C# OpenVino Yolov8 Pose

效果

模型信息

项目

代码

下载

效果

模型信息

Model Properties
-------------------------
date：2023-09-07T17:11:43.091306
description：Ultralytics YOLOv8n-pose model trained on /usr/src/app/ultralytics/datasets/coco-pose.yaml
author：Ultralytics
kpt_shape：[17, 3]
task：pose
license：AGPL-3.0 https://ultralytics.com/license
version：8.0.172
stride：32
batch：1
imgsz：[640, 640]
names：{0: 'person'}
---------------------------------------------------------------

Inputs
-------------------------
name：images
tensor：Float[1, 3, 640, 640]
---------------------------------------------------------------

Outputs
-------------------------
name：output0
tensor：Float[1, 56, 8400]
---------------------------------------------------------------

项目

代码

using OpenCvSharp;
using Sdcb.OpenVINO;
using System;
using System.Diagnostics;
using System.Drawing;
using System.Text;
using System.Windows.Forms;

namespace OpenVino_Yolov8_Pose
{
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
}

string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
string image_path = "";
string model_path;
string classer_path;

Mat src;

StringBuilder sb = new StringBuilder();
PoseResult result_pro;
Result result;

CompiledModel cm;
InferRequest ir;
Shape inputShape;

private void Form1_Load(object sender, EventArgs e)
{
model_path = "model\\yolov8n-pose.onnx";
classer_path = "model\\lable.txt";

Model rawModel = OVCore.Shared.ReadModel(model_path);

var ad = OVCore.Shared.AvailableDevices;
Console.WriteLine("可用设备");
foreach (var item in ad)
{
Console.WriteLine(item);
}

cm = OVCore.Shared.CompileModel(rawModel, "CPU");
ir = cm.CreateInferRequest();
inputShape = cm.Inputs.Primary.Shape;

image_path = "test_img\\demo_2.jpg";
pictureBox1.Image = new Bitmap(image_path);
textBox1.Text = "";
src = new Mat(image_path);
pictureBox2.Image = null;

result_pro = new PoseResult();
}

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 = "";
src = new Mat(image_path);
pictureBox2.Image = null;
}

unsafe private void button2_Click(object sender, EventArgs e)
{
if (pictureBox1.Image == null)
{
return;
}

pictureBox2.Image = null;
textBox1.Text = "";
sb.Clear();
button2.Enabled = false;
Application.DoEvents();

Stopwatch stopwatch = new Stopwatch();

//图片缩放
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));

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));

float[] factors = new float[2];
factors[0] = factors[1] = (float)(max_image_length / 640.0);
result_pro.scales = factors;

resize_image.ConvertTo(resize_image, MatType.CV_32FC3, 1.0 / 255);

float[] input_tensor_data = Common.ExtractMat(resize_image);

resize_image.Dispose();
image_rgb.Dispose();

using (Tensor input_x = Tensor.FromArray(input_tensor_data, new Shape(1, 3, 640, 640)))
{
ir.Inputs[0] = input_x;
}

double preprocessTime = stopwatch.Elapsed.TotalMilliseconds;
stopwatch.Restart();

ir.Run();
double inferTime = stopwatch.Elapsed.TotalMilliseconds;
stopwatch.Restart();

using (Tensor output = ir.Outputs[0])
{
float[] result_array = output.GetData<float>().ToArray();

result = result_pro.process_result(result_array);

double postprocessTime = stopwatch.Elapsed.TotalMilliseconds;
stopwatch.Stop();
double totalTime = preprocessTime + inferTime + postprocessTime;

Mat result_image = result_pro.draw_result(result, image.Clone());

sb.AppendLine($"Preprocess: {preprocessTime:F2}ms");
sb.AppendLine($"Infer: {inferTime:F2}ms");
sb.AppendLine($"Postprocess: {postprocessTime:F2}ms");
sb.AppendLine($"Total: {totalTime:F2}ms");
sb.AppendLine("---------------------------------------");

for (int i = 0; i < result.length; i++)
{
sb.AppendLine("person " + result.scores[i].ToString("P2"));
}

if (pictureBox2.Image != null)
{
pictureBox2.Image.Dispose();
}

pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
textBox1.Text = sb.ToString();
}

image.Dispose();
button2.Enabled = true;
}

}
}

using OpenCvSharp;
using Sdcb.OpenVINO;
using System;
using System.Diagnostics;
using System.Drawing;
using System.Text;
using System.Windows.Forms;namespace OpenVino_Yolov8_Pose
{public partial class Form1 : Form{public Form1(){InitializeComponent();}string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";string image_path = "";string model_path;string classer_path;Mat src;StringBuilder sb = new StringBuilder();PoseResult result_pro;Result result;CompiledModel cm;InferRequest ir;Shape inputShape;private void Form1_Load(object sender, EventArgs e){model_path = "model\\yolov8n-pose.onnx";classer_path = "model\\lable.txt";Model rawModel = OVCore.Shared.ReadModel(model_path);var ad = OVCore.Shared.AvailableDevices;Console.WriteLine("可用设备");foreach (var item in ad){Console.WriteLine(item);}cm = OVCore.Shared.CompileModel(rawModel, "CPU");ir = cm.CreateInferRequest();inputShape = cm.Inputs.Primary.Shape;image_path = "test_img\\demo_2.jpg";pictureBox1.Image = new Bitmap(image_path);textBox1.Text = "";src = new Mat(image_path);pictureBox2.Image = null;result_pro = new PoseResult();}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 = "";src = new Mat(image_path);pictureBox2.Image = null;}unsafe private void button2_Click(object sender, EventArgs e){if (pictureBox1.Image == null){return;}pictureBox2.Image = null;textBox1.Text = "";sb.Clear();button2.Enabled = false;Application.DoEvents();Stopwatch stopwatch = new Stopwatch();//图片缩放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));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));float[] factors = new float[2];factors[0] = factors[1] = (float)(max_image_length / 640.0);result_pro.scales = factors;resize_image.ConvertTo(resize_image, MatType.CV_32FC3, 1.0 / 255);float[] input_tensor_data = Common.ExtractMat(resize_image);resize_image.Dispose();image_rgb.Dispose();using (Tensor input_x = Tensor.FromArray(input_tensor_data, new Shape(1, 3, 640, 640))){ir.Inputs[0] = input_x;}double preprocessTime = stopwatch.Elapsed.TotalMilliseconds;stopwatch.Restart();ir.Run();double inferTime = stopwatch.Elapsed.TotalMilliseconds;stopwatch.Restart();using (Tensor output = ir.Outputs[0]){float[] result_array = output.GetData<float>().ToArray();result = result_pro.process_result(result_array);double postprocessTime = stopwatch.Elapsed.TotalMilliseconds;stopwatch.Stop();double totalTime = preprocessTime + inferTime + postprocessTime;Mat result_image = result_pro.draw_result(result, image.Clone());sb.AppendLine($"Preprocess: {preprocessTime:F2}ms");sb.AppendLine($"Infer: {inferTime:F2}ms");sb.AppendLine($"Postprocess: {postprocessTime:F2}ms");sb.AppendLine($"Total: {totalTime:F2}ms");sb.AppendLine("---------------------------------------");for (int i = 0; i < result.length; i++){sb.AppendLine("person " + result.scores[i].ToString("P2"));}if (pictureBox2.Image != null){pictureBox2.Image.Dispose();}pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());textBox1.Text = sb.ToString();}image.Dispose();button2.Enabled = true;}}
}