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// For audio recognition, we need a double-precision (16 bit)
// integral image. We simply turn the sample data into pixels and
// have ourselves a verybig*1 image.
srecord noeq AudioRecognizer {
  interface IAudioSample {
    double length();
    double sampleSum(int channel, double start, double end);
  }
  
  // the 16 bit per channel integral image
  class AudioSample implements IAudioSample {
    int channels;
    int length;
    // 5 bytes are needed to store a sum table entry
    int[] data;
    byte[] highBytes;
    
    double length() { ret length; }
    
    public double sampleSum(int channel, double start, double end) {
      int a = ifloor(start), b = ifloor(end);
      ret getEntry(b)-getEntry(a);
    }
    
    public getPixel(int channel, double start, double end) {
      ret doubleRatio(sampleSum, end-start);
    }
    
    // get an entry of the sum table
    long getEntry(int i) {
      ret ((long) highBytes[i]) & 0xFF) << 16 | uintToLong(data[i]);
    }

    *(IntegralImage img) {
      w = img.w;
      h = img.h;
      data = img.data;
    }
     
    *(short[] samples) {
      w = img.getWidth(); h = img.getHeight();
      if (longMul(w, h) > 8000000) fail("Image too big: " + w + "*" + h);
      
      data = new int[w*h*channels];
      int i = 0, j = 0;
      int[] sum = new[channels];
      for y to h: {
        for c to channels: sum[c] = 0;
        for x to w: {
          int rgb = pixels[j++] & 0xFFFFFF;
          for c to channels: {
            if (c == grayscale)
              data[i] = iround((sum[0]+sum[1]+sum[2])/3);
            else {
              data[i] = (sum[c] += rgb >> 16);
              rgb = (rgb << 8) & 0xFFFFFF;
            }
            if (y > 0)
              data[i] += data[i-w*channels];
            i++;
          }
        }
      }
    }
    
    public double integralValue(int x, int y, Channel channel) {
      /*if (channel == grayscale)
        ret doubleAvg(countIterator(3, c -> integralValue(x, y, c)));*/
        
      ret x < 0 || y < 0 ? 0
        : data[(min(y, h-1)*w+min(x, w-1))*channels+channel];
    }
  }

  IIntegralImage newClip(IIntegralImage fullImage, Rect r) {
    assertSame(fullImage, mainImage);
    ret getOrCreate(clipCache, r, () -> new Clip(fullImage, r));
  }

  IIntegralImage liveliestPointIn(IIntegralImage image) {
    ret applyUntilEqual_goOneBackOnNull(c -> c.liveliestSubshape(grayscale), image);
  }

  // level++ <=> a fourth the area
  double level(IIntegralImage image) {
    ret -log(image.relativeArea(), 4);
  }
  
  double descentProbability(IIntegralImage image, int channel) {
    // what depth we at
    double level = level(image);

    // descent limit reached?
    if (level >= maxDescentLevel+0.5) {
      if (verboseDecentLevelReached) printVars_str("Descent limit reached", +level, +image);
      ret 0;
    }

    // liveliness of area
    double liveliness = rebaseZeroTo(minLiveliness, image.liveliness(channel));

    // correct liveliness for child-ness (distance from root)
    double levelFactor = pow(1.0/childLivelinessFactor, level-1);
    double corrected = liveliness*levelFactor;

    if (verbose || verboseDescentProbabilities)
      printVars(level := formatDouble(level, 1),
        rawDescentProbability := formatDouble(corrected, 5), +image, +liveliness, +levelFactor);
    
    //ret scoreToProbability(corrected);
    ret corrected;
  }

  // featureSize = relative to smaller image dimension
  double actualFeatureSize() {
    ret featureSize*min(mainImage.w, mainImage.h);
  }

  Rect featureArea(IIntegralImage image) {
   ret rectAround(image.center(),
      iround(max(actualFeatureSize(), 1)));
  }

  // keeps 0 liveliness as 0 value (=the point is discarded)
  // Any other liveliness is proceeding to possibly make it
  // into the "list of interesting points"
  double leafValue(IIntegralImage image, int channel) {
    Pt center = image.center();
    int actualFeatureSize = iround(max(actualFeatureSize(), 1));
    Rect r = featureArea(image);
    double value = mainImage.clip(r).liveliness(channel);
    double scaled = value*finalLivelinessFactor;
    if (verbose || verboseValues) printVars(+scaled, +value, +image, pos := image.positionInImage(), +center, +actualFeatureSize, +r);
    ret scaled;
  }

  void clearCaches {
    clipCache.clear();
  }

  // this prevents a really small feature area being scanned
  // on a low descent level (which would mean we are basically
  // scanning a random area)
  void fixFeatureSize {
    featureSize = max(featureSize, pow(.5, maxDescentLevel-1));
  }
  
  void prepareImage {
    if (mainImage == null)
      // make integral image
      mainImage = new IntegralImage(inputImage);
    else
      // not sure why we are doing this one or whether we should do it
      mainImage = new IntegralImage(mainImage);
    //inputImage = null; // save space
    
    //print(liveliness := mainImage.liveliness(grayscale));
    if (verbose || verboseImageSize) print("Full image size: " + mainImage.w + "*" + mainImage.h);
  }
  
  run {
    prepareImage();

    time "Recognition" {
      liveliestPoints = new ProbabilisticList;
      scheduler = new ProbabilisticList;
      lookedAt = new Set;
      lowestExecutedProbability = 1;
      steps = 0;
      
      scheduler.add(WithProbability(mainImage));
  
      int channel = grayscale;
      while (nempty(scheduler) && steps++ < maxSteps) {
        WithProbability<IIntegralImage> clip = popFirst(scheduler);
        var cp = clip.probability();
        lowestExecutedProbability = min(lowestExecutedProbability, cp);
        if (!lookedAt.add(clip!))
          continue; // We were here before...
          
        if (verbose || verboseLookAt)
          print("LEVEL " + formatDouble(level(clip!), 1) + " (p=" 
            + cp + ") - "
            + clip);
  
        L<WithProbability<IIntegralImage>> subs1
          = mapToProbabilities(clip->descentShapes_cleaned(),
            shape -> descentProbability(shape, channel));
        var preferredSub = getVar(first(subs1));
        
        ProbabilisticList<IIntegralImage> subs = new ProbabilisticList<>(subs1);
  
        if (empty(subs)) {
          if (verbose) print("  Is leaf");
          // leaf (single point) - save with value based on
          // liveliness of surroundings on a certain level (=scale)
          if (!liveliestPoints.containsElement(clip!)) {
            if (verboseFound) print("Found point: " + clip);
            clip->discoveredInStep = steps;
            liveliestPoints.add(withProbability(leafValue(clip!, channel), clip!));
            if (l(liveliestPoints) >= maxPoints) break;
          }
        } else {
          if (verbose) print("  Has " + n2(subs, "sub") + ":");
          if (verbose) pnlIndent(subs);
          for (var sub : subs) {
            // always force at least one descent of every area we actually looked at
            //var p = descentProbability(sub!, channel);
            var p = sub.probability();
            if (p == 0) continue;
            if (sub! == preferredSub) p = drillDownProbability;
            if (verbose) print("  Descending at " + p + " to " + sub!);
            scheduler.at(p, sub!);
          }
        }
      }
    }
  }

  BufferedImage markedImage() {
    print("Have " + nPoints(liveliestPoints) + " after " + nSteps(steps) + " (areas looked at: " + n2(lookedAt) + ", cache size=" + n2(clipCache) + ")");
    print("p=" + lowestExecutedProbability);
    pnl(takeFirst(10, liveliestPoints));
    int n = l(liveliestPoints);
    liveliestPoints.truncateBelow(finalMinLiveliness);
    int m = l(liveliestPoints);
    if (m < n)
      print("Truncated to " + nPoints(m));
    L<Long> stepList = map(liveliestPoints, p -> p->discoveredInStep);
    print("Points found in steps: " + sorted(stepList));

    var markedImage = mainImage.render();
    int markSize = max(3, iround(actualFeatureSize()*markScale));
    forEach(liveliestPoints, p ->
      markPointInImageWithAlpha(
        markedImage,
        p->center(),
        Color.red,
        rebaseZeroTo(minMarkAlpha, p.probability()),
        markSize));
    ret markedImage;
  }
        
  void show {
    showImage(markedImage());
  }
  
  void setInputImage aka setImage(BufferedImage image) {
    inputImage = image;
    mainImage = null;
  }
  
  // one-stop shop method
  Set<Pt> interestingPoints aka points(BufferedImage image) {
    setInputImage(image);
    run();
    ret points();
  }
  
  // accessor after run()
  Set<Pt> interestingPoints aka points() {
    ret mapToSet(liveliestPoints, p -> p->center());
  }
}