skia/ext/convolver_unittest.cc - chromium/src - Git at Google

 // Copyright (c) 2011 The Chromium Authors. All rights reserved.
 // Use of this source code is governed by a BSD-style license that can be
 // found in the LICENSE file.

 #include <string.h>
 #include <time.h>
 #include <vector>

 #include "base/basictypes.h"
 #include "base/logging.h"
 #include "base/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);
 }

 TEST(Convolver, SIMDVerification) {
 #if defined(SIMD_SSE2)
   base::CPU cpu;
   if (!cpu.has_sse2()) return;

   int source_sizes[][2] = { {1920, 1080}, {720, 480}, {1377, 523}, {325, 241} };
   int dest_sizes[][2] = { {1280, 1024}, {480, 270}, {177, 123} };
   float filter[] = { 0.05f, -0.15f, 0.6f, 0.6f, -0.15f, 0.05f };

   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 = source_sizes[j][0];
       unsigned int dest_height = source_sizes[j][1];

       // 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;
         if (offset > source_width - arraysize(filter))
           offset = source_width - arraysize(filter);
         x_filter.AddFilter(offset, filter, arraysize(filter));
       }
       for (unsigned int p = 0; p < dest_height; ++p) {
         unsigned int offset = source_height * p / dest_height;
         if (offset > source_height - arraysize(filter))
           offset = source_height - arraysize(filter);
         y_filter.AddFilter(offset, filter, arraysize(filter));
       }

       // Allocate input and output skia bitmap.
       SkBitmap source, result_c, result_sse;
       source.setConfig(SkBitmap::kARGB_8888_Config,
                        source_width, source_height);
       source.allocPixels();
       result_c.setConfig(SkBitmap::kARGB_8888_Config,
                          dest_width, dest_height);
       result_c.allocPixels();
       result_sse.setConfig(SkBitmap::kARGB_8888_Config,
                            dest_width, dest_height);
       result_sse.allocPixels();

       // Randomize source bitmap for testing.
       unsigned char* src_ptr = static_cast<unsigned char*>(source.getPixels());
       for (int y = 0; y < source.height(); y++) {
         for (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*>(source.getPixels()),
                        static_cast<int>(source.rowBytes()),
                        alpha ? true : false, 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*>(source.getPixels()),
                        static_cast<int>(source.rowBytes()),
                        alpha ? true : false, 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 c_us = delta_c.InMicroseconds();
         int64 sse_us = delta_sse.InMicroseconds();
         LOG(INFO) << "from:" << source_width << "x" << source_height
                   << " to:" << dest_width << "x" << dest_height
                   << (alpha ? " with alpha" : " w/o alpha");
         LOG(INFO) << "c:" << c_us << " sse:" << sse_us;
         LOG(INFO) << "ratio:" << static_cast<float>(c_us) / sse_us;

         // Comparing result.
         for (unsigned int i = 0; i < dest_height; i++) {
           for (unsigned int x = 0; x < dest_width * 4; x++) {  // RGBA always.
             EXPECT_EQ(r1[x], r2[x]);
           }
           r1 += result_c.rowBytes();
           r2 += result_sse.rowBytes();
         }
       }
     }
   }
 #endif
 }

 }  // namespace skia
	// Copyright (c) 2011 The Chromium Authors. All rights reserved.
	// Use of this source code is governed by a BSD-style license that can be
	// found in the LICENSE file.

	#include <string.h>
	#include <time.h>
	#include <vector>

	#include "base/basictypes.h"
	#include "base/logging.h"
	#include "base/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);
	}

	TEST(Convolver, SIMDVerification) {
	#if defined(SIMD_SSE2)
	base::CPU cpu;
	if (!cpu.has_sse2()) return;

	int source_sizes[][2] = { {1920, 1080}, {720, 480}, {1377, 523}, {325, 241} };
	int dest_sizes[][2] = { {1280, 1024}, {480, 270}, {177, 123} };
	float filter[] = { 0.05f, -0.15f, 0.6f, 0.6f, -0.15f, 0.05f };

	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 = source_sizes[j][0];
	unsigned int dest_height = source_sizes[j][1];

	// 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;
	if (offset > source_width - arraysize(filter))
	offset = source_width - arraysize(filter);
	x_filter.AddFilter(offset, filter, arraysize(filter));
	}
	for (unsigned int p = 0; p < dest_height; ++p) {
	unsigned int offset = source_height * p / dest_height;
	if (offset > source_height - arraysize(filter))
	offset = source_height - arraysize(filter);
	y_filter.AddFilter(offset, filter, arraysize(filter));
	}

	// Allocate input and output skia bitmap.
	SkBitmap source, result_c, result_sse;
	source.setConfig(SkBitmap::kARGB_8888_Config,
	source_width, source_height);
	source.allocPixels();
	result_c.setConfig(SkBitmap::kARGB_8888_Config,
	dest_width, dest_height);
	result_c.allocPixels();
	result_sse.setConfig(SkBitmap::kARGB_8888_Config,
	dest_width, dest_height);
	result_sse.allocPixels();

	// Randomize source bitmap for testing.
	unsigned char* src_ptr = static_cast<unsigned char*>(source.getPixels());
	for (int y = 0; y < source.height(); y++) {
	for (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*>(source.getPixels()),
	static_cast<int>(source.rowBytes()),
	alpha ? true : false, 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*>(source.getPixels()),
	static_cast<int>(source.rowBytes()),
	alpha ? true : false, 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 c_us = delta_c.InMicroseconds();
	int64 sse_us = delta_sse.InMicroseconds();
	LOG(INFO) << "from:" << source_width << "x" << source_height
	<< " to:" << dest_width << "x" << dest_height
	<< (alpha ? " with alpha" : " w/o alpha");
	LOG(INFO) << "c:" << c_us << " sse:" << sse_us;
	LOG(INFO) << "ratio:" << static_cast<float>(c_us) / sse_us;

	// Comparing result.
	for (unsigned int i = 0; i < dest_height; i++) {
	for (unsigned int x = 0; x < dest_width * 4; x++) { // RGBA always.
	EXPECT_EQ(r1[x], r2[x]);
	}
	r1 += result_c.rowBytes();
	r2 += result_sse.rowBytes();
	}
	}
	}
	}
	#endif
	}

	} // namespace skia