添加矩阵PHSH元素，矩阵笛卡尔积

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

@@ -1,5 +1,6 @@
 package org.wlld.MatrixTools;
 
+import com.sun.org.apache.bcel.internal.generic.FADD;
 import org.wlld.tools.ArithUtil;
 
 public class MatrixOperation {
@@ -23,6 +24,91 @@ public class MatrixOperation {
         }
     }
 
+    public static Matrix pushVector(Matrix myMatrix, Matrix matrix, boolean addRow) throws Exception {
+        if (matrix.getX() == 1 || matrix.getY() == 1) {
+            Matrix addMatrix;
+            if (addRow) {//增加一行
+                if (matrix.getY() != myMatrix.getY()) {
+                    throw new Exception("this matrix column is not equals");
+                }
+                addMatrix = new Matrix(myMatrix.getX() + 1, myMatrix.getY());
+            } else {//增加一列
+                if (matrix.getX() != myMatrix.getX()) {
+                    throw new Exception("this matrix row is not equals");
+                }
+                addMatrix = new Matrix(myMatrix.getX(), myMatrix.getY() + 1);
+            }
+            for (int i = 0; i < addMatrix.getX(); i++) {
+                for (int j = 0; j < addMatrix.getY(); j++) {
+                    if (addRow) {
+                        if (i == addMatrix.getX() - 1) {//最后一行
+                            addMatrix.setNub(i, j, matrix.getNumber(0, j));
+                        } else {
+                            addMatrix.setNub(i, j, myMatrix.getNumber(i, j));
+                        }
+                    } else {
+                        if (j == addMatrix.getY() - 1) {//最后一列
+                            addMatrix.setNub(i, j, matrix.getNumber(i, 0));
+                        } else {
+                            addMatrix.setNub(i, j, myMatrix.getNumber(i, j));
+                        }
+                    }
+                }
+            }
+            return addMatrix;
+        } else {
+            throw new Exception("this matrix is not a vector");
+        }
+    }
+
+    public static Matrix push(Matrix matrix, double nub) throws Exception {//向一个向量里PUSH一个值
+        if (matrix.getX() == 1 || matrix.getY() == 1) {
+            Matrix myMatrix;
+            int nubs;
+            boolean isRow = true;
+            if (matrix.getX() == 1) {//行向量
+                nubs = matrix.getY() + 1;
+                myMatrix = new Matrix(1, nubs);
+            } else {//列向量
+                isRow = false;
+                nubs = matrix.getX() + 1;
+                myMatrix = new Matrix(nubs, 1);
+            }
+            for (int i = 0; i < nubs; i++) {
+                if (i == nubs - 1) {
+                    if (isRow) {
+                        myMatrix.setNub(0, i, nub);
+                    } else {
+                        myMatrix.setNub(i, 0, nub);
+                    }
+                } else {
+                    if (isRow) {//行向量
+                        myMatrix.setNub(0, i, matrix.getNumber(0, i));
+                    } else {//列向量
+                        myMatrix.setNub(i, 0, matrix.getNumber(i, 0));
+                    }
+                }
+            }
+            return myMatrix;
+        } else {
+            throw new Exception("this matrix is not a vector");
+        }
+    }
+
+    public static Matrix matrixToRow(Matrix matrix) throws Exception {//将一个矩阵转成行向量
+        int x = matrix.getX();
+        int y = matrix.getY();
+        Matrix myMatrix = new Matrix(1, x * y);
+        int t = 0;
+        for (int i = 0; i < x; i++) {
+            for (int j = 0; j < y; j++) {
+                myMatrix.setNub(0, t, matrix.getNumber(i, j));
+                t++;
+            }
+        }
+        return myMatrix;
+    }
+
     public static double innerProduct(Matrix matrix1, Matrix matrix2) throws Exception {//两个向量内积
         Matrix matrix = transPosition(matrix1);
         Matrix matrix3 = mulMatrix(matrix, matrix2);

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

@@ -10,16 +10,16 @@ import org.wlld.config.Kernel;
  * @date 9:23 上午 2020/1/2
  */
 public class Convolution {
-    public Matrix getFeatures(Matrix matrix, int maxNub) throws Exception {
+    public Matrix getFeatures(Matrix matrix, int maxNub, boolean isBorder) throws Exception {
         do {
-            matrix = convolution(matrix, Kernel.ALL_Two);
+            matrix = convolution(matrix, Kernel.ALL_Two, isBorder);
         }
-        while (matrix.getX() > maxNub && matrix.getY() > maxNub);
+        while (matrix.getX() > maxNub && matrix.getY() > maxNub && !isBorder);
         //已经不可以再缩小了，最后做一层卷积，然后提取最大值
         return matrix;
     }
 
-    private Matrix convolution(Matrix matrix, Matrix kernel) throws Exception {
+    private Matrix convolution(Matrix matrix, Matrix kernel, boolean isBorder) throws Exception {
         int x = matrix.getX() - 2;//求导后矩阵的行数
         int y = matrix.getY() - 2;//求导后矩阵的列数
         Matrix myMatrix = new Matrix(x, y);//最终合成矩阵
@@ -31,8 +31,13 @@ public class Convolution {
                 }
             }
         }
-        return late(myMatrix);
+        if (isBorder) {
+            return myMatrix;
+        } else {
+            return late(myMatrix);
+        }
     }
+
     public Matrix late(Matrix matrix) throws Exception {//迟化处理
         int xn = matrix.getX();
         int yn = matrix.getY();

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

@@ -19,7 +19,7 @@ public class Operation {//进行计算
         this.templeConfig = templeConfig;
     }
 
-    public List<Double> convolution(Matrix matrix) throws Exception {
+    public List<Double> convolution(Matrix matrix, boolean isBorder) throws Exception {
         //进行卷积
         int maxNub = 0;
         if (templeConfig.getRow() >= templeConfig.getColumn()) {
@@ -27,14 +27,14 @@ public class Operation {//进行计算
         } else {
             maxNub = templeConfig.getColumn();
         }
-        Matrix matrix1 = convolution.getFeatures(matrix, maxNub);
+        Matrix matrix1 = convolution.getFeatures(matrix, maxNub, isBorder);
         return sub(matrix1);
     }
 
     //模板学习
     public void study(Matrix matrix, Map<Integer, Double> tagging) throws Exception {
         if (templeConfig.getStudyPattern() == StudyPattern.Speed_Pattern) {
-            List<Double> list = convolution(matrix);
+            List<Double> list = convolution(matrix, templeConfig.isHavePosition());
             intoNerve(1, list, templeConfig.getSensoryNerves(), true, tagging);
         } else {
             throw new Exception("pattern is wrong");
@@ -58,7 +58,7 @@ public class Operation {//进行计算
     //图像视觉 speed 模式
     public void look(Matrix matrix, long eventId) throws Exception {
         if (templeConfig.getStudyPattern() == StudyPattern.Speed_Pattern) {
-            List<Double> list = convolution(matrix);
+            List<Double> list = convolution(matrix, templeConfig.isHavePosition());
             intoNerve(eventId, list, templeConfig.getSensoryNerves(), false, null);
         } else if (templeConfig.getStudyPattern() == StudyPattern.Accuracy_Pattern) {
             see(matrix, eventId);

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

@@ -25,6 +25,50 @@ public class TempleConfig {
     private int classificationNub = 1;//分类的数量
     private int studyPattern;//学习模式
     private OutBack outBack;
+    private boolean isHavePosition = false;//是否需要锁定物体位置
+    private int region = 10;//将一个图片均匀分成几个区域 默认是10
+    private int regionRow = 8;//目标检测物体大概会在图像中占的行份
+    private int regionColumn = 8;//目标检测物体大概会在图像中占的列份
+
+    public int getRegionRow() {
+        return regionRow;
+    }
+
+    public void setRegionRow(int regionRow) {
+        this.regionRow = regionRow;
+    }
+
+    public int getRegionColumn() {
+        return regionColumn;
+    }
+
+    public void setRegionColumn(int regionColumn) {
+        this.regionColumn = regionColumn;
+    }
+
+    public NerveManager getNerveManager() {
+        return nerveManager;
+    }
+
+    public void setNerveManager(NerveManager nerveManager) {
+        this.nerveManager = nerveManager;
+    }
+
+    public int getRegion() {
+        return region;
+    }
+
+    public void setRegion(int region) {
+        this.region = region;
+    }
+
+    public boolean isHavePosition() {
+        return isHavePosition;
+    }
+
+    public void setHavePosition(boolean havePosition) {
+        isHavePosition = havePosition;
+    }
 
     public NerveManager getConvolutionNerveManager() {
         return convolutionNerveManager;

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

@@ -0,0 +1,67 @@
+package org.wlld.imageRecognition.border;
+
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.imageRecognition.TempleConfig;
+import org.wlld.test.Ma;
+import org.wlld.tools.ArithUtil;
+
+/**
+ * @author lidapeng
+ * @description 图像边框
+ * @date 10:37 上午 2020/1/22
+ */
+public class Border {
+    private int minX;
+    private int minY;
+    private int maxX;
+    private int maxY;
+    private int width;//宽度
+    private int height;//高度
+    private double modelX;//标准左上角X坐标
+    private double modelY;//标准左上角Y坐标
+    private double modelHeight;//标准高度
+    private double modelWidth;//标准宽度
+
+    Border(TempleConfig templeConfig, int imageWidth, int imageHeight) {
+        int region = templeConfig.getRegion();//将一个区域分成几份
+        double row = ArithUtil.div(imageHeight, region);//每一行的像素
+        double column = ArithUtil.div(imageWidth, region);//每一列的像素
+        int divRow = templeConfig.getRegionRow();//检测物体行占据的份数
+        int divColumn = templeConfig.getRegionColumn();//检测物体列占据的份数
+        modelHeight = ArithUtil.mul(divRow, row);
+        modelWidth = ArithUtil.mul(divColumn, column);
+        double regionRow = (region - divRow) / 2;//左上角X份数
+        double regionColumn = (region - divColumn) / 2;//左上角Y份数
+        modelX = ArithUtil.mul(regionRow, row);
+        modelY = ArithUtil.mul(regionColumn, column);
+    }
+
+    //输入像素值查找边框四角坐标
+    public void setPosition(int x, int y) {
+        if (minX > x || minX == 0) {
+            minX = x;
+        }
+        if (minY > y || minY == 0) {
+            minY = y;
+        }
+        if (x > maxX) {
+            maxX = x;
+        }
+        if (y > maxY) {
+            maxY = y;
+        }
+    }
+
+    public void end(Matrix matrix) {//长宽
+        height = maxX - minX;
+        width = maxY - minY;
+        //多元线性回归的四个输出值
+        double tx = ArithUtil.div(ArithUtil.sub(minX, modelX), modelHeight);
+        double ty = ArithUtil.div(ArithUtil.sub(minY, modelY), modelWidth);
+        double tw = Math.log(ArithUtil.div(width, modelWidth));
+        double th = Math.log(ArithUtil.div(height, modelHeight));
+        //进行参数汇集
+
+    }
+
+}