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https://gitee.com/dromara/easyAi.git
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增加特征选区筛选
This commit is contained in:
lidapeng
parent
382c3ca598
commit
877694fae2
@ -62,6 +62,23 @@ public class MatrixOperation {
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}
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}
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public static double getEDistByMatrix(Matrix matrix1, Matrix matrix2) throws Exception {
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if (matrix1.getX() == matrix2.getX() && matrix1.getY() == matrix2.getY()) {
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int x = matrix1.getX();
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int y = matrix1.getY();
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double sigma = 0;
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for (int i = 0; i < x; i++) {
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for (int j = 0; j < y; j++) {
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double sub = matrix1.getNumber(i, j) - matrix2.getNumber(i, j);
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sigma = sigma + Math.pow(sub, 2);
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}
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}
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return sigma / (x * y);
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} else {
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throw new Exception("two matrixes is not equals");
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}
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}
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//返回两个向量之间的欧氏距离的平方
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public static double getEDist(Matrix matrix1, Matrix matrix2) throws Exception {
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if (matrix1.isRowVector() && matrix2.isRowVector() && matrix1.getY() == matrix2.getY()) {
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@ -322,16 +339,16 @@ public class MatrixOperation {
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return inverserNumber;
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}
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public static Matrix getInverseMatrixs(Matrix matrixs) throws Exception {//矩阵求逆
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double def = matrixs.getDet();
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public static Matrix getInverseMatrixs(Matrix matrix) throws Exception {//矩阵求逆
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double def = matrix.getDet();
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if (def != 0) {
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def = ArithUtil.div(1, def);
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Matrix myMatrix = adjointMatrix(matrixs);//伴随矩阵
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Matrix myMatrix = adjointMatrix(matrix);//伴随矩阵
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mathMul(myMatrix, def);
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return myMatrix;
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} else {
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System.out.println(matrixs.getString());
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throw new Exception("this matrixs do not have InverseMatrixs");
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System.out.println(matrix.getString());
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throw new Exception("this matrix do not have InverseMatrixs");
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}
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}
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@ -82,7 +82,7 @@ public class Convolution extends Frequency {
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meanClustering.setColor(color);
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}
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}
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meanClustering.start();
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meanClustering.start(false);
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List<RGBNorm> rgbNorms = meanClustering.getMatrices();
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Collections.sort(rgbNorms, rgbSort);
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for (RGBNorm rgbNorm : rgbNorms) {
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@ -182,7 +182,7 @@ public class Convolution extends Frequency {
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meanClustering.setColor(color);
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}
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}
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meanClustering.start();
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meanClustering.start(false);
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List<RGBNorm> rgbNorms = meanClustering.getMatrices();
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Collections.sort(rgbNorms, rgbSort);
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List<Double> features = new ArrayList<>();
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@ -203,32 +203,64 @@ public class Convolution extends Frequency {
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public List<Double> getCenterTexture(ThreeChannelMatrix threeChannelMatrix, int size, int poolSize, TempleConfig templeConfig
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, int sqNub) throws Exception {
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RGBSort rgbSort = new RGBSort();
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double dispersedThNub = templeConfig.getFood().getDispersedTh();
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int step = templeConfig.getFood().getStep();
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MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig);
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Matrix matrixR = threeChannelMatrix.getMatrixR();
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Matrix matrixG = threeChannelMatrix.getMatrixG();
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Matrix matrixB = threeChannelMatrix.getMatrixB();
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Matrix matrixH = threeChannelMatrix.getH();
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int xn = matrixR.getX();
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int yn = matrixR.getY();
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for (int i = 0; i <= xn - size; i += size) {
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for (int j = 0; j <= yn - size; j += size) {
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Matrix sonR = late(matrixR.getSonOfMatrix(i, j, size, size), poolSize);
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Matrix sonG = late(matrixG.getSonOfMatrix(i, j, size, size), poolSize);
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Matrix sonB = late(matrixB.getSonOfMatrix(i, j, size, size), poolSize);
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int tSize = sonR.getX();
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int kSize = sonR.getY();
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double[] rgb = new double[tSize * kSize * 3];
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for (int t = 0; t < tSize; t++) {
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for (int k = 0; k < kSize; k++) {
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int index = t * kSize + k;
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rgb[index] = sonR.getNumber(t, k);
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rgb[tSize * kSize + index] = sonG.getNumber(t, k);
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rgb[tSize * kSize * 2 + index] = sonB.getNumber(t, k);
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// for (int i = 0; i < xn; i++) {
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// for (int j = 0; j < yn; j++) {
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// double[] rgb = new double[]{matrixR.getNumber(i, j), matrixG.getNumber(i, j)
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// , matrixB.getNumber(i, j)};
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// meanClustering.setColor(rgb);
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// }
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// }
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//局部特征选区筛选
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double sigma = 0;
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int nub = 0;
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for (int i = 0; i <= xn - size; i += step) {
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for (int j = 0; j <= yn - size; j += step) {
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Matrix sonH = matrixH.getSonOfMatrix(i, j, size, size);
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double[] h = new double[size * size];
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nub++;
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for (int t = 0; t < size; t++) {
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for (int k = 0; k < size; k++) {
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int index = t * size + k;
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h[index] = sonH.getNumber(t, k);
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}
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}
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meanClustering.setColor(rgb);
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sigma = dc(h) + sigma;
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}
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}
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meanClustering.start();//开始聚类
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double dispersedTh = (sigma / nub) * dispersedThNub;//离散阈值
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for (int i = 0; i <= xn - size; i += step) {
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for (int j = 0; j <= yn - size; j += step) {
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Matrix sonR = matrixR.getSonOfMatrix(i, j, size, size);
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Matrix sonG = matrixG.getSonOfMatrix(i, j, size, size);
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Matrix sonB = matrixB.getSonOfMatrix(i, j, size, size);
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Matrix sonH = matrixH.getSonOfMatrix(i, j, size, size);
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double[] h = new double[size * size];
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double[] rgb = new double[size * size * 3];
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for (int t = 0; t < size; t++) {
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for (int k = 0; k < size; k++) {
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int index = t * size + k;
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h[index] = sonH.getNumber(t, k);
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rgb[index] = sonR.getNumber(t, k);
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rgb[size * size + index] = sonG.getNumber(t, k);
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rgb[size * size * 2 + index] = sonB.getNumber(t, k);
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}
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}
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double dispersed = dc(h);
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if (dispersed < dispersedTh) {
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meanClustering.setColor(rgb);
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}
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}
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}
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meanClustering.start(false);//开始聚类
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List<RGBNorm> rgbNorms = meanClustering.getMatrices();
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Collections.sort(rgbNorms, rgbSort);
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List<Double> features = new ArrayList<>();
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@ -238,6 +270,7 @@ public class Convolution extends Frequency {
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features.add(rgb[j]);
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}
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}
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//System.out.println(features);
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return features;
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}
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@ -256,6 +289,24 @@ public class Convolution extends Frequency {
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return border;
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}
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public void imgNormalization(ThreeChannelMatrix threeChannelMatrix) throws Exception {
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Matrix matrixR = threeChannelMatrix.getMatrixR();
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Matrix matrixG = threeChannelMatrix.getMatrixG();
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Matrix matrixB = threeChannelMatrix.getMatrixB();
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int x = matrixR.getX();
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int y = matrixR.getY();
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for (int i = 0; i < x; i++) {
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for (int j = 0; j < y; j++) {
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double colorR = matrixR.getNumber(i, j) / 255;
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double colorG = matrixG.getNumber(i, j) / 255;
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double colorB = matrixB.getNumber(i, j) / 255;
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matrixR.setNub(i, j, colorR);
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matrixG.setNub(i, j, colorG);
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matrixB.setNub(i, j, colorB);
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}
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}
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}
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public ThreeChannelMatrix getRegionMatrix(ThreeChannelMatrix threeChannelMatrix, int x, int y, int xSize, int ySize) {
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ThreeChannelMatrix threeChannelMatrix1 = new ThreeChannelMatrix();
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Matrix matrixR = threeChannelMatrix.getMatrixR().getSonOfMatrix(x, y, xSize, ySize);
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24
src/main/java/org/wlld/imageRecognition/DistSort.java
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24
src/main/java/org/wlld/imageRecognition/DistSort.java
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@ -0,0 +1,24 @@
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package org.wlld.imageRecognition;
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import org.wlld.imageRecognition.border.DistBody;
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import java.util.Comparator;
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/**
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* @param
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* @DATA
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* @Author LiDaPeng
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* @Description
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*/
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public class DistSort implements Comparator<DistBody> {
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@Override
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public int compare(DistBody o1, DistBody o2) {
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if (o1.getDist() < o2.getDist()) {
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return -1;
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} else if (o1.getDist() > o2.getDist()) {
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return 1;
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} else {
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return 0;
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}
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}
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}
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@ -90,7 +90,7 @@ public class MeanClustering {
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}
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}
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public void start() throws Exception {//开始聚类
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public void start(boolean isRegression) throws Exception {//开始聚类
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if (matrixList.size() > 1) {
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Random random = new Random();
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for (int i = 0; i < speciesQuantity; i++) {//初始化均值向量
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@ -111,7 +111,9 @@ public class MeanClustering {
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break;
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}
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}
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// startRegression();//开始进行回归
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if (isRegression) {
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startRegression();//开始进行回归
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}
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} else {
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throw new Exception("matrixList number less than 2");
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}
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@ -103,43 +103,49 @@ public class Operation {//进行计算
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int maxY = regionBody.getMaxY() - dif;
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int xSize = maxX - minX;
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int ySize = maxY - minY;
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//convolution.imgNormalization(threeChannelMatrix);
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ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
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int times = templeConfig.getFood().getTimes();
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for (int i = 0; i < times; i++) {
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// List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
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// templeConfig.getFeatureNub(), templeConfig);
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List<Double> feature = convolution.getCenterTexture(threeChannelMatrix1, templeConfig.getFood().getRegionSize(),
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templeConfig.getPoolSize(), templeConfig, templeConfig.getFeatureNub());
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if (templeConfig.isShowLog()) {
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System.out.println(tag + ":" + feature);
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}
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int classifier = templeConfig.getClassifier();
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switch (classifier) {
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case Classifier.DNN:
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Map<Integer, Double> map = new HashMap<>();
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map.put(tag, 1.0);
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if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub() * 3) {
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intoDnnNetwork(1, feature, templeConfig.getSensoryNerves(), true, map, null);
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} else {
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throw new Exception("nerves number is not equal featureNub");
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}
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break;
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case Classifier.LVQ:
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Matrix vector = MatrixOperation.listToRowVector(feature);
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lvqStudy(tag, vector);
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break;
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case Classifier.VAvg:
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Matrix vec = MatrixOperation.listToRowVector(feature);
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avgStudy(tag, vec);
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break;
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case Classifier.KNN:
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Matrix veck = MatrixOperation.listToRowVector(feature);
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knnStudy(tag, veck);
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break;
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}
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List<Double> feature = convolution.getCenterTexture(threeChannelMatrix1, templeConfig.getFood().getRegionSize(),
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templeConfig.getPoolSize(), templeConfig, templeConfig.getFeatureNub());
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if (templeConfig.isShowLog()) {
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System.out.println(tag + ":" + feature);
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}
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int classifier = templeConfig.getClassifier();
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switch (classifier) {
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case Classifier.DNN:
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Map<Integer, Double> map = new HashMap<>();
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map.put(tag, 1.0);
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if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub() * 3) {
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intoDnnNetwork(1, feature, templeConfig.getSensoryNerves(), true, map, null);
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} else {
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throw new Exception("nerves number is not equal featureNub");
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}
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break;
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case Classifier.LVQ:
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Matrix vector = MatrixOperation.listToRowVector(feature);
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lvqStudy(tag, vector);
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break;
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case Classifier.VAvg:
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Matrix vec = MatrixOperation.listToRowVector(feature);
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avgStudy(tag, vec);
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break;
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case Classifier.KNN:
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Matrix veck = MatrixOperation.listToRowVector(feature);
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knnStudy(tag, veck);
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break;
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}
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return regionBody;
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} else {
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for (RegionBody regionBody : regionBodies) {
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int minX = regionBody.getMinX();
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int minY = regionBody.getMinY();
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int maxX = regionBody.getMaxX();
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int maxY = regionBody.getMaxY();
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System.out.println("异常：minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY);
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}
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throw new Exception("Parameter exception region size==" + regionBodies.size());
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}
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}
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@ -45,6 +45,7 @@ public class RGBNorm {
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for (int i = 0; i < rgb.length; i++) {
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rgbUp[i] = rgb[i];
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}
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rgbs.clear();
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//System.out.println("clear==" + Arrays.toString(rgbUp));
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}
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@ -79,6 +80,7 @@ public class RGBNorm {
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for (int i = 0; i < rgb.length; i++) {
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rgbAll[i] = rgbAll[i] + rgb[i];
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}
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rgbs.add(rgb);
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nub++;
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}
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@ -8,6 +8,24 @@ public class ThreeChannelMatrix {
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Matrix matrixB;
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Matrix H;
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Matrix matrixRGB;
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int similarId;//最大相似id
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boolean isLine = false;//是否被连接
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public int getSimilarId() {
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return similarId;
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}
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public boolean isLine() {
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return isLine;
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}
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public void setLine(boolean line) {
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isLine = line;
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}
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public void setSimilarId(int similarId) {
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this.similarId = similarId;
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}
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public Matrix getMatrixRGB() {
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return matrixRGB;
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28
src/main/java/org/wlld/imageRecognition/border/DistBody.java
Normal file
28
src/main/java/org/wlld/imageRecognition/border/DistBody.java
Normal file
@ -0,0 +1,28 @@
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package org.wlld.imageRecognition.border;
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/**
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* @param
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* @DATA
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* @Author LiDaPeng
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* @Description
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*/
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public class DistBody {
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private int id;//id
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private double dist;//距离
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public int getId() {
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return id;
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}
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public void setId(int id) {
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this.id = id;
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}
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public double getDist() {
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return dist;
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}
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public void setDist(double dist) {
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this.dist = dist;
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}
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}
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@ -0,0 +1,138 @@
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package org.wlld.imageRecognition.segmentation;
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import org.wlld.MatrixTools.Matrix;
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import org.wlld.MatrixTools.MatrixOperation;
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import org.wlld.imageRecognition.ThreeChannelMatrix;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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/**
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* @param
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* @DATA
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* @Author LiDaPeng
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* @Description 寻找相似度最大的选区
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*/
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public class FindMaxSimilar {
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public void findMaxSimilar(ThreeChannelMatrix threeChannelMatrix, int size) throws Exception {
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Map<Integer, ThreeChannelMatrix> threeChannelMatrices = new HashMap<>();
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Matrix matrixR = threeChannelMatrix.getMatrixR();
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Matrix matrixG = threeChannelMatrix.getMatrixG();
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Matrix matrixB = threeChannelMatrix.getMatrixB();
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Matrix matrixRGB = threeChannelMatrix.getMatrixRGB();
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int x = matrixR.getX();
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int y = matrixR.getY();
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System.out.println("初始区域数量:" + (x * y));
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int index = 0;
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for (int i = 0; i <= x - size; i += size) {
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for (int j = 0; j <= y - size; j += size) {
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ThreeChannelMatrix threeChannelMatrix1 = new ThreeChannelMatrix();
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Matrix bodyR = matrixR.getSonOfMatrix(i, j, size, size);
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Matrix bodyG = matrixG.getSonOfMatrix(i, j, size, size);
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Matrix bodyB = matrixB.getSonOfMatrix(i, j, size, size);
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Matrix bodyRGB = matrixRGB.getSonOfMatrix(i, j, size, size);
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threeChannelMatrix1.setMatrixR(bodyR);
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threeChannelMatrix1.setMatrixG(bodyG);
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threeChannelMatrix1.setMatrixB(bodyB);
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threeChannelMatrix1.setMatrixRGB(bodyRGB);
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threeChannelMatrices.put(index, threeChannelMatrix1);
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index++;
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}
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}
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//切割完毕 ，开始寻找最大相似性
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for (Map.Entry<Integer, ThreeChannelMatrix> entry : threeChannelMatrices.entrySet()) {
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int key = entry.getKey();
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ThreeChannelMatrix threeChannelMatrix1 = entry.getValue();
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Matrix matrix1 = threeChannelMatrix1.getMatrixRGB();
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double minDist = -1;
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int similarId = 0;
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for (Map.Entry<Integer, ThreeChannelMatrix> entrySon : threeChannelMatrices.entrySet()) {
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int sonKey = entrySon.getKey();
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if (key != sonKey) {
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Matrix matrix2 = entrySon.getValue().getMatrixRGB();
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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<Integer, List<Integer>> lineMap = line(threeChannelMatrices);
|
||||
//System.out.println("size:" + lineMap.size());
|
||||
int max = 0;
|
||||
int maxK = 0;
|
||||
for (Map.Entry<Integer, List<Integer>> 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<Integer, List<Integer>> lineMap, Map<Integer, ThreeChannelMatrix> map,
|
||||
int x, int y) throws Exception {
|
||||
Map<Integer, Matrix> map2 = new HashMap<>();
|
||||
for (Map.Entry<Integer, List<Integer>> entry : lineMap.entrySet()) {
|
||||
int key = entry.getKey();
|
||||
Matrix matrixAll = new Matrix(x, y);
|
||||
List<Integer> 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<Integer, List<Integer>> line(Map<Integer, ThreeChannelMatrix> threeChannelMatrices) {//开始进行连线
|
||||
Map<Integer, List<Integer>> lineMap = new HashMap<>();
|
||||
for (Map.Entry<Integer, ThreeChannelMatrix> 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<Integer> 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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,206 @@
|
||||
package org.wlld.imageRecognition.segmentation;
|
||||
|
||||
import org.wlld.MatrixTools.Matrix;
|
||||
import org.wlld.MatrixTools.MatrixOperation;
|
||||
import org.wlld.imageRecognition.DistSort;
|
||||
import org.wlld.imageRecognition.TempleConfig;
|
||||
import org.wlld.imageRecognition.border.DistBody;
|
||||
|
||||
import java.util.*;
|
||||
|
||||
/**
|
||||
* @param
|
||||
* @DATA
|
||||
* @Author LiDaPeng
|
||||
* @Description 修正映射
|
||||
*/
|
||||
public class RegionMapping {
|
||||
private Map<Integer, List<Matrix>> featureMap;
|
||||
private double studyPoint = 0.01;
|
||||
private Map<Integer, Matrix> powerMatrixMap = new HashMap<>();//映射层的权重矩阵
|
||||
private int times = 200;
|
||||
|
||||
public RegionMapping(TempleConfig templeConfig) {
|
||||
this.featureMap = templeConfig.getKnn().getFeatureMap();
|
||||
}
|
||||
|
||||
public void detection() throws Exception {//对模型进行检查,先对当前模型最相近的三个模型进行检出
|
||||
Map<Integer, List<DistBody>> map = new HashMap<>();
|
||||
DistSort distSort = new DistSort();
|
||||
for (Map.Entry<Integer, List<Matrix>> entry : featureMap.entrySet()) {
|
||||
int key = entry.getKey();
|
||||
List<Matrix> myFeature = entry.getValue();
|
||||
//记录 哪个类别 每个类别距离这个类别的最小距离
|
||||
List<DistBody> distBodies = new ArrayList<>();
|
||||
map.put(key, distBodies);
|
||||
for (Map.Entry<Integer, List<Matrix>> entrySon : featureMap.entrySet()) {
|
||||
int type = entrySon.getKey();
|
||||
if (key != type) {//当前其余类别
|
||||
DistBody distBody = new DistBody();
|
||||
List<Matrix> features = entrySon.getValue();//其余类别的所有特征
|
||||
double minError = -1;
|
||||
for (Matrix myMatrix : myFeature) {//待验证类别所有特征
|
||||
for (Matrix otherFeature : features) {
|
||||
double dist = MatrixOperation.getEDistByMatrix(myMatrix, otherFeature);
|
||||
if (minError < 0 || dist < minError) {
|
||||
minError = dist;
|
||||
}
|
||||
}
|
||||
}
|
||||
//当前类别遍历结束
|
||||
distBody.setId(type);
|
||||
distBody.setDist(minError);
|
||||
distBodies.add(distBody);
|
||||
}
|
||||
}
|
||||
}
|
||||
//进行排序
|
||||
for (Map.Entry<Integer, List<DistBody>> entry : map.entrySet()) {
|
||||
List<DistBody> list = entry.getValue();
|
||||
Collections.sort(list, distSort);
|
||||
}
|
||||
adjustWeight(map, 3);
|
||||
test(12, map, 3);
|
||||
// int testType = 5;
|
||||
// List<DistBody> testBody = map.get(testType);
|
||||
// Matrix matrix = powerMatrixMap.get(testType);
|
||||
// System.out.println(matrix.getString());
|
||||
}
|
||||
|
||||
private Matrix mapping(Matrix feature, Matrix mapping) throws Exception {
|
||||
int y = feature.getY();
|
||||
if (y == mapping.getY()) {
|
||||
Matrix matrix = new Matrix(1, y);
|
||||
for (int i = 0; i < y; i++) {
|
||||
double nub = feature.getNumber(0, i) * mapping.getNumber(0, i);
|
||||
matrix.setNub(0, i, nub);
|
||||
}
|
||||
return matrix;
|
||||
} else {
|
||||
throw new Exception("matrix is not equals");
|
||||
}
|
||||
}
|
||||
|
||||
private void test(int key, Map<Integer, List<DistBody>> map, int nub) throws Exception {
|
||||
Matrix matrixMapping = powerMatrixMap.get(key);//映射
|
||||
List<Matrix> rightFeature = featureMap.get(key);//正确的特征
|
||||
System.out.println("正确特征====================");
|
||||
for (Matrix matrix : rightFeature) {
|
||||
System.out.println(matrix.getString());
|
||||
}
|
||||
int y = rightFeature.get(0).getY();
|
||||
Matrix featureMatrix = getFeatureAvg(rightFeature, y);//均值
|
||||
List<DistBody> distBodies = map.get(key);
|
||||
List<Matrix> wrongFeature = new ArrayList<>();
|
||||
for (int i = 0; i < nub; i++) {
|
||||
DistBody distBody = distBodies.get(i);
|
||||
int id = distBody.getId();//相近的特征id
|
||||
wrongFeature.addAll(featureMap.get(id));
|
||||
}
|
||||
System.out.println("错误特征=============");
|
||||
for (Matrix matrix : wrongFeature) {
|
||||
System.out.println(matrix.getString());
|
||||
}
|
||||
//做验证
|
||||
double minOtherDist = -1;//其余特征最小距离
|
||||
for (Matrix wrongMatrix : wrongFeature) {
|
||||
Matrix wrongMapping = mapping(wrongMatrix, matrixMapping);
|
||||
double dist = MatrixOperation.getEDistByMatrix(featureMatrix, wrongMapping);
|
||||
System.out.println("异类距离：" + dist);
|
||||
if (minOtherDist < 0 || dist < minOtherDist) {
|
||||
minOtherDist = dist;
|
||||
}
|
||||
}
|
||||
System.out.println("异类最小距离：" + minOtherDist);
|
||||
for (Matrix rightMatrix : rightFeature) {
|
||||
Matrix rightMapping = mapping(rightMatrix, matrixMapping);
|
||||
double dist = MatrixOperation.getEDistByMatrix(featureMatrix, rightMapping);
|
||||
System.out.println("同类距离：" + dist);
|
||||
}
|
||||
}
|
||||
|
||||
private void adjustWeight(Map<Integer, List<DistBody>> map, int nub) throws Exception {//进行权重调整
|
||||
for (Map.Entry<Integer, List<DistBody>> entry : map.entrySet()) {
|
||||
int key = entry.getKey();//当前类别的id
|
||||
List<Matrix> rightFeature = featureMap.get(key);//正确的特征
|
||||
List<DistBody> distBodies = entry.getValue();
|
||||
List<Matrix> wrongFeature = new ArrayList<>();
|
||||
for (int i = 0; i < nub; i++) {
|
||||
DistBody distBody = distBodies.get(i);
|
||||
int id = distBody.getId();//相近的特征id
|
||||
wrongFeature.addAll(featureMap.get(id));
|
||||
}
|
||||
//对每个分类的特征图进行权重调整
|
||||
updatePower(wrongFeature, rightFeature, key);
|
||||
}
|
||||
}
|
||||
|
||||
//调整权重矩阵
|
||||
private void updatePower(List<Matrix> wrongFeature, List<Matrix> rightFeature, int key) throws Exception {
|
||||
int size = wrongFeature.size();
|
||||
int y = rightFeature.get(0).getY();
|
||||
//特征均值向量
|
||||
Matrix featureMatrix = getFeatureAvg(rightFeature, y);
|
||||
Matrix powerMatrix = new Matrix(1, y);
|
||||
powerMatrixMap.put(key, powerMatrix);
|
||||
Random random = new Random();
|
||||
for (int j = 0; j < times; j++) {
|
||||
for (int i = 0; i < size; i++) {
|
||||
Matrix feature = rightFeature.get(random.nextInt(rightFeature.size()));
|
||||
Matrix noFeature = wrongFeature.get(i);
|
||||
powerDeflection(feature, featureMatrix, true, powerMatrix);
|
||||
powerDeflection(noFeature, featureMatrix, false, powerMatrix);
|
||||
}
|
||||
}
|
||||
end();
|
||||
}
|
||||
|
||||
private void end() throws Exception {
|
||||
for (Map.Entry<Integer, Matrix> entry : powerMatrixMap.entrySet()) {
|
||||
Matrix powerMatrix = entry.getValue();
|
||||
int y = powerMatrix.getY();
|
||||
double min = 0;
|
||||
for (int i = 0; i < y; i++) {
|
||||
double nub = powerMatrix.getNumber(0, i);
|
||||
if (nub < min) {
|
||||
min = nub;
|
||||
}
|
||||
}
|
||||
//获取最小值完毕
|
||||
for (int i = 0; i < y; i++) {
|
||||
double nub = powerMatrix.getNumber(0, i);
|
||||
powerMatrix.setNub(0, i, nub - min);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private void powerDeflection(Matrix matrix1, Matrix matrix2, boolean polymerization,
|
||||
Matrix powerMatrix) throws Exception {
|
||||
int y = matrix1.getY();
|
||||
for (int i = 0; i < y; i++) {
|
||||
double sub = Math.abs(matrix1.getNumber(0, i) - matrix2.getNumber(0, i))
|
||||
* studyPoint;
|
||||
double power = powerMatrix.getNumber(0, i);//当前矩阵中的权值
|
||||
if (polymerization) {//同类聚合 聚合是减
|
||||
power = power - sub;
|
||||
} else {//异类离散
|
||||
power = power + sub;
|
||||
}
|
||||
powerMatrix.setNub(0, i, power);
|
||||
}
|
||||
}
|
||||
|
||||
private Matrix getFeatureAvg(List<Matrix> rightFeature, int size) throws Exception {//求特征均值
|
||||
Matrix feature = new Matrix(1, size);
|
||||
int nub = rightFeature.size();
|
||||
for (int i = 0; i < nub; i++) {
|
||||
Matrix matrix = rightFeature.get(i);
|
||||
for (int j = 0; j < size; j++) {
|
||||
double sigma = matrix.getNumber(0, j) + feature.getNumber(0, j);
|
||||
feature.setNub(0, j, sigma);
|
||||
}
|
||||
}
|
||||
MatrixOperation.mathDiv(feature, nub);
|
||||
return feature;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,55 @@
|
||||
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<RGBNorm> 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;
|
||||
}
|
||||
}
|
||||
@ -75,15 +75,15 @@ 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;
|
||||
}
|
||||
}
|
||||
// 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;
|
||||
}
|
||||
|
||||
@ -354,10 +354,12 @@ public class Watershed {
|
||||
int width = maxY - minY;
|
||||
int height = maxX - minX;
|
||||
boolean isCenter = true;
|
||||
if (edgeSize > 0) {
|
||||
double h = this.height / edgeSize;
|
||||
double w = this.width / edgeSize;
|
||||
isCenter = maxX > h && maxY > w && maxX < (h * (edgeSize - 1)) && maxY < (w * (edgeSize - 1));
|
||||
if (edgeSize > 0) {//边缘过滤
|
||||
double top = this.height / edgeSize;//上边缘界限
|
||||
double left = this.width / edgeSize;//左边缘界限
|
||||
double bottom = this.height - top;//下边缘界限
|
||||
double right = this.width - left;//右边缘界限
|
||||
isCenter = maxX > top && maxY > left && minX < bottom && minY < right;
|
||||
}
|
||||
if (width >= specification.getMinWidth() && height >= specification.getMinHeight()
|
||||
&& width <= specification.getMaxWidth() && height <= specification.getMaxHeight()
|
||||
|
||||
@ -19,7 +19,25 @@ public class Food {
|
||||
private List<RgbRegression> trayBody = new ArrayList<>();//托盘实体参数
|
||||
private int regressionNub = 10000;//回归次数
|
||||
private double trayTh = 0.1;//托盘回归阈值
|
||||
private int regionSize = 10;//纹理区域大小
|
||||
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
|
||||
|
||||
public int getStep() {
|
||||
return step;
|
||||
}
|
||||
|
||||
public void setStep(int step) {
|
||||
this.step = step;
|
||||
}
|
||||
|
||||
public double getDispersedTh() {
|
||||
return dispersedTh;
|
||||
}
|
||||
|
||||
public void setDispersedTh(double dispersedTh) {
|
||||
this.dispersedTh = dispersedTh;
|
||||
}
|
||||
|
||||
public int getRegionSize() {
|
||||
return regionSize;
|
||||
|
||||
@ -9,6 +9,7 @@ import org.wlld.config.RZ;
|
||||
import org.wlld.config.StudyPattern;
|
||||
import org.wlld.imageRecognition.*;
|
||||
import org.wlld.imageRecognition.segmentation.RegionBody;
|
||||
import org.wlld.imageRecognition.segmentation.RegionMapping;
|
||||
import org.wlld.imageRecognition.segmentation.Specifications;
|
||||
import org.wlld.nerveEntity.ModelParameter;
|
||||
import org.wlld.param.Cutting;
|
||||
@ -56,25 +57,27 @@ public class FoodTest {
|
||||
|
||||
public static TempleConfig getTemple(ModelParameter modelParameter) throws Exception {
|
||||
TempleConfig templeConfig = new TempleConfig();
|
||||
templeConfig.setEdge(10);
|
||||
//templeConfig.isShowLog(true);//是否打印日志
|
||||
Cutting cutting = templeConfig.getCutting();
|
||||
Food food = templeConfig.getFood();
|
||||
//
|
||||
cutting.setMaxRain(360);//切割阈值
|
||||
cutting.setTh(0.6);
|
||||
cutting.setRegionNub(200);
|
||||
cutting.setMaxIou(2.0);
|
||||
cutting.setTh(0.8);
|
||||
cutting.setRegionNub(100);
|
||||
cutting.setMaxIou(2);
|
||||
//knn参数
|
||||
templeConfig.setKnnNub(1);
|
||||
//池化比例
|
||||
templeConfig.setPoolSize(2);//缩小比例
|
||||
//聚类
|
||||
templeConfig.setFeatureNub(5);//聚类特征数量
|
||||
templeConfig.setFeatureNub(3);//聚类特征数量
|
||||
//菜品识别实体类
|
||||
food.setShrink(20);//缩紧像素
|
||||
food.setTimes(2);//聚类数据增强
|
||||
food.setRowMark(0.12);//0.12
|
||||
food.setColumnMark(0.12);//0.25
|
||||
food.setShrink(5);//缩紧像素
|
||||
food.setTimes(1);//聚类数据增强
|
||||
food.setRegionSize(5);
|
||||
food.setRowMark(0.15);//0.12
|
||||
food.setColumnMark(0.15);//0.25
|
||||
food.setRegressionNub(20000);
|
||||
food.setTrayTh(0.08);
|
||||
templeConfig.setClassifier(Classifier.KNN);
|
||||
@ -91,19 +94,121 @@ public class FoodTest {
|
||||
Operation operation = new Operation(templeConfig);
|
||||
List<Specifications> specificationsList = new ArrayList<>();
|
||||
Specifications specifications = new Specifications();
|
||||
specifications.setMinWidth(100);
|
||||
specifications.setMinHeight(100);
|
||||
specifications.setMaxWidth(1000);
|
||||
specifications.setMaxHeight(1000);
|
||||
specifications.setMinWidth(150);//150
|
||||
specifications.setMinHeight(150);//150
|
||||
specifications.setMaxWidth(600);
|
||||
specifications.setMaxHeight(600);
|
||||
specificationsList.add(specifications);
|
||||
ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/d.jpg");
|
||||
operation.setTray(threeChannelMatrix);
|
||||
for (int i = 1; i <= 1; i++) {
|
||||
ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("/Users/lidapeng/Desktop/test/test.jpg");
|
||||
// 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++) {
|
||||
System.out.println("轮数============================" + i);
|
||||
ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix(name + "a" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix(name + "b" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix(name + "c" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix4 = picture.getThreeMatrix(name + "d" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix5 = picture.getThreeMatrix(name + "e" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix6 = picture.getThreeMatrix(name + "f" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix7 = picture.getThreeMatrix(name + "g" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix8 = picture.getThreeMatrix(name + "h" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix9 = picture.getThreeMatrix(name + "i" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix10 = picture.getThreeMatrix(name + "j" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix11 = picture.getThreeMatrix(name + "k" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix12 = picture.getThreeMatrix(name + "l" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix13 = picture.getThreeMatrix(name + "m" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix14 = picture.getThreeMatrix(name + "n" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix15 = picture.getThreeMatrix(name + "o" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix16 = picture.getThreeMatrix(name + "p" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix17 = picture.getThreeMatrix(name + "q" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix18 = picture.getThreeMatrix(name + "r" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix19 = picture.getThreeMatrix(name + "s" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix20 = picture.getThreeMatrix(name + "t" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix21 = picture.getThreeMatrix(name + "u" + i + ".jpg");
|
||||
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);
|
||||
}
|
||||
// test2(templeConfig);
|
||||
int i = 4;
|
||||
ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix(name + "a" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix(name + "b" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix(name + "c" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix4 = picture.getThreeMatrix(name + "d" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix5 = picture.getThreeMatrix(name + "e" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix6 = picture.getThreeMatrix(name + "f" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix7 = picture.getThreeMatrix(name + "g" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix8 = picture.getThreeMatrix(name + "h" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix9 = picture.getThreeMatrix(name + "i" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix10 = picture.getThreeMatrix(name + "j" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix11 = picture.getThreeMatrix(name + "k" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix12 = picture.getThreeMatrix(name + "l" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix13 = picture.getThreeMatrix(name + "m" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix14 = picture.getThreeMatrix(name + "n" + i + ".jpg");
|
||||
|
||||
ThreeChannelMatrix threeChannelMatrix15 = picture.getThreeMatrix(name + "o" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix16 = picture.getThreeMatrix(name + "p" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix17 = picture.getThreeMatrix(name + "q" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix18 = picture.getThreeMatrix(name + "r" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix19 = picture.getThreeMatrix(name + "s" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix20 = picture.getThreeMatrix(name + "t" + i + ".jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix21 = picture.getThreeMatrix(name + "u" + i + ".jpg");
|
||||
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);
|
||||
|
||||
}
|
||||
|
||||
private static void test3(ThreeChannelMatrix threeChannelMatrix, Operation operation, List<Specifications> specifications) throws Exception {
|
||||
int type = operation.colorLook(threeChannelMatrix, specifications).get(0).getType();
|
||||
System.out.println(type);
|
||||
}
|
||||
|
||||
public static void study() throws Exception {
|
||||
|
||||
@ -3,10 +3,8 @@ package coverTest;
|
||||
import org.wlld.MatrixTools.Matrix;
|
||||
import org.wlld.config.Classifier;
|
||||
import org.wlld.config.StudyPattern;
|
||||
import org.wlld.imageRecognition.Convolution;
|
||||
import org.wlld.imageRecognition.Picture;
|
||||
import org.wlld.imageRecognition.TempleConfig;
|
||||
import org.wlld.imageRecognition.ThreeChannelMatrix;
|
||||
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;
|
||||
@ -27,7 +25,7 @@ public class ForestTest {
|
||||
private static Convolution convolution = new Convolution();
|
||||
|
||||
public static void main(String[] args) throws Exception {
|
||||
test();
|
||||
testPic();
|
||||
//int a = (int) (Math.log(4) / Math.log(2));//id22是第几层
|
||||
//double a = Math.pow(2, 5) - 1; 第五层的第一个数
|
||||
// System.out.println("a==" + a);
|
||||
@ -37,24 +35,32 @@ public class ForestTest {
|
||||
public static void testPic() throws Exception {
|
||||
Picture picture = new Picture();
|
||||
TempleConfig templeConfig = getTemple(null);
|
||||
Operation operation = new Operation(templeConfig);
|
||||
List<Specifications> specificationsList = new ArrayList<>();
|
||||
Specifications specifications = new Specifications();
|
||||
specifications.setMinWidth(100);
|
||||
specifications.setMinHeight(100);
|
||||
specifications.setMaxWidth(1000);
|
||||
specifications.setMaxHeight(1000);
|
||||
specifications.setMaxWidth(600);
|
||||
specifications.setMaxHeight(600);
|
||||
specificationsList.add(specifications);
|
||||
ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/d.jpg");
|
||||
ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("/Users/lidapeng/Desktop/train/a2.jpg");
|
||||
Watershed watershed = new Watershed(threeChannelMatrix, specificationsList, templeConfig);
|
||||
List<RegionBody> regionBodies = watershed.rainfall();
|
||||
RegionBody regionBody = regionBodies.get(0);
|
||||
int minX = regionBody.getMinX();
|
||||
int minY = regionBody.getMinY();
|
||||
int maxX = regionBody.getMaxX();
|
||||
int maxY = regionBody.getMaxY();
|
||||
int xSize = maxX - minX;
|
||||
int ySize = maxY - minY;
|
||||
ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
|
||||
if (regionBodies.size() == 1) {
|
||||
RegionBody regionBody = regionBodies.get(0);
|
||||
int minX = regionBody.getMinX();
|
||||
int minY = regionBody.getMinY();
|
||||
int maxX = regionBody.getMaxX();
|
||||
int maxY = regionBody.getMaxY();
|
||||
int xSize = maxX - minX;
|
||||
int ySize = maxY - minY;
|
||||
ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
|
||||
List<Double> feature = convolution.getCenterTexture(threeChannelMatrix1, templeConfig.getFood().getRegionSize(),
|
||||
templeConfig.getPoolSize(), templeConfig, templeConfig.getFeatureNub());
|
||||
System.out.println(feature);
|
||||
} else {
|
||||
System.out.println("size===" + regionBodies.size());
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@ -70,9 +76,9 @@ public class ForestTest {
|
||||
Food food = templeConfig.getFood();
|
||||
//
|
||||
cutting.setMaxRain(360);//切割阈值
|
||||
cutting.setTh(0.6);
|
||||
cutting.setRegionNub(200);
|
||||
cutting.setMaxIou(0.5);
|
||||
cutting.setTh(0.8);
|
||||
cutting.setRegionNub(100);
|
||||
cutting.setMaxIou(0.2);
|
||||
//knn参数
|
||||
templeConfig.setKnnNub(1);
|
||||
//池化比例
|
||||
@ -80,10 +86,10 @@ public class ForestTest {
|
||||
//聚类
|
||||
templeConfig.setFeatureNub(3);//聚类特征数量
|
||||
//菜品识别实体类
|
||||
food.setShrink(10);//缩紧像素
|
||||
food.setTimes(2);//聚类数据增强
|
||||
food.setRowMark(0.1);//0.12
|
||||
food.setColumnMark(0.1);//0.25
|
||||
food.setShrink(5);//缩紧像素
|
||||
food.setTimes(1);//聚类数据增强
|
||||
food.setRowMark(0.15);//0.12
|
||||
food.setColumnMark(0.15);//0.25
|
||||
food.setRegressionNub(20000);
|
||||
food.setTrayTh(0.08);
|
||||
templeConfig.setClassifier(Classifier.KNN);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user