稍微修正一下说明文档细节

.idea/compiler.xml

@@ -6,8 +6,8 @@
         <sourceOutputDir name="target/generated-sources/annotations" />
         <sourceTestOutputDir name="target/generated-test-sources/test-annotations" />
         <outputRelativeToContentRoot value="true" />
-        <module name="ImageMarket" />
         <module name="myBrain" />
+        <module name="ImageMarket" />
       </profile>
     </annotationProcessing>
   </component>

README.md

@@ -202,7 +202,7 @@
 学习1200万像素的照片物体，1000张需耗时5-7个小时。
 #### 本包为性能优化而对AI算法的修改
 * 本包对图像AI算法进行了修改，为应对CPU部署。
-* 卷积神经网络后的全连接层直接替换成了K均值算法进行聚类，通过卷积结果与K均值矩阵欧式距离来进行判定。
+* 卷积神经网络后的全连接层直接替换成了LVQ算法进行特征向量量化学习聚类，通过卷积结果与K均值矩阵欧式距离来进行判定。
 * 物体的边框检测通过卷积后的特征向量进行多元线性回归获得，检测边框的候选区并没有使用图像分割（cpu对图像分割算法真是超慢），
 而是通过Frame类让用户自定义先验图框大小和先验图框每次移动的检测步长，然后再通过多次检测的IOU来确定是否为同一物体。
 * 所以添加定位模式，用户要确定Frame的大小和步长，来替代基于图像分割的候选区推荐算法。

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

@@ -57,8 +57,7 @@ public class MatrixOperation {
     //返回两个向量之间的欧氏距离的平方
     public static double getEDist(Matrix matrix1, Matrix matrix2) throws Exception {
         if (matrix1.isRowVector() && matrix2.isRowVector() && matrix1.getY() == matrix2.getY()) {
-            mathMul(matrix2, -1);
+            Matrix matrix = sub(matrix1, matrix2);
-            Matrix matrix = add(matrix1, matrix2);
             return getNorm(matrix);
         } else {
             throw new Exception("this matrix is not  rowVector or length different");

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

@@ -82,20 +82,13 @@ public class Operation {//进行计算
                 if (templeConfig.isHavePosition() && tagging > 0) {
                     border.end(myMatrix, tagging);
                 }
+                //进行聚类
                 LVQ lvq = templeConfig.getLvq();
                 Matrix vector = MatrixOperation.matrixToVector(myMatrix, true);
                 MatrixBody matrixBody = new MatrixBody();
                 matrixBody.setMatrix(vector);
                 matrixBody.setId(tagging);
                 lvq.insertMatrixBody(matrixBody);
-                //进行聚类
-                Map<Integer, KMatrix> kMatrixMap = templeConfig.getkMatrixMap();
-                if (kMatrixMap.containsKey(tagging)) {
-                    KMatrix kMatrix = kMatrixMap.get(tagging);
-                    kMatrix.addMatrix(myMatrix);
-                } else {
-                    throw new Exception("not find tag");
-                }
             }
         } else {
             throw new Exception("pattern is wrong");
@@ -288,7 +281,6 @@ public class Operation {//进行计算
                     false, -1, matrixBack);
             Matrix myMatrix = matrixBack.getMatrix();
             Matrix vector = MatrixOperation.matrixToVector(myMatrix, true);
-
             return getClassificationId2(vector);
         } else {
             throw new Exception("pattern is wrong");

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

@@ -0,0 +1,29 @@
+package org.wlld.imageRecognition.border;
+
+import org.wlld.MatrixTools.Matrix;
+
+/**
+ * @author lidapeng
+ * @description
+ * @date 9:11 上午 2020/2/6
+ */
+public class Box {
+    private Matrix matrix;//特征向量
+    private Matrix matrixFather;//坐标向量
+
+    public Matrix getMatrix() {
+        return matrix;
+    }
+
+    public void setMatrix(Matrix matrix) {
+        this.matrix = matrix;
+    }
+
+    public Matrix getMatrixFather() {
+        return matrixFather;
+    }
+
+    public void setMatrixFather(Matrix matrixFather) {
+        this.matrixFather = matrixFather;
+    }
+}

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

@@ -12,17 +12,17 @@ import java.util.*;
  * @date 10:14 上午 2020/2/4
  */
 public class KClustering {
-    private List<MatrixBody> matrixList = new ArrayList<>();//聚类集合
+    private List<Box> matrixList = new ArrayList<>();//聚类集合
     private int length;//向量长度
     private int speciesQuantity;//种类数量
     private Matrix[] matrices;//均值K
-    private Map<Integer, List<MatrixBody>> clusterMap = new HashMap<>();//簇
+    private Map<Integer, List<Box>> clusterMap = new HashMap<>();//簇
 
     public Matrix[] getMatrices() {
         return matrices;
     }
 
-    public Map<Integer, List<MatrixBody>> getClusterMap() {
+    public Map<Integer, List<Box>> getClusterMap() {
         return clusterMap;
     }
 
@@ -34,7 +34,7 @@ public class KClustering {
         }
     }
 
-    public void setMatrixList(MatrixBody matrixBody) throws Exception {
+    public void setMatrixList(Box matrixBody) throws Exception {
         if (matrixBody.getMatrix().isVector() && matrixBody.getMatrix().isRowVector()) {
             Matrix matrix = matrixBody.getMatrix();
             if (matrixList.size() == 0) {
@@ -54,7 +54,7 @@ public class KClustering {
 
     private Matrix[] averageMatrix() throws Exception {
         Matrix[] matrices2 = new Matrix[speciesQuantity];//待比较均值K
-        for (MatrixBody matrixBody : matrixList) {//遍历当前集合
+        for (Box matrixBody : matrixList) {//遍历当前集合
             Matrix matrix = matrixBody.getMatrix();
             double min = 0;
             int id = 0;
@@ -65,11 +65,11 @@ public class KClustering {
                     id = i;
                 }
             }
-            List<MatrixBody> matrixList1 = clusterMap.get(id);
+            List<Box> matrixList1 = clusterMap.get(id);
             matrixList1.add(matrixBody);
         }
         //重新计算均值
-        for (Map.Entry<Integer, List<MatrixBody>> entry : clusterMap.entrySet()) {
+        for (Map.Entry<Integer, List<Box>> entry : clusterMap.entrySet()) {
             Matrix matrix = average(entry.getValue());
             matrices2[entry.getKey()] = matrix;
         }
@@ -77,30 +77,33 @@ public class KClustering {
     }
 
     private void clear() {
-        for (Map.Entry<Integer, List<MatrixBody>> entry : clusterMap.entrySet()) {
+        for (Map.Entry<Integer, List<Box>> entry : clusterMap.entrySet()) {
             entry.getValue().clear();
         }
     }
 
-    private Matrix average(List<MatrixBody> matrixList) throws Exception {//进行矩阵均值计算
+    private Matrix average(List<Box> matrixList) throws Exception {//进行矩阵均值计算
         double nub = ArithUtil.div(1, matrixList.size());
-        Matrix matrix = new Matrix(0, length);
+        Matrix matrix = new Matrix(1, length);
-        for (MatrixBody matrixBody1 : matrixList) {
+        for (Box matrixBody1 : matrixList) {
             matrix = MatrixOperation.add(matrix, matrixBody1.getMatrix());
         }
         MatrixOperation.mathMul(matrix, nub);
         return matrix;
     }
 
+
     public void start() throws Exception {//开始聚类
         if (matrixList.size() > 1) {
             Random random = new Random();
             for (int i = 0; i < matrices.length; i++) {//初始化均值向量
                 int index = random.nextInt(matrixList.size());
+                //要进行深度克隆
                 matrices[i] = matrixList.get(index).getMatrix();
             }
             //进行两者的比较
             boolean isEqual = false;
+            int nub = 0;
             do {
                 Matrix[] matrices2 = averageMatrix();
                 isEqual = equals(matrices, matrices2);
@@ -108,8 +111,12 @@ public class KClustering {
                     matrices = matrices2;
                     clear();
                 }
+                nub++;
             }
-            while (isEqual);
+            while (!isEqual);
+            //聚类结束，进行坐标均值矩阵计算
+            System.out.println("聚类循环次数：" + nub);
+            
         } else {
             throw new Exception("matrixList number less than 2");
         }
@@ -126,6 +133,9 @@ public class KClustering {
                     break;
                 }
             }
+            if (!isEquals) {
+                break;
+            }
         }
         return isEquals;
     }

src/test/java/org/wlld/HelloWorld.java

@@ -41,7 +41,7 @@ public class HelloWorld {
         templeConfig.init(StudyPattern.Accuracy_Pattern, true, 3204, 4032, 1);
         templeConfig.insertModel(modelParameter);
         Operation operation = new Operation(templeConfig);
-        for (int i = 1; i < 30; i++) {//faster rcnn神经网络学习
+        for (int i = 1; i < 100; i++) {//faster rcnn神经网络学习
             System.out.println("study==" + i);
             //读取本地URL地址图片,并转化成矩阵
             Matrix right = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/c/c" + i + ".png");
@@ -63,14 +63,14 @@ public class HelloWorld {
 //            System.out.println("j===" + j);
 //        }
         //测试集图片,进行识别测试
-//        for (int j = 121; j < 140; j++) {
+        for (int j = 121; j < 140; j++) {
-//            Matrix right = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/c/c" + j + ".png");
+            Matrix right = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/c/c" + j + ".png");
-//            Matrix wrong = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/b/b" + j + ".png");
+            Matrix wrong = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/b/b" + j + ".png");
-//            int rightId = operation.toSee(right);
+            int rightId = operation.toSee(right);
-//            int wrongId = operation.toSee(wrong);
+            int wrongId = operation.toSee(wrong);
-//            System.out.println("该图是菜单：" + rightId);
+            System.out.println("该图是菜单：" + rightId);
-//            System.out.println("该图是桌子:" + wrongId);
+            System.out.println("该图是桌子:" + wrongId);
-//        }
+        }
 
     }