| // 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 |
| base::CPU cpu; |
| if (cpu.has_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 |
