效果

项目

代码

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Imaging;
using System.Linq;
using System.Windows.Forms;
using UMapx.Core;
using UMapx.Imaging;

namespace PortraitModeFilter_模糊背景
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        string startupPath;
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
        string model_path;
        Mat image;
        Bitmap src;
        Bitmap mask;
        int modelSize = 512;

        SessionOptions options;
        InferenceSession onnx_session;
        Tensor<byte> input_tensor;
        List<NamedOnnxValue> input_ontainer;
        IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
        DisposableNamedOnnxValue[] results_onnxvalue;
        Tensor<long> result_tensors;
        long[] result_array;

        double strength = 1;// 0 到 1

        OpenFileDialog ofd;
        private void button1_Click(object sender, EventArgs e)
        {
            if (ofd.ShowDialog() != DialogResult.OK) return;
            pictureBox1.Image = null;
            image_path = ofd.FileName;
            src = new Bitmap(image_path);
            pictureBox1.Image = src;
            textBox1.Text = "";
            image = new Mat(image_path);
            pictureBox2.Image = null;
            trackBar1.Enabled = false;
        }

        private void Form2_Load(object sender, EventArgs e)
        {
            trackBar1.Enabled = false;

            startupPath = Application.StartupPath;

            ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;

            model_path = startupPath + "\\deeplabv3_mnv2_pascal_train_aug.onnx";

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

            // 创建推理模型类，读取本地模型文件
            onnx_session = new InferenceSession(model_path, options);

            // 创建输入容器
            input_ontainer = new List<NamedOnnxValue>();

            // 创建输入容器
            input_ontainer = new List<NamedOnnxValue>();
        }

        private void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }

            textBox1.Text = "生成中……";
            pictureBox2.Image = null;

            Application.DoEvents();

            //缩放图片大小
            int oldwidth = image.Cols;
            int oldheight = image.Rows;
            int maxEdge = Math.Max(image.Rows, image.Cols);
            float ratio = 1.0f * modelSize / maxEdge;
            int newHeight = (int)(image.Rows * ratio);
            int newWidth = (int)(image.Cols * ratio);
            Mat resize_image = image.Resize(new OpenCvSharp.Size(newWidth, newHeight));

            input_tensor = new DenseTensor<byte>(new[] { 1, newHeight, newWidth, 3 });

            // 输入Tensor
            for (int y = 0; y < resize_image.Height; y++)
            {
                for (int x = 0; x < resize_image.Width; x++)
                {
                    input_tensor[0, y, x, 2] = resize_image.At<Vec3b>(y, x)[0];
                    input_tensor[0, y, x, 1] = resize_image.At<Vec3b>(y, x)[1];
                    input_tensor[0, y, x, 0] = resize_image.At<Vec3b>(y, x)[2];
                }
            }

            //将 input_tensor 放入一个输入参数的容器，并指定名称
            input_ontainer.Add(NamedOnnxValue.CreateFromTensor(onnx_session.InputNames[0].ToString(), input_tensor));

            dt1 = DateTime.Now;
            //运行 Inference 并获取结果
            result_infer = onnx_session.Run(input_ontainer);
            dt2 = DateTime.Now;

            //将输出结果转为DisposableNamedOnnxValue数组
            results_onnxvalue = result_infer.ToArray();

            //读取第一个节点输出并转为Tensor数据
            result_tensors = results_onnxvalue[0].AsTensor<Int64>();

            result_array = result_tensors.ToArray();

            //得到掩码图
            mask = SegmentationMap(result_array, newWidth, newHeight);

            mask = new Bitmap(mask, new System.Drawing.Size(oldwidth, oldheight));

            trackBar1.Enabled = true;

            //模糊背景
            pictureBox2.Image = Filter(src, mask);

            textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";

        }

        /// <summary>
        /// Converts an RGB tensor array to a color image.
        /// </summary>
        /// <param name="tensor">RGBA tensor array</param>
        /// <param name="width">Bitmap width</param>
        /// <param name="height">Bitmap height</param>
        public unsafe Bitmap SegmentationMap(long[] tensor, int width, int height)
        {
            Bitmap Data = new Bitmap(width, height);
            BitmapData bmData = Data.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.ReadWrite, PixelFormat.Format24bppRgb);

            int stride = bmData.Stride;
            byte* p = (byte*)bmData.Scan0.ToPointer();
            int pos = 0;

            for (int j = 0; j < height; j++)
            {
                int k, jstride = j * stride;

                for (int i = 0; i < width; i++, pos++)
                {
                    k = jstride + i * 3;

                    var z = (tensor[pos] == 15) ? (byte)255 : (byte)0;

                    // rgb
                    p[k + 2] = z;
                    p[k + 1] = z;
                    p[k + 0] = z;
                }
            }

            Data.UnlockBits(bmData);

            return Data;
        }

        private void button3_Click(object sender, EventArgs e)
        {
            if (pictureBox2.Image == null)
            {
                return;
            }
            Bitmap output = new Bitmap(pictureBox2.Image);
            var 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 trackBar1_Scroll(object sender, EventArgs e)
        {
            strength = trackBar1.Value / 100.0;
            label1.Text = $"Strenght: {strength}";
            pictureBox2.Image = Filter(src, mask);
        }

        BoxBlur _boxBlur = new BoxBlur();
        AlphaChannelFilter _alphaChannelFilter = new AlphaChannelFilter();
        Merge _merge = new Merge(0, 0, 255);

        Bitmap Filter(Bitmap image, Bitmap mask)
        {
            int radius = (int)(strength * 2 * ((Math.Max(image.Height, image.Width) / 100) + 1));

            // deep person lab
            var alphaMask = (Bitmap)src.Clone();
            var portrait = (Bitmap)src.Clone();
            var segmentantionMask = (Bitmap)mask.Clone();

            // gaussian blur approximation
            _boxBlur.Size = new SizeInt(radius, radius);
            _boxBlur.Apply(portrait);
            _boxBlur.Apply(segmentantionMask);

            _boxBlur.Size = new SizeInt(radius / 2, radius / 2);
            _boxBlur.Apply(portrait);
            _boxBlur.Apply(segmentantionMask);

            // merging images
            _alphaChannelFilter.Apply(alphaMask, segmentantionMask);
            _merge.Apply(portrait, alphaMask);
            alphaMask.Dispose();
            segmentantionMask.Dispose();

            return portrait;
        }
    }
}

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