YOLOv8 营业执照提取统一社会信用代码、企业名称

背景

尝试一：整图OCR识别，然后正则匹配

尝试二：利用显著特征，直接传统方法定位，切出来识别

尝试三：yolov8训练一个统一社会信用代码、企业名称位置检测

编辑

效果

模型信息

项目

编辑

代码

下载

其他

背景

因项目需要，需要从营业执照中提取统一社会信用代码、企业名称。

尝试一：整图OCR识别，然后正则匹配

统一社会信用代码大多情况是18位数字加英文的组合，比较好正则匹配，名称结尾太多不好匹配，放弃。

尝试二：利用显著特征，直接传统方法定位，切出来识别

国徽就是个显著特征，利用国徽模板匹配，角度和位置就有了，然后用相对固定的比例系数乘以输入图片宽高，切出来后整个主要文字区域就有了，然后还是按比例从主区域中一块块的切，由于图片拍摄质量问题放弃。

尝试三：yolov8训练一个统一社会信用代码、企业名称位置检测

效果还不错，先检测出位置，再裁剪出图片OCR。

效果

模型信息

Model Properties
-------------------------
author：Ultralytics
task：detect
license：AGPL-3.0 https://ultralytics.com/license
version：8.0.184
stride：32
batch：1
imgsz：[640, 640]
names：{0: 'code', 1: 'name'}
---------------------------------------------------------------

Inputs
-------------------------
name：images
tensor：Float[1, 3, 640, 640]
---------------------------------------------------------------

Outputs
-------------------------
name：output0
tensor：Float[1, 6, 8400]
---------------------------------------------------------------

项目

VS2022+.net framework 4.8

OpenCvSharp 4.8

Microsoft.ML.OnnxRuntime 1.16.2

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.Text;
using System.Windows.Forms;
 
namespace Onnx_Yolov8_Detect
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }
 
        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        string startupPath;
        string classer_path;
        string model_path;
 
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
 
        Mat image;
        Mat result_image;
 
        SessionOptions options;
        InferenceSession onnx_session;
        Tensor<float> input_tensor;
        List<NamedOnnxValue> input_ontainer;
        IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
        DisposableNamedOnnxValue[] results_onnxvalue;
 
        Tensor<float> result_tensors;
        float[] result_array;
        float[] factors = new float[2];
 
        Result result;
        DetectionResult result_pro;
        StringBuilder sb = new StringBuilder();
 
        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);
        }
 
        private void Form1_Load(object sender, EventArgs e)
        {
            startupPath = Application.StartupPath + "\\model\\";
 
            model_path = startupPath + "best.onnx";
            classer_path = startupPath + "lable.txt";
 
            // 创建输出会话
            options = new SessionOptions();
            options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
            options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行
 
            // 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_ontainer = new List<NamedOnnxValue>();
 
        }
 
        private void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }
            textBox1.Text = "检测中，请稍等……";
            pictureBox2.Image = null;
            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));
 
            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; y < resize_image.Height; y++)
            {
                for (int x = 0; x < resize_image.Width; x++)
                {
                    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_ontainer.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));
 
            dt1 = DateTime.Now;
            //运行 Inference 并获取结果
            result_infer = onnx_session.Run(input_ontainer);
            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, 0.8f, 0.5f);
            result = result_pro.process_result(result_array);
            result_image = result_pro.draw_result(result, image.Clone());
 
            if (!result_image.Empty())
            {
                pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
                sb.Clear();
                sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
                sb.AppendLine("------------------------------");
                for (int i = 0; i < result.length; i++)
                {
                    sb.AppendLine(string.Format("{0}：{1},({2},{3},{4},{5})"
                        , result.classes[i]
                        , result.scores[i].ToString("0.00")
                        , result.rects[i].TopLeft.X
                        , result.rects[i].TopLeft.Y
                        , result.rects[i].BottomRight.X
                        , result.rects[i].BottomRight.Y
                        ));
                }
                textBox1.Text = sb.ToString();
            }
            else
            {
                textBox1.Text = "无信息";
            }
        }
 
        private void pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox2.Image);
        }
 
        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox1.Image);
        }
    }
}