Java Code Examples for org.opencv.core.Mat#reshape()

/**
 * 
* @Title: reconstructFace 
* @Description: 从输入的预处理图像在人脸模型中重构人脸
* @param model 包含预处理的人脸模型
* @param preprocessedFace 输入的预处理过的图像
* @return
* Mat 
* @throws
 */
public static Mat reconstructFace(BasicFaceRecognizer model, Mat preprocessedFace){
	try {
		// 获取每个人脸的特征值
		Mat eigenvectors = model.getEigenVectors();
		// 获取平均人脸
		Mat averageFaceRow = model.getMean();
		int faceHeight = preprocessedFace.rows();
		// subspaceProject将人脸图像投影到特征空间
		Mat projection = subspaceProject(eigenvectors, averageFaceRow, preprocessedFace.reshape(1, 1));
		// subspaceReconstruct从特征空间重构图像
		Mat reconstructionRow = subspaceReconstruct(eigenvectors, averageFaceRow, projection);
		
		Mat reconstructionMat = reconstructionRow.reshape(1, faceHeight);
		Mat reconstructedFace = new Mat(reconstructionMat.size(), CvType.CV_8U);
		reconstructionMat.convertTo(reconstructedFace, CvType.CV_8U, 1, 0);
		
		return reconstructedFace;
	} catch(CvException e) {
		e.printStackTrace();
	}
	return null;
}
 

/**
 * Checks if two OpenCV Mats are equal.
 * The matrices must be equal size and type.
 * Floating-point mats are not supported.
 *
 * @param expected expected mat.
 * @param actual   actual mat.
 * @return true if they are equal.
 */
private static boolean equals(Mat expected, Mat actual) {
    if (expected.type() != actual.type() || expected.cols() != actual.cols()
            || expected.rows() != actual.rows()) {
        throw new UnsupportedOperationException(
                "Can not compare " + expected + " and " + actual);
    } else if (expected.depth() == CvType.CV_32F || expected.depth() == CvType.CV_64F) {
        throw new UnsupportedOperationException(
                "Floating-point mats must not be checked for exact match.");
    }
    // Subtract matrices
    Mat diff = new Mat();
    Core.absdiff(expected, actual, diff);
    // Count non zero pixels
    Mat reshaped = diff.reshape(1); // One channel
    int mistakes = Core.countNonZero(reshaped);
    // Free
    reshaped.release();
    diff.release();
    // Check mistakes
    return 0 == mistakes;
}
 

public String recognize(Mat img, String expectedLabel){
    // Ignore
    img = img.reshape(1,1);
    // Subtract mean
    img.convertTo(img, CvType.CV_32F);
    Core.subtract(img, Psi, img);
    // Project to subspace
    Mat projected = getFeatureVector(img);
    // Save all points of image for tSNE
    img.convertTo(img, CvType.CV_8U);
    addImage(projected, expectedLabel, true);
    //addImage(projected, expectedLabel);
    Mat distance = new Mat(Omega.rows(), 1, CvType.CV_64FC1);
    for (int i=0; i<Omega.rows(); i++){
        double dist = Core.norm(projected.row(0), Omega.row(i), Core.NORM_L2);
        distance.put(i, 0, dist);
    }
    Mat sortedDist = new Mat(Omega.rows(), 1, CvType.CV_8UC1);
    Core.sortIdx(distance, sortedDist, Core.SORT_EVERY_COLUMN + Core.SORT_ASCENDING);
    // Give back the name of the found person
    int index = (int)(sortedDist.get(0,0)[0]);
    return labelMap.getKey(labelList.get(index));
}
 

public static List<Mat> cluster(Mat cutout, int k) {
	Mat samples = cutout.reshape(1, cutout.cols() * cutout.rows());
	Mat samples32f = new Mat();
	samples.convertTo(samples32f, CvType.CV_32F, 1.0 / 255.0);
	
	Mat labels = new Mat();
	TermCriteria criteria = new TermCriteria(TermCriteria.COUNT, 100, 1);
	Mat centers = new Mat();
	Core.kmeans(samples32f, k, labels, criteria, 1, Core.KMEANS_PP_CENTERS, centers);		
	return showClusters(cutout, labels, centers);
}
 

@Override
public Mat getFeatureVector(Mat img) {
    return img.reshape(1,1);
}
 

public Mat getFeatureVector(Mat img){
    return img.reshape(1,1);
}