Remove LUT based filters.
Caching kernel weights makes using the LUT based approach obsolete. Now they seem to be even slower than their counterparts.
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@ -36,9 +36,7 @@ The provided interpolation functions are (from fast to slow execution time)
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- `Bilinear`: [Bilinear interpolation](http://en.wikipedia.org/wiki/Bilinear_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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- `Bicubic`: [Bicubic interpolation](http://en.wikipedia.org/wiki/Bicubic_interpolation)
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- `MitchellNetravali`: [Mitchell-Netravali interpolation](http://dl.acm.org/citation.cfm?id=378514)
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- `MitchellNetravali`: [Mitchell-Netravali interpolation](http://dl.acm.org/citation.cfm?id=378514)
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- `Lanczos2Lut`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=2 using a look-up table for fast computation
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- `Lanczos2`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=2
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- `Lanczos2`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=2
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- `Lanczos3Lut`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=3 using a look-up table for fast computation
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- `Lanczos3`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=3
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- `Lanczos3`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=3
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Which of these methods gives the best results depends on your use case.
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Which of these methods gives the best results depends on your use case.
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43
filters.go
43
filters.go
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@ -156,35 +156,6 @@ func createFilter(img image.Image, factor float32, size int, kernel func(float32
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return
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return
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}
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}
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// Return a filter kernel that performs nearly identically to the provided
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// kernel, but generates and uses a precomputed table rather than executing
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// the kernel for each evaluation. The table is generated with tableSize
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// values that cover the kernal domain from -maxX to +maxX. The input kernel
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// is assumed to be symmetrical around 0, so the table only includes values
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// from 0 to maxX.
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func tableKernel(kernel func(float32) float32, tableSize int,
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maxX float32) func(float32) float32 {
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// precompute an array of filter coefficients
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weights := make([]float32, tableSize+1)
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for i := range weights {
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weights[i] = kernel(maxX * float32(i) / float32(tableSize))
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}
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weights[tableSize] = 0.0
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return func(x float32) float32 {
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if x < 0.0 {
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x = -x
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}
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indf := x / maxX * float32(tableSize)
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ind := int(indf)
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if ind >= tableSize {
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return 0.0
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}
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return weights[ind] + (weights[ind+1]-weights[ind])*(indf-float32(ind))
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}
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}
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// Nearest-neighbor interpolation
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// Nearest-neighbor interpolation
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func NearestNeighbor(img image.Image, factor float32) Filter {
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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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return createFilter(img, factor, 2, func(x float32) (y float32) {
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@ -263,21 +234,7 @@ func Lanczos2(img image.Image, factor float32) Filter {
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return createFilter(img, factor, 4, lanczosKernel(2))
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return createFilter(img, factor, 4, lanczosKernel(2))
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}
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}
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// Lanczos interpolation (a=2) using a look-up table
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// to speed up computation
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func Lanczos2Lut(img image.Image, factor float32) Filter {
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return createFilter(img, factor, 4,
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tableKernel(lanczosKernel(2), lanczosTableSize, 2.0))
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}
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// Lanczos interpolation (a=3)
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// Lanczos interpolation (a=3)
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func Lanczos3(img image.Image, factor float32) Filter {
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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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return createFilter(img, factor, 6, lanczosKernel(3))
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}
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}
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// Lanczos interpolation (a=3) using a look-up table
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// to speed up computation
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func Lanczos3Lut(img image.Image, factor float32) Filter {
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return createFilter(img, factor, 6,
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tableKernel(lanczosKernel(3), lanczosTableSize, 3.0))
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}
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@ -61,14 +61,6 @@ func Benchmark_BigResizeLanczos3(b *testing.B) {
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m.At(0, 0)
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m.At(0, 0)
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}
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}
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func Benchmark_BigResizeLanczos3Lut(b *testing.B) {
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var m image.Image
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for i := 0; i < b.N; i++ {
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m = Resize(1000, 1000, img, Lanczos3Lut)
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}
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m.At(0, 0)
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}
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func Benchmark_Reduction(b *testing.B) {
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func Benchmark_Reduction(b *testing.B) {
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largeImg := image.NewRGBA(image.Rect(0, 0, 1000, 1000))
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largeImg := image.NewRGBA(image.Rect(0, 0, 1000, 1000))
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@ -107,18 +99,10 @@ func Benchmark_LargeJpegThumbMitchellNetravali(b *testing.B) {
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jpegThumb(b, MitchellNetravali)
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jpegThumb(b, MitchellNetravali)
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}
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}
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func Benchmark_LargeJpegThumbLanczos2Lut(b *testing.B) {
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jpegThumb(b, Lanczos2Lut)
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}
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func Benchmark_LargeJpegThumbLanczos2(b *testing.B) {
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func Benchmark_LargeJpegThumbLanczos2(b *testing.B) {
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jpegThumb(b, Lanczos2)
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jpegThumb(b, Lanczos2)
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}
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}
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func Benchmark_LargeJpegThumbLanczos3Lut(b *testing.B) {
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jpegThumb(b, Lanczos3Lut)
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}
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func Benchmark_LargeJpegThumbLanczos3(b *testing.B) {
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func Benchmark_LargeJpegThumbLanczos3(b *testing.B) {
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jpegThumb(b, Lanczos3)
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jpegThumb(b, Lanczos3)
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}
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}
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