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增加集成聚类

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pull/43/head
lidapeng 5 years ago
parent 74f9c021d3
commit a3a09678ac
4 changed files with 134 additions and 80 deletions
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185
src/main/java/org/wlld/imageRecognition/Operation.java
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@ -16,6 +16,7 @@ import org.wlld.nerveEntity.SensoryNerve;
import org.wlld.tools.ArithUtil;
import org.wlld.tools.IdCreator;
import javax.swing.text.html.parser.Entity;
import java.util.*;
public class Operation {//进行计算
@ -24,7 +25,7 @@ public class Operation {//进行计算
private MatrixBack matrixBack = new MatrixBack();
private ImageBack imageBack = new ImageBack();
private OutBack outBack;
private int dif;
private double dif;
public Operation(TempleConfig templeConfig) {
this.templeConfig = templeConfig;
@ -63,45 +64,55 @@ public class Operation {//进行计算
List<RegionBody> regionBodies = watershed.rainfall();
if (regionBodies.size() == 1) {
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 integrate = templeConfig.getFood().getIntegrate();
int minX = regionBody.getMinX();
int minY = regionBody.getMinY();
int maxX = regionBody.getMaxX();
int maxY = regionBody.getMaxY();
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.getFood().getTimes();
for (int i = 0; i < times; i++) {
List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
templeConfig.getFeatureNub());
if (templeConfig.isShowLog()) {
System.out.println(tag + ":" + feature);
}
//System.out.println("=====================================");
int classifier = templeConfig.getClassifier();
switch (classifier) {
case Classifier.DNN:
Map<Integer, Double> map = new HashMap<>();
map.put(tag, 1.0);
if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub() * 3) {
intoDnnNetwork(1, feature, templeConfig.getSensoryNerves(), true, map, null);
} else {
throw new Exception("nerves number is not equal featureNub");
}
break;
case Classifier.LVQ:
Matrix vector = MatrixOperation.listToRowVector(feature);
lvqStudy(tag, vector);
break;
case Classifier.VAvg:
Matrix vec = MatrixOperation.listToRowVector(feature);
avgStudy(tag, vec);
break;
case Classifier.KNN:
Matrix veck = MatrixOperation.listToRowVector(feature);
knnStudy(tag, veck);
break;
for (int j = 1; j <= integrate; j++) {
double move = dif * j;
int moveX = (int) (xSize * move);
int moveY = (int) (ySize * move);
int x = minX + moveX;
int y = minY + moveY;
int xLength = xSize - moveX * 2;
int yLength = ySize - moveY * 2;
ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, x, y, xLength, yLength);
//convolution.filtering(threeChannelMatrix1);//光照过滤
int times = templeConfig.getFood().getTimes();
for (int i = 0; i < times; i++) {
List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
templeConfig.getFeatureNub());
if (templeConfig.isShowLog()) {
System.out.println(tag + ":" + feature);
}
//System.out.println("=====================================");
int classifier = templeConfig.getClassifier();
switch (classifier) {
case Classifier.DNN:
Map<Integer, Double> map = new HashMap<>();
map.put(tag, 1.0);
if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub() * 3) {
intoDnnNetwork(1, feature, templeConfig.getSensoryNerves(), true, map, null);
} else {
throw new Exception("nerves number is not equal featureNub");
}
break;
case Classifier.LVQ:
Matrix vector = MatrixOperation.listToRowVector(feature);
lvqStudy(tag, vector);
break;
case Classifier.VAvg:
Matrix vec = MatrixOperation.listToRowVector(feature);
avgStudy(tag, vec);
break;
case Classifier.KNN:
Matrix veck = MatrixOperation.listToRowVector(feature);
knnStudy(tag, veck, templeConfig.getKnns()[j]);
break;
}
}
}
} else {
@ -109,8 +120,7 @@ public class Operation {//进行计算
}
}
private void knnStudy(int tagging, Matrix vector) throws Exception {
Knn knn = templeConfig.getKnn();
private void knnStudy(int tagging, Matrix vector, Knn knn) throws Exception {
knn.insertMatrix(vector, tagging);
}
@ -132,45 +142,70 @@ public class Operation {//进行计算
List<RegionBody> regionList = watershed.rainfall();
for (RegionBody regionBody : regionList) {
MaxPoint maxPoint = new MaxPoint();
int minX = regionBody.getMinX() + dif;
int minY = regionBody.getMinY() + dif;
int maxX = regionBody.getMaxX() - dif;
int maxY = regionBody.getMaxY() - dif;
int integrate = templeConfig.getFood().getIntegrate();
int minX = regionBody.getMinX();
int minY = regionBody.getMinY();
int maxX = regionBody.getMaxX();
int maxY = regionBody.getMaxY();
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()) {
System.out.println(feature);
Map<Integer, Integer> map = new HashMap<>();
for (int j = 1; j <= integrate; j++) {
double move = dif * j;
int moveX = (int) (xSize * move);
int moveY = (int) (ySize * move);
int x = minX + moveX;
int y = minY + moveY;
int xLength = xSize - moveX * 2;
int yLength = ySize - moveY * 2;
ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, x, y, xLength, yLength);
//convolution.filtering(threeChannelMatrix1);//光照过滤
List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
templeConfig.getFeatureNub());
if (templeConfig.isShowLog()) {
System.out.println(feature);
}
int classifier = templeConfig.getClassifier();
int id = 0;
switch (classifier) {
case Classifier.LVQ:
Matrix myMatrix = MatrixOperation.listToRowVector(feature);
id = getIdByLVQ(myMatrix);
break;
case Classifier.DNN:
if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub() * 3) {
intoDnnNetwork(IdCreator.get().nextId(), feature, templeConfig.getSensoryNerves(), false, null, maxPoint);
id = maxPoint.getId();
} else {
throw new Exception("nerves number is not equal featureNub");
}
break;
case Classifier.VAvg:
Matrix myMatrix1 = MatrixOperation.listToRowVector(feature);
id = getIdByVag(myMatrix1);
break;
case Classifier.KNN:
Matrix myMatrix2 = MatrixOperation.listToRowVector(feature);
Knn knn = templeConfig.getKnns()[j];
id = knn.getType(myMatrix2);
break;
}
//完成一个结果,统计所有结果
if (map.containsKey(id)) {
map.put(id, map.get(id) + 1);
} else {
map.put(id, 1);
}
}
int classifier = templeConfig.getClassifier();
int id = 0;
switch (classifier) {
case Classifier.LVQ:
Matrix myMatrix = MatrixOperation.listToRowVector(feature);
id = getIdByLVQ(myMatrix);
break;
case Classifier.DNN:
if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub() * 3) {
intoDnnNetwork(IdCreator.get().nextId(), feature, templeConfig.getSensoryNerves(), false, null, maxPoint);
id = maxPoint.getId();
} else {
throw new Exception("nerves number is not equal featureNub");
}
break;
case Classifier.VAvg:
Matrix myMatrix1 = MatrixOperation.listToRowVector(feature);
id = getIdByVag(myMatrix1);
break;
case Classifier.KNN:
Matrix myMatrix2 = MatrixOperation.listToRowVector(feature);
Knn knn = templeConfig.getKnn();
id = knn.getType(myMatrix2);
break;
int key = 0;
int max = 0;
for (Map.Entry<Integer, Integer> entry : map.entrySet()) {
int nub = entry.getValue();
if (nub > max) {
key = entry.getKey();
}
}
regionBody.setType(id);
regionBody.setType(key);
//System.out.println("类别" + id);
}
return regionList;

12
src/main/java/org/wlld/imageRecognition/TempleConfig.java
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@ -66,11 +66,16 @@ public class TempleConfig {
private int featureNub = 4;//聚类特征数量
private Food food = new Food();
private Knn knn;//KNN分类器
private Knn[] knns;//集成knn分类
private int knnNub = 7;//KNN投票人数
private ThreeChannelMatrix backGround;//背景面板
private double backGroundError;//背景误差偏移量，0-255
private Cutting cutting = new Cutting();
public Knn[] getKnns() {
return knns;
}
public Cutting getCutting() {
return cutting;
}
@ -325,7 +330,12 @@ public class TempleConfig {
vectorK = new VectorK(featureNub * 3);
break;
case Classifier.KNN:
knn = new Knn(knnNub);
int integrate = food.getIntegrate();//集成分类次数
knns = new Knn[integrate];
for (int i = 0; i < integrate; i++) {
knns[i] = new Knn(knnNub);
}
//knn = new Knn(knnNub);
break;
}
break;

15
src/main/java/org/wlld/param/Food.java
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@ -7,17 +7,26 @@ package org.wlld.param;
* @Description 菜品识别实体类
*/
public class Food {
private int shrink = 60;//收缩参数
private double shrink = 0.03;//收缩参数
private int times = 10;//聚类增强次数
private int integrate = 5;//聚类集成次数
public int getShrink() {
public double getShrink() {
return shrink;
}
public void setShrink(int shrink) {
public void setShrink(double shrink) {
this.shrink = shrink;
}
public int getIntegrate() {
return integrate;
}
public void setIntegrate(int integrate) {
this.integrate = integrate;
}
public int getTimes() {
return times;
}

2
src/test/java/coverTest/FoodTest.java
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@ -80,7 +80,7 @@ public class FoodTest {
//聚类
templeConfig.setFeatureNub(3);//聚类特征数量
//菜品识别实体类
food.setShrink(50);//缩紧像素
//food.setShrink(50);//缩紧像素
food.setTimes(2);//聚类数据增强
templeConfig.setClassifier(Classifier.KNN);
templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 3);

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