C＃OpenCvSharp YOLO v3 Demo

效果

项目

代码

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;
using OpenCvSharp;
using System.IO;
using OpenCvSharp.Dnn;
using System.Diagnostics;
using OpenCvSharp.Extensions;

namespace OpenCvSharp_YoloV3
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }

        //random assign color to each label
        private static readonly Scalar[] Colors = Enumerable.Repeat(false, 80).Select(x => Scalar.RandomColor()).ToArray();

        //get labels from coco.names
        private static readonly string[] Labels = File.ReadAllLines("coco.names").ToArray();

        string cfg = "yolov3.cfg";
        string model = "yolov3.weights";
        const float threshold = 0.5f;       //for confidence
        const float nmsThreshold = 0.3f;    //threshold for nms

        Net net;

        private void frmMain_Load(object sender, EventArgs e)
        {
            //load model and config, if you got error: "separator_index < line.size()", check your cfg file, must be something wrong.
            net = CvDnn.ReadNetFromDarknet(cfg, model);

            #region set preferable
            net.SetPreferableBackend(3);
            /*
            0:DNN_BACKEND_DEFAULT
            1:DNN_BACKEND_HALIDE
            2:DNN_BACKEND_INFERENCE_ENGINE
            3:DNN_BACKEND_OPENCV
             */
            net.SetPreferableTarget(0);
            /*
            0:DNN_TARGET_CPU
            1:DNN_TARGET_OPENCL
            2:DNN_TARGET_OPENCL_FP16
            3:DNN_TARGET_MYRIAD
            4:DNN_TARGET_FPGA
             */
            #endregion
        }

        private void button1_Click(object sender, EventArgs e)
        {
            if (bmp == null) return;

            //get image
            var org = OpenCvSharp.Extensions.BitmapConverter.ToMat(bmp);//bitmap转mat
            Cv2.CvtColor(org, org, ColorConversionCodes.RGBA2RGB);//mat转三通道mat

            //setting blob, size can be:320/416/608
            //opencv blob setting can check here https://github.com/opencv/opencv/tree/master/samples/dnn#object-detection
            var blob = CvDnn.BlobFromImage(org, 1.0 / 255, new OpenCvSharp.Size(416, 416), new Scalar(), true, false);

            //input data
            net.SetInput(blob);

            //get output layer name
            var outNames = net.GetUnconnectedOutLayersNames();
            //create mats for output layer
            var outs = outNames.Select(_ => new Mat()).ToArray();

            #region forward model
            Stopwatch sw = new Stopwatch();
            sw.Start();

            net.Forward(outs, outNames);

            sw.Stop();
            Console.WriteLine("Runtime:{" + sw.ElapsedMilliseconds + "} ms");
            #endregion

            //get result from all output
            GetResult(outs, org, threshold, nmsThreshold);

            Bitmap Bitmap1 = BitmapConverter.ToBitmap(org);
            pictureBox2.Image = Bitmap1;

        }

        /// <summary>
        /// Get result form all output
        /// </summary>
        /// <param name="output"></param>
        /// <param name="image"></param>
        /// <param name="threshold"></param>
        /// <param name="nmsThreshold">threshold for nms</param>
        /// <param name="nms">Enable Non-maximum suppression or not</param>
        private static void GetResult(IEnumerable<Mat> output, Mat image, float threshold, float nmsThreshold, bool nms = true)
        {
            //for nms
            var classIds = new List<int>();
            var confidences = new List<float>();
            var probabilities = new List<float>();
            var boxes = new List<Rect2d>();

            var w = image.Width;
            var h = image.Height;
            /*
             YOLO3 COCO trainval output
             0 1 : center                    2 3 : w/h
             4 : confidence                  5 ~ 84 : class probability
            */
            const int prefix = 5;   //skip 0~4

            foreach (var prob in output)
            {
                for (var i = 0; i < prob.Rows; i++)
                {
                    var confidence = prob.At<float>(i, 4);
                    if (confidence > threshold)
                    {
                        //get classes probability
                        OpenCvSharp.Point max;
                        OpenCvSharp.Point minLoc;
                        Cv2.MinMaxLoc(prob.Row[i].ColRange(prefix, prob.Cols), out minLoc, out  max);
                        var classes = max.X;
                        var probability = prob.At<float>(i, classes + prefix);

                        if (probability > threshold) //more accuracy, you can cancel it
                        {
                            //get center and width/height
                            var centerX = prob.At<float>(i, 0) * w;
                            var centerY = prob.At<float>(i, 1) * h;
                            var width = prob.At<float>(i, 2) * w;
                            var height = prob.At<float>(i, 3) * h;

                            if (!nms)
                            {
                                // draw result (if don't use NMSBoxes)
                                Draw(image, classes, confidence, probability, centerX, centerY, width, height);
                                continue;
                            }

                            //put data to list for NMSBoxes
                            classIds.Add(classes);
                            confidences.Add(confidence);
                            probabilities.Add(probability);
                            boxes.Add(new Rect2d(centerX, centerY, width, height));
                        }
                    }
                }
            }

            if (!nms) return;

            //using non-maximum suppression to reduce overlapping low confidence box
            int[] indices;
            CvDnn.NMSBoxes(boxes, confidences, threshold, nmsThreshold, out indices);

            Console.WriteLine("NMSBoxes drop {" + (confidences.Count - indices.Length) + "} overlapping result.");

            foreach (var i in indices)
            {
                var box = boxes[i];
                Draw(image, classIds[i], confidences[i], probabilities[i], box.X, box.Y, box.Width, box.Height);
            }

        }

        /// <summary>
        /// Draw result to image
        /// </summary>
        /// <param name="image"></param>
        /// <param name="classes"></param>
        /// <param name="confidence"></param>
        /// <param name="probability"></param>
        /// <param name="centerX"></param>
        /// <param name="centerY"></param>
        /// <param name="width"></param>
        /// <param name="height"></param>
        private static void Draw(Mat image, int classes, float confidence, float probability, double centerX, double centerY, double width, double height)
        {
            //label formating
            var label = Labels[classes] + " " + (probability * 100).ToString("0.00") + "%";

            Console.WriteLine("confidence " + (confidence * 100).ToString("0.00") + "%  " + label);

            var x1 = (centerX - width / 2) < 0 ? 0 : centerX - width / 2; //avoid left side over edge
            //draw result
            image.Rectangle(new OpenCvSharp.Point(x1, centerY - height / 2), new OpenCvSharp.Point(centerX + width / 2, centerY + height / 2), Colors[classes], 2);

            int baseline;
            var textSize = Cv2.GetTextSize(label, HersheyFonts.HersheyTriplex, 0.5, 1, out  baseline);

            Cv2.Rectangle(image, new Rect(new OpenCvSharp.Point(x1, centerY - height / 2 - textSize.Height - baseline),
                new OpenCvSharp.Size(textSize.Width, textSize.Height + baseline)), Colors[classes], Cv2.FILLED);

            var textColor = Cv2.Mean(Colors[classes]).Val0 < 70 ? Scalar.White : Scalar.Black;

            Cv2.PutText(image, label, new OpenCvSharp.Point(x1, centerY - height / 2 - baseline), HersheyFonts.HersheyTriplex, 0.5, textColor);
        }

        private string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        Bitmap bmp;

        private void button2_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;
            var imagebyte = File.ReadAllBytes(ofd.FileName);
            bmp = new Bitmap(new MemoryStream(imagebyte));
            pictureBox1.Image = bmp;

        }

    }
}

Demo下载