一秒变身艺术家！U2Net 跨界肖像画，让你的头像瞬间细节完美复刻，打造个性化头像新风潮！

效果

测试图片来自网络，如有侵权，联系删除。

项目

关注微信公众号，回复关键字：“一秒变身艺术家”，获取程序！

模型信息

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Inputs ------------------------- name：input_image tensor：Float[1, 3, 512, 512] ---------------------------------------------------------------   Outputs ------------------------- name：output_image tensor：Float[1, 1, 512, 512] name：2016 tensor：Float[1, 1, 512, 512] name：2017 tensor：Float[1, 1, 512, 512] name：2018 tensor：Float[1, 1, 512, 512] name：2019 tensor：Float[1, 1, 512, 512] name：2020 tensor：Float[1, 1, 512, 512] name：2021 tensor：Float[1, 1, 512, 512] ---------------------------------------------------------------

代码

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225

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;   namespace U2Net_Portrait {     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;         int modelSize = 512;           SessionOptions options;         InferenceSession onnx_session;         Tensor<float> input_tensor;         List<NamedOnnxValue> input_ontainer;         IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;         DisposableNamedOnnxValue[] results_onnxvalue;           Tensor<float> result_tensors;         float[] result_array;           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 = "";             image = new Mat(image_path);             pictureBox2.Image = null;         }           private void button2_Click(object sender, EventArgs e)         {             if (image_path == "")             {                 return;             }               textBox1.Text = "";             pictureBox2.Image = null;               int oldwidth = image.Cols;             int oldheight = image.Rows;               //缩放图片大小             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));             int width = resize_image.Cols;             int height = resize_image.Rows;             if (width != modelSize || height != modelSize)             {                 resize_image = resize_image.CopyMakeBorder(0, modelSize - newHeight, 0, modelSize - newWidth, BorderTypes.Constant, new Scalar(255, 255, 255));             }               Cv2.CvtColor(resize_image, resize_image, ColorConversionCodes.BGR2RGB);               for (int y = 0; y < resize_image.Height; y++)             {                 for (int x = 0; x < resize_image.Width; x++)                 {                     input_tensor[0, 0, y, x] = (resize_image.At<Vec3b>(y, x)[0] / 255f - 0.485f) / 0.229f;                     input_tensor[0, 1, y, x] = (resize_image.At<Vec3b>(y, x)[1] / 255f - 0.456f) / 0.224f;                     input_tensor[0, 2, y, x] = (resize_image.At<Vec3b>(y, x)[2] / 255f - 0.406f) / 0.225f;                 }             }               //将 input_tensor 放入一个输入参数的容器，并指定名称             input_ontainer.Add(NamedOnnxValue.CreateFromTensor("input_image", 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<float>();               result_array = result_tensors.ToArray();               for (int i = 0; i < result_array.Length; i++)             {                 result_array[i] = 1 - result_array[i];             }               float maxVal = result_array.Max();             float minVal = result_array.Min();               for (int i = 0; i < result_array.Length; i++)             {                 result_array[i] = (result_array[i] - minVal) / (maxVal - minVal) * 255;             }               Mat result_image = new Mat(512, 512, MatType.CV_32F, result_array);               //还原图像大小             if (width != modelSize || height != modelSize)             {                 Rect rect = new Rect(0, 0, width, height);                 result_image = result_image.Clone(rect);             }             result_image = result_image.Resize(new OpenCvSharp.Size(oldwidth, oldheight));               pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());             textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";           }           private void Form1_Load(object sender, EventArgs e)         {             startupPath = Application.StartupPath;               model_path = startupPath + "\\model\\u2net_portrait.onnx";               modelSize = 512;               //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>();               //输入Tensor             input_tensor = new DenseTensor<float>(new[] { 1, 3, 512, 512 });           }           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 (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf";             if (sdf.ShowDialog() == DialogResult.OK)             {                 switch (sdf.FilterIndex)                 {                     case 1:                         {                             output.Save(sdf.FileName, ImageFormat.Bmp);                             break;                         }                     case 2:                         {                             output.Save(sdf.FileName, ImageFormat.Emf);                             break;                         }                     case 3:                         {                             output.Save(sdf.FileName, ImageFormat.Exif);                             break;                         }                     case 4:                         {                             output.Save(sdf.FileName, ImageFormat.Gif);                             break;                         }                     case 5:                         {                             output.Save(sdf.FileName, ImageFormat.Icon);                             break;                         }                     case 6:                         {                             output.Save(sdf.FileName, ImageFormat.Jpeg);                             break;                         }                     case 7:                         {                             output.Save(sdf.FileName, ImageFormat.Png);                             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);               }         }     } }

参考

https://github.com/xuebinqin/U-2-Net