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package main
import (
"image"
"image/color"
"math"
)
func mean(i []int) float64 {
sum := 0
for _, n := range i {
sum += n
}
return float64(sum) / float64(len(i))
}
// TODO: is there a prettier way of doing this than float64() all over the place?
func stddev(i []int) float64 {
m := mean(i)
var sum float64
for _, n := range i {
sum += (float64(n) - m) * (float64(n) - m)
}
variance := float64(sum) / float64(len(i) - 1)
return math.Sqrt(variance)
}
func meanstddev(i []int) (float64, float64) {
m := mean(i)
var sum float64
for _, n := range i {
sum += (float64(n) - m) * (float64(n) - m)
}
variance := float64(sum) / float64(len(i) - 1)
return m, math.Sqrt(variance)
}
// gets the pixel values surrounding a point in the image
func surrounding(img *image.Gray, x int, y int, size int) []int {
b := img.Bounds()
miny := y - size/2
if miny < b.Min.Y {
miny = b.Min.Y
}
minx := x - size/2
if minx < b.Min.X {
minx = b.Min.X
}
maxy := y + size/2
if maxy > b.Max.Y {
maxy = b.Max.Y
}
maxx := x + size/2
if maxx > b.Max.X {
maxx = b.Max.X
}
var s []int
for yi := miny; yi < maxy; yi++ {
for xi := minx; xi < maxx; xi++ {
s = append(s, int(img.GrayAt(xi, yi).Y))
}
}
return s
}
// TODO: parallelize
// TODO: switch to using integral images to make faster; see paper
// "Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral Images"
func Sauvola(img *image.Gray, ksize float64, windowsize int) *image.Gray {
b := img.Bounds()
new := image.NewGray(b)
for y := b.Min.Y; y < b.Max.Y; y++ {
for x := b.Min.X; x < b.Max.X; x++ {
window := surrounding(img, x, y, windowsize)
m, dev := meanstddev(window)
threshold := m * (1 + ksize * ((dev / 128) - 1))
if img.GrayAt(x, y).Y < uint8(threshold) {
new.SetGray(x, y, color.Gray{0})
} else {
new.SetGray(x, y, color.Gray{255})
}
}
}
return new
}
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