!7

cmodule TestTextRecognizers > DynSingleFunctionWithPrintLog {

  replace Recognizer with IF1<BufferedImage, LS>.

  S scoreModule;

  long maxMBs = 256; // save dat memory

  transient L<Result> results = syncList();

  new Best<S> best;

  transient long dataSize;

  transient new L<TestImage> images;

  transient JProgressBar progressBar;

  // result for recognizer

  srecord noeq Result(

    S recognizerName, Recognizer recognizer,

    double score, Map<TestResult, Double> individualScores) {}

  // individual test result

  srecord noeq TestResult(

    TestImage test,

    TreeSet<S> linesFound

  ) {}

  srecord noeq TestImage(S caseName, BufferedImage img, TreeSet<S> expectedLines) {

    toString { ret caseName; }

  }

  bool spaceToSpare() { ret toMB(dataSize) < maxMBs; }

  void doIt {

    prepare();

    results.clear();

    dm_rcall clear(scoreModule);

    _testRecognizer('ocr_recognizeMultiLine, lambda1 ocr_recognizeMultiLine);

  }

  void prepare runInQAndWait {

    if (empty(images)) loadImages();

    if (!dm_moduleExists(scoreModule))

      setField(scoreModule := dm_showNewModule("#1024823/ScoreMatrix"));

    else

      dm_activateModule(scoreModule);

  }

  Result scoreRecognizer(S name, Recognizer seg) {

    Result result = new(name, seg, 0, new LinkedHashMap);

    try {

      new Scorer scorer;

      for (TestImage img : images) {

        LS out = seg.get(img.img);

        TreeSet<S> outSet = new(out);

        Set<S> found = setIntersection(outSet, img.expectedLines);

        //Set<S> extra = setMinusSet(outSet, img.expectedLines);

        double score = doubleRatio(l(found), l(img.expectedLines));

        print("Score: " + score);

        scorer.addZeroToOne(score);

        result.individualScores.put(new TestResult(img, outSet), score);

      }

      print(scorer);

      result.score = scorer.score();

    } catch print e {

      print("RECOGNIZER TOTAL FAIL");

    }

    ret result;

  }

  void loadImages {

    File dir = javaxDataDir("Screen shots for text recognition");

    L<File> in = asLinkedList(listFilesWithExtension(".expectedlines", dir));

    while (nempty(in)) {

      if (!spaceToSpare())

        break with print("Out of space (" + toM(dataSize) + " MB used), skipping " + nImages(in));

      File f = popFirst(in);

      LS lines = quotedOnly_unquote(tlft(loadTextFile(f)));

      if (empty(lines)) continue;

      File fImg = imageFileWithSameBaseName(f);

      if (fImg == null) continue;

      pcall {

        BufferedImage img = loadImage2(fImg);

        images.add(new TestImage(fileName(fImg), img, new TreeSet(lines)));

        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 _testRecognizer(S name, Recognizer rec) {

    prepare();

    Result r = scoreRecognizer(name, rec);

    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.recognizerName, r.score, r.individualScores));

  }

  // API

  void testRecognizer(S name, virtual Recognizer _rec) {

    _testRecognizer(name, img -> (LS) quickImport(callF(_rec, img)));

  }

}