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 = 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);
        }
    }
}

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