#OpenCV – Detect and blur faces 😁 using DNN in C#

Hi !

NET is in anniversary mode, so I think it will be a good idea to share some camera samples in C#. For these demos I’m using C# 6 and OpenCVSharp as OpenCV wrapper for .NET.

Today post: detect a face using a DNN model and blur the face area. This demo also show the detected face in a separated window (just for fun). And I added the Frame per second (FPS) as additional information in the main window

Easy to implement, and also easy to read:

Code Sample

	// Copyright (c) 2022
	// Author : Bruno Capuano
	// Create Time : 2022 Feb
	// Change Log :
	// - Open a camera feed from a local USB webcam and analyze each frame to detect faces using a pretrained DNN model
	// - Download model and prototxt from https://github.com/spmallick/learnopencv/tree/master/FaceDetectionComparison/models
	// - When a face is detected, the app will blur the face zone in the main window
	// - When a face is detected, the app will show the original detected face in a separated window
	// - Press [F] to enable / disable the face detection process
	// - Press [Q] to quit the app
	//
	// The MIT License (MIT)
	//
	// Permission is hereby granted, free of charge, to any person obtaining a copy
	// of this software and associated documentation files (the "Software"), to deal
	// in the Software without restriction, including without limitation the rights
	// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	// copies of the Software, and to permit persons to whom the Software is
	// furnished to do so, subject to the following conditions:
	//
	// The above copyright notice and this permission notice shall be included in
	// all copies or substantial portions of the Software.
	//
	// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
	// THE SOFTWARE.
	using OpenCvSharp;
	using OpenCvSharp.Dnn;
	// 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";
	using var faceNet = CvDnn.ReadNetFromCaffe(configFile, faceModel);
	var image = new Mat();
	var detectFaces = true;
	var run = true;
	using var capture = new VideoCapture(0);
	using var window = new Window("El Bruno - Face Detector DNN");
	using var windowFace = new Window("Face");
	while (run)
	{
	var startTime = DateTime.Now;
	capture.Read(image);
	if (image.Empty())
	break;
	var newSize = new Size(640, 480);
	using var frame = new Mat();
	Cv2.Resize(image, frame, newSize);
	if (detectFaces)
	{
	int frameHeight = frame.Rows;
	int frameWidth = frame.Cols;
	using var blob = CvDnn.BlobFromImage(frame, 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(frame, new Point(x1, y1), new Point(x2, y2), Scalar.Green);
	// create a new Mat with the detected face
	var faceImg = new Mat(frame,
	new OpenCvSharp.Range(y1, y2),
	new OpenCvSharp.Range(x1, x2));
	windowFace.Image = faceImg;
	// blur the face area in the original frame
	var faceBlur = new Mat();
	Cv2.GaussianBlur(faceImg, faceBlur, new Size(23, 23), 30);
	frame[new OpenCvSharp.Range(y1, y2), new OpenCvSharp.Range(x1, x2)] = faceBlur;
	}
	}
	}
	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(frame, fpsInfo, new Point(10, 20), HersheyFonts.HersheyComplexSmall, 1, Scalar.White);
	window.Image = frame;
	switch ((char)Cv2.WaitKey(100))
	{
	case (char)27: // ESC
	run = false;
	break;
	case 'f':
	detectFaces = !detectFaces;
	break;
	}
	}

view raw opencvdetectandblurfaceusingdnn.cs hosted with ❤ by GitHub

Original Post

Happy coding!

Greetings

El Bruno

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