C# OpenCvSharp DNN FreeYOLO 人脸检测
���录
效果
模型信息
项目
代码
下载
C# OpenCvSharp DNN FreeYOLO 人脸检测
效果
模型信息
Inputs
-------------------------
name：input
tensor：Float[1, 3, 192, 320]
---------------------------------------------------------------
Outputs
-------------------------
name：output
tensor：Float[1, 1260, 6]
---------------------------------------------------------------
项目
代码
using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
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;
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 class_names;
int num_class;
Net opencv_net;
Mat BN_image;
Mat image;
Mat result_image;
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)
{
confThreshold = 0.8f;
nmsThreshold = 0.5f;
modelpath = "model/yolo_free_huge_widerface_192x320.onnx";
inpHeight = 192;
inpWidth = 320;
opencv_net = CvDnn.ReadNetFromOnnx(modelpath);
class_names = new List();
class_names.Add("face");
num_class = 1;
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……";
pictureBox2.Image = null;
Application.DoEvents();
image = new Mat(image_path);
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();
dt1 = DateTime.Now;
opencv_net.Forward(outs, outBlobNames);
dt2 = DateTime.Now;
int num_proposal = outs[0].Size(1);
int nout = outs[0].Size(2);
float* pdata = (float*)outs[0].Data;
List confidences = new List();
List boxes = new List();
List classIds = new List();
for (int n = 0; n 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]; //cx
float cy = (0.5f + i + pdata[1]) * strides[n]; //cy
float w = (float)(Math.Exp(pdata[2]) * strides[n]); //w
float h = (float)(Math.Exp(pdata[3]) * strides[n]); //h
float 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);
result_image = image.Clone();
for (int ii = 0; ii
下载
可运行程序exe下载
源码下载
The End