Java Code Examples for org.opencv.core.Core#meanStdDev()

private static double calculateScore(Mat im) {
	MatOfDouble mean = new MatOfDouble();
	MatOfDouble std = new MatOfDouble();
	Core.meanStdDev(im, mean, std);
	double[] means = mean.get(0, 0);
	double[] stds = std.get(0, 0);
	double score = 0.0;
	for (int i = 0; i < means.length; i++) score += means[i] - stds[i];
	return score;
}
 

/**
 * Output: resized zero-mean patch/pattern
 * @param inImg INPUT, outPattern OUTPUT
 * @return stdev
 */
private static double resizeZeroMeanStdev(final Mat inImg, Mat outPattern, int patternSize){
	if(inImg == null || outPattern == null){
		return -1;
	}
	
	Imgproc.resize(inImg, outPattern, new Size(patternSize, patternSize));
	final MatOfDouble mean = new MatOfDouble();
	final MatOfDouble stdev = new MatOfDouble();
	Core.meanStdDev(outPattern, mean, stdev);
	outPattern.convertTo(outPattern, CvType.CV_32F);
	Core.subtract(outPattern, new Scalar(mean.toArray()[0]), outPattern);
	
	return stdev.toArray()[0];
}
 

/**
 * @param input - Input mat
 * @return - Difference from perfect score
 */
@Override
public double calculateScore(Mat input) {
    Core.meanStdDev(input, mean, std);
    return MathFTC.mean(std.get(0,0));
}
 

public void init(Mat frame1, Rect trackedBox) {
	// get Bounding boxes
	if(Math.min(trackedBox.width, trackedBox.height) < _params.min_win) {
		throw new IllegalArgumentException("Provided trackedBox: " + trackedBox + " is too small (min " + _params.min_win + ")");
	}
	_grid = new Grid(frame1, trackedBox, _params.min_win);
	Log.i(Util.TAG, "Init Created " + _grid.getSize() + " bounding boxes.");
	_grid.updateGoodBadBoxes(trackedBox, _params.num_closest_init);
	
	_iiRows = frame1.rows();
	_iiCols = frame1.cols();
	_iisum.create(_iiRows, _iiCols, CvType.CV_32F);
	_iisqsum.create(_iiRows, _iiCols, CvType.CV_64F);
	
	// correct bounding box
	_lastbox = _grid.getBestBox();
	
	_classifierFern.init(_grid.getTrackedBoxScales(), _rng);
	
	
	// generate DATA
	// generate POSITIVE DATA
	generatePositiveData(frame1, _params.num_warps_init, _grid);
	
	// Set variance threshold
	MatOfDouble stddev = new MatOfDouble();
	Core.meanStdDev(frame1.submat(_grid.getBestBox()), new MatOfDouble(), stddev);
	updateIntegralImgs(frame1);
	// this is directly half of the variance of the initial box, which will be used the the 1st stage of the classifier
	_var = (float)Math.pow(stddev.toArray()[0], 2d) * 0.5f;
	// check variance
	final double checkVar = Util.getVar(_grid.getBestBox(), _iisumJava, _iisqsumJava, _iiCols) * 0.5;
	Log.i(Util.TAG, "Variance: " + _var + " / Check variance: " + checkVar);
	
	
	// generate NEGATIVE DATA
	final Pair<List<Pair<int[], Boolean>>, List<Mat>> negData = generateNegativeData(frame1);
	
	// Split Negative Ferns <features, labels=false> into Training and Testing sets (they are already shuffled)
	final int nFernsSize = negData.first.size();
	final List<Pair<int[], Boolean>> nFernsTest = new ArrayList<Pair<int[], Boolean>>(negData.first.subList(0, nFernsSize/2));
	final List<Pair<int[], Boolean>> nFerns = new ArrayList<Pair<int[], Boolean>>(negData.first.subList(nFernsSize/2, nFernsSize));
	
	// Split Negative NN Examples into Training and Testing sets
	final int nExSize = negData.second.size();
	final List<Mat> nExamplesTest = new ArrayList<Mat>(negData.second.subList(0, nExSize/2));
	_nExamples = new ArrayList<Mat>(negData.second.subList(nExSize/2, nExSize));

	
	//MERGE Negative Data with Positive Data and shuffle it
	final List<Pair<int[], Boolean>> fernsData = new ArrayList<Pair<int[], Boolean>>(_pFerns);
	fernsData.addAll(nFerns);
	Collections.shuffle(fernsData);
	
	// TRAINING
	Log.i(Util.TAG, "Init Start Training with " + fernsData.size() + " ferns, " 
	+ _nExamples.size() + " nExamples, " + nFernsTest.size() + " nFernsTest, " + nExamplesTest.size() + " nExamplesTest");
	_classifierFern.trainF(fernsData, 10);
	_classifierNN.trainNN(_pExample, _nExamples);
	// Threshold evaluation on testing sets
	_classifierFern.evaluateThreshold(nFernsTest);
	_classifierNN.evaluateThreshold(nExamplesTest);
}