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Author: Yagami360

Example_6_2/LDA1.R

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+#install.packages("lda")
+#install.packages("jpeg")
+
+# expand lib on memory
+library( MASS )   # MASS package
+library( jpeg )   # jpeg画像の読み込み
+
+# set options
+options( digits=7 ) # 表示桁数
+
+# load image data (jpeg)
+jpeg_NekoSensei <- readJPEG( "nekosensei_greyscale.jpg" ) # 560*420 = 235200 pixel(3data<rgb> per 1 pixel)
+class( jpeg_NekoSensei )      # 3次元配列 um[1:420, 1:560, 1:3]
+attributes( jpeg_NekoSensei )
+
+########################################
+# convert to thresholding/binary image #
+########################################
+dfNekoSensei <- as.data.frame( expand.grid(jpeg_NekoSensei) ) # convert to data frame
+dat_levels <- c(
+  rep( min(dfNekoSensei$Var1), length(dfNekoSensei$Var1)/2 ), 
+  rep( max(dfNekoSensei$Var1), length(dfNekoSensei$Var1)/2 ) 
+) # binary data set (0 or 1)
+
+dat_lda <- lda( dat_levels ~ . , data = dfNekoSensei )
+print( dat_lda )
+
+# 線形判別係数 [coefficients of linear discriminants] から線形識別関数を求める
+linerC <- apply( dat_lda$means%*%dat_lda$scaling, 2, mean ) # 定数項C
+cat("\nConstant term:\n")
+print(linerC)
+
+# print idification result in table
+result <- predict( dat_lda )
+tbl <- table( dat_levels, result$class )
+cat("\nIdification result:")
+print(tbl)
+
+# result$class convert to vector
+imgNekosensei1 <- array( 
+  data = rep( 0,length(dfNekoSensei$Var1) ),
+  dim = c(420,560,3)
+)
+imgNekosensei2 <- array( 
+  data = rep( 0,length(dfNekoSensei$Var1) ),
+  dim = c(420,560,3)
+)
+
+for( i in 1:length(imgNekosensei) )
+{
+  if( result$class[i] == 1 )
+  {
+    imgNekosensei1[i] <- 1
+    imgNekosensei2[i] <- 0
+  }
+  else
+  {
+    imgNekosensei1[i] <- 0    
+    imgNekosensei2[i] <- 1
+  }
+}
+
+############################
+# set graphics parameters  #
+############################
+# 軸に関してのデータリスト
+lstAxis <- list(                        
+  xMin = 0.0, xMax = 1.0,  # x軸の最小値、最大値
+  yMin = 0.0, yMax = 1.0,  # y軸の最小値、最大値
+  zMin = 0.0, zMax = 1.0,  # z軸の最小値、最大値
+  xlim = range( c(0.0, 1.0) ), 
+  ylim = range( c(0.0, 1.0) ), 
+  zlim = range( c(0.0, 1.0) ),
+  mainTitle = "mainTitle", # 図のメインタイトル（図の上）
+  subTitle  = "subTitle",  # 図のサブタイトル（図の下）
+  xlab      = "x", # x軸の名前
+  ylab      = "y", # y軸の名前
+  zlab      = "z"  # z軸の名前
+)
+lstAxis$xMin <- 0
+lstAxis$xMax <- 560
+lstAxis$yMin <- 0
+lstAxis$yMax <- 420
+lstAxis$xlim = range( c(lstAxis$xMin, lstAxis$xMax) )
+lstAxis$ylim = range( c(lstAxis$yMin, lstAxis$yMax) )
+lstAxis$zlim = range( c(lstAxis$zMin, lstAxis$zMax) )
+lstAxis$xlab <- "x1"
+lstAxis$ylab <- "x2"
+lstAxis$mainTitle <- "ねこ先生（グレースケール）[Greyscale]" # 図のメインタイトル（図の上）
+
+# plot frame only
+par(new=F)
+plot.new()  # clear
+plot( c(), type='n',
+      main = lstAxis$mainTitle,
+      xlim=lstAxis$xlim, ylim=lstAxis$ylim,
+      xlab=lstAxis$xlab, ylab=lstAxis$ylab
+)
+#grid() #グリッド線を追加
+
+############################
+# Draw Image and figure    #
+############################
+# draw original image
+rasterImage( 
+  image = jpeg_NekoSensei, 
+  xleft = lstAxis$xMin, xright = lstAxis$xMax, 
+  ybottom = lstAxis$yMin, ytop = lstAxis$yMax
+)
+
+# draw converted image
+lstAxis$mainTitle <- "ねこ先生（２値化処理後）"  # 図のメインタイトル（図の上）
+plot( c(), type='n',
+      main = lstAxis$mainTitle,
+      xlim=lstAxis$xlim, ylim=lstAxis$ylim,
+      xlab=lstAxis$xlab, ylab=lstAxis$ylab
+)
+rasterImage(
+  image = imgNekosensei1, # 変換後のデータ
+  xleft = lstAxis$xMin, xright = lstAxis$xMax, 
+  ybottom = lstAxis$yMin, ytop = lstAxis$yMax
+)
+
+lstAxis$mainTitle <- "ねこ先生（２値化処理後＜反転＞）"  # 図のメインタイトル（図の上）
+plot( c(), type='n',
+      main = lstAxis$mainTitle,
+      xlim=lstAxis$xlim, ylim=lstAxis$ylim,
+      xlab=lstAxis$xlab, ylab=lstAxis$ylab
+)
+rasterImage(
+  image = imgNekosensei2, # 変換後のデータ
+  xleft = lstAxis$xMin, xright = lstAxis$xMax, 
+  ybottom = lstAxis$yMin, ytop = lstAxis$yMax
+)