1 // Copyright (c) 2012 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 <string.h> 6 #include <time.h> 7 #include <algorithm> 8 #include <numeric> 9 #include <vector> 10 11 #include "base/basictypes.h" 12 #include "base/logging.h" 13 #include "base/time/time.h" 14 #include "skia/ext/convolver.h" 15 #include "testing/gtest/include/gtest/gtest.h" 16 #include "third_party/skia/include/core/SkBitmap.h" 17 #include "third_party/skia/include/core/SkColorPriv.h" 18 #include "third_party/skia/include/core/SkRect.h" 19 #include "third_party/skia/include/core/SkTypes.h" 20 21 namespace skia { 22 23 namespace { 24 25 // Fills the given filter with impulse functions for the range 0->num_entries. 26 void FillImpulseFilter(int num_entries, ConvolutionFilter1D* filter) { 27 float one = 1.0f; 28 for (int i = 0; i < num_entries; i++) 29 filter->AddFilter(i, &one, 1); 30 } 31 32 // Filters the given input with the impulse function, and verifies that it 33 // does not change. 34 void TestImpulseConvolution(const unsigned char* data, int width, int height) { 35 int byte_count = width * height * 4; 36 37 ConvolutionFilter1D filter_x; 38 FillImpulseFilter(width, &filter_x); 39 40 ConvolutionFilter1D filter_y; 41 FillImpulseFilter(height, &filter_y); 42 43 std::vector<unsigned char> output; 44 output.resize(byte_count); 45 BGRAConvolve2D(data, width * 4, true, filter_x, filter_y, 46 filter_x.num_values() * 4, &output[0], false); 47 48 // Output should exactly match input. 49 EXPECT_EQ(0, memcmp(data, &output[0], byte_count)); 50 } 51 52 // Fills the destination filter with a box filter averaging every two pixels 53 // to produce the output. 54 void FillBoxFilter(int size, ConvolutionFilter1D* filter) { 55 const float box[2] = { 0.5, 0.5 }; 56 for (int i = 0; i < size; i++) 57 filter->AddFilter(i * 2, box, 2); 58 } 59 60 } // namespace 61 62 // Tests that each pixel, when set and run through the impulse filter, does 63 // not change. 64 TEST(Convolver, Impulse) { 65 // We pick an "odd" size that is not likely to fit on any boundaries so that 66 // we can see if all the widths and paddings are handled properly. 67 int width = 15; 68 int height = 31; 69 int byte_count = width * height * 4; 70 std::vector<unsigned char> input; 71 input.resize(byte_count); 72 73 unsigned char* input_ptr = &input[0]; 74 for (int y = 0; y < height; y++) { 75 for (int x = 0; x < width; x++) { 76 for (int channel = 0; channel < 3; channel++) { 77 memset(input_ptr, 0, byte_count); 78 input_ptr[(y * width + x) * 4 + channel] = 0xff; 79 // Always set the alpha channel or it will attempt to "fix" it for us. 80 input_ptr[(y * width + x) * 4 + 3] = 0xff; 81 TestImpulseConvolution(input_ptr, width, height); 82 } 83 } 84 } 85 } 86 87 // Tests that using a box filter to halve an image results in every square of 4 88 // pixels in the original get averaged to a pixel in the output. 89 TEST(Convolver, Halve) { 90 static const int kSize = 16; 91 92 int src_width = kSize; 93 int src_height = kSize; 94 int src_row_stride = src_width * 4; 95 int src_byte_count = src_row_stride * src_height; 96 std::vector<unsigned char> input; 97 input.resize(src_byte_count); 98 99 int dest_width = src_width / 2; 100 int dest_height = src_height / 2; 101 int dest_byte_count = dest_width * dest_height * 4; 102 std::vector<unsigned char> output; 103 output.resize(dest_byte_count); 104 105 // First fill the array with a bunch of random data. 106 srand(static_cast<unsigned>(time(NULL))); 107 for (int i = 0; i < src_byte_count; i++) 108 input[i] = rand() * 255 / RAND_MAX; 109 110 // Compute the filters. 111 ConvolutionFilter1D filter_x, filter_y; 112 FillBoxFilter(dest_width, &filter_x); 113 FillBoxFilter(dest_height, &filter_y); 114 115 // Do the convolution. 116 BGRAConvolve2D(&input[0], src_width, true, filter_x, filter_y, 117 filter_x.num_values() * 4, &output[0], false); 118 119 // Compute the expected results and check, allowing for a small difference 120 // to account for rounding errors. 121 for (int y = 0; y < dest_height; y++) { 122 for (int x = 0; x < dest_width; x++) { 123 for (int channel = 0; channel < 4; channel++) { 124 int src_offset = (y * 2 * src_row_stride + x * 2 * 4) + channel; 125 int value = input[src_offset] + // Top left source pixel. 126 input[src_offset + 4] + // Top right source pixel. 127 input[src_offset + src_row_stride] + // Lower left. 128 input[src_offset + src_row_stride + 4]; // Lower right. 129 value /= 4; // Average. 130 int difference = value - output[(y * dest_width + x) * 4 + channel]; 131 EXPECT_TRUE(difference >= -1 || difference <= 1); 132 } 133 } 134 } 135 } 136 137 // Tests the optimization in Convolver1D::AddFilter that avoids storing 138 // leading/trailing zeroes. 139 TEST(Convolver, AddFilter) { 140 skia::ConvolutionFilter1D filter; 141 142 const skia::ConvolutionFilter1D::Fixed* values = NULL; 143 int filter_offset = 0; 144 int filter_length = 0; 145 146 // An all-zero filter is handled correctly, all factors ignored 147 static const float factors1[] = { 0.0f, 0.0f, 0.0f }; 148 filter.AddFilter(11, factors1, arraysize(factors1)); 149 ASSERT_EQ(0, filter.max_filter()); 150 ASSERT_EQ(1, filter.num_values()); 151 152 values = filter.FilterForValue(0, &filter_offset, &filter_length); 153 ASSERT_TRUE(values == NULL); // No values => NULL. 154 ASSERT_EQ(11, filter_offset); // Same as input offset. 155 ASSERT_EQ(0, filter_length); // But no factors since all are zeroes. 156 157 // Zeroes on the left are ignored 158 static const float factors2[] = { 0.0f, 1.0f, 1.0f, 1.0f, 1.0f }; 159 filter.AddFilter(22, factors2, arraysize(factors2)); 160 ASSERT_EQ(4, filter.max_filter()); 161 ASSERT_EQ(2, filter.num_values()); 162 163 values = filter.FilterForValue(1, &filter_offset, &filter_length); 164 ASSERT_TRUE(values != NULL); 165 ASSERT_EQ(23, filter_offset); // 22 plus 1 leading zero 166 ASSERT_EQ(4, filter_length); // 5 - 1 leading zero 167 168 // Zeroes on the right are ignored 169 static const float factors3[] = { 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.0f, 0.0f }; 170 filter.AddFilter(33, factors3, arraysize(factors3)); 171 ASSERT_EQ(5, filter.max_filter()); 172 ASSERT_EQ(3, filter.num_values()); 173 174 values = filter.FilterForValue(2, &filter_offset, &filter_length); 175 ASSERT_TRUE(values != NULL); 176 ASSERT_EQ(33, filter_offset); // 33, same as input due to no leading zero 177 ASSERT_EQ(5, filter_length); // 7 - 2 trailing zeroes 178 179 // Zeroes in leading & trailing positions 180 static const float factors4[] = { 0.0f, 0.0f, 1.0f, 1.0f, 1.0f, 0.0f, 0.0f }; 181 filter.AddFilter(44, factors4, arraysize(factors4)); 182 ASSERT_EQ(5, filter.max_filter()); // No change from existing value. 183 ASSERT_EQ(4, filter.num_values()); 184 185 values = filter.FilterForValue(3, &filter_offset, &filter_length); 186 ASSERT_TRUE(values != NULL); 187 ASSERT_EQ(46, filter_offset); // 44 plus 2 leading zeroes 188 ASSERT_EQ(3, filter_length); // 7 - (2 leading + 2 trailing) zeroes 189 190 // Zeroes surrounded by non-zero values are ignored 191 static const float factors5[] = { 0.0f, 0.0f, 192 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 193 0.0f }; 194 filter.AddFilter(55, factors5, arraysize(factors5)); 195 ASSERT_EQ(6, filter.max_filter()); 196 ASSERT_EQ(5, filter.num_values()); 197 198 values = filter.FilterForValue(4, &filter_offset, &filter_length); 199 ASSERT_TRUE(values != NULL); 200 ASSERT_EQ(57, filter_offset); // 55 plus 2 leading zeroes 201 ASSERT_EQ(6, filter_length); // 9 - (2 leading + 1 trailing) zeroes 202 203 // All-zero filters after the first one also work 204 static const float factors6[] = { 0.0f }; 205 filter.AddFilter(66, factors6, arraysize(factors6)); 206 ASSERT_EQ(6, filter.max_filter()); 207 ASSERT_EQ(6, filter.num_values()); 208 209 values = filter.FilterForValue(5, &filter_offset, &filter_length); 210 ASSERT_TRUE(values == NULL); // filter_length == 0 => values is NULL 211 ASSERT_EQ(66, filter_offset); // value passed in 212 ASSERT_EQ(0, filter_length); 213 } 214 215 void VerifySIMD(unsigned int source_width, 216 unsigned int source_height, 217 unsigned int dest_width, 218 unsigned int dest_height) { 219 float filter[] = { 0.05f, -0.15f, 0.6f, 0.6f, -0.15f, 0.05f }; 220 // Preparing convolve coefficients. 221 ConvolutionFilter1D x_filter, y_filter; 222 for (unsigned int p = 0; p < dest_width; ++p) { 223 unsigned int offset = source_width * p / dest_width; 224 EXPECT_LT(offset, source_width); 225 x_filter.AddFilter(offset, filter, 226 std::min<int>(arraysize(filter), 227 source_width - offset)); 228 } 229 x_filter.PaddingForSIMD(); 230 for (unsigned int p = 0; p < dest_height; ++p) { 231 unsigned int offset = source_height * p / dest_height; 232 y_filter.AddFilter(offset, filter, 233 std::min<int>(arraysize(filter), 234 source_height - offset)); 235 } 236 y_filter.PaddingForSIMD(); 237 238 // Allocate input and output skia bitmap. 239 SkBitmap source, result_c, result_sse; 240 source.allocN32Pixels(source_width, source_height); 241 result_c.allocN32Pixels(dest_width, dest_height); 242 result_sse.allocN32Pixels(dest_width, dest_height); 243 244 // Randomize source bitmap for testing. 245 unsigned char* src_ptr = static_cast<unsigned char*>(source.getPixels()); 246 for (int y = 0; y < source.height(); y++) { 247 for (unsigned int x = 0; x < source.rowBytes(); x++) 248 src_ptr[x] = rand() % 255; 249 src_ptr += source.rowBytes(); 250 } 251 252 // Test both cases with different has_alpha. 253 for (int alpha = 0; alpha < 2; alpha++) { 254 // Convolve using C code. 255 base::TimeTicks resize_start; 256 base::TimeDelta delta_c, delta_sse; 257 unsigned char* r1 = static_cast<unsigned char*>(result_c.getPixels()); 258 unsigned char* r2 = static_cast<unsigned char*>(result_sse.getPixels()); 259 260 resize_start = base::TimeTicks::Now(); 261 BGRAConvolve2D(static_cast<const uint8*>(source.getPixels()), 262 static_cast<int>(source.rowBytes()), 263 (alpha != 0), x_filter, y_filter, 264 static_cast<int>(result_c.rowBytes()), r1, false); 265 delta_c = base::TimeTicks::Now() - resize_start; 266 267 resize_start = base::TimeTicks::Now(); 268 // Convolve using SSE2 code 269 BGRAConvolve2D(static_cast<const uint8*>(source.getPixels()), 270 static_cast<int>(source.rowBytes()), 271 (alpha != 0), x_filter, y_filter, 272 static_cast<int>(result_sse.rowBytes()), r2, true); 273 delta_sse = base::TimeTicks::Now() - resize_start; 274 275 // Unfortunately I could not enable the performance check now. 276 // Most bots use debug version, and there are great difference between 277 // the code generation for intrinsic, etc. In release version speed 278 // difference was 150%-200% depend on alpha channel presence; 279 // while in debug version speed difference was 96%-120%. 280 // TODO(jiesun): optimize further until we could enable this for 281 // debug version too. 282 // EXPECT_LE(delta_sse, delta_c); 283 284 int64 c_us = delta_c.InMicroseconds(); 285 int64 sse_us = delta_sse.InMicroseconds(); 286 VLOG(1) << "from:" << source_width << "x" << source_height 287 << " to:" << dest_width << "x" << dest_height 288 << (alpha ? " with alpha" : " w/o alpha"); 289 VLOG(1) << "c:" << c_us << " sse:" << sse_us; 290 VLOG(1) << "ratio:" << static_cast<float>(c_us) / sse_us; 291 292 // Comparing result. 293 for (unsigned int i = 0; i < dest_height; i++) { 294 EXPECT_FALSE(memcmp(r1, r2, dest_width * 4)); // RGBA always 295 r1 += result_c.rowBytes(); 296 r2 += result_sse.rowBytes(); 297 } 298 } 299 } 300 301 TEST(Convolver, VerifySIMDEdgeCases) { 302 srand(static_cast<unsigned int>(time(0))); 303 // Loop over all possible (small) image sizes 304 for (unsigned int width = 1; width < 20; width++) { 305 for (unsigned int height = 1; height < 20; height++) { 306 VerifySIMD(width, height, 8, 8); 307 VerifySIMD(8, 8, width, height); 308 } 309 } 310 } 311 312 // Verify that lage upscales/downscales produce the same result 313 // with and without SIMD. 314 TEST(Convolver, VerifySIMDPrecision) { 315 int source_sizes[][2] = { {1920, 1080}, {1377, 523}, {325, 241} }; 316 int dest_sizes[][2] = { {1280, 1024}, {177, 123} }; 317 318 srand(static_cast<unsigned int>(time(0))); 319 320 // Loop over some specific source and destination dimensions. 321 for (unsigned int i = 0; i < arraysize(source_sizes); ++i) { 322 unsigned int source_width = source_sizes[i][0]; 323 unsigned int source_height = source_sizes[i][1]; 324 for (unsigned int j = 0; j < arraysize(dest_sizes); ++j) { 325 unsigned int dest_width = dest_sizes[j][0]; 326 unsigned int dest_height = dest_sizes[j][1]; 327 VerifySIMD(source_width, source_height, dest_width, dest_height); 328 } 329 } 330 } 331 332 TEST(Convolver, SeparableSingleConvolution) { 333 static const int kImgWidth = 1024; 334 static const int kImgHeight = 1024; 335 static const int kChannelCount = 3; 336 static const int kStrideSlack = 22; 337 ConvolutionFilter1D filter; 338 const float box[5] = { 0.2f, 0.2f, 0.2f, 0.2f, 0.2f }; 339 filter.AddFilter(0, box, 5); 340 341 // Allocate a source image and set to 0. 342 const int src_row_stride = kImgWidth * kChannelCount + kStrideSlack; 343 int src_byte_count = src_row_stride * kImgHeight; 344 std::vector<unsigned char> input; 345 const int signal_x = kImgWidth / 2; 346 const int signal_y = kImgHeight / 2; 347 input.resize(src_byte_count, 0); 348 // The image has a single impulse pixel in channel 1, smack in the middle. 349 const int non_zero_pixel_index = 350 signal_y * src_row_stride + signal_x * kChannelCount + 1; 351 input[non_zero_pixel_index] = 255; 352 353 // Destination will be a single channel image with stide matching width. 354 const int dest_row_stride = kImgWidth; 355 const int dest_byte_count = dest_row_stride * kImgHeight; 356 std::vector<unsigned char> output; 357 output.resize(dest_byte_count); 358 359 // Apply convolution in X. 360 SingleChannelConvolveX1D(&input[0], src_row_stride, 1, kChannelCount, 361 filter, SkISize::Make(kImgWidth, kImgHeight), 362 &output[0], dest_row_stride, 0, 1, false); 363 for (int x = signal_x - 2; x <= signal_x + 2; ++x) 364 EXPECT_GT(output[signal_y * dest_row_stride + x], 0); 365 366 EXPECT_EQ(output[signal_y * dest_row_stride + signal_x - 3], 0); 367 EXPECT_EQ(output[signal_y * dest_row_stride + signal_x + 3], 0); 368 369 // Apply convolution in Y. 370 SingleChannelConvolveY1D(&input[0], src_row_stride, 1, kChannelCount, 371 filter, SkISize::Make(kImgWidth, kImgHeight), 372 &output[0], dest_row_stride, 0, 1, false); 373 for (int y = signal_y - 2; y <= signal_y + 2; ++y) 374 EXPECT_GT(output[y * dest_row_stride + signal_x], 0); 375 376 EXPECT_EQ(output[(signal_y - 3) * dest_row_stride + signal_x], 0); 377 EXPECT_EQ(output[(signal_y + 3) * dest_row_stride + signal_x], 0); 378 379 EXPECT_EQ(output[signal_y * dest_row_stride + signal_x - 1], 0); 380 EXPECT_EQ(output[signal_y * dest_row_stride + signal_x + 1], 0); 381 382 // The main point of calling this is to invoke the routine on input without 383 // padding. 384 std::vector<unsigned char> output2; 385 output2.resize(dest_byte_count); 386 SingleChannelConvolveX1D(&output[0], dest_row_stride, 0, 1, 387 filter, SkISize::Make(kImgWidth, kImgHeight), 388 &output2[0], dest_row_stride, 0, 1, false); 389 // This should be a result of 2D convolution. 390 for (int x = signal_x - 2; x <= signal_x + 2; ++x) { 391 for (int y = signal_y - 2; y <= signal_y + 2; ++y) 392 EXPECT_GT(output2[y * dest_row_stride + x], 0); 393 } 394 EXPECT_EQ(output2[0], 0); 395 EXPECT_EQ(output2[dest_row_stride - 1], 0); 396 EXPECT_EQ(output2[dest_byte_count - 1], 0); 397 } 398 399 TEST(Convolver, SeparableSingleConvolutionEdges) { 400 // The purpose of this test is to check if the implementation treats correctly 401 // edges of the image. 402 static const int kImgWidth = 600; 403 static const int kImgHeight = 800; 404 static const int kChannelCount = 3; 405 static const int kStrideSlack = 22; 406 static const int kChannel = 1; 407 ConvolutionFilter1D filter; 408 const float box[5] = { 0.2f, 0.2f, 0.2f, 0.2f, 0.2f }; 409 filter.AddFilter(0, box, 5); 410 411 // Allocate a source image and set to 0. 412 int src_row_stride = kImgWidth * kChannelCount + kStrideSlack; 413 int src_byte_count = src_row_stride * kImgHeight; 414 std::vector<unsigned char> input(src_byte_count); 415 416 // Draw a frame around the image. 417 for (int i = 0; i < src_byte_count; ++i) { 418 int row = i / src_row_stride; 419 int col = i % src_row_stride / kChannelCount; 420 int channel = i % src_row_stride % kChannelCount; 421 if (channel != kChannel || col > kImgWidth) { 422 input[i] = 255; 423 } else if (row == 0 || col == 0 || 424 col == kImgWidth - 1 || row == kImgHeight - 1) { 425 input[i] = 100; 426 } else if (row == 1 || col == 1 || 427 col == kImgWidth - 2 || row == kImgHeight - 2) { 428 input[i] = 200; 429 } else { 430 input[i] = 0; 431 } 432 } 433 434 // Destination will be a single channel image with stide matching width. 435 int dest_row_stride = kImgWidth; 436 int dest_byte_count = dest_row_stride * kImgHeight; 437 std::vector<unsigned char> output; 438 output.resize(dest_byte_count); 439 440 // Apply convolution in X. 441 SingleChannelConvolveX1D(&input[0], src_row_stride, 1, kChannelCount, 442 filter, SkISize::Make(kImgWidth, kImgHeight), 443 &output[0], dest_row_stride, 0, 1, false); 444 445 // Sadly, comparison is not as simple as retaining all values. 446 int invalid_values = 0; 447 const unsigned char first_value = output[0]; 448 EXPECT_NEAR(first_value, 100, 1); 449 for (int i = 0; i < dest_row_stride; ++i) { 450 if (output[i] != first_value) 451 ++invalid_values; 452 } 453 EXPECT_EQ(0, invalid_values); 454 455 int test_row = 22; 456 EXPECT_NEAR(output[test_row * dest_row_stride], 100, 1); 457 EXPECT_NEAR(output[test_row * dest_row_stride + 1], 80, 1); 458 EXPECT_NEAR(output[test_row * dest_row_stride + 2], 60, 1); 459 EXPECT_NEAR(output[test_row * dest_row_stride + 3], 40, 1); 460 EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 1], 100, 1); 461 EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 2], 80, 1); 462 EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 3], 60, 1); 463 EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 4], 40, 1); 464 465 SingleChannelConvolveY1D(&input[0], src_row_stride, 1, kChannelCount, 466 filter, SkISize::Make(kImgWidth, kImgHeight), 467 &output[0], dest_row_stride, 0, 1, false); 468 469 int test_column = 42; 470 EXPECT_NEAR(output[test_column], 100, 1); 471 EXPECT_NEAR(output[test_column + dest_row_stride], 80, 1); 472 EXPECT_NEAR(output[test_column + dest_row_stride * 2], 60, 1); 473 EXPECT_NEAR(output[test_column + dest_row_stride * 3], 40, 1); 474 475 EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 1)], 100, 1); 476 EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 2)], 80, 1); 477 EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 3)], 60, 1); 478 EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 4)], 40, 1); 479 } 480 481 TEST(Convolver, SetUpGaussianConvolutionFilter) { 482 ConvolutionFilter1D smoothing_filter; 483 ConvolutionFilter1D gradient_filter; 484 SetUpGaussianConvolutionKernel(&smoothing_filter, 4.5f, false); 485 SetUpGaussianConvolutionKernel(&gradient_filter, 3.0f, true); 486 487 int specified_filter_length; 488 int filter_offset; 489 int filter_length; 490 491 const ConvolutionFilter1D::Fixed* smoothing_kernel = 492 smoothing_filter.GetSingleFilter( 493 &specified_filter_length, &filter_offset, &filter_length); 494 EXPECT_TRUE(smoothing_kernel); 495 std::vector<float> fp_smoothing_kernel(filter_length); 496 std::transform(smoothing_kernel, 497 smoothing_kernel + filter_length, 498 fp_smoothing_kernel.begin(), 499 ConvolutionFilter1D::FixedToFloat); 500 // Should sum-up to 1 (nearly), and all values whould be in ]0, 1[. 501 EXPECT_NEAR(std::accumulate( 502 fp_smoothing_kernel.begin(), fp_smoothing_kernel.end(), 0.0f), 503 1.0f, 0.01f); 504 EXPECT_GT(*std::min_element(fp_smoothing_kernel.begin(), 505 fp_smoothing_kernel.end()), 0.0f); 506 EXPECT_LT(*std::max_element(fp_smoothing_kernel.begin(), 507 fp_smoothing_kernel.end()), 1.0f); 508 509 const ConvolutionFilter1D::Fixed* gradient_kernel = 510 gradient_filter.GetSingleFilter( 511 &specified_filter_length, &filter_offset, &filter_length); 512 EXPECT_TRUE(gradient_kernel); 513 std::vector<float> fp_gradient_kernel(filter_length); 514 std::transform(gradient_kernel, 515 gradient_kernel + filter_length, 516 fp_gradient_kernel.begin(), 517 ConvolutionFilter1D::FixedToFloat); 518 // Should sum-up to 0, and all values whould be in ]-1.5, 1.5[. 519 EXPECT_NEAR(std::accumulate( 520 fp_gradient_kernel.begin(), fp_gradient_kernel.end(), 0.0f), 521 0.0f, 0.01f); 522 EXPECT_GT(*std::min_element(fp_gradient_kernel.begin(), 523 fp_gradient_kernel.end()), -1.5f); 524 EXPECT_LT(*std::min_element(fp_gradient_kernel.begin(), 525 fp_gradient_kernel.end()), 0.0f); 526 EXPECT_LT(*std::max_element(fp_gradient_kernel.begin(), 527 fp_gradient_kernel.end()), 1.5f); 528 EXPECT_GT(*std::max_element(fp_gradient_kernel.begin(), 529 fp_gradient_kernel.end()), 0.0f); 530 } 531 532 } // namespace skia 533
