rgbtoyuv.java

SNP算法: 基于背景缩减和阴影检测的统计方法,适用于室内检测.

package bin;

import javax.imageio.ImageIO;
import java.awt.*;
import java.awt.image.*;
import java.io.File;
import java.io.IOException;
import java.io.FileOutputStream;

public class RGBToYUV{
	static float[] tb10_257x=new float[256];
    static float[] tb10_504x=new float[256];
    static float[] tb10_098x=new float[256];
    static float[] tb10_148x=new float[256];
    static float[] tb10_291x=new float[256];
    static float[] tb10_439x=new float[256];
    static float[] tb10_368x=new float[256];
    static float[] tb10_071x=new float[256];
	static void createtable(float[] pTable,float fMul){
	  for(int i=0;i<256;i++)
		pTable[i]=fMul*i;
    }
	static void createtables(){
	 createtable(tb10_257x,0.257f);
	 createtable(tb10_504x,0.504f);
	 createtable(tb10_098x,0.098f);
	 createtable(tb10_148x,0.148f);
	 createtable(tb10_291x,0.291f);
	 createtable(tb10_439x,0.439f);
	 createtable(tb10_368x,0.368f);
	 createtable(tb10_071x,0.071f);
   }
   RGBToYUV(){
   		createtables();
   }
   public static float[] rgbtoyuv(int[] rgb) {
   		float[] yuv=new float[3];
        yuv[0] = tb10_257x[rgb[0]] + tb10_504x[rgb[1]] +tb10_098x[rgb[2]] + 16;
    	yuv[1] = -tb10_148x[rgb[0]] - tb10_291x[rgb[1]]+tb10_439x[rgb[2]] + 128;
    	yuv[2] = tb10_439x[rgb[0]] - tb10_368x[rgb[1]] -tb10_071x[rgb[2]] + 128;
    	return yuv; 
    }  
    public static float gety(int[] rgb){
    	return	tb10_257x[rgb[0]] + tb10_504x[rgb[1]] +tb10_098x[rgb[2]] + 16;
    }
}