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, 0 insertions, 572 deletions
diff --git a/vendor/github.com/disintegration/imaging/resize.go b/vendor/github.com/disintegration/imaging/resize.go deleted file mode 100644 index 97f498a..0000000 --- a/vendor/github.com/disintegration/imaging/resize.go +++ /dev/null @@ -1,572 +0,0 @@ -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 - }, - } -} |