Unify filters and their dependencies
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parent
3fc31c95cc
commit
e96bbe5547
72
filters.go
72
filters.go
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@ -41,17 +41,24 @@ func clampToUint16(x float32) (y uint16) {
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return
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}
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func convolution1d(x float32, kernel func(float32) float32, p []rgba16) (c rgba16) {
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type filterModel struct {
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src image.Image
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size int
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kernel func(float32) float32
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tempRow []rgba16
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tempCol []rgba16
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}
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func (f *filterModel) convolution1d(x float32, p []rgba16) (c rgba16) {
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x -= float32(int(x))
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m := float32(len(p)/2-1)
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m := float32(f.size/2 - 1)
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var k float32
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var sum float32 = 0
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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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k = kernel(x+m-float32(j))
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k = f.kernel(x + m - float32(j))
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sum += k
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for i := range c {
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l[i] += float32(p[j][i]) * k
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@ -63,51 +70,43 @@ func convolution1d(x float32, kernel func(float32) float32, p []rgba16) (c rgba1
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return
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}
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func filter(x, y float32, img image.Image, n int, kernel func(x float32) float32) color.RGBA64 {
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xf, yf := int(x)-n/2+1, int(y)-n/2+1
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func (f *filterModel) Interpolate(x, y float32) color.RGBA64 {
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xf, yf := int(x)-f.size/2+1, int(y)-f.size/2+1
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row := make([]rgba16, n)
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col := make([]rgba16, n)
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for i := 0; i < n; i++ {
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for j := 0; j < n; j++ {
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row[j] = toRGBA(img.At(xf+j, yf+i))
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for i := 0; i < f.size; i++ {
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for j := 0; j < f.size; j++ {
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f.tempRow[j] = toRGBA(f.src.At(xf+j, yf+i))
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}
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col[i] = convolution1d(x, kernel, row)
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f.tempCol[i] = f.convolution1d(x, f.tempRow)
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}
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c := convolution1d(y, kernel, col)
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c := f.convolution1d(y, f.tempCol)
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return color.RGBA64{c[0], c[1], c[2], c[3]}
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}
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// Nearest-neighbor interpolation.
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// Approximates a value by returning the value of the nearest point.
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func NearestNeighbor(x, y float32, img image.Image) color.RGBA64 {
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n := 2
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kernel := func(x float32) (y float32) {
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func NearestNeighbor(img image.Image) Filter {
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return &filterModel{img, 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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return filter(x, y, img, n, kernel)
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}, make([]rgba16, 2), make([]rgba16, 2)}
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}
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// Bicubic interpolation
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func Bilinear(x, y float32, img image.Image) color.RGBA64 {
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n := 2
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kernel := func(x float32) float32 {
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func Bilinear(img image.Image) Filter {
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return &filterModel{img, 2, func(x float32) float32 {
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return 1 - float32(math.Abs(float64(x)))
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}
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return filter(x, y, img, n, kernel)
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}, make([]rgba16, 2), make([]rgba16, 2)}
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}
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// Bicubic interpolation
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func Bicubic(x, y float32, img image.Image) color.RGBA64 {
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n := 4
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kernel := func(x float32) (y float32) {
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func Bicubic(img image.Image) Filter {
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return &filterModel{img, 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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@ -115,24 +114,19 @@ func Bicubic(x, y float32, img image.Image) color.RGBA64 {
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y = absX*(absX*(2.5-0.5*absX)-4) + 2
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}
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return
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}
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return filter(x, y, img, n, kernel)
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}, make([]rgba16, 4), make([]rgba16, 4)}
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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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n := 4
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kernel := func(x float32) float32 {
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func Lanczos2(img image.Image) Filter {
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return &filterModel{img, 4, func(x float32) float32 {
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return float32(Sinc(float64(x))) * float32(Sinc(float64((x)/float32(2))))
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}
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return filter(x, y, img, n, kernel)
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}, make([]rgba16, 4), make([]rgba16, 4)}
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}
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// Lanczos interpolation (a=3).
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func Lanczos3(x, y float32, img image.Image) color.RGBA64 {
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n := 6
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kernel := func(x float32) float32 {
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func Lanczos3(img image.Image) Filter {
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return &filterModel{img, 6, func(x float32) float32 {
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return float32(Sinc(float64(x))) * float32(Sinc(float64((x)/float32(3))))
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}
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return filter(x, y, img, n, kernel)
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}, make([]rgba16, 6), make([]rgba16, 6)}
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}
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52
resize.go
52
resize.go
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@ -40,30 +40,14 @@ func (t *Trans2) Eval(x, y float32) (u, v float32) {
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return
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}
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// Calculate scaling factors using old and new image dimensions.
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func calcFactors(width, height uint, oldWidth, oldHeight float32) (scaleX, scaleY float32) {
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if width == 0 {
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if height == 0 {
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scaleX = 1.0
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scaleY = 1.0
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} else {
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scaleY = oldHeight / float32(height)
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scaleX = scaleY
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}
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} else {
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scaleX = oldWidth / float32(width)
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if height == 0 {
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scaleY = scaleX
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} else {
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scaleY = oldHeight / float32(height)
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}
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}
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return
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// Filter can interpolate at points (x,y)
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type Filter interface {
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Interpolate(x, y float32) color.RGBA64
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}
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// InterpolationFunction return a color for an arbitrary point inside
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// an image
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type InterpolationFunction func(float32, float32, image.Image) color.RGBA64
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// InterpolationFunction return a Filter implementation
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// that operates on an image
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type InterpolationFunction func(image.Image) Filter
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// Resize an image to new width and height using the interpolation function interp.
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// A new image with the given dimensions will be returned.
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@ -85,11 +69,12 @@ func Resize(width, height uint, img image.Image, interp InterpolationFunction) i
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c := make(chan int, n)
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for i := 0; i < n; i++ {
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go func(b image.Rectangle, c chan int) {
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filter := interp(img)
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var u, v float32
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for y := b.Min.Y; y < b.Max.Y; y++ {
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for x := b.Min.X; x < b.Max.X; x++ {
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u, v = t.Eval(float32(x), float32(y))
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resizedImg.SetRGBA64(x, y, interp(u, v, img))
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resizedImg.SetRGBA64(x, y, filter.Interpolate(u, v))
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}
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}
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c <- 1
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@ -103,6 +88,27 @@ func Resize(width, height uint, img image.Image, interp InterpolationFunction) i
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return resizedImg
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}
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// Calculate scaling factors using old and new image dimensions.
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func calcFactors(width, height uint, oldWidth, oldHeight float32) (scaleX, scaleY float32) {
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if width == 0 {
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if height == 0 {
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scaleX = 1.0
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scaleY = 1.0
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} else {
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scaleY = oldHeight / float32(height)
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scaleX = scaleY
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}
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} else {
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scaleX = oldWidth / float32(width)
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if height == 0 {
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scaleY = scaleX
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} else {
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scaleY = oldHeight / float32(height)
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}
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}
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return
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}
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// Set number of parallel jobs
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// but prevent resize from doing too much work
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// if #CPUs > width
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