b293696c81
This change only *prepares* the codegen to handle multiple Ops. The actual generated code still only supports one Op (Src) and not the other (Over). A follow-up change will add Over. This Op switch (an eventual x2 multiplier in the amount of code generated) should be the last of the codegen LoC multipliers. The dst and src mask options will be implemented in the slow path fallback. Change-Id: Iecbcc6fad063e2aac36d78d5380c0a0947c709df Reviewed-on: https://go-review.googlesource.com/8488 Reviewed-by: Rob Pike <r@golang.org>
434 lines
12 KiB
Go
434 lines
12 KiB
Go
// Copyright 2015 The Go Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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//go:generate go run gen.go
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package draw
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import (
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"image"
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"image/color"
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"math"
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"sync"
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"golang.org/x/image/math/f64"
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)
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// Copy copies the part of the source image defined by src and sr and writes to
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// the part of the destination image defined by dst and the translation of sr
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// so that sr.Min translates to dp.
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func Copy(dst Image, dp image.Point, src image.Image, sr image.Rectangle, opts *Options) {
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mask, mp := image.Image(nil), image.Point{}
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if opts != nil {
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// TODO: set mask and mp.
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}
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dr := sr.Add(dp.Sub(sr.Min))
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DrawMask(dst, dr, src, sr.Min, mask, mp, opts.op())
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}
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// Scaler scales the part of the source image defined by src and sr and writes
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// to the part of the destination image defined by dst and dr.
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//
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// A Scaler is safe to use concurrently.
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type Scaler interface {
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Scale(dst Image, dr image.Rectangle, src image.Image, sr image.Rectangle, opts *Options)
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}
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// Transformer transforms the part of the source image defined by src and sr
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// and writes to the part of the destination image defined by dst and the
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// affine transform m applied to sr.
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//
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// For example, if m is the matrix
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//
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// m00 m01 m02
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// m10 m11 m12
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//
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// then the src-space point (sx, sy) maps to the dst-space point
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// (m00*sx + m01*sy + m02, m10*sx + m11*sy + m12).
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//
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// A Transformer is safe to use concurrently.
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type Transformer interface {
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Transform(dst Image, m *f64.Aff3, src image.Image, sr image.Rectangle, opts *Options)
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}
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// Options are optional parameters to Copy, Scale and Transform.
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//
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// A nil *Options means to use the default (zero) values of each field.
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type Options struct {
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// Op is the compositing operator. The default value is Over.
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Op Op
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// TODO: add fields a la
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// https://groups.google.com/forum/#!topic/golang-dev/fgn_xM0aeq4
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}
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func (o *Options) op() Op {
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if o == nil {
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return Over
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}
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return o.Op
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}
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// Interpolator is an interpolation algorithm, when dst and src pixels don't
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// have a 1:1 correspondence.
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//
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// Of the interpolators provided by this package:
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// - NearestNeighbor is fast but usually looks worst.
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// - CatmullRom is slow but usually looks best.
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// - ApproxBiLinear has reasonable speed and quality.
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//
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// The time taken depends on the size of dr. For kernel interpolators, the
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// speed also depends on the size of sr, and so are often slower than
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// non-kernel interpolators, especially when scaling down.
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type Interpolator interface {
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Scaler
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Transformer
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}
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// Kernel is an interpolator that blends source pixels weighted by a symmetric
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// kernel function.
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type Kernel struct {
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// Support is the kernel support and must be >= 0. At(t) is assumed to be
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// zero when t >= Support.
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Support float64
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// At is the kernel function. It will only be called with t in the
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// range [0, Support).
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At func(t float64) float64
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}
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// Scale implements the Scaler interface.
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func (q *Kernel) Scale(dst Image, dr image.Rectangle, src image.Image, sr image.Rectangle, opts *Options) {
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q.newScaler(dr.Dx(), dr.Dy(), sr.Dx(), sr.Dy(), false).Scale(dst, dr, src, sr, opts)
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}
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// NewScaler returns a Scaler that is optimized for scaling multiple times with
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// the same fixed destination and source width and height.
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func (q *Kernel) NewScaler(dw, dh, sw, sh int) Scaler {
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return q.newScaler(dw, dh, sw, sh, true)
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}
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func (q *Kernel) newScaler(dw, dh, sw, sh int, usePool bool) Scaler {
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z := &kernelScaler{
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kernel: q,
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dw: int32(dw),
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dh: int32(dh),
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sw: int32(sw),
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sh: int32(sh),
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horizontal: newDistrib(q, int32(dw), int32(sw)),
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vertical: newDistrib(q, int32(dh), int32(sh)),
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}
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if usePool {
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z.pool.New = func() interface{} {
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tmp := z.makeTmpBuf()
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return &tmp
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}
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}
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return z
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}
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var (
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// NearestNeighbor is the nearest neighbor interpolator. It is very fast,
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// but usually gives very low quality results. When scaling up, the result
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// will look 'blocky'.
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NearestNeighbor = Interpolator(nnInterpolator{})
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// ApproxBiLinear is a mixture of the nearest neighbor and bi-linear
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// interpolators. It is fast, but usually gives medium quality results.
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//
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// It implements bi-linear interpolation when upscaling and a bi-linear
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// blend of the 4 nearest neighbor pixels when downscaling. This yields
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// nicer quality than nearest neighbor interpolation when upscaling, but
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// the time taken is independent of the number of source pixels, unlike the
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// bi-linear interpolator. When downscaling a large image, the performance
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// difference can be significant.
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ApproxBiLinear = Interpolator(ablInterpolator{})
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// BiLinear is the tent kernel. It is slow, but usually gives high quality
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// results.
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BiLinear = &Kernel{1, func(t float64) float64 {
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return 1 - t
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}}
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// CatmullRom is the Catmull-Rom kernel. It is very slow, but usually gives
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// very high quality results.
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//
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// It is an instance of the more general cubic BC-spline kernel with parameters
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// B=0 and C=0.5. See Mitchell and Netravali, "Reconstruction Filters in
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// Computer Graphics", Computer Graphics, Vol. 22, No. 4, pp. 221-228.
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CatmullRom = &Kernel{2, func(t float64) float64 {
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if t < 1 {
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return (1.5*t-2.5)*t*t + 1
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}
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return ((-0.5*t+2.5)*t-4)*t + 2
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}}
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// TODO: a Kaiser-Bessel kernel?
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)
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type nnInterpolator struct{}
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type ablInterpolator struct{}
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type kernelScaler struct {
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kernel *Kernel
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dw, dh, sw, sh int32
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horizontal, vertical distrib
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pool sync.Pool
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}
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func (z *kernelScaler) makeTmpBuf() [][4]float64 {
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return make([][4]float64, z.dw*z.sh)
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}
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// source is a range of contribs, their inverse total weight, and that ITW
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// divided by 0xffff.
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type source struct {
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i, j int32
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invTotalWeight float64
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invTotalWeightFFFF float64
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}
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// contrib is the weight of a column or row.
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type contrib struct {
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coord int32
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weight float64
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}
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// distrib measures how source pixels are distributed over destination pixels.
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type distrib struct {
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// sources are what contribs each column or row in the source image owns,
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// and the total weight of those contribs.
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sources []source
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// contribs are the contributions indexed by sources[s].i and sources[s].j.
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contribs []contrib
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}
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// newDistrib returns a distrib that distributes sw source columns (or rows)
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// over dw destination columns (or rows).
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func newDistrib(q *Kernel, dw, sw int32) distrib {
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scale := float64(sw) / float64(dw)
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halfWidth, kernelArgScale := q.Support, 1.0
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// When shrinking, broaden the effective kernel support so that we still
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// visit every source pixel.
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if scale > 1 {
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halfWidth *= scale
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kernelArgScale = 1 / scale
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}
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// Make the sources slice, one source for each column or row, and temporarily
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// appropriate its elements' fields so that invTotalWeight is the scaled
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// co-ordinate of the source column or row, and i and j are the lower and
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// upper bounds of the range of destination columns or rows affected by the
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// source column or row.
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n, sources := int32(0), make([]source, dw)
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for x := range sources {
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center := (float64(x)+0.5)*scale - 0.5
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i := int32(math.Floor(center - halfWidth))
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if i < 0 {
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i = 0
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}
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j := int32(math.Ceil(center + halfWidth))
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if j > sw {
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j = sw
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if j < i {
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j = i
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}
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}
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sources[x] = source{i: i, j: j, invTotalWeight: center}
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n += j - i
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}
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contribs := make([]contrib, 0, n)
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for k, b := range sources {
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totalWeight := 0.0
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l := int32(len(contribs))
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for coord := b.i; coord < b.j; coord++ {
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t := abs((b.invTotalWeight - float64(coord)) * kernelArgScale)
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if t >= q.Support {
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continue
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}
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weight := q.At(t)
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if weight == 0 {
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continue
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}
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totalWeight += weight
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contribs = append(contribs, contrib{coord, weight})
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}
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totalWeight = 1 / totalWeight
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sources[k] = source{
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i: l,
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j: int32(len(contribs)),
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invTotalWeight: totalWeight,
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invTotalWeightFFFF: totalWeight / 0xffff,
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}
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}
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return distrib{sources, contribs}
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}
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// abs is like math.Abs, but it doesn't care about negative zero, infinities or
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// NaNs.
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func abs(f float64) float64 {
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if f < 0 {
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f = -f
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}
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return f
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}
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// ftou converts the range [0.0, 1.0] to [0, 0xffff].
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func ftou(f float64) uint16 {
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i := int32(0xffff*f + 0.5)
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if i > 0xffff {
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return 0xffff
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}
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if i > 0 {
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return uint16(i)
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}
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return 0
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}
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// fffftou converts the range [0.0, 65535.0] to [0, 0xffff].
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func fffftou(f float64) uint16 {
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i := int32(f + 0.5)
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if i > 0xffff {
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return 0xffff
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}
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if i > 0 {
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return uint16(i)
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}
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return 0
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}
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// invert returns the inverse of m.
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//
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// TODO: move this into the f64 package, once we work out the convention for
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// matrix methods in that package: do they modify the receiver, take a dst
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// pointer argument, or return a new value?
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func invert(m *f64.Aff3) f64.Aff3 {
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m00 := +m[3*1+1]
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m01 := -m[3*0+1]
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m02 := +m[3*1+2]*m[3*0+1] - m[3*1+1]*m[3*0+2]
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m10 := -m[3*1+0]
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m11 := +m[3*0+0]
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m12 := +m[3*1+0]*m[3*0+2] - m[3*1+2]*m[3*0+0]
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det := m00*m11 - m10*m01
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return f64.Aff3{
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m00 / det,
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m01 / det,
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m02 / det,
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m10 / det,
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m11 / det,
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m12 / det,
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}
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}
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func matMul(p, q *f64.Aff3) f64.Aff3 {
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return f64.Aff3{
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p[3*0+0]*q[3*0+0] + p[3*0+1]*q[3*1+0],
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p[3*0+0]*q[3*0+1] + p[3*0+1]*q[3*1+1],
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p[3*0+0]*q[3*0+2] + p[3*0+1]*q[3*1+2] + p[3*0+2],
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p[3*1+0]*q[3*0+0] + p[3*1+1]*q[3*1+0],
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p[3*1+0]*q[3*0+1] + p[3*1+1]*q[3*1+1],
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p[3*1+0]*q[3*0+2] + p[3*1+1]*q[3*1+2] + p[3*1+2],
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}
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}
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// transformRect returns a rectangle dr that contains sr transformed by s2d.
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func transformRect(s2d *f64.Aff3, sr *image.Rectangle) (dr image.Rectangle) {
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ps := [...]image.Point{
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{sr.Min.X, sr.Min.Y},
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{sr.Max.X, sr.Min.Y},
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{sr.Min.X, sr.Max.Y},
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{sr.Max.X, sr.Max.Y},
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}
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for i, p := range ps {
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sxf := float64(p.X)
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syf := float64(p.Y)
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dx := int(math.Floor(s2d[0]*sxf + s2d[1]*syf + s2d[2]))
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dy := int(math.Floor(s2d[3]*sxf + s2d[4]*syf + s2d[5]))
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// The +1 adjustments below are because an image.Rectangle is inclusive
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// on the low end but exclusive on the high end.
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if i == 0 {
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dr = image.Rectangle{
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Min: image.Point{dx + 0, dy + 0},
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Max: image.Point{dx + 1, dy + 1},
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}
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continue
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}
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if dr.Min.X > dx {
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dr.Min.X = dx
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}
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dx++
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if dr.Max.X < dx {
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dr.Max.X = dx
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}
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if dr.Min.Y > dy {
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dr.Min.Y = dy
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}
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dy++
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if dr.Max.Y < dy {
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dr.Max.Y = dy
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}
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}
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return dr
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}
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func transform_Uniform(dst Image, dr, adr image.Rectangle, d2s *f64.Aff3, src *image.Uniform, sr image.Rectangle, bias image.Point, op Op) {
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// TODO: implement op == Over.
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switch dst := dst.(type) {
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case *image.RGBA:
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pr, pg, pb, pa := src.C.RGBA()
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pr8 := uint8(pr >> 8)
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pg8 := uint8(pg >> 8)
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pb8 := uint8(pb >> 8)
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pa8 := uint8(pa >> 8)
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for dy := int32(adr.Min.Y); dy < int32(adr.Max.Y); dy++ {
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dyf := float64(dr.Min.Y+int(dy)) + 0.5
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d := dst.PixOffset(dr.Min.X+adr.Min.X, dr.Min.Y+int(dy))
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for dx := int32(adr.Min.X); dx < int32(adr.Max.X); dx, d = dx+1, d+4 {
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dxf := float64(dr.Min.X+int(dx)) + 0.5
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sx0 := int(d2s[0]*dxf+d2s[1]*dyf+d2s[2]) + bias.X
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sy0 := int(d2s[3]*dxf+d2s[4]*dyf+d2s[5]) + bias.Y
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if !(image.Point{sx0, sy0}).In(sr) {
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continue
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}
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dst.Pix[d+0] = pr8
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dst.Pix[d+1] = pg8
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dst.Pix[d+2] = pb8
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dst.Pix[d+3] = pa8
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}
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}
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default:
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pr, pg, pb, pa := src.C.RGBA()
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dstColorRGBA64 := &color.RGBA64{
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uint16(pr),
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uint16(pg),
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uint16(pb),
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uint16(pa),
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}
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dstColor := color.Color(dstColorRGBA64)
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for dy := int32(adr.Min.Y); dy < int32(adr.Max.Y); dy++ {
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dyf := float64(dr.Min.Y+int(dy)) + 0.5
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for dx := int32(adr.Min.X); dx < int32(adr.Max.X); dx++ {
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dxf := float64(dr.Min.X+int(dx)) + 0.5
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sx0 := int(d2s[0]*dxf+d2s[1]*dyf+d2s[2]) + bias.X
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sy0 := int(d2s[3]*dxf+d2s[4]*dyf+d2s[5]) + bias.Y
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if !(image.Point{sx0, sy0}).In(sr) {
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continue
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}
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dst.Set(dr.Min.X+int(dx), dr.Min.Y+int(dy), dstColor)
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}
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}
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}
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}
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