/** * @see GThresholdImageOps#localMean */ public LocalMeanBinaryFilter(ConfigLength width, double scale, boolean down, ImageType<T> inputType) { this.regionWidth = width; this.scale = scale; this.down = down; this.inputType = inputType; work1 = inputType.createImage(1,1); work2 = inputType.createImage(1,1); }
protected void declareImages( int width , int height ) { image = imageType.createImage(width, height); image2 = imageType.createImage(width, height); } }
protected void declareImages( int width , int height ) { image = imageType.createImage(width, height); image2 = imageType.createImage(width, height); } }
/** * @see GThresholdImageOps#localGaussian */ public LocalGaussianBinaryFilter(ConfigLength regionWidth, double scale, boolean down, ImageType<T> inputType) { this.regionWidth = regionWidth; this.scale = scale; this.down = down; this.inputType = inputType; work1 = inputType.createImage(1,1); work2 = inputType.createImage(1,1); }
/** * Constructor * * @param alpha Larger values place more importance on flow smoothness consistency over brightness consistency. Try 20 * @param numIterations Number of iterations. Try 1000 */ public HornSchunck(float alpha, int numIterations, ImageType<D> derivType ) { this.alpha2 = alpha*alpha; this.numIterations = numIterations; derivX = derivType.createImage(1,1); derivY = derivType.createImage(1,1); derivT = derivType.createImage(1,1); }
public MjpegStreamSequence( InputStream in , ImageType<T> imageType ) { this.in = new DataInputStream(in); this.imageType = imageType; image = imageType.createImage(1,1); readNext(); }
public JpegByteImageSequence(ImageType<T> imageType, List<byte[]> jpegData, boolean loop) { this.imageType = imageType; this.jpegData = jpegData; this.loop = loop; output = imageType.createImage(1,1); loadNext(); }
public JCodecSimplified(String filename, ImageType<T> typeOutput) { image = typeOutput.createImage(1,1); this.typeOutput = typeOutput; this.filename = filename; reset(); }
public JCodecSimplified(String filename, ImageType<T> typeOutput) { image = typeOutput.createImage(1,1); this.typeOutput = typeOutput; this.filename = filename; reset(); }
public CacheSequenceStream( ImageType<T> imageType ) { queueBoof = imageType.createArray(2); queueBuff = new BufferedImage[2]; for (int i = 0; i < 2; i++) { queueBoof[i] = imageType.createImage(1,1); queueBuff[i] = new BufferedImage(1,1,BufferedImage.TYPE_INT_RGB); } this.imageType = imageType; }
public ImageGradientThenReduce(ImageGradient<Input, Middle> gradient, GradientMultiToSingleBand<Middle, Output> reduce) { this.gradient = gradient; this.reduce = reduce; middleX = gradient.getDerivativeType().createImage(1,1); middleY = gradient.getDerivativeType().createImage(1,1); }
public static <T extends ImageBase> T loadImage( File image, boolean orderRgb, ImageType<T> imageType ) { BufferedImage img = loadImage(image.getAbsolutePath()); if( img == null ) return null; T output = imageType.createImage(img.getWidth(),img.getHeight()); ConvertBufferedImage.convertFrom(img, orderRgb, output); return output; }
/** * */ public BufferedFileImageSequence(ImageType<T> type, BufferedImage[] orig) { this.type = type; this.orig = orig; images = type.createArray( orig.length ); for( int i = 0; i < orig.length; i++ ) { BufferedImage b = orig[i]; images[i] = type.createImage(b.getWidth(),b.getHeight()); ConvertBufferedImage.convertFrom(orig[i], images[i], true); } }
public SimpleSequence(Webcam webcam, ImageType<T> imageType) { this.webcam = webcam; Dimension d = webcam.getDevice().getResolution(); width = d.width; height = d.height; output = imageType.createImage(width,height); }
public SimpleSequence(Webcam webcam, ImageType<T> imageType) { this.webcam = webcam; Dimension d = webcam.getDevice().getResolution(); width = d.width; height = d.height; output = imageType.createImage(width,height); }
public DescribeImageDense_Convert(DescribeImageDense<ImageBase,Desc> describer , ImageType<T> inputType ) { ImageType describerType = describer.getImageType(); if( inputType.getFamily() != describerType.getFamily() ) throw new IllegalArgumentException("Image types must have the same family"); if( inputType.getDataType() == describerType.getDataType() ) throw new IllegalArgumentException("Data types are the same. Why do you want to use this class?"); workspace = describerType.createImage(1,1); this.describer = describer; this.inputType = inputType; }
private void createStorage() { if( inputType.getFamily() == ImageType.Family.PLANAR ) { storage = (T)GeneralizedImageOps.createSingleBand(inputType.getImageClass(),1,1); } else { storage = inputType.createImage(1,1); } }
public SimpleSequence(OpenCVFrameGrabber grabber, ImageType<T> imageType) { this.grabber = grabber; try { this.grabber.start(); } catch (FrameGrabber.Exception e) { throw new RuntimeException(e); } bgr_to_rgb = grabber.getPixelFormat() == 1; width = grabber.getImageWidth(); height = grabber.getImageHeight(); output = imageType.createImage(width,height); }
/** * @see ThresholdBlockOtsu */ public InputToBinarySwitch(InputToBinary alg, Class<T> inputType) { this.alg = alg; this.inputType = ImageType.single(inputType); if( !alg.getInputType().isSameType(this.inputType)) { work = (ImageGray)alg.getInputType().createImage(1,1); } }
public BackgroundProcessing(BackgroundModelStationary<T> model ) { super(model.getImageType()); this.model = model; this.scaled = model.getImageType().createImage(1, 1); this.work = GeneralizedImageOps.createSingleBand(model.getImageType().getDataType(),1,1); }