效果
项目
代码
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 OpenCvSharp;
namespace OpenVino_Yolov8_Demo
{
public partial class Form1 : Form
{
pub服务器托管网lic Form1()
{
InitializeComponent();
}
string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
string image_path = "";
String startupPath;
DateTime dt1 = DateTime.Now;
DateTime dt2 = DateTime.Now;
String model_path;
StringBuilder sb = new StringBuilder();
Core core;
Mat image;
PoseResult result_pro;
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);
}
private void button2_Click(object sender, EventArgs e)
{
if (image_path == "")
{
return;
}
// 配置图片数据
Mat 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 * 56];
float[] factors = new float[2];
factors[0] = factors[1] = (float)(max_image_length / 640.0);
byte[] image_data = max_image.ImEncode(".bmp");
//存储byte的长度
ulong image_size = Convert.ToUInt64(image_data.Length);
// 加载推理图片数据
core.load_input_data("images", image_data, image_size, 1);
dt1 = DateTime.Now;
// 模型推理
core.infer();
dt2 = DateTime.Now;
// 读取推理结果
result_array = core.read_infer_result("output0", 8400 * 56);
result_pro = new PoseResult(factors);
Mat result_image = result_pro.draw_result(result_pro.process服务器托管网_result(result_array), image.Clone());
pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
textBox1.Text = "耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
}
private void Form1_Load(object sender, EventArgs e)
{
startupPath = System.Windows.Forms.Application.StartupPath;
model_path = startupPath + "yolov8n-pose.onnx";
core = new Core(model_path, "CPU");
}
private void Form1_FormClosing(object sender, FormClosingEventArgs e)
{
core.delet();
}
}
}
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import torch import os import numpy as np from PIL import Image def cal_mean_std(path: str): channels_sum, channels_squared_sum, n…