Examples of javax.media.jai.KernelJAI

javax.media.jai.KernelJAI
A kernel representing a matrix with a key position, used by operators such as Convolve .
A KernelJAI is characterized by its width, height, and origin, or key element. The key element is the element which is placed over the current source pixel to perform convolution or error diffusion.
A kernel K is separable it the outer product of two one-dimensional vectors. It can speed up computation. One can construct a kernel from two one-dimensional vectors. <>The symmetry can be useful (such as computation speedup). Currently the protected instance variables isHorizonallySymmetric and isVerticallySymmetric are set to false. @see javax.media.jai.operator.ConvolveDescriptor @see javax.media.jai.operator.OrderedDitherDescriptor @see javax.media.jai.operator.ErrorDiffusionDescriptor

        RenderedOp src, Parameters params, int colorMapSize)
        throws MissingParameterException {


        int bitSize = numBitsToIndexColourmap(colorMapSize);


        KernelJAI kernel = getKernelFor(params, bitSize);
        IndexColorModel cm = getIndexColorModel(src, colorMapSize);
        ImageLayout layout = new ImageLayout();
        layout.setColorModel(cm);
        RenderingHints hints = new RenderingHints(JAI.KEY_IMAGE_LAYOUT, layout);
        if (src.getColorModel().getTransparency() != Transparency.OPAQUE &&

View Full Code Here

     *  range.
     */
    private KernelJAI _filterAssigner(int choice) throws IllegalActionException {
        switch (choice) {
        case _BACKDIAGONAL:
            return new KernelJAI(3, 3, _backDiagonalFilter);


        case _DIAGONAL:
            return new KernelJAI(3, 3, _diagonalFilter);


        case _FREICHEN_HORIZONTAL:
            return new KernelJAI(3, 3, _freiAndChenHorizontalFilter);


        case _FREICHEN_VERTICAL:
            return new KernelJAI(3, 3, _freiAndChenVerticalFilter);


        case _PREWITT_HORIZONTAL:
            return new KernelJAI(3, 3, _prewittHorizontalFilter);


        case _PREWITT_VERTICAL:
            return new KernelJAI(3, 3, _prewittVerticalFilter);


        case _ROBERTS_HORIZONTAL:
            return new KernelJAI(3, 3, _robertsHorizontalFilter);


        case _ROBERTS_VERTICAL:
            return new KernelJAI(3, 3, _robertsVerticalFilter);


        case _SOBEL_HORIZONTAL:
            return new KernelJAI(3, 3, _sobelHorizontalFilter);


        case _SOBEL_VERTICAL:
            return new KernelJAI(3, 3, _sobelVerticalFilter);


        case _TRANSPARENT:
            return new KernelJAI(3, 3, _transparentFilter);


        case _ZERO_FILTER:
            return new KernelJAI(3, 3, _zeroFilter);


        default:
            throw new IllegalActionException("Could not assign filter");
        }
    }

View Full Code Here

                floatArray[count] = (float) matrixValue[i][j];
                count = count + 1;
            }
        }


        return new KernelJAI(width, height, floatArray);
    }

View Full Code Here

                    floatArray[count] = (float) matrixValue[i][j];
                    count = count + 1;
                }
            }


            _filter = new KernelJAI(width, height, floatArray);
        } else {
            super.attributeChanged(attribute);
        }
    }

View Full Code Here

                    true, null ), 256, 256 );


            
            final float[] DEFAULT_KERNEL_1D = {0.25f,0.5f,0.25f};
            ParameterBlock pb = new ParameterBlock();
            KernelJAI kernel = new KernelJAI(DEFAULT_KERNEL_1D.length,
                                             DEFAULT_KERNEL_1D.length,
                                             DEFAULT_KERNEL_1D.length/2,
                                             DEFAULT_KERNEL_1D.length/2,
                                             DEFAULT_KERNEL_1D,
                                             DEFAULT_KERNEL_1D);

View Full Code Here

                            image =  new RenderedImageAdapter( ri );
                            originalSampleModel = image.getSampleModel();
                            originalColorModel = image.getColorModel();
                            final float[] DEFAULT_KERNEL_1D = {0.25f,0.5f,0.25f};
                            ParameterBlock pb = new ParameterBlock();
                            KernelJAI kernel = new KernelJAI(DEFAULT_KERNEL_1D.length,
                                    DEFAULT_KERNEL_1D.length,
                                    DEFAULT_KERNEL_1D.length/2,
                                    DEFAULT_KERNEL_1D.length/2,
                                    DEFAULT_KERNEL_1D,
                                    DEFAULT_KERNEL_1D);

View Full Code Here


        int numMasks = ditherMask.length;
        this.dmask = new int[numMasks][];


        for(int k = 0; k < numMasks; k++) {
            KernelJAI mask = ditherMask[k];


            if(mask.getWidth() != dmaskWidth ||
               mask.getHeight() != dmaskHeight) {
                throw new IllegalArgumentException
                    (JaiI18N.getString("MlibOrderedDitherOpImage0"));
            }


            // Initialize the integral dither mask coefficients.

View Full Code Here

                                RenderingHints hints) {
        // Get source and parameters.
        RenderedImage source = args.getRenderedSource(0);
        LookupTableJAI colorMap =
            (LookupTableJAI)args.getObjectParameter(0);
        KernelJAI errorKernel = (KernelJAI)args.getObjectParameter(1);


        // Check colorMap compatibility.
        if(colorMap.getNumBands() != 1 &&
           colorMap.getNumBands() != 3) {
            // 1 or 3 band colorMaps only.

View Full Code Here


        RenderedImage source = args.getRenderedSource(0);


  // map the input kernel + gain factor to an equivalent
  // convolution kernel and then do a normal convolve.
  KernelJAI unRotatedKernel =
    ImageUtil.getUnsharpMaskEquivalentKernel(
      (KernelJAI)args.getObjectParameter(0),
      args.getFloatParameter(1));


        KernelJAI kJAI = unRotatedKernel.getRotatedKernel();


        int kWidth = kJAI.getWidth();
        int kHeight = kJAI.getHeight();


        /* mediaLib does not handle kernels with either dimension < 2. */
        if (kWidth < 2 || kHeight < 2) {
            return null;
        }


        if (kJAI.isSeparable() && kWidth >= 3 && kWidth <= 7 &&  kWidth == kHeight) {
            return new MlibSeparableConvolveOpImage(source,
                                                    extender, hints, layout,
                                                    kJAI);
        } else if ((kWidth == 3 && kHeight == 3) ||
                   (kWidth == 5 && kHeight == 5)) {

View Full Code Here

            dataV = new float[height];
            Arrays.fill(dataV, 1.0F/(float)height);
        }


        // Construct a separable kernel.
        KernelJAI kernel = new KernelJAI(width, height, xOrigin, yOrigin,
                                         dataH, dataV);


        // Construct the parameters for the "Convolve" RIF.
        ParameterBlock args = new ParameterBlock(paramBlock.getSources());
        args.add(kernel);

View Full Code Here

0 1 2 3 4 5

TOP

Related Classes of javax.media.jai.KernelJAI

com.sun.media.jai.codecimpl.util.ImageUtil

com.sun.media.jai.mlib.MlibBoxFilterRIF

com.sun.media.jai.mlib.MlibConvolveRIF

com.sun.media.jai.mlib.MlibDilateRIF

com.sun.media.jai.mlib.MlibErodeRIF

com.sun.media.jai.mlib.MlibErrorDiffusionRIF

com.sun.media.jai.mlib.MlibGradientRIF

com.sun.media.jai.mlib.MlibOrderedDitherOpImage

com.sun.media.jai.mlib.MlibUnsharpMaskRIF

com.sun.media.jai.opimage.BoxFilterRIF

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.