C#版Facefusion：让你的脸与世界融为一体！-01 人脸检测

目录

说明

效果

模型信息

项目

代码

下载

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说明

C#版Facefusion一共有如下5个步骤：

1、使用yoloface_8n.onnx进行人脸检测
2、使用2dfan4.onnx获取人脸关键点
3、使用arcface_w600k_r50.onnx获取人脸特征值
4、使用inswapper_128.onnx进行人脸交换
5、使用gfpgan_1.4.onnx进行人脸增强

本文分享使用yoloface_8n.onnx实现C#版Facefusion第一步：人脸检测。

效果

模型信息

Model Properties
-------------------------
date：2024-01-13T23:32:01.523479
description：Ultralytics YOLOv8n-pose model trained on /ssd2t/derron/ultralytics/ultralytics/datasets/widerface.yaml
author：Ultralytics
kpt_shape：[5, 3]
task：pose
license：AGPL-3.0 https://ultralytics.com/license
version：8.1.0
stride：32
batch：1
imgsz：[640, 640]
names：{0: 'face'}
---------------------------------------------------------------

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

Outputs
-------------------------
name：output0
tensor：Float[1, 20, 8400]
---------------------------------------------------------------

项目

代码

Form1.cs

using OpenCvSharp;
using OpenCvSharp.Extensions;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Windows.Forms;

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

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";

        string source_path = "";
        string target_path = "";

        Yolov8Face detect_face;

        private void button2_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;

            pictureBox1.Image = null;

            source_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(source_path);
        }

        private void button3_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;

            pictureBox2.Image = null;

            target_path = ofd.FileName;
            pictureBox2.Image = new Bitmap(target_path);
        }

        private void button1_Click(object sender, EventArgs e)
        {
            if (pictureBox1.Image == null)
            {
                return;
            }

            button1.Enabled = false;
            Application.DoEvents();

            Mat source_img = Cv2.ImRead(source_path);
            Mat target_img = Cv2.ImRead(target_path);

            List<Bbox> boxes;
            boxes = detect_face.detect(source_img);
            if (boxes.Count == 0)
            {
                MessageBox.Show("Source中未检测到人脸！");
                button1.Enabled = true;
                return;
            }

            //绘图
            Cv2.Rectangle(source_img, new OpenCvSharp.Point(boxes[0].xmin, boxes[0].ymin), new OpenCvSharp.Point(boxes[0].xmax, boxes[0].ymax), new Scalar(255, 0, 0), 2);
            //显示
            pictureBox1.Image = source_img.ToBitmap();

            if (pictureBox2.Image == null)
            {
                return;
            }

            boxes = detect_face.detect(target_img);
            if (boxes.Count == 0)
            {
                MessageBox.Show("target_img中未检测到人脸！");
                button1.Enabled = true;
                return;
            }

            //绘图
            Cv2.Rectangle(target_img, new OpenCvSharp.Point(boxes[0].xmin, boxes[0].ymin), new OpenCvSharp.Point(boxes[0].xmax, boxes[0].ymax), new Scalar(255, 0, 0), 2);
            //显示
            pictureBox2.Image = target_img.ToBitmap();
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            detect_face = new Yolov8Face("model/yoloface_8n.onnx");

            target_path = "images/target.jpg";
            source_path = "images/source.jpg";

            pictureBox1.Image = new Bitmap(source_path);
            pictureBox2.Image = new Bitmap(target_path);
        }
    }
}

using OpenCvSharp;
using OpenCvSharp.Extensions;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Windows.Forms;

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

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";

        string source_path = "";
        string target_path = "";

        Yolov8Face detect_face;

        private void button2_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;

            pictureBox1.Image = null;

            source_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(source_path);
        }

        private void button3_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;

            pictureBox2.Image = null;

            target_path = ofd.FileName;
            pictureBox2.Image = new Bitmap(target_path);
        }

        private void button1_Click(object sender, EventArgs e)
        {
            if (pictureBox1.Image == null)
            {
                return;
            }

            button1.Enabled = false;
            Application.DoEvents();

            Mat source_img = Cv2.ImRead(source_path);
            Mat target_img = Cv2.ImRead(target_path);

            List<Bbox> boxes;
            boxes = detect_face.detect(source_img);
            if (boxes.Count == 0)
            {
                MessageBox.Show("Source中未检测到人脸！");
                button1.Enabled = true;
                return;
            }

            //绘图
            Cv2.Rectangle(source_img, new OpenCvSharp.Point(boxes[0].xmin, boxes[0].ymin), new OpenCvSharp.Point(boxes[0].xmax, boxes[0].ymax), new Scalar(255, 0, 0), 2);
            //显示
            pictureBox1.Image = source_img.ToBitmap();

            if (pictureBox2.Image == null)
            {
                return;
            }


            boxes = detect_face.detect(target_img);
            if (boxes.Count == 0)
            {
                MessageBox.Show("target_img中未检测到人脸！");
                button1.Enabled = true;
                return;
            }

            //绘图
            Cv2.Rectangle(target_img, new OpenCvSharp.Point(boxes[0].xmin, boxes[0].ymin), new OpenCvSharp.Point(boxes[0].xmax, boxes[0].ymax), new Scalar(255, 0, 0), 2);
            //显示
            pictureBox2.Image = target_img.ToBitmap();
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            detect_face = new Yolov8Face("model/yoloface_8n.onnx");

            target_path = "images/target.jpg";
            source_path = "images/source.jpg";

            pictureBox1.Image = new Bitmap(source_path);
            pictureBox2.Image = new Bitmap(target_path);
        }
    }
}

Yolov8Face.cs

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Linq;

namespace FaceFusionSharp
{
    internal class Yolov8Face
    {
        float[] input_image;
        int input_height;
        int input_width;
        float ratio_height;
        float ratio_width;
        float conf_threshold;
        float iou_threshold;

        SessionOptions options;
        InferenceSession onnx_session;

        public Yolov8Face(string modelpath, float conf_thres = 0.5f, float iou_thresh = 0.4f)
        {
            options = new SessionOptions();
            options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
            options.AppendExecutionProvider_CPU(0);

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

            this.input_height = 640;
            this.input_width = 640;

            conf_threshold = conf_thres;
            iou_threshold = iou_thresh;
        }

        void preprocess(Mat srcimg)
        {
            int height = srcimg.Rows;
            int width = srcimg.Cols;
            Mat temp_image = srcimg.Clone();
            if (height > input_height || width > input_width)
            {
                float scale = Math.Min((float)input_height / height, (float)input_width / width);
                Size new_size = new Size((int)(width * scale), (int)(height * scale));
                Cv2.Resize(srcimg, 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);

            Mat[] bgrChannels = Cv2.Split(input_img);
            for (int c = 0; c < 3; c++)
            {
                bgrChannels[c].ConvertTo(bgrChannels[c], MatType.CV_32FC1, 1 / 128.0, -127.5 / 128.0);
            }

            Cv2.Merge(bgrChannels, input_img);

            foreach (Mat channel in bgrChannels)
            {
                channel.Dispose();
            }

            input_image = Common.ExtractMat(input_img);
            input_img.Dispose();
        }

        public List<Bbox> detect(Mat srcimg)
        {
            preprocess(srcimg);

            Tensor<float> input_tensor = new DenseTensor<float>(input_image, new[] { 1, 3, input_height, input_width });
            List<NamedOnnxValue> input_container = new List<NamedOnnxValue>
            {
                NamedOnnxValue.CreateFromTensor("images", input_tensor)
            };

            var ort_outputs = onnx_session.Run(input_container).ToArray();
            // 形状是(1, 20, 8400),不考虑第0维batchsize，每一列的长度20,前4个元素是检测框坐标(cx,cy,w,h)，第4个元素是置信度，剩下的15个元素是5个关键点坐标x,y和置信度
            float[] pdata = ort_outputs[0].AsTensor<float>().ToArray();
            int num_box = 8400;
            List<Bbox> bounding_box_raw = new List<Bbox>();
            List<float> score_raw = new List<float>();
            for (int i = 0; i < num_box; i++)
            {
                float score = pdata[4 * num_box + i];
                if (score > conf_threshold)
                {
                    float xmin = (float)((pdata[i] - 0.5 * pdata[2 * num_box + i]) * ratio_width);            //(cx,cy,w,h)转到(x,y,w,h)并还原到原图
                    float ymin = (float)((pdata[num_box + i] - 0.5 * pdata[3 * num_box + i]) * ratio_height); //(cx,cy,w,h)转到(x,y,w,h)并还原到原图
                    float xmax = (float)((pdata[i] + 0.5 * pdata[2 * num_box + i]) * ratio_width);            //(cx,cy,w,h)转到(x,y,w,h)并还原到原图
                    float ymax = (float)((pdata[num_box + i] + 0.5 * pdata[3 * num_box + i]) * ratio_height); //(cx,cy,w,h)转到(x,y,w,h)并还原到原图
                    //坐标的越界检查保护，可以添加一下
                    bounding_box_raw.Add(new Bbox(xmin, ymin, xmax, ymax));
                    score_raw.Add(score);
                    //剩下的5个关键点坐标的计算,暂时不写,因为在下游的模块里没有用到5个关键点坐标信息
                }
            }
            List<int> keep_inds = Common.nms(bounding_box_raw, score_raw, iou_threshold);
            int keep_num = keep_inds.Count();
            List<Bbox> boxes = new List<Bbox>();
            for (int i = 0; i < keep_num; i++)
            {
                int ind = keep_inds[i];
                boxes.Add(bounding_box_raw[ind]);
            }
            return boxes;
        }
    }
}

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