C# Onnx DBNet 检测条形码

效果

模型信息

Inputs
-------------------------
name：input
tensor：Float[1, 3, 736, 736]
---------------------------------------------------------------

Outputs
-------------------------
name：output
tensor：Float[736, 736]
--------------------------------------------------------------
           

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.Numerics;
using System.Runtime.InteropServices.WindowsRuntime;
using System.Security.Cryptography;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;

namespace Onnx_Demo
{
 public partial class frmMain : Form
 {
 public frmMain()
 {
 InitializeComponent();
 }

 string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
 string image_path = "";
 string startupPath;
 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;

 StringBuilder sb = new StringBuilder();

float binaryThreshold = 0.5f;
float polygonThreshold = 0.7f;
float unclipRatio = 1.5f;
 int maxCandidates = 1000;

float[] mean = { 0.485f, 0.456f, 0.406f };
float[] std = { 0.229f, 0.224f, 0.225f };

 int inpWidth = 736;
 int inpHeight = 736;

 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);
 }

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

 model_path = startupPath + "model_0.88_depoly.onnx";

 // 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, inpHeight, inpWidth });

 // 创建输入容器
 input_ontainer = new List<NamedOnnxValue>();

 }

float ContourScore(Mat binary, OpenCvSharp.Point[] contour)
 {
 Rect rect = Cv2.BoundingRect(contour);
 int xmin = Math.Max(rect.X, 0);
 int xmax = Math.Min(rect.X + rect.Width, binary.Cols - 1);
 int ymin = Math.Max(rect.Y, 0);
 int ymax = Math.Min(rect.Y + rect.Height, binary.Rows - 1);

 Mat binROI = new Mat(binary, new Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1));

 Mat mask = Mat.Zeros(new OpenCvSharp.Size(xmax - xmin + 1, ymax - ymin + 1), MatType.CV_8UC1);

 List<OpenCvSharp.Point> roiContour = new List<OpenCvSharp.Point>();

 foreach (var item in contour)
 {
 OpenCvSharp.Point pt = new OpenCvSharp.Point(item.X - xmin, item.Y - ymin);
 roiContour.Add(pt);
 }

 List<List<OpenCvSharp.Point>> roiContours = new List<List<OpenCvSharp.Point>>
 {
 roiContour
 };

 Cv2.FillPoly(mask, roiContours, new Scalar(1));

float score = (float)Cv2.Mean(binROI)[0];

return score;
 }

 void Unclip(List<Point2f> inPoly, List<Point2f> outPoly)
 {
float area = (float)Cv2.ContourArea(inPoly);
float length = (float)Cv2.ArcLength(inPoly, true);
float distance = area * unclipRatio / length;

 int numPoints = inPoly.Count();
 List<List<Point2f>> newLines = new List<List<Point2f>>();
for (int i = 0; i < numPoints; i++)
 {
 List<Point2f> newLine = new List<Point2f>();
 OpenCvSharp.Point pt1 = (OpenCvSharp.Point)inPoly[i];
 int index = (i - 1) % numPoints;
if (index <= 0) index = 0;
 OpenCvSharp.Point pt2 = (OpenCvSharp.Point)inPoly[index];
 OpenCvSharp.Point vec = pt1 - pt2;

 Mat mat_vec = new Mat(1, 2, MatType.CV_8U, new int[] { vec.X, vec.Y });
float unclipDis = (float)(distance / Cv2.Norm(mat_vec));

 Point2f rotateVec = new Point2f(vec.Y * unclipDis, -vec.X * unclipDis);
 newLine.Add(new Point2f(pt1.X + rotateVec.X, pt1.Y + rotateVec.Y));
 newLine.Add(new Point2f(pt2.X + rotateVec.X, pt2.Y + rotateVec.Y));
 newLines.Add(newLine);
 }

 int numLines = newLines.Count();
for (int i = 0; i < numLines; i++)
 {
 Point2f a = newLines[i][0];
 Point2f b = newLines[i][1];
 Point2f c = newLines[(i + 1) % numLines][0];
 Point2f d = newLines[(i + 1) % numLines][1];
 Point2f pt;
 Point2f v1 = b - a;
 Point2f v2 = d - c;

 Mat mat_v1 = new Mat(1, 2, MatType.CV_32FC1, new float[] { v1.X, v1.Y });
 Mat mat_v2 = new Mat(1, 2, MatType.CV_32FC1, new float[] { v2.X, v2.Y });
float cosAngle = (float)((v1.X * v2.X + v1.Y * v2.Y) / (Cv2.Norm(mat_v1) * Cv2.Norm(mat_v2)));

if (Math.Abs(cosAngle) > 0.7)
 {
 pt.X = (float)((b.X + c.X) * 0.5);
 pt.Y = (float)((b.Y + c.Y) * 0.5);
 }
else
 {
float denom = a.X * (float)(d.Y - c.Y) + b.X * (float)(c.Y - d.Y) +
 d.X * (float)(b.Y - a.Y) + c.X * (float)(a.Y - b.Y);
float num = a.X * (float)(d.Y - c.Y) + c.X * (float)(a.Y - d.Y) + d.X * (float)(c.Y - a.Y);
float s = num / denom;

 pt.X = a.X + s * (b.X - a.X);
 pt.Y = a.Y + s * (b.Y - a.Y);
 }
 outPoly.Add(pt);
 }
 }

 private void button2_Click(object sender, EventArgs e)
 {
if (image_path == "")
 {
return;
 }
 textBox1.Text = "检测中，请稍等……";
 pictureBox2.Image = ;
 Application.DoEvents();

 //图片
 image = new Mat(image_path);

 //将图片转为RGB通道
 Mat image_rgb = new Mat();
 Cv2.CvtColor(image, image_rgb, ColorConversionCodes.BGR2RGB);

 Mat resize_image = new Mat();
 Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(inpHeight, inpWidth));

 //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 - mean[0]) / std[0];
 input_tensor[0, 1, y, x] = (resize_image.At<Vec3b>(y, x)[1] / 255f - mean[1]) / std[1];
 input_tensor[0, 2, y, x] = (resize_image.At<Vec3b>(y, x)[2] / 255f - mean[2]) / std[2];
 }
 }

 //将 input_tensor 放入一个输入参数的容器，并指定名称
 input_ontainer.Add(NamedOnnxValue.CreateFromTensor("input", 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();

 var result_array = results_onnxvalue[0].AsTensor<float>().ToArray();

 Mat binary = new Mat(resize_image.Rows, resize_image.Cols, MatType.CV_32FC1, result_array);

 // threshold
 Mat threshold = new Mat();
 Cv2.Threshold(binary, threshold, binaryThreshold, 255, ThresholdTypes.Binary);

 Cv2.ImShow("threshold", threshold);

 int h = image.Rows;
 int w = image.Cols;
float scaleHeight = (float)(h) / (float)(binary.Size(0));
float scaleWidth = (float)(w) / (float)(binary.Size(1));

 threshold.ConvertTo(threshold, MatType.CV_8UC1);

 // Find contours
 OpenCvSharp.Point[][] contours;
 HierarchyIndex[] hierarchly;

 Cv2.FindContours(threshold, out contours, out hierarchly, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple);

 // Candidate number limitation
 int numCandidate = Math.Min(contours.Count(), maxCandidates > 0 ? maxCandidates : int.MaxValue);

 List<List<Point2f>> results = new List<List<Point2f>>();

for (int i = 0; i < numCandidate; i++)
 {
 OpenCvSharp.Point[] contour = contours[i];

 // Calculate text contour score
if (ContourScore(binary, contour) < polygonThreshold)
continue;

 // Rescale
 List<OpenCvSharp.Point> contourScaled = new List<OpenCvSharp.Point>();
 foreach (var item in contour)
 {
 contourScaled.Add(new OpenCvSharp.Point((int)(item.X * scaleWidth), (int)(item.Y * scaleHeight)));
 }

 RotatedRect box = Cv2.MinAreaRect(contourScaled);

 // minArea() rect is not normalized, it may return rectangles with angle=-90 or height < width
float angle_threshold = 60; // do not expect vertical text, TODO detection algo property
 bool swap_size = false;
if (box.Size.Width < box.Size.Height) // horizontal-wide text area is expected
 {
 swap_size = true;
 }
else if (Math.Abs(box.Angle) >= angle_threshold) // don't work with vertical rectangles
 {
 swap_size = true;
 }

 if (swap_size)
 {
 float temp = box.Size.Width;
 box.Size.Width = box.Size.Height;
 box.Size.Height = temp;

 if (box.Angle < 0)
 box.Angle += 90;
 else if (box.Angle > 0)
 box.Angle -= 90;
 }

 Point2f[] vertex = new Point2f[4];
 vertex = box.Points(); // order: bl, tl, tr, br

 List<Point2f> approx = new List<Point2f>();

 for (int j = vertex.Length - 1; j >= 0; j--)
 {
 approx.Add(vertex[j]);
 }

 List<Point2f> polygon = new List<Point2f>();

 Unclip(approx, polygon);

 results.Add(approx);

 }

 result_image = image.Clone();

 for (int i = 0; i < results.Count; i++)
 {
 for (int j = 0; j < 4; j++)
 {

 Cv2.Circle(result_image
 , new OpenCvSharp.Point((int)results[i][j].X, (int)results[i][j].Y)
 , 2
 , new Scalar(0, 0, 255)
 , -1);

 if (j < 3)
 {
 Cv2.Line(result_image
 , new OpenCvSharp.Point((int)results[i][j].X, (int)results[i][j].Y)
 , new OpenCvSharp.Point((int)results[i][j + 1].X, (int)results[i][j + 1].Y)
 , new Scalar(0, 255, 0), 2);
 }
 else
 {
 Cv2.Line(result_image
 , new OpenCvSharp.Point((int)results[i][j].X, (int)results[i][j].Y)
 , new OpenCvSharp.Point((int)results[i][0].X, (int)results[i][0].Y)
 , new Scalar(0, 255, 0), 2);
 }


 }
 }

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

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

 }

 private void pictureBox2_DoubleClick(object sender, EventArgs e)
 {
 Common.ShowNormalImg(pictureBox2.Image);
 }

 private void pictureBox1_DoubleClick(object sender, EventArgs e)
 {
 Common.ShowNormalImg(pictureBox1.Image);
 }
 }
}