說(shuō)明
官網(wǎng)地址：
https://github.com/ultralytics/ultralytics
效果
項(xiàng)目
模型信息
Model Properties
-------------------------
date：2024-10-06T16:52:12.968917
description：Ultralytics YOLO11n model trained on /usr/src/ultralytics/ultralytics/cfg/datasets/coco.yaml
author：Ultralytics
version：8.3.5
task：detect
license：AGPL-3.0 License (https:///license)
docs：https://docs.
stride：32
batch：1
imgsz：[640, 640]
names：{0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
---------------------------------------------------------------
Inputs
-------------------------
name：images
tensor：Float[1, 3, 640, 640]
---------------------------------------------------------------
Outputs
-------------------------
name：output0
tensor：Float[1, 84, 8400]
---------------------------------------------------------------
代碼
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Imaging;
using System.IO;
using System.Linq;
using System.Text;
using System.Windows.Forms;
namespace Onnx_Demo
{
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
}
string fileFilter = '*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png';
string image_path = '';
string model_path;
string classer_path;
public string[] class_names;
public int class_num;
DateTime dt1 = DateTime.Now;
DateTime dt2 = DateTime.Now;
int input_height;
int input_width;
float ratio_height;
float ratio_width;
InferenceSession onnx_session;
int box_num;
float conf_threshold;
float nms_threshold;
/// <summary>
/// 選擇圖片
/// </summary>
/// <param name='sender'></param>
/// <param name='e'></param>
private void button1_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 = '';
pictureBox2.Image = null;
}
/// <summary>
/// 推理
/// </summary>
/// <param name='sender'></param>
/// <param name='e'></param>
private void button2_Click(object sender, EventArgs e)
{
if (image_path == '')
{
return;
}
button2.Enabled = false;
pictureBox2.Image = null;
textBox1.Text = '';
Application.DoEvents();
Mat image = new Mat(image_path);
//圖片縮放
int height = image.Rows;
int width = image.Cols;
Mat temp_image = image.Clone();
if (height > input_height || width > input_width)
{
float scale = Math.Min((float)input_height / height, (float)input_width / width);
OpenCvSharp.Size new_size = new OpenCvSharp.Size((int)(width * scale), (int)(height * scale));
Cv2.Resize(image, temp_image, new_size);
}
ratio_height = (float)height / temp_image.Rows;
ratio_width = (float)width / temp_image.Cols;
Mat input_img = new Mat();
Cv2.CopyMakeBorder(temp_image, input_img, 0, input_height - temp_image.Rows, 0, input_width - temp_image.Cols, BorderTypes.Constant, 0);
//Cv2.ImShow('input_img', input_img);
//輸入Tensor
Tensor<float> input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
for (int y = 0; y < input_img.Height; y++)
{
for (int x = 0; x < input_img.Width; x++)
{
input_tensor[0, 0, y, x] = input_img.At<Vec3b>(y, x)[0] / 255f;
input_tensor[0, 1, y, x] = input_img.At<Vec3b>(y, x)[1] / 255f;
input_tensor[0, 2, y, x] = input_img.At<Vec3b>(y, x)[2] / 255f;
}
}
List<NamedOnnxValue> input_container = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor('images', input_tensor)
};
//推理
dt1 = DateTime.Now;
var ort_outputs = onnx_session.Run(input_container).ToArray();
dt2 = DateTime.Now;
float[] data = Transpose(ort_outputs[0].AsTensor<float>().ToArray(), 4 + class_num, box_num);
float[] confidenceInfo = new float[class_num];
float[] rectData = new float[4];
List<DetectionResult> detResults = new List<DetectionResult>();
for (int i = 0; i < box_num; i++)
{
Array.Copy(data, i * (class_num + 4), rectData, 0, 4);
Array.Copy(data, i * (class_num + 4) + 4, confidenceInfo, 0, class_num);
float score = confidenceInfo.Max(); // 獲取最大值
int maxIndex = Array.IndexOf(confidenceInfo, score); // 獲取最大值的位置
int _centerX = (int)(rectData[0] * ratio_width);
int _centerY = (int)(rectData[1] * ratio_height);
int _width = (int)(rectData[2] * ratio_width);
int _height = (int)(rectData[3] * ratio_height);
detResults.Add(new DetectionResult(
maxIndex,
class_names[maxIndex],
new Rect(_centerX - _width / 2, _centerY - _height / 2, _width, _height),
score));
}
//NMS
CvDnn.NMSBoxes(detResults.Select(x => x.Rect), detResults.Select(x => x.Confidence), conf_threshold, nms_threshold, out int[] indices);
detResults = detResults.Where((x, index) => indices.Contains(index)).ToList();
//繪制結(jié)果
Mat result_image = image.Clone();
foreach (DetectionResult r in detResults)
{
Cv2.PutText(result_image, $'{r.Class}:{r.Confidence:P0}', new OpenCvSharp.Point(r.Rect.TopLeft.X, r.Rect.TopLeft.Y - 10), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
Cv2.Rectangle(result_image, r.Rect, Scalar.Red, thickness: 2);
}
pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
textBox1.Text = '推理耗時(shí):' + (dt2 - dt1).TotalMilliseconds + 'ms';
button2.Enabled = true;
}
/// <summary>
///窗體加載
/// </summary>
/// <param name='sender'></param>
/// <param name='e'></param>
private void Form1_Load(object sender, EventArgs e)
{
model_path = 'model/yolo11n.onnx';
//創(chuàng)建輸出會(huì)話，用于輸出模型讀取信息
SessionOptions options = new SessionOptions();
options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
options.AppendExecutionProvider_CPU(0);// 設(shè)置為CPU上運(yùn)行
// 創(chuàng)建推理模型類，讀取模型文件
onnx_session = new InferenceSession(model_path, options);//model_path 為onnx模型文件的路徑
input_height = 640;
input_width = 640;
box_num = 8400;
conf_threshold = 0.25f;
nms_threshold = 0.5f;
classer_path = 'model/lable.txt';
class_names = File.ReadAllLines(classer_path, Encoding.UTF8);
class_num = class_names.Length;
image_path = 'test_img/zidane.jpg';
pictureBox1.Image = new Bitmap(image_path);
}
/// <summary>
/// 保存
/// </summary>
/// <param name='sender'></param>
/// <param name='e'></param>
private void button3_Click(object sender, EventArgs e)
{
if (pictureBox2.Image == null)
{
return;
}
Bitmap output = new Bitmap(pictureBox2.Image);
SaveFileDialog sdf = new SaveFileDialog();
sdf.Title = '保存';
sdf.Filter = 'Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf';
if (sdf.ShowDialog() == DialogResult.OK)
{
switch (sdf.FilterIndex)
{
case 1:
{
output.Save(sdf.FileName, ImageFormat.Jpeg);
break;
}
case 2:
{
output.Save(sdf.FileName, ImageFormat.Png);
break;
}
case 3:
{
output.Save(sdf.FileName, ImageFormat.Bmp);
break;
}
case 4:
{
output.Save(sdf.FileName, ImageFormat.Emf);
break;
}
case 5:
{
output.Save(sdf.FileName, ImageFormat.Exif);
break;
}
case 6:
{
output.Save(sdf.FileName, ImageFormat.Gif);
break;
}
case 7:
{
output.Save(sdf.FileName, ImageFormat.Icon);
break;
}
case 8:
{
output.Save(sdf.FileName, ImageFormat.Tiff);
break;
}
case 9:
{
output.Save(sdf.FileName, ImageFormat.Wmf);
break;
}
}
MessageBox.Show('保存成功，位置：' + sdf.FileName);
}
}
private void pictureBox1_DoubleClick(object sender, EventArgs e)
{
ShowNormalImg(pictureBox1.Image);
}
private void pictureBox2_DoubleClick(object sender, EventArgs e)
{
ShowNormalImg(pictureBox2.Image);
}
public void ShowNormalImg(Image img)
{
if (img == null) return;
frmShow frm = new frmShow();
frm.Width = Screen.PrimaryScreen.Bounds.Width;
frm.Height = Screen.PrimaryScreen.Bounds.Height;
if (frm.Width > img.Width)
{
frm.Width = img.Width;
}
if (frm.Height > img.Height)
{
frm.Height = img.Height;
}
bool b = frm.richTextBox1.ReadOnly;
Clipboard.SetDataObject(img, true);
frm.richTextBox1.ReadOnly = false;
frm.richTextBox1.Paste(DataFormats.GetFormat(DataFormats.Bitmap));
frm.richTextBox1.ReadOnly = b;
frm.ShowDialog();
}
public unsafe float[] Transpose(float[] tensorData, int rows, int cols)
{
float[] transposedTensorData = new float[tensorData.Length];
fixed (float* pTensorData = tensorData)
{
fixed (float* pTransposedData = transposedTensorData)
{
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
int index = i * cols + j;
int transposedIndex = j * rows + i;
pTransposedData[transposedIndex] = pTensorData[index];
}
}
}
}
return transposedTensorData;
}
}
public class DetectionResult
{
public DetectionResult(int ClassId, string Class, Rect Rect, float Confidence)
{
this.ClassId = ClassId;
this.Confidence = Confidence;
this.Rect = Rect;
this.Class = Class;
}
public string Class { get; set; }
public int ClassId { get; set; }
public float Confidence { get; set; }
public Rect Rect { get; set; }
}
}
