目录
效果
模型信息
项目
代码
下载
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<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";
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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