基於opencv的人臉檢測

因爲項目須要瞭解下圖像識別，看到opencv有豐富的類庫，就到官網看了下例子，由於自己項目是基於java的，因此嘗試改爲java版本熟悉下opencv。這裏只是說人臉檢測，還沒能識別是誰的臉。

1、環境準備

1.下載opencv，以下因爲網絡緣由下到了opencv-3.4.1，下載完成後執行安裝，下面的opencv就是解壓後的目錄

2.配置java.library.path

3.配置jar依賴

~\opencv\build\java這個目錄下有個jar包opencv-341.jar添加到工程依賴，用maven的話能夠手動添加jar包依賴，也能夠從中央倉庫下載。阿里雲倉庫找到以下版本了。也能夠用。System.loadLibrary(Core.NATIVE_LIBRARY_NAME);改爲System.loadLibrary("opencv_java341");

<dependency>
            <groupId>org.openpnp</groupId>
            <artifactId>opencv</artifactId>
            <version>3.2.0-1</version>
        </dependency>

2、opencv官網示例代碼

https://docs.opencv.org/4.0.1/d4/d26/samples_2cpp_2facedetect_8cpp-example.html，這個是C++版本，翻譯下來：

public class NesttyMain  {
    public static void main(String[] args){
        System.out.println(System.getProperty("java.library.path"));
        System.loadLibrary("opencv_java341");
        CascadeClassifier cascade= new CascadeClassifier();
        CascadeClassifier nestedCascade = new CascadeClassifier();
//下面的訓練好的文件改爲本身的目錄        
        cascade.load("D:\\3rd_app\\opencv3.4.1\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt.xml");
        nestedCascade.load("D:\\3rd_app\\opencv3.4.1\\opencv\\build\\etc\\haarcascades\\haarcascade_eye_tree_eyeglasses.xml");
        Mat image = Imgcodecs
                .imread("C:\\Users\\Administrator\\Desktop\\3women.jpg");
        detectAndDraw(image,cascade,nestedCascade,1,false);
    }

    static void  detectAndDraw( Mat img, CascadeClassifier cascade,
                        CascadeClassifier nestedCascade,
                        double scale, boolean tryflip ) {
        double t = 0;
        MatOfRect faces = new MatOfRect();
        MatOfRect faces2 = new MatOfRect();
        Scalar colors[] =
                {
                        new Scalar(255, 0, 0),
                        new Scalar(255, 128, 0),
                        new Scalar(255, 255, 0),
                        new Scalar(0, 255, 0),
                        new Scalar(0, 128, 255),
                        new Scalar(0, 255, 255),
                        new Scalar(0, 0, 255),
                        new Scalar(255, 0, 255)
                };
        Mat gray = new Mat();
        Mat smallImg = new Mat();
        Imgproc.cvtColor(img, gray, Imgproc.COLOR_BGR2GRAY);
        double fx = 1 / scale;
        Imgproc.resize(gray, smallImg, new Size(), fx, fx, 5);
        Imgproc.equalizeHist(smallImg, smallImg);
        t = (double) Core.getTickCount();

        cascade.detectMultiScale(smallImg, faces,
                1.1, 2, 0
                        //|CASCADE_FIND_BIGGEST_OBJECT
                        //|CASCADE_DO_ROUGH_SEARCH
                        | CASCADE_SCALE_IMAGE,
                new Size(),
                new Size(150, 150));
        if (tryflip) {
            Core.flip(smallImg, smallImg, 1);
            cascade.detectMultiScale(smallImg, faces2,
                    1.1, 2, 0
                            //|CASCADE_FIND_BIGGEST_OBJECT
                            //|CASCADE_DO_ROUGH_SEARCH
                            | CASCADE_SCALE_IMAGE,
                    new Size(0, 0),
                    new Size(300, 300));
            for (Rect rect : faces2.toArray()) {
                Imgproc.rectangle(img, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
            }
        }
        t = (double) Core.getTickCount() - t;
        System.out.println(String.format("detection time = %g ms\n", t * 1000 / Core.getTickFrequency()));
        System.out.println(String.format("detection faces = %s \n", faces.toArray().length));
        int i = 0;
        for (Rect rect : faces.toArray()) {
            Rect r = rect;
            Mat smallImgROI;
            MatOfRect nestedObjects = new MatOfRect();
            Point center = new Point();
            Scalar color = colors[i % 8];
            int radius;
            double aspect_ratio = (double) r.width / r.height;
            if (0.75 < aspect_ratio && aspect_ratio < 1.3) {
                center.x = Math.round((r.x + r.width * 0.5) * scale);
                center.y = Math.round((r.y + r.height * 0.5) * scale);
                radius = (int) Math.round((r.width + r.height) * 0.25 * scale);
                Imgproc.circle(img, center, radius, color, 3, 8, 0);
            } else
                Imgproc.rectangle(img, new Point(Math.round(r.x * scale), Math.round(r.y * scale)),
                        new Point(Math.round((r.x + r.width - 1) * scale), Math.round((r.y + r.height - 1) * scale)),
                        color, 3, 8, 0);
            if (nestedCascade.empty())
                continue;
            smallImgROI = smallImg.submat(r);
            nestedCascade.detectMultiScale(smallImgROI, nestedObjects,
                    1.1, 2, 0
                            //|CASCADE_FIND_BIGGEST_OBJECT
                            //|CASCADE_DO_ROUGH_SEARCH
                            //|CASCADE_DO_CANNY_PRUNING
                            | CASCADE_SCALE_IMAGE,
                    new Size(),
                    new Size(40, 40));
            System.out.println(String.format("detection eyes = %s \n", nestedObjects.toArray().length));
            for (Rect rectNested : nestedObjects.toArray()) {
                {
                    Rect nr = rectNested;
                    center.x = Math.round((r.x + nr.x + nr.width * 0.5) * scale);
                    center.y = Math.round((r.y + nr.y + nr.height * 0.5) * scale);
                    radius = (int) Math.round((nr.width + nr.height) * 0.25 * scale);
                    Imgproc.circle(img, center, radius, color, 3, 8, 0);
                }
                i++;
            }
            Imgcodecs.imwrite("ouput.png", img);
        }
        img.dump();
    }
}

最終效果：