| // Copyright (c) 2012 The Chromium Authors. All rights reserved. |
| // Use of this source code is governed by a BSD-style license that can be |
| // found in the LICENSE file. |
| |
| #include <stdint.h> |
| #include <string.h> |
| #include <time.h> |
| #include <algorithm> |
| #include <numeric> |
| #include <vector> |
| |
| #include "base/logging.h" |
| #include "base/macros.h" |
| #include "base/time/time.h" |
| #include "skia/ext/convolver.h" |
| #include "testing/gtest/include/gtest/gtest.h" |
| #include "third_party/skia/include/core/SkBitmap.h" |
| #include "third_party/skia/include/core/SkColorPriv.h" |
| #include "third_party/skia/include/core/SkRect.h" |
| #include "third_party/skia/include/core/SkTypes.h" |
| |
| namespace skia { |
| |
| namespace { |
| |
| // Fills the given filter with impulse functions for the range 0->num_entries. |
| void FillImpulseFilter(int num_entries, ConvolutionFilter1D* filter) { |
| float one = 1.0f; |
| for (int i = 0; i < num_entries; i++) |
| filter->AddFilter(i, &one, 1); |
| } |
| |
| // Filters the given input with the impulse function, and verifies that it |
| // does not change. |
| void TestImpulseConvolution(const unsigned char* data, int width, int height) { |
| int byte_count = width * height * 4; |
| |
| ConvolutionFilter1D filter_x; |
| FillImpulseFilter(width, &filter_x); |
| |
| ConvolutionFilter1D filter_y; |
| FillImpulseFilter(height, &filter_y); |
| |
| std::vector<unsigned char> output; |
| output.resize(byte_count); |
| BGRAConvolve2D(data, width * 4, true, filter_x, filter_y, |
| filter_x.num_values() * 4, &output[0], false); |
| |
| // Output should exactly match input. |
| EXPECT_EQ(0, memcmp(data, &output[0], byte_count)); |
| } |
| |
| // Fills the destination filter with a box filter averaging every two pixels |
| // to produce the output. |
| void FillBoxFilter(int size, ConvolutionFilter1D* filter) { |
| const float box[2] = { 0.5, 0.5 }; |
| for (int i = 0; i < size; i++) |
| filter->AddFilter(i * 2, box, 2); |
| } |
| |
| } // namespace |
| |
| // Tests that each pixel, when set and run through the impulse filter, does |
| // not change. |
| TEST(Convolver, Impulse) { |
| // We pick an "odd" size that is not likely to fit on any boundaries so that |
| // we can see if all the widths and paddings are handled properly. |
| int width = 15; |
| int height = 31; |
| int byte_count = width * height * 4; |
| std::vector<unsigned char> input; |
| input.resize(byte_count); |
| |
| unsigned char* input_ptr = &input[0]; |
| for (int y = 0; y < height; y++) { |
| for (int x = 0; x < width; x++) { |
| for (int channel = 0; channel < 3; channel++) { |
| memset(input_ptr, 0, byte_count); |
| input_ptr[(y * width + x) * 4 + channel] = 0xff; |
| // Always set the alpha channel or it will attempt to "fix" it for us. |
| input_ptr[(y * width + x) * 4 + 3] = 0xff; |
| TestImpulseConvolution(input_ptr, width, height); |
| } |
| } |
| } |
| } |
| |
| // Tests that using a box filter to halve an image results in every square of 4 |
| // pixels in the original get averaged to a pixel in the output. |
| TEST(Convolver, Halve) { |
| static const int kSize = 16; |
| |
| int src_width = kSize; |
| int src_height = kSize; |
| int src_row_stride = src_width * 4; |
| int src_byte_count = src_row_stride * src_height; |
| std::vector<unsigned char> input; |
| input.resize(src_byte_count); |
| |
| int dest_width = src_width / 2; |
| int dest_height = src_height / 2; |
| int dest_byte_count = dest_width * dest_height * 4; |
| std::vector<unsigned char> output; |
| output.resize(dest_byte_count); |
| |
| // First fill the array with a bunch of random data. |
| srand(static_cast<unsigned>(time(NULL))); |
| for (int i = 0; i < src_byte_count; i++) |
| input[i] = rand() * 255 / RAND_MAX; |
| |
| // Compute the filters. |
| ConvolutionFilter1D filter_x, filter_y; |
| FillBoxFilter(dest_width, &filter_x); |
| FillBoxFilter(dest_height, &filter_y); |
| |
| // Do the convolution. |
| BGRAConvolve2D(&input[0], src_width, true, filter_x, filter_y, |
| filter_x.num_values() * 4, &output[0], false); |
| |
| // Compute the expected results and check, allowing for a small difference |
| // to account for rounding errors. |
| for (int y = 0; y < dest_height; y++) { |
| for (int x = 0; x < dest_width; x++) { |
| for (int channel = 0; channel < 4; channel++) { |
| int src_offset = (y * 2 * src_row_stride + x * 2 * 4) + channel; |
| int value = input[src_offset] + // Top left source pixel. |
| input[src_offset + 4] + // Top right source pixel. |
| input[src_offset + src_row_stride] + // Lower left. |
| input[src_offset + src_row_stride + 4]; // Lower right. |
| value /= 4; // Average. |
| int difference = value - output[(y * dest_width + x) * 4 + channel]; |
| EXPECT_TRUE(difference >= -1 || difference <= 1); |
| } |
| } |
| } |
| } |
| |
| // Tests the optimization in Convolver1D::AddFilter that avoids storing |
| // leading/trailing zeroes. |
| TEST(Convolver, AddFilter) { |
| skia::ConvolutionFilter1D filter; |
| |
| const skia::ConvolutionFilter1D::Fixed* values = NULL; |
| int filter_offset = 0; |
| int filter_length = 0; |
| |
| // An all-zero filter is handled correctly, all factors ignored |
| static const float factors1[] = { 0.0f, 0.0f, 0.0f }; |
| filter.AddFilter(11, factors1, arraysize(factors1)); |
| ASSERT_EQ(0, filter.max_filter()); |
| ASSERT_EQ(1, filter.num_values()); |
| |
| values = filter.FilterForValue(0, &filter_offset, &filter_length); |
| ASSERT_TRUE(values == NULL); // No values => NULL. |
| ASSERT_EQ(11, filter_offset); // Same as input offset. |
| ASSERT_EQ(0, filter_length); // But no factors since all are zeroes. |
| |
| // Zeroes on the left are ignored |
| static const float factors2[] = { 0.0f, 1.0f, 1.0f, 1.0f, 1.0f }; |
| filter.AddFilter(22, factors2, arraysize(factors2)); |
| ASSERT_EQ(4, filter.max_filter()); |
| ASSERT_EQ(2, filter.num_values()); |
| |
| values = filter.FilterForValue(1, &filter_offset, &filter_length); |
| ASSERT_TRUE(values != NULL); |
| ASSERT_EQ(23, filter_offset); // 22 plus 1 leading zero |
| ASSERT_EQ(4, filter_length); // 5 - 1 leading zero |
| |
| // Zeroes on the right are ignored |
| static const float factors3[] = { 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.0f, 0.0f }; |
| filter.AddFilter(33, factors3, arraysize(factors3)); |
| ASSERT_EQ(5, filter.max_filter()); |
| ASSERT_EQ(3, filter.num_values()); |
| |
| values = filter.FilterForValue(2, &filter_offset, &filter_length); |
| ASSERT_TRUE(values != NULL); |
| ASSERT_EQ(33, filter_offset); // 33, same as input due to no leading zero |
| ASSERT_EQ(5, filter_length); // 7 - 2 trailing zeroes |
| |
| // Zeroes in leading & trailing positions |
| static const float factors4[] = { 0.0f, 0.0f, 1.0f, 1.0f, 1.0f, 0.0f, 0.0f }; |
| filter.AddFilter(44, factors4, arraysize(factors4)); |
| ASSERT_EQ(5, filter.max_filter()); // No change from existing value. |
| ASSERT_EQ(4, filter.num_values()); |
| |
| values = filter.FilterForValue(3, &filter_offset, &filter_length); |
| ASSERT_TRUE(values != NULL); |
| ASSERT_EQ(46, filter_offset); // 44 plus 2 leading zeroes |
| ASSERT_EQ(3, filter_length); // 7 - (2 leading + 2 trailing) zeroes |
| |
| // Zeroes surrounded by non-zero values are ignored |
| static const float factors5[] = { 0.0f, 0.0f, |
| 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, |
| 0.0f }; |
| filter.AddFilter(55, factors5, arraysize(factors5)); |
| ASSERT_EQ(6, filter.max_filter()); |
| ASSERT_EQ(5, filter.num_values()); |
| |
| values = filter.FilterForValue(4, &filter_offset, &filter_length); |
| ASSERT_TRUE(values != NULL); |
| ASSERT_EQ(57, filter_offset); // 55 plus 2 leading zeroes |
| ASSERT_EQ(6, filter_length); // 9 - (2 leading + 1 trailing) zeroes |
| |
| // All-zero filters after the first one also work |
| static const float factors6[] = { 0.0f }; |
| filter.AddFilter(66, factors6, arraysize(factors6)); |
| ASSERT_EQ(6, filter.max_filter()); |
| ASSERT_EQ(6, filter.num_values()); |
| |
| values = filter.FilterForValue(5, &filter_offset, &filter_length); |
| ASSERT_TRUE(values == NULL); // filter_length == 0 => values is NULL |
| ASSERT_EQ(66, filter_offset); // value passed in |
| ASSERT_EQ(0, filter_length); |
| } |
| |
| void VerifySIMD(unsigned int source_width, |
| unsigned int source_height, |
| unsigned int dest_width, |
| unsigned int dest_height) { |
| float filter[] = { 0.05f, -0.15f, 0.6f, 0.6f, -0.15f, 0.05f }; |
| // Preparing convolve coefficients. |
| ConvolutionFilter1D x_filter, y_filter; |
| for (unsigned int p = 0; p < dest_width; ++p) { |
| unsigned int offset = source_width * p / dest_width; |
| EXPECT_LT(offset, source_width); |
| x_filter.AddFilter(offset, filter, |
| std::min<int>(arraysize(filter), |
| source_width - offset)); |
| } |
| x_filter.PaddingForSIMD(); |
| for (unsigned int p = 0; p < dest_height; ++p) { |
| unsigned int offset = source_height * p / dest_height; |
| y_filter.AddFilter(offset, filter, |
| std::min<int>(arraysize(filter), |
| source_height - offset)); |
| } |
| y_filter.PaddingForSIMD(); |
| |
| // Allocate input and output skia bitmap. |
| SkBitmap source, result_c, result_sse; |
| source.allocN32Pixels(source_width, source_height); |
| result_c.allocN32Pixels(dest_width, dest_height); |
| result_sse.allocN32Pixels(dest_width, dest_height); |
| |
| // Randomize source bitmap for testing. |
| unsigned char* src_ptr = static_cast<unsigned char*>(source.getPixels()); |
| for (int y = 0; y < source.height(); y++) { |
| for (unsigned int x = 0; x < source.rowBytes(); x++) |
| src_ptr[x] = rand() % 255; |
| src_ptr += source.rowBytes(); |
| } |
| |
| // Test both cases with different has_alpha. |
| for (int alpha = 0; alpha < 2; alpha++) { |
| // Convolve using C code. |
| base::TimeTicks resize_start; |
| base::TimeDelta delta_c, delta_sse; |
| unsigned char* r1 = static_cast<unsigned char*>(result_c.getPixels()); |
| unsigned char* r2 = static_cast<unsigned char*>(result_sse.getPixels()); |
| |
| resize_start = base::TimeTicks::Now(); |
| BGRAConvolve2D(static_cast<const uint8_t*>(source.getPixels()), |
| static_cast<int>(source.rowBytes()), (alpha != 0), x_filter, |
| y_filter, static_cast<int>(result_c.rowBytes()), r1, false); |
| delta_c = base::TimeTicks::Now() - resize_start; |
| |
| resize_start = base::TimeTicks::Now(); |
| // Convolve using SSE2 code |
| BGRAConvolve2D(static_cast<const uint8_t*>(source.getPixels()), |
| static_cast<int>(source.rowBytes()), (alpha != 0), x_filter, |
| y_filter, static_cast<int>(result_sse.rowBytes()), r2, true); |
| delta_sse = base::TimeTicks::Now() - resize_start; |
| |
| // Unfortunately I could not enable the performance check now. |
| // Most bots use debug version, and there are great difference between |
| // the code generation for intrinsic, etc. In release version speed |
| // difference was 150%-200% depend on alpha channel presence; |
| // while in debug version speed difference was 96%-120%. |
| // TODO(jiesun): optimize further until we could enable this for |
| // debug version too. |
| // EXPECT_LE(delta_sse, delta_c); |
| |
| int64_t c_us = delta_c.InMicroseconds(); |
| int64_t sse_us = delta_sse.InMicroseconds(); |
| VLOG(1) << "from:" << source_width << "x" << source_height |
| << " to:" << dest_width << "x" << dest_height |
| << (alpha ? " with alpha" : " w/o alpha"); |
| VLOG(1) << "c:" << c_us << " sse:" << sse_us; |
| VLOG(1) << "ratio:" << static_cast<float>(c_us) / sse_us; |
| |
| // Comparing result. |
| for (unsigned int i = 0; i < dest_height; i++) { |
| EXPECT_FALSE(memcmp(r1, r2, dest_width * 4)); // RGBA always |
| r1 += result_c.rowBytes(); |
| r2 += result_sse.rowBytes(); |
| } |
| } |
| } |
| |
| TEST(Convolver, VerifySIMDEdgeCases) { |
| srand(static_cast<unsigned int>(time(0))); |
| // Loop over all possible (small) image sizes |
| for (unsigned int width = 1; width < 20; width++) { |
| for (unsigned int height = 1; height < 20; height++) { |
| VerifySIMD(width, height, 8, 8); |
| VerifySIMD(8, 8, width, height); |
| } |
| } |
| } |
| |
| // Verify that lage upscales/downscales produce the same result |
| // with and without SIMD. |
| TEST(Convolver, VerifySIMDPrecision) { |
| int source_sizes[][2] = { {1920, 1080}, {1377, 523}, {325, 241} }; |
| int dest_sizes[][2] = { {1280, 1024}, {177, 123} }; |
| |
| srand(static_cast<unsigned int>(time(0))); |
| |
| // Loop over some specific source and destination dimensions. |
| for (unsigned int i = 0; i < arraysize(source_sizes); ++i) { |
| unsigned int source_width = source_sizes[i][0]; |
| unsigned int source_height = source_sizes[i][1]; |
| for (unsigned int j = 0; j < arraysize(dest_sizes); ++j) { |
| unsigned int dest_width = dest_sizes[j][0]; |
| unsigned int dest_height = dest_sizes[j][1]; |
| VerifySIMD(source_width, source_height, dest_width, dest_height); |
| } |
| } |
| } |
| |
| TEST(Convolver, SeparableSingleConvolution) { |
| static const int kImgWidth = 1024; |
| static const int kImgHeight = 1024; |
| static const int kChannelCount = 3; |
| static const int kStrideSlack = 22; |
| ConvolutionFilter1D filter; |
| const float box[5] = { 0.2f, 0.2f, 0.2f, 0.2f, 0.2f }; |
| filter.AddFilter(0, box, 5); |
| |
| // Allocate a source image and set to 0. |
| const int src_row_stride = kImgWidth * kChannelCount + kStrideSlack; |
| int src_byte_count = src_row_stride * kImgHeight; |
| std::vector<unsigned char> input; |
| const int signal_x = kImgWidth / 2; |
| const int signal_y = kImgHeight / 2; |
| input.resize(src_byte_count, 0); |
| // The image has a single impulse pixel in channel 1, smack in the middle. |
| const int non_zero_pixel_index = |
| signal_y * src_row_stride + signal_x * kChannelCount + 1; |
| input[non_zero_pixel_index] = 255; |
| |
| // Destination will be a single channel image with stide matching width. |
| const int dest_row_stride = kImgWidth; |
| const int dest_byte_count = dest_row_stride * kImgHeight; |
| std::vector<unsigned char> output; |
| output.resize(dest_byte_count); |
| |
| // Apply convolution in X. |
| SingleChannelConvolveX1D(&input[0], src_row_stride, 1, kChannelCount, |
| filter, SkISize::Make(kImgWidth, kImgHeight), |
| &output[0], dest_row_stride, 0, 1, false); |
| for (int x = signal_x - 2; x <= signal_x + 2; ++x) |
| EXPECT_GT(output[signal_y * dest_row_stride + x], 0); |
| |
| EXPECT_EQ(output[signal_y * dest_row_stride + signal_x - 3], 0); |
| EXPECT_EQ(output[signal_y * dest_row_stride + signal_x + 3], 0); |
| |
| // Apply convolution in Y. |
| SingleChannelConvolveY1D(&input[0], src_row_stride, 1, kChannelCount, |
| filter, SkISize::Make(kImgWidth, kImgHeight), |
| &output[0], dest_row_stride, 0, 1, false); |
| for (int y = signal_y - 2; y <= signal_y + 2; ++y) |
| EXPECT_GT(output[y * dest_row_stride + signal_x], 0); |
| |
| EXPECT_EQ(output[(signal_y - 3) * dest_row_stride + signal_x], 0); |
| EXPECT_EQ(output[(signal_y + 3) * dest_row_stride + signal_x], 0); |
| |
| EXPECT_EQ(output[signal_y * dest_row_stride + signal_x - 1], 0); |
| EXPECT_EQ(output[signal_y * dest_row_stride + signal_x + 1], 0); |
| |
| // The main point of calling this is to invoke the routine on input without |
| // padding. |
| std::vector<unsigned char> output2; |
| output2.resize(dest_byte_count); |
| SingleChannelConvolveX1D(&output[0], dest_row_stride, 0, 1, |
| filter, SkISize::Make(kImgWidth, kImgHeight), |
| &output2[0], dest_row_stride, 0, 1, false); |
| // This should be a result of 2D convolution. |
| for (int x = signal_x - 2; x <= signal_x + 2; ++x) { |
| for (int y = signal_y - 2; y <= signal_y + 2; ++y) |
| EXPECT_GT(output2[y * dest_row_stride + x], 0); |
| } |
| EXPECT_EQ(output2[0], 0); |
| EXPECT_EQ(output2[dest_row_stride - 1], 0); |
| EXPECT_EQ(output2[dest_byte_count - 1], 0); |
| } |
| |
| TEST(Convolver, SeparableSingleConvolutionEdges) { |
| // The purpose of this test is to check if the implementation treats correctly |
| // edges of the image. |
| static const int kImgWidth = 600; |
| static const int kImgHeight = 800; |
| static const int kChannelCount = 3; |
| static const int kStrideSlack = 22; |
| static const int kChannel = 1; |
| ConvolutionFilter1D filter; |
| const float box[5] = { 0.2f, 0.2f, 0.2f, 0.2f, 0.2f }; |
| filter.AddFilter(0, box, 5); |
| |
| // Allocate a source image and set to 0. |
| int src_row_stride = kImgWidth * kChannelCount + kStrideSlack; |
| int src_byte_count = src_row_stride * kImgHeight; |
| std::vector<unsigned char> input(src_byte_count); |
| |
| // Draw a frame around the image. |
| for (int i = 0; i < src_byte_count; ++i) { |
| int row = i / src_row_stride; |
| int col = i % src_row_stride / kChannelCount; |
| int channel = i % src_row_stride % kChannelCount; |
| if (channel != kChannel || col > kImgWidth) { |
| input[i] = 255; |
| } else if (row == 0 || col == 0 || |
| col == kImgWidth - 1 || row == kImgHeight - 1) { |
| input[i] = 100; |
| } else if (row == 1 || col == 1 || |
| col == kImgWidth - 2 || row == kImgHeight - 2) { |
| input[i] = 200; |
| } else { |
| input[i] = 0; |
| } |
| } |
| |
| // Destination will be a single channel image with stide matching width. |
| int dest_row_stride = kImgWidth; |
| int dest_byte_count = dest_row_stride * kImgHeight; |
| std::vector<unsigned char> output; |
| output.resize(dest_byte_count); |
| |
| // Apply convolution in X. |
| SingleChannelConvolveX1D(&input[0], src_row_stride, 1, kChannelCount, |
| filter, SkISize::Make(kImgWidth, kImgHeight), |
| &output[0], dest_row_stride, 0, 1, false); |
| |
| // Sadly, comparison is not as simple as retaining all values. |
| int invalid_values = 0; |
| const unsigned char first_value = output[0]; |
| EXPECT_NEAR(first_value, 100, 1); |
| for (int i = 0; i < dest_row_stride; ++i) { |
| if (output[i] != first_value) |
| ++invalid_values; |
| } |
| EXPECT_EQ(0, invalid_values); |
| |
| int test_row = 22; |
| EXPECT_NEAR(output[test_row * dest_row_stride], 100, 1); |
| EXPECT_NEAR(output[test_row * dest_row_stride + 1], 80, 1); |
| EXPECT_NEAR(output[test_row * dest_row_stride + 2], 60, 1); |
| EXPECT_NEAR(output[test_row * dest_row_stride + 3], 40, 1); |
| EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 1], 100, 1); |
| EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 2], 80, 1); |
| EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 3], 60, 1); |
| EXPECT_NEAR(output[(test_row + 1) * dest_row_stride - 4], 40, 1); |
| |
| SingleChannelConvolveY1D(&input[0], src_row_stride, 1, kChannelCount, |
| filter, SkISize::Make(kImgWidth, kImgHeight), |
| &output[0], dest_row_stride, 0, 1, false); |
| |
| int test_column = 42; |
| EXPECT_NEAR(output[test_column], 100, 1); |
| EXPECT_NEAR(output[test_column + dest_row_stride], 80, 1); |
| EXPECT_NEAR(output[test_column + dest_row_stride * 2], 60, 1); |
| EXPECT_NEAR(output[test_column + dest_row_stride * 3], 40, 1); |
| |
| EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 1)], 100, 1); |
| EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 2)], 80, 1); |
| EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 3)], 60, 1); |
| EXPECT_NEAR(output[test_column + dest_row_stride * (kImgHeight - 4)], 40, 1); |
| } |
| |
| TEST(Convolver, SetUpGaussianConvolutionFilter) { |
| ConvolutionFilter1D smoothing_filter; |
| ConvolutionFilter1D gradient_filter; |
| SetUpGaussianConvolutionKernel(&smoothing_filter, 4.5f, false); |
| SetUpGaussianConvolutionKernel(&gradient_filter, 3.0f, true); |
| |
| int specified_filter_length; |
| int filter_offset; |
| int filter_length; |
| |
| const ConvolutionFilter1D::Fixed* smoothing_kernel = |
| smoothing_filter.GetSingleFilter( |
| &specified_filter_length, &filter_offset, &filter_length); |
| EXPECT_TRUE(smoothing_kernel); |
| std::vector<float> fp_smoothing_kernel(filter_length); |
| std::transform(smoothing_kernel, |
| smoothing_kernel + filter_length, |
| fp_smoothing_kernel.begin(), |
| ConvolutionFilter1D::FixedToFloat); |
| // Should sum-up to 1 (nearly), and all values whould be in ]0, 1[. |
| EXPECT_NEAR(std::accumulate( |
| fp_smoothing_kernel.begin(), fp_smoothing_kernel.end(), 0.0f), |
| 1.0f, 0.01f); |
| EXPECT_GT(*std::min_element(fp_smoothing_kernel.begin(), |
| fp_smoothing_kernel.end()), 0.0f); |
| EXPECT_LT(*std::max_element(fp_smoothing_kernel.begin(), |
| fp_smoothing_kernel.end()), 1.0f); |
| |
| const ConvolutionFilter1D::Fixed* gradient_kernel = |
| gradient_filter.GetSingleFilter( |
| &specified_filter_length, &filter_offset, &filter_length); |
| EXPECT_TRUE(gradient_kernel); |
| std::vector<float> fp_gradient_kernel(filter_length); |
| std::transform(gradient_kernel, |
| gradient_kernel + filter_length, |
| fp_gradient_kernel.begin(), |
| ConvolutionFilter1D::FixedToFloat); |
| // Should sum-up to 0, and all values whould be in ]-1.5, 1.5[. |
| EXPECT_NEAR(std::accumulate( |
| fp_gradient_kernel.begin(), fp_gradient_kernel.end(), 0.0f), |
| 0.0f, 0.01f); |
| EXPECT_GT(*std::min_element(fp_gradient_kernel.begin(), |
| fp_gradient_kernel.end()), -1.5f); |
| EXPECT_LT(*std::min_element(fp_gradient_kernel.begin(), |
| fp_gradient_kernel.end()), 0.0f); |
| EXPECT_LT(*std::max_element(fp_gradient_kernel.begin(), |
| fp_gradient_kernel.end()), 1.5f); |
| EXPECT_GT(*std::max_element(fp_gradient_kernel.begin(), |
| fp_gradient_kernel.end()), 0.0f); |
| } |
| |
| } // namespace skia |