// Copyright (c) 2011 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include <algorithm>
#include "base/logging.h"
#include "skia/ext/convolver.h"
#include "skia/ext/convolver_SSE2.h"
#include "skia/ext/convolver_mips_dspr2.h"
#include "third_party/skia/include/core/SkSize.h"
#include "third_party/skia/include/core/SkTypes.h"
namespace skia {
namespace {
// Converts the argument to an 8-bit unsigned value by clamping to the range
// 0-255.
inline unsigned char ClampTo8(int a) {
if (static_cast<unsigned>(a) < 256)
return a; // Avoid the extra check in the common case.
if (a < 0)
return 0;
return 255;
}
// Takes the value produced by accumulating element-wise product of image with
// a kernel and brings it back into range.
// All of the filter scaling factors are in fixed point with kShiftBits bits of
// fractional part.
inline unsigned char BringBackTo8(int a, bool take_absolute) {
a >>= ConvolutionFilter1D::kShiftBits;
if (take_absolute)
a = std::abs(a);
return ClampTo8(a);
}
// Stores a list of rows in a circular buffer. The usage is you write into it
// by calling AdvanceRow. It will keep track of which row in the buffer it
// should use next, and the total number of rows added.
class CircularRowBuffer {
public:
// The number of pixels in each row is given in |source_row_pixel_width|.
// The maximum number of rows needed in the buffer is |max_y_filter_size|
// (we only need to store enough rows for the biggest filter).
//
// We use the |first_input_row| to compute the coordinates of all of the
// following rows returned by Advance().
CircularRowBuffer(int dest_row_pixel_width, int max_y_filter_size,
int first_input_row)
: row_byte_width_(dest_row_pixel_width * 4),
num_rows_(max_y_filter_size),
next_row_(0),
next_row_coordinate_(first_input_row) {
buffer_.resize(row_byte_width_ * max_y_filter_size);
row_addresses_.resize(num_rows_);
}
// Moves to the next row in the buffer, returning a pointer to the beginning
// of it.
unsigned char* AdvanceRow() {
unsigned char* row = &buffer_[next_row_ * row_byte_width_];
next_row_coordinate_++;
// Set the pointer to the next row to use, wrapping around if necessary.
next_row_++;
if (next_row_ == num_rows_)
next_row_ = 0;
return row;
}
// Returns a pointer to an "unrolled" array of rows. These rows will start
// at the y coordinate placed into |*first_row_index| and will continue in
// order for the maximum number of rows in this circular buffer.
//
// The |first_row_index_| may be negative. This means the circular buffer
// starts before the top of the image (it hasn't been filled yet).
unsigned char* const* GetRowAddresses(int* first_row_index) {
// Example for a 4-element circular buffer holding coords 6-9.
// Row 0 Coord 8
// Row 1 Coord 9
// Row 2 Coord 6 <- next_row_ = 2, next_row_coordinate_ = 10.
// Row 3 Coord 7
//
// The "next" row is also the first (lowest) coordinate. This computation
// may yield a negative value, but that's OK, the math will work out
// since the user of this buffer will compute the offset relative
// to the first_row_index and the negative rows will never be used.
*first_row_index = next_row_coordinate_ - num_rows_;
int cur_row = next_row_;
for (int i = 0; i < num_rows_; i++) {
row_addresses_[i] = &buffer_[cur_row * row_byte_width_];
// Advance to the next row, wrapping if necessary.
cur_row++;
if (cur_row == num_rows_)
cur_row = 0;
}
return &row_addresses_[0];
}
private:
// The buffer storing the rows. They are packed, each one row_byte_width_.
std::vector<unsigned char> buffer_;
// Number of bytes per row in the |buffer_|.
int row_byte_width_;
// The number of rows available in the buffer.
int num_rows_;
// The next row index we should write into. This wraps around as the
// circular buffer is used.
int next_row_;
// The y coordinate of the |next_row_|. This is incremented each time a
// new row is appended and does not wrap.
int next_row_coordinate_;
// Buffer used by GetRowAddresses().
std::vector<unsigned char*> row_addresses_;
};
// Convolves horizontally along a single row. The row data is given in
// |src_data| and continues for the num_values() of the filter.
template<bool has_alpha>
void ConvolveHorizontally(const unsigned char* src_data,
const ConvolutionFilter1D& filter,
unsigned char* out_row) {
// Loop over each pixel on this row in the output image.
int num_values = filter.num_values();
for (int out_x = 0; out_x < num_values; out_x++) {
// Get the filter that determines the current output pixel.
int filter_offset, filter_length;
const ConvolutionFilter1D::Fixed* filter_values =
filter.FilterForValue(out_x, &filter_offset, &filter_length);
// Compute the first pixel in this row that the filter affects. It will
// touch |filter_length| pixels (4 bytes each) after this.
const unsigned char* row_to_filter = &src_data[filter_offset * 4];
// Apply the filter to the row to get the destination pixel in |accum|.
int accum[4] = {0};
for (int filter_x = 0; filter_x < filter_length; filter_x++) {
ConvolutionFilter1D::Fixed cur_filter = filter_values[filter_x];
accum[0] += cur_filter * row_to_filter[filter_x * 4 + 0];
accum[1] += cur_filter * row_to_filter[filter_x * 4 + 1];
accum[2] += cur_filter * row_to_filter[filter_x * 4 + 2];
if (has_alpha)
accum[3] += cur_filter * row_to_filter[filter_x * 4 + 3];
}
// Bring this value back in range. All of the filter scaling factors
// are in fixed point with kShiftBits bits of fractional part.
accum[0] >>= ConvolutionFilter1D::kShiftBits;
accum[1] >>= ConvolutionFilter1D::kShiftBits;
accum[2] >>= ConvolutionFilter1D::kShiftBits;
if (has_alpha)
accum[3] >>= ConvolutionFilter1D::kShiftBits;
// Store the new pixel.
out_row[out_x * 4 + 0] = ClampTo8(accum[0]);
out_row[out_x * 4 + 1] = ClampTo8(accum[1]);
out_row[out_x * 4 + 2] = ClampTo8(accum[2]);
if (has_alpha)
out_row[out_x * 4 + 3] = ClampTo8(accum[3]);
}
}
// Does vertical convolution to produce one output row. The filter values and
// length are given in the first two parameters. These are applied to each
// of the rows pointed to in the |source_data_rows| array, with each row
// being |pixel_width| wide.
//
// The output must have room for |pixel_width * 4| bytes.
template<bool has_alpha>
void ConvolveVertically(const ConvolutionFilter1D::Fixed* filter_values,
int filter_length,
unsigned char* const* source_data_rows,
int pixel_width,
unsigned char* out_row) {
// We go through each column in the output and do a vertical convolution,
// generating one output pixel each time.
for (int out_x = 0; out_x < pixel_width; out_x++) {
// Compute the number of bytes over in each row that the current column
// we're convolving starts at. The pixel will cover the next 4 bytes.
int byte_offset = out_x * 4;
// Apply the filter to one column of pixels.
int accum[4] = {0};
for (int filter_y = 0; filter_y < filter_length; filter_y++) {
ConvolutionFilter1D::Fixed cur_filter = filter_values[filter_y];
accum[0] += cur_filter * source_data_rows[filter_y][byte_offset + 0];
accum[1] += cur_filter * source_data_rows[filter_y][byte_offset + 1];
accum[2] += cur_filter * source_data_rows[filter_y][byte_offset + 2];
if (has_alpha)
accum[3] += cur_filter * source_data_rows[filter_y][byte_offset + 3];
}
// Bring this value back in range. All of the filter scaling factors
// are in fixed point with kShiftBits bits of precision.
accum[0] >>= ConvolutionFilter1D::kShiftBits;
accum[1] >>= ConvolutionFilter1D::kShiftBits;
accum[2] >>= ConvolutionFilter1D::kShiftBits;
if (has_alpha)
accum[3] >>= ConvolutionFilter1D::kShiftBits;
// Store the new pixel.
out_row[byte_offset + 0] = ClampTo8(accum[0]);
out_row[byte_offset + 1] = ClampTo8(accum[1]);
out_row[byte_offset + 2] = ClampTo8(accum[2]);
if (has_alpha) {
unsigned char alpha = ClampTo8(accum[3]);
// Make sure the alpha channel doesn't come out smaller than any of the
// color channels. We use premultipled alpha channels, so this should
// never happen, but rounding errors will cause this from time to time.
// These "impossible" colors will cause overflows (and hence random pixel
// values) when the resulting bitmap is drawn to the screen.
//
// We only need to do this when generating the final output row (here).
int max_color_channel = std::max(out_row[byte_offset + 0],
std::max(out_row[byte_offset + 1], out_row[byte_offset + 2]));
if (alpha < max_color_channel)
out_row[byte_offset + 3] = max_color_channel;
else
out_row[byte_offset + 3] = alpha;
} else {
// No alpha channel, the image is opaque.
out_row[byte_offset + 3] = 0xff;
}
}
}
void ConvolveVertically(const ConvolutionFilter1D::Fixed* filter_values,
int filter_length,
unsigned char* const* source_data_rows,
int pixel_width,
unsigned char* out_row,
bool source_has_alpha) {
if (source_has_alpha) {
ConvolveVertically<true>(filter_values, filter_length,
source_data_rows,
pixel_width,
out_row);
} else {
ConvolveVertically<false>(filter_values, filter_length,
source_data_rows,
pixel_width,
out_row);
}
}
} // namespace
// ConvolutionFilter1D ---------------------------------------------------------
ConvolutionFilter1D::ConvolutionFilter1D()
: max_filter_(0) {
}
ConvolutionFilter1D::~ConvolutionFilter1D() {
}
void ConvolutionFilter1D::AddFilter(int filter_offset,
const float* filter_values,
int filter_length) {
SkASSERT(filter_length > 0);
std::vector<Fixed> fixed_values;
fixed_values.reserve(filter_length);
for (int i = 0; i < filter_length; ++i)
fixed_values.push_back(FloatToFixed(filter_values[i]));
AddFilter(filter_offset, &fixed_values[0], filter_length);
}
void ConvolutionFilter1D::AddFilter(int filter_offset,
const Fixed* filter_values,
int filter_length) {
// It is common for leading/trailing filter values to be zeros. In such
// cases it is beneficial to only store the central factors.
// For a scaling to 1/4th in each dimension using a Lanczos-2 filter on
// a 1080p image this optimization gives a ~10% speed improvement.
int filter_size = filter_length;
int first_non_zero = 0;
while (first_non_zero < filter_length && filter_values[first_non_zero] == 0)
first_non_zero++;
if (first_non_zero < filter_length) {
// Here we have at least one non-zero factor.
int last_non_zero = filter_length - 1;
while (last_non_zero >= 0 && filter_values[last_non_zero] == 0)
last_non_zero--;
filter_offset += first_non_zero;
filter_length = last_non_zero + 1 - first_non_zero;
SkASSERT(filter_length > 0);
for (int i = first_non_zero; i <= last_non_zero; i++)
filter_values_.push_back(filter_values[i]);
} else {
// Here all the factors were zeroes.
filter_length = 0;
}
FilterInstance instance;
// We pushed filter_length elements onto filter_values_
instance.data_location = (static_cast<int>(filter_values_.size()) -
filter_length);
instance.offset = filter_offset;
instance.trimmed_length = filter_length;
instance.length = filter_size;
filters_.push_back(instance);
max_filter_ = std::max(max_filter_, filter_length);
}
const ConvolutionFilter1D::Fixed* ConvolutionFilter1D::GetSingleFilter(
int* specified_filter_length,
int* filter_offset,
int* filter_length) const {
const FilterInstance& filter = filters_[0];
*filter_offset = filter.offset;
*filter_length = filter.trimmed_length;
*specified_filter_length = filter.length;
if (filter.trimmed_length == 0)
return NULL;
return &filter_values_[filter.data_location];
}
typedef void (*ConvolveVertically_pointer)(
const ConvolutionFilter1D::Fixed* filter_values,
int filter_length,
unsigned char* const* source_data_rows,
int pixel_width,
unsigned char* out_row,
bool has_alpha);
typedef void (*Convolve4RowsHorizontally_pointer)(
const unsigned char* src_data[4],
const ConvolutionFilter1D& filter,
unsigned char* out_row[4]);
typedef void (*ConvolveHorizontally_pointer)(
const unsigned char* src_data,
const ConvolutionFilter1D& filter,
unsigned char* out_row,
bool has_alpha);
struct ConvolveProcs {
// This is how many extra pixels may be read by the
// conolve*horizontally functions.
int extra_horizontal_reads;
ConvolveVertically_pointer convolve_vertically;
Convolve4RowsHorizontally_pointer convolve_4rows_horizontally;
ConvolveHorizontally_pointer convolve_horizontally;
};
void SetupSIMD(ConvolveProcs *procs) {
#ifdef SIMD_SSE2
procs->extra_horizontal_reads = 3;
procs->convolve_vertically = &ConvolveVertically_SSE2;
procs->convolve_4rows_horizontally = &Convolve4RowsHorizontally_SSE2;
procs->convolve_horizontally = &ConvolveHorizontally_SSE2;
#elif defined SIMD_MIPS_DSPR2
procs->extra_horizontal_reads = 3;
procs->convolve_vertically = &ConvolveVertically_mips_dspr2;
procs->convolve_horizontally = &ConvolveHorizontally_mips_dspr2;
#endif
}
void BGRAConvolve2D(const unsigned char* source_data,
int source_byte_row_stride,
bool source_has_alpha,
const ConvolutionFilter1D& filter_x,
const ConvolutionFilter1D& filter_y,
int output_byte_row_stride,
unsigned char* output,
bool use_simd_if_possible) {
ConvolveProcs simd;
simd.extra_horizontal_reads = 0;
simd.convolve_vertically = NULL;
simd.convolve_4rows_horizontally = NULL;
simd.convolve_horizontally = NULL;
if (use_simd_if_possible) {
SetupSIMD(&simd);
}
int max_y_filter_size = filter_y.max_filter();
// 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 filter_offset, filter_length;
const ConvolutionFilter1D::Fixed* filter_values =
filter_y.FilterForValue(0, &filter_offset, &filter_length);
int next_x_row = filter_offset;
// 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 padding 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 row_buffer_width = (filter_x.num_values() + 15) & ~0xF;
int row_buffer_height = max_y_filter_size +
(simd.convolve_4rows_horizontally ? 4 : 0);
CircularRowBuffer row_buffer(row_buffer_width,
row_buffer_height,
filter_offset);
// Loop over every possible output row, processing just enough horizontal
// convolutions to run each subsequent vertical convolution.
SkASSERT(output_byte_row_stride >= filter_x.num_values() * 4);
int num_output_rows = filter_y.num_values();
// We need to check which is the last line to convolve before we advance 4
// lines in one iteration.
int last_filter_offset, last_filter_length;
// 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.
filter_x.FilterForValue(filter_x.num_values() - 1, &last_filter_offset,
&last_filter_length);
int avoid_simd_rows = 1 + simd.extra_horizontal_reads /
(last_filter_offset + last_filter_length);
filter_y.FilterForValue(num_output_rows - 1, &last_filter_offset,
&last_filter_length);
for (int out_y = 0; out_y < num_output_rows; out_y++) {
filter_values = filter_y.FilterForValue(out_y,
&filter_offset, &filter_length);
// Generate output rows until we have enough to run the current filter.
while (next_x_row < filter_offset + filter_length) {
if (simd.convolve_4rows_horizontally &&
next_x_row + 3 < last_filter_offset + last_filter_length -
avoid_simd_rows) {
const unsigned char* src[4];
unsigned char* out_row[4];
for (int i = 0; i < 4; ++i) {
src[i] = &source_data[(next_x_row + i) * source_byte_row_stride];
out_row[i] = row_buffer.AdvanceRow();
}
simd.convolve_4rows_horizontally(src, filter_x, out_row);
next_x_row += 4;
} else {
// Check if we need to avoid SSE2 for this row.
if (simd.convolve_horizontally &&
next_x_row < last_filter_offset + last_filter_length -
avoid_simd_rows) {
simd.convolve_horizontally(
&source_data[next_x_row * source_byte_row_stride],
filter_x, row_buffer.AdvanceRow(), source_has_alpha);
} else {
if (source_has_alpha) {
ConvolveHorizontally<true>(
&source_data[next_x_row * source_byte_row_stride],
filter_x, row_buffer.AdvanceRow());
} else {
ConvolveHorizontally<false>(
&source_data[next_x_row * source_byte_row_stride],
filter_x, row_buffer.AdvanceRow());
}
}
next_x_row++;
}
}
// Compute where in the output image this row of final data will go.
unsigned char* cur_output_row = &output[out_y * output_byte_row_stride];
// Get the list of rows that the circular buffer has, in order.
int first_row_in_circular_buffer;
unsigned char* const* rows_to_convolve =
row_buffer.GetRowAddresses(&first_row_in_circular_buffer);
// Now compute the start of the subset of those rows that the filter
// needs.
unsigned char* const* first_row_for_filter =
&rows_to_convolve[filter_offset - first_row_in_circular_buffer];
if (simd.convolve_vertically) {
simd.convolve_vertically(filter_values, filter_length,
first_row_for_filter,
filter_x.num_values(), cur_output_row,
source_has_alpha);
} else {
ConvolveVertically(filter_values, filter_length,
first_row_for_filter,
filter_x.num_values(), cur_output_row,
source_has_alpha);
}
}
}
void SingleChannelConvolveX1D(const unsigned char* source_data,
int source_byte_row_stride,
int input_channel_index,
int input_channel_count,
const ConvolutionFilter1D& filter,
const SkISize& image_size,
unsigned char* output,
int output_byte_row_stride,
int output_channel_index,
int output_channel_count,
bool absolute_values) {
int filter_offset, filter_length, filter_size;
// Very much unlike BGRAConvolve2D, here we expect to have the same filter
// for all pixels.
const ConvolutionFilter1D::Fixed* filter_values =
filter.GetSingleFilter(&filter_size, &filter_offset, &filter_length);
if (filter_values == NULL || image_size.width() < filter_size) {
NOTREACHED();
return;
}
int centrepoint = filter_length / 2;
if (filter_size - filter_offset != 2 * filter_offset) {
// This means the original filter was not symmetrical AND
// got clipped from one side more than from the other.
centrepoint = filter_size / 2 - filter_offset;
}
const unsigned char* source_data_row = source_data;
unsigned char* output_row = output;
for (int r = 0; r < image_size.height(); ++r) {
unsigned char* target_byte = output_row + output_channel_index;
// Process the lead part, padding image to the left with the first pixel.
int c = 0;
for (; c < centrepoint; ++c, target_byte += output_channel_count) {
int accval = 0;
int i = 0;
int pixel_byte_index = input_channel_index;
for (; i < centrepoint - c; ++i) // Padding part.
accval += filter_values[i] * source_data_row[pixel_byte_index];
for (; i < filter_length; ++i, pixel_byte_index += input_channel_count)
accval += filter_values[i] * source_data_row[pixel_byte_index];
*target_byte = BringBackTo8(accval, absolute_values);
}
// Now for the main event.
for (; c < image_size.width() - centrepoint;
++c, target_byte += output_channel_count) {
int accval = 0;
int pixel_byte_index = (c - centrepoint) * input_channel_count +
input_channel_index;
for (int i = 0; i < filter_length;
++i, pixel_byte_index += input_channel_count) {
accval += filter_values[i] * source_data_row[pixel_byte_index];
}
*target_byte = BringBackTo8(accval, absolute_values);
}
for (; c < image_size.width(); ++c, target_byte += output_channel_count) {
int accval = 0;
int overlap_taps = image_size.width() - c + centrepoint;
int pixel_byte_index = (c - centrepoint) * input_channel_count +
input_channel_index;
int i = 0;
for (; i < overlap_taps - 1; ++i, pixel_byte_index += input_channel_count)
accval += filter_values[i] * source_data_row[pixel_byte_index];
for (; i < filter_length; ++i)
accval += filter_values[i] * source_data_row[pixel_byte_index];
*target_byte = BringBackTo8(accval, absolute_values);
}
source_data_row += source_byte_row_stride;
output_row += output_byte_row_stride;
}
}
void SingleChannelConvolveY1D(const unsigned char* source_data,
int source_byte_row_stride,
int input_channel_index,
int input_channel_count,
const ConvolutionFilter1D& filter,
const SkISize& image_size,
unsigned char* output,
int output_byte_row_stride,
int output_channel_index,
int output_channel_count,
bool absolute_values) {
int filter_offset, filter_length, filter_size;
// Very much unlike BGRAConvolve2D, here we expect to have the same filter
// for all pixels.
const ConvolutionFilter1D::Fixed* filter_values =
filter.GetSingleFilter(&filter_size, &filter_offset, &filter_length);
if (filter_values == NULL || image_size.height() < filter_size) {
NOTREACHED();
return;
}
int centrepoint = filter_length / 2;
if (filter_size - filter_offset != 2 * filter_offset) {
// This means the original filter was not symmetrical AND
// got clipped from one side more than from the other.
centrepoint = filter_size / 2 - filter_offset;
}
for (int c = 0; c < image_size.width(); ++c) {
unsigned char* target_byte = output + c * output_channel_count +
output_channel_index;
int r = 0;
for (; r < centrepoint; ++r, target_byte += output_byte_row_stride) {
int accval = 0;
int i = 0;
int pixel_byte_index = c * input_channel_count + input_channel_index;
for (; i < centrepoint - r; ++i) // Padding part.
accval += filter_values[i] * source_data[pixel_byte_index];
for (; i < filter_length; ++i, pixel_byte_index += source_byte_row_stride)
accval += filter_values[i] * source_data[pixel_byte_index];
*target_byte = BringBackTo8(accval, absolute_values);
}
for (; r < image_size.height() - centrepoint;
++r, target_byte += output_byte_row_stride) {
int accval = 0;
int pixel_byte_index = (r - centrepoint) * source_byte_row_stride +
c * input_channel_count + input_channel_index;
for (int i = 0; i < filter_length;
++i, pixel_byte_index += source_byte_row_stride) {
accval += filter_values[i] * source_data[pixel_byte_index];
}
*target_byte = BringBackTo8(accval, absolute_values);
}
for (; r < image_size.height();
++r, target_byte += output_byte_row_stride) {
int accval = 0;
int overlap_taps = image_size.height() - r + centrepoint;
int pixel_byte_index = (r - centrepoint) * source_byte_row_stride +
c * input_channel_count + input_channel_index;
int i = 0;
for (; i < overlap_taps - 1;
++i, pixel_byte_index += source_byte_row_stride) {
accval += filter_values[i] * source_data[pixel_byte_index];
}
for (; i < filter_length; ++i)
accval += filter_values[i] * source_data[pixel_byte_index];
*target_byte = BringBackTo8(accval, absolute_values);
}
}
}
void SetUpGaussianConvolutionKernel(ConvolutionFilter1D* filter,
float kernel_sigma,
bool derivative) {
DCHECK(filter != NULL);
DCHECK_GT(kernel_sigma, 0.0);
const int tail_length = static_cast<int>(4.0f * kernel_sigma + 0.5f);
const int kernel_size = tail_length * 2 + 1;
const float sigmasq = kernel_sigma * kernel_sigma;
std::vector<float> kernel_weights(kernel_size, 0.0);
float kernel_sum = 1.0f;
kernel_weights[tail_length] = 1.0f;
for (int ii = 1; ii <= tail_length; ++ii) {
float v = std::exp(-0.5f * ii * ii / sigmasq);
kernel_weights[tail_length + ii] = v;
kernel_weights[tail_length - ii] = v;
kernel_sum += 2.0f * v;
}
for (int i = 0; i < kernel_size; ++i)
kernel_weights[i] /= kernel_sum;
if (derivative) {
kernel_weights[tail_length] = 0.0;
for (int ii = 1; ii <= tail_length; ++ii) {
float v = sigmasq * kernel_weights[tail_length + ii] / ii;
kernel_weights[tail_length + ii] = v;
kernel_weights[tail_length - ii] = -v;
}
}
filter->AddFilter(0, &kernel_weights[0], kernel_weights.size());
}
} // namespace skia