效果
项目
代码
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;
using static System.Net.Mime.MediaTypeNames;
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;
SegmentationResult result_pro;
Mat result_image;
SessionOptions options;
InferenceSession onnx_session;
Tensor input_tensor;
List input_ontainer;
IDisposableReadOnlyCollection result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;
Tensor result_tensors_det;
Tensor result_tensors_proto;
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;
}
// 配置图片数据
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[] det_result_array = new float[8400 * 116];
float[] proto_result_array = new float[32 * 160 * 160];
float[] factors = new float[4];
factors[0] = factors[1] = (float)(max_image_length / 640.0);
factors[2] = image.Rows;
factors[3] = image.Cols;
// 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
// input_tensor = new DenseTensor(new[] { 1, 3, 640, 640 });
for (int y = 0; y (y, x)[0] / 255f;
input_tensor[0, 1, y, x] = resize_image.At(y, x)[1] / 255f;
input_tensor[0, 2, y, x] = resize_image.At(y, x)[2] / 255f;
}
}
//将 input_tensor 放入一个输入参数的容器,并指定名称
input_ontainer.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));
dt1 = DateTime.Now;
//运行 Inference 并获取结果
result_infer = onnx_session.Run(input_ontainer);
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_det = results_onnxvalue[0].AsTensor();
result_tensors_proto = results_onnxvalue[1].AsTensor();
det_result_array = result_tensors_det.ToArray();
proto_result_array = result_tensors_proto.ToArray(服务器托管网);
resize_image.Dispose();
image_rgb.Dispose();
result_pro = new SegmentationResult(classer_path, factors);
result_image = result_pro.draw_result(result_pro.process_result(det_result_array, proto_result_array), image.Clone());
if (!result_image.Empty())
{
pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
}
else
{
textBox1.Text = "无信息";
}
}
private void Form1_Load(object sender, EventArgs e)
{
startupPath = System.Windows.Forms.Application.StartupPath;
model_path = startupPath + "yolov8n-seg.onnx";
classer_path = startupPath + "yolov8-detect-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;
// 设置为CPU上运行
options服务器托管网.AppendExecutionProvider_CPU(0);
// 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(new[] { 1, 3, 640, 640 });
// 创建输入容器
input_ontainer = new List();
}
}
}
完整Demo下载
exe程序下载
服务器托管,北京服务器托管,服务器租用 http://www.fwqtg.net
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