yolo+openvino视频推理

【核心代码】

using OpenCvSharp.Dnn;
using OpenCvSharp;
using OpenVinoSharp;
using OpenVinoSharp.Extensions.result;
using OpenVinoSharp.Extensions.process;
using System.Diagnostics;
using OpenVinoSharp.preprocess;


namespace openvino_asyn_csharp
{
    internal class Program
    {
        static void Main(string[] args)
        {
            Console.WriteLine("Hello, World!");
            //yolov8_det();
            yolov8_async_det();
        }


        static void yolov8_async_det()
        {
            Core core = new Core();
            Model model = core.read_model(model_path);
            CompiledModel compiled_model = core.compile_model(model, "CPU");
            VideoCapture capture = new VideoCapture(video_path);
            if (!capture.IsOpened())
            {
                Console.WriteLine("ERROR: 视频无法打开");
                return;
            }
            List<InferRequest> requests = new List<InferRequest> { compiled_model.create_infer_request(), compiled_model.create_infer_request() };
            Mat frame = new Mat();
            capture.Read(frame);
            float factor = 0f;
            float[] input_data = yolov8_preprocess(frame, out factor);
            requests[0].get_input_tensor().set_data(input_data);
            requests[0].start_async();
            Stopwatch sw = new Stopwatch();
            float[] total_infs = new float[3];

            while (true)
            {
                Mat next_frame = new Mat();
                if (!capture.Read(next_frame))
                {
                    break;
                }
                sw.Restart();
                input_data = yolov8_preprocess(frame, out factor);
                requests[1].get_input_tensor().set_data(input_data);
                sw.Stop();
                total_infs[0] = sw.ElapsedMilliseconds;
                sw.Restart();
                requests[1].start_async();
                requests[0].wait();
                sw.Stop();
                total_infs[1] = sw.ElapsedMilliseconds;
                sw.Restart();
                float[] output_data = requests[0].get_output_tensor().get_data<float>(8400 * 84);
                DetResult result = yolov8_postprocess(output_data, factor);
                sw.Stop();
                total_infs[2] = sw.ElapsedMilliseconds;

                Cv2.PutText(frame, "PreProcess: " (1000.0 / total_infs[0]).ToString("0.00") "FPS  " (total_infs[0]).ToString("0.00") "ms",
                    new Point(20, 40), HersheyFonts.HersheyPlain, 2, new Scalar(255, 0, 255), 2);
                Cv2.PutText(frame, "Inference: " (1000.0 / total_infs[1]).ToString("0.00") "FPS  " (total_infs[1]).ToString("0.00") "ms",
                    new Point(20, 70), HersheyFonts.HersheyPlain, 2, new Scalar(255, 0, 255), 2);
                Cv2.PutText(frame, "PostProcess: " (1000.0 / total_infs[2]).ToString("0.00") "FPS  " (total_infs[2]).ToString("0.00") "ms",
                    new Point(20, 100), HersheyFonts.HersheyPlain, 2, new Scalar(255, 0, 255), 2);
                Cv2.PutText(frame, "Total: " (1000.0 / (total_infs[0] total_infs[1] total_infs[2])).ToString("0.00")
                    "FPS   " ((total_infs[0] total_infs[1] total_infs[2])).ToString("0.00") "ms",
                    new Point(20, 130), HersheyFonts.HersheyPlain, 2, new Scalar(255, 0, 255), 2);
                Mat res_mat = Visualize.draw_det_result(result, frame);
                Cv2.ImShow("Result", res_mat);
                Cv2.WaitKey(10);
                swap(requests);
                frame = next_frame;
            }
        }