效果

项目

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 = 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 + "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Tensorinput_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 = null;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Tensorfor (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);// thresholdMat 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 contoursOpenCvSharp.Point[][] contours;HierarchyIndex[] hierarchly;Cv2.FindContours(threshold, out contours, out hierarchly, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple);// Candidate number limitationint 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 scoreif (ContourScore(binary, contour) < polygonThreshold)continue;// RescaleList<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 < widthfloat angle_threshold = 60;  // do not expect vertical text, TODO detection algo propertybool 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, brList<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);}}
}

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