效果
项目
代码
using Microsoft.ML.OnnxRuntime.Tensors;
using Microsoft.ML.OnnxRuntime;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Windows.Forms;
using System.Linq;
using System.Numerics;
namespace Onnx_Demo
{
public partial class frmMain : Form
{
public frmMain()
{
InitializeComponent();
}
string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
string image_path = "";
DateTime dt1 = DateTime.Now;
DateTime dt2 = DateTime.Now;
Mat image;
string model_path = "";
float[] factors = new float[2];
SessionOptions options;
InferenceSession onnx_session;
Tensor<float> input_tensor;
List<NamedOnnxValue> input_ontainer;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;
Tensor<float> result_tensors;
float[] result_array;
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 System.Drawing.Bitmap(image_path);
image = new Mat(image_path);
}
private void Form1_Load(object sender, EventArgs e)
{
// 创建输入容器
input_ontainer = new List<NamedOnnxValue>();
// 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
model_path = "model/directmhp_cmu_m_post_640x640.onnx";
onnx_session = new InferenceSession(model_path, options);
// 输入Tensor
input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
// 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();
//图片缩放
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));
factors[0] = factors[1] = (float)(max_image_length / 640.0);
//将图片转为RGB通道
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));
//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;
input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1] / 255f;
input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2] / 255f;
}
}
resize_image.Dispose();
image_rgb.Dispose();
//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();
//读取第一个节点输出并转为Tensor数据
result_tensors = results_onnxvalue[0].AsTensor<float>();
result_array = result_tensors.ToArray();
int num_face = result_tensors.Dimensions[0];
int len = result_tensors.Dimensions[1];
List<BoxInfo> faceboxes = new List<BoxInfo>();
float scale_h = factors[0];
float scale_w = factors[1];
float confThreshold = 0.5f;
for (int i = 0; i < num_face; i++)
{
float score = result_array[i * len + 6];
if (score > confThreshold)
{
float xmin = Math.Max(result_array[i * len + 2] * scale_w, 0f);
float ymin = Math.Max(result_array[i * len + 3] * scale_h, 0f);
float xmax = Math.Min(result_array[i * len + 4] * scale_w, (float)image.Cols);
float ymax = Math.Min(result_array[i * len + 5] * scale_h, (float)image.Rows);
faceboxes.Add(new BoxInfo(xmin, ymin, xmax, ymax, score, result_array[i * len + 7], result_array[i * len + 8], result_array[i * len + 9]));
}
}
Mat result_image = image.Clone();
foreach (BoxInfo item in faceboxes)
{
Cv2.Rectangle(result_image, new OpenCvSharp.Point(item.xmin, item.ymin), new OpenCvSharp.Point(item.xmax, item.ymax), new Scalar(0, 0, 255), 2);
float pitch = (float)(item.pitch * Math.PI / 180);
float yaw = (float)(-item.yaw * Math.PI / 180);
float roll = (float)(item.roll * Math.PI / 180);
float tdx = (float)((item.xmin + item.xmax) * 0.5);
float tdy = (float)((item.ymin + item.ymax) * 0.5);
int size_ = (int)(Math.Floor(item.xmax - item.xmin) / 3);
//X - Axis pointing to right.drawn in red
float x1 = (float)(size_ * (Math.Cos(yaw) * Math.Cos(roll)) + tdx);
float y1 = (float)(size_ * (Math.Cos(pitch) * Math.Sin(roll) + Math.Cos(roll) * Math.Sin(pitch) * Math.Sin(yaw)) + tdy);
//Y-Axis | drawn in green
float x2 = (float)(size_ * (-Math.Cos(yaw) * Math.Sin(roll)) + tdx);
float y2 = (float)(size_ * (Math.Cos(pitch) * Math.Cos(roll) - Math.Sin(pitch) * Math.Sin(yaw) * Math.Sin(roll)) + tdy);
//Z-Axis (out of the screen) drawn in blue
float x3 = (float)(size_ * (Math.Sin(yaw)) + tdx);
float y3 = (float)(size_ * (-Math.Cos(yaw) * Math.Sin(pitch)) + tdy);
Cv2.Line(result_image, new OpenCvSharp.Point(tdx, tdy), new OpenCvSharp.Point(x1, y1), new Scalar(0, 0, 255), 2);
Cv2.Line(result_image, new OpenCvSharp.Point(tdx, tdy), new OpenCvSharp.Point(x2, y2), new Scalar(0, 255, 0), 2);
Cv2.Line(result_image, new OpenCvSharp.Point(tdx, tdy), new OpenCvSharp.Point(x3, y3), new Scalar(255, 0, 0), 2);
}
pictureBox2.Image = new System.Drawing.Bitmap(result_image.ToMemoryStream());
textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
}
private void pictureBox2_DoubleClick(object sender, EventArgs e)
{
Common.ShowNormalImg(pictureBox2.Image);
}
private void pictureBox1_DoubleClick(object sender, EventArgs e)
{
Common.ShowNormalImg(pictureBox1.Image);
}
}
}
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