opencvdetectandblurfaceusingdnn.cs

opencvdetectandblurfaceusingdnn.cs

//    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;
    }
}