Algorithm-Repo/SourceCode/CVAlgorithmUtils/YOLODetectPostProcessUtils/YOLODetectProcessLib/BreastLesionDetectImg.cs

using System;
using System.Collections.Generic;
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using System.Runtime.InteropServices;
using System.IO;

namespace YOLODetectProcessLib
{
    public class BreastLesionDetectImg
    {
        private IInferenceNetwork _inferNet;

        /// <summary>
        /// 通知订阅者，推理过程中发生了错误
        /// </summary>
        public event EventHandler<ErrorEventArgs> NotifyError;

        public BreastLesionDetectImg(int numCPU, string netDir)
        {
            try
            {
                _inferNet = new InferNetOnnxDetectBreastLesion();
                _inferNet.LoadNetwork(numCPU, netDir);
            }
            catch (Exception excep)
            {
                throw new Exception("Failed at Loading network:" + excep);
            }
        }

        /// <summary>
        /// 实现单幅图的乳腺病灶检测
        /// </summary>
        /// <param name="rawImg">rawImg格式图像</param>
        /// <param name="isCropped">是否已经裁切</param>
        /// <returns></returns>
        public List<DetectedBreastLesionResult> ProcessBreastLesionImg(RawImage rawImg, bool isCropped)
        {
            try
            {
                YOLODetectProcessLib.Rect cropRect = new YOLODetectProcessLib.Rect(0, 0, rawImg.Width, rawImg.Height); ;
                if (!isCropped)
                {
                    if (!UsImageRegionSegUtils.CropWithCvCore(rawImg, out cropRect))
                    {
                        NotifyError?.Invoke(this, new ErrorEventArgs(new Exception("Failed at UsImageRegionSegUtils.CropWithCvCore")));
                    }
                }

                //对一张图进行推理
                InferenceNetworkInputImage inferInput = new InferenceNetworkInputImage(rawImg, cropRect);
                var inferResult = _inferNet.Process(new InferenceNetworkInputImage[] { inferInput });
                if (inferResult == null)
                {
                    NotifyError?.Invoke(this, new ErrorEventArgs(new Exception("Unexpected result size for the breast object detect model of one image.")));
                }
                if (inferResult.Length != 1)
                {
                    NotifyError?.Invoke(this, new ErrorEventArgs(new Exception("Unexpected result size for the breast object detect model of one image.")));
                }
                if (inferResult[0].Length == 0)
                {
                    return new List<DetectedBreastLesionResult>();
                }
                else
                {
                    List<DetectedBreastLesionResult> results = new List<DetectedBreastLesionResult>();
                    for (int ni = 0; ni < inferResult[0].Length; ni++)
                    {
                        results.Add(new DetectedBreastLesionResult(inferResult[0][ni].Label, inferResult[0][ni].Confidence, inferResult[0][ni].BoundingBox));
                    }
                    return results;
                }
            }
            catch (Exception excep)
            {
                NotifyError?.Invoke(this, new ErrorEventArgs(excep));
                return new List<DetectedBreastLesionResult>();
            }
        }

        /// <summary>
        /// 销毁
        /// </summary>
        public void Dispose()
        {
            DoDispose();
            GC.SuppressFinalize(this);
        }

        /// <summary>
        /// 析构函数
        /// </summary>
        ~BreastLesionDetectImg()
        {
            DoDispose();
        }

        private void DoDispose()
        {
            _inferNet?.Dispose();
            _inferNet = null;
        }
    }
}