C# Onnx DirectMHP 全范围角度2D多人头部姿势估计

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Tensorinput_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Tensorfor (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 redfloat 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 greenfloat 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 bluefloat 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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