sclass G22DataWrangler > Stages {
  settable BufferedImage inputImage;
  
  settable bool withDiagonals = true;
  settable int blurPixels = 1;
  
  // kilobytes per compressed image (pessimistic estimate
  // counting 2 bytes for each int)
  new TargetAndActual<Double> kilobytes;
  new TargetAndActual<Double> coveredPixelsPercentage;
  new TargetAndActual<Double> detailLevel;
  
  settable IPosterizer posterizer = new SinglePixelPosterizer(4);
  
  BufferedImage blurredImage;
  int maxLines;
  VectorOptimizedSSIList vectorizedSSIs, cutVectorizedSSIs;
  
  // We store the posterized image as Hi15
  Hi15Image posterizedImage;
  
  FastRegions_Hi15Image regionMaker;
  L<IImageRegion<Hi15Image>> regions;
  
  L<SSI> initialSSIs, sortedSSIs, cutSSIs;
  
  TargetAndActual<Double> kb() { ret kilobytes; }

  double detailDivisor() {
    ret areaRoot(inputImage);
  }
  
  // choose number of colors for posterized image
  selfType colors(int colors) {
    int perChannel = iceil(cbrt(colors));
    posterizer = new SinglePixelPosterizer(perChannel);
    this;
  }

  stage "Blur" {
    blurredImage = blurBufferedImage(blurPixels, inputImage);
  }
  
  stage "Posterize" {
    posterizedImage = new Hi15Image(posterizeBufferedImage(blurredImage, posterizer));
  }
  
  stage "Regions" {
    regionMaker = new FastRegions_Hi15Image(posterizedImage);
    regionMaker.withDiagonals(withDiagonals);
    regions = regionMaker!;
  }
  
  stage "SSIs" {
    initialSSIs = new L;
    for (region : regions)
      initialSSIs.addAll(new G22_RegionToSSIs_v2(region).withDiagonals (withDiagonals)!);
  }
  
  stage "Sort SSIs" {
    sortedSSIs = biggestSSIsFirst(initialSSIs);
  }
  
  int initialSSILines() {
    ret totalSSILines(sortedSSIs);
  }
  
  stage "Cut SSI List" {
    maxLines = !detailLevel.hasTarget() ? Int.MAX_VALUE
      : iround(detailDivisor()*detailLevel.target());
    cutSSIs = takeFirstNSSILines(maxLines, sortedSSIs);
    detailLevel.set(l(cutSSIs)/detailDivisor());
  }
  
  stage "Vector-Optimize" {
    vectorizedSSIs = new VectorOptimizedSSIList(initialSSIs);
  }
  
  stage "Cut Vector-Optimized SSIs by file size" {
    int maxInts = !kilobytes.hasTarget() ? Int.MAX_VALUE
      : iround(kilobytes.target()*512); // assuming 16 bit ints
    var cutter = new CutListToBudget<>(ssi -> (double) ssi.sizeInInts(), maxInts, vectorizedSSIs);
    cutVectorizedSSIs = VectorOptimizedSSIList.alreadyOptimized(cutter!);
    kilobytes.set(totalSizeInInts(cutVectorizedSSIs)/512.0);
  }
      
  /*
 
  // take biggest ones, cut off at selected detail level
  optimizedSSIs <- VectorOptimizedSSIList alreadyOptimized < biggestSSIsFirst optimizedSSIs
  //print "optimizedSSIs" < l optimizedSSIs
  optimizedSSIs <- VectorOptimizedSSIList alreadyOptimized < takeFirstNSSILines maxLines optimizedSSIs
  //print "optimizedSSIs" < l optimizedSSIs

  // get stats
  keptLines <- totalSSILines optimizedSSIs
  vectorSSIs <- optimizedSSIs vectorSSIs
  ssis <- optimizedSSIs directSSIs
  totalSSIs <- l ssis  
  directSSILines <- totalSSILines ssis
  vectorSSIPoints <- totalVectorSSIPoints vectorSSIs
  
  stats <- commaCombine list {
    resolution
    n2 totalLines "SSI line"
    n2_noPlural keptLines "kept"
    n2 optimizedSSIs "SSI"
    //n2 totalSSIs "Direct SSI"
    //n2 vectorSSIs "Vector SSI"
    n2 directSSILines "Direct SSI line"
    n2 vectorSSIPoints "Vector SSI point"
  }
  
  imageOut <- transparentImage resolution
  optimizedSSIs render imageOut

  infiller <- new ImageInfiller imageOut
  //infiller maxDepth 2
  infiller unlimited
  infiller infillOpacity 254
  //infiller eraseOriginalPixels
  imageOut <- infiller get
  
  imageOut <- renderImageOnBackground imageOut < Color black
  */
}