如何在 Java 中找到不同颜色的深浅？

Question

如果我有一个数字的 RBG 代码，例如 -16777216（黑色），如何使用此颜色代码找到其他类似的黑色阴影？

我正在尝试通过将所有不是-16777216 的像素标记为白色来将图像转换为单色。但是，通常会发现不同深浅的黑色，但由于不完全匹配而丢失了。

编辑：我遇到了一些麻烦。当我尝试使用这种颜色来查找黑色阴影时，我可以在将其他像素转换为白色时忽略它们，这是我的结果：

来源：

结果：

代码：

package test;

import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.net.URL;
import javax.imageio.ImageIO;

public class Test
{       
    public static void main(String[] args)
    {
        try
        {
            BufferedImage source = ImageIO.read( new URL("http://i.imgur.com/UgdqfUY.png"));
            //-16777216 = black:
            BufferedImage dest = makeMonoChromeFast(source, -16777216);
            File result = new File("D:/result.png");
            ImageIO.write(dest, "png", result);
        }
        catch (Exception e)
        {
            e.printStackTrace();;
        }
    }

    public static BufferedImage makeMonoChromeFast(BufferedImage source, int foreground)
    {        
        int background = -1; //white;

        Color fg = new Color(foreground);

        int color = 0;
        for (int y = 0; y < source.getHeight(); y++)
        {
            for (int x = 0; x < source.getWidth(); x++)
            {
                color = source.getRGB(x, y);
                if ( color == foreground )
                    continue;
                if (! isIncluded(fg, color, 50))
                    source.setRGB(x, y, background);;
            }
        }

        return source;
    }

    public static boolean isIncluded(Color target, int pixelColor, int tolerance)
    {
        Color pixel = new Color(pixelColor);
        int rT = target.getRed();
        int gT = target.getGreen();
        int bT = target.getBlue();
        int rP = pixel.getRed();
        int gP = pixel.getGreen();
        int bP = pixel.getBlue();
        return(
            (rP-tolerance<=rT) && (rT<=rP+tolerance) &&
            (gP-tolerance<=gT) && (gT<=gP+tolerance) &&
            (bP-tolerance<=bT) && (bT<=bP+tolerance) );
    }
}

Answer 1

您可以使用这种“寻找具有差异容差的颜色”的方法。

public static boolean isIncluded(Color target, Color pixel, int tolerance) {
    int rT = target.getRed();
    int gT = target.getGreen();
    int bT = target.getBlue();
    int rP = pixel.getRed();
    int gP = pixel.getGreen();
    int bP = pixel.getBlue();
    return(
        (rP-tolerance<=rT) && (rT<=rP+tolerance) &&
        (gP-tolerance<=gT) && (gT<=gP+tolerance) &&
        (bP-tolerance<=bT) && (bT<=bP+tolerance) );
}

这里用于获取摩托车 (motorcycle.jpg) 的轮廓 (motorcycle-03.jpg)，同时去除“浅灰色覆盖层”。

摩托车.jpg

摩托车-03.png

ImageOutline.java

此代码需要一些耐心（运行时）。请参阅 Smoothing a jagged path 以获取更快执行相同操作的代码。

import java.awt.*;
import java.awt.image.BufferedImage;
import java.awt.geom.Area;
import javax.imageio.ImageIO;
import java.io.File;
import java.util.Date;
import javax.swing.*;

/* Motorcycle image courtesy of ShutterStock
http://www.shutterstock.com/pic-13585165/stock-vector-travel-motorcycle-silhouette.html */
class ImageOutline {

    public static Area getOutline(BufferedImage image, Color color, boolean include, int tolerance) {
        Area area = new Area();
        for (int x=0; x<image.getWidth(); x++) {
            for (int y=0; y<image.getHeight(); y++) {
                Color pixel = new Color(image.getRGB(x,y));
                if (include) {
                    if (isIncluded(color, pixel, tolerance)) {
                        Rectangle r = new Rectangle(x,y,1,1);
                        area.add(new Area(r));
                    }
                } else {
                    if (!isIncluded(color, pixel, tolerance)) {
                        Rectangle r = new Rectangle(x,y,1,1);
                        area.add(new Area(r));
                    }
                }
            }
        }
        return area;
    }

    public static boolean isIncluded(Color target, Color pixel, int tolerance) {
        int rT = target.getRed();
        int gT = target.getGreen();
        int bT = target.getBlue();
        int rP = pixel.getRed();
        int gP = pixel.getGreen();
        int bP = pixel.getBlue();
        return(
            (rP-tolerance<=rT) && (rT<=rP+tolerance) &&
            (gP-tolerance<=gT) && (gT<=gP+tolerance) &&
            (bP-tolerance<=bT) && (bT<=bP+tolerance) );
    }

    public static BufferedImage drawOutline(int w, int h, Area area) {
        final BufferedImage result = new BufferedImage(
            w,
            h,
            BufferedImage.TYPE_INT_RGB);
        Graphics2D g = result.createGraphics();

        g.setColor(Color.white);
        g.fillRect(0,0,w,h);

        g.setClip(area);
        g.setColor(Color.red);
        g.fillRect(0,0,w,h);

        g.setClip(null);
        g.setStroke(new BasicStroke(1));
        g.setColor(Color.blue);
        g.draw(area);

        return result;
    }

    public static BufferedImage createAndWrite(
        BufferedImage image,
        Color color,
        boolean include,
        int tolerance,
        String name)
        throws Exception {
        int w = image.getWidth();
        int h = image.getHeight();

        System.out.println("Get Area: " + new Date() + " - " + name);
        Area area = getOutline(image, color, include, tolerance);
        System.out.println("Got Area: " + new Date() + " - " + name);

        final BufferedImage result = drawOutline(w,h,area);
        displayAndWriteImage(result, name);

        return result;
    }

    public static void displayAndWriteImage(BufferedImage image, String fileName) throws Exception {
        ImageIO.write(image, "png", new File(fileName));
        JOptionPane.showMessageDialog(null, new JLabel(new ImageIcon(image)));
    }

    public static void main(String[] args) throws Exception {
        final BufferedImage outline = ImageIO.read(new File("motorcycle.jpg"));
        BufferedImage crop = outline.getSubimage(17,35,420,270);
        displayAndWriteImage(crop, "motorcycle-01.png");

        BufferedImage crude = createAndWrite(crop, Color.white, false, 60, "motorcycle-02.png");

        BufferedImage combo = createAndWrite(crude, Color.red, true, 0, "motorcycle-03.png");
    }
}

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使用问题中看到的代码，容差为 150，我看到了这个。

Answer 2

总的来说，我认为要走的路是使用this Wikipedia page上描述的sRGB到灰度转换公式，然后选择一个特定的“灰度”值作为黑白之间的边界。 （选择权在你...）

但是假设您已经有表示灰度点的 RGB 值，您应该会发现它们都具有相等的红色、绿色和蓝色值。如果情况确实如此，那么您只需选择 RGB 的颜色分量之一，并将其​​与您选择的“灰色”的相同颜色值进行比较。

如果您需要区分黑色、灰色和白色的多种色调，请选择多种边界“颜色”。

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编辑：我遇到了一些麻烦。当我尝试使用这种颜色来查找黑色阴影时，我可以在将其他像素转换为白色时忽略它们，这是我的结果：

您所看到的是抗锯齿的效果。图像中实际上很少有“纯”黑色。很多人眼看起来黑色的东西实际上是黑色的，或者不是那么深灰色。您需要使边界颜色（即“黑色”和“非黑色”之间的边界）更灰。