596 lines
14 KiB
Go
596 lines
14 KiB
Go
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package imaging
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import (
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"image"
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"math"
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)
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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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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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scale = 1.0
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}
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ru := math.Ceil(scale * filter.Support)
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out := make([][]indexWeight, dstSize)
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tmp := make([]indexWeight, 0, dstSize*int(ru+2)*2)
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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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begin := int(math.Ceil(fu - ru))
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if begin < 0 {
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begin = 0
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}
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end := int(math.Floor(fu + ru))
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if end > srcSize-1 {
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end = srcSize - 1
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}
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var sum float64
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for u := begin; u <= end; u++ {
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w := filter.Kernel((float64(u) - fu) / scale)
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if w != 0 {
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sum += w
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tmp = append(tmp, 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 tmp {
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tmp[i].weight /= sum
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}
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}
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out[v] = tmp
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tmp = tmp[len(tmp):]
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}
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return out
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}
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// Resize resizes the image to the specified width and height using the specified resampling
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// filter and returns the transformed image. If one of width or height is 0, the image aspect
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// ratio is preserved.
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//
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// Example:
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//
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// dstImage := imaging.Resize(srcImage, 800, 600, imaging.Lanczos)
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//
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func Resize(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
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dstW, dstH := width, height
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if dstW < 0 || dstH < 0 {
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return &image.NRGBA{}
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}
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if dstW == 0 && dstH == 0 {
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return &image.NRGBA{}
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}
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srcW := img.Bounds().Dx()
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srcH := img.Bounds().Dy()
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if srcW <= 0 || srcH <= 0 {
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return &image.NRGBA{}
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}
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// If new width or height is 0 then preserve aspect ratio, minimum 1px.
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if dstW == 0 {
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tmpW := float64(dstH) * float64(srcW) / float64(srcH)
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dstW = int(math.Max(1.0, math.Floor(tmpW+0.5)))
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}
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if dstH == 0 {
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tmpH := float64(dstW) * float64(srcH) / float64(srcW)
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dstH = int(math.Max(1.0, math.Floor(tmpH+0.5)))
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}
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if filter.Support <= 0 {
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// Nearest-neighbor special case.
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return resizeNearest(img, dstW, dstH)
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}
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if srcW != dstW && srcH != dstH {
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return resizeVertical(resizeHorizontal(img, dstW, filter), dstH, filter)
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}
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if srcW != dstW {
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return resizeHorizontal(img, dstW, filter)
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}
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if srcH != dstH {
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return resizeVertical(img, dstH, filter)
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}
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return Clone(img)
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}
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func resizeHorizontal(img image.Image, width int, filter ResampleFilter) *image.NRGBA {
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src := newScanner(img)
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dst := image.NewNRGBA(image.Rect(0, 0, width, src.h))
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weights := precomputeWeights(width, src.w, filter)
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parallel(0, src.h, func(ys <-chan int) {
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scanLine := make([]uint8, src.w*4)
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for y := range ys {
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src.scan(0, y, src.w, y+1, scanLine)
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j0 := y * dst.Stride
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for x := range weights {
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var r, g, b, a float64
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for _, w := range weights[x] {
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i := w.index * 4
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s := scanLine[i : i+4 : i+4]
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aw := float64(s[3]) * w.weight
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r += float64(s[0]) * aw
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g += float64(s[1]) * aw
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b += float64(s[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 + x*4
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d := dst.Pix[j : j+4 : j+4]
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d[0] = clamp(r * aInv)
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d[1] = clamp(g * aInv)
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d[2] = clamp(b * aInv)
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d[3] = clamp(a)
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}
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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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func resizeVertical(img image.Image, height int, filter ResampleFilter) *image.NRGBA {
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src := newScanner(img)
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dst := image.NewNRGBA(image.Rect(0, 0, src.w, height))
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weights := precomputeWeights(height, src.h, filter)
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parallel(0, src.w, func(xs <-chan int) {
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scanLine := make([]uint8, src.h*4)
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for x := range xs {
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src.scan(x, 0, x+1, src.h, scanLine)
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for y := range weights {
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var r, g, b, a float64
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for _, w := range weights[y] {
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i := w.index * 4
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s := scanLine[i : i+4 : i+4]
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aw := float64(s[3]) * w.weight
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r += float64(s[0]) * aw
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g += float64(s[1]) * aw
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b += float64(s[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 := y*dst.Stride + x*4
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d := dst.Pix[j : j+4 : j+4]
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d[0] = clamp(r * aInv)
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d[1] = clamp(g * aInv)
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d[2] = clamp(b * aInv)
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d[3] = clamp(a)
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}
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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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// resizeNearest is a fast nearest-neighbor resize, no filtering.
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func resizeNearest(img image.Image, width, height int) *image.NRGBA {
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dst := image.NewNRGBA(image.Rect(0, 0, width, height))
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dx := float64(img.Bounds().Dx()) / float64(width)
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dy := float64(img.Bounds().Dy()) / float64(height)
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if dx > 1 && dy > 1 {
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src := newScanner(img)
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parallel(0, height, func(ys <-chan int) {
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for y := range ys {
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srcY := int((float64(y) + 0.5) * dy)
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dstOff := y * dst.Stride
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for x := 0; x < width; x++ {
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srcX := int((float64(x) + 0.5) * dx)
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src.scan(srcX, srcY, srcX+1, srcY+1, dst.Pix[dstOff:dstOff+4])
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dstOff += 4
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}
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}
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})
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} else {
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src := toNRGBA(img)
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parallel(0, height, func(ys <-chan int) {
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for y := range ys {
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srcY := int((float64(y) + 0.5) * dy)
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srcOff0 := srcY * src.Stride
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dstOff := y * dst.Stride
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for x := 0; x < width; x++ {
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srcX := int((float64(x) + 0.5) * dx)
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srcOff := srcOff0 + srcX*4
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copy(dst.Pix[dstOff:dstOff+4], src.Pix[srcOff:srcOff+4])
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dstOff += 4
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}
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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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// Fit scales down the image using the specified resample filter to fit the specified
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// maximum width and height and returns the transformed image.
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//
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// Example:
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//
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// dstImage := imaging.Fit(srcImage, 800, 600, imaging.Lanczos)
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//
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func Fit(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
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maxW, maxH := width, height
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if maxW <= 0 || maxH <= 0 {
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return &image.NRGBA{}
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}
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srcBounds := img.Bounds()
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srcW := srcBounds.Dx()
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srcH := srcBounds.Dy()
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if srcW <= 0 || srcH <= 0 {
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return &image.NRGBA{}
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}
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if srcW <= maxW && srcH <= maxH {
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return Clone(img)
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}
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srcAspectRatio := float64(srcW) / float64(srcH)
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maxAspectRatio := float64(maxW) / float64(maxH)
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var newW, newH int
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if srcAspectRatio > maxAspectRatio {
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newW = maxW
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newH = int(float64(newW) / srcAspectRatio)
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} else {
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newH = maxH
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newW = int(float64(newH) * srcAspectRatio)
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}
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return Resize(img, newW, newH, filter)
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}
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// Fill creates an image with the specified dimensions and fills it with the scaled source image.
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// To achieve the correct aspect ratio without stretching, the source image will be cropped.
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//
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// Example:
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//
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// dstImage := imaging.Fill(srcImage, 800, 600, imaging.Center, imaging.Lanczos)
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//
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func Fill(img image.Image, width, height int, anchor Anchor, filter ResampleFilter) *image.NRGBA {
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dstW, dstH := width, height
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if dstW <= 0 || dstH <= 0 {
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return &image.NRGBA{}
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}
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srcBounds := img.Bounds()
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srcW := srcBounds.Dx()
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srcH := srcBounds.Dy()
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if srcW <= 0 || srcH <= 0 {
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return &image.NRGBA{}
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}
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if srcW == dstW && srcH == dstH {
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return Clone(img)
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}
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if srcW >= 100 && srcH >= 100 {
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return cropAndResize(img, dstW, dstH, anchor, filter)
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}
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return resizeAndCrop(img, dstW, dstH, anchor, filter)
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}
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// cropAndResize crops the image to the smallest possible size that has the required aspect ratio using
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// the given anchor point, then scales it to the specified dimensions and returns the transformed image.
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//
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// This is generally faster than resizing first, but may result in inaccuracies when used on small source images.
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func cropAndResize(img image.Image, width, height int, anchor Anchor, filter ResampleFilter) *image.NRGBA {
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dstW, dstH := width, height
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srcBounds := img.Bounds()
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srcW := srcBounds.Dx()
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srcH := srcBounds.Dy()
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srcAspectRatio := float64(srcW) / float64(srcH)
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dstAspectRatio := float64(dstW) / float64(dstH)
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var tmp *image.NRGBA
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if srcAspectRatio < dstAspectRatio {
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cropH := float64(srcW) * float64(dstH) / float64(dstW)
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tmp = CropAnchor(img, srcW, int(math.Max(1, cropH)+0.5), anchor)
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} else {
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cropW := float64(srcH) * float64(dstW) / float64(dstH)
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tmp = CropAnchor(img, int(math.Max(1, cropW)+0.5), srcH, anchor)
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}
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return Resize(tmp, dstW, dstH, filter)
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}
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// resizeAndCrop resizes the image to the smallest possible size that will cover the specified dimensions,
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// crops the resized image to the specified dimensions using the given anchor point and returns
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// the transformed image.
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func resizeAndCrop(img image.Image, width, height int, anchor Anchor, filter ResampleFilter) *image.NRGBA {
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dstW, dstH := width, height
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srcBounds := img.Bounds()
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srcW := srcBounds.Dx()
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srcH := srcBounds.Dy()
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srcAspectRatio := float64(srcW) / float64(srcH)
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dstAspectRatio := float64(dstW) / float64(dstH)
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var tmp *image.NRGBA
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if srcAspectRatio < dstAspectRatio {
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tmp = Resize(img, dstW, 0, filter)
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} else {
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tmp = Resize(img, 0, dstH, filter)
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}
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return CropAnchor(tmp, dstW, dstH, anchor)
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}
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// Thumbnail scales the image up or down using the specified resample filter, crops it
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// to the specified width and hight and returns the transformed image.
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//
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// Example:
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//
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// dstImage := imaging.Thumbnail(srcImage, 100, 100, imaging.Lanczos)
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//
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func Thumbnail(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
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return Fill(img, width, height, Center, filter)
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}
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// ResampleFilter specifies a resampling filter to be used for image resizing.
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//
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// General filter recommendations:
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//
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// - Lanczos
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// A high-quality resampling filter for photographic images yielding sharp results.
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//
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// - CatmullRom
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// A sharp cubic filter that is faster than Lanczos filter while providing similar results.
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//
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// - MitchellNetravali
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// A cubic filter that produces smoother results with less ringing artifacts than CatmullRom.
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//
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// - Linear
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// Bilinear resampling filter, produces a smooth output. Faster than cubic filters.
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//
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// - Box
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// Simple and fast averaging filter appropriate for downscaling.
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// When upscaling it's similar to NearestNeighbor.
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//
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// - NearestNeighbor
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// Fastest resampling filter, no antialiasing.
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//
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type ResampleFilter struct {
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Support float64
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Kernel func(float64) float64
|
||
|
|
}
|
||
|
|
|
||
|
|
// NearestNeighbor is a nearest-neighbor filter (no anti-aliasing).
|
||
|
|
var NearestNeighbor ResampleFilter
|
||
|
|
|
||
|
|
// Box filter (averaging pixels).
|
||
|
|
var Box ResampleFilter
|
||
|
|
|
||
|
|
// Linear filter.
|
||
|
|
var Linear ResampleFilter
|
||
|
|
|
||
|
|
// Hermite cubic spline filter (BC-spline; B=0; C=0).
|
||
|
|
var Hermite ResampleFilter
|
||
|
|
|
||
|
|
// MitchellNetravali is Mitchell-Netravali cubic filter (BC-spline; B=1/3; C=1/3).
|
||
|
|
var MitchellNetravali ResampleFilter
|
||
|
|
|
||
|
|
// CatmullRom is a Catmull-Rom - sharp cubic filter (BC-spline; B=0; C=0.5).
|
||
|
|
var CatmullRom ResampleFilter
|
||
|
|
|
||
|
|
// BSpline is a smooth cubic filter (BC-spline; B=1; C=0).
|
||
|
|
var BSpline ResampleFilter
|
||
|
|
|
||
|
|
// Gaussian is a Gaussian blurring filter.
|
||
|
|
var Gaussian ResampleFilter
|
||
|
|
|
||
|
|
// Bartlett is a Bartlett-windowed sinc filter (3 lobes).
|
||
|
|
var Bartlett ResampleFilter
|
||
|
|
|
||
|
|
// Lanczos filter (3 lobes).
|
||
|
|
var Lanczos ResampleFilter
|
||
|
|
|
||
|
|
// Hann is a Hann-windowed sinc filter (3 lobes).
|
||
|
|
var Hann ResampleFilter
|
||
|
|
|
||
|
|
// Hamming is a Hamming-windowed sinc filter (3 lobes).
|
||
|
|
var Hamming ResampleFilter
|
||
|
|
|
||
|
|
// Blackman is a Blackman-windowed sinc filter (3 lobes).
|
||
|
|
var Blackman ResampleFilter
|
||
|
|
|
||
|
|
// Welch is a Welch-windowed sinc filter (parabolic window, 3 lobes).
|
||
|
|
var Welch ResampleFilter
|
||
|
|
|
||
|
|
// Cosine is a Cosine-windowed sinc filter (3 lobes).
|
||
|
|
var Cosine ResampleFilter
|
||
|
|
|
||
|
|
func bcspline(x, b, c float64) float64 {
|
||
|
|
var y float64
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 1.0 {
|
||
|
|
y = ((12-9*b-6*c)*x*x*x + (-18+12*b+6*c)*x*x + (6 - 2*b)) / 6
|
||
|
|
} else if x < 2.0 {
|
||
|
|
y = ((-b-6*c)*x*x*x + (6*b+30*c)*x*x + (-12*b-48*c)*x + (8*b + 24*c)) / 6
|
||
|
|
}
|
||
|
|
return y
|
||
|
|
}
|
||
|
|
|
||
|
|
func sinc(x float64) float64 {
|
||
|
|
if x == 0 {
|
||
|
|
return 1
|
||
|
|
}
|
||
|
|
return math.Sin(math.Pi*x) / (math.Pi * x)
|
||
|
|
}
|
||
|
|
|
||
|
|
func init() {
|
||
|
|
NearestNeighbor = ResampleFilter{
|
||
|
|
Support: 0.0, // special case - not applying the filter
|
||
|
|
}
|
||
|
|
|
||
|
|
Box = ResampleFilter{
|
||
|
|
Support: 0.5,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x <= 0.5 {
|
||
|
|
return 1.0
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
Linear = ResampleFilter{
|
||
|
|
Support: 1.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 1.0 {
|
||
|
|
return 1.0 - x
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
Hermite = ResampleFilter{
|
||
|
|
Support: 1.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 1.0 {
|
||
|
|
return bcspline(x, 0.0, 0.0)
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
MitchellNetravali = ResampleFilter{
|
||
|
|
Support: 2.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 2.0 {
|
||
|
|
return bcspline(x, 1.0/3.0, 1.0/3.0)
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
CatmullRom = ResampleFilter{
|
||
|
|
Support: 2.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 2.0 {
|
||
|
|
return bcspline(x, 0.0, 0.5)
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
BSpline = ResampleFilter{
|
||
|
|
Support: 2.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 2.0 {
|
||
|
|
return bcspline(x, 1.0, 0.0)
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
Gaussian = ResampleFilter{
|
||
|
|
Support: 2.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 2.0 {
|
||
|
|
return math.Exp(-2 * x * x)
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
Bartlett = ResampleFilter{
|
||
|
|
Support: 3.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 3.0 {
|
||
|
|
return sinc(x) * (3.0 - x) / 3.0
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
Lanczos = ResampleFilter{
|
||
|
|
Support: 3.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 3.0 {
|
||
|
|
return sinc(x) * sinc(x/3.0)
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
Hann = ResampleFilter{
|
||
|
|
Support: 3.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 3.0 {
|
||
|
|
return sinc(x) * (0.5 + 0.5*math.Cos(math.Pi*x/3.0))
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
Hamming = ResampleFilter{
|
||
|
|
Support: 3.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 3.0 {
|
||
|
|
return sinc(x) * (0.54 + 0.46*math.Cos(math.Pi*x/3.0))
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
Blackman = ResampleFilter{
|
||
|
|
Support: 3.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 3.0 {
|
||
|
|
return sinc(x) * (0.42 - 0.5*math.Cos(math.Pi*x/3.0+math.Pi) + 0.08*math.Cos(2.0*math.Pi*x/3.0))
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
Welch = ResampleFilter{
|
||
|
|
Support: 3.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 3.0 {
|
||
|
|
return sinc(x) * (1.0 - (x * x / 9.0))
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
Cosine = ResampleFilter{
|
||
|
|
Support: 3.0,
|
||
|
|
Kernel: func(x float64) float64 {
|
||
|
|
x = math.Abs(x)
|
||
|
|
if x < 3.0 {
|
||
|
|
return sinc(x) * math.Cos((math.Pi/2.0)*(x/3.0))
|
||
|
|
}
|
||
|
|
return 0
|
||
|
|
},
|
||
|
|
}
|
||
|
|
}
|