// Copyright 2015 The Go Authors. All rights reserved. // Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. //go:generate go run gen.go package draw // TODO: add an Options type a la // https://groups.google.com/forum/#!topic/golang-dev/fgn_xM0aeq4 import ( "image" "math" ) // Scaler scales the part of the source image defined by src and sr and writes // to the part of the destination image defined by dst and dr. // // A Scaler is safe to use concurrently. type Scaler interface { Scale(dst Image, dr image.Rectangle, src image.Image, sr image.Rectangle) } // Interpolator is an interpolation algorithm, when dst and src pixels don't // have a 1:1 correspondance. // // Of the interpolators provided by this package: // - NearestNeighbor is fast but usually looks worst. // - CatmullRom is slow but usually looks best. // - ApproxBiLinear has reasonable speed and quality. // // The time taken depends on the size of dr. For kernel interpolators, the // speed also depends on the size of sr, and so are often slower than // non-kernel interpolators, especially when scaling down. type Interpolator interface { Scaler // TODO: Transformer } // Kernel is an interpolator that blends source pixels weighted by a symmetric // kernel function. type Kernel struct { // Support is the kernel support and must be >= 0. At(t) is assumed to be // zero when t >= Support. Support float64 // At is the kernel function. It will only be called with t in the // range [0, Support). At func(t float64) float64 } // Scale implements the Scaler interface. func (k *Kernel) Scale(dst Image, dr image.Rectangle, src image.Image, sr image.Rectangle) { k.NewScaler(dr.Dx(), dr.Dy(), sr.Dx(), sr.Dy()).Scale(dst, dr, src, sr) } // NewScaler returns a Scaler that is optimized for scaling multiple times with // the same fixed destination and source width and height. func (k *Kernel) NewScaler(dw, dh, sw, sh int) Scaler { return &kernelScaler{ kernel: k, dw: int32(dw), dh: int32(dh), sw: int32(sw), sh: int32(sh), horizontal: newDistrib(k, int32(dw), int32(sw)), vertical: newDistrib(k, int32(dh), int32(sh)), } } var ( // NearestNeighbor is the nearest neighbor interpolator. It is very fast, // but usually gives very low quality results. When scaling up, the result // will look 'blocky'. NearestNeighbor = Interpolator(nnInterpolator{}) // ApproxBiLinear is a mixture of the nearest neighbor and bi-linear // interpolators. It is fast, but usually gives medium quality results. // // It implements bi-linear interpolation when upscaling and a bi-linear // blend of the 4 nearest neighbor pixels when downscaling. This yields // nicer quality than nearest neighbor interpolation when upscaling, but // the time taken is independent of the number of source pixels, unlike the // bi-linear interpolator. When downscaling a large image, the performance // difference can be significant. ApproxBiLinear = Interpolator(ablInterpolator{}) // BiLinear is the tent kernel. It is slow, but usually gives high quality // results. BiLinear = &Kernel{1, func(t float64) float64 { return 1 - t }} // CatmullRom is the Catmull-Rom kernel. It is very slow, but usually gives // very high quality results. // // It is an instance of the more general cubic BC-spline kernel with parameters // B=0 and C=0.5. See Mitchell and Netravali, "Reconstruction Filters in // Computer Graphics", Computer Graphics, Vol. 22, No. 4, pp. 221-228. CatmullRom = &Kernel{2, func(t float64) float64 { if t < 1 { return (1.5*t-2.5)*t*t + 1 } return ((-0.5*t+2.5)*t-4)*t + 2 }} // TODO: a Kaiser-Bessel kernel? ) type nnInterpolator struct{} type ablInterpolator struct{} type kernelScaler struct { kernel *Kernel dw, dh, sw, sh int32 horizontal, vertical distrib } // source is a range of contribs, their inverse total weight, and that ITW // divided by 0xffff. type source struct { i, j int32 invTotalWeight float64 invTotalWeightFFFF float64 } // contrib is the weight of a column or row. type contrib struct { coord int32 weight float64 } // distrib measures how source pixels are distributed over destination pixels. type distrib struct { // sources are what contribs each column or row in the source image owns, // and the total weight of those contribs. sources []source // contribs are the contributions indexed by sources[s].i and sources[s].j. contribs []contrib } // newDistrib returns a distrib that distributes sw source columns (or rows) // over dw destination columns (or rows). func newDistrib(q *Kernel, dw, sw int32) distrib { scale := float64(sw) / float64(dw) halfWidth, kernelArgScale := q.Support, 1.0 if scale > 1 { halfWidth *= scale kernelArgScale = 1 / scale } // Make the sources slice, one source for each column or row, and temporarily // appropriate its elements' fields so that invTotalWeight is the scaled // co-ordinate of the source column or row, and i and j are the lower and // upper bounds of the range of destination columns or rows affected by the // source column or row. n, sources := int32(0), make([]source, dw) for x := range sources { center := (float64(x)+0.5)*scale - 0.5 i := int32(math.Floor(center - halfWidth)) if i < 0 { i = 0 } j := int32(math.Ceil(center + halfWidth)) if j >= sw { j = sw - 1 if j < i { j = i } } sources[x] = source{i: i, j: j, invTotalWeight: center} n += j - i + 1 } contribs := make([]contrib, 0, n) for k, b := range sources { totalWeight := 0.0 l := int32(len(contribs)) for coord := b.i; coord <= b.j; coord++ { t := (b.invTotalWeight - float64(coord)) * kernelArgScale if t < 0 { t = -t } if t >= q.Support { continue } weight := q.At(t) if weight == 0 { continue } totalWeight += weight contribs = append(contribs, contrib{coord, weight}) } totalWeight = 1 / totalWeight sources[k] = source{ i: l, j: int32(len(contribs)), invTotalWeight: totalWeight, invTotalWeightFFFF: totalWeight / 0xffff, } } return distrib{sources, contribs} } func ftou(f float64) uint16 { i := int32(0xffff*f + 0.5) if i > 0xffff { return 0xffff } else if i > 0 { return uint16(i) } return 0 }