Examples of java.awt.image.Kernel

java.awt.image.Kernel
The Kernel class defines a matrix that describes how a specified pixel and its surrounding pixels affect the value computed for the pixel's position in the output image of a filtering operation. The X origin and Y origin indicate the kernel matrix element that corresponds to the pixel position for which an output value is being computed. @see ConvolveOp @version 10 Feb 1997

    
    private void sharpen() {
        img = picture.getImage();
        BufferedImage src = Utilities.toBufferedImage(new DcImageIcon(img));
        BufferedImageOp op = new ConvolveOp(
                new Kernel(3, 3, new float[] { 0.0f, -0.75f, 0.0f, -0.75f, 4.0f, 
                                              -0.75f, 0.0f, -0.75f, 0.0f }));
        update(op, src);
    }

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    private void blur() {
        img = picture.getImage();
        BufferedImage src = Utilities.toBufferedImage(new DcImageIcon(img));
        BufferedImageOp op = new ConvolveOp(
                new Kernel(3, 3, new float[] {.1111f, .1111f, .1111f, .1111f, .1111f, 
                                              .1111f, .1111f, .1111f, .1111f, }));
        update(op, src);
    }

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        float[] data = new float[size * size];
        float value = 1 / (float) (size * size);
        for (int i = 0; i < data.length; i++) {
            data[i] = value;
        }
        return new ConvolveOp(new Kernel(size, size, data));
    }

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        renderingHints = new RenderingHints(map);


        float ninth = 1.0f / 9.0f;
        float[] blurKernel = {ninth, ninth, ninth, ninth, ninth, ninth, ninth,
            ninth, ninth};
        convolutionKernel = new Kernel(3, 3, blurKernel);
        blurOperator = new ConvolveOp(convolutionKernel, ConvolveOp.EDGE_NO_OP,
                renderingHints);
    }

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        float[] data = new float[size * size];
        float value = 1.0f / (float) (size * size);
        for (int i = 0; i < data.length; i++) {
            data[i] = value;
        }
        return new ConvolveOp(new Kernel(size, size, data));
    }

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            index++;
        }
        for (int i = 0; i < rows; i++)
            matrix[i] /= total;


        return new Kernel(rows, 1, matrix);
    }

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        float[] data = new float[size * size];
        float value = 1.0f / (float) (size * size);
        for (int i = 0; i < data.length; i++) {
            data[i] = value;
        }
        return new ConvolveOp(new Kernel(size, size, data));
    }

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        for (int i = 0; i < data.length; i++) {
            data[i] /= total;
        }        
        
        Kernel kernel = null;
        if (horizontal) {
            kernel = new Kernel(size, 1, data);
        } else {
            kernel = new Kernel(1, size, data);
        }
        return new ConvolveOp(kernel, ConvolveOp.EDGE_NO_OP, null);
    }

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    }


    private void updateKernel() {
        float[] kernel = new float[kernelSize * kernelSize];
        Arrays.fill(kernel, blurIntensity);
        blur = new ConvolveOp(new Kernel(kernelSize, kernelSize, kernel));
    }

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        return kernelData;
    }


    private Kernel makeQualityKernelX(int len) {
        return new Kernel(len, 1, computeQualityKernelData(len, stdDevX));
    }

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