添加多种类干食切图

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

@@ -0,0 +1,147 @@
+package org.wlld.imageRecognition;
+
+
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.imageRecognition.border.GMClustering;
+import org.wlld.imageRecognition.modelEntity.GMBody;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description
+ */
+public class CutFood {
+    private TempleConfig templeConfig;
+    private Map<Integer, GMClustering> meanMap = new HashMap<>();
+    private Matrix regionMap;
+
+    public CutFood(TempleConfig templeConfig) {
+        this.templeConfig = templeConfig;
+    }
+
+    private void mean(ThreeChannelMatrix threeChannelMatrix, GMClustering mean) throws Exception {
+        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[] rgb = new double[]{matrixR.getNumber(i, j) / 255, matrixG.getNumber(i, j) / 255
+                        , matrixB.getNumber(i, j) / 255};
+                mean.setColor(rgb);
+            }
+        }
+        mean.start(true);
+    }
+
+    private double getAvg(Matrix matrix) throws Exception {
+        double sigma = 0;
+        int x = matrix.getX();
+        int y = matrix.getY();
+        for (int i = 0; i < x; i++) {
+            for (int j = 0; j < y; j++) {
+                sigma = sigma + matrix.getNumber(i, j);
+            }
+        }
+        return sigma / (x * y);
+    }
+
+    public void createRegion(ThreeChannelMatrix threeChannelMatrix) throws Exception {
+        int size = templeConfig.getFood().getRegionSize();
+        Matrix matrixR = threeChannelMatrix.getMatrixR();
+        Matrix matrixG = threeChannelMatrix.getMatrixG();
+        Matrix matrixB = threeChannelMatrix.getMatrixB();
+        int x = matrixR.getX();
+        int y = matrixR.getY();
+        double s = Math.pow(size, 2);
+        regionMap = new Matrix(x / size, y / size);
+        for (int i = 0; i <= x - size; i += size) {
+            for (int j = 0; j <= y - size; j += size) {
+                double r = getAvg(matrixR.getSonOfMatrix(i, j, size, size));
+                double g = getAvg(matrixG.getSonOfMatrix(i, j, size, size));
+                double b = getAvg(matrixB.getSonOfMatrix(i, j, size, size));
+                double[] rgb = new double[]{r / 255, g / 255, b / 255};
+                int index = getType(rgb);//该区域所属类别
+                regionMap.setNub(i / size, j / size, index);
+            }
+        }
+        int xr = regionMap.getX();
+        int yr = regionMap.getY();
+        List<GMBody> gmBodies = new ArrayList<>();
+        for (int i = 0; i < xr; i++) {
+            for (int j = 0; j < yr; j++) {
+                int type = (int) regionMap.getNumber(i, j);
+                if (!insertBodies(gmBodies, i, j, type)) {//需要创建一个新的
+                    gmBodies.add(new GMBody(type, i, j));
+                }
+            }
+        }
+        List<GMBody> gmBodies2 = new ArrayList<>();
+        for (int i = 0; i < gmBodies.size(); i++) {//过滤侯选区
+            GMBody gmBody = gmBodies.get(i);
+            double regionSize = gmBody.getPixelNub() * s;
+            int type = gmBody.getType();
+            if (type != 1) {//背景直接过滤
+                int oneSize = meanMap.get(type).getRegionSize();
+                if (regionSize > oneSize * 0.8) {
+                    gmBodies2.add(gmBody);
+                }
+            }
+        }
+        for (GMBody gmBody : gmBodies2) {
+            int type = gmBody.getType();
+            double regionSize = gmBody.getPixelNub() * s;
+            int oneSize = meanMap.get(type).getRegionSize();
+            double nub = regionSize / (double) oneSize;
+            System.out.println("type==" + type + ",nub==" + nub + ",onSize==" + oneSize + ",gmNub=="
+                    + gmBody.getPixelNub());
+        }
+
+    }
+
+    private boolean insertBodies(List<GMBody> gmBodies, int x, int y, int type) {
+        boolean isInsert = false;
+        for (GMBody gmBody : gmBodies) {
+            if (gmBody.insertRgb(x, y, type)) {
+                isInsert = true;
+                break;
+            }
+        }
+        return isInsert;
+    }
+
+    private int getType(double[] rgb) throws Exception {
+        int index = 0;
+        double max = 0;
+        for (Map.Entry<Integer, GMClustering> entry : meanMap.entrySet()) {
+            GMClustering gmClustering = entry.getValue();
+            double probability = gmClustering.getProbabilityDensity(rgb);
+            if (probability > max) {
+                max = probability;
+                index = entry.getKey();
+            }
+        }
+        if (max < 2) {
+            index = 1;
+        }
+        return index;
+    }
+
+    public void study(int type, ThreeChannelMatrix threeChannelMatrix) throws Exception {
+        Matrix matrixR = threeChannelMatrix.getMatrixR();
+        int x = matrixR.getX();
+        int y = matrixR.getY();
+        GMClustering mean = new GMClustering(templeConfig.getFeatureNub(), templeConfig);
+        mean.setRegionSize(x * y);
+        meanMap.put(type, mean);
+        mean(threeChannelMatrix, mean);
+        //记录非背景的单物体面积
+    }
+}

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

@@ -131,10 +131,9 @@ public class Operation {//进行计算
                 int minY = regionBody.getMinY();
                 int maxX = regionBody.getMaxX();
                 int maxY = regionBody.getMaxY();
-                System.out.println("异常：minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY + ",tag==" + tag
+                System.out.println("异常：minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY + ",tag==" + tag);
-                        + "url==" + url);
             }
-            throw new Exception("Parameter exception region size==" + regionBodies.size());
+            throw new Exception("Parameter exception region size==" + regionBodies.size() + ",url" + url);
         }
     }
 

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

@@ -202,7 +202,7 @@ public class RGBNorm {
         }
     }
 
-    public double getGMProbability(double[] feature) throws Exception {//计算正态分布概率
+    public double getGMProbability(double[] feature) throws Exception {//计算正态分布概率密度
         double zSigma = 0;
         int size = feature.length;
         for (int i = 0; i < size; i++) {

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

@@ -4,7 +4,6 @@ import org.wlld.imageRecognition.MeanClustering;
 import org.wlld.imageRecognition.RGBNorm;
 import org.wlld.imageRecognition.TempleConfig;
 
-import java.util.Arrays;
 
 /**
  * @param
@@ -13,11 +12,28 @@ import java.util.Arrays;
  * @Description
  */
 public class GMClustering extends MeanClustering {
+    private int regionSize;//单区域面积
+
+    public int getRegionSize() {
+        return regionSize;
+    }
+
+    public void setRegionSize(int regionSize) {
+        this.regionSize = regionSize;
+    }
 
     public GMClustering(int speciesQuantity, TempleConfig templeConfig) throws Exception {
         super(speciesQuantity, templeConfig);
     }
 
+    public double getProbabilityDensity(double[] feature) throws Exception {//获取总概率密度
+        double sigma = 0;
+        for (RGBNorm rgbNorm : matrices) {
+            sigma = sigma + rgbNorm.getGMProbability(feature);
+        }
+        return sigma;
+    }
+
     @Override
     public void start(boolean isRegression) throws Exception {
         super.start(isRegression);

src/main/java/org/wlld/imageRecognition/modelEntity/GMBody.java

@@ -0,0 +1,55 @@
+package org.wlld.imageRecognition.modelEntity;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description 混合高斯切割区域
+ */
+public class GMBody {
+    private int type;//类别
+    private List<Integer> pixels = new ArrayList<>();
+    private int pixelNub = 0;
+
+    public GMBody(int type, int x, int y) {
+        this.type = type;
+        pixelNub++;
+        pixels.add((x << 12) | y);
+    }
+
+    public int getPixelNub() {
+        return pixelNub;
+    }
+
+    public int getType() {
+        return type;
+    }
+
+    private boolean isAdjacent(int x, int y, int i, int j) {
+        boolean adjacent = false;
+        if (Math.abs(x - i) == 1 || Math.abs(y - j) == 1) {
+            adjacent = true;
+        }
+        return adjacent;
+    }
+
+    public boolean insertRgb(int x, int y, int type) {
+        boolean isRight = false;
+        if (this.type == type) {
+            for (int pixel : pixels) {
+                int i = (pixel >> 12) & 0xfff;
+                int j = pixel & 0xfff;
+                if (isAdjacent(x, y, i, j)) {//相邻
+                    isRight = true;
+                    pixelNub++;
+                    pixels.add((x << 12) | y);
+                    break;
+                }
+            }
+        }
+        return isRight;
+    }
+}

src/main/java/org/wlld/imageRecognition/segmentation/Watershed.java

@@ -75,7 +75,7 @@ public class Watershed {
 
     private boolean isTray(int x, int y) throws Exception {
         boolean isTray = false;
-        if (trayBody != null && trayBody.size() > 0) {
+        if (trayBody.size() > 0) {
             double[] rgb = new double[]{matrixR.getNumber(x, y) / 255, matrixG.getNumber(x, y) / 255,
                     matrixB.getNumber(x, y) / 255};
             for (RgbRegression rgbRegression : trayBody) {
@@ -279,14 +279,16 @@ public class Watershed {
                 regionBodies.add(regionBody);
             }
         }
-        for (RegionBody regionBody : regionBodies) {
+        regionBodies = iou(regionBodies);
+        for (int i = 0; i < regionBodies.size(); i++) {
+            RegionBody regionBody = regionBodies.get(i);
             int minX = regionBody.getMinX();
             int maxX = regionBody.getMaxX();
             int minY = regionBody.getMinY();
             int maxY = regionBody.getMaxY();
-            System.out.println("minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY);
+            System.out.println("minX==" + minX + ",maxX==" + maxX + ",minY==" + minY + ",maxY==" + maxY);
         }
-        return iou(regionBodies);
+        return regionBodies;
     }
 
     private List<RegionBody> iou(List<RegionBody> regionBodies) {
@@ -361,7 +363,7 @@ public class Watershed {
                 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;
+                isCenter = minX > top && minY > left && minX < bottom && minY < right;
             }
             if (width >= specification.getMinWidth() && height >= specification.getMinHeight()
                     && width <= specification.getMaxWidth() && height <= specification.getMaxHeight()
@@ -494,7 +496,7 @@ public class Watershed {
         int minIdx = 0;
         for (int i = 0; i < array.length; i++) {
             double nub = array[i];
-            if (nub > -1 && nub < mySelf) {
+            if (nub > -1 && nub < mySelf && nub < 280) {
                 minIdx = minIdx | (1 << i);
             }
         }