## Tuesday, November 6, 2018

### Lindenmayer system: Implementation

I recently got interested in Lindenmayer systems or L-system for short and I also wrote some code.  These are generative
systems that generate strings, that in turn can be interpreted as turtle graphics. For example,
you can model koch curves and the famous dragon curve. This book describes how to generate plants which you can see below.

### Implementation

For example, given a initial string $+A-Bf$, we have terminal symbols $\{+,-,f\}$ and non terminals $\{A, B\}$ that can still be replaced by rules like $A \rightarrow AfA$, which reads: each A can be replaced in the string. Furthermore, the string $+A-Bf$ can be interpreted by a turtle as $+$: turning right, $-$: turning left, all capital letters mean moving forward drawing a line, and all lower case letters mean moving silently to the next position. We start by defining a symbol class.
case class Symbol(id: Char) {

def isSilent: Boolean = id.isLower

def isTerminal: Boolean = !id.isLetter || id.isLower

}


Next we define a Lindenmayer system as a start string and a set of rules. Given the start string and a depth limit, we can recursively replace all non terminal symbols with the associated rules. This enables us to generate string.
case class LSystem(start: List[Symbol], rules: Map[Symbol, List[Symbol]]) {

private def produce(symbol: Symbol): List[Symbol] =
if(symbol.isTerminal) List(symbol)
else rules(symbol)

@tailrec
private def produce(current: List[Symbol], result: List[Symbol]): List[Symbol] = current match {
case Nil          => result
case head :: tail => {
}
}

@tailrec
private def produce(current: List[Symbol], stepsLeft: Int): List[Symbol] =
if(stepsLeft == 0) current
else produce(produce(current, Nil), stepsLeft - 1)

def produce(depth: Int): List[Symbol] = produce(start, depth)

}

Now we are done with the definition of the grammar system. The only thing left is to define a turtle and an interpreter that converts strings from L-systems to a sequence of turtles. I define a turtle by it's posture $[x, y, \alpha]$ where $x,y$ is the turtle's position and the orientation is defined by the angle $\alpha$. For moving forward the next positions are defined as:
• $x + \delta * cos(\alpha)$
• $y + \delta * sin(\alpha)$
A rotation basically involves increasing the angle by some certain degree.
case class Turtle(x: Int, y: Int, angle: Double, sil: Boolean) {

def move(distance: Int): Turtle =
Turtle(
math.round(y + distance * math.sin(math.toRadians(angle))).toInt, angle, false)

def moveSil(distance: Int): Turtle =
Turtle(
math.round(y + distance * math.sin(math.toRadians(angle))).toInt, angle, true)

def turn(by: Double): Turtle = {
Turtle(x, y, (angle + by) % 360, false)
}

def silence: Turtle = Turtle(x, y, angle, true)

Then given a string, we can recursively build a sequence of positions by applying the action associated with each symbol.
class TurtleInterpreter(moveBy: Int, turnBy: Double) {

import TurtleInterpreter._

private def execute(cmd: List[Symbol], state: Turtle, stack: List[Turtle], drawable: List[Turtle]): List[Turtle] = cmd match {
case Nil => drawable
case RotateRight :: tail => execute(tail, state.turn(turnBy), stack, drawable)
case RotateLeft  :: tail => execute(tail, state.turn(-turnBy), stack, drawable)
case PushStack   :: tail => execute(tail, state, state :: stack, drawable)
case head        :: tail => {
val next = if (head.isSilent) state.moveSil(moveBy) else state.move(moveBy)
execute(tail, next, stack, next :: drawable)
}
}

def execute(cmd: List[Symbol], init: Turtle): List[Turtle] =
execute(cmd, init, List.empty[Turtle], List(init))

}

object TurtleInterpreter {

final val RotateRight = Symbol('+')
final val RotateLeft  = Symbol('-')
final val PushStack   = Symbol('[')
final val PopStack    = Symbol(']')

Given that we can define grammars and paint pictures as the ones shown above. I wrote some code that plots all the above images that can be found on github.

## Saturday, November 3, 2018

### Exploring Super Resolution With Neural Networks: Blade Runner Style

In Blade Runner there is a scene in which Deckard (the protagonist) inspects one of the replicant's (Loean's) photos. The scene features an insane zoom which this blog post estimates the final magnification at a factor of 667.9

While this is science fiction (obviously) there where some breakthroughs in super resolution recently. Basically we use machine learning models to upscale an image without gaining pixelated artefacts.
I used this code to upscale the same photo from the movie using the code that is around using the Lasagne neural network framework.

 The top image shows a reflection in a cabinet that is itself a reflection in the round mirror. The bottom image shows the first zoom in Deckard performs.

Below is the original image (top) and the result (bottom) I got when running the 4 times upscale neural net on the image from Blade Runner.

If you zoom into the mirror in the reconstructed image (open in new tab to see it in it's original resolution) and you use your imagination, I believe you can see a face in it. I do not believe
it is actually there but that it is a reconstruction artefact, but who knows ...