Java Mat.empty方法代码示例

import org.opencv.core.Mat; //导入方法依赖的package包/类
/**
 * Analyzes an object in preparation to search for the object in a frame.
 * <p/>
 * This method should be called in an initialize() method.
 * Calling the analyzeObject method twice will overwrite the previous objectAnalysis.
 * <p/>
 * It is recommended to use a GFTT (Good Features To Track) detector for this phase.
 *
 * @param object Object image
 * @return The object descriptor matrix to be piped into locateObject() later
 */
public ObjectAnalysis analyzeObject(Mat object) throws IllegalArgumentException {
    Mat descriptors = new Mat();
    MatOfKeyPoint keypoints = new MatOfKeyPoint();

    Log.d("FTCVision", "Analyzing object...");

    if (object == null || object.empty()) {
        throw new IllegalArgumentException("Object image cannot be empty!");
    }

    //Detect object keypoints
    detector.detect(object, keypoints);

    //Extract object keypoints
    extractor.compute(object, keypoints, descriptors);

    return new ObjectAnalysis(keypoints, descriptors, object);
}
 

import org.opencv.core.Mat; //导入方法依赖的package包/类
private Mat grabFrame()
{
    Mat frame = new Mat(); // empty mat
    if (cap.isOpened())
    {
        try
        {
            cap.read(frame);
            if (!frame.empty()) process(frame);
        }
        catch(Exception ex)
        {
            System.err.println("Error during image processing.");
        }
    }
    
    return frame;
}
 

import org.opencv.core.Mat; //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.Mat; //导入方法依赖的package包/类
public static void main(String[] args) throws Exception {
	final String input = "D:/workspace/SpotSpotter/src/pers/zylo117/spotspotter/image/4.jpg";
	final String output = "D:/workspace/SpotSpotter/src/pers/zylo117/spotspotter/image/output4.jpg";

	System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
	final Mat in = Imgcodecs.imread(input);
	if (in.empty()) {
		throw new Exception("no file");
	}
	final Mat out = LSD(in);
	Imgcodecs.imwrite(output, out);
}
 

import org.opencv.core.Mat; //导入方法依赖的package包/类
public static void main(String[] args) throws Exception {
	final String input = "D:\\tmp\\5.jpg";
	// final String output =
	// "D:/workspace/SpotSpotter/src/pers/zylo117/spotspotter/image/output2.jpg";
	System.loadLibrary("opencv_java330_64");
	final Mat in = Imgcodecs.imread(input);
	if (in.empty()) {
		throw new Exception("no file");
	}
	final Mat mask = binaryzation_OpenCV(in, 300 * 0.6, true);
	MatView.imshow(mask, "mask");
	// Imgcodecs.imwrite(output, mask);
}
 

import org.opencv.core.Mat; //导入方法依赖的package包/类
private Mat grabFrame()
{
    // init everything
    Mat frame = new Mat();

    // check if the capture is open
    if (this.capture.isOpened())
    {
        try
        {
            // read the current frame
            this.capture.read(frame);

            // if the frame is not empty, process it
            if (!frame.empty())
            {
                process(frame);
            }

        }
        catch (Exception e)
        {
            // log the error
            System.err.println("Exception during the image elaboration: " + e);
        }
    }

    return frame;
}
 

import org.opencv.core.Mat; //导入方法依赖的package包/类
private Mat grabFrame() {
    Mat frame = new Mat(); // empty mat
    if (cap.isOpened()) {
        try {
            cap.read(frame);
            if (!frame.empty()) process(frame);
        } catch (Exception ex) {
            System.err.println("Error during image processing.");
        }
    }

    return frame;
}
 

import org.opencv.core.Mat; //导入方法依赖的package包/类
public void grabImage(){
    Mat frame = new Mat();

    //connect
    videoCapture = new VideoCapture(0);
    isOpened = videoCapture.isOpened();
    System.out.println("connected: " + isOpened);
    //setSetting
    videoCapture.set(Videoio.CV_CAP_PROP_FRAME_WIDTH, 1280);
    videoCapture.set(Videoio.CV_CAP_PROP_FRAME_HEIGHT, 720);
    //startGrab
    isSucceed = videoCapture.grab();
    System.out.println("started: " + String.valueOf(isSucceed));
    if ((!isOpened) || (!isSucceed))
        return;
    System.out.println("------- START GRAB -------");

    //Wait for camera starting
    while (true){
        videoCapture.read(frame);
        if (!frame.empty())
            break;
    }

    int frameNo = 0;
    long startSysMillis = System.currentTimeMillis();
    while (frameNo < 1000){
        videoCapture.read(frame);
        frameNo++;
    }
    System.out.println(frameNo + " frames in " + (System.currentTimeMillis() - startSysMillis) + " millis");

    videoCapture.release(); // release device

    System.out.println('\n' + "Done");
}
 

import org.opencv.core.Mat; //导入方法依赖的package包/类
/**
 * Get a frame from the opened video stream (if any)
 * 
 * @return the {@link Image} to show
 */
private Image grabFrame()
{
	// init everything
	Image imageToShow = null;
	Mat frame = new Mat();
	
	// check if the capture is open
	if (this.capture.isOpened())
	{
		try
		{
			// read the current frame
			this.capture.read(frame);
			
			// if the frame is not empty, process it
			if (!frame.empty())
			{
				// face detection
				this.detectAndDisplay(frame);
				
				// convert the Mat object (OpenCV) to Image (JavaFX)
				imageToShow = mat2Image(frame);
			}
			
		}
		catch (Exception e)
		{
			// log the (full) error
			System.err.println("ERROR: " + e);
		}
	}
	
	return imageToShow;
}
 

import org.opencv.core.Mat; //导入方法依赖的package包/类
public static Mat subspaceProject(Mat W, Mat mean, Mat src) {
	int n = src.rows();
	int d = src.cols();
	Mat X = new Mat();
	Mat Y = new Mat();
	src.convertTo(X, W.type());
	if(!mean.empty()) {
		for(int i = 0; i < n; i ++) {
			Mat r_i = X.row(i);
			Core.subtract(r_i, mean.reshape(1, 1), r_i);
		}
	}
	Core.gemm(X, W, 1.0, new Mat(), 0.0, Y);
	return Y;
}
 

import org.opencv.core.Mat; //导入方法依赖的package包/类
public static Mat subspaceReconstruct(Mat W, Mat mean, Mat src) {
	int n = src.rows();
	int d = src.cols();
	Mat X = new Mat();
	Mat Y = new Mat();
	src.convertTo(Y, W.type());
	Core.gemm(Y, W, 1.0, new Mat(), 0.0, X, 2);
	if(!mean.empty()) {
		for(int i = 0; i < n; i ++) {
			Mat r_i = X.row(i);
			Core.add(r_i, mean.reshape(1, 1), r_i);
		}
	}
	return X;
}
 

import org.opencv.core.Mat; //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;
	}