Upgrading server dependancies (#6215)
Этот коммит содержится в:
Christopher Speller
коммит произвёл
Joram Wilander
Joram Wilander
родитель
7f68a60f8c
Коммит
f5437632f4
132
vendor/github.com/disintegration/imaging/resize.go
сгенерированный
поставляемый
132
vendor/github.com/disintegration/imaging/resize.go
сгенерированный
поставляемый
@@ -5,17 +5,12 @@ import (
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"math"
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)
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type iwpair struct {
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i int
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w int32
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type indexWeight struct {
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index int
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weight float64
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}
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type pweights struct {
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iwpairs []iwpair
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wsum int32
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}
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func precomputeWeights(dstSize, srcSize int, filter ResampleFilter) []pweights {
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func precomputeWeights(dstSize, srcSize int, filter ResampleFilter) [][]indexWeight {
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du := float64(srcSize) / float64(dstSize)
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scale := du
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if scale < 1.0 {
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@@ -23,7 +18,7 @@ func precomputeWeights(dstSize, srcSize int, filter ResampleFilter) []pweights {
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}
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ru := math.Ceil(scale * filter.Support)
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out := make([]pweights, dstSize)
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out := make([][]indexWeight, dstSize)
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for v := 0; v < dstSize; v++ {
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fu := (float64(v)+0.5)*du - 0.5
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@@ -37,15 +32,19 @@ func precomputeWeights(dstSize, srcSize int, filter ResampleFilter) []pweights {
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endu = srcSize - 1
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}
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wsum := int32(0)
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var sum float64
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for u := startu; u <= endu; u++ {
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w := int32(0xff * filter.Kernel((float64(u)-fu)/scale))
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w := filter.Kernel((float64(u) - fu) / scale)
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if w != 0 {
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wsum += w
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out[v].iwpairs = append(out[v].iwpairs, iwpair{u, w})
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sum += w
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out[v] = append(out[v], indexWeight{index: u, weight: w})
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}
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}
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if sum != 0 {
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for i := range out[v] {
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out[v][i].weight /= sum
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}
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}
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out[v].wsum = wsum
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}
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return out
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@@ -127,22 +126,26 @@ func resizeHorizontal(src *image.NRGBA, width int, filter ResampleFilter) *image
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parallel(dstH, func(partStart, partEnd int) {
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for dstY := partStart; dstY < partEnd; dstY++ {
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i0 := dstY * src.Stride
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j0 := dstY * dst.Stride
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for dstX := 0; dstX < dstW; dstX++ {
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var c [4]int64
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for _, iw := range weights[dstX].iwpairs {
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i := dstY*src.Stride + iw.i*4
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a := int64(src.Pix[i+3]) * int64(iw.w)
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c[0] += int64(src.Pix[i+0]) * a
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c[1] += int64(src.Pix[i+1]) * a
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c[2] += int64(src.Pix[i+2]) * a
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c[3] += a
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var r, g, b, a float64
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for _, w := range weights[dstX] {
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i := i0 + w.index*4
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aw := float64(src.Pix[i+3]) * w.weight
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r += float64(src.Pix[i+0]) * aw
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g += float64(src.Pix[i+1]) * aw
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b += float64(src.Pix[i+2]) * aw
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a += aw
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}
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if a != 0 {
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aInv := 1 / a
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j := j0 + dstX*4
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dst.Pix[j+0] = clamp(r * aInv)
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dst.Pix[j+1] = clamp(g * aInv)
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dst.Pix[j+2] = clamp(b * aInv)
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dst.Pix[j+3] = clamp(a)
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}
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j := dstY*dst.Stride + dstX*4
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sum := weights[dstX].wsum
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dst.Pix[j+0] = clampint32(int32(float64(c[0])/float64(c[3]) + 0.5))
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dst.Pix[j+1] = clampint32(int32(float64(c[1])/float64(c[3]) + 0.5))
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dst.Pix[j+2] = clampint32(int32(float64(c[2])/float64(c[3]) + 0.5))
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dst.Pix[j+3] = clampint32(int32(float64(c[3])/float64(sum) + 0.5))
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}
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}
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})
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@@ -163,33 +166,33 @@ func resizeVertical(src *image.NRGBA, height int, filter ResampleFilter) *image.
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weights := precomputeWeights(dstH, srcH, filter)
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parallel(dstW, func(partStart, partEnd int) {
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for dstX := partStart; dstX < partEnd; dstX++ {
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for dstY := 0; dstY < dstH; dstY++ {
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var c [4]int64
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for _, iw := range weights[dstY].iwpairs {
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i := iw.i*src.Stride + dstX*4
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a := int64(src.Pix[i+3]) * int64(iw.w)
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c[0] += int64(src.Pix[i+0]) * a
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c[1] += int64(src.Pix[i+1]) * a
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c[2] += int64(src.Pix[i+2]) * a
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c[3] += a
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var r, g, b, a float64
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for _, w := range weights[dstY] {
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i := w.index*src.Stride + dstX*4
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aw := float64(src.Pix[i+3]) * w.weight
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r += float64(src.Pix[i+0]) * aw
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g += float64(src.Pix[i+1]) * aw
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b += float64(src.Pix[i+2]) * aw
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a += aw
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}
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if a != 0 {
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aInv := 1 / a
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j := dstY*dst.Stride + dstX*4
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dst.Pix[j+0] = clamp(r * aInv)
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dst.Pix[j+1] = clamp(g * aInv)
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dst.Pix[j+2] = clamp(b * aInv)
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dst.Pix[j+3] = clamp(a)
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}
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j := dstY*dst.Stride + dstX*4
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sum := weights[dstY].wsum
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dst.Pix[j+0] = clampint32(int32(float64(c[0])/float64(c[3]) + 0.5))
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dst.Pix[j+1] = clampint32(int32(float64(c[1])/float64(c[3]) + 0.5))
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dst.Pix[j+2] = clampint32(int32(float64(c[2])/float64(c[3]) + 0.5))
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dst.Pix[j+3] = clampint32(int32(float64(c[3])/float64(sum) + 0.5))
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}
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}
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})
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return dst
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}
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// fast nearest-neighbor resize, no filtering
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// resizeNearest is a fast nearest-neighbor resize, no filtering.
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func resizeNearest(src *image.NRGBA, width, height int) *image.NRGBA {
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dstW, dstH := width, height
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@@ -205,13 +208,16 @@ func resizeNearest(src *image.NRGBA, width, height int) *image.NRGBA {
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parallel(dstH, func(partStart, partEnd int) {
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for dstY := partStart; dstY < partEnd; dstY++ {
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fy := (float64(dstY)+0.5)*dy - 0.5
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srcY := int((float64(dstY) + 0.5) * dy)
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if srcY > srcH-1 {
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srcY = srcH - 1
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}
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for dstX := 0; dstX < dstW; dstX++ {
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fx := (float64(dstX)+0.5)*dx - 0.5
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srcX := int(math.Min(math.Max(math.Floor(fx+0.5), 0.0), float64(srcW)))
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srcY := int(math.Min(math.Max(math.Floor(fy+0.5), 0.0), float64(srcH)))
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srcX := int((float64(dstX) + 0.5) * dx)
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if srcX > srcW-1 {
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srcX = srcW - 1
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}
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srcOff := srcY*src.Stride + srcX*4
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dstOff := dstY*dst.Stride + dstX*4
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@@ -326,7 +332,7 @@ func Thumbnail(img image.Image, width, height int, filter ResampleFilter) *image
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return Fill(img, width, height, Center, filter)
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}
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// Resample filter struct. It can be used to make custom filters.
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// ResampleFilter is a resampling filter struct. It can be used to define custom filters.
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//
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// Supported resample filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali,
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// CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine.
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@@ -361,7 +367,7 @@ type ResampleFilter struct {
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Kernel func(float64) float64
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}
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// Nearest-neighbor filter, no anti-aliasing.
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// NearestNeighbor is a nearest-neighbor filter (no anti-aliasing).
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var NearestNeighbor ResampleFilter
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// Box filter (averaging pixels).
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@@ -373,37 +379,37 @@ var Linear ResampleFilter
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// Hermite cubic spline filter (BC-spline; B=0; C=0).
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var Hermite ResampleFilter
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// Mitchell-Netravali cubic filter (BC-spline; B=1/3; C=1/3).
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// MitchellNetravali is Mitchell-Netravali cubic filter (BC-spline; B=1/3; C=1/3).
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var MitchellNetravali ResampleFilter
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// Catmull-Rom - sharp cubic filter (BC-spline; B=0; C=0.5).
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// CatmullRom is a Catmull-Rom - sharp cubic filter (BC-spline; B=0; C=0.5).
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var CatmullRom ResampleFilter
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// Cubic B-spline - smooth cubic filter (BC-spline; B=1; C=0).
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// BSpline is a smooth cubic filter (BC-spline; B=1; C=0).
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var BSpline ResampleFilter
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// Gaussian Blurring Filter.
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// Gaussian is a Gaussian blurring Filter.
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var Gaussian ResampleFilter
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// Bartlett-windowed sinc filter (3 lobes).
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// Bartlett is a Bartlett-windowed sinc filter (3 lobes).
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var Bartlett ResampleFilter
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// Lanczos filter (3 lobes).
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var Lanczos ResampleFilter
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// Hann-windowed sinc filter (3 lobes).
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// Hann is a Hann-windowed sinc filter (3 lobes).
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var Hann ResampleFilter
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// Hamming-windowed sinc filter (3 lobes).
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// Hamming is a Hamming-windowed sinc filter (3 lobes).
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var Hamming ResampleFilter
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// Blackman-windowed sinc filter (3 lobes).
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// Blackman is a Blackman-windowed sinc filter (3 lobes).
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var Blackman ResampleFilter
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// Welch-windowed sinc filter (parabolic window, 3 lobes).
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// Welch is a Welch-windowed sinc filter (parabolic window, 3 lobes).
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var Welch ResampleFilter
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// Cosine-windowed sinc filter (3 lobes).
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// Cosine is a Cosine-windowed sinc filter (3 lobes).
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var Cosine ResampleFilter
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func bcspline(x, b, c float64) float64 {
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