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C# Onnx 动漫人物人脸检测

news 来源：原创 2025/6/5 18:14:23

目录

效果

模型信息

项目

代码

下载

参考

效果

模型信息

Model Properties
-------------------------
stride：32
names：{0: 'face'}
---------------------------------------------------------------

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

Outputs
-------------------------
name：output0
tensor：Float[-1, -1, -1]
---------------------------------------------------------------

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_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>
        /// 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/face_detect_v1.4_s.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);

        }

        /// <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";
            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;
                        }
                }
                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; }

    }

}
 

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_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>/// 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TensorTensor<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));}//NMSCvDnn.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/face_detect_v1.4_s.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);}/// <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";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;}}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; }}}

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参考

https://github.com/deepghs

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