diff options
Diffstat (limited to 'vendor/github.com/disintegration/imaging/resize.go')
| -rw-r--r-- | vendor/github.com/disintegration/imaging/resize.go | 572 |
1 files changed, 572 insertions, 0 deletions
diff --git a/vendor/github.com/disintegration/imaging/resize.go b/vendor/github.com/disintegration/imaging/resize.go new file mode 100644 index 0000000..97f498a --- /dev/null +++ b/vendor/github.com/disintegration/imaging/resize.go @@ -0,0 +1,572 @@ +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. +// +// Supported resample filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali, +// CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine. +// +// Usage 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 := 0; x < width; x++ { + var r, g, b, a float64 + for _, w := range weights[x] { + i := w.index * 4 + aw := float64(scanLine[i+3]) * w.weight + r += float64(scanLine[i+0]) * aw + g += float64(scanLine[i+1]) * aw + b += float64(scanLine[i+2]) * aw + a += aw + } + if a != 0 { + aInv := 1 / a + j := j0 + x*4 + dst.Pix[j+0] = clamp(r * aInv) + dst.Pix[j+1] = clamp(g * aInv) + dst.Pix[j+2] = clamp(b * aInv) + dst.Pix[j+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 := 0; y < height; y++ { + var r, g, b, a float64 + for _, w := range weights[y] { + i := w.index * 4 + aw := float64(scanLine[i+3]) * w.weight + r += float64(scanLine[i+0]) * aw + g += float64(scanLine[i+1]) * aw + b += float64(scanLine[i+2]) * aw + a += aw + } + if a != 0 { + aInv := 1 / a + j := y*dst.Stride + x*4 + dst.Pix[j+0] = clamp(r * aInv) + dst.Pix[j+1] = clamp(g * aInv) + dst.Pix[j+2] = clamp(b * aInv) + dst.Pix[j+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. +// +// Supported resample filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali, +// CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine. +// +// Usage 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 scales 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. +// +// Supported resample filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali, +// CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine. +// +// Usage 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 { + minW, minH := width, height + + if minW <= 0 || minH <= 0 { + return &image.NRGBA{} + } + + srcBounds := img.Bounds() + srcW := srcBounds.Dx() + srcH := srcBounds.Dy() + + if srcW <= 0 || srcH <= 0 { + return &image.NRGBA{} + } + + if srcW == minW && srcH == minH { + return Clone(img) + } + + srcAspectRatio := float64(srcW) / float64(srcH) + minAspectRatio := float64(minW) / float64(minH) + + var tmp *image.NRGBA + if srcAspectRatio < minAspectRatio { + tmp = Resize(img, minW, 0, filter) + } else { + tmp = Resize(img, 0, minH, filter) + } + + return CropAnchor(tmp, minW, minH, 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. +// +// Supported resample filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali, +// CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine. +// +// Usage 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 is a resampling filter struct. It can be used to define custom filters. +// +// Supported resample filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali, +// CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine. +// +// General filter recommendations: +// +// - Lanczos +// High-quality resampling filter for photographic images yielding sharp results. +// It's slower than cubic filters (see below). +// +// - CatmullRom +// A sharp cubic filter. It's a good filter for both upscaling and downscaling if sharp results are needed. +// +// - MitchellNetravali +// A high quality cubic filter that produces smoother results with less ringing artifacts than CatmullRom. +// +// - BSpline +// A good filter if a very smooth output is needed. +// +// - Linear +// Bilinear interpolation filter, produces reasonably good, smooth output. +// It's 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 + }, + } +} |