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!44 修改切割

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Merge pull request !44 from 逐光/test
pull/44/MERGE
逐光 5 years ago committed by Gitee
parent 049dde22b2 be9d063fb4
commit f74d96139e
14 changed files with 178 additions and 169 deletions
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25
src/main/java/org/wlld/imageRecognition/Convolution.java
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@ -62,26 +62,17 @@ public class Convolution extends Frequency {
return threeChannelMatrixList;
}
public List<List<Double>> kAvg(ThreeChannelMatrix threeMatrix, int poolSize, int sqNub
public List<List<Double>> kAvg(ThreeChannelMatrix threeMatrix, int sqNub
, int regionSize) throws Exception {
RGBSort rgbSort = new RGBSort();
List<List<Double>> features = new ArrayList<>();
Matrix matrixR = threeMatrix.getMatrixR();
Matrix matrixG = threeMatrix.getMatrixG();
Matrix matrixB = threeMatrix.getMatrixB();
matrixR = late(matrixR, poolSize);
matrixG = late(matrixG, poolSize);
matrixB = late(matrixB, poolSize);
threeMatrix.setMatrixR(matrixR);
threeMatrix.setMatrixG(matrixG);
threeMatrix.setMatrixB(matrixB);
List<ThreeChannelMatrix> threeChannelMatrixList = regionThreeChannelMatrix(threeMatrix, regionSize);
for (ThreeChannelMatrix threeChannelMatrix : threeChannelMatrixList) {
List<Double> feature = new ArrayList<>();
MeanClustering meanClustering = new MeanClustering(sqNub);
matrixR = threeChannelMatrix.getMatrixR();
matrixG = threeChannelMatrix.getMatrixG();
matrixB = threeChannelMatrix.getMatrixB();
Matrix matrixR = threeChannelMatrix.getMatrixR();
Matrix matrixG = threeChannelMatrix.getMatrixG();
Matrix matrixB = threeChannelMatrix.getMatrixB();
int x = matrixR.getX();
int y = matrixR.getY();
for (int i = 0; i < x; i++) {
@ -93,14 +84,12 @@ public class Convolution extends Frequency {
meanClustering.start();
List<RGBNorm> rgbNorms = meanClustering.getMatrices();
Collections.sort(rgbNorms, rgbSort);
double[] dm = new double[sqNub];
for (RGBNorm rgbNorm : rgbNorms) {
feature.add(rgbNorm.getNorm());
double[] rgb = rgbNorm.getRgb();
for (int i = 0; i < rgb.length; i++) {
feature.add(rgb[i]);
}
for (int t = 0; t < dm.length; t++) {
dm[t] = rgbNorms.get(t).getNorm();
}
//System.out.println(Arrays.toString(dm));
features.add(feature);
}
return features;

24
src/main/java/org/wlld/imageRecognition/Operation.java
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@ -58,6 +58,17 @@ public class Operation {//进行计算
return sub(matrix1);
}
private void cutPic(ThreeChannelMatrix threeChannelMatrix, int x, int y, int xSize, int ySize) {
Matrix matrixR = threeChannelMatrix.getMatrixR();
Matrix matrixG = threeChannelMatrix.getMatrixG();
Matrix matrixB = threeChannelMatrix.getMatrixB();
Matrix matrixRGB = threeChannelMatrix.getMatrixRGB();
threeChannelMatrix.setMatrixR(matrixR.getSonOfMatrix(x, y, xSize, ySize));
threeChannelMatrix.setMatrixG(matrixG.getSonOfMatrix(x, y, xSize, ySize));
threeChannelMatrix.setMatrixB(matrixB.getSonOfMatrix(x, y, xSize, ySize));
threeChannelMatrix.setMatrixRGB(matrixRGB.getSonOfMatrix(x, y, xSize, ySize));
}
public void colorStudy(ThreeChannelMatrix threeChannelMatrix, int tag, List<Specifications> specificationsList) throws Exception {
Watershed watershed = new Watershed(threeChannelMatrix.getMatrixRGB(), specificationsList, templeConfig);
List<RegionBody> regionBodies = watershed.rainfall();
@ -226,23 +237,22 @@ public class Operation {//进行计算
return id;
}
public void coverStudy(Map<Integer, ThreeChannelMatrix> matrixMap, int poolSize, int sqNub, int regionSize,
int times) throws Exception {
public void coverStudy(Map<Integer, ThreeChannelMatrix> matrixMap, int sqNub, int regionSize) throws Exception {
if (templeConfig.getStudyPattern() == StudyPattern.Cover_Pattern) {
int size = 0;
List<CoverBody> coverBodies = new ArrayList<>();
for (Map.Entry<Integer, ThreeChannelMatrix> entry : matrixMap.entrySet()) {
//先进行切图
CoverBody coverBody = new CoverBody();
Map<Integer, Double> tag = new HashMap<>();
tag.put(entry.getKey(), 1.0);
List<List<Double>> lists = convolution.kAvg(entry.getValue(), poolSize, sqNub, regionSize);
List<List<Double>> lists = convolution.kAvg(entry.getValue(), sqNub, regionSize);
size = lists.size();
coverBody.setFeature(lists);
coverBody.setTag(tag);
coverBodies.add(coverBody);
}
//特征塞入容器完毕
for (int j = 0; j < times; j++) {
for (int i = 0; i < size; i++) {
for (CoverBody coverBody : coverBodies) {
List<Double> list = coverBody.getFeature().get(i);
@ -252,15 +262,15 @@ public class Operation {//进行计算
intoDnnNetwork(1, list, templeConfig.getSensoryNerves(), true, coverBody.getTag(), null);
}
}
}
}
}
public Map<Integer, Double> coverPoint(ThreeChannelMatrix matrix, int poolSize, int sqNub, int regionSize) throws Exception {
public Map<Integer, Double> coverPoint(ThreeChannelMatrix matrix, int sqNub, int regionSize) throws Exception {
if (templeConfig.getStudyPattern() == StudyPattern.Cover_Pattern) {
Map<Integer, Double> coverMap = new HashMap<>();
Map<Integer, Integer> typeNub = new HashMap<>();
List<List<Double>> lists = convolution.kAvg(matrix, poolSize, sqNub, regionSize);
List<List<Double>> lists = convolution.kAvg(matrix, sqNub, regionSize);
//特征塞入容器完毕
int size = lists.size();
int all = 0;

7
src/main/java/org/wlld/imageRecognition/segmentation/Watershed.java
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@ -25,6 +25,8 @@ public class Watershed {
private int xMax;
private int yMax;
private double maxRain;
private double width;
private double height;
private List<Specifications> specifications;
public Watershed(Matrix matrix, List<Specifications> specifications, TempleConfig templeConfig) throws Exception {
@ -35,6 +37,8 @@ public class Watershed {
maxRain = cutting.getMaxRain();
this.matrix = matrix;
this.specifications = specifications;
width = matrix.getY();
height = matrix.getX();
xSize = matrix.getX() / regionNub;
ySize = matrix.getY() / regionNub;
// System.out.println("xSize===" + xSize + ",ysize===" + ySize);
@ -212,6 +216,9 @@ public class Watershed {
for (Specifications specification : specifications) {
int width = maxY - minY;
int height = maxX - minX;
// double h = this.height / 8;
// double w = this.width / 8;
// boolean isCenter = minX > h && minY > w && minX < (h * 4.5) && minY < (w * 4.5);
if (width >= specification.getMinWidth() && height >= specification.getMinHeight()
&& width <= specification.getMaxWidth() && height <= specification.getMaxHeight()) {
isRight = true;

18
src/main/java/org/wlld/naturalLanguage/Talk.java
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@ -13,9 +13,17 @@ import java.util.List;
* @date 4:14 下午 2020/2/23
*/
public class Talk {
private List<WorldBody> allWorld = WordTemple.get().getAllWorld();//所有词集合
private RandomForest randomForest = WordTemple.get().getRandomForest();//获取随机森林模型
private List<List<String>> wordTimes = WordTemple.get().getWordTimes();
private List<WorldBody> allWorld;//所有词集合
private RandomForest randomForest;//获取随机森林模型
private List<List<String>> wordTimes;
private WordTemple wordTemple;
public Talk(WordTemple wordTemple) {
this.wordTemple = wordTemple;
allWorld = wordTemple.getAllWorld();//所有词集合
randomForest = wordTemple.getRandomForest();//获取随机森林模型
wordTimes = wordTemple.getWordTimes();
}
public List<Integer> talk(String sentence) throws Exception {
List<Integer> typeList = new ArrayList<>();
@ -69,7 +77,7 @@ public class Talk {
features.add(nub);
}
int type = 0;
if (ArithUtil.div(wrong, wordNumber) < WordTemple.get().getGarbageTh()) {
if (ArithUtil.div(wrong, wordNumber) < wordTemple.getGarbageTh()) {
LangBody langBody = new LangBody();
langBody.setA1(features.get(0));
langBody.setA2(features.get(1));
@ -135,7 +143,7 @@ public class Talk {
listWord = body.getWorldBodies();//这个body报了一次空指针
word.setWordFrequency(body.getWordFrequency());
}
Tokenizer tokenizer = new Tokenizer();
Tokenizer tokenizer = new Tokenizer(wordTemple);
tokenizer.radiation(words);
}

9
src/main/java/org/wlld/naturalLanguage/TemplateReader.java
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@ -20,7 +20,7 @@ public class TemplateReader {//模板读取类
* @param charsetName 文字编码(一般使用UTF-8)
* @throws Exception 找不到文字抛出异常
*/
public void read(String url, String charsetName) throws Exception {
public void read(String url, String charsetName, WordTemple wordTemple) throws Exception {
this.charsetName = charsetName;
File file = new File(url);
InputStream is = new FileInputStream(file);
@ -68,11 +68,12 @@ public class TemplateReader {//模板读取类
}
}
}
word();
word(wordTemple);
}
public void word() throws Exception {
Tokenizer tokenizer = new Tokenizer();
public void word(WordTemple wordTemple) throws Exception {
//将模版注入分词器进行分词
Tokenizer tokenizer = new Tokenizer(wordTemple);
tokenizer.start(model);
}

24
src/main/java/org/wlld/naturalLanguage/Tokenizer.java
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@ -13,10 +13,18 @@ import java.util.*;
* @date 7:42 上午 2020/2/23
*/
public class Tokenizer extends Frequency {
private List<Sentence> sentences = WordTemple.get().getSentences();//所有断句
private List<WorldBody> allWorld = WordTemple.get().getAllWorld();//所有词集合
private List<List<String>> wordTimes = WordTemple.get().getWordTimes();//所有词编号
private List<Sentence> sentences;//所有断句
private List<WorldBody> allWorld;//所有词集合
private List<List<String>> wordTimes;//所有词编号
private Word nowWord;//上一次出现的关键字
private WordTemple wordTemple;
public Tokenizer(WordTemple wordTemple) {
this.wordTemple = wordTemple;
sentences = wordTemple.getSentences();//所有断句
allWorld = wordTemple.getAllWorld();//所有词集合
wordTimes = wordTemple.getWordTimes();//所有词编号
}
public void start(Map<Integer, List<String>> model) throws Exception {
//model的主键是类别，值是该类别语句的集合
@ -59,7 +67,7 @@ public class Tokenizer extends Frequency {
}
private void number() {//分词编号
System.out.println("开始编码:" + sentences.size());
System.out.println("开始编码:" + (sentences.size() + 1));
for (Sentence sentence : sentences) {
List<Integer> features = sentence.getFeatures();
List<String> sentenceList = sentence.getKeyWords();
@ -85,10 +93,12 @@ public class Tokenizer extends Frequency {
}
column.add("key");
DataTable dataTable = new DataTable(column);
dataTable.setKey("key");
dataTable.setKey("key");//确认结果集主键
//初始化随机森林
RandomForest randomForest = new RandomForest(11);
WordTemple.get().setRandomForest(randomForest);//保存随机森林到模版
RandomForest randomForest = new RandomForest(wordTemple.getTreeNub());
randomForest.setTrustTh(wordTemple.getTrustTh());
randomForest.setTrustPunishment(wordTemple.getTrustPunishment());
wordTemple.setRandomForest(randomForest);//保存随机森林到模版
randomForest.init(dataTable);
for (Sentence sentence : sentences) {
LangBody langBody = new LangBody();

40
src/main/java/org/wlld/naturalLanguage/WordTemple.java
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@ -11,33 +11,30 @@ import java.util.List;
* @date 4:15 下午 2020/2/23
*/
public class WordTemple {
private static WordTemple Word_Temple = new WordTemple();
private List<Sentence> sentences = new ArrayList<>();//所有断句
private List<WorldBody> allWorld = new ArrayList<>();//所有词集合
private List<List<String>> wordTimes = new ArrayList<>();//词编号
private RandomForest randomForest;//保存的随机森林模型
//四大参数
private double garbageTh = 0.5;//垃圾分类的阈值默认0.5
private double trustPunishment = 0.1;//信任惩罚
private double trustTh = 0.1;//信任阈值,相当于一次信任惩罚的数值
private int treeNub = 11;//丛林里面树的数量
public WordModel getModel() {//获取模型
WordModel wordModel = new WordModel();
wordModel.setAllWorld(allWorld);
wordModel.setWordTimes(wordTimes);
wordModel.setGarbageTh(garbageTh);
wordModel.setTrustPunishment(trustPunishment);
wordModel.setTrustTh(randomForest.getTrustTh());
wordModel.setRfModel(randomForest.getModel());
return wordModel;
public int getTreeNub() {
return treeNub;
}
public void insertModel(WordModel wordModel) throws Exception {//注入模型
allWorld = wordModel.getAllWorld();
wordTimes = wordModel.getWordTimes();
garbageTh = wordModel.getGarbageTh();
trustPunishment = wordModel.getTrustPunishment();
randomForest = new RandomForest();
randomForest.setTrustTh(wordModel.getTrustTh());
randomForest.insertModel(wordModel.getRfModel());
public void setTreeNub(int treeNub) {
this.treeNub = treeNub;
}
public double getTrustTh() {
return trustTh;
}
public void setTrustTh(double trustTh) {
this.trustTh = trustTh;
}
public double getTrustPunishment() {
@ -64,9 +61,6 @@ public class WordTemple {
this.randomForest = randomForest;
}
private WordTemple() {
}
public List<List<String>> getWordTimes() {
return wordTimes;
}
@ -75,10 +69,6 @@ public class WordTemple {
this.wordTimes = wordTimes;
}
public static WordTemple get() {
return Word_Temple;
}
public List<Sentence> getSentences() {
return sentences;
}

28
src/main/java/org/wlld/randomForest/RandomForest.java
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@ -13,6 +13,15 @@ public class RandomForest {
private Random random = new Random();
private Tree[] forest;
private double trustTh = 0.1;//信任阈值
private double trustPunishment = 0.1;//信任惩罚
public double getTrustPunishment() {
return trustPunishment;
}
public void setTrustPunishment(double trustPunishment) {
this.trustPunishment = trustPunishment;
}
public double getTrustTh() {
return trustTh;
@ -33,21 +42,6 @@ public class RandomForest {
}
}
public void insertModel(RfModel rfModel) throws Exception {//注入模型
if (rfModel != null) {
Map<Integer, Node> nodeMap = rfModel.getNodeMap();
forest = new Tree[nodeMap.size()];
for (Map.Entry<Integer, Node> entry : nodeMap.entrySet()) {
int key = entry.getKey();
Tree tree = new Tree();
forest[key] = tree;
tree.setRootNode(entry.getValue());
}
} else {
throw new Exception("model is null");
}
}
public RfModel getModel() {//获取模型
RfModel rfModel = new RfModel();
Map<Integer, Node> nodeMap = new HashMap<>();
@ -89,6 +83,7 @@ public class RandomForest {
return type;
}
//rf初始化
public void init(DataTable dataTable) throws Exception {
//一棵树属性的数量
if (dataTable.getSize() > 4) {
@ -96,7 +91,7 @@ public class RandomForest {
//int kNub = dataTable.getSize() - 1;
// System.out.println("knNub==" + kNub);
for (int i = 0; i < forest.length; i++) {
Tree tree = new Tree(getRandomData(dataTable, kNub));
Tree tree = new Tree(getRandomData(dataTable, kNub), trustPunishment);
forest[i] = tree;
}
} else {
@ -119,6 +114,7 @@ public class RandomForest {
}
}
//从总属性列表中随机挑选属性kNub个属性数量
private DataTable getRandomData(DataTable dataTable, int kNub) throws Exception {
Set<String> attr = dataTable.getKeyType();
Set<String> myName = new HashSet<>();

9
src/main/java/org/wlld/randomForest/Tree.java
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@ -18,6 +18,7 @@ public class Tree {//决策树
private List<Integer> endList;//最终结果分类
private List<Node> lastNodes = new ArrayList<>();//最后一层节点集合
private Random random = new Random();
private double trustPunishment;//信任惩罚
public Node getRootNode() {
return rootNode;
@ -36,11 +37,13 @@ public class Tree {//决策树
private double gainRatio;//信息增益率
}
public Tree() {
public Tree(double trustPunishment) {
this.trustPunishment = trustPunishment;
}
public Tree(DataTable dataTable) throws Exception {
public Tree(DataTable dataTable, double trustPunishment) throws Exception {
if (dataTable != null && dataTable.getKey() != null) {
this.trustPunishment = trustPunishment;
this.dataTable = dataTable;
} else {
throw new Exception("dataTable is empty");
@ -221,7 +224,7 @@ public class Tree {//决策树
private void punishment(TreeWithTrust treeWithTrust) {//信任惩罚
//System.out.println("惩罚");
double trust = treeWithTrust.getTrust();//获取当前信任值
trust = ArithUtil.mul(trust, WordTemple.get().getTrustPunishment());
trust = ArithUtil.mul(trust, trustPunishment);
treeWithTrust.setTrust(trust);
}

49
src/test/java/coverTest/CoverTest.java
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@ -24,7 +24,7 @@ import java.util.Map;
public class CoverTest {
public static void main(String[] args) throws Exception {
//cover();
cover();
}
public static void insertModel(String model) throws Exception {//注入模型
@ -52,18 +52,18 @@ public class CoverTest {
return new Operation(templeConfig);//初始化运算类
}
public static void test(Operation operation, int poolSize, int sqlNub, int regionSize,
public static void test(Operation operation, int sqlNub, int regionSize,
String name, int t) throws Exception {
Picture picture = new Picture();
int wrong = 0;
if (operation == null) {
operation = getModel();
}
for (int i = 1; i < 100; i++) {
String na = "D:\\share\\cai/" + name + i + ".jpg";
for (int i = 1; i < 20; i++) {
String na = "/Users/lidapeng/Desktop/myDocument/food/" + name + i + ".jpg";
//System.out.println("name======================" + na);
ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix(na);
Map<Integer, Double> map1 = operation.coverPoint(threeChannelMatrix, poolSize, sqlNub, regionSize);
Map<Integer, Double> map1 = operation.coverPoint(threeChannelMatrix, sqlNub, regionSize);
int id = 0;
double point = 0;
for (Map.Entry<Integer, Double> entry : map1.entrySet()) {
@ -90,6 +90,8 @@ public class CoverTest {
}
public static void cover() throws Exception {
int sqNub = 1;
int regionSize = 20;
//创建图片解析类 桔梗覆盖，桔梗焚烧，土壤扰动
Picture picture = new Picture();
//创建模版类，参数选false就可以
@ -97,35 +99,36 @@ public class CoverTest {
//初始化模板 注意 width height参数是你训练图片的实际尺寸需要改，其他不用动
//创建运算类进行标注
templeConfig.isShowLog(true);
templeConfig.setStudyPoint(0.01);//不动
templeConfig.setSoftMax(true);
//templeConfig.setDeep(2);
//templeConfig.setHiddenNerveNub(9);
templeConfig.setSensoryNerveNub(3);//多出来的
templeConfig.setStudyPoint(0.005);//不动
//templeConfig.setSoftMax(true);
templeConfig.setDeep(2);
templeConfig.setHiddenNerveNub(9);
templeConfig.setSensoryNerveNub(sqNub);//多出来的
templeConfig.setRzType(RZ.L1);//不动//3 18
templeConfig.setlParam(0.015);//不动
templeConfig.setClassifier(Classifier.DNN);
templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 4);
Operation operation = new Operation(templeConfig);
for (int i = 1; i < 100; i++) {
for (int i = 1; i < 20; i++) {
System.out.println("i====================================" + i);
Map<Integer, ThreeChannelMatrix> matrixMap = new HashMap<>();
ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\share\\cai/a" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("D:\\share\\cai/b" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix("D:\\share\\cai/c" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix4 = picture.getThreeMatrix("D:\\share\\cai/d" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/food/a" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/food/b" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/food/c" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix4 = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/food/d" + i + ".jpg");
matrixMap.put(1, threeChannelMatrix1);//桔梗覆盖
matrixMap.put(2, threeChannelMatrix2);//土地
matrixMap.put(3, threeChannelMatrix3);//桔梗覆盖
matrixMap.put(4, threeChannelMatrix4);//土地
operation.coverStudy(matrixMap, 2, 3, 18, 2);
operation.coverStudy(matrixMap, sqNub, regionSize);
}
ModelParameter modelParameter = templeConfig.getModel();
String model = JSON.toJSONString(modelParameter);
System.out.println(model);
test(operation, 2, 3, 18, "a", 1);
test(operation, 2, 3, 18, "b", 2);
test(operation, 2, 3, 18, "c", 3);
test(operation, 2, 3, 18, "d", 4);
// ModelParameter modelParameter = templeConfig.getModel();
// String model = JSON.toJSONString(modelParameter);
// System.out.println(model);
test(operation, sqNub, regionSize, "a", 1);
test(operation, sqNub, regionSize, "b", 2);
test(operation, sqNub, regionSize, "c", 3);
test(operation, sqNub, regionSize, "d", 4);
}
}

75
src/test/java/coverTest/FoodTest.java
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@ -3,6 +3,7 @@ package coverTest;
import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import org.wlld.MatrixTools.Matrix;
import org.wlld.ModelData;
import org.wlld.config.Classifier;
import org.wlld.config.RZ;
import org.wlld.config.StudyPattern;
@ -20,6 +21,12 @@ import java.util.List;
public class FoodTest {
public static void main(String[] args) throws Exception {
// ModelParameter parameter = JSON.parseObject(ModelData.DATA, ModelParameter.class);
// if (parameter.getKnnVector() != null) {
// System.out.println("空的");
// } else {
// System.out.println("不是空===");
// }
test();
}
@ -40,19 +47,20 @@ public class FoodTest {
public static void test2(TempleConfig templeConfig) throws Exception {
if (templeConfig == null) {
ModelParameter parameter = JSON.parseObject(ModelData.DATA, ModelParameter.class);
templeConfig = getTemple(null);
}
Picture picture = new Picture();
List<Specifications> specificationsList = new ArrayList<>();
Specifications specifications = new Specifications();
specifications.setMinWidth(250);
specifications.setMinHeight(250);
specifications.setMaxWidth(750);
specifications.setMaxHeight(750);
specifications.setMinWidth(400);
specifications.setMinHeight(400);
specifications.setMaxWidth(950);
specifications.setMaxHeight(950);
specificationsList.add(specifications);
Operation operation = new Operation(templeConfig);
for (int i = 1; i <= 28; i++) {
ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\share\\cai\\g/g" + i + ".jpg");
for (int i = 1; i <= 1; i++) {
ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("/Users/lidapeng/Documents/paramterTest/c" + i + ".jpg");
List<RegionBody> regionBody = operation.colorLook(threeChannelMatrix1, specificationsList);
for (int j = 0; j < regionBody.size(); j++) {
RegionBody regionBody1 = regionBody.get(j);
@ -70,17 +78,17 @@ public class FoodTest {
Cutting cutting = templeConfig.getCutting();
Food food = templeConfig.getFood();
//切割
cutting.setMaxRain(320);//切割阈值
cutting.setTh(0.88);
cutting.setMaxRain(310);//切割阈值
cutting.setTh(0.8);
cutting.setRegionNub(200);
//knn参数
templeConfig.setKnnNub(3);
templeConfig.setKnnNub(1);
//池化比例
templeConfig.setPoolSize(2);//缩小比例
//聚类
templeConfig.setFeatureNub(3);//聚类特征数量
//菜品识别实体类
food.setShrink(60);//缩紧像素
food.setShrink(5);//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);
@ -96,35 +104,30 @@ public class FoodTest {
Operation operation = new Operation(templeConfig);
List<Specifications> specificationsList = new ArrayList<>();
Specifications specifications = new Specifications();
specifications.setMinWidth(250);
specifications.setMinHeight(250);
specifications.setMaxWidth(750);
specifications.setMaxHeight(750);
specifications.setMinWidth(400);
specifications.setMinHeight(400);
specifications.setMaxWidth(950);
specifications.setMaxHeight(950);
specificationsList.add(specifications);
for (int i = 1; i <= 10; i++) {
ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\share\\cai\\a/a" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("D:\\share\\cai\\b/b" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix("D:\\share\\cai\\c/c" + i + ".jpg");
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);
for (int i = 1; i <= 1; i++) {
ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("/Users/lidapeng/Documents/paramterTest/a" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("/Users/lidapeng/Documents/paramterTest/b" + i + ".jpg");
operation.colorStudy(threeChannelMatrix1, 83, specificationsList);
System.out.println("=======================================");
operation.colorStudy(threeChannelMatrix2, 86, specificationsList);
System.out.println("=======================================" + i);
}
test2(templeConfig);
ModelParameter modelParameter = templeConfig.getModel();
if (modelParameter.getKnnVector() != null) {
System.out.println("not null 1");
}
String tr = JSON.toJSONString(modelParameter);
System.out.println(tr);
ModelParameter modelParameter1 = JSON.parseObject(tr, ModelParameter.class);
if (modelParameter1.getKnnVector() != null) {
System.out.println("not null 2");
}
//test2(templeConfig);
}
public static void study() throws Exception {

6
src/test/java/coverTest/PicTest.java
Unescape View File

@ -36,14 +36,8 @@ public class PicTest {
//testImage(right, wrong, a, b);
//test();
tm();
}
public static void tm() {
String model = ModelData.DATA4;
int index = model.indexOf("knnVector");
}
public static void test() throws Exception {//对图像进行识别测试
Picture picture = new Picture();

12
src/test/java/org/wlld/LangTest.java
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@ -70,17 +70,15 @@ public class LangTest {
public static void test() throws Exception {
//创建模板读取累
TemplateReader templateReader = new TemplateReader();
WordTemple wordTemple = new WordTemple();//初始化语言模版
wordTemple.setTreeNub(9);
//读取语言模版，第一个参数是模版地址，第二个参数是编码方式 (教程里的第三个参数已经省略)
//同时也是学习过程
templateReader.read("D:\\b/a.txt", "UTF-8");
//学习结束获取模型参数
// WordModel wordModel = WordTemple.get().getModel();
//不用学习注入模型参数
// WordTemple.get().insertModel(wordModel);
Talk talk = new Talk();
templateReader.read("/Users/lidapeng/Desktop/myDocument/model.txt", "UTF-8", wordTemple);
Talk talk = new Talk(wordTemple);
//输入语句进行识别，若有标点符号会形成LIST中的每个元素
//返回的集合中每个值代表了输入语句，每个标点符号前语句的分类
List<Integer> list = talk.talk("有个快递尽快代我邮寄出去");
List<Integer> list = talk.talk("空调坏了");
System.out.println(list);
}
}

5
src/test/java/org/wlld/ModelData.java
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