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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.Linq;
using System.Threading.Tasks;
using System.Windows.Forms;namespace 人像卡通化
{public partial class Form1 : Form{public Form1(){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;Mat result_image;int modelSize = 256;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);// 输入Tensorfor (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 - 0.5f) / 0.5f;input_tensor[0, 1, y, x] = (resize_image.At<Vec3b>(y, x)[1] / 255f - 0.5f) / 0.5f;input_tensor[0, 2, y, x] = (resize_image.At<Vec3b>(y, x)[2] / 255f - 0.5f) / 0.5f;}}//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", 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();float[] temp_r = new float[256 * 256];float[] temp_g = new float[256 * 256];float[] temp_b = new float[256 * 256];Array.Copy(result_array, temp_r, 256 * 256);Array.Copy(result_array, 256 * 256, temp_g, 0, 256 * 256);Array.Copy(result_array, 256 * 256 * 2, temp_b, 0, 256 * 256);Mat rmat = new Mat(256, 256, MatType.CV_32F, temp_r);Mat gmat = new Mat(256, 256, MatType.CV_32F, temp_g);Mat bmat = new Mat(256, 256, MatType.CV_32F, temp_b);rmat = (rmat + 1f) * 127.5f;gmat = (gmat + 1f) * 127.5f;bmat = (bmat + 1f) * 127.5f;result_image = new Mat();Cv2.Merge(new Mat[] { rmat, gmat, bmat }, result_image);if (!result_image.Empty()){//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";}else{textBox1.Text = "无信息";}}private void Form1_Load(object sender, EventArgs e){startupPath = System.Windows.Forms.Application.StartupPath;model_path = startupPath + "\\photo2cartoon_weights.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);// 输入Tensorinput_tensor = new DenseTensor<float>(new[] { 1, 3, 256, 256 });// 创建输入容器input_ontainer = new List<NamedOnnxValue>();}}
}
Demo下载
说明
1、该例子只是人像转卡通像，转之前需要如下前置处理（为了效果更好）
- 检测人脸及关键点。
- 根据关键点旋转校正人脸。
- 将关键点边界框按固定的比例扩张并裁剪出人脸区域。
- 使用人像分割模型将背景置白。
2、该模型不能用于分割半身像，因为该模型是专用模型，需先裁剪出人脸区域再输入
人像分割参考：
C# PaddleInference.PP-HumanSeg 人像分割 替换背景色-CSDN博客
人脸检测参考：
C# DlibDotNet 人脸识别、人脸68特征点识别、人脸5特征点识别、人脸对齐，三角剖分，人脸特征比对-CSDN博客
3、参考
GitHub - minivision-ai/photo2cartoon-paddle: 人像卡通化探索项目 (photo-to-cartoon translation project)
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