C# OpenCvSharp DNN FreeYOLO 人脸检测人脸图像质量评估

本文主要是介绍C# OpenCvSharp DNN FreeYOLO 人脸检测人脸图像质量评估，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

效果

模型信息

yolo_free_huge_widerface_192x320.onnx

face-quality-assessment.onnx

项目

代码

frmMain.cs

FreeYoloFace

FaceQualityAssessment.cs

下载

C# OpenCvSharp DNN FreeYOLO 人脸检测&人脸图像质量评估

效果

模型信息

yolo_free_huge_widerface_192x320.onnx

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

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

face-quality-assessment.onnx

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

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

项目

代码

frmMain.cs

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);
}
}
}

FreeYoloFace.cs

using OpenCvSharp.Dnn;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Linq;namespace OpenCvSharp_DNN_Demo
{public class FreeYoloFace{float confThreshold;float nmsThreshold;int num_stride = 3;float[] strides = new float[3] { 8.0f, 16.0f, 32.0f };string modelpath;int inpHeight;int inpWidth;List<string> class_names;int num_class;Net opencv_net;Mat BN_image;Mat image;public FreeYoloFace(string modelpath){opencv_net = CvDnn.ReadNetFromOnnx(modelpath);class_names = new List<string> { "face" };num_class = 1;confThreshold = 0.8f;nmsThreshold = 0.5f;inpHeight = 192;inpWidth = 320;}unsafe public List<Face> Detect(Mat image){List<Face> ltFace = new List<Face>();float ratio = Math.Min(1.0f * inpHeight / image.Rows, 1.0f * inpWidth / image.Cols);int neww = (int)(image.Cols * ratio);int newh = (int)(image.Rows * ratio);Mat dstimg = new Mat();Cv2.Resize(image, dstimg, new OpenCvSharp.Size(neww, newh));Cv2.CopyMakeBorder(dstimg, dstimg, 0, inpHeight - newh, 0, inpWidth - neww, BorderTypes.Constant);BN_image = CvDnn.BlobFromImage(dstimg);//配置图片输入数据opencv_net.SetInput(BN_image);//模型推理，读取推理结果Mat[] outs = new Mat[1] { new Mat() };string[] outBlobNames = opencv_net.GetUnconnectedOutLayersNames().ToArray();opencv_net.Forward(outs, outBlobNames);int num_proposal = outs[0].Size(1);int nout = outs[0].Size(2);float* pdata = (float*)outs[0].Data;List<float> confidences = new List<float>();List<Rect> boxes = new List<Rect>();List<int> classIds = new List<int>();for (int n = 0; n < num_stride; n++){int num_grid_x = (int)Math.Ceiling(inpWidth / strides[n]);int num_grid_y = (int)Math.Ceiling(inpHeight / strides[n]);for (int i = 0; i < num_grid_y; i++){for (int j = 0; j < num_grid_x; j++){float box_score = pdata[4];int max_ind = 0;float max_class_socre = 0;for (int k = 0; k < num_class; k++){if (pdata[k + 5] > max_class_socre){max_class_socre = pdata[k + 5];max_ind = k;}}max_class_socre = max_class_socre * box_score;max_class_socre = (float)Math.Sqrt(max_class_socre);if (max_class_socre > confThreshold){float cx = (0.5f + j + pdata[0]) * strides[n];  //cxfloat cy = (0.5f + i + pdata[1]) * strides[n];   //cyfloat w = (float)(Math.Exp(pdata[2]) * strides[n]);   //wfloat h = (float)(Math.Exp(pdata[3]) * strides[n]);  //hfloat xmin = (float)((cx - 0.5 * w) / ratio);float ymin = (float)((cy - 0.5 * h) / ratio);float xmax = (float)((cx + 0.5 * w) / ratio);float ymax = (float)((cy + 0.5 * h) / ratio);int left = (int)((cx - 0.5 * w) / ratio);int top = (int)((cy - 0.5 * h) / ratio);int width = (int)(w / ratio);int height = (int)(h / ratio);confidences.Add(max_class_socre);boxes.Add(new Rect(left, top, width, height));classIds.Add(max_ind);}pdata += nout;}}}int[] indices;CvDnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, out indices);for (int ii = 0; ii < indices.Length; ++ii){int idx = indices[ii];Rect box = boxes[idx];ltFace.Add(new Face(box, confidences[idx]));}outs[0].Dispose();BN_image.Dispose();dstimg.Dispose();return ltFace;}}
}

FaceQualityAssessment.cs

using OpenCvSharp;
using OpenCvSharp.Dnn;
using System.Linq;namespace OpenCvSharp_DNN_Demo
{public class FaceQualityAssessment{Net net;int inpWidth = 112;int inpHeight = 112;float[] mean = new float[] { 0.5f, 0.5f, 0.5f };float[] std = new float[] { 0.5f, 0.5f, 0.5f };public FaceQualityAssessment(string modelpath){net = CvDnn.ReadNetFromOnnx(modelpath);}unsafe public float Detect(Mat cropped){Mat rgbimg = new Mat();Cv2.CvtColor(cropped, rgbimg, ColorConversionCodes.BGR2RGB);Cv2.Resize(rgbimg, rgbimg, new Size(inpWidth, inpHeight));Mat normalized_mat = Normalize(rgbimg);Mat blob = CvDnn.BlobFromImage(normalized_mat);//配置图片输入数据net.SetInput(blob);//模型推理，读取推理结果Mat[] outs = new Mat[1] { new Mat() };string[] outBlobNames = net.GetUnconnectedOutLayersNames().ToArray();net.Forward(outs, outBlobNames);float* pdata = (float*)outs[0].Data;  //形状1x10int length = outs[0].Size(1);float fqa_prob_mean = 0;for (int i = 0; i < length; i++){fqa_prob_mean += pdata[i];}fqa_prob_mean /= length;rgbimg.Dispose();normalized_mat.Dispose();blob.Dispose();outs[0].Dispose();return fqa_prob_mean;}Mat Normalize(Mat src){Mat[] bgr = src.Split();for (int i = 0; i < bgr.Length; ++i){bgr[i].ConvertTo(bgr[i], MatType.CV_32FC1, 1.0 / (255.0 * std[i]), (0.0 - mean[i]) / std[i]);}Cv2.Merge(bgr, src);foreach (Mat channel in bgr){channel.Dispose();}return src;}}
}