C# OpenCvSharp DNN 部署YOLOV6目标检测

目录

效果

模型信息

项目

代码

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C# OpenCvSharp DNN 部署YOLOV6目标检测

效果

模型信息

Inputs
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name：image_arrays
tensor：Float[1, 3, 640, 640]
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Outputs
————————-
name：outputs
tensor：Float[1, 8400, 85]
—————————————————————

项目

代码

using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
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;
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 = 0.3f;
nmsThreshold = 0.5f;
modelpath = “model/yolov6s.onnx”;

inpHeight = 640;
inpWidth = 640;

opencv_net = CvDnn.ReadNetFromOnnx(modelpath);

class_names = new List();
StreamReader sr = new StreamReader(“model/coco.names”);
string line;
while ((line = sr.ReadLine()) != null)
{
class_names.Add(line);
}
num_class = class_names.Count();

image_path = “test_img/image3.jpg”;
pictureBox1.Image = new Bitmap(image_path);

}

float sigmoid(float x)
{
return (float)(1.0 / (1 + Math.Exp(-x)));
}

Mat ResizeImage(Mat srcimg, out int newh, out int neww, out int top, out int left)
{
int srch = srcimg.Rows, srcw = srcimg.Cols;
top = 0;
left = 0;
newh = inpHeight;
neww = inpWidth;
Mat dstimg = new Mat();
if (srch != srcw)
{
float hw_scale = (float)srch / srcw;
if (hw_scale > 1)
{
newh = inpHeight;
neww = (int)(inpWidth / hw_scale);
Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh), 0, 0, InterpolationFlags.Area);
left = (int)((inpWidth – neww) * 0.5);
Cv2.CopyMakeBorder(dstimg, dstimg, 0, 0, left, inpWidth – neww – left, BorderTypes.Constant);
}
else
{
newh = (int)(inpHeight * hw_scale);
neww = inpWidth;
Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh), 0, 0, InterpolationFlags.Area);
top = (int)((inpHeight – newh) * 0.5);
Cv2.CopyMakeBorder(dstimg, dstimg, top, inpHeight – newh – top, 0, 0, BorderTypes.Constant);
}
}
else
{
Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh));
}
return dstimg;
}

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);

int newh = 0, neww = 0, padh = 0, padw = 0;
Mat dstimg = ResizeImage(image, out newh, out neww, out padh, out padw);

BN_image = CvDnn.BlobFromImage(dstimg, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(0, 0, 0), true, false);

//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[3] { new Mat(), new Mat(), 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(0);
int nout = outs[0].Size(1);

if (outs[0].Dims > 2)
{
num_proposal = outs[0].Size(1);
nout = outs[0].Size(2);
outs[0] = outs[0].Reshape(0, num_proposal);
}

float ratioh = 1.0f * image.Rows / newh, ratiow = 1.0f * image.Cols / neww;
int n = 0, row_ind = 0; ///cx,cy,w,h,box_score,class_score
float* pdata = (float*)outs[0].Data;

List boxes = new List();
List confidences = new List();
List classIds = new List();

for (n = 0; n < num_proposal; n++)
{
float box_score = pdata[4];

if (box_score > confThreshold)
{
Mat scores = outs[0].Row(row_ind).ColRange(5, nout);
double minVal, max_class_socre;
OpenCvSharp.Point minLoc, classIdPoint;
// Get the value and location of the maximum score
Cv2.MinMaxLoc(scores, out minVal, out max_class_socre, out minLoc, out classIdPoint);
max_class_socre *= box_score;

int class_idx = classIdPoint.X;

float cx = (pdata[0] – padw) * ratiow; //cx
float cy = (pdata[1] – padh) * ratioh;//cy
float w = pdata[2] * ratiow;//w
float h = pdata[3] * ratioh; //h

int left = (int)(cx – 0.5 * w);
int top = (int)(cy – 0.5 * h);

confidences.Add((float)max_class_socre);
boxes.Add(new Rect(left, top, (int)w, (int)h));
classIds.Add(class_idx);
}
row_ind++;
pdata += nout;

}

int[] indices;
CvDnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, out indices);

result_image = image.Clone();

for (int ii = 0; ii < indices.Length; ++ii)
{
int idx = indices[ii];
Rect box = boxes[idx];
Cv2.Rectangle(result_image, new OpenCvSharp.Point(box.X, box.Y), new OpenCvSharp.Point(box.X + box.Width, box.Y + box.Height), new Scalar(0, 0, 255), 2);
string label = class_names[classIds[idx]] + “:” + confidences[idx].ToString(“0.00”);
Cv2.PutText(result_image, label, new OpenCvSharp.Point(box.X, box.Y – 5), HersheyFonts.HersheySimplex, 0.75, new Scalar(0, 0, 255), 1);
}

pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
textBox1.Text = “推理耗时:” + (dt2 – dt1).TotalMilliseconds + “ms”;
}

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

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

using OpenCvSharp;using OpenCvSharp.Dnn;using System;using System.Collections.Generic;using System.Drawing;using System.IO;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;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 = 0.3f;nmsThreshold = 0.5f;modelpath = "model/yolov6s.onnx";inpHeight = 640;inpWidth = 640;opencv_net = CvDnn.ReadNetFromOnnx(modelpath);class_names = new List();StreamReader sr = new StreamReader("model/coco.names");string line;while ((line = sr.ReadLine()) != null){class_names.Add(line);}num_class = class_names.Count();image_path = "test_img/image3.jpg";pictureBox1.Image = new Bitmap(image_path);}float sigmoid(float x){return (float)(1.0 / (1 + Math.Exp(-x)));}Mat ResizeImage(Mat srcimg, out int newh, out int neww, out int top, out int left){int srch = srcimg.Rows, srcw = srcimg.Cols;top = 0;left = 0;newh = inpHeight;neww = inpWidth;Mat dstimg = new Mat();if (srch != srcw){float hw_scale = (float)srch / srcw;if (hw_scale > 1){newh = inpHeight;neww = (int)(inpWidth / hw_scale);Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh), 0, 0, InterpolationFlags.Area);left = (int)((inpWidth - neww) * 0.5);Cv2.CopyMakeBorder(dstimg, dstimg, 0, 0, left, inpWidth - neww - left, BorderTypes.Constant);}else{newh = (int)(inpHeight * hw_scale);neww = inpWidth;Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh), 0, 0, InterpolationFlags.Area);top = (int)((inpHeight - newh) * 0.5);Cv2.CopyMakeBorder(dstimg, dstimg, top, inpHeight - newh - top, 0, 0, BorderTypes.Constant);}}else{Cv2.Resize(srcimg, dstimg, new OpenCvSharp.Size(neww, newh));}return dstimg;}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);int newh = 0, neww = 0, padh = 0, padw = 0;Mat dstimg = ResizeImage(image, out newh, out neww, out padh, out padw);BN_image = CvDnn.BlobFromImage(dstimg, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(0, 0, 0), true, false);//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[3] { new Mat(), new Mat(), 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(0);int nout = outs[0].Size(1);if (outs[0].Dims > 2){num_proposal = outs[0].Size(1);nout = outs[0].Size(2);outs[0] = outs[0].Reshape(0, num_proposal);}float ratioh = 1.0f * image.Rows / newh, ratiow = 1.0f * image.Cols / neww;int n = 0, row_ind = 0; ///cx,cy,w,h,box_score,class_scorefloat* pdata = (float*)outs[0].Data;List boxes = new List();List confidences = new List();List classIds = new List();for (n = 0; n  confThreshold){Mat scores = outs[0].Row(row_ind).ColRange(5, nout);double minVal, max_class_socre;OpenCvSharp.Point minLoc, classIdPoint;// Get the value and location of the maximum scoreCv2.MinMaxLoc(scores, out minVal, out max_class_socre, out minLoc, out classIdPoint);max_class_socre *= box_score;int class_idx = classIdPoint.X;float cx = (pdata[0] - padw) * ratiow;//cxfloat cy = (pdata[1] - padh) * ratioh; //cyfloat w = pdata[2] * ratiow; //wfloat h = pdata[3] * ratioh;//hint left = (int)(cx - 0.5 * w);int top = (int)(cy - 0.5 * h);confidences.Add((float)max_class_socre);boxes.Add(new Rect(left, top, (int)w, (int)h));classIds.Add(class_idx);}row_ind++;pdata += nout;}int[] indices;CvDnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, out indices);result_image = image.Clone();for (int ii = 0; ii < indices.Length; ++ii){int idx = indices[ii];Rect box = boxes[idx];Cv2.Rectangle(result_image, new OpenCvSharp.Point(box.X, box.Y), new OpenCvSharp.Point(box.X + box.Width, box.Y + box.Height), new Scalar(0, 0, 255), 2);string label = class_names[classIds[idx]] + ":" + confidences[idx].ToString("0.00");Cv2.PutText(result_image, label, new OpenCvSharp.Point(box.X, box.Y - 5), HersheyFonts.HersheySimplex, 0.75, new Scalar(0, 0, 255), 1);}pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";}private void pictureBox2_DoubleClick(object sender, EventArgs e){Common.ShowNormalImg(pictureBox2.Image);}private void pictureBox1_DoubleClick(object sender, EventArgs e){Common.ShowNormalImg(pictureBox1.Image);}}}

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