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| 1 // Copyright (c) 2011 The Chromium Authors. All rights reserved. | |
| 2 // Use of this source code is governed by a BSD-style license that can be | |
| 3 // found in the LICENSE file. | |
| 4 | |
| 5 #include <algorithm> | |
| 6 | |
| 7 #include "base/logging.h" | |
| 8 #include "skia/ext/convolver.h" | |
| 9 #include "skia/ext/convolver_SSE2.h" | |
| 10 #include "skia/ext/convolver_mips_dspr2.h" | |
| 11 #include "third_party/skia/include/core/SkSize.h" | |
| 12 #include "third_party/skia/include/core/SkTypes.h" | |
| 13 | |
| 14 namespace skia { | |
| 15 | |
| 16 namespace { | |
| 17 | |
| 18 // Converts the argument to an 8-bit unsigned value by clamping to the range | |
| 19 // 0-255. | |
| 20 inline unsigned char ClampTo8(int a) { | |
| 21 if (static_cast<unsigned>(a) < 256) | |
| 22 return a; // Avoid the extra check in the common case. | |
| 23 if (a < 0) | |
| 24 return 0; | |
| 25 return 255; | |
| 26 } | |
| 27 | |
| 28 // Takes the value produced by accumulating element-wise product of image with | |
| 29 // a kernel and brings it back into range. | |
| 30 // All of the filter scaling factors are in fixed point with kShiftBits bits of | |
| 31 // fractional part. | |
| 32 inline unsigned char BringBackTo8(int a, bool take_absolute) { | |
| 33 a >>= ConvolutionFilter1D::kShiftBits; | |
| 34 if (take_absolute) | |
| 35 a = std::abs(a); | |
| 36 return ClampTo8(a); | |
| 37 } | |
| 38 | |
| 39 // Stores a list of rows in a circular buffer. The usage is you write into it | |
| 40 // by calling AdvanceRow. It will keep track of which row in the buffer it | |
| 41 // should use next, and the total number of rows added. | |
| 42 class CircularRowBuffer { | |
| 43 public: | |
| 44 // The number of pixels in each row is given in |source_row_pixel_width|. | |
| 45 // The maximum number of rows needed in the buffer is |max_y_filter_size| | |
| 46 // (we only need to store enough rows for the biggest filter). | |
| 47 // | |
| 48 // We use the |first_input_row| to compute the coordinates of all of the | |
| 49 // following rows returned by Advance(). | |
| 50 CircularRowBuffer(int dest_row_pixel_width, int max_y_filter_size, | |
| 51 int first_input_row) | |
| 52 : row_byte_width_(dest_row_pixel_width * 4), | |
| 53 num_rows_(max_y_filter_size), | |
| 54 next_row_(0), | |
| 55 next_row_coordinate_(first_input_row) { | |
| 56 buffer_.resize(row_byte_width_ * max_y_filter_size); | |
| 57 row_addresses_.resize(num_rows_); | |
| 58 } | |
| 59 | |
| 60 // Moves to the next row in the buffer, returning a pointer to the beginning | |
| 61 // of it. | |
| 62 unsigned char* AdvanceRow() { | |
| 63 unsigned char* row = &buffer_[next_row_ * row_byte_width_]; | |
| 64 next_row_coordinate_++; | |
| 65 | |
| 66 // Set the pointer to the next row to use, wrapping around if necessary. | |
| 67 next_row_++; | |
| 68 if (next_row_ == num_rows_) | |
| 69 next_row_ = 0; | |
| 70 return row; | |
| 71 } | |
| 72 | |
| 73 // Returns a pointer to an "unrolled" array of rows. These rows will start | |
| 74 // at the y coordinate placed into |*first_row_index| and will continue in | |
| 75 // order for the maximum number of rows in this circular buffer. | |
| 76 // | |
| 77 // The |first_row_index_| may be negative. This means the circular buffer | |
| 78 // starts before the top of the image (it hasn't been filled yet). | |
| 79 unsigned char* const* GetRowAddresses(int* first_row_index) { | |
| 80 // Example for a 4-element circular buffer holding coords 6-9. | |
| 81 // Row 0 Coord 8 | |
| 82 // Row 1 Coord 9 | |
| 83 // Row 2 Coord 6 <- next_row_ = 2, next_row_coordinate_ = 10. | |
| 84 // Row 3 Coord 7 | |
| 85 // | |
| 86 // The "next" row is also the first (lowest) coordinate. This computation | |
| 87 // may yield a negative value, but that's OK, the math will work out | |
| 88 // since the user of this buffer will compute the offset relative | |
| 89 // to the first_row_index and the negative rows will never be used. | |
| 90 *first_row_index = next_row_coordinate_ - num_rows_; | |
| 91 | |
| 92 int cur_row = next_row_; | |
| 93 for (int i = 0; i < num_rows_; i++) { | |
| 94 row_addresses_[i] = &buffer_[cur_row * row_byte_width_]; | |
| 95 | |
| 96 // Advance to the next row, wrapping if necessary. | |
| 97 cur_row++; | |
| 98 if (cur_row == num_rows_) | |
| 99 cur_row = 0; | |
| 100 } | |
| 101 return &row_addresses_[0]; | |
| 102 } | |
| 103 | |
| 104 private: | |
| 105 // The buffer storing the rows. They are packed, each one row_byte_width_. | |
| 106 std::vector<unsigned char> buffer_; | |
| 107 | |
| 108 // Number of bytes per row in the |buffer_|. | |
| 109 int row_byte_width_; | |
| 110 | |
| 111 // The number of rows available in the buffer. | |
| 112 int num_rows_; | |
| 113 | |
| 114 // The next row index we should write into. This wraps around as the | |
| 115 // circular buffer is used. | |
| 116 int next_row_; | |
| 117 | |
| 118 // The y coordinate of the |next_row_|. This is incremented each time a | |
| 119 // new row is appended and does not wrap. | |
| 120 int next_row_coordinate_; | |
| 121 | |
| 122 // Buffer used by GetRowAddresses(). | |
| 123 std::vector<unsigned char*> row_addresses_; | |
| 124 }; | |
| 125 | |
| 126 // Convolves horizontally along a single row. The row data is given in | |
| 127 // |src_data| and continues for the num_values() of the filter. | |
| 128 template<bool has_alpha> | |
| 129 void ConvolveHorizontally(const unsigned char* src_data, | |
| 130 const ConvolutionFilter1D& filter, | |
| 131 unsigned char* out_row) { | |
| 132 // Loop over each pixel on this row in the output image. | |
| 133 int num_values = filter.num_values(); | |
| 134 for (int out_x = 0; out_x < num_values; out_x++) { | |
| 135 // Get the filter that determines the current output pixel. | |
| 136 int filter_offset, filter_length; | |
| 137 const ConvolutionFilter1D::Fixed* filter_values = | |
| 138 filter.FilterForValue(out_x, &filter_offset, &filter_length); | |
| 139 | |
| 140 // Compute the first pixel in this row that the filter affects. It will | |
| 141 // touch |filter_length| pixels (4 bytes each) after this. | |
| 142 const unsigned char* row_to_filter = &src_data[filter_offset * 4]; | |
| 143 | |
| 144 // Apply the filter to the row to get the destination pixel in |accum|. | |
| 145 int accum[4] = {0}; | |
| 146 for (int filter_x = 0; filter_x < filter_length; filter_x++) { | |
| 147 ConvolutionFilter1D::Fixed cur_filter = filter_values[filter_x]; | |
| 148 accum[0] += cur_filter * row_to_filter[filter_x * 4 + 0]; | |
| 149 accum[1] += cur_filter * row_to_filter[filter_x * 4 + 1]; | |
| 150 accum[2] += cur_filter * row_to_filter[filter_x * 4 + 2]; | |
| 151 if (has_alpha) | |
| 152 accum[3] += cur_filter * row_to_filter[filter_x * 4 + 3]; | |
| 153 } | |
| 154 | |
| 155 // Bring this value back in range. All of the filter scaling factors | |
| 156 // are in fixed point with kShiftBits bits of fractional part. | |
| 157 accum[0] >>= ConvolutionFilter1D::kShiftBits; | |
| 158 accum[1] >>= ConvolutionFilter1D::kShiftBits; | |
| 159 accum[2] >>= ConvolutionFilter1D::kShiftBits; | |
| 160 if (has_alpha) | |
| 161 accum[3] >>= ConvolutionFilter1D::kShiftBits; | |
| 162 | |
| 163 // Store the new pixel. | |
| 164 out_row[out_x * 4 + 0] = ClampTo8(accum[0]); | |
| 165 out_row[out_x * 4 + 1] = ClampTo8(accum[1]); | |
| 166 out_row[out_x * 4 + 2] = ClampTo8(accum[2]); | |
| 167 if (has_alpha) | |
| 168 out_row[out_x * 4 + 3] = ClampTo8(accum[3]); | |
| 169 } | |
| 170 } | |
| 171 | |
| 172 // Does vertical convolution to produce one output row. The filter values and | |
| 173 // length are given in the first two parameters. These are applied to each | |
| 174 // of the rows pointed to in the |source_data_rows| array, with each row | |
| 175 // being |pixel_width| wide. | |
| 176 // | |
| 177 // The output must have room for |pixel_width * 4| bytes. | |
| 178 template<bool has_alpha> | |
| 179 void ConvolveVertically(const ConvolutionFilter1D::Fixed* filter_values, | |
| 180 int filter_length, | |
| 181 unsigned char* const* source_data_rows, | |
| 182 int pixel_width, | |
| 183 unsigned char* out_row) { | |
| 184 // We go through each column in the output and do a vertical convolution, | |
| 185 // generating one output pixel each time. | |
| 186 for (int out_x = 0; out_x < pixel_width; out_x++) { | |
| 187 // Compute the number of bytes over in each row that the current column | |
| 188 // we're convolving starts at. The pixel will cover the next 4 bytes. | |
| 189 int byte_offset = out_x * 4; | |
| 190 | |
| 191 // Apply the filter to one column of pixels. | |
| 192 int accum[4] = {0}; | |
| 193 for (int filter_y = 0; filter_y < filter_length; filter_y++) { | |
| 194 ConvolutionFilter1D::Fixed cur_filter = filter_values[filter_y]; | |
| 195 accum[0] += cur_filter * source_data_rows[filter_y][byte_offset + 0]; | |
| 196 accum[1] += cur_filter * source_data_rows[filter_y][byte_offset + 1]; | |
| 197 accum[2] += cur_filter * source_data_rows[filter_y][byte_offset + 2]; | |
| 198 if (has_alpha) | |
| 199 accum[3] += cur_filter * source_data_rows[filter_y][byte_offset + 3]; | |
| 200 } | |
| 201 | |
| 202 // Bring this value back in range. All of the filter scaling factors | |
| 203 // are in fixed point with kShiftBits bits of precision. | |
| 204 accum[0] >>= ConvolutionFilter1D::kShiftBits; | |
| 205 accum[1] >>= ConvolutionFilter1D::kShiftBits; | |
| 206 accum[2] >>= ConvolutionFilter1D::kShiftBits; | |
| 207 if (has_alpha) | |
| 208 accum[3] >>= ConvolutionFilter1D::kShiftBits; | |
| 209 | |
| 210 // Store the new pixel. | |
| 211 out_row[byte_offset + 0] = ClampTo8(accum[0]); | |
| 212 out_row[byte_offset + 1] = ClampTo8(accum[1]); | |
| 213 out_row[byte_offset + 2] = ClampTo8(accum[2]); | |
| 214 if (has_alpha) { | |
| 215 unsigned char alpha = ClampTo8(accum[3]); | |
| 216 | |
| 217 // Make sure the alpha channel doesn't come out smaller than any of the | |
| 218 // color channels. We use premultipled alpha channels, so this should | |
| 219 // never happen, but rounding errors will cause this from time to time. | |
| 220 // These "impossible" colors will cause overflows (and hence random pixel | |
| 221 // values) when the resulting bitmap is drawn to the screen. | |
| 222 // | |
| 223 // We only need to do this when generating the final output row (here). | |
| 224 int max_color_channel = std::max(out_row[byte_offset + 0], | |
| 225 std::max(out_row[byte_offset + 1], out_row[byte_offset + 2])); | |
| 226 if (alpha < max_color_channel) | |
| 227 out_row[byte_offset + 3] = max_color_channel; | |
| 228 else | |
| 229 out_row[byte_offset + 3] = alpha; | |
| 230 } else { | |
| 231 // No alpha channel, the image is opaque. | |
| 232 out_row[byte_offset + 3] = 0xff; | |
| 233 } | |
| 234 } | |
| 235 } | |
| 236 | |
| 237 void ConvolveVertically(const ConvolutionFilter1D::Fixed* filter_values, | |
| 238 int filter_length, | |
| 239 unsigned char* const* source_data_rows, | |
| 240 int pixel_width, | |
| 241 unsigned char* out_row, | |
| 242 bool source_has_alpha) { | |
| 243 if (source_has_alpha) { | |
| 244 ConvolveVertically<true>(filter_values, filter_length, | |
| 245 source_data_rows, | |
| 246 pixel_width, | |
| 247 out_row); | |
| 248 } else { | |
| 249 ConvolveVertically<false>(filter_values, filter_length, | |
| 250 source_data_rows, | |
| 251 pixel_width, | |
| 252 out_row); | |
| 253 } | |
| 254 } | |
| 255 | |
| 256 } // namespace | |
| 257 | |
| 258 // ConvolutionFilter1D --------------------------------------------------------- | |
| 259 | |
| 260 ConvolutionFilter1D::ConvolutionFilter1D() | |
| 261 : max_filter_(0) { | |
| 262 } | |
| 263 | |
| 264 ConvolutionFilter1D::~ConvolutionFilter1D() { | |
| 265 } | |
| 266 | |
| 267 void ConvolutionFilter1D::AddFilter(int filter_offset, | |
| 268 const float* filter_values, | |
| 269 int filter_length) { | |
| 270 SkASSERT(filter_length > 0); | |
| 271 | |
| 272 std::vector<Fixed> fixed_values; | |
| 273 fixed_values.reserve(filter_length); | |
| 274 | |
| 275 for (int i = 0; i < filter_length; ++i) | |
| 276 fixed_values.push_back(FloatToFixed(filter_values[i])); | |
| 277 | |
| 278 AddFilter(filter_offset, &fixed_values[0], filter_length); | |
| 279 } | |
| 280 | |
| 281 void ConvolutionFilter1D::AddFilter(int filter_offset, | |
| 282 const Fixed* filter_values, | |
| 283 int filter_length) { | |
| 284 // It is common for leading/trailing filter values to be zeros. In such | |
| 285 // cases it is beneficial to only store the central factors. | |
| 286 // For a scaling to 1/4th in each dimension using a Lanczos-2 filter on | |
| 287 // a 1080p image this optimization gives a ~10% speed improvement. | |
| 288 int filter_size = filter_length; | |
| 289 int first_non_zero = 0; | |
| 290 while (first_non_zero < filter_length && filter_values[first_non_zero] == 0) | |
| 291 first_non_zero++; | |
| 292 | |
| 293 if (first_non_zero < filter_length) { | |
| 294 // Here we have at least one non-zero factor. | |
| 295 int last_non_zero = filter_length - 1; | |
| 296 while (last_non_zero >= 0 && filter_values[last_non_zero] == 0) | |
| 297 last_non_zero--; | |
| 298 | |
| 299 filter_offset += first_non_zero; | |
| 300 filter_length = last_non_zero + 1 - first_non_zero; | |
| 301 SkASSERT(filter_length > 0); | |
| 302 | |
| 303 for (int i = first_non_zero; i <= last_non_zero; i++) | |
| 304 filter_values_.push_back(filter_values[i]); | |
| 305 } else { | |
| 306 // Here all the factors were zeroes. | |
| 307 filter_length = 0; | |
| 308 } | |
| 309 | |
| 310 FilterInstance instance; | |
| 311 | |
| 312 // We pushed filter_length elements onto filter_values_ | |
| 313 instance.data_location = (static_cast<int>(filter_values_.size()) - | |
| 314 filter_length); | |
| 315 instance.offset = filter_offset; | |
| 316 instance.trimmed_length = filter_length; | |
| 317 instance.length = filter_size; | |
| 318 filters_.push_back(instance); | |
| 319 | |
| 320 max_filter_ = std::max(max_filter_, filter_length); | |
| 321 } | |
| 322 | |
| 323 const ConvolutionFilter1D::Fixed* ConvolutionFilter1D::GetSingleFilter( | |
| 324 int* specified_filter_length, | |
| 325 int* filter_offset, | |
| 326 int* filter_length) const { | |
| 327 const FilterInstance& filter = filters_[0]; | |
| 328 *filter_offset = filter.offset; | |
| 329 *filter_length = filter.trimmed_length; | |
| 330 *specified_filter_length = filter.length; | |
| 331 if (filter.trimmed_length == 0) | |
| 332 return NULL; | |
| 333 | |
| 334 return &filter_values_[filter.data_location]; | |
| 335 } | |
| 336 | |
| 337 typedef void (*ConvolveVertically_pointer)( | |
| 338 const ConvolutionFilter1D::Fixed* filter_values, | |
| 339 int filter_length, | |
| 340 unsigned char* const* source_data_rows, | |
| 341 int pixel_width, | |
| 342 unsigned char* out_row, | |
| 343 bool has_alpha); | |
| 344 typedef void (*Convolve4RowsHorizontally_pointer)( | |
| 345 const unsigned char* src_data[4], | |
| 346 const ConvolutionFilter1D& filter, | |
| 347 unsigned char* out_row[4]); | |
| 348 typedef void (*ConvolveHorizontally_pointer)( | |
| 349 const unsigned char* src_data, | |
| 350 const ConvolutionFilter1D& filter, | |
| 351 unsigned char* out_row, | |
| 352 bool has_alpha); | |
| 353 | |
| 354 struct ConvolveProcs { | |
| 355 // This is how many extra pixels may be read by the | |
| 356 // conolve*horizontally functions. | |
| 357 int extra_horizontal_reads; | |
| 358 ConvolveVertically_pointer convolve_vertically; | |
| 359 Convolve4RowsHorizontally_pointer convolve_4rows_horizontally; | |
| 360 ConvolveHorizontally_pointer convolve_horizontally; | |
| 361 }; | |
| 362 | |
| 363 void SetupSIMD(ConvolveProcs *procs) { | |
| 364 #ifdef SIMD_SSE2 | |
| 365 procs->extra_horizontal_reads = 3; | |
| 366 procs->convolve_vertically = &ConvolveVertically_SSE2; | |
| 367 procs->convolve_4rows_horizontally = &Convolve4RowsHorizontally_SSE2; | |
| 368 procs->convolve_horizontally = &ConvolveHorizontally_SSE2; | |
| 369 #elif defined SIMD_MIPS_DSPR2 | |
| 370 procs->extra_horizontal_reads = 3; | |
| 371 procs->convolve_vertically = &ConvolveVertically_mips_dspr2; | |
| 372 procs->convolve_horizontally = &ConvolveHorizontally_mips_dspr2; | |
| 373 #endif | |
| 374 } | |
| 375 | |
| 376 void BGRAConvolve2D(const unsigned char* source_data, | |
| 377 int source_byte_row_stride, | |
| 378 bool source_has_alpha, | |
| 379 const ConvolutionFilter1D& filter_x, | |
| 380 const ConvolutionFilter1D& filter_y, | |
| 381 int output_byte_row_stride, | |
| 382 unsigned char* output, | |
| 383 bool use_simd_if_possible) { | |
| 384 ConvolveProcs simd; | |
| 385 simd.extra_horizontal_reads = 0; | |
| 386 simd.convolve_vertically = NULL; | |
| 387 simd.convolve_4rows_horizontally = NULL; | |
| 388 simd.convolve_horizontally = NULL; | |
| 389 if (use_simd_if_possible) { | |
| 390 SetupSIMD(&simd); | |
| 391 } | |
| 392 | |
| 393 int max_y_filter_size = filter_y.max_filter(); | |
| 394 | |
| 395 // The next row in the input that we will generate a horizontally | |
| 396 // convolved row for. If the filter doesn't start at the beginning of the | |
| 397 // image (this is the case when we are only resizing a subset), then we | |
| 398 // don't want to generate any output rows before that. Compute the starting | |
| 399 // row for convolution as the first pixel for the first vertical filter. | |
| 400 int filter_offset, filter_length; | |
| 401 const ConvolutionFilter1D::Fixed* filter_values = | |
| 402 filter_y.FilterForValue(0, &filter_offset, &filter_length); | |
| 403 int next_x_row = filter_offset; | |
| 404 | |
| 405 // We loop over each row in the input doing a horizontal convolution. This | |
| 406 // will result in a horizontally convolved image. We write the results into | |
| 407 // a circular buffer of convolved rows and do vertical convolution as rows | |
| 408 // are available. This prevents us from having to store the entire | |
| 409 // intermediate image and helps cache coherency. | |
| 410 // We will need four extra rows to allow horizontal convolution could be done | |
| 411 // simultaneously. We also padding each row in row buffer to be aligned-up to | |
| 412 // 16 bytes. | |
| 413 // TODO(jiesun): We do not use aligned load from row buffer in vertical | |
| 414 // convolution pass yet. Somehow Windows does not like it. | |
| 415 int row_buffer_width = (filter_x.num_values() + 15) & ~0xF; | |
| 416 int row_buffer_height = max_y_filter_size + | |
| 417 (simd.convolve_4rows_horizontally ? 4 : 0); | |
| 418 CircularRowBuffer row_buffer(row_buffer_width, | |
| 419 row_buffer_height, | |
| 420 filter_offset); | |
| 421 | |
| 422 // Loop over every possible output row, processing just enough horizontal | |
| 423 // convolutions to run each subsequent vertical convolution. | |
| 424 SkASSERT(output_byte_row_stride >= filter_x.num_values() * 4); | |
| 425 int num_output_rows = filter_y.num_values(); | |
| 426 | |
| 427 // We need to check which is the last line to convolve before we advance 4 | |
| 428 // lines in one iteration. | |
| 429 int last_filter_offset, last_filter_length; | |
| 430 | |
| 431 // SSE2 can access up to 3 extra pixels past the end of the | |
| 432 // buffer. At the bottom of the image, we have to be careful | |
| 433 // not to access data past the end of the buffer. Normally | |
| 434 // we fall back to the C++ implementation for the last row. | |
| 435 // If the last row is less than 3 pixels wide, we may have to fall | |
| 436 // back to the C++ version for more rows. Compute how many | |
| 437 // rows we need to avoid the SSE implementation for here. | |
| 438 filter_x.FilterForValue(filter_x.num_values() - 1, &last_filter_offset, | |
| 439 &last_filter_length); | |
| 440 int avoid_simd_rows = 1 + simd.extra_horizontal_reads / | |
| 441 (last_filter_offset + last_filter_length); | |
| 442 | |
| 443 filter_y.FilterForValue(num_output_rows - 1, &last_filter_offset, | |
| 444 &last_filter_length); | |
| 445 | |
| 446 for (int out_y = 0; out_y < num_output_rows; out_y++) { | |
| 447 filter_values = filter_y.FilterForValue(out_y, | |
| 448 &filter_offset, &filter_length); | |
| 449 | |
| 450 // Generate output rows until we have enough to run the current filter. | |
| 451 while (next_x_row < filter_offset + filter_length) { | |
| 452 if (simd.convolve_4rows_horizontally && | |
| 453 next_x_row + 3 < last_filter_offset + last_filter_length - | |
| 454 avoid_simd_rows) { | |
| 455 const unsigned char* src[4]; | |
| 456 unsigned char* out_row[4]; | |
| 457 for (int i = 0; i < 4; ++i) { | |
| 458 src[i] = &source_data[(next_x_row + i) * source_byte_row_stride]; | |
| 459 out_row[i] = row_buffer.AdvanceRow(); | |
| 460 } | |
| 461 simd.convolve_4rows_horizontally(src, filter_x, out_row); | |
| 462 next_x_row += 4; | |
| 463 } else { | |
| 464 // Check if we need to avoid SSE2 for this row. | |
| 465 if (simd.convolve_horizontally && | |
| 466 next_x_row < last_filter_offset + last_filter_length - | |
| 467 avoid_simd_rows) { | |
| 468 simd.convolve_horizontally( | |
| 469 &source_data[next_x_row * source_byte_row_stride], | |
| 470 filter_x, row_buffer.AdvanceRow(), source_has_alpha); | |
| 471 } else { | |
| 472 if (source_has_alpha) { | |
| 473 ConvolveHorizontally<true>( | |
| 474 &source_data[next_x_row * source_byte_row_stride], | |
| 475 filter_x, row_buffer.AdvanceRow()); | |
| 476 } else { | |
| 477 ConvolveHorizontally<false>( | |
| 478 &source_data[next_x_row * source_byte_row_stride], | |
| 479 filter_x, row_buffer.AdvanceRow()); | |
| 480 } | |
| 481 } | |
| 482 next_x_row++; | |
| 483 } | |
| 484 } | |
| 485 | |
| 486 // Compute where in the output image this row of final data will go. | |
| 487 unsigned char* cur_output_row = &output[out_y * output_byte_row_stride]; | |
| 488 | |
| 489 // Get the list of rows that the circular buffer has, in order. | |
| 490 int first_row_in_circular_buffer; | |
| 491 unsigned char* const* rows_to_convolve = | |
| 492 row_buffer.GetRowAddresses(&first_row_in_circular_buffer); | |
| 493 | |
| 494 // Now compute the start of the subset of those rows that the filter | |
| 495 // needs. | |
| 496 unsigned char* const* first_row_for_filter = | |
| 497 &rows_to_convolve[filter_offset - first_row_in_circular_buffer]; | |
| 498 | |
| 499 if (simd.convolve_vertically) { | |
| 500 simd.convolve_vertically(filter_values, filter_length, | |
| 501 first_row_for_filter, | |
| 502 filter_x.num_values(), cur_output_row, | |
| 503 source_has_alpha); | |
| 504 } else { | |
| 505 ConvolveVertically(filter_values, filter_length, | |
| 506 first_row_for_filter, | |
| 507 filter_x.num_values(), cur_output_row, | |
| 508 source_has_alpha); | |
| 509 } | |
| 510 } | |
| 511 } | |
| 512 | |
| 513 void SingleChannelConvolveX1D(const unsigned char* source_data, | |
| 514 int source_byte_row_stride, | |
| 515 int input_channel_index, | |
| 516 int input_channel_count, | |
| 517 const ConvolutionFilter1D& filter, | |
| 518 const SkISize& image_size, | |
| 519 unsigned char* output, | |
| 520 int output_byte_row_stride, | |
| 521 int output_channel_index, | |
| 522 int output_channel_count, | |
| 523 bool absolute_values) { | |
| 524 int filter_offset, filter_length, filter_size; | |
| 525 // Very much unlike BGRAConvolve2D, here we expect to have the same filter | |
| 526 // for all pixels. | |
| 527 const ConvolutionFilter1D::Fixed* filter_values = | |
| 528 filter.GetSingleFilter(&filter_size, &filter_offset, &filter_length); | |
| 529 | |
| 530 if (filter_values == NULL || image_size.width() < filter_size) { | |
| 531 NOTREACHED(); | |
| 532 return; | |
| 533 } | |
| 534 | |
| 535 int centrepoint = filter_length / 2; | |
| 536 if (filter_size - filter_offset != 2 * filter_offset) { | |
| 537 // This means the original filter was not symmetrical AND | |
| 538 // got clipped from one side more than from the other. | |
| 539 centrepoint = filter_size / 2 - filter_offset; | |
| 540 } | |
| 541 | |
| 542 const unsigned char* source_data_row = source_data; | |
| 543 unsigned char* output_row = output; | |
| 544 | |
| 545 for (int r = 0; r < image_size.height(); ++r) { | |
| 546 unsigned char* target_byte = output_row + output_channel_index; | |
| 547 // Process the lead part, padding image to the left with the first pixel. | |
| 548 int c = 0; | |
| 549 for (; c < centrepoint; ++c, target_byte += output_channel_count) { | |
| 550 int accval = 0; | |
| 551 int i = 0; | |
| 552 int pixel_byte_index = input_channel_index; | |
| 553 for (; i < centrepoint - c; ++i) // Padding part. | |
| 554 accval += filter_values[i] * source_data_row[pixel_byte_index]; | |
| 555 | |
| 556 for (; i < filter_length; ++i, pixel_byte_index += input_channel_count) | |
| 557 accval += filter_values[i] * source_data_row[pixel_byte_index]; | |
| 558 | |
| 559 *target_byte = BringBackTo8(accval, absolute_values); | |
| 560 } | |
| 561 | |
| 562 // Now for the main event. | |
| 563 for (; c < image_size.width() - centrepoint; | |
| 564 ++c, target_byte += output_channel_count) { | |
| 565 int accval = 0; | |
| 566 int pixel_byte_index = (c - centrepoint) * input_channel_count + | |
| 567 input_channel_index; | |
| 568 | |
| 569 for (int i = 0; i < filter_length; | |
| 570 ++i, pixel_byte_index += input_channel_count) { | |
| 571 accval += filter_values[i] * source_data_row[pixel_byte_index]; | |
| 572 } | |
| 573 | |
| 574 *target_byte = BringBackTo8(accval, absolute_values); | |
| 575 } | |
| 576 | |
| 577 for (; c < image_size.width(); ++c, target_byte += output_channel_count) { | |
| 578 int accval = 0; | |
| 579 int overlap_taps = image_size.width() - c + centrepoint; | |
| 580 int pixel_byte_index = (c - centrepoint) * input_channel_count + | |
| 581 input_channel_index; | |
| 582 int i = 0; | |
| 583 for (; i < overlap_taps - 1; ++i, pixel_byte_index += input_channel_count) | |
| 584 accval += filter_values[i] * source_data_row[pixel_byte_index]; | |
| 585 | |
| 586 for (; i < filter_length; ++i) | |
| 587 accval += filter_values[i] * source_data_row[pixel_byte_index]; | |
| 588 | |
| 589 *target_byte = BringBackTo8(accval, absolute_values); | |
| 590 } | |
| 591 | |
| 592 source_data_row += source_byte_row_stride; | |
| 593 output_row += output_byte_row_stride; | |
| 594 } | |
| 595 } | |
| 596 | |
| 597 void SingleChannelConvolveY1D(const unsigned char* source_data, | |
| 598 int source_byte_row_stride, | |
| 599 int input_channel_index, | |
| 600 int input_channel_count, | |
| 601 const ConvolutionFilter1D& filter, | |
| 602 const SkISize& image_size, | |
| 603 unsigned char* output, | |
| 604 int output_byte_row_stride, | |
| 605 int output_channel_index, | |
| 606 int output_channel_count, | |
| 607 bool absolute_values) { | |
| 608 int filter_offset, filter_length, filter_size; | |
| 609 // Very much unlike BGRAConvolve2D, here we expect to have the same filter | |
| 610 // for all pixels. | |
| 611 const ConvolutionFilter1D::Fixed* filter_values = | |
| 612 filter.GetSingleFilter(&filter_size, &filter_offset, &filter_length); | |
| 613 | |
| 614 if (filter_values == NULL || image_size.height() < filter_size) { | |
| 615 NOTREACHED(); | |
| 616 return; | |
| 617 } | |
| 618 | |
| 619 int centrepoint = filter_length / 2; | |
| 620 if (filter_size - filter_offset != 2 * filter_offset) { | |
| 621 // This means the original filter was not symmetrical AND | |
| 622 // got clipped from one side more than from the other. | |
| 623 centrepoint = filter_size / 2 - filter_offset; | |
| 624 } | |
| 625 | |
| 626 for (int c = 0; c < image_size.width(); ++c) { | |
| 627 unsigned char* target_byte = output + c * output_channel_count + | |
| 628 output_channel_index; | |
| 629 int r = 0; | |
| 630 | |
| 631 for (; r < centrepoint; ++r, target_byte += output_byte_row_stride) { | |
| 632 int accval = 0; | |
| 633 int i = 0; | |
| 634 int pixel_byte_index = c * input_channel_count + input_channel_index; | |
| 635 | |
| 636 for (; i < centrepoint - r; ++i) // Padding part. | |
| 637 accval += filter_values[i] * source_data[pixel_byte_index]; | |
| 638 | |
| 639 for (; i < filter_length; ++i, pixel_byte_index += source_byte_row_stride) | |
| 640 accval += filter_values[i] * source_data[pixel_byte_index]; | |
| 641 | |
| 642 *target_byte = BringBackTo8(accval, absolute_values); | |
| 643 } | |
| 644 | |
| 645 for (; r < image_size.height() - centrepoint; | |
| 646 ++r, target_byte += output_byte_row_stride) { | |
| 647 int accval = 0; | |
| 648 int pixel_byte_index = (r - centrepoint) * source_byte_row_stride + | |
| 649 c * input_channel_count + input_channel_index; | |
| 650 for (int i = 0; i < filter_length; | |
| 651 ++i, pixel_byte_index += source_byte_row_stride) { | |
| 652 accval += filter_values[i] * source_data[pixel_byte_index]; | |
| 653 } | |
| 654 | |
| 655 *target_byte = BringBackTo8(accval, absolute_values); | |
| 656 } | |
| 657 | |
| 658 for (; r < image_size.height(); | |
| 659 ++r, target_byte += output_byte_row_stride) { | |
| 660 int accval = 0; | |
| 661 int overlap_taps = image_size.height() - r + centrepoint; | |
| 662 int pixel_byte_index = (r - centrepoint) * source_byte_row_stride + | |
| 663 c * input_channel_count + input_channel_index; | |
| 664 int i = 0; | |
| 665 for (; i < overlap_taps - 1; | |
| 666 ++i, pixel_byte_index += source_byte_row_stride) { | |
| 667 accval += filter_values[i] * source_data[pixel_byte_index]; | |
| 668 } | |
| 669 | |
| 670 for (; i < filter_length; ++i) | |
| 671 accval += filter_values[i] * source_data[pixel_byte_index]; | |
| 672 | |
| 673 *target_byte = BringBackTo8(accval, absolute_values); | |
| 674 } | |
| 675 } | |
| 676 } | |
| 677 | |
| 678 void SetUpGaussianConvolutionKernel(ConvolutionFilter1D* filter, | |
| 679 float kernel_sigma, | |
| 680 bool derivative) { | |
| 681 DCHECK(filter != NULL); | |
| 682 DCHECK_GT(kernel_sigma, 0.0); | |
| 683 const int tail_length = static_cast<int>(4.0f * kernel_sigma + 0.5f); | |
| 684 const int kernel_size = tail_length * 2 + 1; | |
| 685 const float sigmasq = kernel_sigma * kernel_sigma; | |
| 686 std::vector<float> kernel_weights(kernel_size, 0.0); | |
| 687 float kernel_sum = 1.0f; | |
| 688 | |
| 689 kernel_weights[tail_length] = 1.0f; | |
| 690 | |
| 691 for (int ii = 1; ii <= tail_length; ++ii) { | |
| 692 float v = std::exp(-0.5f * ii * ii / sigmasq); | |
| 693 kernel_weights[tail_length + ii] = v; | |
| 694 kernel_weights[tail_length - ii] = v; | |
| 695 kernel_sum += 2.0f * v; | |
| 696 } | |
| 697 | |
| 698 for (int i = 0; i < kernel_size; ++i) | |
| 699 kernel_weights[i] /= kernel_sum; | |
| 700 | |
| 701 if (derivative) { | |
| 702 kernel_weights[tail_length] = 0.0; | |
| 703 for (int ii = 1; ii <= tail_length; ++ii) { | |
| 704 float v = sigmasq * kernel_weights[tail_length + ii] / ii; | |
| 705 kernel_weights[tail_length + ii] = v; | |
| 706 kernel_weights[tail_length - ii] = -v; | |
| 707 } | |
| 708 } | |
| 709 | |
| 710 filter->AddFilter(0, &kernel_weights[0], kernel_weights.size()); | |
| 711 } | |
| 712 | |
| 713 } // namespace skia | |
| OLD | NEW |
|---|
Chromium Code Reviews
Issue 1519243002:
Remove many unused files from //skia/ext (Closed)
Base URL: git@github.com:domokit/mojo.git@master