Java Code Examples for org.opencv.objdetect.CascadeClassifier#detectMultiScale()

/**
 * OpenCV-4.0.0 人脸识别
 * 
 * @date: 2019年5月7日12:16:55
 * @param image
 *            待处理Mat图片(视频中的某一帧)
 * @return 处理后的图片
 * @throws IOException
 */
public static Mat getFace(Mat image) throws IOException {
	// 1 读取OpenCV自带的人脸识别特征XML文件
	File faceFile = new ClassPathResourcePatternResolver().getResource("opencv/data/haarcascade_frontalface_alt.xml")
			.getFile();
	CascadeClassifier facebook = new CascadeClassifier(faceFile.getAbsolutePath());
	// 2 特征匹配类
	MatOfRect face = new MatOfRect();
	// 3 特征匹配
	facebook.detectMultiScale(image, face);
	Rect[] rects = face.toArray();
	System.out.println("匹配到 " + rects.length + " 个人脸");
	// 4 为每张识别到的人脸画一个圈
	for (int i = 0; i < rects.length; i++) {
		Imgproc.rectangle(image, new Point(rects[i].x, rects[i].y),
				new Point(rects[i].x + rects[i].width, rects[i].y + rects[i].height), new Scalar(0, 255, 0));
		Imgproc.putText(image, "Human", new Point(rects[i].x, rects[i].y), Imgproc.FONT_HERSHEY_SCRIPT_SIMPLEX, 1.0,
				new Scalar(0, 255, 0), 1, Imgproc.LINE_AA, false);
		// Mat dst=image.clone();
		// Imgproc.resize(image, image, new Size(300,300));
	}
	return image;
}
 

public void run() {
  System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
  String base = "C:/Books in Progress/Java for Data Science/Chapter 10/OpenCVExamples/src/resources";
  CascadeClassifier faceDetector = 
          new CascadeClassifier(base + "/lbpcascade_frontalface.xml");
  
  Mat image = Imgcodecs.imread(base + "/images.jpg");

  MatOfRect faceVectors = new MatOfRect();
  faceDetector.detectMultiScale(image, faceVectors);

  out.println(faceVectors.toArray().length + " faces found");

  for (Rect rect : faceVectors.toArray()) {
      Imgproc.rectangle(image, new Point(rect.x, rect.y), 
              new Point(rect.x + rect.width, rect.y + rect.height), 
              new Scalar(0, 255, 0));
  }
  Imgcodecs.imwrite("faceDetection.png", image);
}
 

/**
 * Detects faces in an image, draws boxes around them, and writes the results
 * @param fileName
 * @param destName
 */
public static void drawRect(String fileName, String destName){
    Mat image = Highgui.imread(fileName);
    // Create a face detector from the cascade file in the resources
    // directory.
    CascadeClassifier faceDetector = new CascadeClassifier("libs/lbpcascade_frontalface.xml");
    // Detect faces in the image.
    // MatOfRect is a special container class for Rect.
    MatOfRect faceDetections = new MatOfRect();
    faceDetector.detectMultiScale(image, faceDetections);
    // Draw a bounding box around each face.
    for (Rect rect : faceDetections.toArray()) {
        Core.rectangle(image, new Point(rect.x, rect.y),
                new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
    }

    Highgui.imwrite(destName, image);

}
 

/**
 * Detects faces in an image, draws boxes around them, and writes the results
 * @param fileName
 * @param destName
 */
public static void drawRect(String fileName, String destName){
    Mat image = Imgcodecs.imread(fileName);
    // Create a face detector from the cascade file in the resources
    // directory.
    CascadeClassifier faceDetector = new CascadeClassifier("libs/lbpcascade_frontalface.xml");
    // Detect faces in the image.
    // MatOfRect is a special container class for Rect.
    MatOfRect faceDetections = new MatOfRect();
    faceDetector.detectMultiScale(image, faceDetections);
    // Draw a bounding box around each face.
    for (Rect rect : faceDetections.toArray()) {
        Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width,
                rect.y + rect.height), new Scalar(0, 255, 0));
    }

    Imgcodecs.imwrite(destName, image);

}
 

public static void loop (CascadeClassifier classifier, Mat mat, ImgWindow window, VideoCapture video) {
	video.read(mat);
	System.out.println(mat);
	if (!mat.empty()) {
		MatOfRect rects = new MatOfRect();
		long start = System.nanoTime();
		classifier.detectMultiScale(mat, rects);
		System.out.println((System.nanoTime()-start)/1000000000.0);
		window.setImage(mat);
		Graphics2D g = window.begin();
		g.setColor(Color.RED);
		for(Rect r: rects.toArray()) {
			g.drawRect(r.x, r.y, r.width, r.height);
		}
		window.end();
	}
}
 

public static Mat detectFace(Mat inputImage) {
    MatOfRect facesDetected = new MatOfRect();
    CascadeClassifier cascadeClassifier = new CascadeClassifier();
    int minFaceSize = Math.round(inputImage.rows() * 0.1f);
    cascadeClassifier.load("./src/main/resources/haarcascades/haarcascade_frontalface_alt.xml");
    cascadeClassifier.detectMultiScale(inputImage,
            facesDetected,
            1.1,
            3,
            Objdetect.CASCADE_SCALE_IMAGE,
            new Size(minFaceSize, minFaceSize),
            new Size()
    );
    Rect[] facesArray =  facesDetected.toArray();
    for(Rect face : facesArray) {
        Imgproc.rectangle(inputImage, face.tl(), face.br(), new Scalar(0, 0, 255), 3 );
    }
    return inputImage;
}
 

public static void detectFace(String sourceImagePath, String targetImagePath) {
    Mat loadedImage = loadImage(sourceImagePath);
    MatOfRect facesDetected = new MatOfRect();
    CascadeClassifier cascadeClassifier = new CascadeClassifier();
    int minFaceSize = Math.round(loadedImage.rows() * 0.1f);
    cascadeClassifier.load("./src/main/resources/haarcascades/haarcascade_frontalface_alt.xml");
    cascadeClassifier.detectMultiScale(loadedImage,
            facesDetected,
            1.1,
            3,
            Objdetect.CASCADE_SCALE_IMAGE,
            new Size(minFaceSize, minFaceSize),
            new Size()
    );
    Rect[] facesArray =  facesDetected.toArray();
    for(Rect face : facesArray) {
        Imgproc.rectangle(loadedImage, face.tl(), face.br(), new Scalar(0, 0, 255), 3 );
    }
    saveImage(loadedImage, targetImagePath);
}
 

/**
 * 获取人脸范围
 * @param fileName
 * @return
 */
public static MatOfRect takeFace(String fileName) {
    CascadeClassifier faceDetector = new CascadeClassifier("libs/lbpcascade_frontalface.xml");
    Mat image = Highgui.imread(fileName);
    MatOfRect faceDetections = new MatOfRect();
    // 指定人脸识别的最大和最小像素范围
    Size minSize = new Size(120, 120);
    Size maxSize = new Size(250, 250);
    // 参数设置为scaleFactor=1.1f, minNeighbors=4, flags=0 以此来增加识别人脸的正确率
    faceDetector.detectMultiScale(image, faceDetections, 1.1f, 4, 0,
            minSize, maxSize);
    return faceDetections;
}
 

/**
 * 获取人脸范围
 * @param fileName
 * @return
 */
public static MatOfRect takeFace(String fileName) {
    CascadeClassifier faceDetector = new CascadeClassifier("libs/lbpcascade_frontalface.xml");
    Mat image = Imgcodecs.imread(fileName);
    MatOfRect faceDetections = new MatOfRect();
    // 指定人脸识别的最大和最小像素范围
    Size minSize = new Size(120, 120);
    Size maxSize = new Size(250, 250);
    // 参数设置为scaleFactor=1.1f, minNeighbors=4, flags=0 以此来增加识别人脸的正确率
    faceDetector.detectMultiScale(image, faceDetections, 1.1f, 4, 0,
            minSize, maxSize);
    return faceDetections;
}
 

public static MatOfRect getFace(Mat src) {
	Mat result = src.clone();
	if (src.cols() > 1000 || src.rows() > 1000) {
		Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
	}

	CascadeClassifier faceDetector = new CascadeClassifier("D:\\mydev\\java\\opencv\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt2.xml");
	MatOfRect objDetections = new MatOfRect();
	faceDetector.detectMultiScale(result, objDetections);
	
	return objDetections;
}
 

/**
 * <p>Build a template from a specific eye area previously substracted
 * uses detectMultiScale for this area, then uses minMaxLoc method to
 * detect iris from the detected eye</p>
 *
 * @param area Preformatted Area
 * @param size minimum iris size
 * @param grayMat image in gray
 * @param rgbaMat image in color
 * @param detectorEye Haar Cascade classifier
 * @return built template
 */
@NonNull
private static Mat buildTemplate(Rect area, final int size,
                                 @NonNull Mat grayMat,
                                 @NonNull Mat rgbaMat,
                                 CascadeClassifier detectorEye) {
    Mat template = new Mat();
    Mat graySubMatEye = grayMat.submat(area);
    MatOfRect eyes = new MatOfRect();
    Rect eyeTemplate;
    detectorEye.detectMultiScale(graySubMatEye, eyes, 1.15, 2,
            Objdetect.CASCADE_FIND_BIGGEST_OBJECT
                    | Objdetect.CASCADE_SCALE_IMAGE, new Size(EYE_MIN_SIZE, EYE_MIN_SIZE),
            new Size());

    Rect[] eyesArray = eyes.toArray();
    if (eyesArray.length > 0) {
        Rect e = eyesArray[0];
        e.x = area.x + e.x;
        e.y = area.y + e.y;
        Rect eyeRectangle = getEyeArea((int) e.tl().x,
                (int) (e.tl().y + e.height * 0.4),
                e.width,
                (int) (e.height * 0.6));
        graySubMatEye = grayMat.submat(eyeRectangle);
        Mat rgbaMatEye = rgbaMat.submat(eyeRectangle);


        Core.MinMaxLocResult minMaxLoc = Core.minMaxLoc(graySubMatEye);

        FaceDrawerOpenCV.drawIrisCircle(rgbaMatEye, minMaxLoc);
        Point iris = new Point();
        iris.x = minMaxLoc.minLoc.x + eyeRectangle.x;
        iris.y = minMaxLoc.minLoc.y + eyeRectangle.y;
        eyeTemplate = getEyeArea((int) iris.x - size / 2,
                (int) iris.y
                        - size / 2, size, size);
        FaceDrawerOpenCV.drawEyeRectangle(eyeTemplate, rgbaMat);
        template = (grayMat.submat(eyeTemplate)).clone();
    }
    return template;
}