From 003804ec222edbc585614357150bf52cb0deca30 Mon Sep 17 00:00:00 2001
From: lidapeng <794757862@qq.com>
Date: Thu, 15 Oct 2020 17:04:21 +0800
Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E7=89=B9=E5=BE=81=E9=80=89?=
=?UTF-8?q?=E5=8C=BA=E7=AD=9B=E9=80=89?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
pom.xml | 2 +-
.../org/wlld/MatrixTools/MatrixOperation.java | 19 +-
src/main/java/org/wlld/config/Kernel.java | 4 +
src/main/java/org/wlld/function/Tanh.java | 8 +-
.../wlld/imageRecognition/Convolution.java | 70 ++---
.../wlld/imageRecognition/MeanClustering.java | 95 ++++--
.../org/wlld/imageRecognition/Operation.java | 10 +-
.../org/wlld/imageRecognition/RGBNorm.java | 27 +-
.../imageRecognition/modelEntity/RgbBack.java | 37 +++
.../segmentation/FindMaxSimilar.java | 138 ---------
.../segmentation/KNerveManger.java | 117 ++++++++
.../segmentation/RgbRegression.java | 99 +++++-
.../segmentation/WFilter.java | 55 ----
.../segmentation/Watershed.java | 20 +-
src/main/java/org/wlld/nerveEntity/Nerve.java | 32 +-
.../java/org/wlld/nerveEntity/SoftMax.java | 8 +-
src/main/java/org/wlld/param/Food.java | 19 ++
src/test/java/coverTest/DataObservation.java | 101 +++++++
src/test/java/coverTest/FoodTest.java | 107 ++++---
src/test/java/coverTest/ForestTest.java | 5 +-
src/test/java/coverTest/PicTest.java | 2 +-
src/test/java/coverTest/RGBBody.java | 46 +++
.../java/coverTest/regionCut/RegionCut.java | 284 ++++++++++++++++++
.../coverTest/regionCut/RegionCutBody.java | 15 +
.../coverTest/regionCut/RegionFeature.java | 84 ++++++
src/test/java/org/wlld/NerveDemo1.java | 4 +-
26 files changed, 1053 insertions(+), 355 deletions(-)
create mode 100644 src/main/java/org/wlld/imageRecognition/modelEntity/RgbBack.java
delete mode 100644 src/main/java/org/wlld/imageRecognition/segmentation/FindMaxSimilar.java
create mode 100644 src/main/java/org/wlld/imageRecognition/segmentation/KNerveManger.java
delete mode 100644 src/main/java/org/wlld/imageRecognition/segmentation/WFilter.java
create mode 100644 src/test/java/coverTest/DataObservation.java
create mode 100644 src/test/java/coverTest/RGBBody.java
create mode 100644 src/test/java/coverTest/regionCut/RegionCut.java
create mode 100644 src/test/java/coverTest/regionCut/RegionCutBody.java
create mode 100644 src/test/java/coverTest/regionCut/RegionFeature.java
diff --git a/pom.xml b/pom.xml
index 4fc633a..34cef63 100644
--- a/pom.xml
+++ b/pom.xml
@@ -6,7 +6,7 @@
com.wlld
easyAi
- 1.0.9
+ 1.0.0
easyAi
diff --git a/src/main/java/org/wlld/MatrixTools/MatrixOperation.java b/src/main/java/org/wlld/MatrixTools/MatrixOperation.java
index 641b31b..0bc6765 100644
--- a/src/main/java/org/wlld/MatrixTools/MatrixOperation.java
+++ b/src/main/java/org/wlld/MatrixTools/MatrixOperation.java
@@ -51,12 +51,16 @@ public class MatrixOperation {
Matrix matrix1 = transPosition(parameter);
//转置的参数矩阵乘以参数矩阵
Matrix matrix2 = mulMatrix(matrix1, parameter);
- //求上一步的逆矩阵
+ //求上一步的逆矩阵 这一步需要矩阵非奇异,若出现奇异矩阵，则返回0矩阵，意味失败
Matrix matrix3 = getInverseMatrixs(matrix2);
- //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);
+ if (matrix3.getX() == 1 && matrix3.getY() == 1) {
+ return matrix3;
+ } else {
+ //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);
+ }
} else {
throw new Exception("invalid regression matrix");
}
@@ -347,8 +351,9 @@ public class MatrixOperation {
mathMul(myMatrix, def);
return myMatrix;
} else {
- System.out.println(matrix.getString());
- throw new Exception("this matrix do not have InverseMatrixs");
+ //System.out.println("matrix def is zero error:");
+ //System.out.println(matrix.getString());
+ return new Matrix(1, 1);
}
}
diff --git a/src/main/java/org/wlld/config/Kernel.java b/src/main/java/org/wlld/config/Kernel.java
index 465295b..c0cf7b5 100644
--- a/src/main/java/org/wlld/config/Kernel.java
+++ b/src/main/java/org/wlld/config/Kernel.java
@@ -8,6 +8,8 @@ public class Kernel {
private static final String Horizontal_Number = "[-1,-2,-1]#[0,0,0]#[1,2,1]#";//横卷积核
private static final String All_Number = "[1,-2,1]#[-2,4,-2]#[1,-2,1]#";//角卷积
private static final String All_Number2 = "[-1,0,-1]#[0,4,0]#[-1,0,-1]#";
+ private static final String All_Big = "[-1,0,0,0,-1]#[0,-1,0,-1,0]#[0,0,8,0,0]#" +
+ "[0,-1,0,-1,0]#[-1,0,0,0,-1]";
public static final int Region_Nub = 60;//一张图有多少份
public static final double th = 0.88;//分水岭灰度阈值
public static final double rgbN = 442.0;//442.0;//RGB范数归一化最大值
@@ -15,6 +17,7 @@ public class Kernel {
public static Matrix Horizontal;
public static Matrix All;
public static Matrix ALL_Two;
+ public static Matrix Big;
public static final int Unit = 100;
public static final double Pi = ArithUtil.div(ArithUtil.div(Math.PI, 2), Unit);
@@ -23,6 +26,7 @@ public class Kernel {
static {
try {
+ Big = new Matrix(5, 5, All_Big);
ALL_Two = new Matrix(3, 3, All_Number2);
All = new Matrix(3, 3, All_Number);
Vertical = new Matrix(3, 3, Vertical_Number);
diff --git a/src/main/java/org/wlld/function/Tanh.java b/src/main/java/org/wlld/function/Tanh.java
index 13b3cc1..776c3e7 100644
--- a/src/main/java/org/wlld/function/Tanh.java
+++ b/src/main/java/org/wlld/function/Tanh.java
@@ -8,13 +8,13 @@ public class Tanh implements ActiveFunction {
public double function(double x) {
double x1 = Math.exp(x);
double x2 = Math.exp(-x);
- double son = ArithUtil.sub(x1, x2);
- double mother = ArithUtil.add(x1, x2);
- return ArithUtil.div(son, mother);
+ double son = x1 - x2;// ArithUtil.sub(x1, x2);
+ double mother = x1 + x2;// ArithUtil.add(x1, x2);
+ return son / mother;//ArithUtil.div(son, mother);
}
@Override
public double functionG(double out) {
- return ArithUtil.sub(1, Math.pow(function(out), 2));
+ return 1 - Math.pow(function(out), 2);//ArithUtil.sub(1, Math.pow(function(out), 2));
}
}
diff --git a/src/main/java/org/wlld/imageRecognition/Convolution.java b/src/main/java/org/wlld/imageRecognition/Convolution.java
index 2e1c468..c7b14be 100644
--- a/src/main/java/org/wlld/imageRecognition/Convolution.java
+++ b/src/main/java/org/wlld/imageRecognition/Convolution.java
@@ -6,8 +6,6 @@ import org.wlld.config.Kernel;
import org.wlld.imageRecognition.border.Border;
import org.wlld.imageRecognition.border.Frame;
import org.wlld.imageRecognition.border.FrameBody;
-import org.wlld.imageRecognition.modelEntity.RegressionBody;
-import org.wlld.imageRecognition.segmentation.RgbRegression;
import org.wlld.tools.ArithUtil;
import org.wlld.tools.Frequency;
@@ -70,7 +68,7 @@ public class Convolution extends Frequency {
List threeChannelMatrixList = regionThreeChannelMatrix(threeMatrix, regionSize);
for (ThreeChannelMatrix threeChannelMatrix : threeChannelMatrixList) {
List feature = new ArrayList<>();
- MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig);
+ MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig, true);
Matrix matrixR = threeChannelMatrix.getMatrixR();
Matrix matrixG = threeChannelMatrix.getMatrixG();
Matrix matrixB = threeChannelMatrix.getMatrixB();
@@ -175,7 +173,7 @@ public class Convolution extends Frequency {
RGBSort rgbSort = new RGBSort();
int x = matrixR.getX();
int y = matrixR.getY();
- MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig);
+ MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig, true);
for (int i = 0; i < x; i++) {
for (int j = 0; j < y; j++) {
double[] color = new double[]{matrixR.getNumber(i, j), matrixG.getNumber(i, j), matrixB.getNumber(i, j)};
@@ -201,15 +199,13 @@ public class Convolution extends Frequency {
}
public List getCenterTexture(ThreeChannelMatrix threeChannelMatrix, int size, int poolSize, TempleConfig templeConfig
- , int sqNub) throws Exception {
+ , int sqNub, int tag) throws Exception {
RGBSort rgbSort = new RGBSort();
- double dispersedThNub = templeConfig.getFood().getDispersedTh();
- int step = templeConfig.getFood().getStep();
- MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig);
+ MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig, true);
Matrix matrixR = threeChannelMatrix.getMatrixR();
Matrix matrixG = threeChannelMatrix.getMatrixG();
Matrix matrixB = threeChannelMatrix.getMatrixB();
- Matrix matrixH = threeChannelMatrix.getH();
+ Matrix matrixRGB = threeChannelMatrix.getMatrixRGB();
int xn = matrixR.getX();
int yn = matrixR.getY();
// for (int i = 0; i < xn; i++) {
@@ -220,47 +216,41 @@ public class Convolution extends Frequency {
// }
// }
//局部特征选区筛选
- double sigma = 0;
- int nub = 0;
- for (int i = 0; i <= xn - size; i += step) {
- for (int j = 0; j <= yn - size; j += step) {
- Matrix sonH = matrixH.getSonOfMatrix(i, j, size, size);
- double[] h = new double[size * size];
- nub++;
- for (int t = 0; t < size; t++) {
- for (int k = 0; k < size; k++) {
- int index = t * size + k;
- h[index] = sonH.getNumber(t, k);
- }
- }
- sigma = dc(h) + sigma;
- }
- }
- double dispersedTh = (sigma / nub) * dispersedThNub;//离散阈值
- for (int i = 0; i <= xn - size; i += step) {
- for (int j = 0; j <= yn - size; j += step) {
+ int nub = size * size;
+ int twoNub = nub * 2;
+ for (int i = 0; i <= xn - size; i++) {
+ for (int j = 0; j <= yn - size; j++) {
Matrix sonR = matrixR.getSonOfMatrix(i, j, size, size);
Matrix sonG = matrixG.getSonOfMatrix(i, j, size, size);
Matrix sonB = matrixB.getSonOfMatrix(i, j, size, size);
- Matrix sonH = matrixH.getSonOfMatrix(i, j, size, size);
- double[] h = new double[size * size];
- double[] rgb = new double[size * size * 3];
+ Matrix sonRGB = matrixRGB.getSonOfMatrix(i, j, size, size);
+ double[] h = new double[nub];
+ double[] rgb = new double[nub * 3];
for (int t = 0; t < size; t++) {
for (int k = 0; k < size; k++) {
int index = t * size + k;
- h[index] = sonH.getNumber(t, k);
- rgb[index] = sonR.getNumber(t, k);
- rgb[size * size + index] = sonG.getNumber(t, k);
- rgb[size * size * 2 + index] = sonB.getNumber(t, k);
+ h[index] = sonRGB.getNumber(t, k);
+ rgb[index] = sonR.getNumber(t, k) / 255;
+ rgb[nub + index] = sonG.getNumber(t, k) / 255;
+ rgb[twoNub + index] = sonB.getNumber(t, k) / 255;
}
}
- double dispersed = dc(h);
- if (dispersed < dispersedTh) {
- meanClustering.setColor(rgb);
+ double dispersed = variance(h);
+ if (dispersed < 900 && dispersed > 200) {
+ for (int m = 0; m < nub; m++) {
+ double[] color = new double[]{rgb[m], rgb[m + nub], rgb[m + twoNub]};
+ meanClustering.setColor(color);
+ }
+ // meanClustering.setColor(rgb);
}
}
}
- meanClustering.start(false);//开始聚类
+ List list = meanClustering.start(true);//开始聚类
+ if (tag == 0) {//识别
+ templeConfig.getFood().getkNerveManger().look(list);
+ } else {//训练
+ templeConfig.getFood().getkNerveManger().setFeature(tag, list);
+ }
List rgbNorms = meanClustering.getMatrices();
Collections.sort(rgbNorms, rgbSort);
List features = new ArrayList<>();
@@ -270,7 +260,7 @@ public class Convolution extends Frequency {
features.add(rgb[j]);
}
}
- //System.out.println(features);
+ // System.out.println(features);
return features;
}
diff --git a/src/main/java/org/wlld/imageRecognition/MeanClustering.java b/src/main/java/org/wlld/imageRecognition/MeanClustering.java
index 8e51792..4b42e41 100644
--- a/src/main/java/org/wlld/imageRecognition/MeanClustering.java
+++ b/src/main/java/org/wlld/imageRecognition/MeanClustering.java
@@ -1,6 +1,6 @@
package org.wlld.imageRecognition;
-import org.wlld.imageRecognition.segmentation.RgbRegression;
+import org.wlld.param.Food;
import java.util.*;
@@ -11,20 +11,31 @@ public class MeanClustering {
private int speciesQuantity;//种类数量(模型需要返回)
private List matrices = new ArrayList<>();//均值K模型(模型需要返回)
private int size = 10000;
+ private TempleConfig templeConfig;
+ private int sensoryNerveNub;//神经元个数
+ private List kList = new ArrayList<>();
public List getMatrices() {
return matrices;
}
- public MeanClustering(int speciesQuantity, TempleConfig templeConfig) {
+ public MeanClustering(int speciesQuantity, TempleConfig templeConfig, boolean isFirst) throws Exception {
this.speciesQuantity = speciesQuantity;//聚类的数量
- size = templeConfig.getFood().getRegressionNub();
+ Food food = templeConfig.getFood();
+ size = food.getRegressionNub();
+ this.templeConfig = templeConfig;
+// if (isFirst) {
+// for (int i = 0; i < speciesQuantity; i++) {
+// kList.add(new MeanClustering(10, templeConfig, false));
+// }
+// }
}
public void setColor(double[] color) throws Exception {
if (matrixList.size() == 0) {
matrixList.add(color);
length = color.length;
+ sensoryNerveNub = templeConfig.getFeatureNub() * length;
} else {
if (length == color.length) {
matrixList.add(color);
@@ -73,24 +84,60 @@ public class MeanClustering {
}
}
- private void startRegression() throws Exception {//开始聚类回归
+ private List startBp() {
+ int times = 2000 + 1;
+ List features = new ArrayList<>();
+ List> lists = new ArrayList<>();
+ for (int j = 0; j < matrices.size(); j++) {
+ List list = matrices.get(j).getRgbs().subList(0, times);
+ lists.add(list);
+ }
+ for (int j = 0; j < times; j++) {
+ double[] feature = new double[sensoryNerveNub];
+ for (int i = 0; i < lists.size(); i++) {
+ double[] data = lists.get(i).get(j);
+ int len = data.length;
+ for (int k = 0; k < len; k++) {
+ feature[i * len + k] = data[k];
+ }
+ }
+ features.add(feature);
+ }
+ return features;
+ }
+
+ private List startRegression() throws Exception {//开始聚类回归
+ for (int i = 0; i < matrices.size(); i++) {
+ List list = matrices.get(i).getRgbs();
+ MeanClustering k = kList.get(i);
+ for (double[] rgb : list) {
+ k.setColor(rgb);
+ }
+ k.start(false);
+ }
+ //遍历子聚类
+ int times = 2000;
Random random = new Random();
- for (RGBNorm rgbNorm : matrices) {
- RgbRegression rgbRegression = new RgbRegression(size);
- List list = rgbNorm.getRgbs();
- for (int i = 0; i < size; i++) {
- double[] rgb = list.get(random.nextInt(list.size()));
- rgb[0] = rgb[0] / 255;
- rgb[1] = rgb[1] / 255;
- rgb[2] = rgb[2] / 255;
- rgbRegression.insertRGB(rgb);
+ List features = new ArrayList<>();
+ for (int i = 0; i < times; i++) {
+ double[] feature = new double[sensoryNerveNub];
+ for (int k = 0; k < kList.size(); k++) {
+ MeanClustering mean = kList.get(k);
+ List rgbNorms = mean.getMatrices();
+ double[] rgb = rgbNorms.get(random.nextInt(rgbNorms.size())).getRgb();
+ int rgbLen = rgb.length;
+ for (int t = 0; t < rgbLen; t++) {
+ int index = k * rgbLen + t;
+ feature[index] = rgb[t];
+ }
}
- rgbRegression.regression();
- rgbNorm.setRgbRegression(rgbRegression);
+ //System.out.println(Arrays.toString(feature));
+ features.add(feature);
}
+ return features;
}
- public void start(boolean isRegression) throws Exception {//开始聚类
+ public List start(boolean isRegression) throws Exception {//开始聚类
if (matrixList.size() > 1) {
Random random = new Random();
for (int i = 0; i < speciesQuantity; i++) {//初始化均值向量
@@ -102,18 +149,26 @@ public class MeanClustering {
}
//进行两者的比较
boolean isNext;
- for (int i = 0; i < 30; i++) {
+ for (int i = 0; i < 40; i++) {
averageMatrix();
isNext = isNext();
- if (isNext && i < 29) {
+ if (isNext && i < 39) {
clear();
} else {
break;
}
}
- if (isRegression) {
- startRegression();//开始进行回归
+ RGBSort rgbSort = new RGBSort();
+ Collections.sort(matrices, rgbSort);
+ for (RGBNorm rgbNorm : matrices) {
+ rgbNorm.finish();
}
+// if (isRegression) {
+// return startRegression();
+// } else {
+// return null;
+// }
+ return startBp();
} else {
throw new Exception("matrixList number less than 2");
}
diff --git a/src/main/java/org/wlld/imageRecognition/Operation.java b/src/main/java/org/wlld/imageRecognition/Operation.java
index 1918278..aa9d8ce 100644
--- a/src/main/java/org/wlld/imageRecognition/Operation.java
+++ b/src/main/java/org/wlld/imageRecognition/Operation.java
@@ -92,7 +92,8 @@ public class Operation {//进行计算
threeChannelMatrix.setMatrixB(matrixB.getSonOfMatrix(x, y, xSize, ySize));
}
- public RegionBody colorStudy(ThreeChannelMatrix threeChannelMatrix, int tag, List specificationsList) throws Exception {
+ public RegionBody colorStudy(ThreeChannelMatrix threeChannelMatrix, int tag, List specificationsList
+ , String url) throws Exception {
Watershed watershed = new Watershed(threeChannelMatrix, specificationsList, templeConfig);
List regionBodies = watershed.rainfall();
if (regionBodies.size() == 1) {
@@ -108,7 +109,7 @@ public class Operation {//进行计算
// List feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
// templeConfig.getFeatureNub(), templeConfig);
List feature = convolution.getCenterTexture(threeChannelMatrix1, templeConfig.getFood().getRegionSize(),
- templeConfig.getPoolSize(), templeConfig, templeConfig.getFeatureNub());
+ templeConfig.getPoolSize(), templeConfig, templeConfig.getFeatureNub(), tag);
if (templeConfig.isShowLog()) {
System.out.println(tag + ":" + feature);
}
@@ -144,7 +145,8 @@ public class Operation {//进行计算
int minY = regionBody.getMinY();
int maxX = regionBody.getMaxX();
int maxY = regionBody.getMaxY();
- System.out.println("异常：minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY);
+ System.out.println("异常：minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY + ",tag==" + tag
+ + "url==" + url);
}
throw new Exception("Parameter exception region size==" + regionBodies.size());
}
@@ -183,7 +185,7 @@ public class Operation {//进行计算
// List feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
// templeConfig.getFeatureNub(), templeConfig);
List feature = convolution.getCenterTexture(threeChannelMatrix1, templeConfig.getFood().getRegionSize(),
- templeConfig.getPoolSize(), templeConfig, templeConfig.getFeatureNub());
+ templeConfig.getPoolSize(), templeConfig, templeConfig.getFeatureNub(), 0);
if (templeConfig.isShowLog()) {
System.out.println(feature);
}
diff --git a/src/main/java/org/wlld/imageRecognition/RGBNorm.java b/src/main/java/org/wlld/imageRecognition/RGBNorm.java
index fef6cd0..0f8d88f 100644
--- a/src/main/java/org/wlld/imageRecognition/RGBNorm.java
+++ b/src/main/java/org/wlld/imageRecognition/RGBNorm.java
@@ -4,6 +4,8 @@ import org.wlld.imageRecognition.segmentation.RgbRegression;
import org.wlld.tools.ArithUtil;
import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Comparator;
import java.util.List;
public class RGBNorm {
@@ -12,7 +14,7 @@ public class RGBNorm {
private int nub;
private double[] rgb;
private double[] rgbUp;
- private List rgbs = new ArrayList<>();
+ private List rgbs = new ArrayList<>();//需要对它进行排序
private RgbRegression rgbRegression;
private int len;
@@ -96,6 +98,11 @@ public class RGBNorm {
}
}
+ public void finish() {//进行排序
+ RGBListSort rgbListSort = new RGBListSort();
+ Collections.sort(rgbs, rgbListSort);
+ }
+
public double getNorm() {
return norm;
}
@@ -103,4 +110,22 @@ public class RGBNorm {
public double[] getRgb() {
return rgb;
}
+
+ class RGBListSort implements Comparator {
+ @Override
+ public int compare(double[] o1, double[] o2) {
+ double o1Norm = 0;
+ double o2Norm = 0;
+ for (int i = 0; i < o1.length; i++) {
+ o1Norm = o1Norm + Math.pow(o1[i], 2);
+ o2Norm = o2Norm + Math.pow(o2[i], 2);
+ }
+ if (o1Norm > o2Norm) {
+ return -1;
+ } else if (o1Norm < o2Norm) {
+ return 1;
+ }
+ return 0;
+ }
+ }
}
diff --git a/src/main/java/org/wlld/imageRecognition/modelEntity/RgbBack.java b/src/main/java/org/wlld/imageRecognition/modelEntity/RgbBack.java
new file mode 100644
index 0000000..7ef5b5f
--- /dev/null
+++ b/src/main/java/org/wlld/imageRecognition/modelEntity/RgbBack.java
@@ -0,0 +1,37 @@
+package org.wlld.imageRecognition.modelEntity;
+
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.i.OutBack;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description
+ */
+public class RgbBack implements OutBack {
+ private int id = 0;
+ private double out = 0;
+
+ public void clear() {
+ out = 0;
+ id = 0;
+ }
+
+ @Override
+ public void getBack(double out, int id, long eventId) {
+ if (out > this.out) {
+ this.out = out;
+ this.id = id;
+ }
+ }
+
+ @Override
+ public void getBackMatrix(Matrix matrix, long eventId) {
+
+ }
+
+ public int getId() {
+ return id;
+ }
+}
diff --git a/src/main/java/org/wlld/imageRecognition/segmentation/FindMaxSimilar.java b/src/main/java/org/wlld/imageRecognition/segmentation/FindMaxSimilar.java
deleted file mode 100644
index 7c38640..0000000
--- a/src/main/java/org/wlld/imageRecognition/segmentation/FindMaxSimilar.java
+++ /dev/null
@@ -1,138 +0,0 @@
-package org.wlld.imageRecognition.segmentation;
-
-import org.wlld.MatrixTools.Matrix;
-import org.wlld.MatrixTools.MatrixOperation;
-import org.wlld.imageRecognition.ThreeChannelMatrix;
-
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-
-/**
- * @param
- * @DATA
- * @Author LiDaPeng
- * @Description 寻找相似度最大的选区
- */
-public class FindMaxSimilar {
-
- public void findMaxSimilar(ThreeChannelMatrix threeChannelMatrix, int size) throws Exception {
- Map threeChannelMatrices = new HashMap<>();
- Matrix matrixR = threeChannelMatrix.getMatrixR();
- Matrix matrixG = threeChannelMatrix.getMatrixG();
- Matrix matrixB = threeChannelMatrix.getMatrixB();
- Matrix matrixRGB = threeChannelMatrix.getMatrixRGB();
- int x = matrixR.getX();
- int y = matrixR.getY();
- System.out.println("初始区域数量:" + (x * y));
- int index = 0;
- for (int i = 0; i <= x - size; i += size) {
- for (int j = 0; j <= y - size; j += size) {
- ThreeChannelMatrix threeChannelMatrix1 = new ThreeChannelMatrix();
- Matrix bodyR = matrixR.getSonOfMatrix(i, j, size, size);
- Matrix bodyG = matrixG.getSonOfMatrix(i, j, size, size);
- Matrix bodyB = matrixB.getSonOfMatrix(i, j, size, size);
- Matrix bodyRGB = matrixRGB.getSonOfMatrix(i, j, size, size);
- threeChannelMatrix1.setMatrixR(bodyR);
- threeChannelMatrix1.setMatrixG(bodyG);
- threeChannelMatrix1.setMatrixB(bodyB);
- threeChannelMatrix1.setMatrixRGB(bodyRGB);
- threeChannelMatrices.put(index, threeChannelMatrix1);
- index++;
- }
- }
- //切割完毕 ，开始寻找最大相似性
- for (Map.Entry entry : threeChannelMatrices.entrySet()) {
- int key = entry.getKey();
- ThreeChannelMatrix threeChannelMatrix1 = entry.getValue();
- Matrix matrix1 = threeChannelMatrix1.getMatrixRGB();
- double minDist = -1;
- int similarId = 0;
- for (Map.Entry entrySon : threeChannelMatrices.entrySet()) {
- int sonKey = entrySon.getKey();
- if (key != sonKey) {
- Matrix matrix2 = entrySon.getValue().getMatrixRGB();
- double dist = MatrixOperation.getEDistByMatrix(matrix1, matrix2);
- if (minDist == -1 || dist < minDist) {
- minDist = dist;
- similarId = sonKey;
- }
- }
- }
- threeChannelMatrix1.setSimilarId(similarId);
- }
- //最大相似性区域已经查找完毕,开始进行连线
- Map> lineMap = line(threeChannelMatrices);
- //System.out.println("size:" + lineMap.size());
- int max = 0;
- int maxK = 0;
- for (Map.Entry> entry : lineMap.entrySet()) {
- int key = entry.getKey();
- int nub = entry.getValue().size();
- if (nub > max) {
- max = nub;
- maxK = key;
- }
- }
- System.out.println("max:" + max);
- int key = lineMap.get(maxK).get(0);
- Matrix matrix = threeChannelMatrices.get(key).getMatrixR();
- System.out.println(matrix.getString());
-
-
- }
-
- private void merge(Map> lineMap, Map map,
- int x, int y) throws Exception {
- Map map2 = new HashMap<>();
- for (Map.Entry> entry : lineMap.entrySet()) {
- int key = entry.getKey();
- Matrix matrixAll = new Matrix(x, y);
- List list = entry.getValue();
- int len = list.size();
- for (int i = 0; i < len; i++) {
- ThreeChannelMatrix threeChannelMatrix = map.get(list.get(i));
- Matrix matrixRGB = threeChannelMatrix.getMatrixRGB();
- matrixAll = MatrixOperation.add(matrixAll, matrixRGB);
- }
- MatrixOperation.mathDiv(matrixAll, len);//矩阵数除
- map2.put(key, matrixAll);
- }
-
- }
-
- private Map> line(Map threeChannelMatrices) {//开始进行连线
- Map> lineMap = new HashMap<>();
- for (Map.Entry entry : threeChannelMatrices.entrySet()) {
- int key = entry.getKey();//当前进行连线的id
- ThreeChannelMatrix myThreeChannelMatrix = entry.getValue();
- boolean isLine = myThreeChannelMatrix.isLine();//是否被连线
- int upIndex = key;//上一个进行连线的id
- if (!isLine) {//可以进行连线
- List list = new ArrayList<>();
- lineMap.put(key, list);
- list.add(key);
- myThreeChannelMatrix.setLine(true);
- boolean line;
- do {
- int similarId = myThreeChannelMatrix.getSimilarId();//距离它最近的id
- ThreeChannelMatrix threeChannelMatrix = threeChannelMatrices.get(similarId);
- line = threeChannelMatrix.isLine();
- if (!line) {//进行连线
- list.add(similarId);
- threeChannelMatrix.setLine(true);
- //如果当前被连线的矩阵的最近id为连线者本身，则连线后停止遍历
- if (upIndex == threeChannelMatrix.getSimilarId()) {//停止连线
- line = true;
- } else {//继续连线z
- upIndex = similarId;
- myThreeChannelMatrix = threeChannelMatrix;
- }
- }
- } while (!line);
- }
- }
- return lineMap;
- }
-}
diff --git a/src/main/java/org/wlld/imageRecognition/segmentation/KNerveManger.java b/src/main/java/org/wlld/imageRecognition/segmentation/KNerveManger.java
new file mode 100644
index 0000000..be1d0f2
--- /dev/null
+++ b/src/main/java/org/wlld/imageRecognition/segmentation/KNerveManger.java
@@ -0,0 +1,117 @@
+package org.wlld.imageRecognition.segmentation;
+
+import org.wlld.config.RZ;
+import org.wlld.function.Sigmod;
+import org.wlld.function.Tanh;
+import org.wlld.imageRecognition.modelEntity.RgbBack;
+import org.wlld.nerveCenter.NerveManager;
+import org.wlld.nerveEntity.SensoryNerve;
+
+import java.awt.image.Kernel;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description
+ */
+public class KNerveManger {
+ private Map> featureMap = new HashMap<>();
+ private int sensoryNerveNub;//输出神经元个数
+ private int speciesNub;//种类数
+ private NerveManager nerveManager;
+ private int times;
+ private RgbBack rgbBack = new RgbBack();
+
+ public KNerveManger(int sensoryNerveNub, int speciesNub, int times) throws Exception {
+ this.sensoryNerveNub = sensoryNerveNub;
+ this.speciesNub = speciesNub;
+ this.times = times;
+ nerveManager = new NerveManager(sensoryNerveNub, 24, speciesNub,
+ 1, new Tanh(),//0.008 l1 0.02
+ false, false, 0.008, RZ.L1, 0.01);
+ nerveManager.init(true, false, true, true);
+ }
+
+ private Map createTag(int tag) {//创建一个标注
+ Map tagging = new HashMap<>();
+ Set set = featureMap.keySet();
+ for (int key : set) {
+ double value = 0.0;
+ if (key == tag) {
+ value = 1.0;
+ }
+ tagging.put(key, value);
+ }
+ return tagging;
+ }
+
+ public void look(List data) throws Exception {
+ int size = data.size();
+ Map map = new HashMap<>();
+ for (int i = 0; i < size; i++) {
+ rgbBack.clear();
+ post(data.get(i), null, false);
+ int type = rgbBack.getId();
+ if (map.containsKey(type)) {
+ map.put(type, map.get(type) + 1);
+ } else {
+ map.put(type, 1);
+ }
+ }
+ double max = 0;
+ int type = 0;
+ for (Map.Entry entry : map.entrySet()) {
+ int nub = entry.getValue();
+ if (nub > max) {
+ max = nub;
+ type = entry.getKey();
+ }
+ }
+ double point = max / size;
+ System.out.println("类型是：" + type + ",总票数:" + size + ",得票率：" + point);
+ System.out.println("=================================完成");
+ }
+
+ public void startStudy() throws Exception {
+ for (int i = 0; i < times; i++) {
+ for (Map.Entry> entry : featureMap.entrySet()) {
+ int type = entry.getKey();
+ System.out.println("=============================" + type);
+ Map tag = createTag(type);//标注
+ double[] feature = entry.getValue().get(i);//数据
+ post(feature, tag, true);
+ }
+ }
+
+// for (Map.Entry> entry : featureMap.entrySet()) {
+// int type = entry.getKey();
+// System.out.println("=============================" + type);
+// List list = entry.getValue();
+// look(list);
+// }
+ }
+
+ private void post(double[] data, Map tagging, boolean isStudy) throws Exception {
+ List sensoryNerveList = nerveManager.getSensoryNerves();
+ int size = sensoryNerveList.size();
+ for (int i = 0; i < size; i++) {
+ sensoryNerveList.get(i).postMessage(1, data[i], isStudy, tagging, rgbBack);
+ }
+ }
+
+ public void setFeature(int type, List feature) {
+ if (type > 0) {
+ if (featureMap.containsKey(type)) {
+ featureMap.get(type).addAll(feature);
+ } else {
+ featureMap.put(type, feature);
+ }
+ }
+ }
+
+}
diff --git a/src/main/java/org/wlld/imageRecognition/segmentation/RgbRegression.java b/src/main/java/org/wlld/imageRecognition/segmentation/RgbRegression.java
index 972e9ee..ac47793 100644
--- a/src/main/java/org/wlld/imageRecognition/segmentation/RgbRegression.java
+++ b/src/main/java/org/wlld/imageRecognition/segmentation/RgbRegression.java
@@ -15,8 +15,56 @@ public class RgbRegression {
private double b;
private Matrix RG;//rg矩阵
private Matrix B;//b矩阵
+ private Matrix RGB;//rgb矩阵
private int xIndex = 0;//记录插入数量
private boolean isRegression = false;//是否进行了回归
+ private int regionNub;
+ private int x;
+ private int y;
+
+ public Matrix getRGB() {
+ return RGB;
+ }
+
+ public int getX() {
+ return x;
+ }
+
+ public void setX(int x) {
+ this.x = x;
+ }
+
+ public int getY() {
+ return y;
+ }
+
+ public void setY(int y) {
+ this.y = y;
+ }
+
+ public Matrix getRGMatrix() {
+ return RG;
+ }
+
+ public Matrix getBMatrix() {
+ return B;
+ }
+
+ public void clear(int size) {
+ RG = new Matrix(size, 3);
+ RGB = new Matrix(size, 3);
+ B = new Matrix(size, 1);
+ xIndex = 0;
+ regionNub = size;
+ }
+
+ public int getRegionNub() {
+ return regionNub;
+ }
+
+ public void setRegionNub(int regionNub) {
+ this.regionNub = regionNub;
+ }
public double getWr() {
return wr;
@@ -32,11 +80,47 @@ public class RgbRegression {
public RgbRegression(int size) {//初始化rgb矩阵
RG = new Matrix(size, 3);
+ RGB = new Matrix(size, 3);
B = new Matrix(size, 1);
+ regionNub = size;
+ xIndex = 0;
+ }
+
+ public void mergeRegion(RgbRegression rgbRegression) throws Exception {
+ Matrix myRG = rgbRegression.getRGMatrix();
+ Matrix myB = rgbRegression.getBMatrix();
+ int nub = rgbRegression.getRegionNub();//合并过来的数据量
+ int size = nub + regionNub;//扩容后新的数据量
+// Matrix NRG = new Matrix(size, 3);
+// Matrix NB = new Matrix(size, 1);
+// for (int i = 0; i < size; i++) {
+// Matrix RGT;
+// Matrix BT;
+// int t = i;
+// if (i < regionNub) {//加载本来的数据
+// RGT = RG;
+// BT = B;
+// } else {
+// RGT = myRG;
+// BT = myB;
+// t = t - regionNub;
+// }
+// for (int j = 0; j < 3; j++) {
+// NRG.setNub(i, j, RGT.getNumber(t, j));
+// }
+// NB.setNub(i, 0, BT.getNumber(t, 0));
+// }
+ regionNub = size;
+// RG = NRG;
+// B = NB;
+// regression();//最新数据进行回归
}
public void insertRGB(double[] rgb) throws Exception {//rgb插入矩阵
if (rgb.length == 3) {
+ RGB.setNub(xIndex, 0, rgb[0]);
+ RGB.setNub(xIndex, 1, rgb[1]);
+ RGB.setNub(xIndex, 2, rgb[2]);
RG.setNub(xIndex, 0, rgb[0]);
RG.setNub(xIndex, 1, rgb[1]);
RG.setNub(xIndex, 2, 1.0);
@@ -47,13 +131,18 @@ public class RgbRegression {
}
}
- public void regression() throws Exception {//开始进行回归
+ public boolean 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;
+ if (ws.getX() == 1 && ws.getY() == 1) {//矩阵奇异
+ isRegression = false;
+ } else {
+ wr = ws.getNumber(0, 0);
+ wg = ws.getNumber(1, 0);
+ b = ws.getNumber(2, 0);
+ isRegression = true;
+ }
+ return isRegression;
// System.out.println("wr==" + wr + ",wg==" + wg + ",b==" + b);
} else {
throw new Exception("regression matrix size is zero");
diff --git a/src/main/java/org/wlld/imageRecognition/segmentation/WFilter.java b/src/main/java/org/wlld/imageRecognition/segmentation/WFilter.java
deleted file mode 100644
index 63c7e75..0000000
--- a/src/main/java/org/wlld/imageRecognition/segmentation/WFilter.java
+++ /dev/null
@@ -1,55 +0,0 @@
-package org.wlld.imageRecognition.segmentation;
-
-import org.wlld.MatrixTools.Matrix;
-import org.wlld.imageRecognition.MeanClustering;
-import org.wlld.imageRecognition.RGBNorm;
-import org.wlld.imageRecognition.TempleConfig;
-import org.wlld.imageRecognition.ThreeChannelMatrix;
-
-import java.util.List;
-
-/**
- * @param
- * @DATA
- * @Author LiDaPeng
- * @Description
- */
-public class WFilter {
- private List rgbNorms;
-
- public void filter(ThreeChannelMatrix threeChannelMatrix, TempleConfig templeConfig,
- int speciesQuantity) throws Exception {
- MeanClustering meanClustering = new MeanClustering(speciesQuantity, templeConfig);
- 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++) {
- for (int j = 0; j < y; j++) {
- double[] color = new double[]{matrixR.getNumber(i, j), matrixG.getNumber(i, j), matrixB.getNumber(i, j)};
- meanClustering.setColor(color);
- }
- }
- meanClustering.start(true);
- rgbNorms = meanClustering.getMatrices();
-
- }
-
- private void getDist() {
- double min = -1;
- for (RGBNorm rgbNorm : rgbNorms) {
- double[] rgb = rgbNorm.getRgb();
-
- }
- }
-
- private double dist(double[] a, double[] b) {
- double sigma = 0;
- for (int i = 0; i < a.length; i++) {
- double sub = Math.pow(a[i] - b[i], 2);
- sigma = sigma + sub;
- }
- return sigma / a.length;
- }
-}
diff --git a/src/main/java/org/wlld/imageRecognition/segmentation/Watershed.java b/src/main/java/org/wlld/imageRecognition/segmentation/Watershed.java
index f968dc4..4885282 100644
--- a/src/main/java/org/wlld/imageRecognition/segmentation/Watershed.java
+++ b/src/main/java/org/wlld/imageRecognition/segmentation/Watershed.java
@@ -75,15 +75,17 @@ public class Watershed {
private boolean isTray(int x, int y) throws Exception {
boolean isTray = false;
-// double[] rgb = new double[]{matrixR.getNumber(x, y) / 255, matrixG.getNumber(x, y) / 255,
-// matrixB.getNumber(x, y) / 255};
-// for (RgbRegression rgbRegression : trayBody) {
-// double dist = rgbRegression.getDisError(rgb);
-// if (dist < trayTh) {
-// isTray = true;
-// break;
-// }
-// }
+ if (trayBody != null && trayBody.size() > 0) {
+ double[] rgb = new double[]{matrixR.getNumber(x, y) / 255, matrixG.getNumber(x, y) / 255,
+ matrixB.getNumber(x, y) / 255};
+ for (RgbRegression rgbRegression : trayBody) {
+ double dist = rgbRegression.getDisError(rgb);
+ if (dist < trayTh) {
+ isTray = true;
+ break;
+ }
+ }
+ }
return isTray;
}
diff --git a/src/main/java/org/wlld/nerveEntity/Nerve.java b/src/main/java/org/wlld/nerveEntity/Nerve.java
index 876d515..c11f463 100644
--- a/src/main/java/org/wlld/nerveEntity/Nerve.java
+++ b/src/main/java/org/wlld/nerveEntity/Nerve.java
@@ -186,10 +186,12 @@ public abstract class Nerve {
private void backGetMessage(double parameter, long eventId) throws Exception {//反向传播
backNub++;
- sigmaW = ArithUtil.add(sigmaW, parameter);
+ //sigmaW = ArithUtil.add(sigmaW, parameter);
+ sigmaW = sigmaW + parameter;
if (backNub == downNub) {//进行新的梯度计算
backNub = 0;
- gradient = ArithUtil.mul(activeFunction.functionG(outNub), sigmaW);
+ //gradient = ArithUtil.mul(activeFunction.functionG(outNub), sigmaW);
+ gradient = activeFunction.functionG(outNub) * sigmaW;
updatePower(eventId);//修改阈值
}
}
@@ -218,8 +220,10 @@ public abstract class Nerve {
}
protected void updatePower(long eventId) throws Exception {//修改阈值
- double h = ArithUtil.mul(gradient, studyPoint);//梯度下降
- threshold = ArithUtil.add(threshold, -h);//更新阈值
+ //double h = ArithUtil.mul(gradient, studyPoint);//梯度下降
+ double h = gradient * studyPoint;
+ //threshold = ArithUtil.add(threshold, -h);//更新阈值
+ threshold = threshold - h;
updateW(h, eventId);
sigmaW = 0;//求和结果归零
backSendMessage(eventId);
@@ -229,7 +233,8 @@ public abstract class Nerve {
double re = 0.0;
if (rzType != RZ.NOT_RZ) {
if (rzType == RZ.L2) {
- re = ArithUtil.mul(param, -w);
+ //re = ArithUtil.mul(param, -w);
+ re = param * -w;
} else if (rzType == RZ.L1) {
if (w > 0) {
re = -param;
@@ -248,11 +253,15 @@ public abstract class Nerve {
int key = entry.getKey();//上层隐层神经元的编号
double w = entry.getValue();//接收到编号为KEY的上层隐层神经元的权重
double bn = list.get(key - 1);//接收到编号为KEY的上层隐层神经元的输入
- double wp = ArithUtil.mul(bn, h);//编号为KEY的上层隐层神经元权重的变化值
+ //double wp = ArithUtil.mul(bn, h);//编号为KEY的上层隐层神经元权重的变化值
+ double wp = bn * h;
double regular = regularization(w, param);//正则化抑制权重s
- w = ArithUtil.add(w, regular);
- w = ArithUtil.add(w, wp);//修正后的编号为KEY的上层隐层神经元权重
- double dm = ArithUtil.mul(w, gradient);//返回给相对应的神经元
+ //w = ArithUtil.add(w, regular);
+ w = w + regular;
+ //w = ArithUtil.add(w, wp);//修正后的编号为KEY的上层隐层神经元权重
+ w = w + wp;
+ // double dm = ArithUtil.mul(w, gradient);//返回给相对应的神经元
+ double dm = w * gradient;
// System.out.println("allG==" + allG + ",dm==" + dm);
wg.put(key, dm);//保存上一层权重与梯度的积
dendrites.put(key, w);//保存修正结果
@@ -287,9 +296,10 @@ public abstract class Nerve {
double value = featuresList.get(i);
double w = dendrites.get(i + 1);
//System.out.println("w==" + w + ",value==" + value);
- sigma = ArithUtil.add(ArithUtil.mul(w, value), sigma);
+ //sigma = ArithUtil.add(ArithUtil.mul(w, value), sigma);
+ sigma = w * value + sigma;
}
- return ArithUtil.sub(sigma, threshold);
+ return sigma - threshold;//ArithUtil.sub(sigma, threshold);
}
private void initPower(boolean init, boolean isDynamic) throws Exception {//初始化权重及阈值
diff --git a/src/main/java/org/wlld/nerveEntity/SoftMax.java b/src/main/java/org/wlld/nerveEntity/SoftMax.java
index 4863bea..11140ee 100644
--- a/src/main/java/org/wlld/nerveEntity/SoftMax.java
+++ b/src/main/java/org/wlld/nerveEntity/SoftMax.java
@@ -51,7 +51,8 @@ public class SoftMax extends Nerve {
private double outGradient() {//生成输出层神经元梯度变化
double g = outNub;
if (E == 1) {
- g = ArithUtil.sub(g, 1);
+ //g = ArithUtil.sub(g, 1);
+ g = g - 1;
}
return g;
}
@@ -63,8 +64,9 @@ public class SoftMax extends Nerve {
double eSelf = Math.exp(self);
for (int i = 0; i < featuresList.size(); i++) {
double value = featuresList.get(i);
- sigma = ArithUtil.add(Math.exp(value), sigma);
+ // sigma = ArithUtil.add(Math.exp(value), sigma);
+ sigma = Math.exp(value) + sigma;
}
- return ArithUtil.div(eSelf, sigma);
+ return eSelf / sigma;//ArithUtil.div(eSelf, sigma);
}
}
diff --git a/src/main/java/org/wlld/param/Food.java b/src/main/java/org/wlld/param/Food.java
index d873fed..9b8f133 100644
--- a/src/main/java/org/wlld/param/Food.java
+++ b/src/main/java/org/wlld/param/Food.java
@@ -1,5 +1,6 @@
package org.wlld.param;
+import org.wlld.imageRecognition.segmentation.KNerveManger;
import org.wlld.imageRecognition.segmentation.RgbRegression;
import java.util.ArrayList;
@@ -22,6 +23,24 @@ public class Food {
private int regionSize = 5;//纹理区域大小
private int step = 1;//特征取样步长
private double dispersedTh = 0.3;//选区筛选离散阈值
+ private int speciesNub = 24;//种类数
+ private KNerveManger kNerveManger;
+
+ public KNerveManger getkNerveManger() {
+ return kNerveManger;
+ }
+
+ public void setkNerveManger(KNerveManger kNerveManger) {
+ this.kNerveManger = kNerveManger;
+ }
+
+ public int getSpeciesNub() {
+ return speciesNub;
+ }
+
+ public void setSpeciesNub(int speciesNub) {
+ this.speciesNub = speciesNub;
+ }
public int getStep() {
return step;
diff --git a/src/test/java/coverTest/DataObservation.java b/src/test/java/coverTest/DataObservation.java
new file mode 100644
index 0000000..16fd9e3
--- /dev/null
+++ b/src/test/java/coverTest/DataObservation.java
@@ -0,0 +1,101 @@
+package coverTest;
+
+import coverTest.regionCut.RegionCut;
+import coverTest.regionCut.RegionFeature;
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.imageRecognition.Convolution;
+import org.wlld.imageRecognition.Picture;
+import org.wlld.imageRecognition.ThreeChannelMatrix;
+import org.wlld.imageRecognition.segmentation.RgbRegression;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description数据观察
+ */
+public class DataObservation {
+ private static Convolution convolution = new Convolution();
+
+ public static void main(String[] args) throws Exception {
+ //372,330,右 最大值 147.44
+ //377 ,330右 最大值 69.6
+ int xp = 123;
+ int yp = 165;//290
+ observation2("/Users/lidapeng/Desktop/test/testOne/a0.jpg", xp, yp);
+ }
+
+ public static void observation2(String url, int xp, int yp) throws Exception {
+ Picture picture = new Picture();
+ ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix(url);
+ ThreeChannelMatrix myThreeChannelMatrix = convolution.getRegionMatrix(threeChannelMatrix, xp, yp, 270, 274);
+ RegionFeature regionFeature = new RegionFeature(myThreeChannelMatrix, xp, yp);
+ regionFeature.start();
+ }
+
+ public static void observation(String url, int xp, int yp, int size) throws Exception {
+ Picture picture = new Picture();
+ ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix(url);
+ ThreeChannelMatrix myThreeChannelMatrix = convolution.getRegionMatrix(threeChannelMatrix, xp, yp, size, size);
+ //右
+ ThreeChannelMatrix threeChannelMatrix2 = convolution.getRegionMatrix(threeChannelMatrix, xp, yp + size, size, size);
+ getDist(myThreeChannelMatrix, threeChannelMatrix2, "右");
+ //左
+ ThreeChannelMatrix threeChannelMatrix3 = convolution.getRegionMatrix(threeChannelMatrix, xp, yp - size, size, size);
+ getDist(myThreeChannelMatrix, threeChannelMatrix3, "左");
+ //上
+ ThreeChannelMatrix threeChannelMatrix4 = convolution.getRegionMatrix(threeChannelMatrix, xp - size, yp, size, size);
+ getDist(myThreeChannelMatrix, threeChannelMatrix4, "上");
+ //下
+ ThreeChannelMatrix threeChannelMatrix5 = convolution.getRegionMatrix(threeChannelMatrix, xp + size, yp, size, size);
+ getDist(myThreeChannelMatrix, threeChannelMatrix5, "下");
+ //左上
+ ThreeChannelMatrix threeChannelMatrix6 = convolution.getRegionMatrix(threeChannelMatrix, xp - size, yp - size, size, size);
+ getDist(myThreeChannelMatrix, threeChannelMatrix6, "左上");
+ //左下
+ ThreeChannelMatrix threeChannelMatrix7 = convolution.getRegionMatrix(threeChannelMatrix, xp + size, yp - size, size, size);
+ getDist(myThreeChannelMatrix, threeChannelMatrix7, "左下");
+ //右上
+ ThreeChannelMatrix threeChannelMatrix8 = convolution.getRegionMatrix(threeChannelMatrix, xp - size, yp + size, size, size);
+ getDist(myThreeChannelMatrix, threeChannelMatrix8, "右上");
+ //右下
+ ThreeChannelMatrix threeChannelMatrix9 = convolution.getRegionMatrix(threeChannelMatrix, xp + size, yp + size, size, size);
+ getDist(myThreeChannelMatrix, threeChannelMatrix9, "右下");
+ //getDist("/Users/lidapeng/Desktop/test/testOne/a1.jpg", 468, 713, rgbRegression, "测");
+
+ }
+
+ private static void getDist(ThreeChannelMatrix threeChannelMatrix1, ThreeChannelMatrix threeChannelMatrix2, String name) throws Exception {
+ Matrix matrixR1 = threeChannelMatrix1.getMatrixR();
+ Matrix matrixG1 = threeChannelMatrix1.getMatrixG();
+ Matrix matrixB1 = threeChannelMatrix1.getMatrixB();
+ Matrix matrixR2 = threeChannelMatrix2.getMatrixR();
+ Matrix matrixG2 = threeChannelMatrix2.getMatrixG();
+ Matrix matrixB2 = threeChannelMatrix2.getMatrixB();
+ int x = matrixR1.getX();
+ int y = matrixR1.getY();
+ int nub = x * y;
+ double sigmaR = 0;
+ double sigmaG = 0;
+ double sigmaB = 0;
+ for (int i = 0; i < x; i++) {
+ for (int j = 0; j < y; j++) {
+ double subR = Math.pow(matrixR1.getNumber(i, j) - matrixR2.getNumber(i, j), 2);
+ double subG = Math.pow(matrixG1.getNumber(i, j) - matrixG2.getNumber(i, j), 2);
+ double subB = Math.pow(matrixB1.getNumber(i, j) - matrixB2.getNumber(i, j), 2);
+ sigmaR = subR + sigmaR;
+ sigmaG = subG + sigmaG;
+ sigmaB = subB + sigmaB;
+ }
+ }
+ sigmaR = sigmaR / nub;
+ sigmaG = sigmaG / nub;
+ sigmaB = sigmaB / nub;
+ double sigma = sigmaR + sigmaG + sigmaB;
+ System.out.println(name + ":" + sigma);
+ }
+}
diff --git a/src/test/java/coverTest/FoodTest.java b/src/test/java/coverTest/FoodTest.java
index d501c38..ef85a42 100644
--- a/src/test/java/coverTest/FoodTest.java
+++ b/src/test/java/coverTest/FoodTest.java
@@ -8,6 +8,7 @@ import org.wlld.config.Classifier;
import org.wlld.config.RZ;
import org.wlld.config.StudyPattern;
import org.wlld.imageRecognition.*;
+import org.wlld.imageRecognition.segmentation.KNerveManger;
import org.wlld.imageRecognition.segmentation.RegionBody;
import org.wlld.imageRecognition.segmentation.RegionMapping;
import org.wlld.imageRecognition.segmentation.Specifications;
@@ -76,10 +77,13 @@ public class FoodTest {
food.setShrink(5);//缩紧像素
food.setTimes(1);//聚类数据增强
food.setRegionSize(5);
+ KNerveManger kNerveManger = new KNerveManger(9, 24, 2000);
+ food.setkNerveManger(kNerveManger);
food.setRowMark(0.15);//0.12
food.setColumnMark(0.15);//0.25
food.setRegressionNub(20000);
food.setTrayTh(0.08);
+ food.setDispersedTh(0.5);
templeConfig.setClassifier(Classifier.KNN);
templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 3);
if (modelParameter != null) {
@@ -99,10 +103,11 @@ public class FoodTest {
specifications.setMaxWidth(600);
specifications.setMaxHeight(600);
specificationsList.add(specifications);
+ KNerveManger kNerveManger = templeConfig.getFood().getkNerveManger();
// ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/d.jpg");
// operation.setTray(threeChannelMatrix);
String name = "/Users/lidapeng/Desktop/test/testOne/";
- for (int i = 0; i < 5; i++) {
+ for (int i = 0; i < 1; i++) {
System.out.println("轮数============================" + i);
ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix(name + "a" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix(name + "b" + i + ".jpg");
@@ -128,32 +133,34 @@ public class FoodTest {
ThreeChannelMatrix threeChannelMatrix22 = picture.getThreeMatrix(name + "v" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix23 = picture.getThreeMatrix(name + "w" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix24 = picture.getThreeMatrix(name + "x" + 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);
- operation.colorStudy(threeChannelMatrix11, 11, specificationsList);
- operation.colorStudy(threeChannelMatrix12, 12, specificationsList);
- operation.colorStudy(threeChannelMatrix13, 13, specificationsList);
- operation.colorStudy(threeChannelMatrix14, 14, specificationsList);
- operation.colorStudy(threeChannelMatrix15, 15, specificationsList);
- operation.colorStudy(threeChannelMatrix16, 16, specificationsList);
- operation.colorStudy(threeChannelMatrix17, 17, specificationsList);
- operation.colorStudy(threeChannelMatrix18, 18, specificationsList);
- operation.colorStudy(threeChannelMatrix19, 19, specificationsList);
- operation.colorStudy(threeChannelMatrix20, 20, specificationsList);
- operation.colorStudy(threeChannelMatrix21, 21, specificationsList);
- operation.colorStudy(threeChannelMatrix22, 22, specificationsList);
- operation.colorStudy(threeChannelMatrix23, 23, specificationsList);
- operation.colorStudy(threeChannelMatrix24, 24, specificationsList);
+ operation.colorStudy(threeChannelMatrix1, 1, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix2, 2, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix3, 3, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix4, 4, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix5, 5, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix6, 6, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix7, 7, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix8, 8, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix9, 9, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix10, 10, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix11, 11, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix12, 12, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix13, 13, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix14, 14, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix15, 15, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix16, 16, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix17, 17, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix18, 18, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix19, 19, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix20, 20, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix21, 21, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix22, 22, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix23, 23, specificationsList, name);
+ operation.colorStudy(threeChannelMatrix24, 24, specificationsList, name);
}
- int i = 4;
+ System.out.println("========================");
+ kNerveManger.startStudy();
+ int i = 0;
ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix(name + "a" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix(name + "b" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix(name + "c" + i + ".jpg");
@@ -179,30 +186,30 @@ public class FoodTest {
ThreeChannelMatrix threeChannelMatrix22 = picture.getThreeMatrix(name + "v" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix23 = picture.getThreeMatrix(name + "w" + i + ".jpg");
ThreeChannelMatrix threeChannelMatrix24 = picture.getThreeMatrix(name + "x" + i + ".jpg");
- test3(threeChannelMatrix1, operation, specificationsList);
- test3(threeChannelMatrix2, operation, specificationsList);
- test3(threeChannelMatrix3, operation, specificationsList);
- test3(threeChannelMatrix4, operation, specificationsList);
- test3(threeChannelMatrix5, operation, specificationsList);
- test3(threeChannelMatrix6, operation, specificationsList);
- test3(threeChannelMatrix7, operation, specificationsList);
- test3(threeChannelMatrix8, operation, specificationsList);
- test3(threeChannelMatrix9, operation, specificationsList);
- test3(threeChannelMatrix10, operation, specificationsList);
- test3(threeChannelMatrix11, operation, specificationsList);
- test3(threeChannelMatrix12, operation, specificationsList);
- test3(threeChannelMatrix13, operation, specificationsList);
- test3(threeChannelMatrix14, operation, specificationsList);
- test3(threeChannelMatrix15, operation, specificationsList);
- test3(threeChannelMatrix16, operation, specificationsList);
- test3(threeChannelMatrix17, operation, specificationsList);
- test3(threeChannelMatrix18, operation, specificationsList);
- test3(threeChannelMatrix19, operation, specificationsList);
- test3(threeChannelMatrix20, operation, specificationsList);
- test3(threeChannelMatrix21, operation, specificationsList);
- test3(threeChannelMatrix22, operation, specificationsList);
- test3(threeChannelMatrix23, operation, specificationsList);
- test3(threeChannelMatrix24, operation, specificationsList);
+ operation.colorLook(threeChannelMatrix1, specificationsList);
+ operation.colorLook(threeChannelMatrix2, specificationsList);
+ operation.colorLook(threeChannelMatrix3, specificationsList);
+ operation.colorLook(threeChannelMatrix4, specificationsList);
+ operation.colorLook(threeChannelMatrix5, specificationsList);
+ operation.colorLook(threeChannelMatrix6, specificationsList);
+ operation.colorLook(threeChannelMatrix7, specificationsList);
+ operation.colorLook(threeChannelMatrix8, specificationsList);
+ operation.colorLook(threeChannelMatrix9, specificationsList);
+ operation.colorLook(threeChannelMatrix10, specificationsList);
+ operation.colorLook(threeChannelMatrix11, specificationsList);
+ operation.colorLook(threeChannelMatrix12, specificationsList);
+ operation.colorLook(threeChannelMatrix13, specificationsList);
+ operation.colorLook(threeChannelMatrix14, specificationsList);
+ operation.colorLook(threeChannelMatrix15, specificationsList);
+ operation.colorLook(threeChannelMatrix16, specificationsList);
+ operation.colorLook(threeChannelMatrix17, specificationsList);
+ operation.colorLook(threeChannelMatrix18, specificationsList);
+ operation.colorLook(threeChannelMatrix19, specificationsList);
+ operation.colorLook(threeChannelMatrix20, specificationsList);
+ operation.colorLook(threeChannelMatrix21, specificationsList);
+ operation.colorLook(threeChannelMatrix22, specificationsList);
+ operation.colorLook(threeChannelMatrix23, specificationsList);
+ operation.colorLook(threeChannelMatrix24, specificationsList);
}
diff --git a/src/test/java/coverTest/ForestTest.java b/src/test/java/coverTest/ForestTest.java
index 03c613e..b306aaa 100644
--- a/src/test/java/coverTest/ForestTest.java
+++ b/src/test/java/coverTest/ForestTest.java
@@ -4,7 +4,6 @@ import org.wlld.MatrixTools.Matrix;
import org.wlld.config.Classifier;
import org.wlld.config.StudyPattern;
import org.wlld.imageRecognition.*;
-import org.wlld.imageRecognition.segmentation.FindMaxSimilar;
import org.wlld.imageRecognition.segmentation.RegionBody;
import org.wlld.imageRecognition.segmentation.Specifications;
import org.wlld.imageRecognition.segmentation.Watershed;
@@ -55,9 +54,7 @@ public class ForestTest {
int xSize = maxX - minX;
int ySize = maxY - minY;
ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
- List feature = convolution.getCenterTexture(threeChannelMatrix1, templeConfig.getFood().getRegionSize(),
- templeConfig.getPoolSize(), templeConfig, templeConfig.getFeatureNub());
- System.out.println(feature);
+
} else {
System.out.println("size===" + regionBodies.size());
}
diff --git a/src/test/java/coverTest/PicTest.java b/src/test/java/coverTest/PicTest.java
index cfc6fc8..3e5deed 100644
--- a/src/test/java/coverTest/PicTest.java
+++ b/src/test/java/coverTest/PicTest.java
@@ -36,8 +36,8 @@ public class PicTest {
//testImage(right, wrong, a, b);
//test();
- }
+ }
public static void test() throws Exception {//对图像进行识别测试
Picture picture = new Picture();
diff --git a/src/test/java/coverTest/RGBBody.java b/src/test/java/coverTest/RGBBody.java
new file mode 100644
index 0000000..337f872
--- /dev/null
+++ b/src/test/java/coverTest/RGBBody.java
@@ -0,0 +1,46 @@
+package coverTest;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description
+ */
+public class RGBBody {
+ private double r;
+ private double g;
+ private double b;
+ private double rgb;
+
+ public double getR() {
+ return r;
+ }
+
+ public void setR(double r) {
+ this.r = r;
+ }
+
+ public double getG() {
+ return g;
+ }
+
+ public void setG(double g) {
+ this.g = g;
+ }
+
+ public double getB() {
+ return b;
+ }
+
+ public void setB(double b) {
+ this.b = b;
+ }
+
+ public double getRgb() {
+ return rgb;
+ }
+
+ public void setRgb(double rgb) {
+ this.rgb = rgb;
+ }
+}
diff --git a/src/test/java/coverTest/regionCut/RegionCut.java b/src/test/java/coverTest/regionCut/RegionCut.java
new file mode 100644
index 0000000..63b0dc4
--- /dev/null
+++ b/src/test/java/coverTest/regionCut/RegionCut.java
@@ -0,0 +1,284 @@
+package coverTest.regionCut;
+
+import org.wlld.Ma;
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.config.Kernel;
+import org.wlld.imageRecognition.ThreeChannelMatrix;
+
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description 分区切割
+ */
+public class RegionCut {
+ private Matrix matrixH;
+ private Matrix regionMatrix;//分区地图
+ private int fatherX;
+ private int fatherY;
+ private int size;
+ private int id = 1;//分区id
+ private Map minMap = new HashMap<>();//保存最小值
+ private Map maxMap = new HashMap<>();//保存最大值
+
+ public RegionCut(Matrix matrixH, int fatherX, int fatherY, int size) {
+ this.matrixH = matrixH;
+ this.fatherX = fatherX;
+ this.fatherY = fatherY;
+ this.size = size;
+ regionMatrix = new Matrix(matrixH.getX(), matrixH.getY());
+ }
+
+ private void setLimit(int id, double pixel) {
+ double min = minMap.get(id);
+ double max = maxMap.get(id);
+ if (pixel > max) {
+ maxMap.put(id, pixel);
+ }
+ if (pixel < min) {
+ minMap.put(id, pixel);
+ }
+ }
+
+ private void firstCut() throws Exception {//进行第一次切割
+ int x = matrixH.getX();
+ int y = matrixH.getY();
+ int size = x * y;
+ System.out.println("像素数量：" + size);
+ for (int i = 0; i < x; i++) {
+ for (int j = 0; j < y; j++) {
+ double regionId = regionMatrix.getNumber(i, j);
+ if (regionId < 0.5) {//该像素没有被连接
+ boolean isStop;
+ double self = matrixH.getNumber(i, j);//灰度值
+ regionMatrix.setNub(i, j, id);
+ minMap.put(id, self);
+ maxMap.put(id, self);
+ //System.out.println(regionMatrix.getString());
+ int xi = i;
+ int yj = j;
+ do {
+ double mySelf = matrixH.getNumber(xi, yj);//灰度值
+ int pixel = pixelLine(xi, yj, mySelf);
+ int column = pixel & 0xfff;
+ int row = (pixel >> 12) & 0xfff;
+ double type = regionMatrix.getNumber(row, column);
+ if (type < 0.5) {//可以连接
+ regionMatrix.setNub(row, column, id);//进行连接
+ setLimit(id, mySelf);
+ double mySelfSon = matrixH.getNumber(row, column);//灰度值
+ int pixelOther = pixelLine(row, column, mySelfSon);
+ int column2 = pixelOther & 0xfff;
+ int row2 = (pixelOther >> 12) & 0xfff;
+ isStop = row2 == xi && column2 == yj;
+ xi = row;
+ yj = column;
+ } else {//已经被连接了，跳出
+ isStop = true;
+ }
+ } while (!isStop);
+ id++;
+ }
+ }
+ }
+ System.out.println("第一次选区数量：" + id);
+ }
+
+ public void secondCut() throws Exception {//二切
+ int x = matrixH.getX();
+ int y = matrixH.getY();
+ for (int i = 0; i < x; i++) {
+ for (int j = 0; j < y; j++) {
+ double key = regionMatrix.getNumber(i, j);//与周围八方向比较看是否有异类
+ getOther(i, j, (int) key, matrixH.getNumber(i, j));
+ }
+ }
+ }
+
+ private void updateType(int type, int toType) throws Exception {
+ int x = regionMatrix.getX();
+ int y = regionMatrix.getY();
+ for (int i = 0; i < x; i++) {
+ for (int j = 0; j < y; j++) {
+ if (regionMatrix.getNumber(i, j) == type) {
+ regionMatrix.setNub(i, j, toType);
+ }
+ }
+ }
+ }
+
+ private void getOther(int x, int y, int key, double self) throws Exception {
+ double[] pixels = getPixels(x, y, false);
+ for (int i = 0; i < pixels.length; i++) {
+ int pix = (int) pixels[i];
+ if (pix > 0 && pix != key) {//接壤的非同类
+ double min = minMap.get(pix);
+ double max = maxMap.get(pix);
+ double maxDist = max - min;
+ int row = x;
+ int column = y;
+ switch (i) {
+ case 0://上
+ row = x - 1;
+ break;
+ case 1://左
+ column = y - 1;
+ break;
+ case 2://下
+ row = x + 1;
+ break;
+ case 3://右
+ column = y + 1;
+ break;
+ case 4://左上
+ column = y - 1;
+ row = x - 1;
+ break;
+ case 5://左下
+ column = y - 1;
+ row = x + 1;
+ break;
+ case 6://右下
+ column = y + 1;
+ row = x + 1;
+ break;
+ case 7://右上
+ column = y + 1;
+ row = x - 1;
+ break;
+ }
+ double dist = Math.abs(matrixH.getNumber(row, column) - self);
+ if (dist < maxDist * 0.2) {//两个选区可以合并
+ setLimit(key, min);
+ setLimit(key, max);
+ updateType(pix, key);
+ id--;
+ }
+ break;
+ }
+ }
+ }
+
+ public void start() throws Exception {
+ firstCut();//初切
+ System.out.println("区域数量1：" + id);
+ for (int i = 0; i < 1; i++) {
+ secondCut();//二切
+ }
+ System.out.println("区域数量2：" + id);
+ System.out.println(regionMatrix.getString());
+ }
+
+ private int pixelLine(int x, int y, double self) throws Exception {
+ double[] pixels = getPixels(x, y, true);
+ int minIndex = getMinIndex(pixels, self);
+ int row = x;
+ int column = y;
+ switch (minIndex) {
+ case 0://上
+ row = x - 1;
+ break;
+ case 1://左
+ column = y - 1;
+ break;
+ case 2://下
+ row = x + 1;
+ break;
+ case 3://右
+ column = y + 1;
+ break;
+ case 4://左上
+ column = y - 1;
+ row = x - 1;
+ break;
+ case 5://左下
+ column = y - 1;
+ row = x + 1;
+ break;
+ case 6://右下
+ column = y + 1;
+ row = x + 1;
+ break;
+ case 7://右上
+ column = y + 1;
+ row = x - 1;
+ break;
+ }
+ return row << 12 | column;
+ }
+
+ private double[] getPixels(int x, int y, boolean isFirst) throws Exception {
+ double left = 1, leftTop = 1, leftBottom = 1, right = 1, rightTop = 1, rightBottom = 1, top = 1, bottom = 1;
+ Matrix matrix;
+ if (isFirst) {
+ matrix = matrixH;
+ } else {
+ matrix = regionMatrix;
+ }
+ if (x == 0) {
+ top = -1;
+ leftTop = -1;
+ rightTop = -1;
+ }
+ if (y == 0) {
+ leftTop = -1;
+ left = -1;
+ leftBottom = -1;
+ }
+ if (x == size - 1) {
+ leftBottom = -1;
+ bottom = -1;
+ rightBottom = -1;
+ }
+ if (y == size - 1) {
+ rightTop = -1;
+ right = -1;
+ rightBottom = -1;
+ }
+ if (top > 0) {
+ top = matrix.getNumber(x - 1, y);
+ }
+ if (left > 0) {
+ left = matrix.getNumber(x, y - 1);
+ }
+ if (right > 0) {
+ right = matrix.getNumber(x, y + 1);
+ }
+ if (bottom > 0) {
+ bottom = matrix.getNumber(x + 1, y);
+ }
+ if (leftTop > 0) {
+ leftTop = matrix.getNumber(x - 1, y - 1);
+ }
+ if (leftBottom > 0) {
+ leftBottom = matrix.getNumber(x + 1, y - 1);
+ }
+ if (rightTop > 0) {
+ rightTop = matrix.getNumber(x - 1, y + 1);
+ }
+ if (rightBottom > 0) {
+ rightBottom = matrix.getNumber(x + 1, y + 1);
+ }
+ return new double[]{top, left, bottom, right, leftTop, leftBottom, rightBottom, rightTop};
+
+ }
+
+ private int getMinIndex(double[] array, double self) {//获取最小值
+ double min = -1;
+ int minIdx = 0;
+ for (int i = 0; i < array.length; i++) {
+ double nub = array[i];
+ if (nub > 0) {
+ nub = Math.abs(nub - self);
+ if (min < 0 || nub < min) {
+ min = nub;
+ minIdx = i;
+ }
+ }
+ }
+ return minIdx;
+ }
+}
diff --git a/src/test/java/coverTest/regionCut/RegionCutBody.java b/src/test/java/coverTest/regionCut/RegionCutBody.java
new file mode 100644
index 0000000..5e0d451
--- /dev/null
+++ b/src/test/java/coverTest/regionCut/RegionCutBody.java
@@ -0,0 +1,15 @@
+package coverTest.regionCut;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description
+ */
+public class RegionCutBody {
+ private List pixels = new ArrayList<>();
+
+}
diff --git a/src/test/java/coverTest/regionCut/RegionFeature.java b/src/test/java/coverTest/regionCut/RegionFeature.java
new file mode 100644
index 0000000..068077a
--- /dev/null
+++ b/src/test/java/coverTest/regionCut/RegionFeature.java
@@ -0,0 +1,84 @@
+package coverTest.regionCut;
+
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.MatrixTools.MatrixOperation;
+import org.wlld.config.Kernel;
+import org.wlld.imageRecognition.ThreeChannelMatrix;
+import org.wlld.imageRecognition.segmentation.RgbRegression;
+import org.wlld.tools.Frequency;
+
+import java.util.Arrays;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description 利用边缘算子提取特征
+ */
+public class RegionFeature extends Frequency {
+ private Matrix matrix;
+ private Matrix matrixR;
+ private Matrix matrixG;
+ private Matrix matrixB;
+ private Matrix kernel = Kernel.Big;
+ private int fatherX;
+ private int fatherY;
+ private RgbRegression rgbRegression = new RgbRegression(25);
+
+ public RegionFeature(ThreeChannelMatrix threeChannelMatrix, int fatherX, int fatherY) {
+ this.matrix = threeChannelMatrix.getMatrixRGB();
+ this.fatherX = fatherX;
+ this.fatherY = fatherY;
+ matrixR = threeChannelMatrix.getMatrixR();
+ matrixG = threeChannelMatrix.getMatrixG();
+ matrixB = threeChannelMatrix.getMatrixB();
+ }
+
+ private double getMatrixVar(Matrix matrix) throws Exception {
+ int x = matrix.getX();
+ int y = matrix.getY();
+ double[] data = new double[x * y];
+ for (int i = 0; i < x; i++) {
+ for (int j = 0; j < y; j++) {
+ int index = i * y + j;
+ data[index] = matrix.getNumber(i, j);
+ }
+ }
+ //System.out.println(Arrays.toString(data));
+ return variance(data);
+ }
+
+ public void start() throws Exception {
+ int x = matrix.getX();
+ int y = matrix.getY();
+ int size = kernel.getX();
+ for (int i = 0; i <= x - size; i += 2) {
+ int xSize = fatherX + i;
+ System.out.println("==================================" + xSize);
+ for (int j = 0; j <= y - size; j += 2) {
+ // double conNub = Math.abs(convolution(i, j));//卷积值
+ double var = getMatrixVar(matrix.getSonOfMatrix(i, j, size, size));
+ int ySize = fatherY + j;
+ System.out.println("x:" + xSize + ",y:" + ySize + ",var:" + var);
+
+
+ }
+ }
+ }
+
+ private double convolution(int x, int y) throws Exception {//计算卷积
+ double allNub = 0;
+ int xr;
+ int yr;
+ int kxMax = kernel.getX();
+ int kyMax = kernel.getY();
+ for (int i = 0; i < kxMax; i++) {
+ xr = i + x;
+ for (int j = 0; j < kyMax; j++) {
+ yr = j + y;
+ allNub = matrix.getNumber(xr, yr) * kernel.getNumber(i, j) + allNub;
+ }
+ }
+ return allNub;
+ }
+}
diff --git a/src/test/java/org/wlld/NerveDemo1.java b/src/test/java/org/wlld/NerveDemo1.java
index 76806f2..9b2752c 100644
--- a/src/test/java/org/wlld/NerveDemo1.java
+++ b/src/test/java/org/wlld/NerveDemo1.java
@@ -36,13 +36,13 @@ public class NerveDemo1 {
* @param isDynamic 是否是动态神经元
*/
NerveManager nerveManager = new NerveManager(2, 6, 1, 2, new Tanh(),
- false, true, 0, RZ.NOT_RZ, 0);
+ false, false, 0, RZ.NOT_RZ, 0);
nerveManager.init(true, false, false, false);
//创建训练
List> list_right = new LinkedList<>();//存放正确的值
List> list_wrong = new LinkedList<>();//存放错误的值
Random random = new Random();
- for (int i = 0; i < 1000; i++) {
+ for (int i = 0; i < 3000; i++) {
Map mp1 = new HashMap<>();
Map mp2 = new HashMap<>();
mp1.put(0, random.nextDouble());