目录
效果
模型信息
项目
代码
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C# OpenCvSharp DNN 部署yolov5不规则四边形目标检测
效果
模型信息
Inputs
————————-
name：images
tensor：Float[1, 3, 1024, 1024]
—————————————————————
Outputs
————————-
name：output
tensor：Float[1, 64512, 11]
—————————————————————
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.Linq.Expressions;
using System.Numerics;
using System.Reflection;
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;
float objThreshold;
float[,] anchors = new float[3, 6] {
{31, 30, 28, 49, 50, 31},
{46, 45, 58, 58, 74, 74},
{94, 94, 115, 115, 151, 151}
};
float[] stride = 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 = 0.5f;
nmsThreshold = 0.5f;
objThreshold = 0.5f;
modelpath = “model/best.onnx”;
inpHeight = 1024;
inpWidth = 1024;
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/1.png”;
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;
服务器托管网 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;
}
float IoU(BoxInfo polya, BoxInfo polyb, int max_w, int max_h)
{
List> poly_array0 = new List>();
List> poly_array1 = new List>();
poly_array0.Add(polya.pts);
poly_array1.Add(polyb.pts);
Mat _poly0 = Mat.Zeros(max_h, max_w, MatType.CV_8UC1);
Mat _poly1 = Mat.Zeros(max_h, max_w, MatType.CV_8UC1);
Mat _result = new Mat();
List> _pts0 = new List>();
List _npts0 = new List();
foreach (var item in poly_array0)
{
if (item.Count return -1f;
_pts0.Add(item);
_npts0.Add(item.Count);
}
List> _pts1 = new List>();
List _npts1 = new List();
foreach (var item in poly_array1)
{
if (item.Count return -1f;
_pts1.Add(item);
_npts1.Add(item.Count);
}
Cv2.FillPoly(_poly0, _pts0, new Scalar(1));
Cv2.FillPoly(_poly1, _pts1, new Scalar(1));
Cv2.BitwiseAnd(_poly0, _poly1, _result);
int _area0 = Cv2.CountNonZero(_poly0);
int _area1 = Cv2.CountNonZero(_poly1);
int _intersection_area = Cv2.CountNonZero(_result);
float _iou = (float)_intersection_area / (float)(_area0 + _area1 – _intersection_area);
return _iou;
}
void nms(List input_boxes, int max_w, int max_h)
{
input_boxes.Sort((a, b) => { return a.score > b.score ? -1 : 1; });
bool[] isSuppressed = new bool[input_boxes.Count];
for (int i = 0; i {
if (isSuppressed[i]) { continue; }
for (int j = i + 1; j {
if (isSuppressed[j]) { continue; }
float ovr = IoU(input_boxes[i], input_boxes[j], max_w, max_h);
if (ovr >= nmsThreshold)
{
isSuppressed[j] = true;
}
}
}
for (int i = isSuppressed.Length – 1; i >= 0; i–)
{
if (isSuppressed[i])
{
input_boxes.RemoveAt(i);
}
}
}
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(1);
int nout = outs[0].Size(2);
if (outs[0].Dims > 2)
{
outs[0] = outs[0].Reshape(0, num_proposal);
}
float ratioh = 1.0f * image.Rows / newh, ratiow = 1.0f * image.Cols / neww;
float* pdata = (float*)outs[0].Data;
List generate_boxes = new List();
int row_ind = 0;
for (int n = 0; n {
int num_grid_x = (int)(inpWidth / stride[n]);
int num_grid_y = (int)(inpHeight / stride[n]);
for (int q = 0; q {
float anchor_w = anchors[n, q * 2];
float anchor_h = anchors[n, q * 2 + 1];
for (int i = 0; i {
for (int j = 0; j {
float box_score = sigmoid(pdata[8]);
if (box_score > objThreshold)
{
Mat scores = outs[0].Row(row_ind).ColRange(9, 9 + num_class);
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);
int class_idx = classIdPoint.X;
max_class_socre = sigmoid((float)max_class_socre) * box_score;
if (max_class_socre > confThreshold)
{
List pts = new List();
for (int k = 0; k {
float x = (pdata[k] + j) * stride[n]; //x
float y = (pdata[k + 1] + i) * stride[n]; //y
x = (x – padw) * ratiow;
y = (y – padh) * ratioh;
pts.Add(new OpenCvSharp.Point(x, y));
}
Rect r = Cv2.BoundingRect(pts);
generate_boxes.Add(new BoxInfo(pts, (float)max_class_socre, class_idx));
}
}
row_ind++;
pdata += nout;
}
}
}
}
nms(generate_boxes, image.Cols, image.Rows);
result_image = image.Clone();
for (int ii = 0; ii {
int idx = generate_boxes[ii].label;
for (int jj = 0; jj {
Cv2.Line(result_image, generate_boxes[ii].pts[jj], generate_boxes[ii].pts[(jj + 1) % 4], new Scalar(0, 0, 255), 2);
}
string label = class_names[idx] + “:” + generate_boxes[ii].score.ToString(“0.00”);
int xmin = (int)generate_boxes[ii].pts[0].X;
int ymin = (int)generate_boxes[ii].pts[0].Y – 10;
Cv2.PutText(result_image, label, new OpenCvSharp.Point(xmin, ymin – 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.Linq.Expressions;
using System.Numerics;
using System.Reflection;
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;
float objThreshold;
float[,] anchors = new float[3, 6] {
{31, 30, 28, 49, 50, 31},
{46, 45, 58, 58, 74, 74},
{94, 94, 115, 115, 151, 151}
};
float[] stride = 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 = 0.5f;
nmsThreshold = 0.5f;
objThreshold = 0.5f;
modelpath = "model/best.onnx";
inpHeight = 1024;
inpWidth = 1024;
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/1.png";
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;
}
float IoU(BoxInfo polya, BoxInfo polyb, int max_w, int max_h)
{
List> poly_array0 = new List>();
List> poly_array1 = new List>();
poly_array0.Add(polya.pts);
poly_array1.Add(polyb.pts);
Mat _poly0 = Mat.Zeros(max_h, max_w, MatType.CV_8UC1);
Mat _poly1 = Mat.Zeros(max_h, max_w, MatType.CV_8UC1);
Mat _result = new Mat();
List> _pts0 = new List>();
List _npts0 = new List();
foreach (var item in poly_array0)
{
if (item.Count > _pts1 = new List>();
List _npt服务器托管网s1 = new List();
foreach (var item in poly_array1)
{
if (item.Count input_boxes, int max_w, int max_h)
{
input_boxes.Sort((a, b) => { return a.score > b.score ? -1 : 1; });
bool[] isSuppressed = new bool[input_boxes.Count];
for (int i = 0; i = nmsThreshold)
{
isSuppressed[j] = true;
}
}
}
for (int i = isSuppressed.Length - 1; i >= 0; i--)
{
if (isSuppressed[i])
{
input_boxes.RemoveAt(i);
}
}
}
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(1);
int nout = outs[0].Size(2);
if (outs[0].Dims > 2)
{
outs[0] = outs[0].Reshape(0, num_proposal);
}
float ratioh = 1.0f * image.Rows / newh, ratiow = 1.0f * image.Cols / neww;
float* pdata = (float*)outs[0].Data;
List generate_boxes = new List();
int row_ind = 0;
for (int n = 0; n objThreshold)
{
Mat scores = outs[0].Row(row_ind).ColRange(9, 9 + num_class);
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);
int class_idx = classIdPoint.X;
max_class_socre = sigmoid((float)max_class_socre) * box_score;
if (max_class_socre > confThreshold)
{
List pts = new List();
for (int k = 0; k 下载
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