• C#如何实现图片检测功能?
  • 发布于 1个月前
  • 68 热度
    0 评论

先看效果

模型信息
Model Properties
-------------------------
date:2023-12-25T17:55:44.583431
author:Ultralytics
task:detect
license:AGPL-3.0 https://ultralytics.com/license
version:8.0.172
stride:32
batch:1
imgsz:[640, 640]
names:{0: 'pikachu', 1: 'charmander', 2: 'bulbasaur', 3: 'squirtle', 4: 'eevee', 5: 'other', 6: 'jigglypuff'}
---------------------------------------------------------------

Inputs
-------------------------
name:images
tensor:Float[1, 3, 640, 640]
---------------------------------------------------------------

Outputs
-------------------------
name:output0
tensor:Float[1, 11, 8400]
---------------------------------------------------------------
项目

代码
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Imaging;
using System.IO;
using System.Linq;
using System.Text;
using System.Windows.Forms;

namespace Onnx_Yolov8_Demo
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = ""; 
        string model_path;
        string classer_path;
        public string[] class_names;
        public int class_num;

        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;

        int input_height;
        int input_width;
        float ratio_height;
        float ratio_width;

        InferenceSession onnx_session;

        int box_num;
        float conf_threshold;
        float nms_threshold;

        /// <summary>
        /// 堆代码 duidaima.com
        /// 选择图片
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        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 = "";
            pictureBox2.Image = null;
        }

        /// <summary>
        /// 推理
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }

            button2.Enabled = false;
            pictureBox2.Image = null;
            textBox1.Text = "";
            Application.DoEvents();

            Mat image = new Mat(image_path);
            
            //图片缩放
            int height = image.Rows;
            int width = image.Cols;
            Mat temp_image = image.Clone();
            if (height > input_height || width > input_width)
            {
                float scale = Math.Min((float)input_height / height, (float)input_width / width);
                OpenCvSharp.Size new_size = new OpenCvSharp.Size((int)(width * scale), (int)(height * scale));
                Cv2.Resize(image, temp_image, new_size);
            }
            ratio_height = (float)height / temp_image.Rows;
            ratio_width = (float)width / temp_image.Cols;
            Mat input_img = new Mat();
            Cv2.CopyMakeBorder(temp_image, input_img, 0, input_height - temp_image.Rows, 0, input_width - temp_image.Cols, BorderTypes.Constant, 0);

            //Cv2.ImShow("input_img", input_img);

            //输入Tensor
            Tensor<float> input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
            for (int y = 0; y < input_img.Height; y++)
            {
                for (int x = 0; x < input_img.Width; x++)
                {
                    input_tensor[0, 0, y, x] = input_img.At<Vec3b>(y, x)[0] / 255f;
                    input_tensor[0, 1, y, x] = input_img.At<Vec3b>(y, x)[1] / 255f;
                    input_tensor[0, 2, y, x] = input_img.At<Vec3b>(y, x)[2] / 255f;
                }
            }

            List<NamedOnnxValue> input_container = new List<NamedOnnxValue>
            {
                NamedOnnxValue.CreateFromTensor("images", input_tensor)
            };

            //推理
            dt1 = DateTime.Now;
            var ort_outputs = onnx_session.Run(input_container).ToArray();
            dt2 = DateTime.Now;

            float[] data = Transpose(ort_outputs[0].AsTensor<float>().ToArray(), 4 + class_num, box_num);

            float[] confidenceInfo = new float[class_num];
            float[] rectData = new float[4];

            List<DetectionResult> detResults = new List<DetectionResult>();

            for (int i = 0; i < box_num; i++)
            {
                Array.Copy(data, i * (class_num + 4), rectData, 0, 4);
                Array.Copy(data, i * (class_num + 4) + 4, confidenceInfo, 0, class_num);

                float score = confidenceInfo.Max(); // 获取最大值

                int maxIndex = Array.IndexOf(confidenceInfo, score); // 获取最大值的位置

                int _centerX = (int)(rectData[0] * ratio_width);
                int _centerY = (int)(rectData[1] * ratio_height);
                int _width = (int)(rectData[2] * ratio_width);
                int _height = (int)(rectData[3] * ratio_height);

                detResults.Add(new DetectionResult(
                   maxIndex,
                   class_names[maxIndex],
                   new Rect(_centerX - _width / 2, _centerY - _height / 2, _width, _height),
                   score));
            }

            //NMS
            CvDnn.NMSBoxes(detResults.Select(x => x.Rect), detResults.Select(x => x.Confidence), conf_threshold, nms_threshold, out int[] indices);
            detResults = detResults.Where((x, index) => indices.Contains(index)).ToList();

            //绘制结果
            Mat result_image = image.Clone();
            foreach (DetectionResult r in detResults)
            {
                Cv2.PutText(result_image, $"{r.Class}:{r.Confidence:P0}", new OpenCvSharp.Point(r.Rect.TopLeft.X, r.Rect.TopLeft.Y - 10), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
                Cv2.Rectangle(result_image, r.Rect, Scalar.Red, thickness: 2);
            }

            pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
            textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";

            button2.Enabled = true;
        }

        /// <summary>
        ///窗体加载
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void Form1_Load(object sender, EventArgs e)
        {
            model_path = "model/yolov8-pokemon-detection.onnx";

            //创建输出会话,用于输出模型读取信息
            SessionOptions options = new SessionOptions();
            options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
            options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行

            // 创建推理模型类,读取模型文件
            onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径

            input_height = 640;
            input_width = 640;

            box_num = 8400;
            conf_threshold = 0.25f;
            nms_threshold = 0.5f;

            classer_path = "model/lable.txt";
            class_names = File.ReadAllLines(classer_path, Encoding.UTF8);
            class_num = class_names.Length;

            image_path = "test_img/1.jpg";
            pictureBox1.Image = new Bitmap(image_path);

            this.Text = "C# Onnx yolov8 pokemon detection";

        }

        /// <summary>
        /// 保存
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void button3_Click(object sender, EventArgs e)
        {
            if (pictureBox2.Image == null)
            {
                return;
            }
            Bitmap output = new Bitmap(pictureBox2.Image);
            SaveFileDialog sdf = new SaveFileDialog();
            sdf.Title = "保存";
            sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf";
            if (sdf.ShowDialog() == DialogResult.OK)
            {
                switch (sdf.FilterIndex)
                {
                    case 1:
                        {
                            output.Save(sdf.FileName, ImageFormat.Jpeg);
                            break;
                        }
                    case 2:
                        {
                            output.Save(sdf.FileName, ImageFormat.Png);
                            break;
                        }
                    case 3:
                        {
                            output.Save(sdf.FileName, ImageFormat.Bmp);
                            break;
                        }
                    case 4:
                        {
                            output.Save(sdf.FileName, ImageFormat.Emf);
                            break;
                        }
                    case 5:
                        {
                            output.Save(sdf.FileName, ImageFormat.Exif);
                            break;
                        }
                    case 6:
                        {
                            output.Save(sdf.FileName, ImageFormat.Gif);
                            break;
                        }
                    case 7:
                        {
                            output.Save(sdf.FileName, ImageFormat.Icon);
                            break;
                        }

                    case 8:
                        {
                            output.Save(sdf.FileName, ImageFormat.Tiff);
                            break;
                        }
                    case 9:
                        {
                            output.Save(sdf.FileName, ImageFormat.Wmf);
                            break;
                        }
                }
                MessageBox.Show("保存成功,位置:" + sdf.FileName);
            }
        }

        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            ShowNormalImg(pictureBox1.Image);
        }

        private void pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            ShowNormalImg(pictureBox2.Image);
        }

        public  void ShowNormalImg(Image img)
        {
            if (img == null) return;

            frmShow frm = new frmShow();

            frm.Width = Screen.PrimaryScreen.Bounds.Width;
            frm.Height = Screen.PrimaryScreen.Bounds.Height;

            if (frm.Width > img.Width)
            {
                frm.Width = img.Width;
            }

            if (frm.Height > img.Height)
            {
                frm.Height = img.Height;
            }

            bool b = frm.richTextBox1.ReadOnly;
            Clipboard.SetDataObject(img, true);
            frm.richTextBox1.ReadOnly = false;
            frm.richTextBox1.Paste(DataFormats.GetFormat(DataFormats.Bitmap));
            frm.richTextBox1.ReadOnly = b;

            frm.ShowDialog();

        }

        public unsafe float[] Transpose(float[] tensorData, int rows, int cols)
        {
            float[] transposedTensorData = new float[tensorData.Length];

            fixed (float* pTensorData = tensorData)
            {
                fixed (float* pTransposedData = transposedTensorData)
                {
                    for (int i = 0; i < rows; i++)
                    {
                        for (int j = 0; j < cols; j++)
                        {
                            int index = i * cols + j;
                            int transposedIndex = j * rows + i;
                            pTransposedData[transposedIndex] = pTensorData[index];
                        }
                    }
                }
            }
            return transposedTensorData;
        }
    }

    public class DetectionResult
    {
        public DetectionResult(int ClassId, string Class, Rect Rect, float Confidence)
        {
            this.ClassId = ClassId;
            this.Confidence = Confidence;
            this.Rect = Rect;
            this.Class = Class;
        }

        public string Class { get; set; }
        public int ClassId { get; set; }
        public float Confidence { get; set; }
        public Rect Rect { get; set; }

    }

}

用户评论