Algorithm-Repo/SourceCode/CVAlgorithmUtils/YOLOInstanceSegPostProcessUtils/YOLOInstanceSegProcessLib/InferNetOnnxInstanceSegYOLOV5.cs

using System;
using System.Collections.Generic;
using System.Text;
using AI.Common.Implements;
using AI.Common.Interface;


namespace YOLOInstanceSegProcessLib
{
    public class InferNetOnnxInstanceSegYOLOV5 : InferNetOnnxInstanceSegYOLOBasic
    {
        #region override 

        protected override string NetworkHashCode => "6a6fd0d2fcc4b280177c15c5c4324c82";

        public override string NetworkName => "ThyroidLesionSegYOLOV5.emd";

        /// <summary>
        /// 复制
        /// </summary>
        /// <returns></returns>
        public override IInferenceNetwork Clone()
        {
            if (!_modelLoaded)
            {
                return new InferNetOnnxInstanceSegYOLOV5();
            }
            return new InferNetOnnxInstanceSegYOLOV5(_trainedModel, _numCPU);
        }

        #endregion

        #region constructor
        public InferNetOnnxInstanceSegYOLOV5()
        {
            ModelParamsAssign();
            ConstructIntermediateVariables();
        }

        public InferNetOnnxInstanceSegYOLOV5(byte[] trainedModel, int numCPU)
        {
            ModelParamsAssign();
            ConstructIntermediateVariables();
            ConstructClonedModelRelatedVariables(trainedModel, numCPU);
        }

        private void ModelParamsAssign()
        {
            _inputTensorNames = new[] { "images" };
            _inputAxisOrder = EnumAxisOrder.CHW;
            _outputTensorNames = new[] { "output0", "output1" };
            _meanValueType = EnumMeanValueType.None;
            _meanR = 0;
            _meanG = 0;
            _meanB = 0;
            _resizeMode = EnumResizeMode.Warp;
            _meanValueType = EnumMeanValueType.None;
            _scaleType = EnumScaleValueType.ConstantScale;
            _scaleR = 255;
            _scaleG = 255;
            _scaleB = 255;
            _normType = EnumNormalizationType.None;
            _channelOrder = EnumChannelOrder.RGB;

            _yoloType = YoloType.yolov5;
            _boxConfThres = 0.5f;
            _clsConfThres = 0.5f;
            _batchSize = 1;
            _maxDet = 100;
            _minBoxRatio = 0.001f;
            _postProcessTopK = 6400;
            _enableIouFilt = true;
            _enableIosFilt = true;
            _iouFltth = 0.3f;
            _iosFltth = 0.3f;
            _enableIouFiltDiffCls = true;
            _enableIosFiltDiffCls = true;
            _iouFltthDiffCls = 0.5f;
            _iosFltthDiffCls = 0.5f;

            _maskThres = 0.7f;
            //模型输入输出的尺寸，netron直接读取即可，此处可以直接赋值，暂时放这边是为了大家理解输出的组成
            _inputVariableShapes = new List<int[]> { new int[] { 1, 3, 320, 320 } };
            _outputVariableShapes = new List<int[]> { new int[] { 1, 6300, 6 + 5 + 32 },
                                                      new int[] { 1, 32, 80, 80 } };

            _segPostProcessParam = new Dictionary<int, InstanceSegmentationPostProcessParams>
            {
                // 1:TIRADS 2
                { 1, new InstanceSegmentationPostProcessParams( new List<LesionAreaSelectInfo>(){new LesionAreaSelectInfo(EnumLesionAreaThresholdType.RatioInMask, 1.0f/1000, 1.0f),
                                                                                                 new LesionAreaSelectInfo(EnumLesionAreaThresholdType.PixelInOrigImg, 0, 50000)}, 5)},
                // 2:TIRADS 3
                { 2, new InstanceSegmentationPostProcessParams( new List<LesionAreaSelectInfo>(){new LesionAreaSelectInfo(EnumLesionAreaThresholdType.RatioInMask, 1.0f/1000, 1.0f)}, 5)},
           
                // 3:TIRADS 4a
                { 3, new InstanceSegmentationPostProcessParams( new List<LesionAreaSelectInfo>(){new LesionAreaSelectInfo(EnumLesionAreaThresholdType.RatioInMask, 1.0f/1000, 1.0f)}, 5)},
                //// 4:TIRADS 4b
                { 4, new InstanceSegmentationPostProcessParams( new List<LesionAreaSelectInfo>(){new LesionAreaSelectInfo(EnumLesionAreaThresholdType.RatioInMask, 1.0f/1000, 1.0f)}, 5)},
                // 5:TIRADS 4c
                { 5, new InstanceSegmentationPostProcessParams( new List<LesionAreaSelectInfo>(){new LesionAreaSelectInfo(EnumLesionAreaThresholdType.RatioInMask, 1.0f/1000, 1.0f)}, 5)},
                // 6:TIRADS 5
                { 6, new InstanceSegmentationPostProcessParams( new List<LesionAreaSelectInfo>(){new LesionAreaSelectInfo(EnumLesionAreaThresholdType.RatioInMask, 1.0f/1000, 1.0f)}, 5)},
            };


            ConfidenceMapInfo confidenceMap = new ConfidenceMapInfo(0.5f, 1.0f, 0.5f, 0.9f);
            _confidenceMapInfo = new Dictionary<int, ConfidenceMapInfo>
            {
                { 1, confidenceMap},
                { 2, confidenceMap},
                { 3, confidenceMap},
                { 4, confidenceMap},
                { 5, confidenceMap},
            };

            var validTypes = new List<Type>
            {
                typeof(LesionMalignantDegreeLabelExtraInfo),
                typeof(LesionInvolvedScopeLabelExtraInfo),
            };
            _labelGroup = new LabelGroup("AI.Vaid.Thyroid.FocalLesion", validTypes);
            _labelGroup.RegistLabel(0, "NoObviousLesion");
            _labelGroup.RegistLabel(1, "TI-RADS 2", new List<ILabelExtraInfo>
            {
                new LesionMalignantDegreeLabelExtraInfo(EnumLesionMalignantDegree.BenignLesion),
                new LesionInvolvedScopeLabelExtraInfo(EnumLesionInvolvedScope.FocalLesion),
            });
            _labelGroup.RegistLabel(2, "TI-RADS 3", new List<ILabelExtraInfo>
            {
                new LesionMalignantDegreeLabelExtraInfo(EnumLesionMalignantDegree.BenignLesion),
                new LesionInvolvedScopeLabelExtraInfo(EnumLesionInvolvedScope.FocalLesion),
            });
            _labelGroup.RegistLabel(3, "TI-RADS 4a", new List<ILabelExtraInfo>
            {
                new LesionMalignantDegreeLabelExtraInfo(EnumLesionMalignantDegree.MalignantLesion),
                new LesionInvolvedScopeLabelExtraInfo(EnumLesionInvolvedScope.FocalLesion),
            });
            _labelGroup.RegistLabel(4, "TI-RADS 4b", new List<ILabelExtraInfo>
            {
                new LesionMalignantDegreeLabelExtraInfo(EnumLesionMalignantDegree.MalignantLesion),
                new LesionInvolvedScopeLabelExtraInfo(EnumLesionInvolvedScope.FocalLesion),
            });
            _labelGroup.RegistLabel(5, "TI-RADS 4c", new List<ILabelExtraInfo>
            {
                new LesionMalignantDegreeLabelExtraInfo(EnumLesionMalignantDegree.MalignantLesion),
                new LesionInvolvedScopeLabelExtraInfo(EnumLesionInvolvedScope.FocalLesion),
            });
            _labelGroup.RegistLabel(6, "TI-RADS 5", new List<ILabelExtraInfo>
            {
                new LesionMalignantDegreeLabelExtraInfo(EnumLesionMalignantDegree.MalignantLesion),
                new LesionInvolvedScopeLabelExtraInfo(EnumLesionInvolvedScope.FocalLesion),
            });
        }

        #endregion

    }
}