ini
复制代码
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Imaging;
using System.Linq;
using System.Text;
using System.Windows.Forms;
namespace Onnx_Yolov8_Demo
{
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
}
string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
string image_path = "";
string startupPath;
string classer_path;
DateTime dt1 = DateTime.Now;
DateTime dt2 = DateTime.Now;
string model_path;
Mat image;
DetectionResult result_pro;
Mat result_image;
Result result;
SessionOptions options;
InferenceSession onnx_session;
Tensor<float> input_tensor;
List<NamedOnnxValue> input_container;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;
Tensor<float> result_tensors;
private void button1_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = fileFilter;
if (ofd.ShowDialog() != DialogResult.OK) return;
pictureBox1.Image = null;
image_path = ofd.FileName;
pictureBox1.Image = new Bitmap(image_path);
textBox1.Text = "";
image = new Mat(image_path);
pictureBox2.Image = null;
}
private void button2_Click(object sender, EventArgs e)
{
if (image_path == "")
{
return;
}
float score_threshold = 0.5f;
float nms_threshold = 0.5f;
button2.Enabled = false;
pictureBox2.Image = null;
textBox1.Text = "";
Application.DoEvents();
//图片缩放
image = new Mat(image_path);
int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;
Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
Rect roi = new Rect(0, 0, image.Cols, image.Rows);
image.CopyTo(new Mat(max_image, roi));
float[] result_array = new float[8400 * 84];
float[] factors = new float[2];
factors[0] = factors[1] = (float)(max_image_length / 640.0);
// 将图片转为RGB通道
Mat image_rgb = new Mat();
Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB);
Mat resize_image = new Mat();
Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(640, 640));
// 输入Tensor
for (int y = 0; y < resize_image.Height; y++)
{
for (int x = 0; x < resize_image.Width; x++)
{
input_tensor[0, 0, y, x] = resize_image.At<Vec3b>(y, x)[0] / 255f;
input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1] / 255f;
input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2] / 255f;
}
}
//将 input_tensor 放入一个输入参数的容器，并指定名称
input_container.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));
dt1 = DateTime.Now;
//运行 Inference 并获取结果
result_infer = onnx_session.Run(input_container);
dt2 = DateTime.Now;
// 将输出结果转为DisposableNamedOnnxValue数组
results_onnxvalue = result_infer.ToArray();
// 读取第一个节点输出并转为Tensor数据
result_tensors = results_onnxvalue[0].AsTensor<float>();
result_array = result_tensors.ToArray();
resize_image.Dispose();
image_rgb.Dispose();
result_pro = new DetectionResult(classer_path, factors, score_threshold, nms_threshold);
result = result_pro.process_result(result_array);
result_image = result_pro.draw_result(result, image.Clone());
StringBuilder sb = new StringBuilder();
if (!result_image.Empty())
{
pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
//textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
sb.AppendLine("--------------------------------------------------");
sb.AppendLine("{lable}{scores}({X},{Y},{Width},{Height})");
sb.AppendLine("--------------------------------------------------");
// 识别结果
for (int i = 0; i < result.length; i++)
{
//Scalar color= Scalar.RandomColor();
Scalar color = new Scalar(0, 0, 255);
string lable = string.Format("{0}\t{1}\t({2},{3},{4},{5})"
, result.classes[i]
, result.scores[i].ToString("P2")
, result.rects[i].X
, result.rects[i].Y
, result.rects[i].Width
, result.rects[i].Height
);
sb.AppendLine(lable);
//Cv2.Rectangle(image, result.rects[i], color, 2, LineTypes.Link8);
//Cv2.Rectangle(image
// , new Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20)
// , new Point(result.rects[i].TopLeft.X - 1 + lable.Length * 12, result.rects[i].TopLeft.Y)
// , color
// , -1);
//Cv2.PutText(image, lable, new Point(result.rects[i].X, result.rects[i].Y - 4), HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);
}
textBox1.Text = sb.ToString();
}
else
{
textBox1.Text = "无信息";
}
button2.Enabled = true;
}
private void Form1_Load(object sender, EventArgs e)
{
startupPath = System.Windows.Forms.Application.StartupPath;
model_path = "model/carton.onnx";
classer_path = "model/lable.txt";
// 创建输出会话，用于输出模型读取信息
options = new SessionOptions();
options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行
// 创建推理模型类，读取本地模型文件
onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径
// 输入Tensor
input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
// 创建输入容器
input_container = new List<NamedOnnxValue>();
image_path = "test_img/4.jpg";
pictureBox1.Image = new Bitmap(image_path);
image = new Mat(image_path);
}
private void pictureBox1_DoubleClick(object sender, EventArgs e)
{
Common.ShowNormalImg(pictureBox1.Image);
}
private void pictureBox2_DoubleClick(object sender, EventArgs e)
{
Common.ShowNormalImg(pictureBox2.Image);
}
SaveFileDialog sdf = new SaveFileDialog();
private void button3_Click(object sender, EventArgs e)
{
if (pictureBox2.Image == null)
{
return;
}
Bitmap output = new Bitmap(pictureBox2.Image);
sdf.Title = "保存";
sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf";
if (sdf.ShowDialog() == DialogResult.OK)
{
switch (sdf.FilterIndex)
{
case 1:
{
output.Save(sdf.FileName, ImageFormat.Jpeg);
break;
}
case 2:
{
output.Save(sdf.FileName, ImageFormat.Png);
break;
}
case 3:
{
output.Save(sdf.FileName, ImageFormat.Bmp);
break;
}
case 4:
{
output.Save(sdf.FileName, ImageFormat.Emf);
break;
}
case 5:
{
output.Save(sdf.FileName, ImageFormat.Exif);
break;
}
case 6:
{
output.Save(sdf.FileName, ImageFormat.Gif);
break;
}
case 7:
{
output.Save(sdf.FileName, ImageFormat.Icon);
break;
}
case 8:
{
output.Save(sdf.FileName, ImageFormat.Tiff);
break;
}
case 9:
{
output.Save(sdf.FileName, ImageFormat.Wmf);
break;
}
}
MessageBox.Show("保存成功，位置：" + sdf.FileName);
}
}
}
public class DetectionResult : ResultBase
{
/// <summary>
/// 结果处理类构造
/// </summary>
/// <param name="path">识别类别文件地址</param>
/// <param name="scales">缩放比例</param>
/// <param name="score_threshold">分数阈值</param>
/// <param name="nms_threshold">非极大值抑制阈值</param>
public DetectionResult(string path, float[] scales, float score_threshold = 0.25f, float nms_threshold = 0.5f)
{
read_class_names(path);
this.scales = scales;
this.score_threshold = score_threshold;
this.nms_threshold = nms_threshold;
}
/// <summary>
/// 结果处理
/// </summary>
/// <param name="result">模型预测输出</param>
/// <returns>模型识别结果</returns>
public Result process_result(float[] result)
{
Mat result_data = new Mat(4 + class_num, 8400, MatType.CV_32F, result);
result_data = result_data.T();
// 存放结果list
List<Rect> position_boxes = new List<Rect>();
List<int> class_ids = new List<int>();
List<float> confidences = new List<float>();
// 预处理输出结果
for (int i = 0; i < result_data.Rows; i++)
{
Mat classes_scores = result_data.Row(i).ColRange(4, 4 + class_num);
OpenCvSharp.Point max_classId_point, min_classId_point;
double max_score, min_score;
// 获取一组数据中最大值及其位置
Cv2.MinMaxLoc(classes_scores, out min_score, out max_score,
out min_classId_point, out max_classId_point);
// 置信度 0～1之间
// 获取识别框信息
if (max_score > this.score_threshold)
{
float cx = result_data.At<float>(i, 0);
float cy = result_data.At<float>(i, 1);
float ow = result_data.At<float>(i, 2);
float oh = result_data.At<float>(i, 3);
int x = (int)((cx - 0.5 * ow) * this.scales[0]);
int y = (int)((cy - 0.5 * oh) * this.scales[1]);
int width = (int)(ow * this.scales[0]);
int height = (int)(oh * this.scales[1]);
Rect box = new Rect();
box.X = x;
box.Y = y;
box.Width = width;
box.Height = height;
position_boxes.Add(box);
class_ids.Add(max_classId_point.X);
confidences.Add((float)max_score);
}
}
// NMS非极大值抑制
int[] indexes = new int[position_boxes.Count];
CvDnn.NMSBoxes(position_boxes, confidences, this.score_threshold, this.nms_threshold, out indexes);
Result re_result = new Result();
// 将识别结果绘制到图片上
for (int i = 0; i < indexes.Length; i++)
{
int index = indexes[i];
int idx = class_ids[index];
re_result.add(confidences[index], position_boxes[index], this.class_names[class_ids[index]]);
}
return re_result;
}
/// <summary>
/// 结果绘制
/// </summary>
/// <param name="result">识别结果</param>
/// <param name="image">绘制图片</param>
/// <returns></returns>
public Mat draw_result(Result result, Mat image)
{
// 将识别结果绘制到图片上
for (int i = 0; i < result.length; i++)
{
//Scalar color= Scalar.RandomColor();
Scalar color = new Scalar(0, 0, 255);
string lable = result.classes[i] + "-" + result.scores[i].ToString("0.00");
Cv2.Rectangle(image, result.rects[i], color, 2, LineTypes.Link8);
Cv2.Rectangle(image
, new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20)
, new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1 + lable.Length * 12, result.rects[i].TopLeft.Y)
, color
, -1);
Cv2.PutText(image, lable, new OpenCvSharp.Point(result.rects[i].X, result.rects[i].Y - 4), HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);
}
return image;
}
}
}
