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-rw-r--r--binarize/binarize.go8
-rw-r--r--binarize/sauvola.go91
2 files changed, 96 insertions, 3 deletions
diff --git a/binarize/binarize.go b/binarize/binarize.go
index f95ee22..fa8a30f 100644
--- a/binarize/binarize.go
+++ b/binarize/binarize.go
@@ -7,14 +7,15 @@ import (
"os"
"github.com/Ernyoke/Imger/imgio"
- "github.com/Ernyoke/Imger/threshold"
)
func main() {
flag.Usage = func() {
- fmt.Fprintf(os.Stderr, "Usage: binarize inimg outimg\n")
+ fmt.Fprintf(os.Stderr, "Usage: binarize [-w num] [-k num] inimg outimg\n")
flag.PrintDefaults()
}
+ wsize := flag.Int("w", 31, "Window size for sauvola algorithm")
+ ksize := flag.Float64("k", 0.5, "K for sauvola algorithm")
flag.Parse()
if flag.NArg() < 2 {
flag.Usage()
@@ -26,7 +27,8 @@ func main() {
log.Fatalf("Could not read image %s\n", flag.Arg(0))
}
- thresh, err := threshold.OtsuThreshold(img, threshold.ThreshBinary)
+ // TODO: should be able to estimate an appropriate window size based on resolution
+ thresh := Sauvola(img, *ksize, *wsize)
if err != nil {
log.Fatal("Error binarising image\n")
}
diff --git a/binarize/sauvola.go b/binarize/sauvola.go
new file mode 100644
index 0000000..f1d0512
--- /dev/null
+++ b/binarize/sauvola.go
@@ -0,0 +1,91 @@
+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
+}