mirror of
https://github.com/danth/stylix
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Refactor palette generator ♻️
Simplified a lot of code which was unnecessarily generic. Now using monads to manage the state of the random number generator rather than passing it around by hand. Also made some performance improvements, then increased the population size so more combinations are tried in a similar length of time.
This commit is contained in:
Daniel Thwaites
parent
c354350b9a
commit
ba5565d698
6 changed files with 147 additions and 234 deletions
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@ -1,48 +1,27 @@
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{-# LANGUAGE MultiParamTypeClasses #-}
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module Ai.Evolutionary ( EvolutionConfig(..), Species(..), evolve ) where
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module Ai.Evolutionary ( Species(..), evolve ) where
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import Control.Applicative ( liftA2 )
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import Data.Bifunctor ( first, second )
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import Data.List ( mapAccumR, sortBy )
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import Data.Ord ( Down(Down, getDown), comparing )
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import System.Random ( RandomGen, mkStdGen, randomR )
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import Data.Ord ( Down(Down), comparing )
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import Data.Vector ( (!) )
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import qualified Data.Vector as V
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import Data.Vector.Algorithms.Intro ( selectBy )
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import System.Random ( randomRIO )
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import Text.Printf ( printf )
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{- |
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Find every possible combination of two values, with the first value
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coming from one list and the second value coming from a different list.
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-}
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cartesianProduct :: [a] -> [b] -> [(a, b)]
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cartesianProduct = liftA2 (,)
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numSurvivors :: Int
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numSurvivors = 500
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{- |
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Find every possible combination of two values, with both values coming
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from the same list. Values are allowed to be paired with themself.
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-}
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cartesianSquare :: [a] -> [(a, a)]
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cartesianSquare as = as `cartesianProduct` as
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numNewborns :: Int
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numNewborns = 50000 - numSurvivors
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-- | Pick a random element from a list using a random generator.
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randomFromList :: (RandomGen r) => r -> [a] -> (a, r)
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randomFromList generator list
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= let (index, generator') = randomR (0, length list - 1) generator
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in (list !! index, generator')
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mutationProbability :: Double
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mutationProbability = 0.75
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{- |
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Map over a list, passing a random generator into the mapped
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function each time it is called. A random generator is returned
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along with the new list.
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-}
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mapWithGen :: (r -> a -> (r, b)) -> (r, [a]) -> (r, [b])
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mapWithGen = uncurry . mapAccumR
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unfoldWithGen :: (r -> (r, a)) -> Int -> r -> (r, [a])
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unfoldWithGen _ 0 generator = (generator, [])
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unfoldWithGen f size generator =
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let (generator', as) = unfoldWithGen f (size - 1) generator
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(generator'', a) = f generator'
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in (generator'', a:as)
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randomFromVector :: V.Vector a -> IO a
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randomFromVector vector = do
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index <- randomRIO (0, V.length vector - 1)
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return $ vector ! index
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{- |
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A genotype is a value which is generated by the genetic algorithm.
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@ -51,141 +30,98 @@ The environment is used to specify the problem for which
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we are trying to find the optimal genotype.
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-}
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class Species environment genotype where
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-- | Generate a new genotype at random.
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generate :: (RandomGen r) => environment -> r -> (r, genotype)
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-- | Randomly generate a new genotype.
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generate :: environment -> IO genotype
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-- | Randomly mutate a single genotype.
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mutate :: environment -> genotype -> IO genotype
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-- | Randomly combine two genotypes.
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crossover :: (RandomGen r) => environment -> r -> genotype -> genotype -> (r, genotype)
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-- | Randomly mutate a genotype using the given environment.
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mutate :: (RandomGen r) => environment -> r -> genotype -> (r, genotype)
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crossover :: environment -> genotype -> genotype -> IO genotype
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-- | Score a genotype. Higher numbers are better.
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fitness :: environment -> genotype -> Double
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-- | Parameters for the genetic algorithm.
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data EvolutionConfig = EvolutionConfig
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{ -- | The number of genotypes processed on each pass.
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populationSize :: Int,
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-- | How many genotypes make it through to the next pass.
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survivors :: Int,
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-- | The chance of a genotype being randomly changed
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-- before crossover. Between 0 and 1.
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mutationProbability :: Double,
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-- | When the fitness score improves by less than this percentage,
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-- the algorithm will stop.
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changeThreshold :: Double
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}
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{- |
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Randomly mutate the given genotype, if the mutation probability
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from the 'EvolutionConfig' says yes.
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-}
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randomMutation :: (RandomGen r, Species e g)
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initialPopulation :: Species e g
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=> e -- ^ Environment
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-> EvolutionConfig
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-> r -- ^ Random generator
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-> g -- ^ Genotype to mutate
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-> (r, g)
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randomMutation environment config generator chromosome
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= let (r, generator') = randomR (0.0, 1.0) generator
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in if r <= mutationProbability config
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then mutate environment generator' chromosome
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else (generator', chromosome)
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-> IO (V.Vector g) -- ^ Population
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initialPopulation environment
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= V.replicateM numSurvivors (generate environment)
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{- |
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Select the fittest survivors from a population,
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to be moved to the next pass of the algorithm.
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-}
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naturalSelection :: (Species e g)
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-- | Expand a population by crossovers followed by mutations.
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evolvePopulation :: Species e g
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=> e -- ^ Environment
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-> EvolutionConfig
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-> [g] -- ^ Original population
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-> [(Double, g)] -- ^ Survivors with fitness scores
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naturalSelection environment config
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= take (survivors config)
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. map (first getDown)
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. sortBy (comparing fst)
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-- Avoid computing fitness multiple times during sorting
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-- Down reverses the sort order so that the best fitness comes first
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. map (\genotype -> (Down $ fitness environment genotype, genotype))
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-> V.Vector g -- ^ Survivors from previous generation
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-> IO (V.Vector g) -- ^ New population
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evolvePopulation environment population = do
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let randomCrossover = do
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a <- randomFromVector population
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b <- randomFromVector population
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crossover environment a b
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-- | Run one pass of the genetic algorithm over a given population.
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evolveGeneration :: (RandomGen r, Species e g)
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randomMutation chromosome = do
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r <- randomRIO (0.0, 1.0)
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if r <= mutationProbability
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then mutate environment chromosome
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else return chromosome
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newborns <- V.replicateM numNewborns randomCrossover
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let nonElites = V.tail population V.++ newborns
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nonElites' <- V.mapM randomMutation nonElites
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return $ V.head population `V.cons` nonElites'
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selectSurvivors :: Species e g
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=> e -- ^ Environment
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-> EvolutionConfig
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-> (r, [g]) -- ^ Random generator, population from previous generation
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-> (r, Double, [g]) -- ^ New random generator, maximum fitness, new population
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evolveGeneration environment config (generator, population)
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= (newGenerator, maximum fitnesses, newPopulation)
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where
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(fitnesses, newPopulation) = unzip newPopulationWithFitness
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-> V.Vector g -- ^ Original population
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-> (Double, V.Vector g) -- ^ Best fitness, survivors
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selectSurvivors environment population =
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let -- Fitness is stored to avoid calculating it for each comparison.
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calculateFitness g = (fitness environment g, g)
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getFitness = fst
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getGenotype = snd
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compareFitness = comparing $ Down . fst
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(newGenerator, newPopulationWithFitness) =
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second (naturalSelection environment config)
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$ mapWithGen (randomMutation environment config)
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$ unfoldWithGen randomCrossover (populationSize config) generator
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-- Moves k best genotypes to the front, but doesn't sort them further.
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selectBest k vector = selectBy compareFitness vector k
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randomCrossover gen = let (pair, gen') = randomFromList gen pairs
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in (uncurry $ crossover environment gen') pair
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selected = V.modify (selectBest 1)
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$ V.take numSurvivors
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$ V.modify (selectBest numSurvivors)
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$ V.map calculateFitness population
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pairs = cartesianSquare population
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in ( getFitness $ V.head selected
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, V.map getGenotype selected
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)
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evolveUntilThreshold :: (RandomGen r, Species e g)
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shouldContinue :: [Double] -- ^ Fitness history
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-> Bool
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shouldContinue (x:y:_) = x /= y
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shouldContinue _ = True
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evolutionLoop :: Species e g
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=> e -- ^ Environment
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-> EvolutionConfig
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-> [Double] -- ^ Fitnesses of previous generations
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-> (r, [g]) -- ^ Random generator, population from previous generation
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-> IO (r, [g]) -- ^ New random generator, final population
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evolveUntilThreshold environment config fitnesses (generator, population) =
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-> [Double] -- ^ Fitness history
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-> V.Vector g -- ^ Survivors from previous generation
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-> IO (V.Vector g) -- ^ Final population
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evolutionLoop environment history survivors =
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do
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let (generator', fitness, population') =
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evolveGeneration environment config (generator, population)
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population <- evolvePopulation environment survivors
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-- Begins at 0 on the first iteration
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generationNumber = length fitnesses
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let (bestFitness, survivors') = selectSurvivors environment population
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history' = bestFitness : history
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fitnesses' = fitness : fitnesses
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recentFitnesses = take 5 fitnesses'
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printf "Generation: %3i Fitness: %7.1f\n"
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(length history') (head history')
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{-
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On the first iteration there is only one recent fitness, so the
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improvement would be calculated as 0%. To prevent the algorithm
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stopping immediately, we fall back to 100% in this case.
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-}
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change =
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if generationNumber < 1
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then 1
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else 1 - (head recentFitnesses / last recentFitnesses);
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if shouldContinue history'
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then evolutionLoop environment history' survivors'
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else return survivors'
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printf "Generation: %3i Fitness: %7.1f Improvement: %5.1f%%\n"
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generationNumber fitness (change * 100)
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if change < changeThreshold config
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then return (generator', population')
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else evolveUntilThreshold environment config fitnesses' (generator', population')
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{- |
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Create the initial population, to be fed into the first
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pass of the genetic algorithm.
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-}
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initialGeneration :: (RandomGen r, Species e g)
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=> e -- ^ Environment
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-> EvolutionConfig
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-> r -- ^ Random generator
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-> (r, [g]) -- ^ New random generator, population
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initialGeneration environment config
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= unfoldWithGen (generate environment) (survivors config)
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-- | Run the full genetic algorithm.
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-- | Run the genetic algorithm.
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evolve :: Species e g
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=> e -- ^ Environment
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-> EvolutionConfig
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-> IO g -- ^ Optimal genotype
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evolve environment config = do
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(_, population) <-
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evolveUntilThreshold environment config []
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$ initialGeneration environment config
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$ mkStdGen 0 -- Fixed seed for determinism
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return $ head population
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evolve environment = do
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population <- initialPopulation environment
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survivors <- evolutionLoop environment [] population
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return $ V.head survivors
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@ -1,20 +1,18 @@
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module Data.Colour ( LAB(..), RGB(..), deltaE, lab2rgb, rgb2lab ) where
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-- | Lightness A-B
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data LAB a = LAB { lightness :: a
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, channelA :: a
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, channelB :: a
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data LAB = LAB { lightness :: Double
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, channelA :: Double
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, channelB :: Double
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}
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-- | Red, Green, Blue
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data RGB a = RGB { red :: a
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, green :: a
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, blue :: a
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data RGB = RGB { red :: Double
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, green :: Double
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, blue :: Double
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}
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-- Based on https://github.com/antimatter15/rgb-lab/blob/master/color.js
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deltaE :: (Floating a, Ord a) => LAB a -> LAB a -> a
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deltaE :: LAB -> LAB -> Double
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deltaE (LAB l1 a1 b1) (LAB l2 a2 b2) =
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let deltaL = l1 - l2
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deltaA = a1 - a2
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@ -32,7 +30,7 @@ deltaE (LAB l1 a1 b1) (LAB l2 a2 b2) =
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in if i < 0 then 0 else sqrt i
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-- | Convert a 'LAB' colour to a 'RGB' colour
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lab2rgb :: (Floating a, Ord a) => LAB a -> RGB a
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lab2rgb :: LAB -> RGB
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lab2rgb (LAB l a bx) =
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let y = (l + 16) / 116
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x = a / 500 + y
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@ -52,7 +50,7 @@ lab2rgb (LAB l a bx) =
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}
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-- | Convert a 'RGB' colour to a 'LAB' colour
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rgb2lab :: (Floating a, Ord a) => RGB a -> LAB a
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rgb2lab :: RGB -> LAB
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rgb2lab (RGB r g b) =
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let r' = r / 255
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g' = g / 255
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@ -1,21 +1,14 @@
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import Ai.Evolutionary ( EvolutionConfig(EvolutionConfig), evolve )
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import Codec.Picture ( DynamicImage, Image, PixelRGB8, convertRGB8, readImage )
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import Data.Colour ( LAB, RGB(RGB), lab2rgb )
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import Ai.Evolutionary ( evolve )
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import Codec.Picture ( DynamicImage, convertRGB8, readImage )
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import Data.Colour ( lab2rgb )
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import qualified Data.Vector as V
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import Stylix.Output ( makeOutputTable )
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import Stylix.Palette ( )
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import System.Environment ( getArgs )
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import System.Exit ( die )
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import System.Random ( setStdGen, mkStdGen )
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import Text.JSON ( encode )
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-- | Run the genetic algorithm to generate a palette from the given image.
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selectColours :: (Floating a, Real a)
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=> String -- ^ Scheme type: "either", "light" or "dark"
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-> Image PixelRGB8 -- ^ Source image
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-> IO (V.Vector (LAB a)) -- ^ Generated palette
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selectColours polarity image
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= evolve (polarity, image) (EvolutionConfig 1000 100 0.5 0.01)
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-- | Load an image file.
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loadImage :: String -- ^ Path to the file
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-> IO DynamicImage
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@ -25,8 +18,11 @@ mainProcess :: (String, String, String) -> IO ()
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mainProcess (polarity, input, output) = do
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putStrLn $ "Processing " ++ input
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-- Random numbers must be deterministic when running inside Nix.
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setStdGen $ mkStdGen 0
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image <- loadImage input
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palette <- selectColours polarity (convertRGB8 image)
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palette <- evolve (polarity, convertRGB8 image)
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let outputTable = makeOutputTable $ V.map lab2rgb palette
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writeFile output $ encode outputTable
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@ -6,23 +6,22 @@ import Data.Word ( Word8 )
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import Text.JSON ( JSObject, toJSObject )
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import Text.Printf ( printf )
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-- | Convert any 'RGB' colour to store integers between 0 and 255.
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toWord8 :: (RealFrac a) => RGB a -> RGB Word8
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toWord8 (RGB r g b) = RGB (truncate r) (truncate g) (truncate b)
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toHexNum :: Double -> Word8
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toHexNum = truncate
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{- |
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Convert a colour to a hexdecimal string.
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>>> toHex (RGB 255 255 255)
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"#ffffff"
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"ffffff"
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-}
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toHex :: RGB Word8 -> String
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toHex (RGB r g b) = printf "%02x%02x%02x" r g b
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toHex :: RGB -> String
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toHex (RGB r g b) = printf "%02x%02x%02x" (toHexNum r) (toHexNum g) (toHexNum b)
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-- | Convert a palette to the JSON format expected by Stylix's NixOS modules.
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makeOutputTable :: (RealFrac a) => V.Vector (RGB a) -> JSObject String
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makeOutputTable :: V.Vector RGB -> JSObject String
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makeOutputTable
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= toJSObject
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. V.toList
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. V.imap (\i c -> (printf "base%02X" i, c))
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. V.map (toHex . toWord8)
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. V.map toHex
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@ -4,12 +4,11 @@ module Stylix.Palette ( ) where
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import Ai.Evolutionary ( Species(..) )
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import Codec.Picture ( Image(imageWidth, imageHeight), PixelRGB8(PixelRGB8), pixelAt )
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import Data.Bifunctor ( second )
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import Data.Colour ( LAB(lightness), RGB(RGB), deltaE, rgb2lab )
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import Data.List ( delete )
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import Data.Vector ( (//) )
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import qualified Data.Vector as V
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import System.Random ( RandomGen, randomR )
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import System.Random ( randomRIO )
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-- | Extract the primary scale from a pallete.
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primary :: V.Vector a -> V.Vector a
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@ -27,39 +26,23 @@ taken enough colours for a new palette.
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alternatingZip :: V.Vector a -> V.Vector a -> V.Vector a
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alternatingZip = V.izipWith (\i a b -> if even i then a else b)
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-- | Select a random color from an image.
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randomFromImage :: (RandomGen r, Floating a, Num a, Ord a)
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=> r -- ^ Random generator
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-> Image PixelRGB8
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-> (LAB a, r) -- ^ Chosen color, new random generator
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randomFromImage generator image
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= let (x, generator') = randomR (0, imageWidth image - 1) generator
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(y, generator'') = randomR (0, imageHeight image - 1) generator'
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(PixelRGB8 r g b) = pixelAt image x y
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randomFromImage :: Image PixelRGB8 -> IO LAB
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randomFromImage image = do
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x <- randomRIO (0, imageWidth image - 1)
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y <- randomRIO (0, imageHeight image - 1)
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let (PixelRGB8 r g b) = pixelAt image x y
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color = RGB (fromIntegral r) (fromIntegral g) (fromIntegral b)
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in (rgb2lab color, generator'')
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return $ rgb2lab color
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instance (Floating a, Real a) => Species (String, (Image PixelRGB8)) (V.Vector (LAB a)) where
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{- |
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Palettes in the initial population are created by randomly
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sampling 16 colours from the source image.
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-}
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generate (_, image) = generateColour 16
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where generateColour 0 generator = (generator, V.empty)
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generateColour n generator
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= let (colour, generator') = randomFromImage generator image
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in second (V.cons colour) $ generateColour (n - 1) generator'
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instance Species (String, Image PixelRGB8) (V.Vector LAB) where
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generate (_, image) = V.replicateM 16 $ randomFromImage image
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crossover _ generator a b = (generator, alternatingZip a b)
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crossover _ a b = return $ alternatingZip a b
|
||||
|
||||
{- |
|
||||
Mutation is done by replacing a random slot in the palette with
|
||||
a new colour, which is randomly sampled from the source image.
|
||||
-}
|
||||
mutate (_, image) generator palette
|
||||
= let (index, generator') = randomR (0, 15) generator
|
||||
(colour, generator'') = randomFromImage generator' image
|
||||
in (generator'', palette // [(index, colour)])
|
||||
mutate (_, image) palette = do
|
||||
index <- randomRIO (0, 15)
|
||||
colour <- randomFromImage image
|
||||
return $ palette // [(index, colour)]
|
||||
|
||||
fitness (polarity, _) palette
|
||||
= realToFrac $ accentDifference - (primarySimilarity/10) - scheme
|
||||
|
|
@ -72,11 +55,11 @@ instance (Floating a, Real a) => Species (String, (Image PixelRGB8)) (V.Vector (
|
|||
|
||||
-- The accent colours should be as different as possible.
|
||||
accentDifference = minimum $ do
|
||||
index_x <- [0 .. (V.length $ accent palette) - 1]
|
||||
index_y <- delete index_x [0 .. (V.length $ accent palette) - 1]
|
||||
let x = (V.!) (accent palette) index_x
|
||||
let y = (V.!) (accent palette) index_y
|
||||
return $ (deltaE x y)
|
||||
index_x <- [0..7]
|
||||
index_y <- delete index_x [0..7]
|
||||
let x = accent palette V.! index_x
|
||||
y = accent palette V.! index_y
|
||||
return $ deltaE x y
|
||||
|
||||
-- Helpers for the function below.
|
||||
lightnesses = V.map lightness palette
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ let
|
|||
JuicyPixels
|
||||
json
|
||||
random
|
||||
vector-algorithms
|
||||
]);
|
||||
|
||||
# `nix build .#palette-generator.passthru.docs` and open in a web browser
|
||||
|
|
|
|||
Loading…
Reference in a new issue