!35 增加缩小选区

Merge pull request !35 from 逐光/test

easyAi.iml

@@ -0,0 +1,16 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module org.jetbrains.idea.maven.project.MavenProjectsManager.isMavenModule="true" type="JAVA_MODULE" version="4">
+  <component name="NewModuleRootManager" LANGUAGE_LEVEL="JDK_1_8">
+    <output url="file://$MODULE_DIR$/target/classes" />
+    <output-test url="file://$MODULE_DIR$/target/test-classes" />
+    <content url="file://$MODULE_DIR$">
+      <sourceFolder url="file://$MODULE_DIR$/src/main/java" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/src/main/resources" type="java-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/test/java" isTestSource="true" />
+      <excludeFolder url="file://$MODULE_DIR$/target" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+    <orderEntry type="library" scope="TEST" name="Maven: com.alibaba:fastjson:1.2.51" level="project" />
+  </component>
+</module>

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

@@ -3,6 +3,7 @@ package org.wlld.MatrixTools;
 import org.wlld.tools.ArithUtil;
 
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 
 public class MatrixOperation {
@@ -67,6 +68,27 @@ public class MatrixOperation {
         }
     }
 
+    public static double errorNub(Matrix matrix, Matrix avgMatrix, int excNub) throws Exception {//求均方误差
+        int y = matrix.getY();
+        if (matrix.isRowVector() && avgMatrix.isRowVector() && y == avgMatrix.getY()) {
+            double[] subAll = new double[y];
+            for (int j = 0; j < y; j++) {
+                double mySelf = matrix.getNumber(0, j);
+                double avg = avgMatrix.getNumber(0, j);
+                double sub = Math.pow(avg - mySelf, 2);
+                subAll[j] = sub;
+            }
+            Arrays.sort(subAll);
+            double sigma = 0;
+            for (int i = 0; i < y - excNub; i++) {
+                sigma = sigma + subAll[i];
+            }
+            return sigma / (y - excNub);
+        } else {
+            throw new Exception("this matrix is not  rowVector or length different");
+        }
+    }
+
     public static Matrix pushVector(Matrix myMatrix, Matrix matrix, boolean addRow) throws Exception {
         //向一个矩阵里合并一个行向量或者列向量到矩阵行或者列的末尾
         if (matrix.getX() == 1 || matrix.getY() == 1) {
@@ -353,6 +375,22 @@ public class MatrixOperation {
         }
     }
 
+    public static Matrix matrixPointDiv(Matrix matrix1, Matrix matrix2) throws Exception {//矩阵点除
+        int x = matrix1.getX();
+        int y = matrix1.getY();
+        Matrix matrix = new Matrix(x, y);
+        if (matrix2.getX() == x && matrix2.getY() == y) {
+            for (int i = 0; i < x; i++) {
+                for (int j = 0; j < y; j++) {
+                    matrix.setNub(i, j, matrix1.getNumber(i, j) / matrix2.getNumber(i, j));
+                }
+            }
+        } else {
+            throw new Exception("two matrix is not equals");
+        }
+        return matrix;
+    }
+
     public static Matrix mulMatrix(Matrix matrix1, Matrix matrix2) throws Exception {//矩阵相乘
         if (matrix1.getY() == matrix2.getX()) {
             Matrix matrix = new Matrix(matrix1.getX(), matrix2.getY());
@@ -384,6 +422,14 @@ public class MatrixOperation {
         }
     }
 
+    public static void mathDiv(Matrix matrix, double nub) throws Exception {//矩阵数除
+        for (int i = 0; i < matrix.getX(); i++) {
+            for (int j = 0; j < matrix.getY(); j++) {
+                matrix.setNub(i, j, matrix.getNumber(i, j) / nub);
+            }
+        }
+    }
+
     //行向量转LIST
     public static List<Double> rowVectorToList(Matrix matrix) throws Exception {
         List<Double> list = new ArrayList<>();

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

@@ -106,6 +106,82 @@ public class Convolution extends Frequency {
         return features;
     }
 
+    public void filtering(ThreeChannelMatrix threeChannelMatrix) throws Exception {//平滑滤波
+        Matrix matrixR = threeChannelMatrix.getMatrixR();
+        Matrix matrixG = threeChannelMatrix.getMatrixG();
+        Matrix matrixB = threeChannelMatrix.getMatrixB();
+        int x = matrixR.getX();
+        int y = matrixR.getY();
+        Matrix matrixRFilter = new Matrix(x, y);//滤波后的R通道
+        Matrix matrixGFilter = new Matrix(x, y);//滤波后的G通道
+        Matrix matrixBFilter = new Matrix(x, y);//滤波后的B通道
+        int row = 0;
+        int column = 0;
+        for (int i = 0; i < x; i++) {
+            for (int j = 0; j < y; j++) {
+                double sigmaR = 0;
+                double sigmaG = 0;
+                double sigmaB = 0;
+                double nub = 0;
+                for (int t = 0; t < 8; t++) {
+                    row = 0;
+                    column = 0;
+                    switch (t) {
+                        case 0://上
+                            row = i - 1;
+                            break;
+                        case 1://左
+                            column = j - 1;
+                            break;
+                        case 2://下
+                            row = i + 1;
+                            break;
+                        case 3://右
+                            column = j + 1;
+                            break;
+                        case 4://左上
+                            column = j - 1;
+                            row = i - 1;
+                            break;
+                        case 5://左下
+                            column = j - 1;
+                            row = i + 1;
+                            break;
+                        case 6://右下
+                            column = j + 1;
+                            row = i + 1;
+                            break;
+                        case 7://右上
+                            column = j + 1;
+                            row = i - 1;
+                            break;
+                    }
+                    if (row >= 0 && column >= 0 && row < x && column < y) {
+                        double r = matrixR.getNumber(row, column);
+                        double g = matrixG.getNumber(row, column);
+                        double b = matrixB.getNumber(row, column);
+                        sigmaR = sigmaR + r;
+                        sigmaG = sigmaG + g;
+                        sigmaB = sigmaB + b;
+                        nub++;
+                    }
+                }
+                double pixelR = sigmaR / nub;
+                double pixelG = sigmaG / nub;
+                double pixelB = sigmaB / nub;
+                matrixRFilter.setNub(i, j, pixelR);
+                matrixGFilter.setNub(i, j, pixelG);
+                matrixBFilter.setNub(i, j, pixelB);
+            }
+        }
+        Matrix rPic = MatrixOperation.matrixPointDiv(matrixR, matrixRFilter);
+        Matrix gPic = MatrixOperation.matrixPointDiv(matrixG, matrixGFilter);
+        Matrix bPic = MatrixOperation.matrixPointDiv(matrixB, matrixBFilter);
+        threeChannelMatrix.setMatrixR(rPic);
+        threeChannelMatrix.setMatrixG(gPic);
+        threeChannelMatrix.setMatrixB(bPic);
+    }
+
     public List<Double> getCenterColor(ThreeChannelMatrix threeChannelMatrix, int poolSize, int sqNub) throws Exception {
         Matrix matrixR = threeChannelMatrix.getMatrixR();
         Matrix matrixG = threeChannelMatrix.getMatrixG();
@@ -119,7 +195,7 @@ public class Convolution extends Frequency {
         MeanClustering meanClustering = new MeanClustering(sqNub);
         for (int i = 0; i < x; i++) {
             for (int j = 0; j < y; j++) {
-                double[] color = new double[]{matrixR.getNumber(i, j) / 255, matrixG.getNumber(i, j) / 255, matrixB.getNumber(i, j) / 255};
+                double[] color = new double[]{matrixR.getNumber(i, j), matrixG.getNumber(i, j), matrixB.getNumber(i, j)};
                 meanClustering.setColor(color);
             }
         }
@@ -269,7 +345,7 @@ public class Convolution extends Frequency {
         return myMatrix;
     }
 
-    protected Matrix late(Matrix matrix, int size) throws Exception {//迟化处理
+    protected Matrix late(Matrix matrix, int size) throws Exception {//池化处理
         int xn = matrix.getX();
         int yn = matrix.getY();
         int x = xn / size;//求导后矩阵的行数

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

@@ -26,14 +26,17 @@ public class Operation {//进行计算
     private MatrixBack matrixBack = new MatrixBack();
     private ImageBack imageBack = new ImageBack();
     private OutBack outBack;
+    private int dif;
 
     public Operation(TempleConfig templeConfig) {
         this.templeConfig = templeConfig;
+        dif = templeConfig.getShrink();
     }
 
     public Operation(TempleConfig templeConfig, OutBack outBack) {
         this.templeConfig = templeConfig;
         this.outBack = outBack;
+        dif = templeConfig.getShrink();
     }
 
     public List<Double> convolution(Matrix matrix, Map<Integer, Double> tagging) throws Exception {
@@ -59,20 +62,22 @@ 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);
-        RegionBody regionBody = watershed.rainfall().get(0);
-        int minX = regionBody.getMinX();
-        int minY = regionBody.getMinY();
-        int maxX = regionBody.getMaxX();
-        int maxY = regionBody.getMaxY();
+        List<RegionBody> regionBodies = watershed.rainfall();
+        RegionBody regionBody = regionBodies.get(0);
+        int minX = regionBody.getMinX() + dif;
+        int minY = regionBody.getMinY() + dif;
+        int maxX = regionBody.getMaxX() - dif;
+        int maxY = regionBody.getMaxY() - dif;
         int xSize = maxX - minX;
         int ySize = maxY - minY;
         ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
+        // convolution.filtering(threeChannelMatrix1);//光照过滤
         int times = templeConfig.getTimes();
         for (int i = 0; i < times; i++) {
             List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
                     templeConfig.getFeatureNub());
             if (templeConfig.isShowLog()) {
-                System.out.println(feature);
+                System.out.println(tag + ":" + feature);
             }
             //System.out.println("=====================================");
             int classifier = templeConfig.getClassifier();
@@ -125,13 +130,14 @@ public class Operation {//进行计算
         List<RegionBody> regionList = watershed.rainfall();
         for (RegionBody regionBody : regionList) {
             MaxPoint maxPoint = new MaxPoint();
-            int minX = regionBody.getMinX();
-            int minY = regionBody.getMinY();
-            int maxX = regionBody.getMaxX();
-            int maxY = regionBody.getMaxY();
+            int minX = regionBody.getMinX() + dif;
+            int minY = regionBody.getMinY() + dif;
+            int maxX = regionBody.getMaxX() - dif;
+            int maxY = regionBody.getMaxY() - dif;
             int xSize = maxX - minX;
             int ySize = maxY - minY;
             ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
+            //convolution.filtering(threeChannelMatrix1);//光照过滤
             List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
                     templeConfig.getFeatureNub());
             if (templeConfig.isShowLog()) {
@@ -812,6 +818,49 @@ public class Operation {//进行计算
         return id;
     }
 
+    public double isCover(ThreeChannelMatrix threeChannelMatrix) throws Exception {//固定背景覆盖率计算
+        ThreeChannelMatrix backGround = templeConfig.getBackGround();
+        double minCover = templeConfig.getMinCover();
+        double maxCover = templeConfig.getMaxCover();
+        double errorBack = templeConfig.getBackGroundError();
+        if (backGround != null && maxCover > minCover && errorBack >= 0 && errorBack <= 255) {
+            Matrix matrixR = threeChannelMatrix.getMatrixR();
+            Matrix matrixG = threeChannelMatrix.getMatrixG();
+            Matrix matrixB = threeChannelMatrix.getMatrixB();
+            Matrix matrixRBg = backGround.getMatrixR();
+            Matrix matrixGBg = backGround.getMatrixG();
+            Matrix matrixBBg = backGround.getMatrixB();
+            int x = matrixR.getX();
+            int y = matrixR.getY();
+            double size = x * y;
+            double cover = 0;
+            if (x == matrixRBg.getX() && y == matrixRBg.getY()) {
+                for (int i = 0; i < x; i++) {
+                    for (int j = 0; j < y; j++) {
+                        double rB = matrixRBg.getNumber(i, j);
+                        double gB = matrixGBg.getNumber(i, j);
+                        double bB = matrixBBg.getNumber(i, j);
+                        double r = matrixR.getNumber(i, j);
+                        double g = matrixG.getNumber(i, j);
+                        double b = matrixB.getNumber(i, j);
+                        double subR = Math.abs(r - rB);
+                        double subG = Math.abs(g - gB);
+                        double subB = Math.abs(b - bB);
+                        double error = (subR + subB + subG) / 3;
+                        if (error > errorBack) {
+                            cover++;
+                        }
+                    }
+                }
+                return cover / size;
+            } else {
+                throw new Exception("Temple matrix is different");
+            }
+        } else {
+            throw new Exception("value is null");
+        }
+    }
+
     private List<Double> sub(Matrix matrix) throws Exception {//
         List<Double> list = new ArrayList<>();
         int x = matrix.getX() - 1;

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

@@ -67,6 +67,51 @@ public class TempleConfig {
     private Knn knn;//KNN分类器
     private int knnNub = 7;//KNN投票人数
     private int times = 10;//聚类循环次数
+    private int shrink = 60;//收缩参数
+    private ThreeChannelMatrix backGround;//背景面板
+    private double minCover;//覆盖下限
+    private double maxCover;//覆盖上限
+    private double backGroundError;//背景误差偏移量，0-255
+
+    public double getBackGroundError() {
+        return backGroundError;
+    }
+
+    public void setBackGroundError(double backGroundError) {
+        this.backGroundError = backGroundError;
+    }
+
+    public double getMinCover() {
+        return minCover;
+    }
+
+    public void setMinCover(double minCover) {
+        this.minCover = minCover;
+    }
+
+    public double getMaxCover() {
+        return maxCover;
+    }
+
+    public void setMaxCover(double maxCover) {
+        this.maxCover = maxCover;
+    }
+
+    public ThreeChannelMatrix getBackGround() {
+        return backGround;
+    }
+
+    public void setBackGround(ThreeChannelMatrix backGround) {
+        this.backGround = backGround;
+    }
+
+    public int getShrink() {
+        return shrink;
+    }
+
+    public void setShrink(int shrink) {
+        this.shrink = shrink;
+    }
 
     public int getTimes() {
         return times;

src/main/java/org/wlld/imageRecognition/border/Knn.java

@@ -3,10 +3,7 @@ package org.wlld.imageRecognition.border;
 import org.wlld.MatrixTools.Matrix;
 import org.wlld.MatrixTools.MatrixOperation;
 
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
+import java.util.*;
 
 public class Knn {//KNN分类器
     private Map<Integer, List<Matrix>> featureMap = new HashMap<>();
@@ -71,9 +68,109 @@ public class Knn {//KNN分类器
         }
     }
 
+    public int getType2(Matrix vector) throws Exception {
+        Map<Integer, Matrix> map = new HashMap<>();
+        double[] sub = new double[vector.getY()];//记录最小差
+        for (int t = 0; t < sub.length; t++) {
+            sub[t] = -1;
+        }
+        int[] types = new int[vector.getY()];//记录每个位置的最小类别
+        for (Map.Entry<Integer, List<Matrix>> entry : featureMap.entrySet()) {
+            int type = entry.getKey();
+            List<Matrix> matrices = entry.getValue();
+            double minDist = -1;
+            Matrix minMatrix = null;
+            for (Matrix matrix : matrices) {
+                double dist = MatrixOperation.getEDist(matrix, vector);
+                if (minDist > dist || minDist == -1) {
+                    minDist = dist;
+                    minMatrix = matrix;
+                }
+            }
+            map.put(type, minMatrix);
+        }
+        //从所有最小的里面选取三名最小的
+        int[] ty = getTypes(vector);
+        System.out.println(Arrays.toString(ty));
+        //从所有最小的里面选取
+        for (Map.Entry<Integer, Matrix> entry : map.entrySet()) {
+            int type = entry.getKey();
+            if (isLive(ty, type)) {
+                Matrix matrix = entry.getValue();
+                setMin(matrix, types, sub, vector, type);
+            }
+        }
+        System.out.println("最小位置：" + Arrays.toString(types));
+        Map<Integer, Integer> typeMap = new HashMap<>();
+        for (int i = 0; i < types.length; i++) {
+            int type = types[i];
+            if (typeMap.containsKey(type)) {
+                typeMap.put(type, typeMap.get(type) + 1);
+            } else {
+                typeMap.put(type, 1);
+            }
+        }
+        int type = 0;
+        int max = 0;
+        for (Map.Entry<Integer, Integer> entry : typeMap.entrySet()) {
+            int key = entry.getKey();
+            int nub = entry.getValue();
+            if (nub > max) {
+                max = nub;
+                type = key;
+            }
+        }
+        return type;
+    }
+
+    private boolean isLive(int[] types, int type) {
+        boolean isLive = false;
+        for (int ty : types) {
+            if (ty == type) {
+                isLive = true;
+                break;
+            }
+        }
+        return isLive;
+    }
+
+    private void setMin(Matrix matrix, int[] types, double[] sub, Matrix vector, int type) throws Exception {
+        int y = vector.getY();
+        for (int j = 0; j < y; j++) {
+            double nub = vector.getNumber(0, j);
+            double myNub = matrix.getNumber(0, j);
+            double subs = sub[j];
+            double cha = Math.abs(nub - myNub);
+            if (subs > cha || subs == -1) {
+                sub[j] = cha;
+                types[j] = type;
+            }
+        }
+    }
+
+    public int[] getTypes(Matrix vector) throws Exception {
+        double[] dists = new double[nub];
+        // System.out.println("测试：" + vector.getString());
+        int[] types = new int[nub];
+        for (int i = 0; i < nub; i++) {
+            dists[i] = -1;
+        }
+        for (Map.Entry<Integer, List<Matrix>> entry : featureMap.entrySet()) {
+            int type = entry.getKey();
+            List<Matrix> matrices = entry.getValue();
+            for (Matrix matrix : matrices) {
+                //double dist = MatrixOperation.errorNub(vector, matrix, 0);
+                double dist = MatrixOperation.getEDist(matrix, vector);
+                compare(dists, types, dist, type);
+            }
+        }
+        return types;
+    }
+
     public int getType(Matrix vector) throws Exception {//识别分类
         int ty = 0;
         double[] dists = new double[nub];
+        // System.out.println("测试：" + vector.getString());
         int[] types = new int[nub];
         for (int i = 0; i < nub; i++) {
             dists[i] = -1;
@@ -82,10 +179,12 @@ public class Knn {//KNN分类器
             int type = entry.getKey();
             List<Matrix> matrices = entry.getValue();
             for (Matrix matrix : matrices) {
-                double dist = MatrixOperation.getEDist(matrix, vector);
+                double dist = MatrixOperation.errorNub(vector, matrix, 0);
+                // double dist = MatrixOperation.getEDist(matrix, vector);
                 compare(dists, types, dist, type);
             }
         }
+        System.out.println(Arrays.toString(types));
         Map<Integer, Integer> map = new HashMap<>();
         for (int i = 0; i < nub; i++) {
             int type = types[i];
@@ -101,9 +200,9 @@ public class Knn {//KNN分类器
             int type = entry.getKey();
             if (value > max) {
                 ty = type;
+                max = value;
             }
         }
         return ty;
     }
-
 }

src/test/java/coverTest/FoodTest.java

@@ -21,6 +21,21 @@ public class FoodTest {
         test();
     }
 
+    public static void one(double[] test, double[] right, double[] wrong) {
+        int nub = 0;
+        for (int i = 0; i < test.length; i++) {
+            double test1 = test[i];
+            double right1 = right[i];
+            double wrong1 = wrong[i];
+            double sub1 = Math.abs(ArithUtil.sub(test1, right1));
+            double sub2 = Math.abs(ArithUtil.sub(test1, wrong1));
+            if (sub1 > sub2) {
+                nub++;
+            }
+        }
+        System.out.println(nub);
+    }
+
     public static void test2(TempleConfig templeConfig) throws Exception {
         //test();
         System.out.println("开始测试");
@@ -32,8 +47,8 @@ public class FoodTest {
         Specifications specifications = new Specifications();
         specifications.setMinWidth(250);
         specifications.setMinHeight(250);
-        specifications.setMaxWidth(700);
-        specifications.setMaxHeight(700);
+        specifications.setMaxWidth(750);
+        specifications.setMaxHeight(750);
         specificationsList.add(specifications);
         Operation operation = new Operation(templeConfig);
         for (int i = 1; i <= 28; i++) {
@@ -53,12 +68,13 @@ public class FoodTest {
         TempleConfig templeConfig = new TempleConfig();
         templeConfig.isShowLog(true);//是否打印日志
         templeConfig.setMaxRain(320);//切割阈值
-        templeConfig.setFeatureNub(4);
+        templeConfig.setFeatureNub(3);
         templeConfig.sethTh(0.88);
-        templeConfig.setKnnNub(7);
+        templeConfig.setKnnNub(3);
         templeConfig.setPoolSize(2);
         templeConfig.setRegionNub(200);
-        templeConfig.setTimes(10);//聚类数据增强
+        templeConfig.setShrink(60);
+        templeConfig.setTimes(2);//聚类数据增强
         templeConfig.setClassifier(Classifier.KNN);
         templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 3);
         return templeConfig;
@@ -72,8 +88,8 @@ public class FoodTest {
         Specifications specifications = new Specifications();
         specifications.setMinWidth(250);
         specifications.setMinHeight(250);
-        specifications.setMaxWidth(700);
-        specifications.setMaxHeight(700);
+        specifications.setMaxWidth(750);
+        specifications.setMaxHeight(750);
         specificationsList.add(specifications);
         for (int i = 1; i <= 10; i++) {
             ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\share\\cai\\a/a" + i + ".jpg");
@@ -82,12 +98,20 @@ public class FoodTest {
             ThreeChannelMatrix threeChannelMatrix4 = picture.getThreeMatrix("D:\\share\\cai\\d/d" + i + ".jpg");
             ThreeChannelMatrix threeChannelMatrix5 = picture.getThreeMatrix("D:\\share\\cai\\e/e" + i + ".jpg");
             ThreeChannelMatrix threeChannelMatrix6 = picture.getThreeMatrix("D:\\share\\cai\\f/f" + i + ".jpg");
+            ThreeChannelMatrix threeChannelMatrix7 = picture.getThreeMatrix("D:\\share\\cai\\h/h" + i + ".jpg");
+            ThreeChannelMatrix threeChannelMatrix8 = picture.getThreeMatrix("D:\\share\\cai\\i/i" + i + ".jpg");
+            ThreeChannelMatrix threeChannelMatrix9 = picture.getThreeMatrix("D:\\share\\cai\\j/j" + i + ".jpg");
+            ThreeChannelMatrix threeChannelMatrix10 = picture.getThreeMatrix("D:\\share\\cai\\k/k" + i + ".jpg");
             operation.colorStudy(threeChannelMatrix1, 1, specificationsList);
             operation.colorStudy(threeChannelMatrix2, 2, specificationsList);
             operation.colorStudy(threeChannelMatrix3, 3, specificationsList);
             operation.colorStudy(threeChannelMatrix4, 4, specificationsList);
             operation.colorStudy(threeChannelMatrix5, 5, specificationsList);
             operation.colorStudy(threeChannelMatrix6, 6, specificationsList);
+            operation.colorStudy(threeChannelMatrix7, 7, specificationsList);
+            operation.colorStudy(threeChannelMatrix8, 8, specificationsList);
+            operation.colorStudy(threeChannelMatrix9, 9, specificationsList);
+            operation.colorStudy(threeChannelMatrix10, 10, specificationsList);
             System.out.println("=======================================" + i);
         }