// TODO(egouriou): Take advantage of periods in the convolution.
// Practical resizing filters are periodic outside of the border area.
// For Lanczos, a scaling by a (reduced) factor of p/q (q pixels in the
// source become p pixels in the destination) will have a period of p.
// A nice consequence is a period of 1 when downscaling by an integral
// factor. Downscaling from typical display resolutions is also bound
// to produce interesting periods as those are chosen to have multiple
// small factors.
// Small periods reduce computational load and improve cache usage if
// the coefficients can be shared. For periods of 1 we can consider
// loading the factors only once outside the borders.
void SkResizeFilter::computeFilters(int srcSize,
float destSubsetLo, float destSubsetSize,
float scale,
SkConvolutionFilter1D* output,
const SkConvolutionProcs& convolveProcs) {
float destSubsetHi = destSubsetLo + destSubsetSize; // [lo, hi)
// When we're doing a magnification, the scale will be larger than one. This
// means the destination pixels are much smaller than the source pixels, and
// that the range covered by the filter won't necessarily cover any source
// pixel boundaries. Therefore, we use these clamped values (max of 1) for
// some computations.
float clampedScale = SkTMin(1.0f, scale);
// This is how many source pixels from the center we need to count
// to support the filtering function.
float srcSupport = fBitmapFilter->width() / clampedScale;
// Speed up the divisions below by turning them into multiplies.
float invScale = 1.0f / scale;
SkTArray<float> filterValues(64);
SkTArray<short> fixedFilterValues(64);
// Loop over all pixels in the output range. We will generate one set of
// filter values for each one. Those values will tell us how to blend the
// source pixels to compute the destination pixel.
for (int destSubsetI = SkScalarFloorToInt(destSubsetLo); destSubsetI < SkScalarCeilToInt(destSubsetHi);
destSubsetI++) {
// Reset the arrays. We don't declare them inside so they can re-use the
// same malloc-ed buffer.
filterValues.reset();
fixedFilterValues.reset();
// This is the pixel in the source directly under the pixel in the dest.
// Note that we base computations on the "center" of the pixels. To see
// why, observe that the destination pixel at coordinates (0, 0) in a 5.0x
// downscale should "cover" the pixels around the pixel with *its center*
// at coordinates (2.5, 2.5) in the source, not those around (0, 0).
// Hence we need to scale coordinates (0.5, 0.5), not (0, 0).
float srcPixel = (static_cast<float>(destSubsetI) + 0.5f) * invScale;
// Compute the (inclusive) range of source pixels the filter covers.
int srcBegin = SkTMax(0, SkScalarFloorToInt(srcPixel - srcSupport));
int srcEnd = SkTMin(srcSize - 1, SkScalarCeilToInt(srcPixel + srcSupport));
// Compute the unnormalized filter value at each location of the source
// it covers.
float filterSum = 0.0f; // Sub of the filter values for normalizing.
for (int curFilterPixel = srcBegin; curFilterPixel <= srcEnd;
curFilterPixel++) {
// Distance from the center of the filter, this is the filter coordinate
// in source space. We also need to consider the center of the pixel
// when comparing distance against 'srcPixel'. In the 5x downscale
// example used above the distance from the center of the filter to
// the pixel with coordinates (2, 2) should be 0, because its center
// is at (2.5, 2.5).
float srcFilterDist =
((static_cast<float>(curFilterPixel) + 0.5f) - srcPixel);
// Since the filter really exists in dest space, map it there.
float destFilterDist = srcFilterDist * clampedScale;
// Compute the filter value at that location.
float filterValue = fBitmapFilter->evaluate(destFilterDist);
filterValues.push_back(filterValue);
filterSum += filterValue;
}
SkASSERT(!filterValues.empty());
// The filter must be normalized so that we don't affect the brightness of
// the image. Convert to normalized fixed point.
short fixedSum = 0;
for (int i = 0; i < filterValues.count(); i++) {
short curFixed = output->FloatToFixed(filterValues[i] / filterSum);
fixedSum += curFixed;
fixedFilterValues.push_back(curFixed);
}
// The conversion to fixed point will leave some rounding errors, which
// we add back in to avoid affecting the brightness of the image. We
// arbitrarily add this to the center of the filter array (this won't always
// be the center of the filter function since it could get clipped on the
// edges, but it doesn't matter enough to worry about that case).
short leftovers = output->FloatToFixed(1.0f) - fixedSum;
fixedFilterValues[fixedFilterValues.count() / 2] += leftovers;
// Now it's ready to go.
output->AddFilter(srcBegin, &fixedFilterValues[0],
static_cast<int>(fixedFilterValues.count()));
}
if (convolveProcs.fApplySIMDPadding) {
convolveProcs.fApplySIMDPadding( output );
}
}
// TODO(egouriou): Take advantage of periods in the convolution.
// Practical resizing filters are periodic outside of the border area.
// For Lanczos, a scaling by a (reduced) factor of p/q (q pixels in the
// source become p pixels in the destination) will have a period of p.
// A nice consequence is a period of 1 when downscaling by an integral
// factor. Downscaling from typical display resolutions is also bound
// to produce interesting periods as those are chosen to have multiple
// small factors.
// Small periods reduce computational load and improve cache usage if
// the coefficients can be shared. For periods of 1 we can consider
// loading the factors only once outside the borders.
void SkResizeFilter::computeFilters(int srcSize,
float destSubsetLo, float destSubsetSize,
float scale,
SkConvolutionFilter1D* output,
const SkConvolutionProcs& convolveProcs) {
float destSubsetHi = destSubsetLo + destSubsetSize; // [lo, hi)
// When we're doing a magnification, the scale will be larger than one. This
// means the destination pixels are much smaller than the source pixels, and
// that the range covered by the filter won't necessarily cover any source
// pixel boundaries. Therefore, we use these clamped values (max of 1) for
// some computations.
float clampedScale = SkTMin(1.0f, scale);
// This is how many source pixels from the center we need to count
// to support the filtering function.
float srcSupport = fBitmapFilter->width() / clampedScale;
float invScale = 1.0f / scale;
SkSTArray<64, float, true> filterValuesArray;
SkSTArray<64, SkConvolutionFilter1D::ConvolutionFixed, true> fixedFilterValuesArray;
// Loop over all pixels in the output range. We will generate one set of
// filter values for each one. Those values will tell us how to blend the
// source pixels to compute the destination pixel.
// This is the pixel in the source directly under the pixel in the dest.
// Note that we base computations on the "center" of the pixels. To see
// why, observe that the destination pixel at coordinates (0, 0) in a 5.0x
// downscale should "cover" the pixels around the pixel with *its center*
// at coordinates (2.5, 2.5) in the source, not those around (0, 0).
// Hence we need to scale coordinates (0.5, 0.5), not (0, 0).
destSubsetLo = SkScalarFloorToScalar(destSubsetLo);
destSubsetHi = SkScalarCeilToScalar(destSubsetHi);
float srcPixel = (destSubsetLo + 0.5f) * invScale;
int destLimit = SkScalarTruncToInt(destSubsetHi - destSubsetLo);
output->reserveAdditional(destLimit, SkScalarCeilToInt(destLimit * srcSupport * 2));
for (int destI = 0; destI < destLimit; srcPixel += invScale, destI++)
{
// Compute the (inclusive) range of source pixels the filter covers.
float srcBegin = SkTMax(0.f, SkScalarFloorToScalar(srcPixel - srcSupport));
float srcEnd = SkTMin(srcSize - 1.f, SkScalarCeilToScalar(srcPixel + srcSupport));
// Compute the unnormalized filter value at each location of the source
// it covers.
// Sum of the filter values for normalizing.
// Distance from the center of the filter, this is the filter coordinate
// in source space. We also need to consider the center of the pixel
// when comparing distance against 'srcPixel'. In the 5x downscale
// example used above the distance from the center of the filter to
// the pixel with coordinates (2, 2) should be 0, because its center
// is at (2.5, 2.5).
float destFilterDist = (srcBegin + 0.5f - srcPixel) * clampedScale;
int filterCount = SkScalarTruncToInt(srcEnd - srcBegin) + 1;
if (filterCount <= 0) {
// true when srcSize is equal to srcPixel - srcSupport; this may be a bug
return;
}
filterValuesArray.reset(filterCount);
float filterSum = fBitmapFilter->evaluate_n(destFilterDist, clampedScale, filterCount,
filterValuesArray.begin());
// The filter must be normalized so that we don't affect the brightness of
// the image. Convert to normalized fixed point.
int fixedSum = 0;
fixedFilterValuesArray.reset(filterCount);
const float* filterValues = filterValuesArray.begin();
SkConvolutionFilter1D::ConvolutionFixed* fixedFilterValues = fixedFilterValuesArray.begin();
float invFilterSum = 1 / filterSum;
for (int fixedI = 0; fixedI < filterCount; fixedI++) {
int curFixed = SkConvolutionFilter1D::FloatToFixed(filterValues[fixedI] * invFilterSum);
fixedSum += curFixed;
fixedFilterValues[fixedI] = SkToS16(curFixed);
}
SkASSERT(fixedSum <= 0x7FFF);
// The conversion to fixed point will leave some rounding errors, which
// we add back in to avoid affecting the brightness of the image. We
// arbitrarily add this to the center of the filter array (this won't always
// be the center of the filter function since it could get clipped on the
// edges, but it doesn't matter enough to worry about that case).
int leftovers = SkConvolutionFilter1D::FloatToFixed(1) - fixedSum;
fixedFilterValues[filterCount / 2] += leftovers;
// Now it's ready to go.
output->AddFilter(SkScalarFloorToInt(srcBegin), fixedFilterValues, filterCount);
}
if (convolveProcs.fApplySIMDPadding) {
convolveProcs.fApplySIMDPadding(output);
}
}
void BGRAConvolve2D(const unsigned char* sourceData,
int sourceByteRowStride,
bool sourceHasAlpha,
const SkConvolutionFilter1D& filterX,
const SkConvolutionFilter1D& filterY,
int outputByteRowStride,
unsigned char* output,
const SkConvolutionProcs& convolveProcs,
bool useSimdIfPossible) {
int maxYFilterSize = filterY.maxFilter();
// The next row in the input that we will generate a horizontally
// convolved row for. If the filter doesn't start at the beginning of the
// image (this is the case when we are only resizing a subset), then we
// don't want to generate any output rows before that. Compute the starting
// row for convolution as the first pixel for the first vertical filter.
int filterOffset, filterLength;
const SkConvolutionFilter1D::ConvolutionFixed* filterValues =
filterY.FilterForValue(0, &filterOffset, &filterLength);
int nextXRow = filterOffset;
// We loop over each row in the input doing a horizontal convolution. This
// will result in a horizontally convolved image. We write the results into
// a circular buffer of convolved rows and do vertical convolution as rows
// are available. This prevents us from having to store the entire
// intermediate image and helps cache coherency.
// We will need four extra rows to allow horizontal convolution could be done
// simultaneously. We also pad each row in row buffer to be aligned-up to
// 16 bytes.
// TODO(jiesun): We do not use aligned load from row buffer in vertical
// convolution pass yet. Somehow Windows does not like it.
int rowBufferWidth = (filterX.numValues() + 15) & ~0xF;
int rowBufferHeight = maxYFilterSize +
(convolveProcs.fConvolve4RowsHorizontally ? 4 : 0);
CircularRowBuffer rowBuffer(rowBufferWidth,
rowBufferHeight,
filterOffset);
// Loop over every possible output row, processing just enough horizontal
// convolutions to run each subsequent vertical convolution.
SkASSERT(outputByteRowStride >= filterX.numValues() * 4);
int numOutputRows = filterY.numValues();
// We need to check which is the last line to convolve before we advance 4
// lines in one iteration.
int lastFilterOffset, lastFilterLength;
// SSE2 can access up to 3 extra pixels past the end of the
// buffer. At the bottom of the image, we have to be careful
// not to access data past the end of the buffer. Normally
// we fall back to the C++ implementation for the last row.
// If the last row is less than 3 pixels wide, we may have to fall
// back to the C++ version for more rows. Compute how many
// rows we need to avoid the SSE implementation for here.
filterX.FilterForValue(filterX.numValues() - 1, &lastFilterOffset,
&lastFilterLength);
int avoidSimdRows = 1 + convolveProcs.fExtraHorizontalReads /
(lastFilterOffset + lastFilterLength);
filterY.FilterForValue(numOutputRows - 1, &lastFilterOffset,
&lastFilterLength);
for (int outY = 0; outY < numOutputRows; outY++) {
filterValues = filterY.FilterForValue(outY,
&filterOffset, &filterLength);
// Generate output rows until we have enough to run the current filter.
while (nextXRow < filterOffset + filterLength) {
if (convolveProcs.fConvolve4RowsHorizontally &&
nextXRow + 3 < lastFilterOffset + lastFilterLength -
avoidSimdRows) {
const unsigned char* src[4];
unsigned char* outRow[4];
for (int i = 0; i < 4; ++i) {
src[i] = &sourceData[(uint64_t)(nextXRow + i) * sourceByteRowStride];
outRow[i] = rowBuffer.advanceRow();
}
convolveProcs.fConvolve4RowsHorizontally(src, filterX, outRow, 4*rowBufferWidth);
nextXRow += 4;
} else {
// Check if we need to avoid SSE2 for this row.
if (convolveProcs.fConvolveHorizontally &&
nextXRow < lastFilterOffset + lastFilterLength -
avoidSimdRows) {
convolveProcs.fConvolveHorizontally(
&sourceData[(uint64_t)nextXRow * sourceByteRowStride],
filterX, rowBuffer.advanceRow(), sourceHasAlpha);
} else {
if (sourceHasAlpha) {
ConvolveHorizontallyAlpha(
&sourceData[(uint64_t)nextXRow * sourceByteRowStride],
filterX, rowBuffer.advanceRow());
} else {
ConvolveHorizontallyNoAlpha(
&sourceData[(uint64_t)nextXRow * sourceByteRowStride],
filterX, rowBuffer.advanceRow());
}
}
nextXRow++;
}
}
// Compute where in the output image this row of final data will go.
unsigned char* curOutputRow = &output[(uint64_t)outY * outputByteRowStride];
// Get the list of rows that the circular buffer has, in order.
int firstRowInCircularBuffer;
unsigned char* const* rowsToConvolve =
rowBuffer.GetRowAddresses(&firstRowInCircularBuffer);
// Now compute the start of the subset of those rows that the filter
// needs.
unsigned char* const* firstRowForFilter =
&rowsToConvolve[filterOffset - firstRowInCircularBuffer];
if (convolveProcs.fConvolveVertically) {
convolveProcs.fConvolveVertically(filterValues, filterLength,
firstRowForFilter,
filterX.numValues(), curOutputRow,
sourceHasAlpha);
} else {
ConvolveVertically(filterValues, filterLength,
firstRowForFilter,
filterX.numValues(), curOutputRow,
sourceHasAlpha);
}
}
}
