C# OpenCvSharp 部署表格检测

说明

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。

效果

模型信息

paddle_cls.onnx

Model Properties
-------------------------
---------------------------------------------------------------

Inputs
-------------------------
name：input
tensor：Float[-1, 3, 624, 624]
---------------------------------------------------------------

Outputs
-------------------------
name：linear_1.tmp_1
tensor：Float[-1, 4]
---------------------------------------------------------------

yolo_edge_det.onnx

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]
---------------------------------------------------------------

yolo_obj_det.onnx

Model Properties
-------------------------
date：2024-10-28T13:52:42.181333
description：Ultralytics YOLO11l model trained on coco.yaml
author：Ultralytics
version：8.3.23
task：detect
license：AGPL-3.0 License (https://ultralytics.com/license)
docs：https://docs.ultralytics.com
stride：32
batch：1
imgsz：[928, 928]
names：{0: 'table'}
---------------------------------------------------------------

Inputs
-------------------------
name：images
tensor：Float[1, 3, 928, 928]
---------------------------------------------------------------

Outputs
-------------------------
name：output0
tensor：Float[1, 5, 17661]
---------------------------------------------------------------

项目

C++封装DLL，C#调用

代码

using OpenCvSharp;
using System;
using System.Diagnostics;
using System.Drawing;
using System.Drawing.Imaging;
using System.Runtime.InteropServices;
using System.Text;
using System.Windows.Forms;

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

 Stopwatch stopwatch = new Stopwatch();
 Mat image;
 string image_path;
 string startupPath;
 string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
 const string DllName = "TableDetectionSharp.dll";
 IntPtr engine;
 /*
 //初始化
 extern "C" _declspec(dllexport) int __cdecl init(void** engine, char* obj_model_path, char* edge_model_path, char* cls_model_path, char* msg);

 //tableDet
 extern "C" _declspec(dllexport) int __cdecl tableDet(void* engine, Mat* image, char* msg, int* out_imgs_size, Mat* out_img1, Mat* out_img2, Mat* out_img3);

 //释放
 extern "C" _declspec(dllexport) void __cdecl destroy(void* engine);
 */

 [DllImport(DllName, EntryPoint = "init", CallingConvention = CallingConvention.Cdecl)]
 internal extern static int init(ref IntPtr engine, string obj_model_path, string edge_model_path, string cls_model_path, StringBuilder msg);

 [DllImport(DllName, EntryPoint = "tableDet", CallingConvention = CallingConvention.Cdecl)]
 internal extern static int tableDet(IntPtr engine, IntPtr image, StringBuilder msg, ref int out_imgs_size, IntPtr out_img1, IntPtr out_img2, IntPtr out_img3);

 [DllImport(DllName, EntryPoint = "destroy", CallingConvention = CallingConvention.Cdecl)]
 internal extern static int destroy(IntPtr engine);

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

 string obj_model_path = startupPath + "\\model\\yolo_obj_det.onnx";
 string edge_model_path = startupPath + "\\model\\yolo_edge_det.onnx";
 string cls_model_path = startupPath + "\\model\\paddle_cls.onnx";

 StringBuilder msg = new StringBuilder(512);

 int res = init(ref engine, obj_model_path, edge_model_path, cls_model_path, msg);
 if (res == -1)
 {
 MessageBox.Show(msg.ToString());
 return;
 }
 else
 {
 Console.WriteLine(msg.ToString());
 }
 image_path = startupPath + "\\test_img\\real5.jpg";
 pictureBox1.Image = new Bitmap(image_path);
 image = new Mat(image_path);
 }

 private void button1_Click(object sender, EventArgs e)
 {
 OpenFileDialog ofd = new OpenFileDialog();
 ofd.Filter = fileFilter;
 if (ofd.ShowDialog() != DialogResult.OK) return;

 pictureBox1.Image = ;
 pictureBox2.Image = ;
 textBox1.Text = "";

 image_path = ofd.FileName;
 pictureBox1.Image = new Bitmap(image_path);
 image = new Mat(image_path);
 }

 Mat out_img1;
 Mat out_img2;
 Mat out_img3;

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

 button2.Enabled = false;

 Application.DoEvents();

 Cv2.DestroyAllWindows();
 if (image != ) image.Dispose();
 if (out_img1 != ) out_img1.Dispose();
 if (out_img2 != ) out_img2.Dispose();
 if (out_img3 != ) out_img3.Dispose();
 if (pictureBox1.Image != ) pictureBox1.Image.Dispose();

 StringBuilder msg = new StringBuilder(512);
 int out_imgs_size = 0;
 image = new Mat(image_path);
 out_img1 = new Mat();
 out_img2 = new Mat();
 out_img3 = new Mat();

 stopwatch.Restart();

 int res = tableDet(engine, image.CvPtr, msg, ref out_imgs_size, out_img1.CvPtr, out_img2.CvPtr, out_img3.CvPtr);
 if (res == 0)
 {
 stopwatch.Stop();
 double costTime = stopwatch.Elapsed.TotalMilliseconds;
 if (out_imgs_size >= 1)
 {
 pictureBox2.Image = new Bitmap(out_img1.ToMemoryStream());
 }

 if (out_imgs_size >= 2)
 {
 Cv2.ImShow("2", out_img2);
 }

 if (out_imgs_size >= 3)
 {
 Cv2.ImShow("3", out_img3);
 }

 textBox1.Text = $"耗时:{costTime:F2}ms";
 }
 else
 {
 textBox1.Text = "识别失败";
 }
 button2.Enabled = true;
 }

 private void Form1_FormClosed(object sender, FormClosedEventArgs e)
 {
 destroy(engine);
 }

 private void button3_Click(object sender, EventArgs e)
 {
 if (pictureBox2.Image == )
 {
 return;
 }
 Bitmap output = new Bitmap(pictureBox2.Image);
 var sdf = new SaveFileDialog();
 sdf.Title = "保存";
 sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp";
 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;
 }
 }
 MessageBox.Show("保存成功，位置：" + sdf.FileName);
 }

 }
 }
}

参考

https://github.com/hpc203/TableDetection