trunk/src/jp/nyatla/nyartoolkit/core/analyzer/histgram/NyARHistgramAnalyzer_DiscriminantThreshold.java

   1 package jp.nyatla.nyartoolkit.core.analyzer.histgram;\r
   2 \r
   3 import jp.nyatla.nyartoolkit.core.types.NyARHistgram;\r
   4 \r
   5 /**\r
   6  * 判別法で閾値を求めます。\r
   7  * 画素数が2048^2に満たない場合は、fixedint(24-8)で計算できます。\r
   8  * @param i_histgram\r
   9  * @param o_value\r
  10  * @return\r
  11  */\r
  12 public class NyARHistgramAnalyzer_DiscriminantThreshold implements INyARHistgramAnalyzer_Threshold\r
  13 {\r
  14         private double _score;\r
  15 \r
  16 \r
  17         public int getThreshold(NyARHistgram i_histgram)\r
  18         {\r
  19                 int[] hist=i_histgram.data;\r
  20                 int n=i_histgram.length;\r
  21                 int da,sa,db,sb,dt,pt,st;\r
  22                 int i;          \r
  23                 int th=0;\r
  24                 //後で使う\r
  25                 dt=pt=0;\r
  26                 for(i=0;i<n;i++){\r
  27                         int h=hist[i];\r
  28                         dt+=h*i;\r
  29                         pt+=h*i*i;//正規化の時に使う定数\r
  30                 }\r
  31                 st=i_histgram.total_of_data;\r
  32                 //Low側(0<=i<=n-2)\r
  33                 da=dt;\r
  34                 sa=st;\r
  35                 //High側(i=n-1)\r
  36                 db=sb=0;                \r
  37                 \r
  38                 double max=-1;\r
  39                 double max_mt=0;\r
  40                 //各ヒストグラムの分離度を計算する(1<=i<=n-1の範囲で評価)\r
  41                 for(i=n-1;i>0;i--){\r
  42                         //次のヒストグラムを計算\r
  43                         int hist_count=hist[i];\r
  44                         int hist_val=hist_count*i;\r
  45                         da-=hist_val;\r
  46                         sa-=hist_count;\r
  47                         db+=hist_val;\r
  48                         sb+=hist_count;\r
  49                         \r
  50                         //クラス間分散を計算\r
  51                         double dv=(sa+sb);\r
  52                         double mt=(double)(da+db)/dv;\r
  53                         double ma=(sa!=0?((double)da/(double)sa):0)-mt;\r
  54                         double mb=(sb!=0?((double)db/(double)sb):0)-mt;\r
  55                         double kai=((double)(sa*(ma*ma)+sb*(mb*mb)))/dv;\r
  56                         if(max<kai){\r
  57                                 max_mt=mt;\r
  58                                 max=kai;\r
  59                                 th=i;\r
  60                         }\r
  61                         //System.out.println(kai);\r
  62                 }\r
  63                 //max_mtを元に正規化\r
  64                 this._score=max/((double)(pt+max_mt*max_mt*st-2*max_mt*dt)/st);//129,0.8888888888888887\r
  65                 return th;\r
  66         }\r
  67         /**\r
  68          * 最後に実行したgetThresholdのスコアを返します。\r
  69          * スコアは正規化された分離度。1.0-0.0の範囲を取る。0.7以上なら概ね双峰的です。\r
  70          * @return\r
  71          */\r
  72         public final double getLastScore()\r
  73         {\r
  74                 return this._score;\r
  75         }\r
  76         /**\r
  77          * Debug\r
  78          */\r
  79         public static void main(String[] args)\r
  80         {\r
  81                 NyARHistgram data=new NyARHistgram(256);\r
  82                 for(int i=0;i<256;i++){\r
  83                         data.data[i]=128-i>0?128-i:i-128;\r
  84                 }\r
  85                 data.total_of_data=data.getTotal(0,255);\r
  86                 NyARHistgramAnalyzer_DiscriminantThreshold an=new NyARHistgramAnalyzer_DiscriminantThreshold();\r
  87                 int th=an.getThreshold(data);\r
  88                 System.out.print(th);\r
  89                 return;\r
  90         }\r
  91 }\r