abstract sclass ChessOCR_DynTestBoardFinders > DynSingleFunctionWithPrintLog {
  replace Segmenter with IF1<BufferedImage, L<Rect>>.
  
  S scoreModule;
  long maxMBs = 256; // save dat memory
  transient L<Result> results = syncList();
  new Best<S> best;
  
  int triesAllowed = 10; // how many rects each segmenter may produce
  
  transient long dataSize;
  transient new L<TestImage> images;
  transient JProgressBar progressBar;
  
  // result for segmenter
  srecord noeq Result(
    S segmenterName, Segmenter segmenter,
    double score, Map<TestResult, Double> individualScores) {}
    
  // individual test result
  srecord noeq TestResult(
    TestImage test,
    L<Rect> rectsFound
  ) {}
  
  srecord noeq TestImage(S caseName, BufferedImage img, Rect boardLocation) {
    toString { ret caseName; }
  }
  
  bool spaceToSpare() { ret toMB(dataSize) < maxMBs; }

  void doIt {
    prepare();
    results.clear();
    dm_call(scoreModule, 'clear);

    Cl<S> presets = agiBlue_chessBoardSegmenterPresetNames();
    for ping (S preset : iterateUpdatingProgressBar(progressBar, presets))
      _testSegmenter(preset, parameterizedSegmenterFromAGIBlue(preset));
  }
  
  void prepare runInQAndWait {
    if (empty(images)) loadImages();
    
    if (!dm_moduleExists(scoreModule))
      setField(scoreModule := dm_showNewModule("#1024787/ScoreMatrix"));
    else
      dm_activateModule(scoreModule);
  }

  Result scoreSegmenter(S name, Segmenter seg) {
    Result result = new(name, seg, 0, new LinkedHashMap);
    
    try {
      new Scorer scorer;
      for (TestImage img : images) {
        L<Rect> out = seg.get(img.img);
        double tolerance = imageDiameter(img.img)/48;
        double score2 = 0;
        for (Rect r : takeFirst(triesAllowed, out))
          score2 = max(score2, video_scoreRect_score(img.boardLocation, r, tolerance));
        print("Score: " + score2);
        scorer.addZeroToOne(score2);
        result.individualScores.put(new TestResult(img, out), score2);
      }
    
      print(scorer);
      result.score = scorer.score();
    } catch print e {
      print("SEGMENTER TOTAL FAIL");
    }
    
    ret result;
  }
  
  void loadImages {
    LPair<S, Rect> in = asLinkedList(agiBlue_chess_trainingImagesWithBoardLocations());
    while (nempty(in)) {
      if (!spaceToSpare())
        break with print("Out of space (" + toM(dataSize) + " MB used), skipping " + nImages(in));
      S name, Rect location = unpair popFirst(in);
      pcall {
        BufferedImage img = loadImage2(parseImageSnippetURL(agiBlue_lookupInSlice(agiBlue_chessBoardRecognitionTrainingImagesSliceID(), name, "input image")));
        images.add(new TestImage(name, img, location));
        dataSize += bufferedImageDataSize(img);
        print("Have " + nImages(images) + ", data size: " + toM(dataSize) + " MB");
      }
    }
    print("Loading done");
  }
  
  start { setFunctionName("Run tests"); }
  
  visual centerAndSouthWithMargin(super, withMargin(progressBar = jProgressBarWithText()));
  
  void _testSegmenter(S name, Segmenter seg) {
    prepare();
    Result r = scoreSegmenter(name, seg);
    print("Score for " + name + ": " + r.score);
    results.add(r);
    if (best.put(name, r.score))
      print("NEW BEST!");
    change();
    dm_call(scoreModule, 'add, dm_call(scoreModule, 'newEntry, r.segmenterName, r.score, r.individualScores));
  }
  
  // API
  
  void testSegmenter(S name, virtual Segmenter _seg) {
    Segmenter seg = img -> (L<Rect>) quickImport(callF(_seg, img));
    _testSegmenter(name, seg);
  }
}