!7

cmodule LearnDogsVsCats > DynSingleFunctionWithPrintLog {
  /*switchable*/ transient File dir = userDir("dev/dogs-vs-cats/train");
  LPair<RGBImage, Rect> scaledImages;

  void doIt {
    // load scaled down test images
    
    L<File> allImageFiles = listImageFiles(dir);
    Map<File, LPair<Rect, S>> markingsMap = mapKeyAndFunction loadImageFileMarkings(allImageFiles);
    pnlStruct(markingsMap);
    if (empty(markingsMap)) ret with print("No marked images found in " + dir);
    
    new Map<File, BWIntegralImage> iImages;
    for (File f : keys(markingsMap))
      iImages.put(f, BWIntegralImage(loadImage2(f)));
    print("Have " + n2(iImages, "integral image") + ", total size: " + str_toM(deepObjectSize(iImages)));

    // make "random & vary" module
    
    RandomAndVary_Record<CloseToColorPredicate> rav = new(CloseToColorPredicate, 
      color := new RandomAndVary_RGBInt,
      maxDistance := new RandomAndVary_Double);
      
    // optimize
      
    CloseToColorPredicate e = pairA(racerOptimize(rav, func(CloseToColorPredicate e) -> double { scoreColorPredicateByRect(e, scaledImages) }, minRuntimeUnless100 := 10.0));
    
    print(e);
    showImage(renderColorPredicateOnTestImages(e, pairsA(scaledImages));
    
    // use centroid to detect rects
    
    for (Pair<RGBImage, Rect> p : scaledImages) {
      DoublePt pt = centroidForColorPredicate(e, p.a);
      if (pt == null) continue with print("No result");
      double dist = ptDistance(pt, centerOfRect(p.b));
      print(pt + " / " + centerOfRect(p.b));
      print("Error: " + percentRatio(dist, hypotenuse(p.a.getWidth(), p.a.getHeight())) + "%");
    }
  }
}