C# OnnxRuntime 实现百度网盘AI大赛-表格检测第2名方案第二部分-表格分割

360影视 2024-12-19 08:41 2

摘要：Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);

百度网盘AI大赛-表格检测的第2名方案。

该算法包含表格边界框检测、表格分割和表格方向识别三个部分，首先，ppyoloe-plus-x 对边界框进行预测，并对置信度较高的表格边界框（box）进行裁剪。裁剪后的单个表格实例会送入到DBNet中进行语义分割，分割结果通过opencv轮廓处理获得表格关键点(point)。之后，我们根据DBNet计算的关键点在裁剪后的单个表格实例上绘制表格边界。最后，PP-LCNet结合表格边界先验和表格实例图像，对表格的方向进行预测，并根据之前定义的几何轮廓点与语义轮廓点的对应关系，将几何轮廓点映射为语义轮廓点。

本文使用C# OnnxRuntime 实现百度网盘AI大赛-表格检测第2名方案第二部分-表格分割。

C# OpenCvSharp DNN 实现百度网盘AI大赛-表格检测第2名方案第一部分-表格边界框检测

Model Properties

date：2024-10-28T08:16:43.725877
description：Ultralytics YOLO11l-seg model trained on coco-seg.yaml
author：Ultralytics
version：8.3.23
task：segment
license：AGPL-3.0 License (https://ultralytics.com/license)
docs：https://docs.ultralytics.com
stride：32
batch：1
imgsz：[800, 800]
names：{0:'table'}

Inputs

name：images
tensor：Float[1, 3, 800, 800]

Outputs

name：output0
tensor：Float[1, 37, 13125]
name：output1
tensor：Float[1, 32, 200, 200]

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRunTime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Security.Cryptography;
using System.Text;
using System.Web;
using System.Windows.Forms;
using static System.Net.Mime.MediaTypeNames;

namespace Onnx_Yolov8_Demo
{
public partial class Form1 : Form
{
publicForm1
{
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;
SegmentationResult result_pro;
Mat result_image;

SessionOptions options;
InferenceSession onnx_session;
Tensorfloat> input_tensor;
List input_container;
IDisposableReadOnlyCollection result_infer;
DisposableNamedOnnxValue results_onnxvalue;

Tensorfloat> result_tensors_det;
Tensorfloat> result_tensors_proto;

private void button1_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog;
ofd.Filter = fileFilter;
if(ofd.ShowDialog != DialogResult.OK)return;
pictureBox1.Image = ;
image_path = ofd.FileName;
pictureBox1.Image = new Bitmap(image_path);
textBox1.Text ="";
image = new Mat(image_path);
pictureBox2.Image = ;
}

private void button2_Click(object sender, EventArgs e)
{
if(image_path =="")
{
return;
}

button2.Enabled =false;

System.Windows.Forms.Application.DoEvents;

// 配置图片数据

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 det_result_array = newfloat[37 * 13125];
float proto_result_array = newfloat[32 * 200 * 200];
float factors = newfloat[4];
factors[0] = factors[1] = (float)(max_image_length / 800.0);
factors[2] = image.Rows;
factors[3] = image.Cols;

// 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(800, 800));

// 输入Tensor
for(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(y, x)[0] / 255f;
input_tensor[0, 1, y, x] = resize_image.At(y, x)[1] / 255f;
input_tensor[0, 2, y, x] = resize_image.At(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_det = results_onnxvalue[0].AsTensorfloat>;
result_tensors_proto = results_onnxvalue[1].AsTensorfloat>;

det_result_array = result_tensors_det.ToArray;
proto_result_array = result_tensors_proto.ToArray;

resize_image.Dispose;
image_rgb.Dispose;

result_pro = new SegmentationResult(classer_path, factors);
result_image = result_pro.draw_result(result_pro.process_result(det_result_array, proto_result_array), image.Clone);

if(!result_image.Empty)
{
pictureBox2.Image = new Bitmap(result_image.ToMemoryStream);
textBox1.Text ="推理耗时:"+ (dt2 - dt1).TotalMilliseconds +"ms";
}
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 = startupPath +"\\yolo_edge_det.onnx";
classer_path = startupPath +"\\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 DenseTensorfloat>(new { 1, 3, 800, 800 });

// 创建输入容器
input_container = new List;

image_path ="1.jpg";

}
}
}

https://aistudio.baidu.com/projectdetail/5398861?searchKeyword=表格检测大赛&searchTab=ALL

来源：opendotnet

标签：网盘百度 onnxruntime

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