avg_fps) << std::endl; return 0; }最后测试效果：
源码地址：https://download.csdn.net/download/FL1623863129/92566174
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