【转载】C#使用DNN推理实现FreeYOLO人脸检测&人脸图像质量评估

效果

效果

模型信息

yolo_free_huge_widerface_192x320.onnx

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Inputs
-------------------------
name:input
tensor:Float[1, 3, 192, 320]
---------------------------------------------------------------

Outputs
-------------------------
name:output
tensor:Float[1, 1260, 6]
---------------------------------------------------------------

face-quality-assessment.onnx

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Inputs
-------------------------
name:input
tensor:Float[1, 3, 112, 112]
---------------------------------------------------------------

Outputs
-------------------------
name:quality
tensor:Float[1, 10]
---------------------------------------------------------------

项目源代码

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

namespace OpenCvSharp_DNN_Demo
{
public partial class frmMain : Form
{
public frmMain()
{
InitializeComponent();
}

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

DateTime dt1 = DateTime.Now;
DateTime dt2 = DateTime.Now;

StringBuilder sb = new StringBuilder();

Mat image;
Mat result_image;

FaceQualityAssessment fqa = new FaceQualityAssessment("model/face-quality-assessment.onnx");
FreeYoloFace face = new FreeYoloFace("model/yolo_free_huge_widerface_192x320.onnx");

private void button1_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = fileFilter;
if (ofd.ShowDialog() != DialogResult.OK) return;

pictureBox1.Image = null;
pictureBox2.Image = null;
textBox1.Text = "";

image_path = ofd.FileName;
pictureBox1.Image = new Bitmap(image_path);
image = new Mat(image_path);
}

private void Form1_Load(object sender, EventArgs e)
{
image_path = "test_img/1.jpg";
pictureBox1.Image = new Bitmap(image_path);
}

private unsafe void button2_Click(object sender, EventArgs e)
{
if (image_path == "")
{
return;
}
textBox1.Text = "检测中,请稍等……";
if (pictureBox2.Image != null)
{
pictureBox2.Image.Dispose();
}
pictureBox2.Image = null;
sb.Clear();
Application.DoEvents();

image = new Mat(image_path);

dt1 = DateTime.Now;
List<Face> ltFace = face.Detect(image);
dt2 = DateTime.Now;

if (ltFace.Count > 0)
{
sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
result_image = image.Clone();
foreach (var item in ltFace)
{
Mat crop_img = new Mat(image, item.rect);
float fqa_prob_mean = fqa.Detect(crop_img);
crop_img.Dispose();
Cv2.Rectangle(result_image, new OpenCvSharp.Point(item.rect.X, item.rect.Y), new OpenCvSharp.Point(item.rect.X + item.rect.Width, item.rect.Y + item.rect.Height), new Scalar(0, 0, 255), 2);
string label = "prob:" + item.prob.ToString("0.00") + " fqa_score:" + fqa_prob_mean.ToString("0.00");
sb.AppendLine(label);
Cv2.PutText(result_image, label, new OpenCvSharp.Point(item.rect.X, item.rect.Y - 5), HersheyFonts.HersheySimplex, 1, new Scalar(0, 0, 255), 2);
}
pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
textBox1.Text = sb.ToString();
}
else
{
textBox1.Text = "未检测到人脸";
}
}

private void pictureBox2_DoubleClick(object sender, EventArgs e)
{
Common.ShowNormalImg(pictureBox2.Image);
}

private void pictureBox1_DoubleClick(object sender, EventArgs e)
{
Common.ShowNormalImg(pictureBox1.Image);
}
}
}

From 公众号:天天代码码天天


【转载】C#使用DNN推理实现FreeYOLO人脸检测&人脸图像质量评估
https://bgmh.work/2024/04/20/CSharp-使用DNN推理实现FreeYOLO人脸检测-人脸图像质量评估/
作者
OuHuanHua
发布于
2024年4月20日
许可协议