Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 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> |
Torne (Richard Coles) | b2df76e | 2013-05-13 16:52:09 +0100 | [diff] [blame] | 7 | #include <algorithm> |
| 8 | #include <numeric> |
Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 9 | #include <vector> |
| 10 | |
| 11 | #include "base/basictypes.h" |
| 12 | #include "base/logging.h" |
Ben Murdoch | eb525c5 | 2013-07-10 11:40:50 +0100 | [diff] [blame] | 13 | #include "base/time/time.h" |
Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 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 | |
Ben Murdoch | bb1529c | 2013-08-08 10:24:53 +0100 | [diff] [blame^] | 215 | #if defined(THREAD_SANITIZER) |
| 216 | // Times out under ThreadSanitizer, http://crbug.com/134400. |
| 217 | #define MAYBE_SIMDVerification DISABLED_SIMDVerification |
| 218 | #else |
| 219 | #define MAYBE_SIMDVerification SIMDVerification |
| 220 | #endif |
| 221 | TEST(Convolver, MAYBE_SIMDVerification) { |
Torne (Richard Coles) | 2a99a7e | 2013-03-28 15:31:22 +0000 | [diff] [blame] | 222 | int source_sizes[][2] = { |
| 223 | {1,1}, {1,2}, {1,3}, {1,4}, {1,5}, |
| 224 | {2,1}, {2,2}, {2,3}, {2,4}, {2,5}, |
| 225 | {3,1}, {3,2}, {3,3}, {3,4}, {3,5}, |
| 226 | {4,1}, {4,2}, {4,3}, {4,4}, {4,5}, |
| 227 | {1920, 1080}, |
| 228 | {720, 480}, |
| 229 | {1377, 523}, |
| 230 | {325, 241} }; |
Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 231 | int dest_sizes[][2] = { {1280, 1024}, {480, 270}, {177, 123} }; |
| 232 | float filter[] = { 0.05f, -0.15f, 0.6f, 0.6f, -0.15f, 0.05f }; |
| 233 | |
| 234 | srand(static_cast<unsigned int>(time(0))); |
| 235 | |
| 236 | // Loop over some specific source and destination dimensions. |
| 237 | for (unsigned int i = 0; i < arraysize(source_sizes); ++i) { |
| 238 | unsigned int source_width = source_sizes[i][0]; |
| 239 | unsigned int source_height = source_sizes[i][1]; |
| 240 | for (unsigned int j = 0; j < arraysize(dest_sizes); ++j) { |
Torne (Richard Coles) | 2a99a7e | 2013-03-28 15:31:22 +0000 | [diff] [blame] | 241 | unsigned int dest_width = dest_sizes[j][0]; |
| 242 | unsigned int dest_height = dest_sizes[j][1]; |
Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 243 | |
| 244 | // Preparing convolve coefficients. |
| 245 | ConvolutionFilter1D x_filter, y_filter; |
| 246 | for (unsigned int p = 0; p < dest_width; ++p) { |
| 247 | unsigned int offset = source_width * p / dest_width; |
Torne (Richard Coles) | 2a99a7e | 2013-03-28 15:31:22 +0000 | [diff] [blame] | 248 | EXPECT_LT(offset, source_width); |
| 249 | x_filter.AddFilter(offset, filter, |
| 250 | std::min<int>(arraysize(filter), |
| 251 | source_width - offset)); |
Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 252 | } |
Torne (Richard Coles) | c2e0dbd | 2013-05-09 18:35:53 +0100 | [diff] [blame] | 253 | x_filter.PaddingForSIMD(); |
Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 254 | for (unsigned int p = 0; p < dest_height; ++p) { |
| 255 | unsigned int offset = source_height * p / dest_height; |
Torne (Richard Coles) | 2a99a7e | 2013-03-28 15:31:22 +0000 | [diff] [blame] | 256 | y_filter.AddFilter(offset, filter, |
| 257 | std::min<int>(arraysize(filter), |
| 258 | source_height - offset)); |
Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 259 | } |
Torne (Richard Coles) | c2e0dbd | 2013-05-09 18:35:53 +0100 | [diff] [blame] | 260 | y_filter.PaddingForSIMD(); |
Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 261 | |
| 262 | // Allocate input and output skia bitmap. |
| 263 | SkBitmap source, result_c, result_sse; |
| 264 | source.setConfig(SkBitmap::kARGB_8888_Config, |
| 265 | source_width, source_height); |
| 266 | source.allocPixels(); |
| 267 | result_c.setConfig(SkBitmap::kARGB_8888_Config, |
| 268 | dest_width, dest_height); |
| 269 | result_c.allocPixels(); |
| 270 | result_sse.setConfig(SkBitmap::kARGB_8888_Config, |
| 271 | dest_width, dest_height); |
| 272 | result_sse.allocPixels(); |
| 273 | |
| 274 | // Randomize source bitmap for testing. |
| 275 | unsigned char* src_ptr = static_cast<unsigned char*>(source.getPixels()); |
| 276 | for (int y = 0; y < source.height(); y++) { |
Torne (Richard Coles) | 2a99a7e | 2013-03-28 15:31:22 +0000 | [diff] [blame] | 277 | for (unsigned int x = 0; x < source.rowBytes(); x++) |
Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 278 | src_ptr[x] = rand() % 255; |
| 279 | src_ptr += source.rowBytes(); |
| 280 | } |
| 281 | |
| 282 | // Test both cases with different has_alpha. |
| 283 | for (int alpha = 0; alpha < 2; alpha++) { |
| 284 | // Convolve using C code. |
| 285 | base::TimeTicks resize_start; |
| 286 | base::TimeDelta delta_c, delta_sse; |
| 287 | unsigned char* r1 = static_cast<unsigned char*>(result_c.getPixels()); |
| 288 | unsigned char* r2 = static_cast<unsigned char*>(result_sse.getPixels()); |
| 289 | |
| 290 | resize_start = base::TimeTicks::Now(); |
| 291 | BGRAConvolve2D(static_cast<const uint8*>(source.getPixels()), |
| 292 | static_cast<int>(source.rowBytes()), |
| 293 | (alpha != 0), x_filter, y_filter, |
| 294 | static_cast<int>(result_c.rowBytes()), r1, false); |
| 295 | delta_c = base::TimeTicks::Now() - resize_start; |
| 296 | |
| 297 | resize_start = base::TimeTicks::Now(); |
| 298 | // Convolve using SSE2 code |
| 299 | BGRAConvolve2D(static_cast<const uint8*>(source.getPixels()), |
| 300 | static_cast<int>(source.rowBytes()), |
| 301 | (alpha != 0), x_filter, y_filter, |
| 302 | static_cast<int>(result_sse.rowBytes()), r2, true); |
| 303 | delta_sse = base::TimeTicks::Now() - resize_start; |
| 304 | |
| 305 | // Unfortunately I could not enable the performance check now. |
| 306 | // Most bots use debug version, and there are great difference between |
| 307 | // the code generation for intrinsic, etc. In release version speed |
| 308 | // difference was 150%-200% depend on alpha channel presence; |
| 309 | // while in debug version speed difference was 96%-120%. |
| 310 | // TODO(jiesun): optimize further until we could enable this for |
| 311 | // debug version too. |
| 312 | // EXPECT_LE(delta_sse, delta_c); |
| 313 | |
| 314 | int64 c_us = delta_c.InMicroseconds(); |
| 315 | int64 sse_us = delta_sse.InMicroseconds(); |
| 316 | VLOG(1) << "from:" << source_width << "x" << source_height |
| 317 | << " to:" << dest_width << "x" << dest_height |
| 318 | << (alpha ? " with alpha" : " w/o alpha"); |
| 319 | VLOG(1) << "c:" << c_us << " sse:" << sse_us; |
| 320 | VLOG(1) << "ratio:" << static_cast<float>(c_us) / sse_us; |
| 321 | |
| 322 | // Comparing result. |
| 323 | for (unsigned int i = 0; i < dest_height; i++) { |
| 324 | for (unsigned int x = 0; x < dest_width * 4; x++) { // RGBA always. |
| 325 | EXPECT_EQ(r1[x], r2[x]); |
| 326 | } |
| 327 | r1 += result_c.rowBytes(); |
| 328 | r2 += result_sse.rowBytes(); |
| 329 | } |
| 330 | } |
| 331 | } |
| 332 | } |
Torne (Richard Coles) | c2e0dbd | 2013-05-09 18:35:53 +0100 | [diff] [blame] | 333 | } |
| 334 | |
| 335 | TEST(Convolver, SeparableSingleConvolution) { |
| 336 | static const int kImgWidth = 1024; |
| 337 | static const int kImgHeight = 1024; |
| 338 | static const int kChannelCount = 3; |
| 339 | static const int kStrideSlack = 22; |
| 340 | ConvolutionFilter1D filter; |
| 341 | const float box[5] = { 0.2f, 0.2f, 0.2f, 0.2f, 0.2f }; |
| 342 | filter.AddFilter(0, box, 5); |
| 343 | |
| 344 | // Allocate a source image and set to 0. |
| 345 | const int src_row_stride = kImgWidth * kChannelCount + kStrideSlack; |
| 346 | int src_byte_count = src_row_stride * kImgHeight; |
| 347 | std::vector<unsigned char> input; |
| 348 | const int signal_x = kImgWidth / 2; |
| 349 | const int signal_y = kImgHeight / 2; |
| 350 | input.resize(src_byte_count, 0); |
| 351 | // The image has a single impulse pixel in channel 1, smack in the middle. |
| 352 | const int non_zero_pixel_index = |
| 353 | signal_y * src_row_stride + signal_x * kChannelCount + 1; |
| 354 | input[non_zero_pixel_index] = 255; |
| 355 | |
| 356 | // Destination will be a single channel image with stide matching width. |
| 357 | const int dest_row_stride = kImgWidth; |
| 358 | const int dest_byte_count = dest_row_stride * kImgHeight; |
| 359 | std::vector<unsigned char> output; |
| 360 | output.resize(dest_byte_count); |
| 361 | |
| 362 | // Apply convolution in X. |
| 363 | SingleChannelConvolveX1D(&input[0], src_row_stride, 1, kChannelCount, |
| 364 | filter, SkISize::Make(kImgWidth, kImgHeight), |
| 365 | &output[0], dest_row_stride, 0, 1, false); |
| 366 | for (int x = signal_x - 2; x <= signal_x + 2; ++x) |
| 367 | EXPECT_GT(output[signal_y * dest_row_stride + x], 0); |
| 368 | |
| 369 | EXPECT_EQ(output[signal_y * dest_row_stride + signal_x - 3], 0); |
| 370 | EXPECT_EQ(output[signal_y * dest_row_stride + signal_x + 3], 0); |
| 371 | |
| 372 | // Apply convolution in Y. |
| 373 | SingleChannelConvolveY1D(&input[0], src_row_stride, 1, kChannelCount, |
| 374 | filter, SkISize::Make(kImgWidth, kImgHeight), |
| 375 | &output[0], dest_row_stride, 0, 1, false); |
| 376 | for (int y = signal_y - 2; y <= signal_y + 2; ++y) |
| 377 | EXPECT_GT(output[y * dest_row_stride + signal_x], 0); |
| 378 | |
| 379 | EXPECT_EQ(output[(signal_y - 3) * dest_row_stride + signal_x], 0); |
| 380 | EXPECT_EQ(output[(signal_y + 3) * dest_row_stride + signal_x], 0); |
| 381 | |
| 382 | EXPECT_EQ(output[signal_y * dest_row_stride + signal_x - 1], 0); |
| 383 | EXPECT_EQ(output[signal_y * dest_row_stride + signal_x + 1], 0); |
| 384 | |
| 385 | // The main point of calling this is to invoke the routine on input without |
| 386 | // padding. |
| 387 | std::vector<unsigned char> output2; |
| 388 | output2.resize(dest_byte_count); |
| 389 | SingleChannelConvolveX1D(&output[0], dest_row_stride, 0, 1, |
| 390 | filter, SkISize::Make(kImgWidth, kImgHeight), |
| 391 | &output2[0], dest_row_stride, 0, 1, false); |
| 392 | // This should be a result of 2D convolution. |
| 393 | for (int x = signal_x - 2; x <= signal_x + 2; ++x) { |
| 394 | for (int y = signal_y - 2; y <= signal_y + 2; ++y) |
| 395 | EXPECT_GT(output2[y * dest_row_stride + x], 0); |
| 396 | } |
| 397 | EXPECT_EQ(output2[0], 0); |
| 398 | EXPECT_EQ(output2[dest_row_stride - 1], 0); |
| 399 | EXPECT_EQ(output2[dest_byte_count - 1], 0); |
| 400 | } |
| 401 | |
| 402 | TEST(Convolver, SeparableSingleConvolutionEdges) { |
| 403 | // The purpose of this test is to check if the implementation treats correctly |
| 404 | // edges of the image. |
| 405 | static const int kImgWidth = 600; |
| 406 | static const int kImgHeight = 800; |
| 407 | static const int kChannelCount = 3; |
| 408 | static const int kStrideSlack = 22; |
| 409 | static const int kChannel = 1; |
| 410 | ConvolutionFilter1D filter; |
| 411 | const float box[5] = { 0.2f, 0.2f, 0.2f, 0.2f, 0.2f }; |
| 412 | filter.AddFilter(0, box, 5); |
| 413 | |
| 414 | // Allocate a source image and set to 0. |
| 415 | int src_row_stride = kImgWidth * kChannelCount + kStrideSlack; |
| 416 | int src_byte_count = src_row_stride * kImgHeight; |
| 417 | std::vector<unsigned char> input(src_byte_count); |
| 418 | |
| 419 | // Draw a frame around the image. |
| 420 | for (int i = 0; i < src_byte_count; ++i) { |
| 421 | int row = i / src_row_stride; |
| 422 | int col = i % src_row_stride / kChannelCount; |
| 423 | int channel = i % src_row_stride % kChannelCount; |
| 424 | if (channel != kChannel || col > kImgWidth) { |
| 425 | input[i] = 255; |
| 426 | } else if (row == 0 || col == 0 || |
| 427 | col == kImgWidth - 1 || row == kImgHeight - 1) { |
| 428 | input[i] = 100; |
| 429 | } else if (row == 1 || col == 1 || |
| 430 | col == kImgWidth - 2 || row == kImgHeight - 2) { |
| 431 | input[i] = 200; |
| 432 | } else { |
| 433 | input[i] = 0; |
| 434 | } |
| 435 | } |
| 436 | |
| 437 | // Destination will be a single channel image with stide matching width. |
| 438 | int dest_row_stride = kImgWidth; |
| 439 | int dest_byte_count = dest_row_stride * kImgHeight; |
| 440 | std::vector<unsigned char> output; |
| 441 | output.resize(dest_byte_count); |
| 442 | |
| 443 | // Apply convolution in X. |
| 444 | SingleChannelConvolveX1D(&input[0], src_row_stride, 1, kChannelCount, |
| 445 | filter, SkISize::Make(kImgWidth, kImgHeight), |
| 446 | &output[0], dest_row_stride, 0, 1, false); |
| 447 | |
| 448 | // Sadly, comparison is not as simple as retaining all values. |
| 449 | int invalid_values = 0; |
| 450 | const unsigned char first_value = output[0]; |
| 451 | EXPECT_NEAR(first_value, 100, 1); |
| 452 | for (int i = 0; i < dest_row_stride; ++i) { |
| 453 | if (output[i] != first_value) |
| 454 | ++invalid_values; |
| 455 | } |
| 456 | EXPECT_EQ(0, invalid_values); |
| 457 | |
| 458 | int test_row = 22; |
| 459 | EXPECT_NEAR(output[test_row * dest_row_stride], 100, 1); |
| 460 | EXPECT_NEAR(output[test_row * dest_row_stride + 1], 80, 1); |
| 461 | EXPECT_NEAR(output[test_row * dest_row_stride + 2], 60, 1); |
| 462 | EXPECT_NEAR(output[test_row * dest_row_stride + 3], 40, 1); |
| 463 | EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 1], 100, 1); |
| 464 | EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 2], 80, 1); |
| 465 | EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 3], 60, 1); |
| 466 | EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 4], 40, 1); |
| 467 | |
| 468 | SingleChannelConvolveY1D(&input[0], src_row_stride, 1, kChannelCount, |
| 469 | filter, SkISize::Make(kImgWidth, kImgHeight), |
| 470 | &output[0], dest_row_stride, 0, 1, false); |
| 471 | |
| 472 | int test_column = 42; |
| 473 | EXPECT_NEAR(output[test_column], 100, 1); |
| 474 | EXPECT_NEAR(output[test_column + dest_row_stride], 80, 1); |
| 475 | EXPECT_NEAR(output[test_column + dest_row_stride * 2], 60, 1); |
| 476 | EXPECT_NEAR(output[test_column + dest_row_stride * 3], 40, 1); |
| 477 | |
| 478 | EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 1)], 100, 1); |
| 479 | EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 2)], 80, 1); |
| 480 | EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 3)], 60, 1); |
| 481 | EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 4)], 40, 1); |
Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 482 | } |
| 483 | |
Torne (Richard Coles) | b2df76e | 2013-05-13 16:52:09 +0100 | [diff] [blame] | 484 | TEST(Convolver, SetUpGaussianConvolutionFilter) { |
| 485 | ConvolutionFilter1D smoothing_filter; |
| 486 | ConvolutionFilter1D gradient_filter; |
| 487 | SetUpGaussianConvolutionKernel(&smoothing_filter, 4.5f, false); |
| 488 | SetUpGaussianConvolutionKernel(&gradient_filter, 3.0f, true); |
| 489 | |
| 490 | int specified_filter_length; |
| 491 | int filter_offset; |
| 492 | int filter_length; |
| 493 | |
| 494 | const ConvolutionFilter1D::Fixed* smoothing_kernel = |
| 495 | smoothing_filter.GetSingleFilter( |
| 496 | &specified_filter_length, &filter_offset, &filter_length); |
| 497 | EXPECT_TRUE(smoothing_kernel); |
| 498 | std::vector<float> fp_smoothing_kernel(filter_length); |
| 499 | std::transform(smoothing_kernel, |
| 500 | smoothing_kernel + filter_length, |
| 501 | fp_smoothing_kernel.begin(), |
| 502 | ConvolutionFilter1D::FixedToFloat); |
| 503 | // Should sum-up to 1 (nearly), and all values whould be in ]0, 1[. |
| 504 | EXPECT_NEAR(std::accumulate( |
| 505 | fp_smoothing_kernel.begin(), fp_smoothing_kernel.end(), 0.0f), |
| 506 | 1.0f, 0.01f); |
| 507 | EXPECT_GT(*std::min_element(fp_smoothing_kernel.begin(), |
| 508 | fp_smoothing_kernel.end()), 0.0f); |
| 509 | EXPECT_LT(*std::max_element(fp_smoothing_kernel.begin(), |
| 510 | fp_smoothing_kernel.end()), 1.0f); |
| 511 | |
| 512 | const ConvolutionFilter1D::Fixed* gradient_kernel = |
| 513 | gradient_filter.GetSingleFilter( |
| 514 | &specified_filter_length, &filter_offset, &filter_length); |
| 515 | EXPECT_TRUE(gradient_kernel); |
| 516 | std::vector<float> fp_gradient_kernel(filter_length); |
| 517 | std::transform(gradient_kernel, |
| 518 | gradient_kernel + filter_length, |
| 519 | fp_gradient_kernel.begin(), |
| 520 | ConvolutionFilter1D::FixedToFloat); |
| 521 | // Should sum-up to 0, and all values whould be in ]-1.5, 1.5[. |
| 522 | EXPECT_NEAR(std::accumulate( |
| 523 | fp_gradient_kernel.begin(), fp_gradient_kernel.end(), 0.0f), |
| 524 | 0.0f, 0.01f); |
| 525 | EXPECT_GT(*std::min_element(fp_gradient_kernel.begin(), |
| 526 | fp_gradient_kernel.end()), -1.5f); |
| 527 | EXPECT_LT(*std::min_element(fp_gradient_kernel.begin(), |
| 528 | fp_gradient_kernel.end()), 0.0f); |
| 529 | EXPECT_LT(*std::max_element(fp_gradient_kernel.begin(), |
| 530 | fp_gradient_kernel.end()), 1.5f); |
| 531 | EXPECT_GT(*std::max_element(fp_gradient_kernel.begin(), |
| 532 | fp_gradient_kernel.end()), 0.0f); |
| 533 | } |
| 534 | |
Torne (Richard Coles) | 5821806 | 2012-11-14 11:43:16 +0000 | [diff] [blame] | 535 | } // namespace skia |