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 = null;
pictureBox2.Image = null;
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 != null) image.Dispose();
if (out_img1 != null) out_img1.Dispose();
if (out_img2 != null) out_img2.Dispose();
if (out_img3 != null) out_img3.Dispose();
if (pictureBox1.Image != null) 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();
