#dotnet – Age and Gender estimation from the 🎦 camera feed using #OpenCV and #net5

Hi !

Face detected, so next step is to use some prebuild models to perform additional actions: like estimate the Age of a face, and also the Gender. In order to do this, I downloaded a couple of models from here.

Disclaimer: these models are just sample models, do not use them in production. These model does not covers all the necessary scenarios for a real implementation.

And the final winform app is kind of cute!

Below you can find the complete Form1 source code, before let’s take a look at the sample analyzing a magazine photo.

So let’s analyze the code. For this sample, we load 3 models to work with age, faces and gender.

// # detect faces, age and gender using models from https://github.com/spmallick/learnopencv/tree/08e61fe80b8c0244cc4029ac11e44cd0fbb008c3/AgeGender
const string faceProto = "models/deploy.prototxt";
const string faceModel = "models/res10_300x300_ssd_iter_140000_fp16.caffemodel";
const string ageProto = @"models/age_deploy.prototxt";
const string ageModel = @"models/age_net.caffemodel";
const string genderProto = @"models/gender_deploy.prototxt";
const string genderModel = @"models/gender_net.caffemodel";
_ageNet = CvDnn.ReadNetFromCaffe(ageProto, ageModel);
_genderNet = CvDnn.ReadNetFromCaffe(genderProto, genderModel);
_faceNet = CvDnn.ReadNetFromCaffe(faceProto, faceModel);

Once the models are loaded, in the loop to analyze camera frames, we perform face detection, and then age and gender estimation.

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));
    var newImage = imageRes.Clone();

    if (_doFaceDetection) DetectFaces(newImage, imageRes);

    if (_fps) CalculateFps(startTime, newImage);

    var bmpWebCam = BitmapConverter.ToBitmap(imageRes);
    var bmpEffect = BitmapConverter.ToBitmap(newImage);

    pictureBoxWebCam.Image = bmpWebCam;
    pictureBoxEffect.Image = bmpEffect;
}

For each detected face, we perform the age and gender estimation. In order to do this, we crop the detected face (plus a padding), and perform the estimation on the cropped image.

private void AnalyzeAgeAndGender(int x1, int y1, int x2, int y2, Mat imageRes, Mat newImage)
{
    // get face frame
    var x = x1 - Padding;
    var y = y1 - Padding;
    var w = (x2 - x1) + Padding * 3;
    var h = (y2 - y1) + Padding * 3;
    Rect roiNew = new Rect(x, y, w, h);
    var face = imageRes[roi: roiNew];

    var meanValues = new Scalar(78.4263377603, 87.7689143744, 114.895847746);
    var blobGender = CvDnn.BlobFromImage(face, 1.0, new Size(227, 227), mean: meanValues,
        swapRB: false);
    _genderNet.SetInput(blobGender);
    var genderPreds = _genderNet.Forward();

    GetMaxClass(genderPreds, out int classId, out double classProbGender);
    var gender = _genderList[classId];

    _ageNet.SetInput(blobGender);
    var agePreds = _ageNet.Forward();
    GetMaxClass(agePreds, out int classIdAge, out double classProbAge);
    var age = _ageList[classIdAge];

    var label = $"{gender},{age}";
    Cv2.PutText(newImage, label, new Point(x1 - 10, y2 + 20), HersheyFonts.HersheyComplexSmall, 1, Scalar.Yellow, 1);
}

private void GetMaxClass(Mat probBlob, out int classId, out double classProb)
{
    // reshape the blob to 1x1000 matrix
    using var probMat = probBlob.Reshape(1, 1);
    Cv2.MinMaxLoc(probMat, out _, out classProb, out _, out var classNumber);
    classId = classNumber.X;
    Debug.WriteLine($"X: {classNumber.X} - Y: {classNumber.Y} ");
}

It’s also important to mention to the GetMaxClass() function, to retrieve the best detected element in the prob result.

And the complete source code:

	using System;
	using System.Collections.Generic;
	using System.Diagnostics;
	using System.Threading;
	using System.Windows.Forms;
	using OpenCvSharp;
	using OpenCvSharp.Dnn;
	using OpenCvSharp.Extensions;
	using Point = OpenCvSharp.Point;
	using Size = OpenCvSharp.Size;
	namespace Demo10_WinFormAgeAndGender
	{
	public partial class Form1 : Form
	{
	private bool _run = true;
	private bool _doFaceDetection = true;
	private bool _doAgeGender = false;
	private VideoCapture _capture;
	private Mat _image;
	private Thread _cameraThread;
	private bool _fps = false;
	private Net _faceNet;
	private Net _ageNet;
	private Net _genderNet;
	private const int LineThickness = 2;
	private const int Padding = 10;
	private readonly List<string> _genderList = new List<string> { "Male", "Female" };
	private readonly List<string> _ageList = new List<string> { "(0-2)", "(4-6)", "(8-12)", "(15-20)", "(25-32)", "(38-43)", "(48-53)", "(60-100)" };
	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 btnFDDNN_Click(object sender, EventArgs e)
	{
	_doFaceDetection = !_doFaceDetection;
	}
	private void buttonFPS_Click(object sender, EventArgs e)
	{
	_fps = !_fps;
	}
	private void btnAgeGender_Click(object sender, EventArgs e)
	{
	_doAgeGender = !_doAgeGender;
	}
	private void Form1_Load(object sender, EventArgs e)
	{
	// # detect faces, age and gender using models from https://github.com/spmallick/learnopencv/tree/08e61fe80b8c0244cc4029ac11e44cd0fbb008c3/AgeGender
	const string faceProto = "models/deploy.prototxt";
	const string faceModel = "models/res10_300x300_ssd_iter_140000_fp16.caffemodel";
	const string ageProto = @"models/age_deploy.prototxt";
	const string ageModel = @"models/age_net.caffemodel";
	const string genderProto = @"models/gender_deploy.prototxt";
	const string genderModel = @"models/gender_net.caffemodel";
	_ageNet = CvDnn.ReadNetFromCaffe(ageProto, ageModel);
	_genderNet = CvDnn.ReadNetFromCaffe(genderProto, genderModel);
	_faceNet = CvDnn.ReadNetFromCaffe(faceProto, faceModel);
	_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));
	var newImage = imageRes.Clone();
	if (_doFaceDetection) DetectFaces(newImage, imageRes);
	if (_fps) CalculateFps(startTime, newImage);
	var bmpWebCam = BitmapConverter.ToBitmap(imageRes);
	var bmpEffect = BitmapConverter.ToBitmap(newImage);
	pictureBoxWebCam.Image = bmpWebCam;
	pictureBoxEffect.Image = bmpEffect;
	}
	}
	private static void CalculateFps(DateTime startTime, Mat imageRes)
	{
	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);
	}
	private void DetectFaces(Mat newImage, Mat imageRes)
	{
	// DNN
	int frameHeight = newImage.Rows;
	int frameWidth = newImage.Cols;
	using var blob = CvDnn.BlobFromImage(newImage, 1.0, new Size(300, 300), new Scalar(104, 117, 123), false, false);
	_faceNet.SetInput(blob, "data");
	using var detection = _faceNet.Forward("detection_out");
	using var detectionMat = new Mat(detection.Size(2), detection.Size(3), MatType.CV_32F, detection.Ptr(0));
	for (int i = 0; i < detectionMat.Rows; i++)
	{
	float confidence = detectionMat.At<float>(i, 2);
	if (confidence > 0.7)
	{
	int x1 = (int)(detectionMat.At<float>(i, 3) * frameWidth);
	int y1 = (int)(detectionMat.At<float>(i, 4) * frameHeight);
	int x2 = (int)(detectionMat.At<float>(i, 5) * frameWidth);
	int y2 = (int)(detectionMat.At<float>(i, 6) * frameHeight);
	Cv2.Rectangle(newImage, new Point(x1, y1), new Point(x2, y2), Scalar.Green, LineThickness);
	if (_doAgeGender)
	AnalyzeAgeAndGender(x1, y1, x2, y2, imageRes, newImage);
	}
	}
	}
	private void AnalyzeAgeAndGender(int x1, int y1, int x2, int y2, Mat imageRes, Mat newImage)
	{
	// get face frame
	var x = x1 – Padding;
	var y = y1 – Padding;
	var w = (x2 – x1) + Padding * 3;
	var h = (y2 – y1) + Padding * 3;
	Rect roiNew = new Rect(x, y, w, h);
	var face = imageRes[roi: roiNew];
	var meanValues = new Scalar(78.4263377603, 87.7689143744, 114.895847746);
	var blobGender = CvDnn.BlobFromImage(face, 1.0, new Size(227, 227), mean: meanValues,
	swapRB: false);
	_genderNet.SetInput(blobGender);
	var genderPreds = _genderNet.Forward();
	GetMaxClass(genderPreds, out int classId, out double classProbGender);
	var gender = _genderList[classId];
	_ageNet.SetInput(blobGender);
	var agePreds = _ageNet.Forward();
	GetMaxClass(agePreds, out int classIdAge, out double classProbAge);
	var age = _ageList[classIdAge];
	var label = $"{gender},{age}";
	Cv2.PutText(newImage, label, new Point(x1 – 10, y2 + 20), HersheyFonts.HersheyComplexSmall, 1, Scalar.Yellow, 1);
	}
	private void GetMaxClass(Mat probBlob, out int classId, out double classProb)
	{
	// reshape the blob to 1×1000 matrix
	using var probMat = probBlob.Reshape(1, 1);
	Cv2.MinMaxLoc(probMat, out _, out classProb, out _, out var classNumber);
	classId = classNumber.X;
	Debug.WriteLine($"X: {classNumber.X} – Y: {classNumber.Y} ");
	}
	}
	}

view raw opencvnet5agegenderestimation.cs hosted with ❤ by GitHub

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