C# OpenCvSharp DNN 部署FastestDet
目录
C# OpenCvSharp DNN 部署FastestDet
效果
模型信息
Inputs
-------------------------
name：input.1
tensor：Float[1, 3, 512, 512]
---------------------------------------------------------------
Outputs
-------------------------
name：761
tensor：Float[1024, 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<string> 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 = 0.35f;
? ? ? ? ? ? modelpath = "model/FastestDet.onnx";
? ? ? ? ? ? inpHeight = 512;
? ? ? ? ? ? inpWidth = 512;
? ? ? ? ? ? opencv_net = CvDnn.ReadNetFromOnnx(modelpath);
? ? ? ? ? ? class_names = new List<string>();
? ? ? ? ? ? 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/4.jpg";
? ? ? ? ? ? pictureBox1.Image = new Bitmap(image_path);
? ? ? ? }
? ? ? ? float sigmoid(float x)
? ? ? ? {
? ? ? ? ? ? return (float)(1.0 / (1 + Math.Exp(-x)));
? ? ? ? }
? ? ? ? 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);
? ? ? ? ? ? dt1 = DateTime.Now;
? ? ? ? ? ? BN_image = CvDnn.BlobFromImage(image, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(0, 0, 0), false, 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();
? ? ? ? ? ? opencv_net.Forward(outs, outBlobNames);
? ? ? ? ? ? dt2 = DateTime.Now;
? ? ? ? ? ? int num_proposal = outs[0].Size(0);
? ? ? ? ? ? int nout = outs[0].Size(1);
? ? ? ? ? ? int i = 0, j = 0, row_ind = 0; //box_score, xmin,ymin,xamx,ymax,class_score
? ? ? ? ? ? int num_grid_x = 32;
? ? ? ? ? ? int num_grid_y = 32;
? ? ? ? ? ? float* pdata = (float*)outs[0].Data;
? ? ? ? ? ? List<Rect> boxes = new List<Rect>();
? ? ? ? ? ? List<float> confidences = new List<float>();
? ? ? ? ? ? List<int> classIds = new List<int>();
? ? ? ? ? ? for (i = 0; i < num_grid_y; i++)
? ? ? ? ? ? {
? ? ? ? ? ? ? ? for (j = 0; j < num_grid_x; j++)
? ? ? ? ? ? ? ? {
? ? ? ? ? ? ? ? ? ? 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 *= pdata[0];
? ? ? ? ? ? ? ? ? ? if (max_class_socre > confThreshold)
? ? ? ? ? ? ? ? ? ? {
? ? ? ? ? ? ? ? ? ? ? ? int class_idx = classIdPoint.X;
? ? ? ? ? ? ? ? ? ? ? ? float cx = (float)((Math.Tanh(pdata[1]) + j) / (float)num_grid_x); ?//cx
? ? ? ? ? ? ? ? ? ? ? ? float cy = (float)((Math.Tanh(pdata[2]) + i) / (float)num_grid_y); ? //cy
? ? ? ? ? ? ? ? ? ? ? ? float w = sigmoid(pdata[3]); ? //w
? ? ? ? ? ? ? ? ? ? ? ? float h = sigmoid(pdata[4]); ?//h
? ? ? ? ? ? ? ? ? ? ? ? cx *= image.Cols;
? ? ? ? ? ? ? ? ? ? ? ? cy *= image.Rows;
? ? ? ? ? ? ? ? ? ? ? ? w *= image.Cols;
? ? ? ? ? ? ? ? ? ? ? ? h *= image.Rows;
? ? ? ? ? ? ? ? ? ? ? ? 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<string> 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 = 0.35f;
modelpath = "model/FastestDet.onnx";
inpHeight = 512;
inpWidth = 512;
opencv_net = CvDnn.ReadNetFromOnnx(modelpath);
class_names = new List<string>();
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/4.jpg";
pictureBox1.Image = new Bitmap(image_path);
}
float sigmoid(float x)
{
return (float)(1.0 / (1 + Math.Exp(-x)));
}
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);
dt1 = DateTime.Now;
BN_image = CvDnn.BlobFromImage(image, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(0, 0, 0), false, 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();
opencv_net.Forward(outs, outBlobNames);
dt2 = DateTime.Now;
int num_proposal = outs[0].Size(0);
int nout = outs[0].Size(1);
int i = 0, j = 0, row_ind = 0; //box_score, xmin,ymin,xamx,ymax,class_score
int num_grid_x = 32;
int num_grid_y = 32;
float* pdata = (float*)outs[0].Data;
List<Rect> boxes = new List<Rect>();
List<float> confidences = new List<float>();
List<int> classIds = new List<int>();
for (i = 0; i < num_grid_y; i++)
{
for (j = 0; j < num_grid_x; j++)
{
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 *= pdata[0];
if (max_class_socre > confThreshold)
{
int class_idx = classIdPoint.X;
float cx = (float)((Math.Tanh(pdata[1]) + j) / (float)num_grid_x); //cx
float cy = (float)((Math.Tanh(pdata[2]) + i) / (float)num_grid_y); //cy
float w = sigmoid(pdata[3]); //w
float h = sigmoid(pdata[4]); //h
cx *= image.Cols;
cy *= image.Rows;
w *= image.Cols;
h *= image.Rows;
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);
}
}
}
下载
本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。 如若内容造成侵权/违法违规/事实不符，请联系我的编程经验分享网邮箱：veading@qq.com进行投诉反馈，一经查实，立即删除！