1. AnimatedRotor
  2. Resolution 200.0
    1. 1.0
    2. 3.0
    3. 5.0
    4. 4.0
    5. 2.0
  3. Resolution 453.0
    1. 1.0
    2. 2.0
    3. 3.0
    4. 4.0
    5. 5.0
    6. 4.5
    7. 3.5
    8. 2.5
    9. 1.5
  4. Resolution 1024.0
    1. 1.0
    2. 1.5
    3. 2.0
    4. 2.5
    5. 3.0
    6. 3.5
    7. 4.0
    8. 4.5
    9. 5.0
    10. 4.75
    11. 4.25
    12. 3.75
    13. 3.25
    14. 2.75
    15. 2.25
    16. 1.75
    17. 1.25

AnimatedRotor

Paints a series of images, each to match the content of one while in the style of another using:
  1. Random noise initialization
  2. Standard VGG19 layers
  3. Operators to match content and constrain and enhance style
  4. Progressive resolution increase
  5. Rotational symmerty constraint caused by a kaliedoscopic image layer
  6. A content seed image to guide the aspect ratio
The parameters for each frame are fixed, but due to the random initialization and loose constraints we can achive a dynamic effect.

Color Permutations:

  1. Order: 1
    • 1,2,3
  2. Order: 2
    • -1,-3,-2
    • -3,-2,-1
    • -2,-1,-3
    • -1,-2,-3
    • 1,-2,-3
    • 1,-3,-2
    • -1,2,-3
    • -3,2,-1
    • 1,2,-3
    • 2,1,-3
    • -2,-1,3
    • -1,-2,3
    • 3,-2,1
    • 1,-2,3
    • -1,2,3
    • -1,3,2
    • 3,2,1
    • 1,3,2
    • 2,1,3
  3. Order: 3
    • -3,1,-2
    • -2,-3,1
    • -2,3,-1
    • 2,3,1
  4. Order: 4
    • -3,-2,1
    • -2,1,-3
    • -1,-3,2
    • 1,-3,2
    • -3,2,1
    • -2,1,3
  5. Order: 6
    • -2,-3,-1
    • 2,-3,1
    • -3,1,2
    • -2,3,1

Tiling Aspect Ratios:

  1. Triangular or Hexagonal: 1.732 or 0.5774
  2. Square: 1.0

Subreport: com.simiacryptus.mindseye.art.examples.AnimatedRotorEC2%24_17b7d40a-b317-4a19-a25c-0e3da1fdbd84_log_com.simiacryptus.ref.lang.ReferenceCountingBase

Code from AnimatedRotor.scala:71 executed in 0.00 seconds (0.000 gc):

    () => {
      implicit val implicitLog = log
      // First, basic configuration so we publish to our s3 site
      log.setArchiveHome(URI.create(s"s3://$s3bucket/$className/${log.getId}/"))
      log.onComplete(() => upload(log): Unit)
      ImageArtUtil.loadImages(log, styleUrl, (maxResolution * Math.sqrt(magnification)).toInt).foreach(x => log.p(log.jpg(x, "Input Style")))
      log.p(log.jpg(ImageArtUtil.loadImage(log, contentUrl, maxResolution), "Input Content"))
  
      def frames = keyframes * 2 - 1
  
      val canvases = (1 to frames).map(_ => new RefAtomicReference[Tensor](null)).toList.toBuffer
      // Execute the main process while registered with the site index
      val registration = registerWithIndexGIF_Cyclic(canvases.map(_.get())
        .filter(_ != null)
        .map(t => {
          val kaleidoscope = getKaleidoscope(t.getDimensions)
          val result = kaleidoscope.eval(t)
          kaleidoscope.freeRef()
          val tensorList = result.getData
          result.freeRef()
          val transformed = tensorList.get(0)
          tensorList.freeRef()
          transformed
        }))
      try {
        animate(
          contentUrl = contentUrl,
          initUrl = initUrl,
          canvases = canvases,
          networks = (1 to frames).map(f => f.toDouble -> {
            new VisualStyleNetwork(
              styleLayers = List(
                // We select all the lower-level layers to achieve a good balance between speed and accuracy.
                VGG19.VGG19_1a,
                VGG19.VGG19_1b1,
                VGG19.VGG19_1b2,
                VGG19.VGG19_1c1,
                VGG19.VGG19_1c2,
                VGG19.VGG19_1c3,
                VGG19.VGG19_1c4,
                VGG19.VGG19_1d1,
                VGG19.VGG19_1d2,
                VGG19.VGG19_1d3,
                VGG19.VGG19_1d4
              ),
              styleModifiers = List(
                // These two operators are a good combination for a vivid yet accurate style
                new GramMatrixEnhancer(),
                new MomentMatcher()
              ),
              styleUrls = Option(styleUrl),
              magnification = magnification,
              // Our optimization network should be built with a kaliedoscope appended to the canvas
              viewLayer = dims => getKaleidoscope(dims.toArray)
            )
          }).toList.toBuffer,
          optimizer = new BasicOptimizer {
            override val trainingMinutes: Int = 60
            override val trainingIterations: Int = 10
            override val maxRate = 1e9
  
            // The canvas result should be processed by the kaliedoscope
            override def renderingNetwork(dims: Seq[Int]) = getKaleidoscope(dims.toArray)
  
            // By default, we use a trust region that constrains the canvas pixel values from 0-256.
            // This conflicts with using a kaliedoscope, whose output is bounded and input may be out of that range.
            override def trustRegion(layer: Layer): TrustRegion = null
          },
          resolutions = new GeometricSequence {
            override val min: Double = minResolution
            override val max: Double = maxResolution
            override val steps = AnimatedRotor.this.steps
          }.toStream.map(_.round.toDouble),
          renderingFn = (dims: Seq[Int]) => getKaleidoscope(dims.toArray))
        null
      } finally {
        registration.foreach(_.stop()(s3client, ec2client))
      }
    }

Returns:

    com.simiacryptus.mindseye.art.examples.AnimatedRotor$$Lambda$368/1495889844@3afbf53b

Input Style

Input Style

Input Content

Resolution 200.0

1.0

Subreport: Optimization

3.0

Subreport: Optimization

5.0

Subreport: Optimization

4.0

Subreport: Optimization

2.0

Subreport: Optimization

Resolution 453.0

1.0

Subreport: Optimization

2.0

Subreport: Optimization

3.0

Subreport: Optimization

4.0

Subreport: Optimization

5.0

Subreport: Optimization

4.5

Subreport: Optimization

3.5

Subreport: Optimization

2.5

Subreport: Optimization

1.5

Subreport: Optimization

Resolution 1024.0

1.0

Subreport: Optimization

1.5

Subreport: Optimization

2.0

Subreport: Optimization

2.5

Subreport: Optimization

3.0

Subreport: Optimization

3.5

Subreport: Optimization

4.0

Subreport: Optimization

4.5

Subreport: Optimization

5.0

Subreport: Optimization

4.75

Subreport: Optimization

4.25

Subreport: Optimization

3.75

Subreport: Optimization

3.25

Subreport: Optimization

2.75

Subreport: Optimization

2.25

Subreport: Optimization

1.75

Subreport: Optimization

1.25

Subreport: Optimization