效果
模型信息
Model Properties
-------------------------
author：Ultralytics
version：8.1.29
task：detect
license：AGPL-3.0 License (https://ultralytics.com/license)
docs：https://docs.ultralytics.com
stride：32
batch：1
imgsz：[640, 640]
names：{0: 'carton'}
---------------------------------------------------------------
Inputs
-------------------------
name：images
tensor：Float[1, 3, 640, 640]
---------------------------------------------------------------
Outputs
-------------------------
name：output0
tensor：Float[1, 5, 8400]
---------------------------------------------------------------
项目
数据集
代码
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.Linq
using System.Text
using System.Windows.Forms
namespace Onnx_Yolov8_Demo
{
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent()
}
string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png"
string image_path = ""
string startupPath
string classer_path
DateTime dt1 = DateTime.Now
DateTime dt2 = DateTime.Now
string model_path
Mat image
DetectionResult result_pro
Mat result_image
Result result
SessionOptions options
InferenceSession onnx_session
Tensor<float> input_tensor
List<NamedOnnxValue> input_container
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer
DisposableNamedOnnxValue[] results_onnxvalue
Tensor<float> result_tensors
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 = ""
image = new Mat(image_path)
pictureBox2.Image = null
}
private void button2_Click(object sender, EventArgs e)
{
if (image_path == "")
{
return
}
float score_threshold = 0.5f
float nms_threshold = 0.5f
button2.Enabled = false
pictureBox2.Image = null
textBox1.Text = ""
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))
float[] result_array = new float[8400 * 84]
float[] factors = new float[2]
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
{
for (int x = 0
{
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
}
}
//将 input_tensor 放入一个输入参数的容器，并指定名称
input_container.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor))
dt1 = DateTime.Now
//运行 Inference 并获取结果
result_infer = onnx_session.Run(input_container)
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()
resize_image.Dispose()
image_rgb.Dispose()
result_pro = new DetectionResult(classer_path, factors, score_threshold, nms_threshold)
result = result_pro.process_result(result_array)
result_image = result_pro.draw_result(result, image.Clone())
StringBuilder sb = new StringBuilder()
if (!result_image.Empty())
{
pictureBox2.Image = new Bitmap(result_image.ToMemoryStream())
//textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms"
sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms")
sb.AppendLine("--------------------------------------------------")
sb.AppendLine("{lable}{scores}({X},{Y},{Width},{Height})")
sb.AppendLine("--------------------------------------------------")
// 识别结果
for (int i = 0
{
//Scalar color= Scalar.RandomColor()
Scalar color = new Scalar(0, 0, 255)
string lable = string.Format("{0}\t{1}\t({2},{3},{4},{5})"
, result.classes[i]
, result.scores[i].ToString("P2")
, result.rects[i].X
, result.rects[i].Y
, result.rects[i].Width
, result.rects[i].Height
)
sb.AppendLine(lable)
//Cv2.Rectangle(image, result.rects[i], color, 2, LineTypes.Link8)
//Cv2.Rectangle(image
// , new Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20)
// , new Point(result.rects[i].TopLeft.X - 1 + lable.Length * 12, result.rects[i].TopLeft.Y)
// , color
// , -1)
//Cv2.PutText(image, lable, new Point(result.rects[i].X, result.rects[i].Y - 4), HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1)
}
textBox1.Text = sb.ToString()
}
else
{
textBox1.Text = "无信息"
}
button2.Enabled = true
}
private void Form1_Load(object sender, EventArgs e)
{
startupPath = System.Windows.Forms.Application.StartupPath
model_path = "model/carton.onnx"
classer_path = "model/lable.txt"
// 创建输出会话，用于输出模型读取信息
options = new SessionOptions()
options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO
options.AppendExecutionProvider_CPU(0)
// 创建推理模型类，读取本地模型文件
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_container = new List<NamedOnnxValue>()
image_path = "test_img/4.jpg"
pictureBox1.Image = new Bitmap(image_path)
image = new Mat(image_path)
}
private void pictureBox1_DoubleClick(object sender, EventArgs e)
{
Common.ShowNormalImg(pictureBox1.Image)
}
private void pictureBox2_DoubleClick(object sender, EventArgs e)
{
Common.ShowNormalImg(pictureBox2.Image)
}
SaveFileDialog sdf = new SaveFileDialog()
private void button3_Click(object sender, EventArgs e)
{
if (pictureBox2.Image == null)
{
return
}
Bitmap output = new Bitmap(pictureBox2.Image)
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)
}
}
}
public class DetectionResult : ResultBase
{
/// <summary>
/// 结果处理类构造
/// </summary>
/// <param name="path">识别类别文件地址</param>
/// <param name="scales">缩放比例</param>
/// <param name="score_threshold">分数阈值</param>
/// <param name="nms_threshold">非极大值抑制阈值</param>
public DetectionResult(string path, float[] scales, float score_threshold = 0.25f, float nms_threshold = 0.5f)
{
read_class_names(path)
this.scales = scales
this.score_threshold = score_threshold
this.nms_threshold = nms_threshold
}
/// <summary>
/// 结果处理
/// </summary>
/// <param name="result">模型预测输出</param>
/// <returns>模型识别结果</returns>
public Result process_result(float[] result)
{
Mat result_data = new Mat(4 + class_num, 8400, MatType.CV_32F, result)
result_data = result_data.T()
// 存放结果list
List<Rect> position_boxes = new List<Rect>()
List<int> class_ids = new List<int>()
List<float> confidences = new List<float>()
// 预处理输出结果
for (int i = 0
{
Mat classes_scores = result_data.Row(i).ColRange(4, 4 + class_num)
OpenCvSharp.Point max_classId_point, min_classId_point
double max_score, min_score
// 获取一组数据中最大值及其位置
Cv2.MinMaxLoc(classes_scores, out min_score, out max_score,
out min_classId_point, out max_classId_point)
// 置信度 0～1之间
// 获取识别框信息
if (max_score > this.score_threshold)
{
float cx = result_data.At<float>(i, 0)
float cy = result_data.At<float>(i, 1)
float ow = result_data.At<float>(i, 2)
float oh = result_data.At<float>(i, 3)
int x = (int)((cx - 0.5 * ow) * this.scales[0])
int y = (int)((cy - 0.5 * oh) * this.scales[1])
int width = (int)(ow * this.scales[0])
int height = (int)(oh * this.scales[1])
Rect box = new Rect()
box.X = x
box.Y = y
box.Width = width
box.Height = height
position_boxes.Add(box)
class_ids.Add(max_classId_point.X)
confidences.Add((float)max_score)
}
}
// NMS非极大值抑制
int[] indexes = new int[position_boxes.Count]
CvDnn.NMSBoxes(position_boxes, confidences, this.score_threshold, this.nms_threshold, out indexes)
Result re_result = new Result()
// 将识别结果绘制到图片上
for (int i = 0
{
int index = indexes[i]
int idx = class_ids[index]
re_result.add(confidences[index], position_boxes[index], this.class_names[class_ids[index]])
}
return re_result
}
/// <summary>
/// 结果绘制
/// </summary>
/// <param name="result">识别结果</param>
/// <param name="image">绘制图片</param>
/// <returns></returns>
public Mat draw_result(Result result, Mat image)
{
// 将识别结果绘制到图片上
for (int i = 0
{
//Scalar color= Scalar.RandomColor()
Scalar color = new Scalar(0, 0, 255)
string lable = result.classes[i] + "-" + result.scores[i].ToString("0.00")
Cv2.Rectangle(image, result.rects[i], color, 2, LineTypes.Link8)
Cv2.Rectangle(image
, new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20)
, new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1 + lable.Length * 12, result.rects[i].TopLeft.Y)
, color
, -1)
Cv2.PutText(image, lable, new OpenCvSharp.Point(result.rects[i].X, result.rects[i].Y - 4), HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1)
}
return image
}
}
}
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