#dotnet – GoogleNet detection from the 🎦 camera feed using #OpenCV and #net5. Bonus: C++ to C# time!

Hi !

So I was browsing in the OpenCV documentation and I find a nice sample that uses opencv_dnn module for image classification by using GoogLeNet trained network from Caffe model zoo.

So I give it a try, and get a decent .Net 5 Winforms App running at ~30 FPS.

The model was trained with 1000 classes, and once you get the main focus on the camera it work great with objects like a machine, mug, bottle, etc. There is a nice amount of code here, and because the DNN analysis is performed in a separated thread, I need to update the label details using PInvoke functions.

	using System;
	using System.IO;
	using System.Linq;
	using System.Threading;
	using System.Windows.Forms;
	using OpenCvSharp;
	using OpenCvSharp.Dnn;
	using OpenCvSharp.Extensions;
	using Point = OpenCvSharp.Point;
	using Size = OpenCvSharp.Size;
	namespace Demo11_WinFormGoogleNet
	{
	public partial class Form1 : Form
	{
	private bool _run = true;
	private bool _useGoogleNet = false;
	private VideoCapture _capture;
	private Mat _image;
	private Thread _cameraThread;
	private bool _fps = false;
	private Net _netGoogleNet;
	private string[] _classNames;
	const string ProtoTxt = @"models\bvlc_googlenet.prototxt";
	const string CaffeModel = @"models\bvlc_googlenet.caffemodel";
	const string SynsetWords = @"models\synset_words.txt";
	private delegate void SafeCallDelegate(string text);
	public Form1()
	{
	InitializeComponent();
	Load += Form1_Load;
	Closed += Form1_Closed;
	}
	private void Form1_Closed(object sender, EventArgs e)
	{
	_cameraThread.Interrupt();
	_capture.Release();
	}
	private void btnStart_Click(object sender, EventArgs e)
	{
	_run = true;
	}
	private void btnStop_Click(object sender, EventArgs e)
	{
	_run = false;
	}
	private void btnGoogleNet_Click(object sender, EventArgs e)
	{
	_useGoogleNet = !_useGoogleNet;
	}
	private void buttonFPS_Click(object sender, EventArgs e)
	{
	_fps = !_fps;
	}
	private void Form1_Load(object sender, EventArgs e)
	{
	_classNames = File.ReadAllLines(SynsetWords)
	.Select(line => line.Split(' ').Last())
	.ToArray();
	_netGoogleNet = CvDnn.ReadNetFromCaffe(ProtoTxt, CaffeModel);
	_capture = new VideoCapture(0);
	_image = new Mat();
	_cameraThread = new Thread(new ThreadStart(CaptureCameraCallback));
	_cameraThread.Start();
	}
	private void CaptureCameraCallback()
	{
	while (true)
	{
	if (!_run) continue;
	var startTime = DateTime.Now;
	_capture.Read(_image);
	if (_image.Empty()) return;
	var imageRes = new Mat();
	Cv2.Resize(_image, imageRes, new Size(320, 240));
	if (_useGoogleNet)
	{
	// Convert Mat to batch of images
	using var inputBlob = CvDnn.BlobFromImage(imageRes, 1, new Size(224, 224), new Scalar(104, 117, 123));
	_netGoogleNet.SetInput(inputBlob, "data");
	using var prob = _netGoogleNet.Forward("prob");
	// find the best class
	GetMaxClass(prob, out int classId, out double classProb);
	var msg = @$"Best class: #{classId} '{_classNames[classId]}' – Probability: {classProb:P2}";
	// display output
	WriteTextSafe(msg);
	}
	if (_fps)
	{
	var diff = DateTime.Now – startTime;
	var fpsInfo = $"FPS: Nan";
	if (diff.Milliseconds > 0)
	{
	var fpsVal = 1.0 / diff.Milliseconds * 1000;
	fpsInfo = $"FPS: {fpsVal:00}";
	}
	Cv2.PutText(imageRes, fpsInfo, new Point(10, 20), HersheyFonts.HersheyComplexSmall, 1, Scalar.White);
	}
	var bmpWebCam = BitmapConverter.ToBitmap(imageRes);
	pictureBoxWebCam.Image = bmpWebCam;
	}
	}
	private void WriteTextSafe(string text)
	{
	if (lblOutputAnalysis.InvokeRequired)
	{
	var d = new SafeCallDelegate(WriteTextSafe);
	lblOutputAnalysis.Invoke(d, new object[] { text });
	}
	else
	{
	lblOutputAnalysis.Text = text;
	}
	}
	private static void GetMaxClass(Mat probBlob, out int classId, out double classProb)
	{
	// reshape the blob to 1×1000 matrix
	using (var probMat = probBlob.Reshape(1, 1))
	{
	Cv2.MinMaxLoc(probMat, out _, out classProb, out _, out var classNumber);
	classId = classNumber.X;
	}
	}
	}
	}

view raw opencvnet5googlenet.cs hosted with ❤ by GitHub

Super fun ! and check the references for the model and support files download location.

References

• OpenCVSharp, https://github.com/shimat/opencvsharp
• Wikipedia, Canny Edge Detector
• OpenCV, Canny tutorial
• OpenCV, FAST tutorial
• Wikipedia, FAST
• OpenCV, Perform face detection using Haar Cascades
• OpenCV, Load Caffe framework models
• Caffe Model Zoo