446eea2f7b
Caching kernel weights makes using the LUT based approach obsolete. Now they seem to be even slower than their counterparts.
241 lines
5.5 KiB
Go
241 lines
5.5 KiB
Go
/*
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Copyright (c) 2012, Jan Schlicht <jan.schlicht@gmail.com>
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Permission to use, copy, modify, and/or distribute this software for any purpose
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with or without fee is hereby granted, provided that the above copyright notice
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and this permission notice appear in all copies.
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THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
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REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
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FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
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INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
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OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER
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TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
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THIS SOFTWARE.
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*/
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package resize
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import (
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"image"
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"image/color"
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"math"
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)
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// restrict an input float32 to the range of uint16 values
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func clampToUint16(x float32) (y uint16) {
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y = uint16(x)
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if x < 0 {
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y = 0
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} else if x > float32(0xfffe) {
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// "else if x > float32(0xffff)" will cause overflows!
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y = 0xffff
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}
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return
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}
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// describe a resampling filter
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type filterModel struct {
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// resampling is done by convolution with a (scaled) kernel
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kernel func(float32) float32
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// instead of blurring an image before downscaling to avoid aliasing,
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// the filter is scaled by a factor which leads to a similar effect
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factorInv float32
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// for optimized access to image points
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converter
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// temporary used by Interpolate
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tempRow []colorArray
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kernelWeight []float32
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weightSum float32
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}
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func (f *filterModel) SetKernelWeights(u float32) {
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uf := int(u) - len(f.tempRow)/2 + 1
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u -= float32(uf)
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f.weightSum = 0
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for j := range f.tempRow {
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f.kernelWeight[j] = f.kernel((u - float32(j)) * f.factorInv)
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f.weightSum += f.kernelWeight[j]
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}
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}
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func (f *filterModel) convolution1d(x float32) (c colorArray) {
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for j := range f.tempRow {
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for i := range c {
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c[i] += f.tempRow[j][i] * f.kernelWeight[j]
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}
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}
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// normalize values
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for i := range c {
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c[i] = c[i] / f.weightSum
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}
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return
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}
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func (f *filterModel) Interpolate(u float32, y int) color.RGBA64 {
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uf := int(u) - len(f.tempRow)/2 + 1
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u -= float32(uf)
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for i := range f.tempRow {
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f.at(uf+i, y, &f.tempRow[i])
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}
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c := f.convolution1d(u)
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return color.RGBA64{
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clampToUint16(c[0]),
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clampToUint16(c[1]),
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clampToUint16(c[2]),
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clampToUint16(c[3]),
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}
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}
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// createFilter tries to find an optimized converter for the given input image
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// and initializes all filterModel members to their defaults
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func createFilter(img image.Image, factor float32, size int, kernel func(float32) float32) (f Filter) {
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sizeX := size * (int(math.Ceil(float64(factor))))
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switch img.(type) {
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default:
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f = &filterModel{
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kernel, 1. / factor,
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&genericConverter{img},
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make([]colorArray, sizeX),
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make([]float32, sizeX),
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0,
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}
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case *image.RGBA:
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f = &filterModel{
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kernel, 1. / factor,
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&rgbaConverter{img.(*image.RGBA)},
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make([]colorArray, sizeX),
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make([]float32, sizeX),
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0,
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}
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case *image.RGBA64:
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f = &filterModel{
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kernel, 1. / factor,
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&rgba64Converter{img.(*image.RGBA64)},
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make([]colorArray, sizeX),
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make([]float32, sizeX),
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0,
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}
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case *image.Gray:
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f = &filterModel{
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kernel, 1. / factor,
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&grayConverter{img.(*image.Gray)},
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make([]colorArray, sizeX),
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make([]float32, sizeX),
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0,
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}
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case *image.Gray16:
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f = &filterModel{
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kernel, 1. / factor,
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&gray16Converter{img.(*image.Gray16)},
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make([]colorArray, sizeX),
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make([]float32, sizeX),
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0,
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}
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case *image.YCbCr:
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f = &filterModel{
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kernel, 1. / factor,
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&ycbcrConverter{img.(*image.YCbCr)},
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make([]colorArray, sizeX),
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make([]float32, sizeX),
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0,
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}
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}
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return
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}
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// Nearest-neighbor interpolation
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func NearestNeighbor(img image.Image, factor float32) Filter {
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return createFilter(img, factor, 2, func(x float32) (y float32) {
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if x >= -0.5 && x < 0.5 {
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y = 1
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} else {
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y = 0
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}
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return
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})
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}
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// Bilinear interpolation
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func Bilinear(img image.Image, factor float32) Filter {
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return createFilter(img, factor, 2, func(x float32) (y float32) {
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absX := float32(math.Abs(float64(x)))
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if absX <= 1 {
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y = 1 - absX
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} else {
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y = 0
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}
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return
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})
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}
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// Bicubic interpolation (with cubic hermite spline)
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func Bicubic(img image.Image, factor float32) Filter {
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return createFilter(img, factor, 4, splineKernel(0, 0.5))
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}
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// Mitchell-Netravali interpolation
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func MitchellNetravali(img image.Image, factor float32) Filter {
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return createFilter(img, factor, 4, splineKernel(1.0/3.0, 1.0/3.0))
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}
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func splineKernel(B, C float32) func(float32) float32 {
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factorA := 2.0 - 1.5*B - C
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factorB := -3.0 + 2.0*B + C
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factorC := 1.0 - 1.0/3.0*B
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factorD := -B/6.0 - C
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factorE := B + 5.0*C
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factorF := -2.0*B - 8.0*C
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factorG := 4.0/3.0*B + 4.0*C
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return func(x float32) (y float32) {
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absX := float32(math.Abs(float64(x)))
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if absX <= 1 {
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y = absX*absX*(factorA*absX+factorB) + factorC
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} else if absX <= 2 {
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y = absX*(absX*(absX*factorD+factorE)+factorF) + factorG
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} else {
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y = 0
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}
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return
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}
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}
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func lanczosKernel(a uint) func(float32) float32 {
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return func(x float32) (y float32) {
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if x > -float32(a) && x < float32(a) {
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y = float32(Sinc(float64(x))) * float32(Sinc(float64(x/float32(a))))
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} else {
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y = 0
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}
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return
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}
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}
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const lanczosTableSize = 300
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// Lanczos interpolation (a=2)
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func Lanczos2(img image.Image, factor float32) Filter {
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return createFilter(img, factor, 4, lanczosKernel(2))
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}
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// Lanczos interpolation (a=3)
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func Lanczos3(img image.Image, factor float32) Filter {
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return createFilter(img, factor, 6, lanczosKernel(3))
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}
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