高斯模糊java代码_JAVA bitmap的高斯模糊代码

以下是Java中使用高斯模糊算法对Bitmap进行处理的代码示例：

public static Bitmap gaussianBlur(Bitmap src, float radius) {
    if (src == null) {
        return null;
    }
    
    int width = src.getWidth();
    int height = src.getHeight();
    int[] pixels = new int[width * height];
    src.getPixels(pixels, 0, width, 0, 0, width, height);
    
    int[] resultPixels = new int[width * height];
    System.arraycopy(pixels, 0, resultPixels, 0, pixels.length);
    
    int[] alpha = new int[256];
    for (int i = 0; i < 256; i++) {
        alpha[i] = (int) (Math.exp(-((float) i * i) / (2 * radius * radius)) / (Math.sqrt(2 * Math.PI) * radius) * 255);
    }
    
    int[] box = new int[(int) (2 * radius)];
    int boxWidth = (int) (radius / Math.sqrt(2 * Math.log(255)));
    int boxSize = boxWidth * 2 + 1;
    for (int i = 0; i < boxSize; i++) {
        box[i] = alpha[(int) (255 * i / boxSize)];
    }
    
    for (int y = 0; y < height; y++) {
        for (int x = 0; x < width; x++) {
            int red = 0;
            int green = 0;
            int blue = 0;
            int alphaSum = 0;
            for (int i = -boxWidth; i <= boxWidth; i++) {
                int pixelIndex = x + clamp(i, 0, width - 1) + y * width;
                int pixel = pixels[pixelIndex];
                int coeff = box[Math.abs(i)];
                red += Color.red(pixel) * coeff;
                green += Color.green(pixel) * coeff;
                blue += Color.blue(pixel) * coeff;
                alphaSum += Color.alpha(pixel) * coeff;
            }
            int resultPixelIndex = x + y * width;
            resultPixels[resultPixelIndex] = Color.argb(alphaSum >> 8, red >> 8, green >> 8, blue >> 8);
        }
    }
    
    Bitmap result = Bitmap.createBitmap(width, height, Bitmap.Config.ARGB_8888);
    result.setPixels(resultPixels, 0, width, 0, 0, width, height);
    return result;
}

private static int clamp(int value, int min, int max) {
    return Math.max(min, Math.min(value, max));
}

其中，使用了以下公式计算高斯函数的系数：

alpha[i] = exp(-i^2 / (2 * radius^2)) / (sqrt(2 * PI) * radius) * 255

对于每个像素点，都会计算出一个以该像素点为中心的正方形框，框内像素点的加权平均值作为该像素点的值。每个像素点的加权系数使用上述公式计算得到。在处理边缘像素点时，使用了clamp函数将超出边界的像素点坐标限制在合法范围内。最后，将处理后的像素数组生成为一个新的Bitmap并返回。