Question

我正在做一个学校作业，我们应该对图像进行sobel边缘检测。我们应该用sobel核心进行卷积，然后计算每个像素的梯度幅度。之后，我们应该使用阈值方法为像素提供值255（白色）或0（黑色），具体取决于阈值。边缘检测的输出图像必须属于 BufferedImage.TYPE_BYTE_BINARY 类型。我使用灰度图像作为输入，但结果看起来非常奇怪..它绝对不会检测到边缘。

我用Google搜索并设法找到工作代码（here，看到标记的正确答案），但是，这里的输出图像是BufferedImage.TYPE_INT_RGB类型，这是不允许的...在这个问题，还使用BufferedImage.TYPE.INT.RGB作为边缘检测的输入。

非常感谢帮助解决这个问题！

执行程序时的结果。边缘检测结果位于最右侧。

边缘检测结果应该的样子。

我的代码：

/**
 * turns an image to a grayscale version of the image
 */
public void alterImageGrayScale() throws IOException {
    imageGrayScale = new BufferedImage(imageOriginal.getWidth(), imageOriginal.getHeight(), BufferedImage.TYPE_BYTE_GRAY);

    for(int i = 0; i < imageOriginal.getWidth(); i++) {
        for(int j = 0; j < imageOriginal.getHeight(); j++) {
            Color c = new Color(imageOriginal.getRGB(i, j));
            int red = c.getRed();
            int green = c.getGreen();
            int blue = c.getBlue();
            int gray = (int) (0.2126*red + 0.7152*green + 0.0722*blue);
            imageGrayScale.setRGB(i, j, new Color(gray, gray, gray).getRGB());
        }
    }
}

/**
 * edge detection
 * @throws IOException
 */
public void alterEdgeDetection() throws IOException {
    imageBlackAndWhite = new BufferedImage(imageGrayScale.getWidth(), imageGrayScale.getHeight(), BufferedImage.TYPE_INT_RGB);
    int x = imageGrayScale.getWidth();
    int y = imageGrayScale.getHeight();
    int threshold = 250;

    for (int i = 1; i < x - 1; i++) {
        for (int j = 1; j < y - 1; j++) {

            int val00 = imageGrayScale.getRGB(i - 1, j - 1);
            int val01 = imageGrayScale.getRGB(i - 1, j);
            int val02 = imageGrayScale.getRGB(i - 1, j + 1);
            int val10 = imageGrayScale.getRGB(i, j - 1);
            int val11 = imageGrayScale.getRGB(i, j);
            int val12 = imageGrayScale.getRGB(i, j + 1);
            int val20 = imageGrayScale.getRGB(i + 1, j - 1);
            int val21 = imageGrayScale.getRGB(i + 1, j);
            int val22 = imageGrayScale.getRGB(i + 1, j + 1);

            int gradientX = ((-1 * val00) + (0 * val01) + (1 * val02)) + ((-2 * val10) + (0 * val11) + (2 * val12))
                    + ((-1 * val20) + (0 * val21) + (1 * val22));
            int gradientY = ((-1 * val00) + (-2 * val01) + (-1 * val02)) + ((0 * val10) + (0 * val11) + (0 * val12))
                    + ((1 * val20) + (2 * val21) + (1 * val22));
            int gradientValue = (int) Math.sqrt(Math.pow(gradientX, 2) + Math.pow(gradientY, 2));

            //???? feel like something should be done here, but dont know what

            if(threshold > gradientValue) {
                imageBlackAndWhite.setRGB(i, j, new Color(0, 0, 0).getRGB());
            } else {
                imageBlackAndWhite.setRGB(i, j, new Color(255, 255, 255).getRGB());
            }

        }
    }
}

Answer 1

根据评论中的回答，将边缘检测计算的灰度图像转换为TYPE_BYTE_BINARY类型的图像应该足够了。

以下是MCVE，它加载问题中链接的灰度图像（包含边缘检测结果），并将其转换为二进制图像。

对于转换为二进制图像，可以使用屏幕底部的滑块修改阈值：它分别确定将哪个灰度值转换为黑色或白色像素。

import java.awt.BorderLayout;
import java.awt.Color;
import java.awt.GridLayout;
import java.awt.image.BufferedImage;
import java.net.URL;

import javax.imageio.ImageIO;
import javax.swing.ImageIcon;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JPanel;
import javax.swing.JSlider;
import javax.swing.SwingUtilities;

public class ImageToBinary
{
    public static void main(String[] args) throws Exception
    {
        BufferedImage input = 
            ImageIO.read(new URL("https://i.stack.imgur.com/jvOan.png"));        

        BufferedImage output = convertToBinary(input, 10);

        SwingUtilities.invokeLater(() -> createAndShowGui(input, output));
    }

    private static void createAndShowGui(
        BufferedImage input, BufferedImage output)
    {
        JFrame f = new JFrame();
        f.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);

        JPanel p = new JPanel(new GridLayout(1,2));
        JLabel outputLabel = new JLabel(new ImageIcon(output));
        p.add(new JLabel(new ImageIcon(input)));    
        p.add(outputLabel);
        f.getContentPane().setLayout(new BorderLayout());
        f.getContentPane().add(p, BorderLayout.NORTH);   

        JSlider slider = new JSlider(0, 256, 10);
        slider.addChangeListener(e -> 
        {
            int threshold = slider.getValue();
            BufferedImage newOutput = convertToBinary(input, threshold);
            outputLabel.setIcon(new ImageIcon(newOutput));
        });

        f.getContentPane().add(slider, BorderLayout.SOUTH);
        f.pack();

        f.setLocationRelativeTo(null);
        f.setVisible(true);

    }

    private static BufferedImage convertToBinary(
        BufferedImage input, int threshold)
    {
        int w = input.getWidth();
        int h = input.getHeight();
        BufferedImage output = new BufferedImage(
            w, h, BufferedImage.TYPE_BYTE_BINARY);

        int blackRgb = Color.BLACK.getRGB();
        int whiteRgb = Color.WHITE.getRGB();

        for (int y = 0; y < h; y++)
        {
            for (int x = 0; x < w; x++)
            {

                int rgb = input.getRGB(x, y);
                int r = (rgb >> 16) & 0xFF;
                int g = (rgb >> 8) & 0xFF;
                int b = (rgb) & 0xFF;
                int gray = (int) (0.2126 * r + 0.7152 * g + 0.0722 * b);
                if (gray >= threshold)
                {
                    output.setRGB(x, y, whiteRgb);
                }
                else
                {
                    output.setRGB(x, y, blackRgb);
                }
            }
        }
        return output;
    }        

}

使用BufferedImage.TYPE_BYTE_BINARY作为输出进行Sobel边缘检测

1 个答案: