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* Copyright 2014 Google Inc.
* Use of this source code is governed by a BSD-style license that can be
* found in the LICENSE file.
#ifndef GrMatrixConvolutionEffect_DEFINED
#define GrMatrixConvolutionEffect_DEFINED
#include "src/gpu/ganesh/GrFragmentProcessor.h"
#include "src/gpu/ganesh/GrProcessorUnitTest.h"
#include "src/gpu/ganesh/GrSamplerState.h"
#include <array>
#include <new>
class GrSurfaceProxyView;
class GrMatrixConvolutionEffect : public GrFragmentProcessor {
// A little bit less than the minimum # uniforms required by DX9SM2 (32).
// Allows for a 5x5 kernel (or 28x1, for that matter).
// Must be a multiple of 4, since we upload these in vec4s.
inline static constexpr int kMaxUniformSize = 28;
static std::unique_ptr<GrFragmentProcessor> Make(GrRecordingContext*,
GrSurfaceProxyView srcView,
const SkIRect& srcBounds,
const SkISize& kernelSize,
const SkScalar* kernel,
SkScalar gain,
SkScalar bias,
const SkIPoint& kernelOffset,
bool convolveAlpha,
const GrCaps&);
const char* name() const override { return "MatrixConvolution"; }
std::unique_ptr<GrFragmentProcessor> clone() const override;
class Impl;
* Small kernels are represented as float-arrays and uploaded as uniforms.
* Large kernels go over the uniform limit and are uploaded as textures and sampled.
* If Float16 textures are supported, we use those. Otherwise we use A8.
class KernelWrapper {
struct BiasAndGain {
// Only used in A8 mode. Applied before any other math.
float fBias;
// Only used in A8 mode. Premultiplied in with user gain to save time.
float fGain;
bool operator==(const BiasAndGain&) const;
using MakeResult = std::tuple<KernelWrapper, std::unique_ptr<GrFragmentProcessor>>;
static MakeResult Make(GrRecordingContext*, SkISize, const GrCaps&, const float* values);
KernelWrapper() = default;
KernelWrapper(const KernelWrapper& that) : fSize(that.fSize) {
if (that.isSampled()) {
fBiasAndGain = that.fBiasAndGain;
} else {
new (&fArray) std::array<float, kMaxUniformSize>(that.fArray);
bool isValid() const { return !fSize.isEmpty(); }
SkISize size() const { return fSize; }
bool isSampled() const { return fSize.area() > kMaxUniformSize; }
const std::array<float, kMaxUniformSize>& array() const {
return fArray;
const BiasAndGain& biasAndGain() const {
return fBiasAndGain;
bool operator==(const KernelWrapper&) const;
KernelWrapper(SkISize size) : fSize(size) {
if (this->isSampled()) {
fBiasAndGain = {0.f , 1.f};
SkISize fSize = {};
union {
std::array<float, kMaxUniformSize> fArray;
BiasAndGain fBiasAndGain;
GrMatrixConvolutionEffect(std::unique_ptr<GrFragmentProcessor> child,
const KernelWrapper& kernel,
std::unique_ptr<GrFragmentProcessor> kernelFP,
SkScalar gain,
SkScalar bias,
const SkIPoint& kernelOffset,
bool convolveAlpha);
explicit GrMatrixConvolutionEffect(const GrMatrixConvolutionEffect&);
std::unique_ptr<ProgramImpl> onMakeProgramImpl() const override;
void onAddToKey(const GrShaderCaps&, skgpu::KeyBuilder*) const override;
bool onIsEqual(const GrFragmentProcessor&) const override;
KernelWrapper fKernel;
float fGain;
float fBias;
SkVector fKernelOffset;
bool fConvolveAlpha;
using INHERITED = GrFragmentProcessor;