cs
复制代码
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;
namespace Onnx_Demo
{
public partial class frmMain : Form
{
public frmMain()
{
InitializeComponent();
}
string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
string image_path = "";
string startupPath;
string model_path;
DateTime dt1 = DateTime.Now;
DateTime dt2 = DateTime.Now;
Mat image;
Mat result_image;
SessionOptions options;
InferenceSession onnx_session;
Tensor<float> input_tensor;
List<NamedOnnxValue> input_ontainer;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;
StringBuilder sb = new StringBuilder();
float confThreshold = 0.5f;
float[] mean = { 0.485f, 0.456f, 0.406f };
float[] std = { 0.229f, 0.224f, 0.225f };
private void button1_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = fileFilter;
if (ofd.ShowDialog() != DialogResult.OK) return;
pictureBox1.Image = null;
pictureBox2.Image = null;
textBox1.Text = "";
image_path = ofd.FileName;
pictureBox1.Image = new Bitmap(image_path);
image = new Mat(image_path);
}
private void Form1_Load(object sender, EventArgs e)
{
startupPath = Application.StartupPath + "\\model\\";
model_path = startupPath + "SHTechA.onnx";
// 创建输出会话
options = new SessionOptions();
options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行
// 创建推理模型类,读取本地模型文件
onnx_session = new InferenceSession(model_path, options);
// 创建输入容器
input_ontainer = new List<NamedOnnxValue>();
}
private void button2_Click(object sender, EventArgs e)
{
if (image_path == "")
{
return;
}
textBox1.Text = "检测中,请稍等......";
pictureBox2.Image = null;
Application.DoEvents();
//图片
image = new Mat(image_path);
//将图片转为RGB通道
Mat image_rgb = new Mat();
Cv2.CvtColor(image, image_rgb, ColorConversionCodes.BGR2RGB);
Mat resize_image = new Mat();
int srch = image.Rows, srcw = image.Cols;
int new_width = srcw / 128 * 128;
int new_height = srch / 128 * 128;
// 输入Tensor
input_tensor = new DenseTensor<float>(new[] { 1, 3, new_height, new_width });
Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(new_width, new_height));
//输入Tensor
for (int y = 0; y < resize_image.Height; y++)
{
for (int x = 0; x < resize_image.Width; x++)
{
input_tensor[0, 0, y, x] = (resize_image.At<Vec3b>(y, x)[0] / 255f - mean[0]) / std[0];
input_tensor[0, 1, y, x] = (resize_image.At<Vec3b>(y, x)[1] / 255f - mean[1]) / std[1];
input_tensor[0, 2, y, x] = (resize_image.At<Vec3b>(y, x)[2] / 255f - mean[2]) / std[2];
}
}
//将 input_tensor 放入一个输入参数的容器,并指定名称
input_ontainer.Add(NamedOnnxValue.CreateFromTensor("input", 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();
List<int> pyramid_levels = new List<int>(1) { 3 };
List<float> all_anchor_points = new List<float>();
Common.generate_anchor_points(resize_image.Cols, resize_image.Rows, pyramid_levels, 2, 2, all_anchor_points);
var pscore = results_onnxvalue[0].AsTensor<float>().ToArray();
var pcoord = results_onnxvalue[1].AsTensor<float>().ToArray();
int num_proposal = pscore.Length;
List<CrowdPoint> crowd_points = new List<CrowdPoint>();
for (int i = 0; i < num_proposal; i++)
{
if (pscore[i] >= confThreshold)
{
float x = (pcoord[i] + all_anchor_points[i * 2]) / (float)resize_image.Width * (float)image.Width;
float y = (pcoord[i + 1] + all_anchor_points[i * 2 + 1]) / (float)resize_image.Height * (float)image.Height;
crowd_points.Add(new CrowdPoint(new OpenCvSharp.Point(x, y), pscore[i]));
}
}
result_image = image.Clone();
sb.Clear();
sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
sb.AppendLine("------------------------------");
sb.AppendLine("人数:" + crowd_points.Count);
for (int i = 0; i < crowd_points.Count; i++)
{
Cv2.Circle(result_image, crowd_points[i].pt.X, crowd_points[i].pt.Y, 2, new Scalar(0, 0, 255), -1);
//Cv2.PutText(result_image, (i+1).ToString()+"-" + crowd_points[i].prob.ToString("0.00"), crowd_points[i].pt, HersheyFonts.HersheySimplex, 1.0, new Scalar(0, 255, 0), 2); ;
sb.AppendLine((i + 1).ToString() + "-" + crowd_points[i].prob.ToString("0.00"));
}
pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
textBox1.Text = sb.ToString();
}
private void pictureBox2_DoubleClick(object sender, EventArgs e)
{
Common.ShowNormalImg(pictureBox2.Image);
}
private void pictureBox1_DoubleClick(object sender, EventArgs e)
{
Common.ShowNormalImg(pictureBox1.Image);
}
}
}