neuquant

README

NeuQuant

Neural network based color quantizer.

Almost one-to-one port of this rust implementation.

Install

$ go get github.com/dveselov/NeuQuant

Usage

// Open & read image file
file, _ := os.Open("image.png")
reader := bufio.NewReader(file)
img, _ := png.Decode(reader)
bounds := img.Bounds()

// Quantize image to 256 colors and create color palette
q := NewNeuquant(10, 256, img)
palette := q.GetPalette()

// Create new paketted image
dist := image.NewPaletted(bounds, palette)

// Iterate over all pixels in source image
// and set appropriate color in output image
for x := 0; x < bounds.Max.X; x++ {
    for y := 0; y < bounds.Max.Y; y++ {
        r, g, b, a := img.At(x, y).RGBA()
        index := q.indexSearch(b, g, r, a)
        c := q.Colormap[index]
        dist.Set(x, y, color.RGBA{
            R: uint8(c.r >> 8),
            G: uint8(c.g >> 8),
            B: uint8(c.b >> 8),
            A: uint8(c.a >> 8),
        })
    }
}

out, err := os.Create("output.png")
if err != nil {
    panic(err)
}
writer := bufio.NewWriter(out)
err = png.Encode(writer, dist)
if err != nil {
    panic(err)
}
writer.Flush()

Examples

Source image	Result image	Source filesize	Result filesize
		50 KB	11 KB
		148 KB	56 KB

Documentation

Index

• Constants
• type Color
• type NeuQuant
  • func NewNeuquant(samplefrac, colors int, img image.Image) NeuQuant
  • func (n *NeuQuant) GetPalette() []color.Color
  • func (n *NeuQuant) Init(img image.Image)
  • func (n *NeuQuant) Learn(img image.Image)
• type Neuron

Constants

const (
	RADIUS_DEC      = 30                   // factor of 1/30 each cycle
	ALPHA_BIASSHIFT = 10                   // alpha starts at 1
	INIT_ALPHA      = 1 << ALPHA_BIASSHIFT // biased by 10 bits
	GAMMA           = 1024.0
	BETA            = 1.0 / GAMMA
	BETAGAMMA       = BETA * GAMMA
	NCYCLES         = 600
)

Types

type Color

type Color struct {
	// contains filtered or unexported fields
}

type NeuQuant

type NeuQuant struct {
	Network    []*Neuron
	Colormap   []*Color
	Netindex   []int
	Bias       []float64
	Freq       []float64
	Samplefrac int
	Netsize    int
}

func NewNeuquant

func NewNeuquant(samplefrac, colors int, img image.Image) NeuQuant

func (*NeuQuant) GetPalette

func (n *NeuQuant) GetPalette() []color.Color

func (*NeuQuant) Init

func (n *NeuQuant) Init(img image.Image)

Initializes the neuronal network and trains it with the supplied data

func (*NeuQuant) Learn

func (n *NeuQuant) Learn(img image.Image)

Main learning loop Note: the number of learning cycles is crucial and the parameters are not optimized for net sizes < 26 or > 256. 1064 colors seems to work fine

type Neuron

type Neuron struct {
	// contains filtered or unexported fields
}