C# OpenVINO Nail Seg 指甲分割 指甲检测

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C# OpenVINO Nail Seg 指甲分割  指甲检测

效果

模型信息

Model Properties

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date：2024-02-29T16:41:28.273760

author：Ultralytics

task：segment

version：8.1.18

stride：32

batch：1

imgsz：[640, 640]

names：{0: 'Nail'}

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Inputs

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name：images

tensor：Float[1, 3, 640, 640]

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Outputs

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name：output0

tensor：Float[1, 37, 8400]

name：output1

tensor：Float[1, 32, 160, 160]

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项目

代码

using OpenCvSharp;

using Sdcb.OpenVINO;

using Sdcb.OpenVINO.Natives;

using System;

using System.Collections.Generic;

using System.Diagnostics;

using System.Drawing;

using System.IO;

using System.Text;

using System.Windows.Forms;

namespace OpenVINO_Seg

{

    public partial class Form1 : Form

    {

        public Form1()

        {

            InitializeComponent();

        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";

        string image_path = "";

        string startupPath;

        string model_path;

        string classer_path;

        Mat src;

        SegmentationResult result_pro;

        Mat result_image;

        Result seg_result;

        StringBuilder sb = new StringBuilder();

        float[] det_result_array = new float[8400 * 37];

        float[] proto_result_array = new float[32 * 160 * 160];

        // 识别结果类型

        public string[] class_names;

        private void button1_Click(object sender, EventArgs e)

        {

            OpenFileDialog ofd = new OpenFileDialog();

            ofd.Filter = fileFilter;

            if (ofd.ShowDialog() != DialogResult.OK) return;

            pictureBox1.Image = null;

            image_path = ofd.FileName;

            pictureBox1.Image = new Bitmap(image_path);

            textBox1.Text = "";

            src = new Mat(image_path);

            pictureBox2.Image = null;

        }

        unsafe private void button2_Click(object sender, EventArgs e)

        {

            if (pictureBox1.Image == null)

            {

                return;

            }

            pictureBox2.Image = null;

            textBox1.Text = "";

            sb.Clear();

            src = new Mat(image_path);

            Model rawModel = OVCore.Shared.ReadModel(model_path);

            PrePostProcessor pp = rawModel.CreatePrePostProcessor();

            PreProcessInputInfo inputInfo = pp.Inputs.Primary;

            inputInfo.TensorInfo.Layout = Sdcb.OpenVINO.Layout.NHWC;

            inputInfo.ModelInfo.Layout = Sdcb.OpenVINO.Layout.NCHW;

            Model m = pp.BuildModel();

            CompiledModel cm = OVCore.Shared.CompileModel(m, "CPU");

            InferRequest ir = cm.CreateInferRequest();

            Shape inputShape = m.Inputs[0].Shape;

            float[] factors = new float[4];

            factors[0] = 1f * src.Width / inputShape[2];

            factors[1] = 1f * src.Height / inputShape[1];

            factors[2] = src.Rows;

            factors[3] = src.Cols;

            result_pro = new SegmentationResult(class_names, factors,0.3f,0.5f);

            Stopwatch stopwatch = new Stopwatch();

            Mat resized = src.Resize(new OpenCvSharp.Size(inputShape[2], inputShape[1]));

            Mat f32 = new Mat();

            resized.ConvertTo(f32, MatType.CV_32FC3, 1.0 / 255);

            using (Tensor input = Tensor.FromRaw(

                 new ReadOnlySpan((void*)f32.Data, (int)((long)f32.DataEnd - (long)f32.DataStart)),

                new Shape(1, f32.Rows, f32.Cols, 3),

                ov_element_type_e.F32))

            {

                ir.Inputs.Primary = input;

            }

            double preprocessTime = stopwatch.Elapsed.TotalMilliseconds;

            stopwatch.Restart();

            ir.Run();

            double inferTime = stopwatch.Elapsed.TotalMilliseconds;

            stopwatch.Restart();

            using (Tensor output_det = ir.Outputs[0])

            using (Tensor output_proto = ir.Outputs[1])

            {

                det_result_array = output_det.GetData().ToArray();

                proto_result_array = output_proto.GetData().ToArray();

                seg_result = result_pro.process_result(det_result_array, proto_result_array);

                double postprocessTime = stopwatch.Elapsed.TotalMilliseconds;

                stopwatch.Stop();

                double totalTime = preprocessTime + inferTime + postprocessTime;

                result_image = src.Clone();

                Mat masked_img = new Mat();

                // 将识别结果绘制到图片上

                for (int i = 0; i

                {

                    Cv2.Rectangle(result_image, seg_result.rects[i], new Scalar(0, 0, 255), 2, LineTypes.Link8);

                    Cv2.Rectangle(result_image, new OpenCvSharp.Point(seg_result.rects[i].TopLeft.X, seg_result.rects[i].TopLeft.Y - 20),

                        new OpenCvSharp.Point(seg_result.rects[i].BottomRight.X, seg_result.rects[i].TopLeft.Y), new Scalar(0, 255, 255), -1);

                    Cv2.PutText(result_image, seg_result.classes[i] + "-" + seg_result.scores[i].ToString("0.00"),

                        new OpenCvSharp.Point(seg_result.rects[i].X, seg_result.rects[i].Y - 5),

                        HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);

                    Cv2.AddWeighted(result_image, 0.5, seg_result.masks[i], 0.5, 0, masked_img);

                    sb.AppendLine($"{seg_result.classes[i]}:{seg_result.scores[i]:P0}");

                }

                if (seg_result.length > 0)

                {

                    if (pictureBox2.Image != null)

                    {

                        pictureBox2.Image.Dispose();

                    }

                    pictureBox2.Image = new Bitmap(masked_img.ToMemoryStream());

                    sb.AppendLine($"Preprocess: {preprocessTime:F2}ms");

                    sb.AppendLine($"Infer: {inferTime:F2}ms");

                    sb.AppendLine($"Postprocess: {postprocessTime:F2}ms");

                    sb.AppendLine($"Total: {totalTime:F2}ms");

                    textBox1.Text = sb.ToString();

                }

                else

                {

                    textBox1.Text = "无信息";

                }

                masked_img.Dispose();

                result_image.Dispose();

            }

        }

        private void Form1_Load(object sender, EventArgs e)

        {

            image_path = "1.jpg";

            pictureBox1.Image = new Bitmap(image_path);

            startupPath = Application.StartupPath;

            model_path = startupPath + "\\nails_seg_s_yolov8.onnx";

            classer_path = startupPath + "\\lable.txt";

            List str = new List();

            StreamReader sr = new StreamReader(classer_path);

            string line;

            while ((line = sr.ReadLine()) != null)

            {

                str.Add(line);

            }

            class_names = str.ToArray();

        }

    }

}

using OpenCvSharp;using Sdcb.OpenVINO;using Sdcb.OpenVINO.Natives;using System;using System.Collections.Generic;using System.Diagnostics;using System.Drawing;using System.IO;using System.Text;using System.Windows.Forms;namespace OpenVINO_Seg{    public partial class Form1 : Form    {        public Form1()        {            InitializeComponent();        }        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";        string image_path = "";        string startupPath;        string model_path;        string classer_path;        Mat src;        SegmentationResult result_pro;        Mat result_image;        Result seg_result;        StringBuilder sb = new StringBuilder();        float[] det_result_array = new float[8400 * 37];        float[] proto_result_array = new float[32 * 160 * 160];        // 识别结果类型        public string[] class_names;        private void button1_Click(object sender, EventArgs e)        {            OpenFileDialog ofd = new OpenFileDialog();            ofd.Filter = fileFilter;            if (ofd.ShowDialog() != DialogResult.OK) return;            pictureBox1.Image = null;            image_path = ofd.FileName;            pictureBox1.Image = new Bitmap(image_path);            textBox1.Text = "";            src = new Mat(image_path);            pictureBox2.Image = null;        }        unsafe private void button2_Click(object sender, EventArgs e)        {            if (pictureBox1.Image == null)            {                return;            }            pictureBox2.Image = null;            textBox1.Text = "";            sb.Clear();            src = new Mat(image_path);            Model rawModel = OVCore.Shared.ReadModel(model_path);            PrePostProcessor pp = rawModel.CreatePrePostProcessor();            PreProcessInputInfo inputInfo = pp.Inputs.Primary;            inputInfo.TensorInfo.Layout = Sdcb.OpenVINO.Layout.NHWC;            inputInfo.ModelInfo.Layout = Sdcb.OpenVINO.Layout.NCHW;            Model m = pp.BuildModel();            CompiledModel cm = OVCore.Shared.CompileModel(m, "CPU");            InferRequest ir = cm.CreateInferRequest();            Shape inputShape = m.Inputs[0].Shape;            float[] factors = new float[4];            factors[0] = 1f * src.Width / inputShape[2];            factors[1] = 1f * src.Height / inputShape[1];            factors[2] = src.Rows;            factors[3] = src.Cols;            result_pro = new SegmentationResult(class_names, factors,0.3f,0.5f);            Stopwatch stopwatch = new Stopwatch();            Mat resized = src.Resize(new OpenCvSharp.Size(inputShape[2], inputShape[1]));            Mat f32 = new Mat();            resized.ConvertTo(f32, MatType.CV_32FC3, 1.0 / 255);            using (Tensor input = Tensor.FromRaw(                 new ReadOnlySpan((void*)f32.Data, (int)((long)f32.DataEnd - (long)f32.DataStart)),                new Shape(1, f32.Rows, f32.Cols, 3),                ov_element_type_e.F32))            {                ir.Inputs.Primary = input;            }            double preprocessTime = stopwatch.Elapsed.TotalMilliseconds;            stopwatch.Restart();            ir.Run();            double inferTime = stopwatch.Elapsed.TotalMilliseconds;            stopwatch.Restart();            using (Tensor output_det = ir.Outputs[0])            using (Tensor output_proto = ir.Outputs[1])            {                det_result_array = output_det.GetData().ToArray();                proto_result_array = output_proto.GetData().ToArray();                seg_result = result_pro.process_result(det_result_array, proto_result_array);                double postprocessTime = stopwatch.Elapsed.TotalMilliseconds;                stopwatch.Stop();                double totalTime = preprocessTime + inferTime + postprocessTime;                result_image = src.Clone();                Mat masked_img = new Mat();                // 将识别结果绘制到图片上                for (int i = 0; i  0)                {                    if (pictureBox2.Image != null)                    {                        pictureBox2.Image.Dispose();                    }                    pictureBox2.Image = new Bitmap(masked_img.ToMemoryStream());                    sb.AppendLine($"Preprocess: {preprocessTime:F2}ms");                    sb.AppendLine($"Infer: {inferTime:F2}ms");                    sb.AppendLine($"Postprocess: {postprocessTime:F2}ms");                    sb.AppendLine($"Total: {totalTime:F2}ms");                    textBox1.Text = sb.ToString();                }                else                {                    textBox1.Text = "无信息";                }                masked_img.Dispose();                result_image.Dispose();            }        }        private void Form1_Load(object sender, EventArgs e)        {            image_path = "1.jpg";            pictureBox1.Image = new Bitmap(image_path);            startupPath = Application.StartupPath;            model_path = startupPath + "\nails_seg_s_yolov8.onnx";            classer_path = startupPath + "\lable.txt";            List str = new List();            StreamReader sr = new StreamReader(classer_path);            string line;            while ((line = sr.ReadLine()) != null)            {                str.Add(line);            }            class_names = str.ToArray();        }    }}

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