lidapeng
4 years ago
parent
877694fae2
commit
003804ec22
@ -0,0 +1,37 @@
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package org.wlld.imageRecognition.modelEntity;
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import org.wlld.MatrixTools.Matrix;
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import org.wlld.i.OutBack;
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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 RgbBack implements OutBack {
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private int id = 0;
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private double out = 0;
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public void clear() {
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out = 0;
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id = 0;
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}
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@Override
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public void getBack(double out, int id, long eventId) {
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if (out > this.out) {
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this.out = out;
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this.id = id;
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}
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}
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@Override
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public void getBackMatrix(Matrix matrix, long eventId) {
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}
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public int getId() {
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return id;
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}
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}
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@ -0,0 +1,117 @@
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package org.wlld.imageRecognition.segmentation;
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import org.wlld.config.RZ;
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import org.wlld.function.Sigmod;
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import org.wlld.function.Tanh;
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import org.wlld.imageRecognition.modelEntity.RgbBack;
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import org.wlld.nerveCenter.NerveManager;
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import org.wlld.nerveEntity.SensoryNerve;
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import java.awt.image.Kernel;
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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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import java.util.Set;
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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 KNerveManger {
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private Map<Integer, List<double[]>> featureMap = new HashMap<>();
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private int sensoryNerveNub;//输出神经元个数
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private int speciesNub;//种类数
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private NerveManager nerveManager;
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private int times;
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private RgbBack rgbBack = new RgbBack();
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public KNerveManger(int sensoryNerveNub, int speciesNub, int times) throws Exception {
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this.sensoryNerveNub = sensoryNerveNub;
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this.speciesNub = speciesNub;
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this.times = times;
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nerveManager = new NerveManager(sensoryNerveNub, 24, speciesNub,
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1, new Tanh(),//0.008 l1 0.02
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false, false, 0.008, RZ.L1, 0.01);
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nerveManager.init(true, false, true, true);
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}
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private Map<Integer, Double> createTag(int tag) {//创建一个标注
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Map<Integer, Double> tagging = new HashMap<>();
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Set<Integer> set = featureMap.keySet();
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for (int key : set) {
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double value = 0.0;
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if (key == tag) {
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value = 1.0;
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}
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tagging.put(key, value);
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}
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return tagging;
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}
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public void look(List<double[]> data) throws Exception {
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int size = data.size();
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Map<Integer, Integer> map = new HashMap<>();
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for (int i = 0; i < size; i++) {
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rgbBack.clear();
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post(data.get(i), null, false);
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int type = rgbBack.getId();
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if (map.containsKey(type)) {
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map.put(type, map.get(type) + 1);
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} else {
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map.put(type, 1);
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}
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}
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double max = 0;
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int type = 0;
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for (Map.Entry<Integer, Integer> entry : map.entrySet()) {
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int nub = entry.getValue();
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if (nub > max) {
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max = nub;
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type = entry.getKey();
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}
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}
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double point = max / size;
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System.out.println("类型是：" + type + ",总票数:" + size + ",得票率：" + point);
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System.out.println("=================================完成");
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}
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public void startStudy() throws Exception {
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for (int i = 0; i < times; i++) {
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for (Map.Entry<Integer, List<double[]>> entry : featureMap.entrySet()) {
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int type = entry.getKey();
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System.out.println("=============================" + type);
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Map<Integer, Double> tag = createTag(type);//标注
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double[] feature = entry.getValue().get(i);//数据
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post(feature, tag, true);
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}
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}
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// for (Map.Entry<Integer, List<double[]>> entry : featureMap.entrySet()) {
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// int type = entry.getKey();
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// System.out.println("=============================" + type);
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// List<double[]> list = entry.getValue();
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// look(list);
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// }
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}
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private void post(double[] data, Map<Integer, Double> tagging, boolean isStudy) throws Exception {
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List<SensoryNerve> sensoryNerveList = nerveManager.getSensoryNerves();
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int size = sensoryNerveList.size();
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for (int i = 0; i < size; i++) {
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sensoryNerveList.get(i).postMessage(1, data[i], isStudy, tagging, rgbBack);
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}
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}
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public void setFeature(int type, List<double[]> feature) {
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if (type > 0) {
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if (featureMap.containsKey(type)) {
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featureMap.get(type).addAll(feature);
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} else {
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featureMap.put(type, feature);
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}
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}
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}
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}
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@ -1,55 +0,0 @@
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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.imageRecognition.MeanClustering;
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import org.wlld.imageRecognition.RGBNorm;
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import org.wlld.imageRecognition.TempleConfig;
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import org.wlld.imageRecognition.ThreeChannelMatrix;
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import java.util.List;
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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 WFilter {
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private List<RGBNorm> rgbNorms;
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public void filter(ThreeChannelMatrix threeChannelMatrix, TempleConfig templeConfig,
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int speciesQuantity) throws Exception {
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MeanClustering meanClustering = new MeanClustering(speciesQuantity, 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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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[] color = new double[]{matrixR.getNumber(i, j), matrixG.getNumber(i, j), matrixB.getNumber(i, j)};
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meanClustering.setColor(color);
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}
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}
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meanClustering.start(true);
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rgbNorms = meanClustering.getMatrices();
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}
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private void getDist() {
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double min = -1;
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for (RGBNorm rgbNorm : rgbNorms) {
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double[] rgb = rgbNorm.getRgb();
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}
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}
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private double dist(double[] a, double[] b) {
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double sigma = 0;
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for (int i = 0; i < a.length; i++) {
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double sub = Math.pow(a[i] - b[i], 2);
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sigma = sigma + sub;
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}
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return sigma / a.length;
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}
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}
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package coverTest;
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import coverTest.regionCut.RegionCut;
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import coverTest.regionCut.RegionFeature;
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import org.wlld.MatrixTools.Matrix;
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import org.wlld.imageRecognition.Convolution;
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import org.wlld.imageRecognition.Picture;
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import org.wlld.imageRecognition.ThreeChannelMatrix;
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import org.wlld.imageRecognition.segmentation.RgbRegression;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.List;
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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数据观察
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*/
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public class DataObservation {
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private static Convolution convolution = new Convolution();
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public static void main(String[] args) throws Exception {
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//372,330,右 最大值 147.44
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//377 ,330右 最大值 69.6
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int xp = 123;
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int yp = 165;//290
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observation2("/Users/lidapeng/Desktop/test/testOne/a0.jpg", xp, yp);
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}
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public static void observation2(String url, int xp, int yp) throws Exception {
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Picture picture = new Picture();
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ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix(url);
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ThreeChannelMatrix myThreeChannelMatrix = convolution.getRegionMatrix(threeChannelMatrix, xp, yp, 270, 274);
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RegionFeature regionFeature = new RegionFeature(myThreeChannelMatrix, xp, yp);
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regionFeature.start();
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}
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public static void observation(String url, int xp, int yp, int size) throws Exception {
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Picture picture = new Picture();
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ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix(url);
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ThreeChannelMatrix myThreeChannelMatrix = convolution.getRegionMatrix(threeChannelMatrix, xp, yp, size, size);
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//右
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ThreeChannelMatrix threeChannelMatrix2 = convolution.getRegionMatrix(threeChannelMatrix, xp, yp + size, size, size);
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getDist(myThreeChannelMatrix, threeChannelMatrix2, "右");
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//左
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ThreeChannelMatrix threeChannelMatrix3 = convolution.getRegionMatrix(threeChannelMatrix, xp, yp - size, size, size);
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getDist(myThreeChannelMatrix, threeChannelMatrix3, "左");
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//上
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ThreeChannelMatrix threeChannelMatrix4 = convolution.getRegionMatrix(threeChannelMatrix, xp - size, yp, size, size);
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getDist(myThreeChannelMatrix, threeChannelMatrix4, "上");
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//下
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ThreeChannelMatrix threeChannelMatrix5 = convolution.getRegionMatrix(threeChannelMatrix, xp + size, yp, size, size);
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getDist(myThreeChannelMatrix, threeChannelMatrix5, "下");
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//左上
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ThreeChannelMatrix threeChannelMatrix6 = convolution.getRegionMatrix(threeChannelMatrix, xp - size, yp - size, size, size);
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getDist(myThreeChannelMatrix, threeChannelMatrix6, "左上");
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//左下
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ThreeChannelMatrix threeChannelMatrix7 = convolution.getRegionMatrix(threeChannelMatrix, xp + size, yp - size, size, size);
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getDist(myThreeChannelMatrix, threeChannelMatrix7, "左下");
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//右上
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ThreeChannelMatrix threeChannelMatrix8 = convolution.getRegionMatrix(threeChannelMatrix, xp - size, yp + size, size, size);
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getDist(myThreeChannelMatrix, threeChannelMatrix8, "右上");
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//右下
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ThreeChannelMatrix threeChannelMatrix9 = convolution.getRegionMatrix(threeChannelMatrix, xp + size, yp + size, size, size);
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getDist(myThreeChannelMatrix, threeChannelMatrix9, "右下");
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//getDist("/Users/lidapeng/Desktop/test/testOne/a1.jpg", 468, 713, rgbRegression, "测");
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}
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private static void getDist(ThreeChannelMatrix threeChannelMatrix1, ThreeChannelMatrix threeChannelMatrix2, String name) throws Exception {
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Matrix matrixR1 = threeChannelMatrix1.getMatrixR();
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Matrix matrixG1 = threeChannelMatrix1.getMatrixG();
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Matrix matrixB1 = threeChannelMatrix1.getMatrixB();
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Matrix matrixR2 = threeChannelMatrix2.getMatrixR();
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Matrix matrixG2 = threeChannelMatrix2.getMatrixG();
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Matrix matrixB2 = threeChannelMatrix2.getMatrixB();
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int x = matrixR1.getX();
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int y = matrixR1.getY();
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int nub = x * y;
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double sigmaR = 0;
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double sigmaG = 0;
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double sigmaB = 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 subR = Math.pow(matrixR1.getNumber(i, j) - matrixR2.getNumber(i, j), 2);
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double subG = Math.pow(matrixG1.getNumber(i, j) - matrixG2.getNumber(i, j), 2);
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double subB = Math.pow(matrixB1.getNumber(i, j) - matrixB2.getNumber(i, j), 2);
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sigmaR = subR + sigmaR;
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sigmaG = subG + sigmaG;
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sigmaB = subB + sigmaB;
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}
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}
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sigmaR = sigmaR / nub;
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sigmaG = sigmaG / nub;
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sigmaB = sigmaB / nub;
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double sigma = sigmaR + sigmaG + sigmaB;
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System.out.println(name + ":" + sigma);
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}
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}
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@ -0,0 +1,46 @@
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package coverTest;
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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 RGBBody {
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private double r;
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private double g;
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private double b;
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private double rgb;
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public double getR() {
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return r;
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}
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public void setR(double r) {
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this.r = r;
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}
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public double getG() {
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return g;
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}
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public void setG(double g) {
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this.g = g;
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}
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public double getB() {
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return b;
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}
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public void setB(double b) {
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this.b = b;
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}
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public double getRgb() {
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return rgb;
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}
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public void setRgb(double rgb) {
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this.rgb = rgb;
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}
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}
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,15 @@
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package coverTest.regionCut;
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import java.util.ArrayList;
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import java.util.List;
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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 RegionCutBody {
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private List<Integer> pixels = new ArrayList<>();
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}
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@ -0,0 +1,84 @@
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package coverTest.regionCut;
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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.config.Kernel;
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import org.wlld.imageRecognition.ThreeChannelMatrix;
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import org.wlld.imageRecognition.segmentation.RgbRegression;
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import org.wlld.tools.Frequency;
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import java.util.Arrays;
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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 利用边缘算子提取特征
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*/
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public class RegionFeature extends Frequency {
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private Matrix matrix;
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private Matrix matrixR;
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private Matrix matrixG;
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private Matrix matrixB;
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private Matrix kernel = Kernel.Big;
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private int fatherX;
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private int fatherY;
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private RgbRegression rgbRegression = new RgbRegression(25);
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public RegionFeature(ThreeChannelMatrix threeChannelMatrix, int fatherX, int fatherY) {
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this.matrix = threeChannelMatrix.getMatrixRGB();
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this.fatherX = fatherX;
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this.fatherY = fatherY;
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matrixR = threeChannelMatrix.getMatrixR();
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matrixG = threeChannelMatrix.getMatrixG();
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matrixB = threeChannelMatrix.getMatrixB();
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}
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private double getMatrixVar(Matrix matrix) throws Exception {
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int x = matrix.getX();
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int y = matrix.getY();
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double[] data = new double[x * y];
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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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int index = i * y + j;
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data[index] = matrix.getNumber(i, j);
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}
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}
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//System.out.println(Arrays.toString(data));
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return variance(data);
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}
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public void start() throws Exception {
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int x = matrix.getX();
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int y = matrix.getY();
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int size = kernel.getX();
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for (int i = 0; i <= x - size; i += 2) {
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int xSize = fatherX + i;
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System.out.println("==================================" + xSize);
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for (int j = 0; j <= y - size; j += 2) {
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// double conNub = Math.abs(convolution(i, j));//卷积值
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double var = getMatrixVar(matrix.getSonOfMatrix(i, j, size, size));
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int ySize = fatherY + j;
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System.out.println("x:" + xSize + ",y:" + ySize + ",var:" + var);
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}
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}
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}
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private double convolution(int x, int y) throws Exception {//计算卷积
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double allNub = 0;
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int xr;
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int yr;
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int kxMax = kernel.getX();
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int kyMax = kernel.getY();
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for (int i = 0; i < kxMax; i++) {
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xr = i + x;
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for (int j = 0; j < kyMax; j++) {
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yr = j + y;
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allNub = matrix.getNumber(xr, yr) * kernel.getNumber(i, j) + allNub;
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}
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}
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return allNub;
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}
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}
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