C# OpenVinoSharp PP-TinyPose 人体姿态识别

目录

效果

模型信息

项目

代码

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---

效果

模型信息

Inputs
-------------------------
name：image
tensor：Float[1, 3, 256, 192]
---------------------------------------------------------------

Outputs
-------------------------
name：conv2d_441.tmp_1
tensor：Float[1, 17, 64, 48]
name：argmax_0.tmp_0
tensor：Int64[1, 17]
---------------------------------------------------------------

项目

VS2022

.net framework 4.8

OpenCvSharp 4.8

openvino_2023.0.1.11005

代码

//推理模型路径中不能不含中文，否则会报错
mode_path_det = Application.StartupPath + @"\TinyPoseModel\picodet_v2_s_320_pedestrian\ir\picodet_s_320_lcnet_pedestrian.xml";
mode_path_pose = Application.StartupPath + @"\TinyPoseModel\tinypose_256_192\tinypose_256_192.onnx";

//设备名称
device_name = "CPU";
//行人区域检测
pico_det = new PicoDet(mode_path_det, device_name);
//人体姿势检测
tiny_pose = new PPTinyPose(mode_path_pose, device_name);

---

image = Cv2.ImRead(image_path);

OpenCvSharp.Size size_det = new OpenCvSharp.Size(320, 320);
pico_det.set_shape(size_det, 2125);

dt1 = DateTime.Now;
List result_rect = pico_det.predict(image);

//人体姿势检测
OpenCvSharp.Size size_pose = new OpenCvSharp.Size(256, 192);
tiny_pose.set_shape(size_pose);

List point_rects;
List person_rois = tiny_pose.get_point_roi(image, result_rect, out point_rects);

for (int p = 0; p < person_rois.Count; p++)
{
    // 关键点识别
    float[,] person_point = tiny_pose.predict(person_rois[p]);
    tiny_pose.draw_poses(person_point, point_rects[p], ref image);
}
dt2 = DateTime.Now;

for (int i = 0; i < result_rect.Count; i++)
{
    Cv2.Rectangle(image, result_rect[i], new Scalar(255, 0, 0), 2);
}
pictureBox2.Image = BitmapConverter.ToBitmap(image);
textBox1.Text = "耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";

using OpenCvSharp;
using OpenCvSharp.Extensions;
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;
 
namespace OpenVinoSharp_PP_TinyPose人体姿态识别
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }
 
        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        String startupPath;
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
        // 行人检测模型
        string mode_path_det;
        // 关键点检测模型
        string mode_path_pose;
        // 设备名称
        string device_name;
        //行人区域检测
        PicoDet pico_det;
        //人体姿势检测
        PPTinyPose tiny_pose;
        Mat image;
 
        private void button2_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 = "";
        }
 
        private void Form1_Load(object sender, EventArgs e)
        {
            startupPath = Application.StartupPath;
 
            //推理模型路径中不能不含中文，否则会报错
            mode_path_det = Application.StartupPath + @"\TinyPoseModel\picodet_v2_s_320_pedestrian\ir\picodet_s_320_lcnet_pedestrian.xml";
            mode_path_pose = Application.StartupPath + @"\TinyPoseModel\tinypose_256_192\tinypose_256_192.onnx";
 
            // 设备名称
            device_name = "CPU";
            //行人区域检测
            pico_det = new PicoDet(mode_path_det, device_name);
            //人体姿势检测
            tiny_pose = new PPTinyPose(mode_path_pose, device_name);
        }
 
 
        private void button1_Click(object sender, EventArgs e)
        {
            tiny_pose_image();
        }
 
 
        public void tiny_pose_image()
        {
            if (image_path == "")
            {
                return;
            }
 
            image = Cv2.ImRead(image_path);
 
            OpenCvSharp.Size size_det = new OpenCvSharp.Size(320, 320);
            pico_det.set_shape(size_det, 2125);
 
            dt1 = DateTime.Now;
            List<Rect> result_rect = pico_det.predict(image);
 
            //人体姿势检测
            OpenCvSharp.Size size_pose = new OpenCvSharp.Size(256, 192);
            tiny_pose.set_shape(size_pose);
 
            List<Rect> point_rects;
            List<Mat> person_rois = tiny_pose.get_point_roi(image, result_rect, out point_rects);
 
            for (int p = 0; p < person_rois.Count; p++)
            {
                // 关键点识别
                float[,] person_point = tiny_pose.predict(person_rois[p]);
                tiny_pose.draw_poses(person_point, point_rects[p], ref image);
            }
            dt2 = DateTime.Now;
 
            for (int i = 0; i < result_rect.Count; i++)
            {
                Cv2.Rectangle(image, result_rect[i], new Scalar(255, 0, 0), 2);
            }
            pictureBox2.Image = BitmapConverter.ToBitmap(image);
            textBox1.Text = "耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
        }
    }
}

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