Use Kernel normalization for more accurate Lanczos resampling. Lanczos2 filter added
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@ -33,6 +33,7 @@ The provided interpolation functions are
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- `NearestNeighbor`: [Nearest-neighbor interpolation](http://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
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- `Bilinear`: [Bilinear interpolation](http://en.wikipedia.org/wiki/Bilinear_interpolation)
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- `Bicubic`: [Bicubic interpolation](http://en.wikipedia.org/wiki/Bicubic_interpolation)
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- `Lanczos2`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=2
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- `Lanczos3`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=3
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Sample usage:
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@ -43,6 +44,7 @@ package main
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import (
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"github.com/nfnt/resize"
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"image/jpeg"
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"log"
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"os"
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)
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@ -50,13 +52,13 @@ func main() {
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// open "test.jpg"
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file, err := os.Open("test.jpg")
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if err != nil {
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return
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log.Fatal(err)
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}
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// decode jpeg into image.Image
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img, err := jpeg.Decode(file)
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if err != nil {
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return
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log.Fatal(err)
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}
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file.Close()
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@ -66,7 +68,7 @@ func main() {
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out, err := os.Create("test_resized.jpg")
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if err != nil {
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return
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log.Fatal(err)
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}
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defer out.Close()
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58
filters.go
58
filters.go
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@ -97,19 +97,59 @@ func Bicubic(x, y float32, img image.Image) color.RGBA64 {
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return color.RGBA64{c[0], c[1], c[2], c[3]}
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}
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// 1-d convolution with windowed sinc for a=3.
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func lanczos_x(x float32, p *[6]RGBA) (c RGBA) {
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// 1-d convolution with windowed sinc for a=2.
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func lanczos2_x(x float32, p *[4]RGBA) (c RGBA) {
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x -= float32(math.Floor(float64(x)))
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var v float32
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var kernel float32
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var sum float32 = 0 // for kernel normalization
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l := [4]float32{0.0, 0.0, 0.0, 0.0}
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for j := range p {
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v = float32(Sinc(float64(x-float32(j-2)))) * float32(Sinc(float64((x-float32(j-2))/3.0)))
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kernel = float32(Sinc(float64(x-float32(j-1)))) * float32(Sinc(float64((x-float32(j-1))/2.0)))
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sum += kernel
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for i := range c {
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l[i] += float32(p[j][i]) * v
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l[i] += float32(p[j][i]) * kernel
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}
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}
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for i := range c {
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c[i] = clampToUint16(l[i])
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c[i] = clampToUint16(l[i] / sum)
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}
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return
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}
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// Lanczos interpolation (a=2).
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func Lanczos2(x, y float32, img image.Image) color.RGBA64 {
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xf, yf := int(math.Floor(float64(x))), int(math.Floor(float64(y)))
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var row [4]RGBA
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var col [4]RGBA
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for i := range row {
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row = [4]RGBA{toRGBA(img.At(xf-1, yf+i-1)), toRGBA(img.At(xf, yf+i-1)), toRGBA(img.At(xf+1, yf+i-1)), toRGBA(img.At(xf+2, yf+i-1))}
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col[i] = lanczos2_x(x, &row)
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}
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c := lanczos2_x(y, &col)
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return color.RGBA64{c[0], c[1], c[2], c[3]}
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}
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// 1-d convolution with windowed sinc for a=3.
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func lanczos3_x(x float32, p *[6]RGBA) (c RGBA) {
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x -= float32(math.Floor(float64(x)))
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var kernel float32
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var sum float32 = 0 // for kernel normalization
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l := [4]float32{0.0, 0.0, 0.0, 0.0}
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for j := range p {
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kernel = float32(Sinc(float64(x-float32(j-2)))) * float32(Sinc(float64((x-float32(j-2))/3.0)))
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sum += kernel
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for i := range c {
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l[i] += float32(p[j][i]) * kernel
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}
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}
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for i := range c {
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c[i] = clampToUint16(l[i] / sum)
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}
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return
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}
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@ -120,11 +160,11 @@ func Lanczos3(x, y float32, img image.Image) color.RGBA64 {
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var row [6]RGBA
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var col [6]RGBA
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for i := 0; i < 6; i++ {
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for i := range row {
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row = [6]RGBA{toRGBA(img.At(xf-2, yf+i-2)), toRGBA(img.At(xf-1, yf+i-2)), toRGBA(img.At(xf, yf+i-2)), toRGBA(img.At(xf+1, yf+i-2)), toRGBA(img.At(xf+2, yf+i-2)), toRGBA(img.At(xf+3, yf+i-2))}
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col[i] = lanczos_x(x, &row)
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col[i] = lanczos3_x(x, &row)
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}
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c := lanczos_x(y, &col)
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c := lanczos3_x(y, &col)
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return color.RGBA64{c[0], c[1], c[2], c[3]}
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}
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