dd1c157427
Filter kernels should yield Zero if they are evaluted outside their intended size. Though filterModel.Interpolate doesn't do this by design, it's better to include it anyways.
215 lines
5.2 KiB
Go
215 lines
5.2 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
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// 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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type filterModel struct {
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converter
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factor [2]float32
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kernel func(float32) float32
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tempRow, tempCol []colorArray
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}
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func (f *filterModel) convolution1d(x float32, p []colorArray, isRow bool) colorArray {
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var k float32
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var sum float32 = 0
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c := colorArray{0.0, 0.0, 0.0, 0.0}
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var index uint
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if isRow {
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index = 0
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} else {
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index = 1
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}
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for j := range p {
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k = f.kernel((x - float32(j)) / f.factor[index])
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sum += k
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for i := range c {
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c[i] += p[j][i] * k
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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] / sum
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}
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return c
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}
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func (f *filterModel) Interpolate(x, y float32) color.RGBA64 {
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xf, yf := int(x)-len(f.tempRow)/2+1, int(y)-len(f.tempCol)/2+1
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x -= float32(xf)
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y -= float32(yf)
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for i := 0; i < len(f.tempCol); i++ {
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for j := 0; j < len(f.tempRow); j++ {
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f.tempRow[j] = f.at(xf+j, yf+i)
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}
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f.tempCol[i] = f.convolution1d(x, f.tempRow, true)
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}
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c := f.convolution1d(y, f.tempCol, false)
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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 [2]float32, size int, kernel func(float32) float32) (f Filter) {
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sizeX := size * (int(math.Ceil(float64(factor[0]))))
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sizeY := size * (int(math.Ceil(float64(factor[1]))))
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switch img.(type) {
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default:
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f = &filterModel{
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&genericConverter{img},
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factor, kernel,
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make([]colorArray, sizeX), make([]colorArray, sizeY),
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}
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case *image.RGBA:
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f = &filterModel{
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&rgbaConverter{img.(*image.RGBA)},
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factor, kernel,
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make([]colorArray, sizeX), make([]colorArray, sizeY),
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}
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case *image.RGBA64:
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f = &filterModel{
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&rgba64Converter{img.(*image.RGBA64)},
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factor, kernel,
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make([]colorArray, sizeX), make([]colorArray, sizeY),
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}
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case *image.Gray:
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f = &filterModel{
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&grayConverter{img.(*image.Gray)},
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factor, kernel,
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make([]colorArray, sizeX), make([]colorArray, sizeY),
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}
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case *image.Gray16:
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f = &filterModel{
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&gray16Converter{img.(*image.Gray16)},
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factor, kernel,
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make([]colorArray, sizeX), make([]colorArray, sizeY),
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}
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case *image.YCbCr:
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f = &filterModel{
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&ycbcrConverter{img.(*image.YCbCr)},
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factor, kernel,
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make([]colorArray, sizeX), make([]colorArray, sizeY),
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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 [2]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 [2]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 [2]float32) Filter {
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return createFilter(img, factor, 4, 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*(1.5*absX-2.5) + 1
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} else if absX <= 2 {
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y = absX*(absX*(2.5-0.5*absX)-4) + 2
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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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// Mitchell-Netravali interpolation
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func MitchellNetravali(img image.Image, factor [2]float32) Filter {
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return createFilter(img, factor, 4, 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*(7*absX-12) + 16.0/3
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} else if absX <= 2 {
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y = -(absX - 2) * (absX - 2) / 3 * (7*absX - 8)
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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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// Lanczos interpolation (a=2)
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func Lanczos2(img image.Image, factor [2]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 [2]float32) Filter {
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return createFilter(img, factor, 6, lanczosKernel(3))
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}
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