java图像处理——图像读取，二值化转bitset

最近要处理一些新闻中的广告图片，其中比较多的是含二维码的图片。简单写了3种逻辑处理了下（同源不同内容，同图片；含完整二维码的图片；残缺二维码广告图片），基本达到了实际需求。有同样需求的可以参考下，言归正传，今天看到有读者问java图像怎么转bitset，下面举例一些我的处理方法。
首先图片有不同来源，有的是url，那么url首先读取成BufferedImage

URL url_url = new URL(img_url);
Image image = null;
try
{
    image = ImageIO.read(url_url);
} catch (Exception e)
{
    e.printStackTrace();

}
BufferedImage buf = GetImage.toBufferedImage(image);
BufferedImage binatryBuf = BinaryTest.loaclTOBinatry(buf);
ImageData imagedata = BufferedImageHelper.bufferedImageToImageData(binatryBuf);

其中的imagedata是个model类，宽，高，和图像的bitset。

public class ImageData {
    private BitSet bitSet;
    private int width;
    private int height;

    public ImageData(BitSet bitSet, int width, int height) {
        this.bitSet = bitSet;
        this.width = width;
        this.height = height;
    }

    public BitSet getBitSet() {
        return bitSet;
    }

    public void setBitSet(BitSet bitSet) {
        this.bitSet = bitSet;
    }

    public int getWidth() {
        return width;
    }

    public void setWidth(int width) {
        this.width = width;
    }

    public int getHeight() {
        return height;
    }

    public void setHeight(int height) {
        this.height = height;
    }
}

其中的GetImage.toBufferedImage(image)

public static BufferedImage toBufferedImage(Image image) {
        if (image instanceof BufferedImage) {
            return (BufferedImage)image;
         }

         image = new ImageIcon(image).getImage();
         BufferedImage bimage = null;
         GraphicsEnvironment ge = GraphicsEnvironment.getLocalGraphicsEnvironment();
        try {
            int transparency = Transparency.OPAQUE;
             GraphicsDevice gs = ge.getDefaultScreenDevice();
             GraphicsConfiguration gc = gs.getDefaultConfiguration();
             bimage = gc.createCompatibleImage(
             image.getWidth(null), image.getHeight(null), transparency);
         } catch (HeadlessException e) {
         }

        if (bimage == null) {
            int type = BufferedImage.TYPE_INT_RGB;
             bimage = new BufferedImage(image.getWidth(null), image.getHeight(null), type);
         }

         Graphics g = bimage.createGraphics();

         g.drawImage(image, 0, 0, null);
         g.dispose();

        return bimage;
    }

如果BufferedImage本身就是二值的黑白图像，比如商场打印小票，那么可以用下面方法直接转换为bitset（Imagedata）

public static ImageData bufferedImageToImageData(BufferedImage buffer,
            int threshold) {
        if (buffer == null) {
            return null;
        }
        int width = buffer.getWidth();
        int height = buffer.getHeight();
        BitSet bitSet = new BitSet(width * height);
        for (int y = 0; y < height; y++) {
            for (int x = 0; x < width; x++) {
                bitSet.set(y * width + x, buffer.getRGB(x, y) <= threshold);
            }
        }
        return new ImageData(bitSet, width, height);
    }
public static ImageData bufferedImageToImageData(BufferedImage buffer) {
        return bufferedImageToImageData(buffer, new Color(0, 0, 0).getRGB());
    }

如果图像是彩色的，那么就要选择合适的二值化方法，下面举例几种：
适合检测类似二维码这种黑白色彩对比明显的图片，max-min方法

/**
     * 3*3窗口求Max-Min
     * 
     * @param gray
     * @param x
     * @param y
     * @param w
     * @param h
     * @return
     */
    public static int getMaxMinColor(int[][] gray, int x, int y, int w, int h, int edge)
    {
        int max = 0;
        int min = 255;
        for (int i = -edge; i < edge + 1; i++)
        {
            for (int j = -edge; j < edge + 1; j++)
            {
//              System.out.println("x:"+(x+i));
//              System.out.println("y:"+(y+j));
                if (x + i < 0 || x + i > w-1)
                    continue;
                if (y + j < 0 || y + j > h-1)
                    continue;
                if (gray[x + i][y + j] > max)
                    max = gray[x + i][y + j];
                if (gray[x + i][y + j] < min)
                    min = gray[x + i][y + j];
            }
        }

        return max - min > 0 ? max - min : 0;
    }

或者简单的窗口均值，这里也是3×3

/**
     * 自己加周围8个灰度值再除以9，算出其相对灰度值
     * 
     * @param gray
     * @param x
     * @param y
     * @param w
     * @param h
     * @return
     */
    public static int getAverageColor(int[][] gray, int x, int y, int w, int h)
    {
        int rs = gray[x][y] + (x == 0 ? 255 : gray[x - 1][y]) + (x == 0 || y == 0 ? 255 : gray[x - 1][y - 1])
                + (x == 0 || y == h - 1 ? 255 : gray[x - 1][y + 1]) + (y == 0 ? 255 : gray[x][y - 1])
                + (y == h - 1 ? 255 : gray[x][y + 1]) + (x == w - 1 ? 255 : gray[x + 1][y])
                + (x == w - 1 || y == 0 ? 255 : gray[x + 1][y - 1])
                + (x == w - 1 || y == h - 1 ? 255 : gray[x + 1][y + 1]);
        return rs / 9;
    }

这样每个像素点会因为不同的计算方法（或者说不同程度受周边像素加权影响），得到一个表示值，和经验阈值进行对比，进行二值。当然还有其他二值方法，大多是微分或者类卷积的对梯度变化加强。

public static BufferedImage loaclTOBinatry(BufferedImage bi,int T) throws IOException
    {
        int h = bi.getHeight();// 获取图像的高
        int w = bi.getWidth();// 获取图像的宽
        int rgb = bi.getRGB(0, 0);// 获取指定坐标的ARGB的像素值
        int[][] gray = new int[w][h];
        for (int x = 0; x < w; x++)
        {
            for (int y = 0; y < h; y++)
            {
                gray[x][y] = getGray(bi.getRGB(x, y));
            }
        }

        BufferedImage nbi = new BufferedImage(w, h, BufferedImage.TYPE_BYTE_BINARY);
        int SW = T;// 200=0.84 //230=0.94
        for (int x = 0; x < w; x++)
        {
            for (int y = 0; y < h; y++)
            {
                // if(gray[x][y]
                // if (getAverageColor(gray, x, y, w, h) > SW)//局部均值
                // {
                if (getMaxMinColor(gray, x, y, w, h, 1) > SW)//局部拉伸
                {
                    int max = new Color(255, 255, 255).getRGB();
                    nbi.setRGB(x, y, max);
                } else
                {
                    int min = new Color(0, 0, 0).getRGB();
                    nbi.setRGB(x, y, min);
                }
            }
        }
        return nbi;
        // ImageIO.write(nbi, "jpg", new File("D:/Test/binary/二值化后_无压缩.jpg"));
    }

    public static int getGray(int rgb)
    {
        Color c = new Color(rgb);
        int r = c.getRed();
        int g = c.getGreen();
        int b = c.getBlue();
        int top = (r + g + b) / 3;
        return (int) (top);
    }

url转bufferImage还可以用下面的方法

public static BufferedImage getBufferedImage(String img_url)
    {
        BufferedInputStream bis = null;
        HttpURLConnection httpUrl = null;
        URL url = null;
        try
        {
            url = new URL(img_url);
            httpUrl = (HttpURLConnection) url.openConnection();
            httpUrl.connect();
            bis = new BufferedInputStream(httpUrl.getInputStream());
            InputStream iis = bis;
            BufferedImage image = ImageIO.read(iis);

            return image;
        } catch (MalformedURLException e)
        {
            e.printStackTrace();
        } catch (IOException e)
        {
            e.printStackTrace();
        } catch (ArrayIndexOutOfBoundsException e)
        {
            e.printStackTrace();
        }

        return null;
    }

以上就这些吧，有兴趣的可以评论留言，我看到会尽快回答的。