Examples of ConvolveOp

ae.java.awt.image.ConvolveOp
This class implements a convolution from the source to the destination. Convolution using a convolution kernel is a spatial operation that computes the output pixel from an input pixel by multiplying the kernel with the surround of the input pixel. This allows the output pixel to be affected by the immediate neighborhood in a way that can be mathematically specified with a kernel.
This class operates with BufferedImage data in which color components are premultiplied with the alpha component. If the Source BufferedImage has an alpha component, and the color components are not premultiplied with the alpha component, then the data are premultiplied before being convolved. If the Destination has color components which are not premultiplied, then alpha is divided out before storing into the Destination (if alpha is 0, the color components are set to 0). If the Destination has no alpha component, then the resulting alpha is discarded after first dividing it out of the color components.
Rasters are treated as having no alpha channel. If the above treatment of the alpha channel in BufferedImages is not desired, it may be avoided by getting the Raster of a source BufferedImage and using the filter method of this class which works with Rasters.
If a RenderingHints object is specified in the constructor, the color rendering hint and the dithering hint may be used when color conversion is required.
Note that the Source and the Destination may not be the same object. @see Kernel @see ae.java.awt.RenderingHints#KEY_COLOR_RENDERING @see ae.java.awt.RenderingHints#KEY_DITHERING
java.awt.image.ConvolveOp
This class implements a convolution from the source to the destination. Convolution using a convolution kernel is a spatial operation that computes the output pixel from an input pixel by multiplying the kernel with the surround of the input pixel. This allows the output pixel to be affected by the immediate neighborhood in a way that can be mathematically specified with a kernel.
This class operates with BufferedImage data in which color components are premultiplied with the alpha component. If the Source BufferedImage has an alpha component, and the color components are not premultiplied with the alpha component, then the data are premultiplied before being convolved. If the Destination has color components which are not premultiplied, then alpha is divided out before storing into the Destination (if alpha is 0, the color components are set to 0). If the Destination has no alpha component, then the resulting alpha is discarded after first dividing it out of the color components.
Rasters are treated as having no alpha channel. If the above treatment of the alpha channel in BufferedImages is not desired, it may be avoided by getting the Raster of a source BufferedImage and using the filter method of this class which works with Rasters.
If a RenderingHints object is specified in the constructor, the color rendering hint and the dithering hint may be used when color conversion is required.
Note that the Source and the Destination may not be the same object. @version 10 Feb 1997 @see Kernel @see java.awt.RenderingHints#KEY_COLOR_RENDERING @see java.awt.RenderingHints#KEY_DITHERING

Examples of java.awt.image.ConvolveOp

        return new ConvolveOp(kernel, ConvolveOp.EDGE_NO_OP, null);
    }
    
    public static ConvolveOp[] getGaussianBlurFilter1DPair(int radius)
    {
        ConvolveOp op = getGaussianBlurFilter1D(radius, true);
        
        // Copy of data, but faster than recalculating
        float[] data = op.getKernel().getKernelData(null);
        
        ConvolveOp op1 = new ConvolveOp(new Kernel(1, op.getKernel().getWidth(), data),
                ConvolveOp.EDGE_NO_OP, null);
        
        return new ConvolveOp[] { op, op1 };
    }

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Examples of java.awt.image.ConvolveOp

                  weight, weight, weight, weight,
                  weight, weight, weight, weight,
                  weight, weight, weight, weight,
                  weight, weight, weight, weight,
              };
              op = new ConvolveOp(new Kernel(4, 4, blurKernel), ConvolveOp.EDGE_NO_OP, null);              
          }
          else {
            final float weight = 1.0f/18.0f;
            final float[] blurKernel = {
                1*weight, 2*weight, 1*weight,
                2*weight, 6*weight, 2*weight,
                1*weight, 2*weight, 1*weight
            };


            op = new ConvolveOp(new Kernel(3, 3, blurKernel), ConvolveOp.EDGE_NO_OP, null);
          }
          
          BufferedImage blured = op.createCompatibleDestImage(bi, bi.getColorModel());
          
             op.filter(bi, blured);

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