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人工智能目标检测 opencv 计算机视觉机器学习 C# Onnx Yolov8 Detect 物体检测多张图片同时推理

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效果

模型信息

项目

代码

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C# Onnx Yolov8 Detect 物体检测多张图片同时推理

效果

模型信息

Model Properties ------------------------- date：2023-12-18T11:47:29.332397 description：Ultralytics YOLOv8n-detect model trained on coco.yaml author：Ultralytics task：detect license：AGPL-3.0 https://ultralytics.com/license version：8.0.172 stride：32 batch：4 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[4, 3, 640, 640] ---------------------------------------------------------------

Outputs ------------------------- name：output0 tensor：Float[4, 84, 8400] ---------------------------------------------------------------

项目

代码

using Microsoft.ML.OnnxRuntime; using Microsoft.ML.OnnxRuntime.Tensors; using OpenCvSharp; using System; using System.Collections.Generic; using System.Drawing; using System.Linq; using System.Windows.Forms;

namespace Onnx_Yolov8_Demo { public partial class Form1 : Form { public Form1() { InitializeComponent(); }

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;

SessionOptions options; InferenceSession onnx_session; Tensor input_tensor; List input_container; IDisposableReadOnlyCollection result_infer; DisposableNamedOnnxValue[] results_onnxvalue; Tensor result_tensors;

private void button2_Click(object sender, EventArgs e) { float[] result_array = new float[8400 * 84 * 4]; List ltfactors = new List();

for (int i = 0; i < 4; i++) { image_path = "test_img/" + i.ToString() + ".jpg";

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[] factors = new float[2]; factors[0] = factors[1] = (float)(max_image_length / 640.0); ltfactors.Add(factors);

// 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[i, 0, y, x] = resize_image.At(y, x)[0] / 255f; input_tensor[i, 1, y, x] = resize_image.At(y, x)[1] / 255f; input_tensor[i, 2, y, x] = resize_image.At(y, x)[2] / 255f; } }

}

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;

results_onnxvalue = result_infer.ToArray();

result_tensors = results_onnxvalue[0].AsTensor();

result_array = result_tensors.ToArray();

for (int i = 0; i < 4; i++) { image_path = "test_img/" + i.ToString() + ".jpg";

result_pro = new DetectionResult(classer_path, ltfactors[i]);

float[] temp = new float[8400 * 84];

Array.Copy(result_array, 8400 * 84 * i, temp, 0, 8400 * 84);

Result result = result_pro.process_result(temp);

result_image = result_pro.draw_result(result, new Mat(image_path));

Cv2.ImShow(image_path, result_image);

}

pictureBox2.Image = new Bitmap(result_image.ToMemoryStream()); textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";

}

private void Form1_Load(object sender, EventArgs e) { startupPath = System.Windows.Forms.Application.StartupPath;

model_path = "model\\yolov8n-detect-batch4.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; // 设置为CPU上运行 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);//model_path 为onnx模型文件的路径

// 输入Tensor input_tensor = new DenseTensor(new[] { 4, 3, 640, 640 });

// 创建输入容器 input_container = new List();

} } }

using Microsoft.ML.OnnxRuntime;

using Microsoft.ML.OnnxRuntime.Tensors;

using OpenCvSharp;

using System;

using System.Collections.Generic;

using System.Drawing;

using System.Linq;

using System.Windows.Forms;

namespace Onnx_Yolov8_Demo

{

public partial class Form1 : Form

{

public Form1()

{

InitializeComponent();

}

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;

SessionOptions options;

InferenceSession onnx_session;

Tensor input_tensor;

List input_container;

IDisposableReadOnlyCollection result_infer;

DisposableNamedOnnxValue[] results_onnxvalue;

Tensor result_tensors;

private void button2_Click(object sender, EventArgs e)

{

float[] result_array = new float[8400 * 84 * 4];

List ltfactors = new List();

for (int i = 0; i < 4; i++)

{

image_path = "test_img/" + i.ToString() + ".jpg";

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[] factors = new float[2];

factors[0] = factors[1] = (float)(max_image_length / 640.0);

ltfactors.Add(factors);

// 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[i, 0, y, x] = resize_image.At(y, x)[0] / 255f;

input_tensor[i, 1, y, x] = resize_image.At(y, x)[1] / 255f;

input_tensor[i, 2, y, x] = resize_image.At(y, x)[2] / 255f;

}

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;

results_onnxvalue = result_infer.ToArray();

result_tensors = results_onnxvalue[0].AsTensor();

result_array = result_tensors.ToArray();

for (int i = 0; i < 4; i++)

{

image_path = "test_img/" + i.ToString() + ".jpg";

result_pro = new DetectionResult(classer_path, ltfactors[i]);

float[] temp = new float[8400 * 84];

Array.Copy(result_array, 8400 * 84 * i, temp, 0, 8400 * 84);

Result result = result_pro.process_result(temp);

result_image = result_pro.draw_result(result, new Mat(image_path));

Cv2.ImShow(image_path, result_image);

}

pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());

textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";

}

private void Form1_Load(object sender, EventArgs e)

{

startupPath = System.Windows.Forms.Application.StartupPath;

model_path = "model\\yolov8n-detect-batch4.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;

// 设置为CPU上运行

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);//model_path 为onnx模型文件的路径

// 输入Tensor

input_tensor = new DenseTensor(new[] { 4, 3, 640, 640 });

// 创建输入容器

input_container = new List();

}

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人工智能 目标检测 opencv 计算机视觉 机器学习 C# Onnx Yolov8 Detect 物体检测 多张图片同时推理

c# MES 对接之一(XML、JSON、SOAP）

Datawhale Django入门组队学习Task01

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人工智能目标检测 opencv 计算机视觉机器学习 C# Onnx Yolov8 Detect 物体检测多张图片同时推理