我们能否使用Java、ImageMagick或JMagick找出图像中哪种颜色是主导色?
在Java中迭代每个像素并确定颜色
import java.awt.image.BufferedImage;
import java.io.File;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import javax.imageio.ImageIO;
import javax.imageio.ImageReader;
import javax.imageio.stream.ImageInputStream;
public class ImageTester {
public static void main(String args[]) throws Exception {
File file = new File("C:\\Users\\Andrew\\Desktop\\myImage.gif");
ImageInputStream is = ImageIO.createImageInputStream(file);
Iterator iter = ImageIO.getImageReaders(is);
if (!iter.hasNext())
{
System.out.println("Cannot load the specified file "+ file);
System.exit(1);
}
ImageReader imageReader = (ImageReader)iter.next();
imageReader.setInput(is);
BufferedImage image = imageReader.read(0);
int height = image.getHeight();
int width = image.getWidth();
Map m = new HashMap();
for(int i=0; i < width ; i++)
{
for(int j=0; j < height ; j++)
{
int rgb = image.getRGB(i, j);
int[] rgbArr = getRGBArr(rgb);
// Filter out grays....
if (!isGray(rgbArr)) {
Integer counter = (Integer) m.get(rgb);
if (counter == null)
counter = 0;
counter++;
m.put(rgb, counter);
}
}
}
String colourHex = getMostCommonColour(m);
System.out.println(colourHex);
}
public static String getMostCommonColour(Map map) {
List list = new LinkedList(map.entrySet());
Collections.sort(list, new Comparator() {
public int compare(Object o1, Object o2) {
return ((Comparable) ((Map.Entry) (o1)).getValue())
.compareTo(((Map.Entry) (o2)).getValue());
}
});
Map.Entry me = (Map.Entry )list.get(list.size()-1);
int[] rgb= getRGBArr((Integer)me.getKey());
return Integer.toHexString(rgb[0])+" "+Integer.toHexString(rgb[1])+" "+Integer.toHexString(rgb[2]);
}
public static int[] getRGBArr(int pixel) {
int alpha = (pixel >> 24) & 0xff;
int red = (pixel >> 16) & 0xff;
int green = (pixel >> 8) & 0xff;
int blue = (pixel) & 0xff;
return new int[]{red,green,blue};
}
public static boolean isGray(int[] rgbArr) {
int rgDiff = rgbArr[0] - rgbArr[1];
int rbDiff = rgbArr[0] - rgbArr[2];
// Filter out black, white and grays...... (tolerance within 10 pixels)
int tolerance = 10;
if (rgDiff > tolerance || rgDiff < -tolerance)
if (rbDiff > tolerance || rbDiff < -tolerance) {
return false;
}
return true;
}
}
使用纯Java,您只需迭代每个像素并计算每种颜色的包含次数即可...
伪代码:
Map<Color, Integer> color2counter;
for (x : width) {
for (y : height) {
color = image.getPixel(x, y)
occurrences = color2counter.get(color)
color2counter.put(color, occurrences + 1)
}
}
var MAX_DESIRED_COLOR_COUNT = 128;
var file = new File("my-image.jpg");
var image = ImageIO.read(file);
var pixels = image.getRGB(0, 0, image.getWidth(), image.getHeight(), null, 0, image.getWidth());
var colorFrequency = QuantizerCelebi.quantize(pixels, MAX_DESIRED_COLOR_COUNT);
var decentColors = Score.score(colorFrequency);
var desiredColor = decentColors.get(0);
// You can take the desiredColor or any other decentColors and forget the rest of code below
// Could also use Scheme.dark(desiredColor); to get colors suitable for dark themes
var colorScheme = Scheme.light(desiredColor);
System.out.println("Decent colors: " + decentColors);
System.out.println("Primary color (light theme): " + colorScheme.getPrimary());
val MAX_DESIRED_COLOR_COUNT = 128
val file = File("my-image.jpg")
val image = ImageIO.read(file)
val pixels = image.getRGB(0, 0, image.width, image.height, null, 0, image.width)
val colorFrequency = QuantizerCelebi.quantize(pixels, MAX_DESIRED_COLOR_COUNT)
val decentColors = Score.score(colorFrequency)
val desiredColor = decentColors.first()
// You can take the desiredColor or any other decentColors and forget the rest of code below
// Could also use Scheme.dark(desiredColor) to get colors suitable for dark themes
val colorScheme = Scheme.light(desiredColor)
println("Decent colors: ${decentColors.joinToString { it.toHexString() }}")
println("Primary color (light theme): ${colorScheme.primary.toHexString()}")
fun Int.toHexString() = "#%06X".format(this and 0xFFFFFF)
colorScheme.primary
)。假设您使用的是加色方案,其中(0,0,0)为黑色,(255,255,255)为白色(如果我错了,请纠正我)。此外,如果您只想从RGB中找到主要颜色:
我有一个想法,任何人都可以仔细检查,即有3个变量,每个变量存储RGB值之一,并向图像中的每个像素添加相应的值,然后除以(255 * numOfPixels)以获得颜色比率。然后比较3个比率:.60表示红色更占优势,绿色为.5。
这只是一个想法,可能需要调整...