mirror of
https://github.com/cwinfo/matterbridge.git
synced 2024-11-30 01:31:36 +00:00
596 lines
14 KiB
Go
596 lines
14 KiB
Go
package imaging
|
|
|
|
import (
|
|
"image"
|
|
"math"
|
|
)
|
|
|
|
type indexWeight struct {
|
|
index int
|
|
weight float64
|
|
}
|
|
|
|
func precomputeWeights(dstSize, srcSize int, filter ResampleFilter) [][]indexWeight {
|
|
du := float64(srcSize) / float64(dstSize)
|
|
scale := du
|
|
if scale < 1.0 {
|
|
scale = 1.0
|
|
}
|
|
ru := math.Ceil(scale * filter.Support)
|
|
|
|
out := make([][]indexWeight, dstSize)
|
|
tmp := make([]indexWeight, 0, dstSize*int(ru+2)*2)
|
|
|
|
for v := 0; v < dstSize; v++ {
|
|
fu := (float64(v)+0.5)*du - 0.5
|
|
|
|
begin := int(math.Ceil(fu - ru))
|
|
if begin < 0 {
|
|
begin = 0
|
|
}
|
|
end := int(math.Floor(fu + ru))
|
|
if end > srcSize-1 {
|
|
end = srcSize - 1
|
|
}
|
|
|
|
var sum float64
|
|
for u := begin; u <= end; u++ {
|
|
w := filter.Kernel((float64(u) - fu) / scale)
|
|
if w != 0 {
|
|
sum += w
|
|
tmp = append(tmp, indexWeight{index: u, weight: w})
|
|
}
|
|
}
|
|
if sum != 0 {
|
|
for i := range tmp {
|
|
tmp[i].weight /= sum
|
|
}
|
|
}
|
|
|
|
out[v] = tmp
|
|
tmp = tmp[len(tmp):]
|
|
}
|
|
|
|
return out
|
|
}
|
|
|
|
// Resize resizes the image to the specified width and height using the specified resampling
|
|
// filter and returns the transformed image. If one of width or height is 0, the image aspect
|
|
// ratio is preserved.
|
|
//
|
|
// Example:
|
|
//
|
|
// dstImage := imaging.Resize(srcImage, 800, 600, imaging.Lanczos)
|
|
//
|
|
func Resize(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
|
|
dstW, dstH := width, height
|
|
if dstW < 0 || dstH < 0 {
|
|
return &image.NRGBA{}
|
|
}
|
|
if dstW == 0 && dstH == 0 {
|
|
return &image.NRGBA{}
|
|
}
|
|
|
|
srcW := img.Bounds().Dx()
|
|
srcH := img.Bounds().Dy()
|
|
if srcW <= 0 || srcH <= 0 {
|
|
return &image.NRGBA{}
|
|
}
|
|
|
|
// If new width or height is 0 then preserve aspect ratio, minimum 1px.
|
|
if dstW == 0 {
|
|
tmpW := float64(dstH) * float64(srcW) / float64(srcH)
|
|
dstW = int(math.Max(1.0, math.Floor(tmpW+0.5)))
|
|
}
|
|
if dstH == 0 {
|
|
tmpH := float64(dstW) * float64(srcH) / float64(srcW)
|
|
dstH = int(math.Max(1.0, math.Floor(tmpH+0.5)))
|
|
}
|
|
|
|
if filter.Support <= 0 {
|
|
// Nearest-neighbor special case.
|
|
return resizeNearest(img, dstW, dstH)
|
|
}
|
|
|
|
if srcW != dstW && srcH != dstH {
|
|
return resizeVertical(resizeHorizontal(img, dstW, filter), dstH, filter)
|
|
}
|
|
if srcW != dstW {
|
|
return resizeHorizontal(img, dstW, filter)
|
|
}
|
|
if srcH != dstH {
|
|
return resizeVertical(img, dstH, filter)
|
|
}
|
|
return Clone(img)
|
|
}
|
|
|
|
func resizeHorizontal(img image.Image, width int, filter ResampleFilter) *image.NRGBA {
|
|
src := newScanner(img)
|
|
dst := image.NewNRGBA(image.Rect(0, 0, width, src.h))
|
|
weights := precomputeWeights(width, src.w, filter)
|
|
parallel(0, src.h, func(ys <-chan int) {
|
|
scanLine := make([]uint8, src.w*4)
|
|
for y := range ys {
|
|
src.scan(0, y, src.w, y+1, scanLine)
|
|
j0 := y * dst.Stride
|
|
for x := range weights {
|
|
var r, g, b, a float64
|
|
for _, w := range weights[x] {
|
|
i := w.index * 4
|
|
s := scanLine[i : i+4 : i+4]
|
|
aw := float64(s[3]) * w.weight
|
|
r += float64(s[0]) * aw
|
|
g += float64(s[1]) * aw
|
|
b += float64(s[2]) * aw
|
|
a += aw
|
|
}
|
|
if a != 0 {
|
|
aInv := 1 / a
|
|
j := j0 + x*4
|
|
d := dst.Pix[j : j+4 : j+4]
|
|
d[0] = clamp(r * aInv)
|
|
d[1] = clamp(g * aInv)
|
|
d[2] = clamp(b * aInv)
|
|
d[3] = clamp(a)
|
|
}
|
|
}
|
|
}
|
|
})
|
|
return dst
|
|
}
|
|
|
|
func resizeVertical(img image.Image, height int, filter ResampleFilter) *image.NRGBA {
|
|
src := newScanner(img)
|
|
dst := image.NewNRGBA(image.Rect(0, 0, src.w, height))
|
|
weights := precomputeWeights(height, src.h, filter)
|
|
parallel(0, src.w, func(xs <-chan int) {
|
|
scanLine := make([]uint8, src.h*4)
|
|
for x := range xs {
|
|
src.scan(x, 0, x+1, src.h, scanLine)
|
|
for y := range weights {
|
|
var r, g, b, a float64
|
|
for _, w := range weights[y] {
|
|
i := w.index * 4
|
|
s := scanLine[i : i+4 : i+4]
|
|
aw := float64(s[3]) * w.weight
|
|
r += float64(s[0]) * aw
|
|
g += float64(s[1]) * aw
|
|
b += float64(s[2]) * aw
|
|
a += aw
|
|
}
|
|
if a != 0 {
|
|
aInv := 1 / a
|
|
j := y*dst.Stride + x*4
|
|
d := dst.Pix[j : j+4 : j+4]
|
|
d[0] = clamp(r * aInv)
|
|
d[1] = clamp(g * aInv)
|
|
d[2] = clamp(b * aInv)
|
|
d[3] = clamp(a)
|
|
}
|
|
}
|
|
}
|
|
})
|
|
return dst
|
|
}
|
|
|
|
// resizeNearest is a fast nearest-neighbor resize, no filtering.
|
|
func resizeNearest(img image.Image, width, height int) *image.NRGBA {
|
|
dst := image.NewNRGBA(image.Rect(0, 0, width, height))
|
|
dx := float64(img.Bounds().Dx()) / float64(width)
|
|
dy := float64(img.Bounds().Dy()) / float64(height)
|
|
|
|
if dx > 1 && dy > 1 {
|
|
src := newScanner(img)
|
|
parallel(0, height, func(ys <-chan int) {
|
|
for y := range ys {
|
|
srcY := int((float64(y) + 0.5) * dy)
|
|
dstOff := y * dst.Stride
|
|
for x := 0; x < width; x++ {
|
|
srcX := int((float64(x) + 0.5) * dx)
|
|
src.scan(srcX, srcY, srcX+1, srcY+1, dst.Pix[dstOff:dstOff+4])
|
|
dstOff += 4
|
|
}
|
|
}
|
|
})
|
|
} else {
|
|
src := toNRGBA(img)
|
|
parallel(0, height, func(ys <-chan int) {
|
|
for y := range ys {
|
|
srcY := int((float64(y) + 0.5) * dy)
|
|
srcOff0 := srcY * src.Stride
|
|
dstOff := y * dst.Stride
|
|
for x := 0; x < width; x++ {
|
|
srcX := int((float64(x) + 0.5) * dx)
|
|
srcOff := srcOff0 + srcX*4
|
|
copy(dst.Pix[dstOff:dstOff+4], src.Pix[srcOff:srcOff+4])
|
|
dstOff += 4
|
|
}
|
|
}
|
|
})
|
|
}
|
|
|
|
return dst
|
|
}
|
|
|
|
// Fit scales down the image using the specified resample filter to fit the specified
|
|
// maximum width and height and returns the transformed image.
|
|
//
|
|
// Example:
|
|
//
|
|
// dstImage := imaging.Fit(srcImage, 800, 600, imaging.Lanczos)
|
|
//
|
|
func Fit(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
|
|
maxW, maxH := width, height
|
|
|
|
if maxW <= 0 || maxH <= 0 {
|
|
return &image.NRGBA{}
|
|
}
|
|
|
|
srcBounds := img.Bounds()
|
|
srcW := srcBounds.Dx()
|
|
srcH := srcBounds.Dy()
|
|
|
|
if srcW <= 0 || srcH <= 0 {
|
|
return &image.NRGBA{}
|
|
}
|
|
|
|
if srcW <= maxW && srcH <= maxH {
|
|
return Clone(img)
|
|
}
|
|
|
|
srcAspectRatio := float64(srcW) / float64(srcH)
|
|
maxAspectRatio := float64(maxW) / float64(maxH)
|
|
|
|
var newW, newH int
|
|
if srcAspectRatio > maxAspectRatio {
|
|
newW = maxW
|
|
newH = int(float64(newW) / srcAspectRatio)
|
|
} else {
|
|
newH = maxH
|
|
newW = int(float64(newH) * srcAspectRatio)
|
|
}
|
|
|
|
return Resize(img, newW, newH, filter)
|
|
}
|
|
|
|
// Fill creates an image with the specified dimensions and fills it with the scaled source image.
|
|
// To achieve the correct aspect ratio without stretching, the source image will be cropped.
|
|
//
|
|
// Example:
|
|
//
|
|
// dstImage := imaging.Fill(srcImage, 800, 600, imaging.Center, imaging.Lanczos)
|
|
//
|
|
func Fill(img image.Image, width, height int, anchor Anchor, filter ResampleFilter) *image.NRGBA {
|
|
dstW, dstH := width, height
|
|
|
|
if dstW <= 0 || dstH <= 0 {
|
|
return &image.NRGBA{}
|
|
}
|
|
|
|
srcBounds := img.Bounds()
|
|
srcW := srcBounds.Dx()
|
|
srcH := srcBounds.Dy()
|
|
|
|
if srcW <= 0 || srcH <= 0 {
|
|
return &image.NRGBA{}
|
|
}
|
|
|
|
if srcW == dstW && srcH == dstH {
|
|
return Clone(img)
|
|
}
|
|
|
|
if srcW >= 100 && srcH >= 100 {
|
|
return cropAndResize(img, dstW, dstH, anchor, filter)
|
|
}
|
|
return resizeAndCrop(img, dstW, dstH, anchor, filter)
|
|
}
|
|
|
|
// cropAndResize crops the image to the smallest possible size that has the required aspect ratio using
|
|
// the given anchor point, then scales it to the specified dimensions and returns the transformed image.
|
|
//
|
|
// This is generally faster than resizing first, but may result in inaccuracies when used on small source images.
|
|
func cropAndResize(img image.Image, width, height int, anchor Anchor, filter ResampleFilter) *image.NRGBA {
|
|
dstW, dstH := width, height
|
|
|
|
srcBounds := img.Bounds()
|
|
srcW := srcBounds.Dx()
|
|
srcH := srcBounds.Dy()
|
|
srcAspectRatio := float64(srcW) / float64(srcH)
|
|
dstAspectRatio := float64(dstW) / float64(dstH)
|
|
|
|
var tmp *image.NRGBA
|
|
if srcAspectRatio < dstAspectRatio {
|
|
cropH := float64(srcW) * float64(dstH) / float64(dstW)
|
|
tmp = CropAnchor(img, srcW, int(math.Max(1, cropH)+0.5), anchor)
|
|
} else {
|
|
cropW := float64(srcH) * float64(dstW) / float64(dstH)
|
|
tmp = CropAnchor(img, int(math.Max(1, cropW)+0.5), srcH, anchor)
|
|
}
|
|
|
|
return Resize(tmp, dstW, dstH, filter)
|
|
}
|
|
|
|
// resizeAndCrop resizes the image to the smallest possible size that will cover the specified dimensions,
|
|
// crops the resized image to the specified dimensions using the given anchor point and returns
|
|
// the transformed image.
|
|
func resizeAndCrop(img image.Image, width, height int, anchor Anchor, filter ResampleFilter) *image.NRGBA {
|
|
dstW, dstH := width, height
|
|
|
|
srcBounds := img.Bounds()
|
|
srcW := srcBounds.Dx()
|
|
srcH := srcBounds.Dy()
|
|
srcAspectRatio := float64(srcW) / float64(srcH)
|
|
dstAspectRatio := float64(dstW) / float64(dstH)
|
|
|
|
var tmp *image.NRGBA
|
|
if srcAspectRatio < dstAspectRatio {
|
|
tmp = Resize(img, dstW, 0, filter)
|
|
} else {
|
|
tmp = Resize(img, 0, dstH, filter)
|
|
}
|
|
|
|
return CropAnchor(tmp, dstW, dstH, anchor)
|
|
}
|
|
|
|
// Thumbnail scales the image up or down using the specified resample filter, crops it
|
|
// to the specified width and hight and returns the transformed image.
|
|
//
|
|
// Example:
|
|
//
|
|
// dstImage := imaging.Thumbnail(srcImage, 100, 100, imaging.Lanczos)
|
|
//
|
|
func Thumbnail(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
|
|
return Fill(img, width, height, Center, filter)
|
|
}
|
|
|
|
// ResampleFilter specifies a resampling filter to be used for image resizing.
|
|
//
|
|
// General filter recommendations:
|
|
//
|
|
// - Lanczos
|
|
// A high-quality resampling filter for photographic images yielding sharp results.
|
|
//
|
|
// - CatmullRom
|
|
// A sharp cubic filter that is faster than Lanczos filter while providing similar results.
|
|
//
|
|
// - MitchellNetravali
|
|
// A cubic filter that produces smoother results with less ringing artifacts than CatmullRom.
|
|
//
|
|
// - Linear
|
|
// Bilinear resampling filter, produces a smooth output. Faster than cubic filters.
|
|
//
|
|
// - Box
|
|
// Simple and fast averaging filter appropriate for downscaling.
|
|
// When upscaling it's similar to NearestNeighbor.
|
|
//
|
|
// - NearestNeighbor
|
|
// Fastest resampling filter, no antialiasing.
|
|
//
|
|
type ResampleFilter struct {
|
|
Support float64
|
|
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
|
|
},
|
|
}
|
|
}
|