#dotnet – Detecting Faces using DNN from the 🎦 camera feed in a WinForm using #OpenCV and #net5

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Hi !

Let’s do some face detection using a DNN model (See references). As yesterday, I won’t write about details, there are almost 20 years of online documentation available.

And, IMHO opinion code is much more useful that long writing, so let’s go there. 1st load the Caffe model and the config file.

// download model and prototxt from https://github.com/spmallick/learnopencv/tree/master/FaceDetectionComparison/models
const string configFile = "deploy.prototxt";
const string faceModel = "res10_300x300_ssd_iter_140000_fp16.caffemodel";
_faceNet = CvDnn.ReadNetFromCaffe(configFile, faceModel);

And, once we grab the camera frame, let’s perform face detection using the dnn model:

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);
        Cv2.PutText(newImage, "Face Dnn", new Point(x1 + 2, y2 + 20),
            HersheyFonts.HersheyComplexSmall, 1, Scalar.Green, 2);
    }
}

And, the full code is here

using System;
using System.Threading;
using System.Windows.Forms;
using OpenCvSharp;
using OpenCvSharp.Dnn;
using OpenCvSharp.Extensions;
using Point = OpenCvSharp.Point;
using Size = OpenCvSharp.Size;
namespace Demo08_WinFormFaceDetectionDNN
{
public partial class Form1 : Form
{
private bool _run = false;
private bool _doFaceDetection = false;
private VideoCapture _capture;
private Mat _image;
private Thread _cameraThread;
private bool _fps = false;
private Net _faceNet;
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 Form1_Load(object sender, EventArgs e)
{
// download model and prototxt from https://github.com/spmallick/learnopencv/tree/master/FaceDetectionComparison/models
const string configFile = "deploy.prototxt";
const string faceModel = "res10_300x300_ssd_iter_140000_fp16.caffemodel";
_faceNet = CvDnn.ReadNetFromCaffe(configFile, 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)
{
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);
Cv2.PutText(newImage, "Face Dnn", new Point(x1 + 2, y2 + 20),
HersheyFonts.HersheyComplexSmall, 1, Scalar.Green, 2);
}
}
}
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);
var bmpEffect = BitmapConverter.ToBitmap(newImage);
pictureBoxWebCam.Image = bmpWebCam;
pictureBoxEffect.Image = bmpEffect;
}
}
}
}

That’s all for today!

Happy coding!

Greetings

El Bruno

References

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