增加背景识别

pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.wlld</groupId>
     <artifactId>easyAi</artifactId>
-    <version>1.0.2</version>
+    <version>1.0.3</version>
 
     <name>easyAi</name>
     <!-- FIXME change it to the project's website -->

src/main/java/org/wlld/MatrixTools/Matrix.java

@@ -44,6 +44,16 @@ public class Matrix {
         matrix = new double[x][y];
         this.x = x;
         this.y = y;
+        setState(x, y);
+    }
+
+    /**
+     * 设置矩阵属性
+     *
+     * @param x 行数
+     * @param y 列数
+     */
+    private void setState(int x, int y) {
         if (x == 1 && y == 1) {
             isZero = true;
         } else if (x == 1 || y == 1) {
@@ -103,6 +113,7 @@ public class Matrix {
         matrix = new double[x][y];
         this.x = x;
         this.y = y;
+        setState(x, y);
         setAll(matr);
     }
 

src/main/java/org/wlld/MatrixTools/MatrixOperation.java

@@ -45,7 +45,8 @@ public class MatrixOperation {
     }
 
     //多元线性回归
-    public static Matrix getLinearRegression(Matrix parameter, Matrix Out) throws Exception {
+    public static Matrix getLinearRegression(Matrix parameter, Matrix out) throws Exception {
+        if (parameter.getX() == out.getX() && out.isVector() && !out.isRowVector()) {
             //将参数矩阵转置
             Matrix matrix1 = transPosition(parameter);
             //转置的参数矩阵乘以参数矩阵
@@ -55,7 +56,10 @@ public class MatrixOperation {
             //逆矩阵乘以转置矩阵
             Matrix matrix4 = mulMatrix(matrix3, matrix1);
             //最后乘以输出矩阵,生成权重矩阵并返回
-        return mulMatrix(matrix4, Out);
+            return mulMatrix(matrix4, out);
+        } else {
+            throw new Exception("invalid regression matrix");
+        }
     }
 
     //返回两个向量之间的欧氏距离的平方
@@ -402,8 +406,10 @@ public class MatrixOperation {
                     for (int h = 0; h < matrixColumn.getX(); h++) {
                         double columnNumber = matrixColumn.getNumber(h, 0);
                         double rowNumber = matrixRow.getNumber(0, h);
-                        double nowNumber = ArithUtil.mul(columnNumber, rowNumber);
+                        //double nowNumber = ArithUtil.mul(columnNumber, rowNumber);
-                        columnAllNumber = ArithUtil.add(columnAllNumber, nowNumber);
+                        //columnAllNumber = ArithUtil.add(columnAllNumber, nowNumber);
+                        double nowNumber = columnNumber * rowNumber;
+                        columnAllNumber = columnAllNumber + nowNumber;
                     }
                     matrix.setNub(i, j, columnAllNumber);
                 }
@@ -417,7 +423,7 @@ public class MatrixOperation {
     public static void mathMul(Matrix matrix, double nub) throws Exception {//矩阵数乘
         for (int i = 0; i < matrix.getX(); i++) {
             for (int j = 0; j < matrix.getY(); j++) {
-                matrix.setNub(i, j, ArithUtil.mul(matrix.getNumber(i, j), nub));
+                matrix.setNub(i, j, matrix.getNumber(i, j) * nub);
             }
         }
     }

src/main/java/org/wlld/imageRecognition/Operation.java

@@ -7,7 +7,9 @@ import org.wlld.config.Classifier;
 import org.wlld.config.StudyPattern;
 import org.wlld.i.OutBack;
 import org.wlld.imageRecognition.border.*;
+import org.wlld.imageRecognition.modelEntity.TrayBody;
 import org.wlld.imageRecognition.segmentation.RegionBody;
+import org.wlld.imageRecognition.segmentation.RgbRegression;
 import org.wlld.imageRecognition.segmentation.Specifications;
 import org.wlld.imageRecognition.segmentation.Watershed;
 import org.wlld.nerveCenter.NerveManager;
@@ -58,6 +60,30 @@ public class Operation {//进行计算
         return sub(matrix1);
     }
 
+    public void setTray(ThreeChannelMatrix threeChannelMatrix) throws Exception {//设置托盘
+        Matrix matrixR = threeChannelMatrix.getMatrixR();
+        Matrix matrixG = threeChannelMatrix.getMatrixG();
+        Matrix matrixB = threeChannelMatrix.getMatrixB();
+        Matrix matrixRGB = threeChannelMatrix.getMatrixRGB();
+        Random random = new Random();
+        int x = matrixRGB.getX();
+        int y = matrixRGB.getY();
+        int size = 70;
+        RgbRegression rgbRegression = new RgbRegression(size);
+        for (int i = 0; i < size; i++) {
+            int xr = random.nextInt(x);
+            int yr = random.nextInt(y);
+            double[] rgb = new double[]{matrixR.getNumber(xr, yr), matrixG.getNumber(xr, yr),
+                    matrixB.getNumber(xr, yr)};
+            rgbRegression.insertRGB(rgb);
+        }
+        rgbRegression.regression();
+//        double[] rgb = new double[]{164, 189, 193};
+//        double dis = rgbRegression.getDisError(rgb);
+//        System.out.println("dis==" + dis);
+        templeConfig.getFood().getTrayBody().add(rgbRegression);
+    }
+
     private void cutPic(ThreeChannelMatrix threeChannelMatrix, int x, int y, int xSize, int ySize) {
         Matrix matrixR = threeChannelMatrix.getMatrixR();
         Matrix matrixG = threeChannelMatrix.getMatrixG();
@@ -70,7 +96,7 @@ public class Operation {//进行计算
     }
 
     public void colorStudy(ThreeChannelMatrix threeChannelMatrix, int tag, List<Specifications> specificationsList) throws Exception {
-        Watershed watershed = new Watershed(threeChannelMatrix.getMatrixRGB(), specificationsList, templeConfig);
+        Watershed watershed = new Watershed(threeChannelMatrix, specificationsList, templeConfig);
         List<RegionBody> regionBodies = watershed.rainfall();
         if (regionBodies.size() == 1) {
             RegionBody regionBody = regionBodies.get(0);
@@ -139,7 +165,7 @@ public class Operation {//进行计算
     }
 
     public List<RegionBody> colorLook(ThreeChannelMatrix threeChannelMatrix, List<Specifications> specificationsList) throws Exception {
-        Watershed watershed = new Watershed(threeChannelMatrix.getMatrixRGB(), specificationsList, templeConfig);
+        Watershed watershed = new Watershed(threeChannelMatrix, specificationsList, templeConfig);
         List<RegionBody> regionList = watershed.rainfall();
         for (RegionBody regionBody : regionList) {
             MaxPoint maxPoint = new MaxPoint();

src/main/java/org/wlld/imageRecognition/TempleConfig.java

@@ -11,10 +11,8 @@ import org.wlld.function.Sigmod;
 import org.wlld.function.Tanh;
 import org.wlld.i.ActiveFunction;
 import org.wlld.imageRecognition.border.*;
-import org.wlld.imageRecognition.modelEntity.BoxList;
+import org.wlld.imageRecognition.modelEntity.*;
-import org.wlld.imageRecognition.modelEntity.KBorder;
+import org.wlld.imageRecognition.segmentation.RgbRegression;
-import org.wlld.imageRecognition.modelEntity.LvqModel;
-import org.wlld.imageRecognition.modelEntity.MatrixModel;
 import org.wlld.nerveCenter.NerveManager;
 import org.wlld.nerveCenter.Normalization;
 import org.wlld.nerveEntity.BodyList;

src/main/java/org/wlld/imageRecognition/modelEntity/TrayBody.java

@@ -0,0 +1,37 @@
+package org.wlld.imageRecognition.modelEntity;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description 托盘参数实体
+ */
+public class TrayBody {
+    private double wr;
+    private double wg;
+    private double b;
+
+    public double getWr() {
+        return wr;
+    }
+
+    public void setWr(double wr) {
+        this.wr = wr;
+    }
+
+    public double getWg() {
+        return wg;
+    }
+
+    public void setWg(double wg) {
+        this.wg = wg;
+    }
+
+    public double getB() {
+        return b;
+    }
+
+    public void setB(double b) {
+        this.b = b;
+    }
+}

src/main/java/org/wlld/imageRecognition/segmentation/RgbRegression.java

@@ -0,0 +1,75 @@
+package org.wlld.imageRecognition.segmentation;
+
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.MatrixTools.MatrixOperation;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description rgb回归 B = wr *R + wg * G+b
+ */
+public class RgbRegression {
+    private double wr;
+    private double wg;
+    private double b;
+    private Matrix RG;//rg矩阵
+    private Matrix B;//b矩阵
+    private int xIndex = 0;//记录插入数量
+    private boolean isRegression = false;//是否进行了回归
+
+    public double getWr() {
+        return wr;
+    }
+
+    public double getWg() {
+        return wg;
+    }
+
+    public double getB() {
+        return b;
+    }
+
+    public RgbRegression(int size) {//初始化rgb矩阵
+        RG = new Matrix(size, 3);
+        B = new Matrix(size, 1);
+    }
+
+    public void insertRGB(double[] rgb) throws Exception {//rgb插入矩阵
+        if (rgb.length == 3) {
+            RG.setNub(xIndex, 0, rgb[0]);
+            RG.setNub(xIndex, 1, rgb[1]);
+            RG.setNub(xIndex, 2, 1.0);
+            B.setNub(xIndex, 0, rgb[2]);
+            xIndex++;
+        } else {
+            throw new Exception("rgb length is not equals three");
+        }
+    }
+
+    public void regression() throws Exception {//开始进行回归
+        if (xIndex > 0) {
+            Matrix ws = MatrixOperation.getLinearRegression(RG, B);
+            wr = ws.getNumber(0, 0);
+            wg = ws.getNumber(1, 0);
+            b = ws.getNumber(2, 0);
+            isRegression = true;
+            System.out.println("wr==" + wr + ",wg==" + wg + ",b==" + b);
+        } else {
+            throw new Exception("regression matrix size is zero");
+        }
+    }
+
+    public double getDisError(double[] rgb) throws Exception {//误差距离
+        if (isRegression) {
+            if (rgb.length == 3) {
+                double B = wr * rgb[0] + wg * rgb[1] + b;
+                return Math.abs(B - rgb[2]);
+            } else {
+                throw new Exception("rgb length is not equals three");
+            }
+        } else {
+            throw new Exception("matrix does not regression");
+        }
+    }
+}

src/main/java/org/wlld/imageRecognition/segmentation/Watershed.java

@@ -3,6 +3,7 @@ package org.wlld.imageRecognition.segmentation;
 import org.wlld.MatrixTools.Matrix;
 import org.wlld.config.Kernel;
 import org.wlld.imageRecognition.TempleConfig;
+import org.wlld.imageRecognition.ThreeChannelMatrix;
 import org.wlld.param.Cutting;
 import org.wlld.tools.ArithUtil;
 
@@ -15,6 +16,9 @@ import java.util.*;
  */
 public class Watershed {
     private Matrix matrix;//RGB范数图像
+    private Matrix matrixR;//r通道
+    private Matrix matrixG;//g通道
+    private Matrix matrixB;//b通道
     private Matrix rainfallMap;//降雨图
     private Matrix regionMap;//分区图
     private int xSize;//单元高度
@@ -32,29 +36,33 @@ public class Watershed {
     private double maxIou;//最大iou
     private double rowMark;//行过滤
     private double columnMark;//列过滤
-    private List<Specifications> specifications;
+    private List<Specifications> specifications;//过滤候选区参数
+    private List<RgbRegression> trayBody;//托盘参数
 
-    public Watershed(Matrix matrix, List<Specifications> specifications, TempleConfig templeConfig) throws Exception {
+    public Watershed(ThreeChannelMatrix matrix, List<Specifications> specifications, TempleConfig templeConfig) throws Exception {
         if (matrix != null && specifications != null && specifications.size() > 0) {
             Cutting cutting = templeConfig.getCutting();
             th = cutting.getTh();
             regionNub = cutting.getRegionNub();
             maxRain = cutting.getMaxRain();
-            this.matrix = matrix;
+            this.matrix = matrix.getMatrixRGB();
+            matrixR = matrix.getMatrixR();
+            matrixG = matrix.getMatrixG();
+            matrixB = matrix.getMatrixB();
             this.specifications = specifications;
+            this.trayBody = templeConfig.getFood().getTrayBody();
             if (templeConfig.getEdge() > 0) {
                 edgeSize = templeConfig.getEdge();
             }
             rowMark = templeConfig.getFood().getRowMark();
             columnMark = templeConfig.getFood().getColumnMark();
-            width = matrix.getY();
+            width = this.matrix.getY();
-            height = matrix.getX();
+            height = this.matrix.getX();
-            xSize = matrix.getX() / regionNub;
+            xSize = this.matrix.getX() / regionNub;
-            ySize = matrix.getY() / regionNub;
+            ySize = this.matrix.getY() / regionNub;
             maxIou = templeConfig.getCutting().getMaxIou();
-            System.out.println("设置最大iou阈值：" + maxIou);
             // System.out.println("xSize===" + xSize + ",ysize===" + ySize);
-            rainfallMap = new Matrix(matrix.getX(), matrix.getY());
+            rainfallMap = new Matrix(this.matrix.getX(), this.matrix.getY());
             regionMap = new Matrix(regionNub, regionNub);
             xMax = rainfallMap.getX() - 1;
             yMax = rainfallMap.getY() - 1;
@@ -63,6 +71,20 @@ public class Watershed {
         }
     }
 
+    private boolean isTray(int x, int y) throws Exception {
+        boolean isTray = false;
+        double[] rgb = new double[]{matrixR.getNumber(x, y), matrixG.getNumber(x, y),
+                matrixB.getNumber(x, y)};
+        for (RgbRegression rgbRegression : trayBody) {
+            double dist = rgbRegression.getDisError(rgb);
+            if (dist < 10) {
+                isTray = true;
+                break;
+            }
+        }
+        return isTray;
+    }
+
     private double[] getPixels(int x, int y) throws Exception {
         //八方向取值
         double left = -1;//左边
@@ -94,29 +116,61 @@ public class Watershed {
             rightBottom = Kernel.rgbN;
         }
         if (top == -1 && rainfallMap.getNumber(x - 1, y) == 0) {
+            if (isTray(x - 1, y)) {
+                top = Kernel.rgbN;
+            } else {
                 top = matrix.getNumber(x - 1, y);
             }
+        }
         if (left == -1 && rainfallMap.getNumber(x, y - 1) == 0) {
+            if (isTray(x, y - 1)) {
+                left = Kernel.rgbN;
+            } else {
                 left = matrix.getNumber(x, y - 1);
             }
+        }
         if (bottom == -1 && rainfallMap.getNumber(x + 1, y) == 0) {
+            if (isTray(x + 1, y)) {
+                bottom = Kernel.rgbN;
+            } else {
                 bottom = matrix.getNumber(x + 1, y);
             }
+        }
         if (right == -1 && rainfallMap.getNumber(x, y + 1) == 0) {
+            if (isTray(x, y + 1)) {
+                right = Kernel.rgbN;
+            } else {
                 right = matrix.getNumber(x, y + 1);
             }
+        }
         if (leftTop == -1 && rainfallMap.getNumber(x - 1, y - 1) == 0) {
+            if (isTray(x - 1, y - 1)) {
+                leftTop = Kernel.rgbN;
+            } else {
                 leftTop = matrix.getNumber(x - 1, y - 1);
             }
+        }
         if (leftBottom == -1 && rainfallMap.getNumber(x + 1, y - 1) == 0) {
+            if (isTray(x + 1, y - 1)) {
+                leftBottom = Kernel.rgbN;
+            } else {
                 leftBottom = matrix.getNumber(x + 1, y - 1);
             }
+        }
         if (rightTop == -1 && rainfallMap.getNumber(x - 1, y + 1) == 0) {
+            if (isTray(x - 1, y + 1)) {
+                rightTop = Kernel.rgbN;
+            } else {
                 rightTop = matrix.getNumber(x - 1, y + 1);
             }
+        }
         if (rightBottom == -1 && rainfallMap.getNumber(x + 1, y + 1) == 0) {
+            if (isTray(x + 1, y + 1)) {
+                rightBottom = Kernel.rgbN;
+            } else {
                 rightBottom = matrix.getNumber(x + 1, y + 1);
             }
+        }
         return new double[]{top, left, bottom, right, leftTop, leftBottom, rightBottom, rightTop};
     }
 
@@ -220,16 +274,15 @@ public class Watershed {
                 regionBodies.add(regionBody);
             }
         }
-//        for (RegionBody regionBody : regionBodies) {
+        for (RegionBody regionBody : regionBodies) {
-//            int minX = regionBody.getMinX();
+            int minX = regionBody.getMinX();
-//            int maxX = regionBody.getMaxX();
+            int maxX = regionBody.getMaxX();
-//            int minY = regionBody.getMinY();
+            int minY = regionBody.getMinY();
-//            int maxY = regionBody.getMaxY();
+            int maxY = regionBody.getMaxY();
-//            System.out.println("minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY);
+            System.out.println("minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY);
-//        }
+        }
-        return iou(regionBodies);
+        //return iou(regionBodies);
-
+        return regionBodies;
-        // return regionBodies;
     }
 
     private List<RegionBody> iou(List<RegionBody> regionBodies) {
@@ -283,7 +336,6 @@ public class Watershed {
                         double intersectS = ArithUtil.mul(intersectX, intersectY);//相交面积
                         double mergeS = ArithUtil.sub(s, intersectS);//相并面积
                         double iou = ArithUtil.div(intersectS, mergeS);
-                        System.out.println("当前iou==" + iou);
                         if (iou > maxIou) {
                             if (s1 < s2) {//s1 是i ,大的
                                 list.add(j);
@@ -295,7 +347,6 @@ public class Watershed {
                 }
             }
         }
-        System.out.println("要删除的下标：" + list);
         List<RegionBody> regionBodies2 = new ArrayList<>();
         for (int i = 0; i < regionBodies.size(); i++) {
             if (!list.contains(i)) {
@@ -408,13 +459,11 @@ public class Watershed {
                 }
             }
             double cover = sigma / y;
-            // System.out.println(cover);
             if (cover < rowMark) {
                 for (int k = 0; k < y; k++) {
                     regionMap.setNub(i, k, 0.0);
                 }
             }
-            //System.out.println("ma==" + ma);
         }
     }
 
@@ -439,9 +488,9 @@ public class Watershed {
                 }
             }
         }
-        pixFilter();
+        pixFilter();//痕迹过滤
-        createMerge();
+        createMerge();//提取候选区
-        merge();
+        merge();//合并候选区
         //System.out.println(regionMap.getString());
     }
 

src/main/java/org/wlld/param/Food.java

@@ -1,5 +1,7 @@
 package org.wlld.param;
 
+import org.wlld.imageRecognition.segmentation.RgbRegression;
+
 import java.util.ArrayList;
 import java.util.List;
 
@@ -12,8 +14,17 @@ import java.util.List;
 public class Food {
     private int shrink = 60;//收缩参数
     private int times = 10;//聚类增强次数
-    private double rowMark = 0.12;//行痕迹
+    private double rowMark = 0.12;//行痕迹过滤
-    private double columnMark = 0.25;//列过滤
+    private double columnMark = 0.25;//列痕迹过滤
+    private List<RgbRegression> trayBody = new ArrayList<>();//托盘实体参数
+
+    public List<RgbRegression> getTrayBody() {
+        return trayBody;
+    }
+
+    public void setTrayBody(List<RgbRegression> trayBody) {
+        this.trayBody = trayBody;
+    }
 
     public double getRowMark() {
         return rowMark;

src/test/java/coverTest/FoodTest.java

@@ -78,7 +78,7 @@ public class FoodTest {
         Cutting cutting = templeConfig.getCutting();
         Food food = templeConfig.getFood();
         //
-        cutting.setMaxRain(340);//切割阈值
+        cutting.setMaxRain(360);//切割阈值
         cutting.setTh(0.8);
         cutting.setRegionNub(200);
         cutting.setMaxIou(1.0);
@@ -91,7 +91,8 @@ public class FoodTest {
         //菜品识别实体类
         food.setShrink(5);//缩紧像素
         food.setTimes(2);//聚类数据增强
-        // food.setTrayTh(50);
+        food.setRowMark(0.1);//0.12
+        food.setColumnMark(0.1);//0.25
         templeConfig.setClassifier(Classifier.KNN);
         templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 3);
         if (modelParameter != null) {
@@ -106,16 +107,20 @@ public class FoodTest {
         Operation operation = new Operation(templeConfig);
         List<Specifications> specificationsList = new ArrayList<>();
         Specifications specifications = new Specifications();
-        specifications.setMinWidth(400);
+        specifications.setMinWidth(300);
-        specifications.setMinHeight(400);
+        specifications.setMinHeight(300);
         specifications.setMaxWidth(900);
         specifications.setMaxHeight(900);
         specificationsList.add(specifications);
+        ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/d.jpg");
+        operation.setTray(threeChannelMatrix);
         for (int i = 1; i <= 1; i++) {
-            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/c.jpg");
+            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("/Users/lidapeng/Desktop/test/e1.jpg");
             operation.colorStudy(threeChannelMatrix1, 1, specificationsList);
             System.out.println("=======================================");
         }
+//        minX==301,minY==430,maxX==854,maxY==920
+//        minX==497,minY==1090,maxX==994,maxY==1520
         //test2(templeConfig);
     }
 

src/test/java/org/wlld/LangTest.java

@@ -1,5 +1,7 @@
 package org.wlld;
 
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.MatrixTools.MatrixOperation;
 import org.wlld.naturalLanguage.Talk;
 import org.wlld.naturalLanguage.TemplateReader;
 import org.wlld.naturalLanguage.WordModel;
@@ -16,7 +18,74 @@ import java.util.*;
  */
 public class LangTest {
     public static void main(String[] args) throws Exception {
-        test();
+        reTest();
+    }
+
+    public static void reTest() throws Exception {
+        Matrix parameter = new Matrix(29, 3, "[173,198,1]#" +
+                "[174,199,1]#" +
+                "[170,195,1]#" +
+                "[169,194,1]#" +
+                "[159,181,1]#" +
+                "[154,175,1]#" +
+                "[151,175,1]#" +
+                "[152,177,1]#" +
+                "[175,200,1]#" +
+                "[169,194,1]#" +
+                "[172,200,1]#" +
+                "[170,195,1]#" +
+                "[172,197,1]#" +
+                "[173,198,1]#" +
+                "[157,177,1]#" +
+                "[158,178,1]#" +
+                "[156,176,1]#" +
+                "[156,177,1]#" +
+                "[157,177,1]#" +
+                "[162,187,1]#" +
+                "[165,188,1]#" +
+                "[166,189,1]#" +
+                "[163,188,1]#" +
+                "[162,187,1]#" +
+                "[163,186,1]#" +
+                "[164,184,1]#" +
+                "[163,186,1]#" +
+                "[179,202,1]#" +
+                "[182,207,1]#");
+        Matrix out = new Matrix(29, 1, "[203]#" +
+                "[204]#" +
+                "[200]#" +
+                "[199]#" +
+                "[185]#" +
+                "[180]#" +
+                "[179]#" +
+                "[181]#" +
+                "[205]#" +
+                "[198]#" +
+                "[204]#" +
+                "[199]#" +
+                "[202]#" +
+                "[203]#" +
+                "[184]#" +
+                "[185]#" +
+                "[183]#" +
+                "[182]#" +
+                "[184]#" +
+                "[194]#" +
+                "[194]#" +
+                "[195]#" +
+                "[195]#" +
+                "[194]#" +
+                "[192]#" +
+                "[191]#" +
+                "[192]#" +
+                "[210]#" +
+                "[214]#");
+        Matrix w = MatrixOperation.getLinearRegression(parameter, out);
+        System.out.println(w.getString());
+        double r = 162;
+        double g = 187;
+        double b = r * w.getNumber(0, 0) + g * w.getNumber(1, 0) + w.getNumber(2, 0);
+        System.out.println("r:" + r + ",g:" + g + ",b:" + b);
     }
 
     public static void test1() throws Exception {