Java MatOfRect.toArray方法代码示例

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
void HOGDescriptor() {
    Mat grayMat = new Mat();
    Mat people = new Mat();

    //Converting the image to grayscale
    Imgproc.cvtColor(originalMat, grayMat, Imgproc.COLOR_BGR2GRAY);

    HOGDescriptor hog = new HOGDescriptor();
    hog.setSVMDetector(HOGDescriptor.getDefaultPeopleDetector());

    MatOfRect faces = new MatOfRect();
    MatOfDouble weights = new MatOfDouble();

    hog.detectMultiScale(grayMat, faces, weights);
    originalMat.copyTo(people);
    //Draw faces on the image
    Rect[] facesArray = faces.toArray();
    for (int i = 0; i < facesArray.length; i++)
        Imgproc.rectangle(people, facesArray[i].tl(), facesArray[i].br(), new Scalar(100), 3);

    //Converting Mat back to Bitmap
    Utils.matToBitmap(people, currentBitmap);
    imageView.setImageBitmap(currentBitmap);
}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {

        mRgba = inputFrame.rgba();
        mGray = inputFrame.gray();

        if (mAbsoluteFaceSize == 0) {
            int height = mGray.rows();
            if (Math.round(height * mRelativeFaceSize) > 0) {
                mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
            }
            mNativeDetector.setMinFaceSize(mAbsoluteFaceSize);
        }

        MatOfRect faces = new MatOfRect();

        if (mDetectorType == JAVA_DETECTOR) {
            if (mJavaDetector != null)
                mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2, // TODO: objdetect.CV_HAAR_SCALE_IMAGE
                        new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());
        } else if (mDetectorType == NATIVE_DETECTOR) {
            if (mNativeDetector != null)
                mNativeDetector.detect(mGray, faces);
        } else {
            Log.e(TAG, "Detection method is not selected!");
        }

        Rect[] facesArray = faces.toArray();
        for (int i = 0; i < facesArray.length; i++) {
            Imgproc.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);
        }

        //转置90度
        Mat rotateMat = Imgproc.getRotationMatrix2D(new Point(mRgba.rows() / 2, mRgba.cols() / 2), 90, 1);
        Imgproc.warpAffine(mRgba, mRgba, rotateMat, mRgba.size());

        //以y轴翻转
        Core.flip(mRgba, mRgba, 1);

        return mRgba;
    }
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
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);
}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
private static Mat dobj(CascadeClassifier objDetector, Mat src) {
	final Mat dst = src.clone();

	final MatOfRect objDetections = new MatOfRect();

	objDetector.detectMultiScale(dst, objDetections);

	if (objDetections.toArray().length <= 0) {
		return src;
	}
	for (final Rect rect : objDetections.toArray()) {
		Imgproc.rectangle(dst, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
				new Scalar(0, 0, 255), 2);
	}
	return dst;
}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
public static void staticFace(String input, String output) {

		System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
		System.out.println("\nRunning FaceDetector");

		final CascadeClassifier faceDetector = new CascadeClassifier(StaticFacialRecognition.class
				.getResource("../../../../../opencv/sources/data/haarcascades_cuda/haarcascade_frontalface_alt.xml")
				.getPath().substring(1));
		final Mat image = Imgcodecs.imread(input);
		// �������� StaticFacialRecognition.class.getResource(input).getPath().substring(1)

		final MatOfRect faceDetections = new MatOfRect();
		faceDetector.detectMultiScale(image, faceDetections);

		System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));

		for (final Rect rect : faceDetections.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));
		}

		System.out.println(String.format("Writing %s", output));
		Imgcodecs.imwrite(output, image);
		// �������� StaticFacialRecognition.class.getResource("").getPath().substring(1) +
		// output
	}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
/**
 * 
* @Title: detectMany 
* @Description: 检测多人图片，返回检测的人脸区域对象
* @param mImgSRC
* @return
* MatOfRect
 */
public static MatOfRect detectMany(Mat mImgSRC) {
	
	if(mImgSRC.empty()) {
		LOG.info("检测多人图片检测时没有找到图片");
		return null;
	}
	// 人脸检测器文件的所在路径的文件夹名称数组
	String [] pathKey = {ServletContextHolder.getOpenCVHaarcascades(), "haarcascade_frontalface_alt.xml"};
	CascadeClassifier cascade = new CascadeClassifier(FileUtils.buildFilePath(pathKey));
	if(cascade.empty()) {
		LOG.info("人脸检测级联加载器为null");
		return null;
	}
	// 记录搜索到的人脸区域
	MatOfRect mOfRect = new MatOfRect();
	// 用于计算缩放比例
	int scaledWidth = mImgSRC.width();
	detectManyObject(mImgSRC, cascade, mOfRect, scaledWidth);
	if(mOfRect.toArray().length <= 0) {
		LOG.info("没有检测到人脸...");
		return null;
	}
	return mOfRect;
}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
@Override
public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
    //Rotating the input frame
    Mat mGray = inputFrame.gray();
    mRgba = inputFrame.rgba();
    if (mIsFrontCamera)
    {
        Core.flip(mRgba, mRgba, 1);
        Core.flip(mGray, mGray, 1);
    }

    //Detecting face in the frame
    MatOfRect faces = new MatOfRect();
    if(haarCascade != null)
    {
        haarCascade.detectMultiScale(mGray, faces, 1.1, 2, 2, new Size(200,200), new Size());
    }

    Rect[] facesArray = faces.toArray();
    for (int i = 0; i < facesArray.length; i++)
        Imgproc.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), new Scalar(100), 3);
    return mRgba;
}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {

        mRgba = inputFrame.rgba();
        mGray = inputFrame.gray();

        if (mAbsoluteFaceSize == 0) {
            int height = mGray.rows();
            if (Math.round(height * mRelativeFaceSize) > 0) {
                mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
            }
            mNativeDetector.setMinFaceSize(mAbsoluteFaceSize);
        }

        MatOfRect faces = new MatOfRect();

        if (mDetectorType == JAVA_DETECTOR) {
            if (mJavaDetector != null)
                mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2, // TODO: objdetect.CV_HAAR_SCALE_IMAGE
                        new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());
        }
        else if (mDetectorType == NATIVE_DETECTOR) {
            if (mNativeDetector != null)
                mNativeDetector.detect(mGray, faces);
        }
        else {
            Log.e(TAG, "Detection method is not selected!");
        }

        Rect[] facesArray = faces.toArray();
        for (int i = 0; i < facesArray.length; i++)
            Imgproc.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);

        return mRgba;
    }
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
/**
 * <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;
}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
@Override
// 这里执行人脸检测的逻辑, 根据OpenCV提供的例子实现(face-detection)
public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
    mRgba = inputFrame.rgba();
    mGray = inputFrame.gray();
    // 翻转矩阵以适配前后置摄像头
    if (isFrontCamera) {
        Core.flip(mRgba, mRgba, 1);
        Core.flip(mGray, mGray, 1);
    } else {
        Core.flip(mRgba, mRgba, -1);
        Core.flip(mGray, mGray, -1);
    }
    float mRelativeFaceSize = 0.2f;
    if (mAbsoluteFaceSize == 0) {
        int height = mGray.rows();
        if (Math.round(height * mRelativeFaceSize) > 0) {
            mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
        }
    }
    MatOfRect faces = new MatOfRect();
    if (classifier != null)
        classifier.detectMultiScale(mGray, faces, 1.1, 2, 2,
                new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());
    Rect[] facesArray = faces.toArray();
    Scalar faceRectColor = new Scalar(0, 255, 0, 255);
    for (Rect faceRect : facesArray)
        Imgproc.rectangle(mRgba, faceRect.tl(), faceRect.br(), faceRectColor, 3);
    return mRgba;
}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
@Override
public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
    mRgba = inputFrame.rgba();
    mGray = inputFrame.gray();
    rotateFlipImage(ImageControllers.getInstance().getFlipType(),
                        ImageControllers.getInstance().getMakeTranspose(),
                                                mRgba.getNativeObjAddr());
    if (mAbsoluteFaceSize == 0) {
        int height = mGray.rows();
        if (Math.round(height * mRelativeFaceSize) > 0) {
            mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
        }
        mNativeDetector.setMinFaceSize(mAbsoluteFaceSize);
    }

    MatOfRect faces = new MatOfRect();

    if (mDetectorType == JAVA_DETECTOR) {
        if (mJavaDetector != null)
            mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2, // TODO: objdetect.CV_HAAR_SCALE_IMAGE
                    new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());
    }
    else if (mDetectorType == NATIVE_DETECTOR) {
        if (mNativeDetector != null)
            mNativeDetector.detect(mGray, faces);
        Log.v(LOG_TAG, "native detect");
    }
    else {
        Log.e(TAG, "Detection method is not selected!");
    }

    Rect[] facesArray = faces.toArray();
    for (int i = 0; i < facesArray.length; i++)
        Imgproc.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);

    return mRgba;
}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
/**
 * Method for face detection and tracking
 * 
 * @param frame
 *            it looks for faces in this frame
 */
private void detectAndDisplay(Mat frame)
{
	MatOfRect faces = new MatOfRect();
	Mat grayFrame = new Mat();
	
	// convert the frame in gray scale
	Imgproc.cvtColor(frame, grayFrame, Imgproc.COLOR_BGR2GRAY);
	// equalize the frame histogram to improve the result
	Imgproc.equalizeHist(grayFrame, grayFrame);
	
	// compute minimum face size (20% of the frame height, in our case)
	if (this.absoluteFaceSize == 0)
	{
		int height = grayFrame.rows();
		if (Math.round(height * 0.2f) > 0)
		{
			this.absoluteFaceSize = Math.round(height * 0.2f);
		}
	}
	
	// detect faces
	this.faceCascade.detectMultiScale(grayFrame, faces, 1.1, 2, 0 | Objdetect.CASCADE_SCALE_IMAGE,
			new Size(this.absoluteFaceSize, this.absoluteFaceSize), new Size());
			
	// each rectangle in faces is a face: draw them!
	Rect[] facesArray = faces.toArray();
	for (int i = 0; i < facesArray.length; i++)
	{
		Imgproc.rectangle(frame, facesArray[i].tl(), facesArray[i].br(), new Scalar(7, 255, 90), 4);
		System.out.println(facesArray[i].tl());	
		System.out.println(facesArray[i].br());	
	}
	
		
	

}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
/**
	 * 
	* @Title: cutOutImage 
	* @Description: 以Rect坐标裁剪图片 
	* @param src
	* @param mOfRect
	* @return
	* List<Mat> 
	 */
	public static List<Mat> cutOutImage(Mat src, MatOfRect mOfRect) {
		
		List<Mat> outs = new ArrayList<Mat>();
		
		if(src.empty() || mOfRect.toArray().length <= 0) {
			return null;
		}
		
		int enlarge = 50; //  将图片的x，y坐标加大
		for(Rect r : mOfRect.toArray()) {
			int enlargeW = (r.x + enlarge) / r.x;
			int enlargeH = (r.y + enlarge) / r.y;
			r.x -= enlarge;
			r.y -= enlarge;
			r.width += (enlargeW * r.width);
			r.height += (enlargeH * r.height);
			
			// 确定人脸在图像中的整个边界，防止人脸在边框之外
			if (r.x < 0) {
				r.x = 0;
			}
			if (r.y < 0) {
				r.y = 0;
			}
			if (r.x + r.width > src.cols()) {
				r.x = src.cols() - r.width;
			}
			if (r.y + r.height > src.rows()) {
				r.y = src.rows() - r.height;
			}
		
			
			Mat cutting = new Mat(src, r); // 裁剪图片
//			System.out.println(cutting);
			outs.add(cutting);
		}
		return outs;
	}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
@Test
public void testDetectManyObject() {
	String opencvDLL = "G:/java/JavaProjectRelease/classchecks/src/main/webapp/WEB-INF/dll/x64/opencv_java320.dll";
	System.load(opencvDLL);
	
	String haarcascade = "haarcascade_frontalface_alt.xml";
	
	
	CascadeClassifier cascade = new CascadeClassifier(XMLFilePath + haarcascade);
	
	Mat src = Imgcodecs.imread(imageDir + "/split/14.jpg");
	
	MatOfRect objects = new MatOfRect();
	int scaledWidth = src.width();
	
	DetectObject.detectManyObject(src, cascade, objects, scaledWidth);
	
	Rect [] rects = objects.toArray();
	int i = 0;
	for(Rect r : rects) {
		/*Imgproc.rectangle(src, new Point(r.x-100 , r.y-100 ), 
				new Point(r.x + r.width + 80, 
						r.y + r.height + 80), new Scalar(0, 0, 255), 3);*/
		Imgproc.rectangle(src, r.tl(), 
				r.br(), new Scalar(0, 0, 255), 3);
		/*r.width += 120;
		r.height += 120;
		r.x -= 100;
		r.y -= 100;
		System.out.println(r);
		Mat roi = new Mat(src, r);
		Imgcodecs.imwrite("e:/classchecks/2017417/split/"+i+".jpg", roi);
		i ++;*/
	}
	Imgcodecs.imwrite("e:/classchecks/2017417/dectctManyObject.jpg", src);
	//Imgcodecs.imwrite("e:/classchecks/dectctManyObject.jpg", src);
}
 

import org.opencv.core.MatOfRect; //导入方法依赖的package包/类
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {

        mRgba = inputFrame.rgba();
        mGray = inputFrame.gray();

        if (mAbsoluteFaceSize == 0) {
            int height = mGray.rows();
            if (Math.round(height * mRelativeFaceSize) > 0) {
                mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
            }
        }

        MatOfRect faces = new MatOfRect();


        if (mJavaDetector != null)
            mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2, // TODO: objdetect.CV_HAAR_SCALE_IMAGE
                    new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());



        Rect[] facesArray = faces.toArray();
        if(isFaceRectangleEnabled){
            for (int i = 0; i < facesArray.length; i++)
                Core.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);
        }

        if (facesArray.length == 1)
        {
            chooseFaceRect = facesArray[0].clone();
            isFaceRectChosen = true;
        }
        return mRgba;
    }