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// Copyright (c) 2012 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 <stddef.h>
#include <stdint.h>
#include <algorithm>
#include <limits>
#include "skia/ext/image_operations.h"
#include "base/check.h"
#include "base/containers/stack_container.h"
#include "base/macros.h"
#include "base/metrics/histogram_macros.h"
#include "base/notreached.h"
#include "base/numerics/math_constants.h"
#include "base/time/time.h"
#include "base/trace_event/trace_event.h"
#include "build/build_config.h"
#include "skia/ext/convolver.h"
#include "third_party/skia/include/core/SkColorPriv.h"
#include "third_party/skia/include/core/SkRect.h"
namespace skia {
namespace {
// Returns the ceiling/floor as an integer.
inline int CeilInt(float val) {
return static_cast<int>(ceil(val));
}
inline int FloorInt(float val) {
return static_cast<int>(floor(val));
}
// Filter function computation -------------------------------------------------
// Evaluates the box filter, which goes from -0.5 to +0.5.
float EvalBox(float x) {
return (x >= -0.5f && x < 0.5f) ? 1.0f : 0.0f;
}
// Evaluates the Lanczos filter of the given filter size window for the given
// position.
//
// |filter_size| is the width of the filter (the "window"), outside of which
// the value of the function is 0. Inside of the window, the value is the
// normalized sinc function:
// lanczos(x) = sinc(x) * sinc(x / filter_size);
// where
// sinc(x) = sin(pi*x) / (pi*x);
float EvalLanczos(int filter_size, float x) {
if (x <= -filter_size || x >= filter_size)
return 0.0f; // Outside of the window.
if (x > -std::numeric_limits<float>::epsilon() &&
x < std::numeric_limits<float>::epsilon())
return 1.0f; // Special case the discontinuity at the origin.
float xpi = x * base::kPiFloat;
return (sin(xpi) / xpi) * // sinc(x)
sin(xpi / filter_size) / (xpi / filter_size); // sinc(x/filter_size)
}
// Evaluates the Hamming filter of the given filter size window for the given
// position.
//
// The filter covers [-filter_size, +filter_size]. Outside of this window
// the value of the function is 0. Inside of the window, the value is sinus
// cardinal multiplied by a recentered Hamming function. The traditional
// Hamming formula for a window of size N and n ranging in [0, N-1] is:
// hamming(n) = 0.54 - 0.46 * cos(2 * pi * n / (N-1)))
// In our case we want the function centered for x == 0 and at its minimum
// on both ends of the window (x == +/- filter_size), hence the adjusted
// formula:
// hamming(x) = (0.54 -
// 0.46 * cos(2 * pi * (x - filter_size)/ (2 * filter_size)))
// = 0.54 - 0.46 * cos(pi * x / filter_size - pi)
// = 0.54 + 0.46 * cos(pi * x / filter_size)
float EvalHamming(int filter_size, float x) {
if (x <= -filter_size || x >= filter_size)
return 0.0f; // Outside of the window.
if (x > -std::numeric_limits<float>::epsilon() &&
x < std::numeric_limits<float>::epsilon())
return 1.0f; // Special case the sinc discontinuity at the origin.
const float xpi = x * base::kPiFloat;
return ((sin(xpi) / xpi) * // sinc(x)
(0.54f + 0.46f * cos(xpi / filter_size))); // hamming(x)
}
// ResizeFilter ----------------------------------------------------------------
// Encapsulates computation and storage of the filters required for one complete
// resize operation.
class ResizeFilter {
public:
ResizeFilter(ImageOperations::ResizeMethod method,
int src_full_width, int src_full_height,
int dest_width, int dest_height,
const SkIRect& dest_subset);
// Returns the filled filter values.
const ConvolutionFilter1D& x_filter() { return x_filter_; }
const ConvolutionFilter1D& y_filter() { return y_filter_; }
private:
// Returns the number of pixels that the filer spans, in filter space (the
// destination image).
float GetFilterSupport(float scale) {
switch (method_) {
case ImageOperations::RESIZE_BOX:
// The box filter just scales with the image scaling.
return 0.5f; // Only want one side of the filter = /2.
case ImageOperations::RESIZE_HAMMING1:
// The Hamming filter takes as much space in the source image in
// each direction as the size of the window = 1 for Hamming1.
return 1.0f;
case ImageOperations::RESIZE_LANCZOS3:
// The Lanczos filter takes as much space in the source image in
// each direction as the size of the window = 3 for Lanczos3.
return 3.0f;
default:
NOTREACHED();
return 1.0f;
}
}
// Computes one set of filters either horizontally or vertically. The caller
// will specify the "min" and "max" rather than the bottom/top and
// right/bottom so that the same code can be re-used in each dimension.
//
// |src_depend_lo| and |src_depend_size| gives the range for the source
// depend rectangle (horizontally or vertically at the caller's discretion
// -- see above for what this means).
//
// Likewise, the range of destination values to compute and the scale factor
// for the transform is also specified.
void ComputeFilters(int src_size,
int dest_subset_lo, int dest_subset_size,
float scale,
ConvolutionFilter1D* output);
// Computes the filter value given the coordinate in filter space.
inline float ComputeFilter(float pos) {
switch (method_) {
case ImageOperations::RESIZE_BOX:
return EvalBox(pos);
case ImageOperations::RESIZE_HAMMING1:
return EvalHamming(1, pos);
case ImageOperations::RESIZE_LANCZOS3:
return EvalLanczos(3, pos);
default:
NOTREACHED();
return 0;
}
}
ImageOperations::ResizeMethod method_;
ConvolutionFilter1D x_filter_;
ConvolutionFilter1D y_filter_;
DISALLOW_COPY_AND_ASSIGN(ResizeFilter);
};
ResizeFilter::ResizeFilter(ImageOperations::ResizeMethod method,
int src_full_width,
int src_full_height,
int dest_width,
int dest_height,
const SkIRect& dest_subset)
: method_(method) {
// method_ will only ever refer to an "algorithm method".
SkASSERT((ImageOperations::RESIZE_FIRST_ALGORITHM_METHOD <= method) &&
(method <= ImageOperations::RESIZE_LAST_ALGORITHM_METHOD));
float scale_x = static_cast<float>(dest_width) /
static_cast<float>(src_full_width);
float scale_y = static_cast<float>(dest_height) /
static_cast<float>(src_full_height);
ComputeFilters(src_full_width, dest_subset.fLeft, dest_subset.width(),
scale_x, &x_filter_);
ComputeFilters(src_full_height, dest_subset.fTop, dest_subset.height(),
scale_y, &y_filter_);
}
// 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 ResizeFilter::ComputeFilters(int src_size,
int dest_subset_lo, int dest_subset_size,
float scale,
ConvolutionFilter1D* output) {
int dest_subset_hi = dest_subset_lo + dest_subset_size; // [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 clamped_scale = std::min(1.0f, scale);
// This is how many source pixels from the center we need to count
// to support the filtering function.
float src_support = GetFilterSupport(clamped_scale) / clamped_scale;
// Speed up the divisions below by turning them into multiplies.
float inv_scale = 1.0f / scale;
base::StackVector<float, 64> filter_values;
base::StackVector<int16_t, 64> fixed_filter_values;
// 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 dest_subset_i = dest_subset_lo; dest_subset_i < dest_subset_hi;
dest_subset_i++) {
// Reset the arrays. We don't declare them inside so they can re-use the
// same malloc-ed buffer.
filter_values->clear();
fixed_filter_values->clear();
// 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 src_pixel = (static_cast<float>(dest_subset_i) + 0.5f) * inv_scale;
// Compute the (inclusive) range of source pixels the filter covers.
int src_begin = std::max(0, FloorInt(src_pixel - src_support));
int src_end = std::min(src_size - 1, CeilInt(src_pixel + src_support));
// Compute the unnormalized filter value at each location of the source
// it covers.
float filter_sum = 0.0f; // Sub of the filter values for normalizing.
for (int cur_filter_pixel = src_begin; cur_filter_pixel <= src_end;
cur_filter_pixel++) {
// 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 'src_pixel'. 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 src_filter_dist =
((static_cast<float>(cur_filter_pixel) + 0.5f) - src_pixel);
// Since the filter really exists in dest space, map it there.
float dest_filter_dist = src_filter_dist * clamped_scale;
// Compute the filter value at that location.
float filter_value = ComputeFilter(dest_filter_dist);
filter_values->push_back(filter_value);
filter_sum += filter_value;
}
DCHECK(!filter_values->empty()) << "We should always get a filter!";
// The filter must be normalized so that we don't affect the brightness of
// the image. Convert to normalized fixed point.
int16_t fixed_sum = 0;
for (size_t i = 0; i < filter_values->size(); i++) {
int16_t cur_fixed = output->FloatToFixed(filter_values[i] / filter_sum);
fixed_sum += cur_fixed;
fixed_filter_values->push_back(cur_fixed);
}
// 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).
int16_t leftovers = output->FloatToFixed(1.0f) - fixed_sum;
fixed_filter_values[fixed_filter_values->size() / 2] += leftovers;
// Now it's ready to go.
output->AddFilter(src_begin, &fixed_filter_values[0],
static_cast<int>(fixed_filter_values->size()));
}
output->PaddingForSIMD();
}
ImageOperations::ResizeMethod ResizeMethodToAlgorithmMethod(
ImageOperations::ResizeMethod method) {
// Convert any "Quality Method" into an "Algorithm Method"
if (method >= ImageOperations::RESIZE_FIRST_ALGORITHM_METHOD &&
method <= ImageOperations::RESIZE_LAST_ALGORITHM_METHOD) {
return method;
}
// The call to ImageOperationsGtv::Resize() above took care of
// GPU-acceleration in the cases where it is possible. So now we just
// pick the appropriate software method for each resize quality.
switch (method) {
// Users of RESIZE_GOOD are willing to trade a lot of quality to
// get speed, allowing the use of linear resampling to get hardware
// acceleration (SRB). Hence any of our "good" software filters
// will be acceptable, and we use the fastest one, Hamming-1.
case ImageOperations::RESIZE_GOOD:
// Users of RESIZE_BETTER are willing to trade some quality in order
// to improve performance, but are guaranteed not to devolve to a linear
// resampling. In visual tests we see that Hamming-1 is not as good as
// Lanczos-2, however it is about 40% faster and Lanczos-2 itself is
// about 30% faster than Lanczos-3. The use of Hamming-1 has been deemed
// an acceptable trade-off between quality and speed.
case ImageOperations::RESIZE_BETTER:
return ImageOperations::RESIZE_HAMMING1;
default:
return ImageOperations::RESIZE_LANCZOS3;
}
}
} // namespace
// Resize ----------------------------------------------------------------------
// static
SkBitmap ImageOperations::Resize(const SkPixmap& source,
ResizeMethod method,
int dest_width,
int dest_height,
const SkIRect& dest_subset,
SkBitmap::Allocator* allocator) {
TRACE_EVENT2("disabled-by-default-skia", "ImageOperations::Resize",
"src_pixels", source.width() * source.height(), "dst_pixels",
dest_width * dest_height);
// Ensure that the ResizeMethod enumeration is sound.
SkASSERT(((RESIZE_FIRST_QUALITY_METHOD <= method) &&
(method <= RESIZE_LAST_QUALITY_METHOD)) ||
((RESIZE_FIRST_ALGORITHM_METHOD <= method) &&
(method <= RESIZE_LAST_ALGORITHM_METHOD)));
// Time how long this takes to see if it's a problem for users.
base::TimeTicks resize_start = base::TimeTicks::Now();
// If the size of source or destination is 0, i.e. 0x0, 0xN or Nx0, just
// return empty.
if (source.width() < 1 || source.height() < 1 ||
dest_width < 1 || dest_height < 1)
return SkBitmap();
SkIRect dest = {0, 0, dest_width, dest_height};
DCHECK(dest.contains(dest_subset))
<< "The supplied subset does not fall within the destination image.";
method = ResizeMethodToAlgorithmMethod(method);
// Check that we deal with an "algorithm methods" from this point onward.
SkASSERT((ImageOperations::RESIZE_FIRST_ALGORITHM_METHOD <= method) &&
(method <= ImageOperations::RESIZE_LAST_ALGORITHM_METHOD));
if (!source.addr() || source.colorType() != kN32_SkColorType)
return SkBitmap();
ResizeFilter filter(method, source.width(), source.height(),
dest_width, dest_height, dest_subset);
// Get a source bitmap encompassing this touched area. We construct the
// offsets and row strides such that it looks like a new bitmap, while
// referring to the old data.
const uint8_t* source_subset =
reinterpret_cast<const uint8_t*>(source.addr());
// Convolve into the result.
SkBitmap result;
result.setInfo(
source.info().makeWH(dest_subset.width(), dest_subset.height()));
result.allocPixels(allocator);
if (!result.readyToDraw())
return SkBitmap();
BGRAConvolve2D(source_subset, static_cast<int>(source.rowBytes()),
!source.isOpaque(), filter.x_filter(), filter.y_filter(),
static_cast<int>(result.rowBytes()),
static_cast<unsigned char*>(result.getPixels()),
true);
base::TimeDelta delta = base::TimeTicks::Now() - resize_start;
UMA_HISTOGRAM_TIMES("Image.ResampleMS", delta);
return result;
}
// static
SkBitmap ImageOperations::Resize(const SkBitmap& source,
ResizeMethod method,
int dest_width,
int dest_height,
const SkIRect& dest_subset,
SkBitmap::Allocator* allocator) {
SkPixmap pixmap;
if (!source.peekPixels(&pixmap))
return SkBitmap();
return Resize(pixmap, method, dest_width, dest_height, dest_subset,
allocator);
}
// static
SkBitmap ImageOperations::Resize(const SkBitmap& source,
ResizeMethod method,
int dest_width, int dest_height,
SkBitmap::Allocator* allocator) {
SkIRect dest_subset = { 0, 0, dest_width, dest_height };
return Resize(source, method, dest_width, dest_height, dest_subset,
allocator);
}
} // namespace skia