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效果

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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 labelprivate static readonly Scalar[] Colors = Enumerable.Repeat(false, 80).Select(x => Scalar.RandomColor()).ToArray();//get labels from coco.namesprivate 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 nmsNet 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 preferablenet.SetPreferableBackend(3);/*0:DNN_BACKEND_DEFAULT 1:DNN_BACKEND_HALIDE 2:DNN_BACKEND_INFERENCE_ENGINE3:DNN_BACKEND_OPENCV */net.SetPreferableTarget(0);/*0:DNN_TARGET_CPU 1:DNN_TARGET_OPENCL2:DNN_TARGET_OPENCL_FP163:DNN_TARGET_MYRIAD 4:DNN_TARGET_FPGA */#endregion}private void button1_Click(object sender, EventArgs e){if (bmp == null) return;//get imagevar org = OpenCvSharp.Extensions.BitmapConverter.ToMat(bmp);//bitmap转matCv2.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-detectionvar blob = CvDnn.BlobFromImage(org, 1.0 / 255, new OpenCvSharp.Size(416, 416), new Scalar(), true, false);//input datanet.SetInput(blob);//get output layer namevar outNames = net.GetUnconnectedOutLayersNames();//create mats for output layervar outs = outNames.Select(_ => new Mat()).ToArray();#region forward modelStopwatch sw = new Stopwatch();sw.Start();net.Forward(outs, outNames);sw.Stop();Console.WriteLine("Runtime:{" + sw.ElapsedMilliseconds + "} ms");#endregion//get result from all outputGetResult(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 nmsvar 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 output0 1 : center                    2 3 : w/h4 : confidence                  5 ~ 84 : class probability */const int prefix = 5;   //skip 0~4foreach (var prob in output){for (var i = 0; i < prob.Rows; i++){var confidence = prob.At<float>(i, 4);if (confidence > threshold){//get classes probabilityOpenCvSharp.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/heightvar 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 NMSBoxesclassIds.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 boxint[] 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 formatingvar 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 resultimage.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;}}
}

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