目录
- 效果
- 模型信息
- 项目
- 代码
效果
模型信息
Inputs
-------------------------name：input.1tensor：Float[1, 3, 384, 384]---------------------------------------------------------------Outputs-------------------------name：508tensor：Float[1, 80, 96, 96]name：511tensor：Float[1, 2, 96, 96]name：514tensor：Float[1, 2, 96, 96]---------------------------------------------------------------
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.Drawing;
using System.IO;
using OpenCvSharp.Dnn;
using System.Text;
using OpenCvSharp.Flann;
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;
float confThreshold = 0.4f;
float nmsThreshold = 0.5f;
int inpWidth;
int inpHeight;
Mat image;
string model_path = "";
SessionOptions options;
InferenceSession onnx_session;
Tensor<float> input_tensor;
Tensor<float> input_tensor_scale;
List<NamedOnnxValue> input_container;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;
List<string> class_names;
int num_class;
StringBuilder sb = new StringBuilder();
float[] mean = { 0.406f, 0.456f, 0.485f };
float[] std = { 0.225f, 0.224f, 0.229f };
int num_grid_y;
int num_grid_x;
float sigmoid(float x)
{
return (float)(1.0 / (1.0 + Math.Exp(-x)));
}
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_container = 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/ctdet_coco_dlav0_384.onnx";
inpHeight = 384;
inpWidth = 384;
num_grid_y = 96;
num_grid_x = 96;
onnx_session = new InferenceSession(model_path, options);
// 创建输入容器
input_container = new List<NamedOnnxValue>();
image_path = "test_img/person.jpg";
pictureBox1.Image = new Bitmap(image_path);
class_names = new List<string>();
StreamReader sr = new StreamReader("coco.names");
string line;
while ((line = sr.ReadLine()) != null)
{
class_names.Add(line);
}
num_class = class_names.Count();
}
private unsafe void button2_Click(object sender, EventphpArgs e)
{
if (image_path == "")
{
return;
}
textBox1.Text = "检测中，请稍等……";
pictureBox2.Image = null;
sb.Clear();
System.Windows.Forms.Application.DoEvents();
image =python new Mat(image_path);
//-----------------前处理--------------------------
Mat dstimg = new Mat();
Cv2.CvtColor(image, dstimg, ColorConversionCodes.BGR2RGB);
Cv2.Resize(dstimg, dstimg, new OpenCvSharp.Size(inpWidth, inpHeight));
Mat[] mv = new Mat[3];
Cv2.Split(dstimg, oupythont mv);
for (int i = 0; i < mv.Length; i++)
{
mv[i].ConvertTo(mv[i], MatType.CV_32FC1, 1.0 / (255.0 * std[i]), (0.0 - mean[i]) / std[i]);
}
Cv2.Merge(mv, dstimg);
int row = dstimg.Rows;
int col = dstimg.Cols;
float[] input_tensor_data = new float[1 * 3 * row * col];
for (int c = 0; c < 3; c++)
{
for (int i = 0; i < row; i++)
{
for (int j = 0; j < col; j++)
{
float pix = ((float*)(dstimg.Ptr(i).ToPointer()))[j * 3 + c];
input_tensor_data[c * row * col + i * col + j] = pix;
}
}
}
input_tensor = new DenseTensor<float>(input_tensor_data, new[] { 1, 3, inpHeight, inpWidth });
input_container.Add(NamedOnnxValue.CreateFromTensor("input.1", input_tensor));
//-----------------推理--------------------------
dt1 = DateTime.Now;
result_infer = onnx_session.Run(input_container);//运行 Inference 并获取结果
dt2 = DateTime.Now;
//-----------------后处理--------------------------
results_onnxvalue = result_infer.ToArray();
float ratioh = (float)image.Rows / inpHeight;
float ratiow = (float)image.Cols / inpWidth;
float stride = inpHeight / num_grid_y;
float[] pscore = results_onnxvalue[0].AsTensor<float>().ToArray();
float[] pxy = results_onnxvalue[1].AsTensor<float>().ToArray();
float[] pwh = results_onnxvalue[2].AsTensor<float>().ToArray();
int area = num_grid_y * num_grid_x;
List<float> confidences = new List<float>();
List<Rect> posipythontion_boxes = new List<Rect>();
uTwom List<int> class_ids = new List<int>();
Result result = new Result();
for (int i = 0; i < num_grid_y; i++)
{
for (int j = 0; j < num_grid_x; j++)
{
float max_class_score = -1000;
int class_id = -1;
for (int c = 0; c < num_class; c++)
{
float score = sigmoid(pscore[c * area + i * num_grid_x + j]);
if (score > max_class_score)
{
max_class_score = score;
class_id = c;
}
}
if (max_class_score > confThreshold)
{
float cx = (pxy[i * num_grid_x + j] + j) * stride * ratiow; ///cx
float cy = (pxy[area + i * num_grid_x + j] + i) * stride * ratioh; ///cy
float w = pwh[i * num_grid_x + j] * stride * ratiow; ///w
float h = pwh[area + i * num_grid_x + j] * stride * ratioh; ///h
int x = (int)Math.Max(cx - 0.5 * w, 0);
int y = (int)Math.Max(cy - 0.5 * h, 0);
int width = (int)Math.Min(w, image.Cols - 1);
int height = (int)Math.Min(h, image.Rows - 1);
position_boxes.Add(new Rect(x, y, width, height));
class_ids.Add(class_id);
confidences.Add(max_class_score);
}
}
}
// NMS非极大值抑制
int[] indexes = new int[position_boxes.Count];
CvDnn.NMSBoxes(position_boxes, confidences, confThreshold, nmsThreshold, out indexes);
for (int i = 0; i < indexes.Length; i++)
{
int index = indexes[i];
result.add(confidences[index], position_boxes[index], class_names[class_ids[index]]);
}
if (pictureBox2.Image != null)
{
pictureBox2.Image.Dispose();
}
sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
sb.AppendLine("------------------------------");
// 将识别结果绘制到图片上
Mat result_image = image.Clone();
for (int i = 0; i < result.length; i++)
{
Cv2.Rectangle(result_image, result.rects[i], new Scalar(0, 0, 255), 2, LineTypes.Link8);
Cv2.Rectangle(result_image, new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20),
new OpenCvSharp.Point(result.rects[i].BottomRight.X, result.rects[i].TopLeft.Y), new Scalar(0, 0, 255), -1);
Cv2.PutText(result_image, result.classes[i] + "-" + result.scores[i].ToString("0.00"),
new OpenCvSharp.Point(result.rects[i].X, result.rects[i].Y - 4),
HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);
sb.AppendLine(string.Format("{0}：{1},({2},{3},{4},{5})"
, result.classes[i]
, result.scores[i].ToString("0.00")
, result.rects[i].TopLeft.X
, result.rects[i].TopLeft.Y
, result.rects[i].BottomRight.X
, result.rects[i].BottomRight.Y
));
}
textBox1.Text = sb.ToString();
pictureBox2.Image = new System.Drawing.Bitmap(result_image.ToMemoryStream());
result_image.Dispose();
dstimg.Dispose();
image.Dispose();
}
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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